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  • 1.
    Anguelova, M.
    et al.
    Chalmers University of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Johansson, M.
    Chalmers University of Technology.
    Franzen, C J
    Chalmers University of Technology.
    Wennberg, B.
    Chalmers University of Technology.
    Conservation laws and unidentifiability of rate expressions in biochemical models2007In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 1, no 4, p. 230-237Article in journal (Refereed)
    Abstract [en]

    New experimental techniques in bioscience provide us with high-quality data allowing quantitative mathematical modelling. Parameter estimation is often necessary and, in connection with this, it is important to know whether all parameters can be uniquely estimated from available data, (i.e. whether the model is identifiable). Dealing essentially with models for metabolism, we show how the assumption of an algebraic relation between concentrations may cause parameters to be unidentifiable. If a sufficient data set is available, the problem with unidentifiability arises locally in individual rate expressions. A general method for reparameterisation to identifiable rate expressions is provided, together with a Mathematica code to help with the calculations. The general results are exemplified by four well-cited models for glycolysis.

  • 2.
    Biteus, Jonas
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Nielsen, Lars
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Improving Airplane Safety by Incorporating Diagnosis into Existing Safety Practice2004Report (Other academic)
    Abstract [en]

    Safety has always been at premium in airfare. There is a long history of systematic work in the field, and current practice has established a high degree of safety that has resulted in so low failure numbers that the public finds confidence in the process of air worthiness certification. However, the design and development process of airplanes to achieve this is costly and may be even more so since modern airplanes become more and more complex. Furthermore, recent trends towards Unmanned Aerial Vehicles (UAV) are likely to require even more efforts and costs, to fulfill the increased safety requirements. Therefore it is interesting to investigate modern techniques that promises to improve safety at reduced costs. One such technique is diagnosis. Diagnosis in general is a term that includes several research and application fields. Examples of such fields, that are technology drivers, are the fields of supervision both on-line (on-board) and off-line (on ground), operator support that evolved from the Harrisburg accident, and law based emission diagnostics regulation e.g. as stipulated by California Air Resource Board (CARB).

    The current work is an investigation in the cross field between safety assessment and diagnosis techniques. The first step was to root the work in existing safety practice. This means that the Swedish defense procedure has been adopted as described in H SystSäk E. It is a safety framework that uses fault tree analysis and failure mode effect analysis as important tools. Thereafter some flight applications were investigated together with Saab specialists to capture and formulate some aspects that are non-trivial to cast in the existing safety framework. Examples of such aspects found are for instance related to performance requirements in different operational model. A principle case study was then formulated using laboratory equipment, with the aim to capture some of the identified aspects in the problem formulation. A complete process for safety analysis was then completed along the lines of H SystSäk E including all meetings and documents required therein. Several observations were done during this work, but the overall conclusion so far is that the effect of introducing diagnosis algorithms can be handled in the safety analysis, and, yes, that there is a promise that diagnosis algorithms can improve safety in a structured quantitative way by lowering the contribution to the total failure risk from the subsystem being diagnosed.

  • 3.
    Brannmark, Cecilia
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Palmer, Robert
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Glad, Torkel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Mass and Information Feedbacks through Receptor Endocytosis Govern Insulin Signaling as Revealed Using a Parameter-free Modeling Framework2010In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 285, no 26, p. 20171-20179Article in journal (Refereed)
    Abstract [en]

    Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between coexisting submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.

  • 4.
    Brännmark, Cecilia
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Bergenholm, Linnéa
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Ekstrand, Eva-Maria
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Insulin Signaling in Type 2 Diabetes: Experimental and Modeling Analyses Reveal Mechanisms of Insulin Resistance in Human Adipocytes2013In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 288, no 14, p. 9867-9880Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems-level mechanistic understanding of insulin resistance in humans. We developed a dynamic mathematical model of insulin signaling – normally and in diabetes – based on quantitative steady-state and dynamic time-course data on signaling intermediaries in human mature adipocytes. At the core of insulin resistance is attenuation of a positive feedback from mammalian target of rapamycin in complex with raptor (mTORC1) to the insulin receptor substrate-1 (IRS1), which explains reduced sensitivity and signal strength throughout the signaling network. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

  • 5.
    Cedersund, G
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Elimination of the initial value parameters when identifying a system close to a Hopf bifurcation.2006In: IEE Proceedings - Systems Biology, ISSN 1741-2471, E-ISSN 1741-248X, Vol. 153, no 6, p. 448-456Article in journal (Refereed)
    Abstract [en]

    One of the biggest problems when performing system identification of biological systems is that it is seldom possible to measure more than a small fraction of the total number of variables. If that is the case, the initial state, from where the simulation should start, has to be estimated along with the kinetic parameters appearing in the rate expressions. This is often done by introducing extra parameters, describing the initial state, and one way to eliminate them is by starting in a steady state. We report a generalisation of this approach to all systems starting on the centre manifold, close to a Hopf bifurcation. There exist biochemical systems where such data have already been collected, for example, of glycolysis in yeast. The initial value parameters are solved for in an optimisation sub-problem, for each step in the estimation of the other parameters. For systems starting in stationary oscillations, the sub-problem is solved in a straight-forward manner, without integration of the differential equations, and without the problem of local minima. This is possible because of a combination of a centre manifold and normal form reduction, which reveals the special structure of the Hopf bifurcation. The advantage of the method is demonstrated on the Brusselator.

  • 6.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Conclusions via unique predictions obtained despite unidentifiability - new definitions and a general method2012In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 279, no 18, p. 3513-3527Article in journal (Refereed)
    Abstract [en]

    It is often predicted that model-based data analysis will revolutionize biology, just as it has physics and engineering. A widely used tool within such analysis is hypothesis testing, which focuses on model rejections. However, the fact that a systems biology model is non-rejected is often a relatively weak statement, as such models usually are highly over-parametrized with respect to the available data, and both parameters and predictions may therefore be arbitrarily uncertain. For this reason, we formally define and analyse the concept of a core prediction. A core prediction is a uniquely identified property that must be fulfilled if the given model structure is to explain the data, even if the individual parameters are non-uniquely identified. It is shown that such a prediction is as strong a conclusion as a rejection. Furthermore, a new method for core prediction analysis is introduced, which is beneficial for the uncertainty of specific model properties, as the method only characterizes the space of acceptable parameters in the relevant directions. This avoids the curse of dimensionality associated with the generic characterizations used by previously proposed methods. Analysis on examples shows that the new method is comparable to profile likelihood with regard to practical identifiability, and thus generalizes profile likelihood to the more general problem of observability. If used, the concepts and methods presented herein make it possible to distinguish between a conclusion and a mere suggestion, which hopefully will contribute to a more justified confidence in systems biology analyses.

