Abstract. Recently, cDNA microarray experiments have generated large amounts of gene expression data. In time-ordered gene expression data, the expression levels are measured at several points in time following some experimental manipulation. A gene regulatory network can be inferred by describing the gene expression data in terms of a linear system of differential equations. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods to infer a linear system of differential equations, some ad hoc assumptions are made to limit the number of nonzero coefficients in the system. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we determine which coefficients are nonzero by using Akaike’s Information Criterion. 1

We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network construction is in the estimation of the conditional distribution of each random variable. We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes. A problem still remains to be solved in selecting an optimal graph, which gives the best representation of the system among genes. We theoretically derive a new graph selection criterion from Bayes approach in general situations. The proposed method includes previous methods based on Bayesian networks. We demonstrate the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae gene expression data newly obtained by disrupting 100 genes. 1.

Petri nets are a discrete event simulation approach developed for system representation, in particular for their concurrency and synchronization properties. Various extensions to the original theory of Petri nets have been used for modeling molecular biology systems and metabolic networks. These extensions are stochastic, colored, hybrid and functional. This paper carries out an initial review of the various modeling approaches based on Petri net found in the literature, and of the biological systems that have been successfully modeled with these approaches. Moreover, the modeling goals and possibilities of qualitative analysis and system simulation of each approach are discussed.

A novel approach to phylogenetic tree construction using stochastic optimization and clustering

Introduction Genomic Object Net (GON) (http://www.genomicobject.net/) is a biopathway modeling and simulation platform that employs the notion of hybrid functional Petri Net with extension (HFPNe) that extends the hybrid functional Petri Net (HFPN) [3, 4], and is developed with JAVA. With this platform, we have succeeded in modeling and simulating glycolytic pathway of E. coli, boundary formation by notch signaling in Drosophila, and apoptosis induced by Fas ligand, etc. For the modeling and simulation of a biopathway, suitable information selection from public biopathway databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [1] (http://www.genome.ad.jp/kegg/) and BioCyc [2] (http://www.biocyc.org/), would be useful. Although the first aim for these pathway databases is to reorganize biochemical information for usage on computers and is not for modeling and simulation of biopathways. Thus, we have developed a way to transform these pathway databases so that the converted b

Introduction As studies of genome analysis proceed, information processing of biopathway in vivo becomes one of the most important topics in bioinformatics. By processing biopathway information, medical development and/or appropriate treatments would be possible in the future; that is, it could be a great help for us. We have developed Genomic Object Net (GON), with which we simulated biopathway models such as metabolic pathways, signal transduction, and gene control mechanisms, based on hybrid fucntional Petri net with extension (HFPNe) [2, 3]. In GON, places and transitions, are converted to biological/medical terms. It also provides colorful viewpoints to simulations by GON with "Visualizer [1]", the presentation tool for XML documents into visible state. However, GON is not good for simulation with super computers at remote locations despite growing needs of high-speed work to process complex pathways. Besides, users have to download the latest version, each time they want to use

As studies of genome analysis proceed, information processing of biopathway in vivo becomes one of the most important topics in bioinformatics. By processing biopathway information, medical development and/or appropriate treatments would be possible in the future; that is, it could be a great help for us. We have developed Genomic Object Net (GON), with which we simulated biopathway models

Genomic Object Net (GON) is a powerful simulation tool for representing biopathways [2, 4]. In this study, by using GON, we constructed Delta-Notch mechanism [1], which is working in a pattern formation of multicellular organism. In the simulation, the number of the neural precursor cells increases when the value of Notch signal decreases, which is thought to represent the situation of

In the post-genome era, biopathway information processing will be one of the most important issues in Bioinformatics. Development of Genomic Object Net [6] is our approach to this issue. This software aims at describing and simulating structurally complex dynamic causal interactions and processes such as metabolic pathways, signal transduction cascades, gene regulations. We have released Genomic

E-CELL [3] is a cenceputually attractive biosimulation tool for representing and simulating biopathways. With E-CELL, Tomita et al. [4] have modeled several biopathways including biochemical reactions in human erythrocyte, signal transduction for bacterial chemotaxis, energy metabolism in mitochondria and lytic-lysogenic switch network of λ phage.