liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Cedersund, Gunnar
Alternative names
Publications (10 of 46) Show all publications
Schleicher, J., Conrad, T., Gustafsson, M., Cedersund, G., Guthke, R. & Linde, J. (2017). Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions. Briefings in Functional Genomics & Proteomics, 16(2), 57-69
Open this publication in new window or tab >>Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions
Show others...
2017 (English)In: Briefings in Functional Genomics & Proteomics, ISSN 2041-2649, E-ISSN 2041-2657, Vol. 16, no 2, p. 57-69Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Oxford University Press, 2017
Keywords
host–pathogen interaction; infection; mathematical modelling; multiscale modelling
National Category
Genetics
Identifiers
urn:nbn:se:liu:diva-126449 (URN)10.1093/bfgp/elv064 (DOI)000397205400001 ()26857943 (PubMedID)
Available from: 2016-03-24 Created: 2016-03-24 Last updated: 2018-11-26Bibliographically approved
Lundengård, K., Cedersund, G., Sten, S., Leong, F., Smedberg, A., Elinder, F. & Engström, M. (2016). Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI. PloS Computational Biology, 12(6), Article ID e1004971.
Open this publication in new window or tab >>Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI
Show others...
2016 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, article id e1004971Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2016
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:liu:diva-130437 (URN)10.1371/journal.pcbi.1004971 (DOI)000379349700045 ()27310017 (PubMedID)
Note

Funding Agencies|Swedish Research council [2014-6249]; Knut and Alice Wallenbergs foundation, KAW [2013.0076]; Research council of Southeast Sweden [FORSS-481691]; Linkoping University

Available from: 2016-08-06 Created: 2016-08-05 Last updated: 2018-03-19
Cedersund, G., Oscar, S., Ball, G., Tegnér, J. & Gomez-Cabrero, D. (2016). Optimization in biology parameter estimation and the associated optimization problem. In: Liesbet Geris, David Gomez-Cabrero (Ed.), Uncertainty in biology: a computational modeling approach (pp. 177-197). New York: Springer
Open this publication in new window or tab >>Optimization in biology parameter estimation and the associated optimization problem
Show others...
2016 (English)In: 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.

Place, publisher, year, edition, pages
New York: Springer, 2016
Series
Studies in Mechanobiology, Tissue engineering, and Biomaterials, ISSN 1868-2006 ; 17
Keywords
Parameter estimation, Optimization, Heuristic, Fitness function
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130997 (URN)10.1007/978-3-319-21296-8_7 (DOI)9783319212951 (ISBN)9783319212968 (ISBN)
Available from: 2016-09-02 Created: 2016-09-02 Last updated: 2016-09-08Bibliographically approved
Cedersund, G. (2016). Prediction uncertainty estimation despite unidentifiability: an overview of recent developments. In: Liesbet Geris and David Gomez-Cabrero (Ed.), Uncertainty in Biology: a computational modeling approach (pp. 449-466). Springer
Open this publication in new window or tab >>Prediction uncertainty estimation despite unidentifiability: an overview of recent developments
2016 (English)In: 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.

Place, publisher, year, edition, pages
Springer, 2016
Series
Studies in Mechanobiology, Tissue Engineering and Biomaterials, ISSN 1868-2006 ; 17
Keywords
prediction uncertainty, systems biology, core predictions, prediction profile likelihood, ordinary differential equations, Bayesian methods, cluster Newton, neutral parameters
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:liu:diva-130995 (URN)10.1007/978-3-319-21296-8_17 (DOI)978-3-319-21295-1 (ISBN)978-3-319-21296-8 (ISBN)
Available from: 2016-09-02 Created: 2016-09-02 Last updated: 2018-01-10Bibliographically approved
Nyman, E., Rozendaal, Y. J., Helmlinger, G., Hamrén, B., Kjellsson, M. C., Strålfors, P., . . . Cedersund, G. (2016). Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes. Interface Focus, 6(2), 1-14
Open this publication in new window or tab >>Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes
Show others...
2016 (English)In: Interface Focus, ISSN 2042-8898, E-ISSN 2042-8901, Vol. 6, no 2, p. 1-14Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
London, UK: The Royal Society Publishing, 2016
Keywords
mathematical modelling, systems pharmacology, disease progression, decision-support type 2 diabetes, anti-diabetic treatment
National Category
Biophysics
Identifiers
urn:nbn:se:liu:diva-127801 (URN)10.1098/rsfs.2015.0075 (DOI)000375410900001 ()27051506 (PubMedID)
Note