  • 7.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Science & Engineering.
    Prediction uncertainty estimation despite unidentifiability: an overview of recent developments2016In: Uncertainty in Biology: a computational modeling approach / [ed] Liesbet Geris and David Gomez-Cabrero, Springer, 2016, p. 449-466Chapter in book (Refereed)
    Abstract [en]

    One of the most important properties of a mathematical model is the abilityto make predictions: to predict that which has not yet been measured. Suchpredictions can sometimes be obtained from a simple simulation, but that requiresthat the parameters in the model are known from before. In biology, theparameters are usually both not known from before and not identifiable, i.e.the parameter values cannot be determined uniquely from available data. Insuch cases of unidentifiability, the space of acceptable parameters is large, ofteninfinite in certain directions. For such large spaces, sampling-based approachesthat try to characterize the entire space have difficulties. Recently, a new type ofalternative approaches that circumvent this characterization problem has beenproposed: where one only searches those directions in the space of acceptable parametersthat are relevant for the uncertainty of a particular prediction. In thisreview chapter, these recently proposed methods are compared and contrasted,both regarding theoretical properties, and regarding user experience. The focusis on methods from the field of systems biology, but also methods from biostatistics,pharmacodynamics, and biochemometrics are discussed. The hope is thatthis review will increase the usefulness and understanding of already proposedmethods, and thereby help foster a tradition where predictions only are deemedinteresting if their uncertainties have been determined.

  • 8.
    Cedersund, Gunnar
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    System identification of nonlinear thermochemical systems with dynamical instabilities2004Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Thermochemical systems appear in applications as widespread as in combustion engines, in industrial chemical plants and inside biological cells. Science in all these areas is going towards a more model based thinking, and it is therefore important to develop good methods for system identification, especially fit for these kind of systems. The presented systems are described as nonlinear differential equations, and the common feature of the models is the presence of a boundary between an oscillating and a non-oscillating region, i.e. the presence of a bifurcation.

    If it is known that a certain input signal brings the system to a bifurcation manifold, and this is the case for many thermochemical systems, this knowledge can be included as an extra constraint in the parameter estimation. Except for special cases, however, this constraint can not be obtained analytically. For the general case a reformulation, adding variables and equally many constraints, have been done. This formulation allows for efficient use of standard techniques from constrained optimization theory. For systems with large state spaces the parameter vector describing the initial state becomes big (sometimes > 1000), and special treatment is required. New theory for such treatment have been shown, and the results are valid for systems operating close to a Hopf bifurcation. Through a combined center manifold and normal form reduction, the initial state is described in minimal degrees of freedom. Experiment designs are presented that force the minimal degrees of freedom two be 2 or 3, independently of the dimension of the state space. The initial state is determined by solving a sub-problem for each step in the ordinary estimation process. For systems starting in stationary oscillations the normal form reduction reveals the special structure of this sub-problem. Therefore it can be solved in a straight-forward manner, that does not have the problem of local minima, and that does not require any integration of the differential equations. It is also shown how the knowledge, coming from the presence of a bifurcation , can be used for model validation. The validation is formulated as a test quantity, and it has the benefit that it can work also with uncalibrated sensors, i.e. with sensors whose exact relation to the state variables is not known.

    Two new models are presented. The first is a multi-zonal model for cylinder pressure, temperature and ionization currents. It is a physically based model with the main objectives of understanding the correlation between the ionization curve and the pressure peak location. It is shown that heat transfer has a significant effect on this relation. It is further shown that the combination of a geometrically based heat transfer mode! and a dynamical NO-model predicts the correct relationship between the pressure and ionization peak location within one crank angel degree. The second developed model is for the mitogenic response to insulin in fat cells. It is the first developed model for this specific pathway and the model has been compared and estimated to experiment al data. Finally, a 16-dimensional model for activated neutrophils has been used to generate virtual data, on which the presented methods have been applied, and on which the performance of the methods were demonstrated.

  • 9.
    Cedersund, Gunnar
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Knudsen, C.
    Technical University of Denmark.
    Improved parameter estimation for systems with an experimentally located Hopf bifurcation2005In: IEE Proceedings - Systems Biology, ISSN 1741-2471, E-ISSN 1741-248X, Vol. 152, no 3, p. 161-168Article in journal (Refereed)
    Abstract [en]

    When performing system identification, we have two sources of information: experimental data and prior knowledge. Many cell-biological systems are oscillating, and sometimes we know an input where the system reaches a Hopf bifurcation. This is the case, for example, for glycolysis in yeast cells and for the Belousov-Zhabotinsky reaction, and for both of these systems there exist significant numbers of quenching data, ideal for system identification. We present a method that includes prior knowledge of the location of a Hopf bifurcation in estimation based on time-series. The main contribution is a reformulation of the prior knowledge into the standard formulation of a constrained optimisation problem. This formulation allows for any of the standard methods to be applied, including all the theories regarding the methods properties. The reformulation is carried out through an over-parametrisation of the original problem. The over-parametrisation allows for extra constraints to be formed, and the net effect is a reduction of the search space. A method that can solve the new formulation of the problem is presented, and the advantage of adding the prior knowledge is demonstrated on the Brusselator.

