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  • 1.
    Magnusson, Rasmus
    et al.
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Bioinformatik. Linköpings universitet, Tekniska fakulteten.
    Mariotti, Guido
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Bioinformatik. Linköpings universitet, Tekniska fakulteten.
    Köpsén, Mattias
    Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Lövfors, William
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för klinisk och experimentell medicin. Linköpings universitet, Medicinska fakulteten.
    Gawel, Danuta
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för barns och kvinnors hälsa. Linköpings universitet, Medicinska fakulteten.
    Jornsten, Rebecka
    University of Gothenburg, Sweden.
    Linde, Joerg
    Hans Knoell Institute, Germany; Hans Knoell Institute, Germany.
    Nordling, Torbjorn
    National Cheng Kung University, Taiwan; Science Life Lab, Sweden.
    Nyman, Elin
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Schulze, Sylvie
    Hans Knoell Institute, Germany.
    Nestor, Colm
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för barns och kvinnors hälsa. Linköpings universitet, Medicinska fakulteten.
    Zhang, Hanmin
    Linköpings universitet, Institutionen för fysik, kemi och biologi. Linköpings universitet, Tekniska högskolan.
    Cedersund, Gunnar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Benson, Mikael
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för barns och kvinnors hälsa. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Allergicentrum US.
    Tjärnberg, Andreas
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Bioinformatik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Mika
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Bioinformatik. Linköpings universitet, Tekniska fakulteten.
    LASSIM-A network inference toolbox for genome-wide mechanistic modeling2017Inngår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, nr 6, artikkel-id e1005608Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

  • 2.
    Brannmark, Cecilia
    et al.
    University of Gothenburg, Sweden.
    Lövfors, William
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Komai, Ali M.
    University of Gothenburg, Sweden.
    Axelsson, Tom
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    El Hachmane, Mickael F.
    University of Gothenburg, Sweden.
    Musovic, Saliha
    University of Gothenburg, Sweden.
    Paul, Alexandra
    Chalmers University of Technology, Sweden.
    Nyman, Elin
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. AstraZeneca RandD, Sweden.
    Olofsson, Charlotta S.
    University of Gothenburg, Sweden.
    Mathematical modeling of white adipocyte exocytosis predicts adiponectin secretion and quantifies the rates of vesicle exo- and endocytosis2017Inngår i: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, nr 49, s. 20032-20043Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Adiponectin is a hormone secreted from white adipocytes and takes part in the regulation of several metabolic processes. Although the pathophysiological importance of adiponectin has been thoroughly investigated, the mechanisms controlling its release are only partly understood. We have recently shown that adiponectin is secreted via regulated exocytosis of adiponectin-containing vesicles, that adiponectin exocytosis is stimulated by cAMP-dependent mechanisms, and that Ca2+ and ATP augment the cAMP-triggered secretion. However, much remains to be discovered regarding the molecular and cellular regulation of adiponectin release. Here, we have used mathematical modeling to extract detailed information contained within our previously obtained high-resolution patch-clamp time-resolved capacitance recordings to produce the first model of adiponectin exocytosis/secretion that combines all mechanistic knowledge deduced from electrophysiological experimental series. This model demonstrates that our previous understanding of the role of intracellular ATP in the control of adiponectin exocytosis needs to be revised to include an additional ATP-dependent step. Validation of the model by introduction of data of secreted adiponectin yielded a very close resemblance between the simulations and experimental results. Moreover, we could show that Ca2+-dependent adiponectin endocytosis contributes to the measured capacitance signal, and we were able to predict the contribution of endocytosis to the measured exocytotic rate under different experimental conditions. In conclusion, using mathematical modeling of published and newly generated data, we have obtained estimates of adiponectin exo- and endocytosis rates, and we have predicted adiponectin secretion. We believe that our model should have multiple applications in the study of metabolic processes and hormonal control thereof.

  • 3.
    Nyman, Elin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. CVMD iMED DMPK AstraZeneca R&D, Mölndal, Sweden.
    Lindgren, Isa
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Biologi. Linköpings universitet, Tekniska högskolan.
    Lövfors, William
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Lundengård, Karin
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för radiologiska vetenskaper. Linköpings universitet, Hälsouniversitetet.
    Cervin, Ida
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Arbring, Theresia
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Fysik och elektroteknik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik.
    Altimitas, Jordi
    Linköpings universitet, Institutionen för fysik, kemi och biologi, Biologi. Linköpings universitet, Tekniska högskolan.
    Cedersund, Gunnar
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för cellbiologi. Linköpings universitet, Medicinska fakulteten.
    Mathematical modeling improves EC50 estimations from classical dose–response curves2015Inngår i: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, nr 5, s. 951-962Artikkel i tidsskrift (Fagfellevurdert)
    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.

1 - 3 of 3
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