A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks

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Author(s): Nicole Radde (University of Leipzig, Germany)and Lars Kaderali (University of Heidelberg, Germany)
Copyright: 2010
Pages: 30
Source title: Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Source Author(s)/Editor(s): Sanjoy Das (Kansas State University, USA), Doina Caragea (Kansas State University, USA), Stephen Welch (Kansas State University, USA)and William H. Hsu (Kansas State University, USA)
DOI: 10.4018/978-1-60566-685-3.ch006

Keywords: Biologically-Inspired Computing / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

Differential equation models provide a detailed, quantitative description of transcription regulatory networks. However, due to the large number of model parameters, they are usually applicable to small networks only, with at most a few dozen genes. Moreover, they are not well suited to deal with noisy data. In this chapter, we show how to circumvent these limitations by integrating an ordinary differential equation model into a stochastic framework. The resulting model is then embedded into a Bayesian learning approach. We integrate the-biologically motivated-expectation of sparse connectivity in the network into the inference process using a specifically defined prior distribution on model parameters. The approach is evaluated on simulated data and a dataset of the transcriptional network governing the yeast cell cycle.

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A Bayes Regularized Ordinary Differential Equation Model for the Inference of Gene Regulatory Networks

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