  • 10.
    Cedersund, Gunnar
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Science & Engineering.
    Oscar, Samuelsson
    Stockholm, Sweden.
    Ball, Gordon
    Stockholm, Sweden.
    Tegnér, Jesper
    Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Solna, Sweden.
    Gomez-Cabrero, David
    Center for Molecular Medicine, Stockholm, Sweden.
    Optimization in biology parameter estimation and the associated optimization problem2016In: Uncertainty in biology: a computational modeling approach / [ed] Liesbet Geris, David Gomez-Cabrero, New York: Springer, 2016, p. 177-197Chapter in book (Refereed)
    Abstract [en]

    Parameter estimation – the assignment of values to the parameters in a model – is an important and time-consuming task in computational biology. Recent computational and algorithmic developments have provided novel tools to improve this estimation step. One of these improvements concerns the optimization step, where the parameter space is explored to find interesting regions. In this chapter we review the parameter estimation problem, with a special emphasis on the associated optimization methods. In relation to this, we also provide concepts and tools to help you select the appropriate methodology for a specific scenario.

  • 11.
    Cedersund, Gunnar
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Roll, Jacob
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Systems biology: Model Based Evaluation and Comparison of Potential Explanations for Given Biological Data2009In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 276, no 4, p. 903-922Article, review/survey (Refereed)
    Abstract [en]

    Systems biology and its usage of mathematical modeling to analyse biological data is rapidly becoming an established approach to biology. A crucial advantage of this approach is that more information can be extracted from observations of intricate dynamics, which allows nontrivial complex explanations to be evaluated and compared. In this minireview we explain this process, and review some of the most central available analysis tools. The focus is on the evaluation and comparison of given explanations for a given set of experimental data and prior knowledge. Three types of methods are discussed: (a) for evaluation of whether a given model is sufficiently able to describe the given data to be nonrejectable; (b) for evaluation of whether a slightly superior model is significantly better; and (c) for a general evaluation and comparison of the biologically interesting features in a model. The most central methods are reviewed, both in terms of underlying assumptions, including references to more advanced literature for the theoretically oriented reader, and in terms of practical guidelines and examples, for the practically oriented reader. Many of the methods are based upon analysis tools from statistics and engineering, and we emphasize that the systems biology focus on acceptable explanations puts these methods in a nonstandard setting. We highlight some associated future improvements that will be essential for future developments of model based data analysis in biology.

  • 12.
    Cedersund, Gunnar
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    Roll, Jacob
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ulfhielm, Erik
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Danielsson, Anna
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    Tidefelt, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Strålfors, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    Model-Based Hypothesis Testing of Key Mechanisms in Initial Phase of Insulin Signaling2008In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 4, no 6Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is characterized by insulin resistance of target organs, which is due to impaired insulin signal transduction. The skeleton of signaling mediators that provide for normal insulin action has been established. However, the detailed kinetics, and their mechanistic generation, remain incompletely understood. We measured time-courses in primary human adipocytes for the short-term phosphorylation dynamics of the insulin receptor (IR) and the IR substrate-1 in response to a step increase in insulin concentration. Both proteins exhibited a rapid transient overshoot in tyrosine phosphorylation, reaching maximum within 1 min, followed by an intermediate steady-state level after approximately 10 min. We used model-based hypothesis testing to evaluate three mechanistic explanations for this behavior: (A) phosphorylation and dephosphorylation of IR at the plasma membrane only, (B) the additional possibility for IR endocytosis, (C) the alternative additional possibility of feedback signals to IR from downstream intermediates. We concluded that (A) is not a satisfactory explanation, that (B) may serve as an explanation only if both internalization, dephosphorylation, and subsequent recycling are permitted, and that (C) is acceptable. These mechanistic insights cannot be obtained by mere inspection of the datasets, and they are rejections and thus stronger and more final conclusions than ordinary model predictions.

  • 13.
    Cedersund, Gunnar
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors , Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Putting the pieces together in diabetes research: Towards a hierarchical model of whole-body glucose homeostasis2009In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 36, no 1, p. 91-104Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is one of the most widespread and rapidly spreading diseases world-wide and has been subject of extensive research efforts. However, understanding the molecular basis of the disease is increasing piecemeal and a consensus regarding the overall picture of normal metabolic regulation and malfunction in diabetes has not emerged. A systems biology approach, combining mathematical modelling with simultaneous high-throughput measurements, can be of considerable help. On the whole-body level, this has been done in pharmacokinetic and pharmacodynamic models, which recently have started to mature into more physiologically realistic organ-based models. At the other end of the spectrum, detailed models for crucial cellular processes are starting to mature into complete modules that potentially can be fitted into such whole-body organ-based models. The result of such a merge is a multi-level hierarchical model, which is a model type that has been common in technical systems. In this review, we report and exemplify some of the recent progress that has been made to achieve such a hierarchical model, and we argue why it is only through such a model that a Complete picture of diabetes mellitus can be obtained.

  • 14.
    Cedersund, Gunnar
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Jirstrand, Mats
    Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Teknikpark, 41288 Gothenburg, Sweden.
    Core-Box Modeling in the Biosimulation of Drug Action2008In: Biosimulation in Drug Development / [ed] Martin Bertau, Erik Mosekilde, Hans V. Westerhoff, Weinheim, Germany: Wiley-VCH Verlagsgesellschaft, 2008, p. 115-139Chapter in book (Other academic)
  • 15.
    Danø, Sune
    et al.
    Copenhagen University.
    Madsen, Mads F
    Copenhagen University.
    Schmidt, Henning
    Chalmers Technical University.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Reduction of a biochemical model with preservation of its basic dynamic properties.2006In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 273, no 21, p. 4862-4877Article in journal (Refereed)
    Abstract [en]

    The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models.

