Parameter Identification and Model Ranking of Thomas Networks

Klarner, Hannes; Streck, Adam; Šafránek, David; Kolčák, Juraj; Siebert, Heike

doi:10.1007/978-3-642-33636-2_13

Cited by 20 publications

(23 citation statements)

References 22 publications

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“…In [5], the method of [18] based on sensitivity analysis is applied to the automated design of synthetic biological devices from basic ODE modules. In [28], parameter synthesis for discrete GRNs is reduced to coloured LTL model checking and solved through a distributed algorithm. Methods based on SMT are presented in [29,36] to synthesize Boolean network models from time-series and perturbation experiments data.…”

Section: Related Workmentioning

confidence: 99%

Precise parameter synthesis for stochastic biochemical systems

et al. 2016

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We consider the problem of synthesising rate parameters for stochastic biochemical networks so that a given time-bounded CSL property is guaranteed to hold, or, in the case of quantitative properties, the probability of satisfying the property is maximised or minimised. Our method is based on extending CSL model checking and standard uniformisation to parametric models, in order to compute safe bounds on the satisfaction probability of the property. We develop synthesis algorithms that yield answers that are precise to within an arbitrarily small tolerance value. The algorithms combine the computation of probability bounds with the refinement and sampling of the parameter space. Our methods are precise and efficient, and improve on existing approximate techniques that employ discretisation and refinement. We evaluate the usefulness of the methods by synthesising rates for three biologically motivated case studies: infection control for a SIR epidemic model; reliability

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Section: Related Workmentioning

confidence: 99%

Precise parameter synthesis for stochastic biochemical systems

et al. 2016

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“…For building the model pool, every topology of possible combinations of the 5 candidate edges is created, resulting in 32 topologies. Then, for every topology all truth tables, representing a logical function, are generated and subsequently selected for the pool if they agree with the constraints (for more detail see [20]). This process is computationally challenging, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, a tailored model checking software for efficient analysis is employed, which filters and evaluates the specific pool, i.e. Tremppi [21] and TomClass [20]. …”

Section: Methodsmentioning

confidence: 99%

“…finding parameters for one ODE model is already a hard problem usually also rife with uncertainty. Here, we use a logical modeling workflow [20–22] to create and analyze possible topologies and mechanisms of biological systems. This formalism is able to capture qualitative effects of the interactions by analyzing basic behaviors, which was shown to deliver valuable results for signaling processes [23–25].…”

Section: Introductionmentioning

confidence: 99%

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Unraveling the regulation of mTORC2 using logical modeling

2017

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BackgroundThe mammalian target of rapamycin (mTOR) is a regulator of cell proliferation, cell growth and apoptosis working through two distinct complexes: mTORC1 and mTORC2. Although much is known about the activation and inactivation of mTORC1, the processes controlling mTORC2 remain poorly characterized. Experimental and modeling studies have attempted to explain the regulation of mTORC2 but have yielded several conflicting hypotheses. More specifically, the Phosphoinositide 3-kinase (PI3K) pathway was shown to be involved in this process, but the identity of the kinase interacting with and regulating mTORC2 remains to be determined (Cybulski and Hall, Trends Biochem Sci 34:620-7, 2009).MethodWe performed a literature search and identified 5 published hypotheses describing mTORC2 regulation. Based on these hypotheses, we built logical models, not only for each single hypothesis but also for all combinations and possible mechanisms among them. Based on data provided by the original studies, a systematic analysis of all models was performed.ResultsWe were able to find models that account for experimental observations from every original study, but do not require all 5 hypotheses to be implemented. Surprisingly, all hypotheses were in agreement with all tested data gathered from the different studies and PI3K was identified as an essential regulator of mTORC2.ConclusionThe results and additional data suggest that more than one regulator is necessary to explain the behavior of mTORC2. Finally, this study proposes a new experiment to validate mTORC1 as second essential regulator.

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“…Because our identification method can be exhaustive, the framework we propose is suited for the complete Thomas parameters identification for BNs from incomplete time series data [13,6].Thanks to our abstract semantics, our method is able to filter out very efficiently a large number of candidate BNs without a costly exact model-checking, which is postponed to the validation of the results. In that way, future work may further explore the combination of dynamics overapproximations with model-checking approaches to provide scalable and exact inference of BNs from time series data.…”

Section: Resultsmentioning

confidence: 99%

Boolean Network Identification from Multiplex Time Series Data

Ostrowski

Paulevé

Schaub

et al. 2015

Computational Methods in Systems Biology

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Abstract. Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logical models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that goal, we exhibit a necessary condition that must be satisfied by a Boolean network dynamics to be consistent with a discretized time series trace. Based on this condition, we use a declarative programming approach (Answer Set Programming) to compute an over-approximation of the set of Boolean networks which fit best with experimental data. Combined with modelchecking approaches, we end up with a global learning algorithm and compare it to learning approaches based on static data.

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Parameter Identification and Model Ranking of Thomas Networks

Cited by 20 publications

References 22 publications

Precise parameter synthesis for stochastic biochemical systems

Precise parameter synthesis for stochastic biochemical systems

Unraveling the regulation of mTORC2 using logical modeling

Boolean Network Identification from Multiplex Time Series Data

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