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A CoD-based stationary control policy for intervening in large gene regulatory networks.

ABSTRACT: BACKGROUND: One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network. RESULTS: In this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model. CONCLUSIONS: The newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.

SUBMITTER: Ghaffari N

PROVIDER: S-EPMC3236832 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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A CoD-based stationary control policy for intervening in large gene regulatory networks.

Ghaffari Noushin N Ivanov Ivan I Qian Xiaoning X Dougherty Edward R ER

BMC bioinformatics 20111018

<h4>Background</h4>One of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of ...[more]

PMID: 22165980

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Project description:BackgroundThe inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.ResultsThis paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network.ConclusionsBoolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.

Project description:BackgroundVenom systems are ideal models to study genetic regulatory mechanisms that underpin evolutionary novelty. Snake venom glands are thought to share a common origin, but there are major distinctions between venom toxins from the medically significant snake families Elapidae and Viperidae, and toxin gene regulatory investigations in elapid snakes have been limited. Here, we used high-throughput RNA-sequencing to profile gene expression and microRNAs between active (milked) and resting (unmilked) venom glands in an elapid (Eastern Brown Snake, Pseudonaja textilis), in addition to comparative genomics, to identify cis- and trans-acting regulation of venom production in an elapid in comparison to viperids (Crotalus viridis and C. tigris).ResultsAlthough there is conservation in high-level mechanistic pathways regulating venom production (unfolded protein response, Notch signaling and cholesterol homeostasis), there are differences in the regulation of histone methylation enzymes, transcription factors, and microRNAs in venom glands from these two snake families. Histone methyltransferases and transcription factor (TF) specificity protein 1 (Sp1) were highly upregulated in the milked elapid venom gland in comparison to the viperids, whereas nuclear factor I (NFI) TFs were upregulated after viperid venom milking. Sp1 and NFI cis-regulatory elements were common to toxin gene promoter regions, but many unique elements were also present between elapid and viperid toxins. The presence of Sp1 binding sites across multiple elapid toxin gene promoter regions that have been experimentally determined to regulate expression, in addition to upregulation of Sp1 after venom milking, suggests this transcription factor is involved in elapid toxin expression. microRNA profiles were distinctive between milked and unmilked venom glands for both snake families, and microRNAs were predicted to target a diversity of toxin transcripts in the elapid P. textilis venom gland, but only snake venom metalloproteinase transcripts in the viperid C. viridis venom gland. These results suggest differences in toxin gene posttranscriptional regulation between the elapid P. textilis and viperid C. viridis.ConclusionsOur comparative transcriptomic and genomic analyses between toxin genes and isoforms in elapid and viperid snakes suggests independent toxin regulation between these two snake families, demonstrating multiple different regulatory mechanisms underpin a venomous phenotype.

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A CoD-based stationary control policy for intervening in large gene regulatory networks.

Publications

A CoD-based stationary control policy for intervening in large gene regulatory networks.

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