  • 16.
    Durrieu, Lucía
    et al.
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bush, Alan
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Gollvik, Martin
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Cedersund, Gunnar
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Colman-Lerner, Alejandro
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Quantification of nuclear transport in single cells2014Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

  • 17.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Dahlström, Nils
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Faculty of Health Sciences.
    Smedby, Örjan
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Brismar, Torkel
    Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    The First Human Whole Body Pharmacokinetic Minimal Model for the Liver Specific Contrast Agent Gd-EOB-DTPA2011In: Proc. Intl. Soc. Mag. Reson. Med. 19 (2011), 2011, p. 3016-3016Conference paper (Refereed)
  • 18.
    Forsgren, Mikael
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    AdHoc Constraints on Complex Liver DCE-MRI Models Can Reduce Parameter Uncertainty2012Conference paper (Other academic)
  • 19.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Physiologically Realistic and Validated Mathematical Liver Model Revels Hepatobiliary Transfer Rates for Gd-EOB-DTPA Using Human DCE-MRI Data2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 4, p. 0095700-Article in journal (Refereed)
    Abstract [en]

    Objectives: Diffuse liver disease (DLD), such as non-alcoholic fatty liver disease (NASH) and cirrhosis, is a rapidly growing problem throughout the Westernized world. Magnetic resonance imaging (MRI), based on uptake of the hepatocyte-specific contrast agent (CA) Gd-EOB-DTPA, is a promising non-invasive approach for diagnosing DLD. However, to fully utilize the potential of such dynamic measurements for clinical or research purposes, more advanced methods for data analysis are required. Methods: A mathematical model that can be used for such data-analysis was developed. Data was obtained from healthy human subjects using a clinical protocol with high spatial resolution. The model is based on ordinary differential equations and goes beyond local diffusion modeling, taking into account the complete system accessible to the CA. Results: The presented model can describe the data accurately, which was confirmed using chi-square statistics. Furthermore, the model is minimal and identifiable, meaning that all parameters were determined with small degree of uncertainty. The model was also validated using independent data. Conclusions: We have developed a novel approach for determining previously undescribed physiological hepatic parameters in humans, associated with CA transport across the liver. The method has a potential for assessing regional liver function in clinical examinations of patients that are suffering of DLD and compromised hepatic function.

  • 20.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Karlsson, Markus
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Whole Body Mechanistic Minimal Model for Gd-EOB-DTPA Contrast Agent Pharmacokinetics in Evaluation of Diffuse Liver Disease2014Conference paper (Other academic)
    Abstract [en]

    Purpose: Aiming for non-invasive diagnostic tools to decrease the need for biopsy in diffuse liver disease and to quantitatively describe liver function, we applied a mechanistic pharmacokinetic modelling analysis of liver MRI with Gd-EOB-DTPA. This modelling method yields physiologically relevant parameters and was compared to previously developed methods in a patient group with diffuse liver disease. Materials and Methods: Using data from healthy volunteers undergoing liver MRI, an identifiable mechanistic model was developed, based on compartments described by ordinary differential equations and kinetic expressions, and validated with independent data including Gd-EOB-DTPA concentration measurements in blood samples. Patients (n=37) with diffuse liver disease underwent liver biopsy and MRI with Gd-EOB-DTPA. The model was used to derive pharmacokinetic parameters which were then compared with other quantitative estimates in their ability to separate mild from severe liver fibrosis. Results: The estimations produced by the mechanistic model allowed better separation between mild and severe fibrosis than previously described methods for quantifying hepatic Gd-EOB-DTPA uptake. Conclusions: With a mechanistic pharmacokinetic modelling approach, the estimation of liver uptake function and its diagnostic information can be improved compared to current methods.

  • 21.
    Forsgren, Mikael
    et al.
    Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Weber, Patrick
    Linköping University, Department of Clinical and Experimental Medicine, Rheumatology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Rheumatology.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Pena, José
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Bayesian mixed-effect modeling of contrast agent data for decision-support when diagnosing diffuse liver disease2012Conference paper (Other academic)
  • 22.
    Johansson, Rickard
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Kreutz, Clemens
    Physics Department, University of Freiburg, Germany.
    Strålfors, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    Timmer, Jens
    Physics Department, University of Freiburg, Germany.
    Cedersund, Gunnar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    A two-dimensional bootstrap approach to model discriminationManuscript (preprint) (Other academic)
  • 23.
    Johansson, Rikard
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Kreutz, Clemens
    University of Freiburg, Department of Physics.
    Bartolomé Rodríguez, M. M.
    University of Freiburg, Department of Medicine.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Timmer, Jens
    University of Freiburg, Department of Physics.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Elucidating mechanisms of early insulin signaling in primary adipocytes and hepatocytes: a joint systems biology effort2009Conference paper (Other academic)
    Abstract [en]

    Type II diabetes is one of the most common diseases afflicting people today. Understanding how this disease works, not only on a cellular level and between different organs and tissues, but also how it affects whole body level homeostasis is crucial for enhancement of its treatment. We use model-bases analysis as a tool for distinguishing different biological hypothesis on the system behavior.

    The Insulin Receptor (IR), is located in the cell membrane as a dimer, and thus has the potential two bind two different insulin molecules. It can also undergo a series of phosphorylations, as well as having the ability to become internalized, and thus be removed from the cell’s censing area. However, it can then be recycled back to the membrane again. The major target of IR is the Insulin Receptor Substrate 1 (IRS1). IRS1 in turn mediates the signal further downstream through Protein Kinase B (PBK) and mammalian Target of Rapamycin (mTOR).  In adipocytes the end result is the translocation of internal vesicles containing Glucose Transporters (GLUT4) to the membrane, thus increasing the uptake of glucose. The liver, on the other hand, responds by down regulating the endogenous glucose production.

    The activity of IRS1 is determined by its phospho-tyrosine composition. This in turn is regulated by at least two serine-phosphorylations, on ser307 and ser312. The serine levels of this protein are regulated by downstream kinases, of which only one is known, S6K. The ser307 phosphorylation appears to allow for a short term positive feedback while the ser312 phosphorylation has the dynamics of a more long term negative feedback.

    The overall dynamics of the IRS1 tyrosine phosphorylation is a mirror of that of the Insulin Receptor. They both have a quick response to insulin within minutes, manifested as a high overshoot before declining to a steady state level. The overshoot behavior of this system can be explained either by a downstream negative feedback, or by having an advanced internalization and recycling model. Several hypotheses of the negative feedback mechanisms necessary to allow for the receptor to adopt such a behavior have previously been rejected by us. So has the hypothesis of internalization (unpublished data). The internalized Insulin Receptors can account for only a small fraction of the total amount of receptors, it however seems to be necessary for its own down regulation, since without it the overshoot behavior disappears.

