Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

BACKGROUND: Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle. RESULTS: Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G1 fixed point was found for deterministic updating schemes. CONCLUSIONS: In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.

Gene regulatory network models based on time series gene expression data: Recent progress / 第二军医大学学报

Regulation between genes is a dynamic event associated with changes of time and circumstances. Gene regulatory network is a complicated and dynamic system. Time series gene microarray provides a tool for creating dynamic gene regulatory network. In this paper,we review several models of dynamic gene regulatory network based on time series gene expression data, including temporal Boolean network,differential equation,dynamic Bayesian networks,etc.. The advantages and disadvantages of the models were analyzed and the future of the research is predicted.

Modeling of gene regulatory networks / 第二军医大学学报

Gene regulatory networks(GRN),which focuses on the complex interactions of genes in life,is an important part in the study of the functional genomics and is the frontier of bioinformatics research.Application of gene-chip technique in bioinformatics provides a great number of basic data for the research of GRN.This paper reviews the origin and recent development of GRN,explicates the preconditions and rationales for construction of GRN,and analyzes several classic GRN models: Boolean networks,linear models,non-linear models and Bayesian networks.The rationales,basic algorithms,advantages,disadvantages and applicability of the models are reviewed based on the characteristics of gene-chip data.