Synthesis of biological models from mutation experiments

Köksal, Ali Sinan; Pu, Yewen; Srivastava, Saurabh; Bodík, Rastislav; Fisher, Jasmin; Piterman, Nir

doi:10.1145/2429069.2429125

Cited by 32 publications

(19 citation statements)

References 25 publications

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“…Model checking and symbolic reasoning have been used to verify properties of manually constructed biological models , complete partially specified pathways using perturbation data (Köksal et al, 2013), and synthesize gene regulatory networks directly from data (Dunn et al, 2014;Moignard et al, 2015) (reviewed in ). In addition, other types of declarative approaches, such as integer programming (Budak et al, 2015;Chasman et al, 2014;Jain et al, 2016;Ourfali et al, 2007;Sharan and Karp, 2013;Silverbush and Sharan, 2014) and answer set programming (Guziolowski et al, 2013), have been applied to biological pathway analysis.…”

Section: Contrasting Tps With Related Computational Approachesmentioning

confidence: 99%

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Köksal

Beck

Cronin

et al. 2017

Preprint

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Advances in proteomics reveal that pathway databases fail to capture the majority of cellular signaling activity. Our mass spectrometry study of the dynamic epidermal growth factor (EGF) response demonstrates that over 89% of significantly (de)phosphorylated proteins are excluded from individual EGF signaling maps, and 63% are absent from all annotated pathways. We present a computational method, the Temporal Pathway Synthesizer (TPS), to discover missing pathway elements by modeling temporal phosphoproteomic data. TPS uses constraint solving to exhaustively explore all possible structures for a signaling pathway, eliminating structures that are inconsistent with protein-protein interactions or the observed phosphorylation event timing. Applied to our EGF response data, TPS connects 83% of the responding proteins to receptors and signaling proteins in EGF pathway maps. Inhibiting predicted active kinases supports the TPS pathway model. The TPS algorithm is broadly applicable and also recovers an accurate model of the yeast osmotic stress response.

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Section: Contrasting Tps With Related Computational Approachesmentioning

confidence: 99%

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Köksal

Beck

Cronin

et al. 2017

Preprint

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“…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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“…The fifth change occurs at t 5 = 6, change (6). Here, it is z whose value changes from z 1 to z 0 ; thus, the time local transition that has realized this change has the form of: τ = z 1 → δ z 0 where can be any combination of the regulators' value at t 3 − 1 of b.…”

Section: Case Studymentioning

confidence: 99%

“…Reverse engineering of gene regulatory networks from expression data have been handled by various approaches [4][5][6][7][8]. Most of them are only static.…”

Section: Introductionmentioning

confidence: 99%

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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Abstract:Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

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Synthesis of biological models from mutation experiments

Cited by 32 publications

References 25 publications

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Precise parameter synthesis for stochastic biochemical systems

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

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