    The complexity of this system is immense and hence we keep to as minimal models as possible, only considering adding complexity to the system when data indicates so, or when a simpler model structure has been rejected. We model the system with a series of Ordinary Differential Equations (ODEs), optimize and estimate the parameters of a given model structure with the Systems Biology Toolbox (SBTB) and reject, or fail to reject, models based on their statistical agreement with our data. We search the entire approximated parameter space for a sample of all acceptable parameter values for any given parameter. We then look for commonalities shared between model simulations of all parameter sets in the sample. That is, a behavior of e.g. a state in the model that has to be above a certain threshold for it to be able to explain the data, while other states might be of arbitrary sizes. If we find such a commonality, we call it a core prediction. Assuming your data is correct and your analysis thorough, a Core Prediction has the same strength as a model rejection. The common aspect, shared between all acceptable parameter etc, is something that has to be true, no matter how much more data you acquire. One such core prediction, which led to the rejection of the internalization hypothesis, was that the amount of internalized IR had to be above 80% of the total receptor pool.  We subsequently rejected this experimentally.

  • 24.
    Johansson, Rikard
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Combining test statistics and models in bootstrapped model rejection: it is a balancing act2014In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 8, no 46Article in journal (Refereed)
    Abstract [en]

    Background: Model rejections lie at the heart of systems biology, since they provide conclusive statements: that the corresponding mechanistic assumptions do not serve as valid explanations for the experimental data. Rejections are usually done usinge.g. the chi-square test (χ2) or the Durbin-Watson test (DW). Analytical formulas for the corresponding distributions rely on assumptions that typically are not fulfilled. This problem is partly alleviated by the usage of bootstrapping, a computationally heavy approach to calculate an empirical distribution. Bootstrapping also allows for a natural extension to estimation of joint distributions, but this feature has so far been little exploited.

    Results: We herein show that simplistic combinations of bootstrapped tests, like the max or min of the individual p-values, give inconsistent, i.e. overly conservative or liberal, results. A new two-dimensional (2D) approach based on parametric bootstrapping, on the other hand, is found both consistent and with a higher power than the individual tests, when tested on static and dynamic examples where the truth is known. In the same examples, the most superior test is a 2D χ2 vs χ2, where the second χ2-value comes from an additional help model, and its ability to describe bootstraps from the tested model. This superiority is lost if the help model is too simple, or too flexible. If a useful help model is found, the most powerful approach is the bootstrapped log-likelihood ratio (LHR). We show that this is because the LHR is one-dimensional, because the second dimension comes at a cost, and because LHR has retained most of the crucial information in the 2D distribution. These approaches statistically resolve a previously published rejection example for the first time.

    Conclusions: We have shown how to, and how not to, combine tests in a bootstrap setting, when the combinatio is advantageous, and when it is advantageous to include a second model. These results also provide a deeper insight into the original motivation for formulating the LHR, for the more general setting of nonlinear and non-nested models. These insights are valuable in cases when accuracy and power, rather than computational speed, are prioritized.

  • 25.
    Jullesson, David
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.2015In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed)
    Abstract [en]

    As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

  • 26.
    Karlsson, Markus
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering. AstraZeneca, Sweden; Fraunhofer Chalmers Centre, Sweden.
    Durrieu, Lucia
    University of Buenos Aires, Argentina; University of Buenos Aires, Argentina.
    Colman-Lerner, Alejandro
    University of Buenos Aires, Argentina; University of Buenos Aires, Argentina.
    Kjellsson, Maria C.
    Uppsala University, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it2015In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 9, no 52Article in journal (Refereed)
    Abstract [en]

    Background: Studies of cell-to-cell variation have in recent years grown in interest, due to improved bioanalytical techniques which facilitates determination of small changes with high uncertainty. Like much high-quality data, single-cell data is best analysed using a systems biology approach. The most common systems biology approach to single-cell data is the standard two-stage (STS) approach. In STS, data from each cell is analysed in a separate sub-problem, meaning that only data from the same cell is used to calculate the parameter values within that cell. Because only parts of the data are considered, problems with parameter unidentifiability are exaggerated in STS. In contrast, a related approach to data analysis has been developed for the studies of patient-to-patient variations. This approach, called nonlinear mixed-effects modelling (NLME), makes use of all data, when estimating the patient-specific parameters. NLME would therefore be advantageous compared to STS also for the study of cell-to-cell variation. However, no such systematic evaluation of the two approaches exists. Results: Herein, such a systematic comparison between STS and NLME has been performed. Different examples, both linear and nonlinear, and both simulated and real experimental data, have been examined. With informative data, there is no significant difference in the results for either parameter or noise estimation. However, when data becomes uninformative, NLME is significantly superior to STS. These results hold independently of whether the loss of information is due to a low signal-to-noise ratio, too few data points, or a bad input signal. The improvement is shown to come from both the consideration of a joint likelihood (JLH) function, describing all parameters and data, and from an a priori postulated form of the population parameters. Finally, we provide a small tutorial that shows how to use NLME for single-cell analysis, using the free and user-friendly software Monolix. Conclusions: When considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.

  • 27.
    Lundengård, Karin
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Elinder, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Engström, Maria
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Mechanistic Modelling Investigates the Neural Basis behind the Hemodynamic Response in fMRI2015In: 16TH NORDIC-BALTIC CONFERENCE ON BIOMEDICAL ENGINEERING, Springer Science Business Media , 2015, Vol. 48, p. 86-87Conference paper (Refereed)
    Abstract [en]

    This work serves as a basis for a new type of fMRI analysis, which is based on a mechanistic interpretation of the hemodynamic response to synaptic activity. Activation was measured in the visual cortex of 12 healthy controls and ordinary differential equation models were fitted to the time series of the hemodynamic response. This allowed us to reject or refine previously proposed mechanistic hypotheses. This is the first attempt to describe the hemodynamic response quantitatively based on recent neurobiological findings. This mechanistic approach stands in contrast to the standard phenomenological description using the gamma variate function.

  • 28.
    Lundengård, Karin
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Sten, Sebastian
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Leong, Felix
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Smedberg, Alexander
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Elinder, Fredrik
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Engström, Maria
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, article id e1004971Article in journal (Refereed)
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) measures brain activity by detecting the blood-oxygen-level dependent (BOLD) response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new, independent validation data. These results open the door to a new type of fMRI analysis that more accurately reflects the true neuronal activity.

