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Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation.

ABSTRACT: We present an approach for constructing dynamic models for the simulation of gene regulatory networks from simple computational elements. Each element is called a "gene gate" and defines an inputoutput relationship corresponding to the binding and production of transcription factors. The proposed reaction kinetics of the gene gates can be mapped onto stochastic processes and the standard ordinary differential equation (ODE) description. While the ODE approach requires fixing the system's topology before its correct implementation, expressing them in stochastic pi-calculus leads to a fully compositional scheme: network elements become autonomous and only the inputoutput relationships fix their wiring. The modularity of our approach allows to pass easily from a basic first-level description to refined models which capture more details of the biological system. As an illustrative application we present the stochastic repressilator, an artificial cellular clock, which oscillates readily without any cooperative effects.

SUBMITTER: Blossey R

PROVIDER: S-EPMC2640994 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation.

Blossey Ralf R Cardelli Luca L Phillips Andrew A

HFSP journal 20071114 1

We present an approach for constructing dynamic models for the simulation of gene regulatory networks from simple computational elements. Each element is called a "gene gate" and defines an inputoutput relationship corresponding to the binding and production of transcription factors. The proposed reaction kinetics of the gene gates can be mapped onto stochastic processes and the standard ordinary differential equation (ODE) description. While the ODE approach requires fixing the system's topolog ...[more]

PMID: 19404450

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Project description:BACKGROUND: The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. RESULTS: We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. CONCLUSIONS: Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.

Dataset Information

Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation.

Publications

Compositionality, stochasticity, and cooperativity in dynamic models of gene regulation.

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