A simple mass-action model for the eukaryotic heat shock response and its mathematical validation

Petre, Ion; Mizera, Andrzej; Hyder, Claire L.; Meinander, Annika; Mikhailov, Andrey; Morimoto, Richard I.; Sistonen, Lea; Eriksson, John E.; Back, Ralph-Johan

doi:10.1007/s11047-010-9216-y

Cited by 57 publications

(131 citation statements)

References 33 publications

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“…Moreover, Scheff et al [75] considered the effect of temperature changes on the transcriptional and multimerization processes. The above models could not capture data sets in different experimental conditions, yet after extension of the model by Petre et al [74], a wide spectrum of data sets from HeLa cells were described well. From a local sensitivity analysis at steady state, this work also showed that the initial amount of HSF is expected to affect the stress-induced damage outcome much more than the amount of initial HSP and denatured proteins.…”

Section: Modelingmentioning

confidence: 99%

“…To understand this tolerance, Peper et al [23] employed a basic model of HSR dynamics, which was in good agreement with observations in Reuber H35 rat hepatoma cells with both single and double heat-shock stimuli. Petre et al [74] constructed a more detailed model incorporating components describing HSP-regulated transcription. They performed a sensitivity analysis to minimize the model by identifying and eliminating model components that only have marginal effects.…”

Section: Modelingmentioning

confidence: 99%

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Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Kuijper

Yang

Water

et al. 2016

Expert Opinion on Drug Metabolism & Toxicology

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Introduction: Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI. Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver. Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitro testing might offer novel screening methods for the harmful side-effects of drugs. ARTICLE HISTORY

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Section: Modelingmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Kuijper

Yang

Water

et al. 2016

Expert Opinion on Drug Metabolism & Toxicology

View full text Add to dashboard Cite

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“…Moreover, the initial values of the model were sought so that they give a steady state of the model at 37 • C. This latter restriction was imposed since the heat shock response is absent at 37 • C. Once suitable numerical values for the parameters were found, the model was subjected to a number of other validation tests. For a detailed discussion on the fit and the validation of the model we refer to [14] and [15]. The final numerical setup of the model is shown in Tables 2(a) and 2(b).…”

Section: The Heat Shock Response Modelmentioning

confidence: 99%

“…The reason for this behavior is that the reactions having mhsf as a product, i.e. reactions (xiii) and the reverse reaction (xv) have a negligible flux rate, primarily due to the small kinetic rate constant of the protein misfolding law, see (15). Consequently, the reaction producing hsp: mhsf, i.e.…”

Section: The Heat Shock Response Modelmentioning

confidence: 99%

“…In each test, we either change the initial values of some variables, or we change the values of some kinetic rate constants. For each new numerical setup we set the initial values of all variables to their steady state values at 37 • C, similarly as done in [15] (to underline that the heat shock response is missing at 37 • C). Finally, we compare the numerical behavior of the model with that of its simplified version obtained as above, for temperatures between 37 • C and 45 • C.…”

Section: The Heat Shock Response Modelmentioning

confidence: 99%

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Computational Heuristics for Simplifying a Biological Model

Petre

Mizera

Back

2009

Mathematical Theory and Computational Practice

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Abstract. Computational biomodelers adopt either of the following approaches: build rich, as complete as possible models in an effort to obtain very realistic models, or on the contrary, build as simple as possible models focusing only on the core aspects of the process, in an effort to obtain a model that is easier to analyze, fit, and validate. When the latter strategy is adopted, the aspects that are left outside the models are very often up to the subjective options of the modeler. We discuss in this paper a heuristic method to simplify an already fit model in such a way that the numerical fit to the experimental data is not lost. We focus in particular on eliminating some of the variables of the model and the reactions they take part in, while also modifying some of the remaining reactions. We illustrate the method on a computational model for the eukaryotic heat shock response. We also discuss the limitations of this method.

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Computational Methods for Quantitative Submodel Comparison

Mizera

Czeizler

Petre

2012

Biomolecular Information Processing

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Comparing alternative models for a given biochemical system is in general a very difficult problem: the models may focus on different aspects of the same system and may consist of very different species and reactions. The numerical setups of the models also play a crucial role in the quantitative comparison. When the alternative designs are submodels of a reference model, e.g. knockdown mutants of a model, the problem of comparing them becomes simpler: they all have very similar, although not identical, underlying reaction networks, and the biological constraints are given by the ones in the reference model. In the first part of our study we review several known methods for model decomposition and for quantitative comparison of submodels. In the second part of the paper, we consider as a case study the eukaryotic heat shock response, an evolutionary well conserved defence mechanism against the accumulation of misfolded proteins.Keywords: Model comparison; model decomposition; computational knockdown analysis; control-based decomposition; quantitative model refinement; heat shock response.

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A simple mass-action model for the eukaryotic heat shock response and its mathematical validation

Cited by 57 publications

References 33 publications

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Computational Heuristics for Simplifying a Biological Model

Computational Methods for Quantitative Submodel Comparison

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