  • 29.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Palmér, Robert
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Brugård, Jan
    MathCore Engn.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Endocrinology and Gastroenterology UHL.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis2011In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 286, no 29, p. 26028-26041Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

  • 30.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Insulin signaling - mathematical modeling comes of age2012In: Trends in endocrinology and metabolism, ISSN 1043-2760, E-ISSN 1879-3061, Vol. 23, no 3, p. 107-115Article, review/survey (Refereed)
    Abstract [en]

    Signaling pathways that only a few years ago appeared simple and understandable, albeit far from complete, have evolved into very complex multi-layered networks of cellular control mechanisms, which in turn are integrated in a similarly complex whole-body level of control mechanisms. This complexity sets limits for classical biochemical reasoning, such that a correct and complete analysis of experimental data while taking the full complexity into account is not possible. In this Opinion we propose that mathematical modeling can be used as a tool in insulin signaling research, and we demonstrate how recent developments in modeling - and the integration of modeling in the experimental process - provide new possibilities to approach and decipher complex biological systems more efficiently.

  • 31.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Jullesson, David
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes2012In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 279, no 6, p. 987-999Article in journal (Refereed)
    Abstract [en]

    Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling.

  • 32.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMED DMPK AstraZeneca R&D, Mölndal, Sweden.
    Lindgren, Isa
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, The Institute of Technology.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundengård, Karin
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences.
    Cervin, Ida
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Arbring, Theresia
    Linköping University, Department of Science and Technology, Physics and Electronics. Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering.
    Altimitas, Jordi
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, The Institute of Technology.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Mathematical modeling improves EC50 estimations from classical dose–response curves2015In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 5, p. 951-962Article in journal (Refereed)
    Abstract [en]

    The beta-adrenergic response is impaired in failing hearts. When studying beta-adrenergic function in vitro, the half-maximal effective concentration (EC50) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50. Instead, we have applied a mathematical modeling approach to interpret the full dose–response curvein a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose–response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180–525 nM) is higher than the classically interpreted EC50 (46–191 nM). Mathematical modeling thus makes it possible to reinterpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible.

  • 33.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Biomedical Engineering.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes2014In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 289, no 48, p. 33215-33230Article in journal (Refereed)
    Abstract [en]

    The response to insulin is impaired in type 2 diabetes. Much information is available about insulin signaling, but understanding of the cellular mechanisms causing impaired signaling and insulin resistance is hampered by fragmented data, mainly obtained from different cell lines and animals. We have collected quantitative and systems-wide dynamic data on insulin signaling in primary adipocytes and compared cells isolated from healthy and diabetic individuals. Mathematical modeling and experimental verification identified mechanisms of insulin control of the MAPKs ERK1/2. We found that in human adipocytes, insulin stimulates phosphorylation of the ribosomal protein S6 and hence protein synthesis about equally via ERK1/2 and mTORC1. Using mathematical modeling, we examined the signaling network as a whole and show that a single mechanism can explain the insulin resistance of type 2 diabetes throughout the network, involving signaling both through IRS1, PKB, and mTOR and via ERK1/2 to the nuclear transcription factor Elk1. The most important part of the insulin resistance mechanism is an attenuated feedback from the protein kinase mTORC1 to IRS1, which spreads signal attenuation to all parts of the insulin signaling network. Experimental inhibition of mTORC1 using rapamycin in adipocytes from non-diabetic individuals induced and thus confirmed the predicted network-wide insulin resistance.

  • 34.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Rohini Rajan, Meenu
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Fagerholm, Siri
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Brännmark, Cecilia
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    The insulin-signaling network in human adipocytes, normally and in diabetes: role of signaling through ERK1/22014Manuscript (preprint) (Other academic)
    Abstract [en]

    Insulin acutely controls metabolism in adipocytes, but also nuclear transcription through the “mitogenic” signaling pathway mediated by Map-kinases ERK1/2 (ERK). The cellular metabolic response to insulin is attenuated in insulin resistance and type 2 diabetes, but whether this involves also signaling through ERK is unclear. Based on experimental data from primary mature human adipocytes from diabetic and nondiabetic individuals, we demonstrate a network-wide, model-based analysis of insulin signaling through ERK to phosphorylation of transcription factor Elk1 integrated with signaling for “metabolic” control. We use minimal modeling to analyze the idiosyncratic phosphorylation dynamics of ERK, i.e. a slow phosphorylation response that returns to basal in response to insulin, and conclude that sequestration of ERK is the simplest explanation to data. We also demonstrate a significant cross-talk between ERK and mTORC1 signaling to ribosomal protein S6 for control of protein synthesis. A reduced sensitivity and reduced maximal phosphorylation of ERK in response to insulin in the diabetic state can be explained by the same mechanisms that generate insulin resistance in the control of metabolism.

  • 35.
    Nyman, Elin
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden.
    Rozendaal, Yvonne J W
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Helmlinger, Gabriel
    AstraZeneca, Pharmaceuticals LP, Waltham, MA, USA.
    Hamrén, Bengt
    AstraZeneca, Gothenburg, Sweden.
    Kjellsson, Maria C
    Uppsala University, Uppsala, Sweden.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    van Riel, Natal A W
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Gennemark, Peter
    AstraZeneca R&D, Gothenburg, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes2016In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, p. 1-14Article, review/survey (Refereed)
    Abstract [en]

    We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

  • 36.
    Nyman, Elin
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Multilevel-Modeling, Core Predictions, and the Concept of Final Conclusions2012In: Biosimulation in Biomedical Research, Health Care and Drug Development / [ed] Mosekilde, Erik; Sosnovtseva, Olga; Rostami-Hodjegan, Amin, Wien: Springer, 2012, p. 311-328Chapter in book (Other academic)
  • 37.
    Palmér, Robert
    et al.
    Wolfram MathCore AB, Linköping, Sweden;.
    Nyman, Elin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Wolfram MathCore AB, Linköping, Sweden;.
    Penney, Mark
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge, UK.
    Marley, Anna
    Bioscience, Astra Zeneca, Alderley Park, UK.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Agoram, Balaji
    Department of Clinical Pharmacology, Drug Metabolism, and Pharmacokinetics, MedImmune, Cambridge.
    Effects of IL-1β-Blocking Therapies in Type 2 Diabetes Mellitus: A Quantitative Systems Pharmacology Modeling Approach to Explore Underlying Mechanisms2014In: CPT: pharmacometrics & systems pharmacology, ISSN 2163-8306, Vol. 3, no 6, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

