Modeling Regulatory Networks With Weight Matrices

Weaver, Daniel C.; Workman, Christopher; Stormo, Gary D.

doi:10.1142/9789814447300_0011

Cited by 269 publications

(211 citation statements)

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“…More detailed gene network models have been used (eg. [2]), as well as modeling the networks as differential equations [24] [11] or weight matrices [27]. In many of these models, the underlying gene network topology can be or is represented as a graph: nodes indicate genes, and directed labeled edges indicate gene regulation.…”

Section: Introductionmentioning

confidence: 99%

Automating Genetic Network Inference with Minimal Physical Experimentation Using Coevolution

Bongard

Lipson

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract.A major challenge in system biology is the automatic inference of gene regulation network topology-an instance of reverse engineering-based on limited local data whose collection is costly and slow. Reverse engineering implies the reconstruction of a hidden system based only on input and output data sets generated by the target system. Here we present a generalized evolutionary algorithm that can reverse engineer a hidden network based solely on input supplied to the network and the output obtained, using a minimal number of tests of the physical system. The algorithm has two stages: the first stage evolves a system hypothesis, and the second stage evolves a new experiment that should be carried out on the target system in order to extract the most information. We present the general algorithm, which we call the estimation-exploration algorithm, and demonstrate it both for the inference of gene regulatory networks without the need to perform expensive and disruptive knockout studies, and the inference of morphological properties of a robot without extensive physical testing.

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Section: Introductionmentioning

confidence: 99%

Automating Genetic Network Inference with Minimal Physical Experimentation Using Coevolution

Bongard

Lipson

2004

Genetic and Evolutionary Computation – GECCO 2004

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“…Quantitative models based on linear models for gene regulatory networks like the weighted matrix model introduced and inferred by Weaver et al [22] consider the continuous level of gene expression. Another algorithm to infer quantitative models is the singular value decomposition method by Yeung et al [23].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Model Optimization for Inferring Gene Regulatory Networks

Spieth

Streichert

Speer

et al. 2005

Lecture Notes in Computer Science

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Abstract. With the invention of microarray technology, researchers are able to measure the expression levels of ten thousands of genes in parallel at various time points of a biological process. The investigation of gene regulatory networks has become one of the major topics in Systems Biology. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We suggest to use a multi-objective evolutionary algorithm to identify the parameters of a non-linear system given by the observed data. Currently, only limited information on gene regulatory pathways is available in Systems Biology. Not only the actual parameters of the examined system are unknown, also the connectivity of the components is a priori not known. However, this number is crucial for the inference process. Therefore, we propose a method, which uses the connectivity as an optimization objective in addition to the data dissimilarity (relative standard error -RSE) between experimental and simulated data.

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“…GRNs are biochemically dynamic systems to describe highly complex interactions among two main species of gene products: mRNA and proteins, in the interactive transcription and translation process, and they are always modeled by using dynamic systems. Several computational models have been applied to investigate the behaviors of GRNs: Boolean models (Somogyi and Sniegoski 1996;Weaver et al 1999), Bayesian network models (Friedman et al 2000;Hartemink et al 2002), Petri net models (Hardy and Robillard 2004;Chaouiya et al 2008), and the differential equation models (Smolen et al 2000;Bolouri and Davidson 2002;Chen and Aihara 2002;Chesi and Hung 2008).…”

Section: Introductionmentioning

confidence: 99%

Sufficient and necessary conditions for Lyapunov stability of genetic networks with SUM regulatory logic

2015

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In this paper, a nonlinear model for genetic regulator networks (GRNs) with SUM regulatory logic is presented. Four sufficient and necessary conditions of global asymptotical stability and global exponential stability for the equilibrium point of the GRNs are proposed, respectively. Specifically, three weak sufficient conditions and corresponding corollaries are derived by using comparing theorem and Dini derivative method. Then, a famous GRN model is used as the example to illustrate the effectiveness of our theoretical results. Comparing to the results in the previous literature, some novel ideas, study methods and interesting results are explored.

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Modeling Regulatory Networks With Weight Matrices

Cited by 269 publications

References 1 publication

Automating Genetic Network Inference with Minimal Physical Experimentation Using Coevolution

Automating Genetic Network Inference with Minimal Physical Experimentation Using Coevolution

Multi-objective Model Optimization for Inferring Gene Regulatory Networks

Sufficient and necessary conditions for Lyapunov stability of genetic networks with SUM regulatory logic

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