Funding agencies: Swedish Research Council; Swedish Diabetes Foundation; Linkoping Initiative within Life Science Technologies; CENIIT; Ostergotland County Council; EU [FP7-HEALTH-305707]; AstraZeneca

Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2017-04-24Bibliographically approved
Rajan, M. R., Nyman, E., Kjölhede, P., Cedersund, G. & Strålfors, P. (2016). Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes. Journal of Biological Chemistry, 291(30), 15806-15819
Open this publication in new window or tab >>Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes
Show others...
2016 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 291, no 30, p. 15806-15819Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Rockville, Maryland: American Society for Biochemistry and Molecular Biology, 2016
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:liu:diva-130998 (URN)10.1074/jbc.M116.715763 (DOI)000380584200033 ()27226562 (PubMedID)
Note

Funding agencies|Swedish Diabetes Fund, University of Linköping; Swedish Research Council; AstraZeneca

Available from: 2016-09-02 Created: 2016-09-02 Last updated: 2017-04-24Bibliographically approved
Jullesson, D., Johansson, R., Rohini Rajan, M., Strålfors, P. & Cedersund, G. (2015). Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.. The FEBS Journal, 282(4), 788-802
Open this publication in new window or tab >>Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
Show others...
2015 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed) Published
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.

Keywords
insulin signalling, dominant negative data, mathematical modelling
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:liu:diva-115805 (URN)10.1111/febs.13182 (DOI)000350288300011 ()25546185 (PubMedID)
Funder
Swedish Research Council
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2017-12-04
Nyman, E., Lindgren, I., Lövfors, W., Lundengård, K., Cervin, I., Arbring, T., . . . Cedersund, G. (2015). Mathematical modeling improves EC50 estimations from classical dose–response curves. The FEBS Journal, 282(5), 951-962
Open this publication in new window or tab >>Mathematical modeling improves EC50 estimations from classical dose–response curves
Show others...
2015 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 5, p. 951-962Article in journal (Refereed) Published
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.

Keywords
adrenaline; cardiac b-adrenergic signaling; dynamic mathematical modeling; EC50; ordinary differential equations
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-114788 (URN)10.1111/febs.13194 (DOI)000350650200010 ()25586512 (PubMedID)
Available from: 2015-03-04 Created: 2015-03-04 Last updated: 2017-12-04
Lundengård, K., Cedersund, G., Elinder, F. & Engström, M. (2015). Mechanistic Modelling Investigates the Neural Basis behind the Hemodynamic Response in fMRI. In: 16TH NORDIC-BALTIC CONFERENCE ON BIOMEDICAL ENGINEERING: . Paper presented at 16th Nordic-Baltic Conference on Biomedical Engineering (NBC) / 10th MTD Joint Conference (pp. 86-87). Springer Science Business Media, 48
Open this publication in new window or tab >>Mechanistic Modelling Investigates the Neural Basis behind the Hemodynamic Response in fMRI
2015 (English)In: 16TH NORDIC-BALTIC CONFERENCE ON BIOMEDICAL ENGINEERING, Springer Science Business Media , 2015, Vol. 48, p. 86-87Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Science Business Media, 2015
Series
IFMBE Proceedings, ISSN 1680-0737 ; 48
Keywords
functional magnetic resonance imaging (fMRI); blood oxygen level dependent (BOLD) response; mechanistic modeling; ordinary differential equations (ODE); neurovascular coupling
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-114431 (URN)10.1007/978-3-319-12967-9_23 (DOI)000347893000023 ()978-3-319-12966-2 (ISBN)
Conference
16th Nordic-Baltic Conference on Biomedical Engineering (NBC) / 10th MTD Joint Conference
Available from: 2015-03-02 Created: 2015-02-20 Last updated: 2018-01-25
Sips, F. L. P., Nyman, E., Adiels, M., Hilbers, P. A. J., Strålfors, P., van Riel, N. A. W. & Cedersund, G. (2015). Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State. PLoS ONE, 10(9), e0135665
Open this publication in new window or tab >>Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State
Show others...
2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 9, p. e0135665-Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2015
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-121744 (URN)10.1371/journal.pone.0135665 (DOI)000360965800006 ()26356502 (PubMedID)
Note

Funding Agencies|European Union [305707]; Linkoping Initiative within Life Science Technologies; Ostergotland County Council; Swedish Research Council; AstraZeneca

Available from: 2015-10-06 Created: 2015-10-05 Last updated: 2017-12-01
Organisations

Search in DiVA

Show all publications