  • 38.
    Rajan, Meenu Rohini
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Cardiovascular and Metabolic Diseases, Innovative Medicines, and Drug Metabolism and Pharmacokinetics, AstraZeneca Research and Development, Gothenburg, Sweden .
    Kjölhede, Preben
    Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes2016In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 291, no 30, p. 15806-15819Article in journal (Refereed)
    Abstract [en]

    Insulin resistance is a major aspect of type 2 diabetes (T2D), which results from impaired insulin signaling in target cells. Signaling to regulate forkhead box protein O1 (FOXO1) may be the most important mechanism for insulin to control transcription. Despite this, little is known about how insulin regulates FOXO1 and how FOXO1 may contribute to insulin resistance in adipocytes, which are the most critical cell type in the development of insulin resistance. We report a detailed mechanistic analysis of insulin control of FOXO1 in human adipocytes obtained from non-diabetic subjects and from patients with T2D. We show that FOXO1 is mainly phosphorylated through mTORC2-mediated phosphorylation of protein kinase B at Ser(473) and that this mechanism is unperturbed in T2D. We also demonstrate a cross-talk from the MAPK branch of insulin signaling to stimulate phosphorylation of FOXO1. The cellular abundance and consequently activity of FOXO1 are halved in T2D. Interestingly, inhibition of mTORC1 with rapamycin reduces the abundance of FOXO1 to the levels in T2D. This suggests that the reduction of the concentration of FOXO1 is a consequence of attenuation of mTORC1, which defines much of the diabetic state in human adipocytes. We integrate insulin control of FOXO1 in a network-wide mathematical model of insulin signaling dynamics based on compatible data from human adipocytes. The diabetic state is network-wide explained by attenuation of an mTORC1-to-insulin receptor substrate-1 (IRS1) feedback and reduced abundances of insulin receptor, GLUT4, AS160, ribosomal protein S6, and FOXO1. The model demonstrates that attenuation of the mTORC1-to-IRS1 feedback is a major mechanism of insulin resistance in the diabetic state.

  • 39.
    Schleicher, Jana
    et al.
    Hans Knöll Institute (HKI) in Jena, Germany.
    Conrad, Theresia
    Hans Knöll Institute (HKI) in Jena, Germany.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Guthke, Reinhard
    Systems Biology at Jena University,Germany.
    Linde, Jörg
    Jena University,Germany.
    Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions2017In: Briefings in Functional Genomics & Proteomics, ISSN 2041-2649, E-ISSN 2041-2657, Vol. 16, no 2, p. 57-69Article in journal (Refereed)
    Abstract [en]

    Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

  • 40.
    Schmidt, H.
    et al.
    Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.
    Madsen, M.F.
    Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
    Dano, S.
    Danø, S., Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
    Cedersund, Gunnar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Cell Biology.
    Complexity reduction of biochemical rate expressions2008In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, no 6, p. 848-854Article in journal (Refereed)
    Abstract [en]

    Motivation: The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods. Results: We present a new reduction method that reduces complex rational rate expressions, such as those often used to describe enzymatic reactions. The method is a novel term-based identifiability analysis, which is easy to use and allows for user-specified reductions of individual rate expressions in complete models. The method is one of the first methods to meet the classical engineering objective of improved parameter identifiability without losing the systems biology demand of preserved biochemical interpretation. © The Author 2008. Published by Oxford University Press. All rights reserved.

  • 41.
    Sips, Fianne L. P.
    et al.
    Eindhoven University of Technology, Netherlands.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. CVMD iMED DMPK AstraZeneca RandD, Sweden.
    Adiels, Martin
    University of Gothenburg, Sweden.
    Hilbers, Peter A. J.
    Eindhoven University of Technology, Netherlands.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    van Riel, Natal A. W.
    Eindhoven University of Technology, Netherlands.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 9, p. e0135665-Article in journal (Refereed)
    Abstract [en]

    In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.

  • 42.
    Sunnaker, Mikael
    et al.
    Fraunhofer Chalmers Research Centre for Industrial Math.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Jirstrand, Mats
    Fraunhofer Chalmers Research Centre for Industrial Math.
    A method for zooming of nonlinear models of biochemical systems2011In: BMC Systems Biology, ISSN 1752-0509, E-ISSN 1752-0509, Vol. 5, no 140Article in journal (Refereed)
    Abstract [en]

    Background: Models of biochemical systems are typically complex, which may complicate the discovery of cardinal biochemical principles. It is therefore important to single out the parts of a model that are essential for the function of the system, so that the remaining non-essential parts can be eliminated. However, each component of a mechanistic model has a clear biochemical interpretation, and it is desirable to conserve as much of this interpretability as possible in the reduction process. Furthermore, it is of great advantage if we can translate predictions from the reduced model to the original model. less thanbrgreater than less thanbrgreater thanResults: In this paper we present a novel method for model reduction that generates reduced models with a clear biochemical interpretation. Unlike conventional methods for model reduction our method enables the mapping of predictions by the reduced model to the corresponding detailed predictions by the original model. The method is based on proper lumping of state variables interacting on short time scales and on the computation of fraction parameters, which serve as the link between the reduced model and the original model. We illustrate the advantages of the proposed method by applying it to two biochemical models. The first model is of modest size and is commonly occurring as a part of larger models. The second model describes glucose transport across the cell membrane in bakers yeast. Both models can be significantly reduced with the proposed method, at the same time as the interpretability is conserved. less thanbrgreater than less thanbrgreater thanConclusions: We introduce a novel method for reduction of biochemical models that is compatible with the concept of zooming. Zooming allows the modeler to work on different levels of model granularity, and enables a direct interpretation of how modifications to the model on one level affect the model on other levels in the hierarchy. The method extends the applicability of the method that was previously developed for zooming of linear biochemical models to nonlinear models.

  • 43.
    Sunnaker, Mikael
    et al.
    Fraunhofer Chalmers Research Centre Industrial Math.
    Schmidt, Henning
    Fraunhofer Chalmers Research Centre Industrial Math.
    Jirstrand, Mats
    Fraunhofer Chalmers Research Centre Industrial Math.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Zooming of states and parameters using a lumping approach including back-translation2010In: BMC SYSTEMS BIOLOGY, ISSN 1752-0509, Vol. 4, no 28Article in journal (Refereed)
    Abstract [en]

    Background Systems biology models tend to become large since biological systems often consist of complex networks of interacting components, and since the models usually are developed to reflect various mechanistic assumptions of those networks. Nevertheless, not all aspects of the model are equally interesting in a given setting, and normally there are parts that can be reduced without affecting the relevant model performance. There are many methods for model reduction, but few or none of them allow for a restoration of the details of the original model after the simplified model has been simulated. Results We present a reduction method that allows for such a back-translation from the reduced to the original model. The method is based on lumping of states, and includes a general and formal algorithm for both determining appropriate lumps, and for calculating the analytical back-translation formulas. The lumping makes use of efficient methods from graph-theory and epsilon-decomposition and is derived and exemplified on two published models for fluorescence emission in photosynthesis. The bigger of these models is reduced from 26 to 6 states, with a negligible deviation from the reduced model simulations, both when comparing simulations in the states of the reduced model and when comparing back-translated simulations in the states of the original model. The method is developed in a linear setting, but we exemplify how the same concepts and approaches can be applied to non-linear problems. Importantly, the method automatically provides a reduced model with back-translations. Also, the method is implemented as a part of the systems biology toolbox for matlab, and the matlab scripts for the examples in this paper are available in the supplementary material. Conclusions Our novel lumping methodology allows for both automatic reduction of states using lumping, and for analytical retrieval of the original states and parameters without performing a new simulation. The two models can thus be considered as two degrees of zooming of the same model. This is a conceptually new development of model reduction approaches, which we think will stimulate much further research and will prove to be very useful in future modelling projects.

  • 44.
    Tegner, Jesper N
    et al.
    Karolinska University Hospita.
    Compte, Albert
    Hospital Clinic Barcelona.
    Auffray, Charles
    CNRS.
    An, Gary
    Northwestern University.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Clermont, Gilles
    University of Pittsburgh.
    Gutkin, Boris
    DEC ENS.
    Oltvai, Zoltan N
    University of Pittsburgh.
    Enno Stephan, Klaas
    University of Zurich.
    Thomas, Randy
    CNRS.
    Villoslada, Pablo
    Hospital Clinic Barcelona.
    Computational disease modeling - fact or fiction?2009In: BMC SYSTEMS BIOLOGY, ISSN 1752-0509, Vol. 3, no 56Article in journal (Refereed)
    Abstract [en]

    Background: Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity. Results: The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling. Conclusion: During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.

  • 45.
    Tiger, Carl-Fredrik
    et al.
    Humboldt University.
    Krause, Falko
    Humboldt University.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Palmér, Robert
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Klipp, Edda
    Humboldt University.
    Hohmann, Stefan
    University of Gothenburg.
    Kitano, Hiroaki
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Krantz, Marcus
    Humboldt University.
    A framework for mapping, visualisation and automatic model creation of signal-transduction networks2012In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 8, no 578Article in journal (Refereed)
    Abstract [en]

    Intracellular signalling systems are highly complex. This complexity makes handling, analysis and visualisation of available knowledge a major challenge in current signalling research. Here, we present a novel framework for mapping signal-transduction networks that avoids the combinatorial explosion by breaking down the network in reaction and contingency information. It provides two new visualisation methods and automatic export to mathematical models. We use this framework to compile the presently most comprehensive map of the yeast MAP kinase network. Our method improves previous strategies by combining (I) more concise mapping adapted to empirical data, (II) individual referencing for each piece of information, (III) visualisation without simplifications or added uncertainty, (IV) automatic visualisation in multiple formats, (V) automatic export to mathematical models and (VI) compatibility with established formats. The framework is supported by an open source software tool that facilitates integration of the three levels of network analysis: definition, visualisation and mathematical modelling. The framework is species independent and we expect that it will have wider impact in signalling research on any system.

  • 46.
    Tiger, C-F
    et al.
    University of Gothenburg.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Palmer, R
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Hohmann, S
    University of Gothenburg.
    Kitano, H
    Syst Biol Institute, Tokyo.
    Krantz, M
    University of Gothenburg.
    Mapping cellular signal transduction in FEBS JOURNAL, vol 277, issue , pp 142-1422010In: FEBS JOURNAL, Blackwell Publishing Ltd , 2010, Vol. 277, p. 142-142Conference paper (Refereed)
    Abstract [en]

    n/a

  • 47.
    W. Jacobsen, E.
    et al.
    KTH, Control Lab, S-10044 Stockholm, Sweden.
    Cedersund, G.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Structural robustness of biochemical network models-with application to the oscillatory metabolism of activated neutrophils2008In: IET SYSTEMS BIOLOGY, ISSN 1751-8849, Vol. 2, no 1, p. 39-47Article in journal (Refereed)
    Abstract [en]

    Sensitivity of biochemical network models to uncertainties in the model structure, with a focus on autonomously oscillating systems, is addressed. Structural robustness, as defined here, concerns the sensitivity of the model predictions with respect to changes in the specific interactions between the network components and encompass, for instance, uncertain kinetic models, neglected intermediate reaction steps and unmodelled transport phenomena. Traditional parametric sensitivity analysis does not address such structural uncertainties and should therefore be combined with analysis of structural robustness. Here a method for quantifying the structural robustness of models for systems displaying sustained oscillations is proposed. The method adopts concepts from robust control theory and is based on adding dynamic perturbations to the network of interacting biochemical components. In addition to providing a measure of the overall robustness, the method is able to identify specific network fragilities. The importance of considering structural robustness is demonstrated through an analysis of a recently proposed model of the oscillatory metabolism in activated neutrophils. The model displays small parametric sensitivities, but is shown to be highly unrobust to small perturbations in some of the network interactions. Identification of specific fragilities reveals that adding a small delay or diffusion term in one of the involved reactions, likely to exist in vivo, completely removes all oscillatory behaviour in the model.

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