Discovering the complex regulatory networks that govern mRNA expression is an important but dicult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method for revising and improving these initial models, which may be incomplete or partially incorrect, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed by a biologist for Cyanobacteria. Applied to wild type expression data, our system suggested several modi cations consistent with biological knowledge.

On February 12, 2001 the Human Genome Project announced that it had assembled a draft physical map of the human genome- the genetic blueprint for a human being. Now the challenge is to annotate this map, by understanding the functions of genes and their interplay with proteins and the environment to create complex, dynamic living systems. This is the goal of functional genomics. Recent technological advances enable biomedical investigators to observe the genome of entire organisms in action by simultaneously measuring the level of activation of thousands of genes under the same experimental conditions. This technology, known as microarrays, provides today unparalleled discovery opportunities and it is reshaping biomedical sciences. One of the main aspects of this revolution is the introduction of heavily quantitative data-analytical methods in biomedical research. This paper reviews the foundations of this technology

Many different Bayesian network models have been suggested...

Abstract Motivation: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the object of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks apply to time series microarray data. Results: We extend the framework of dynamic Bayesian networks in order to handle perturbations. A new discretization method, specialized for datasets from time series perturbations experiments, is also introduced. We compare networks inferred from realistic simulations data by our method and by dynamic Bayesian networks learning techniques. We conclude that application of our method substantially improves inferring. 1 Introduction As most genetic regulatory systems involve many components connected through complex networks of interactions, formal methods and computer tools for modeling and simulating are needed. Therefore, various formalisms were proposed to describe genetic regulatory systems, including Boolean networks and their generalizations, ordinary and partial differential equations, stochastic equations and Bayesian networks (see [4] for a review). While differential and stochastic equations describe the biophysical processes at a very refined level of detail and prove useful in simulations of well studied systems, Bayesian networks appear attractive in the field of inferring the regulatory network structure from gene expression data. The reason is that their learning techniques have solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way.

Recently a variety of high-troughput experimental techniques, such as DNA microarray, are opening system-level perspectives of living organisms on the molecular level. Inferring genetic network architecture from time series data generated from these technologies is an important computational methods to help us to understand the system behavior of living organisms. We developed an interactive software, GeneNetwork, which supports four representative reverse engineering models and three data interpolation approaches. It enables users readily reconstruct genetic network based on microarray data without being intimately involved with mathematical computation. A simple graphical user interface enables rapid, intuitive mapping, and analysis of the reconstructed network. These high-level capabilities of GeneNetwork lead biologists to explore biological systems at the system-level.

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Introduction A genetic network is a set of genes in which individual genes inuence the activity of other genes. The core task in identifying genetic networks is to distinguish direct from indirect regulatory interactions. Static and dynamic Bayesian network models have been suggested to reconstruct gene expression networks from microarray data [1,3,8]. However, little attention has been payed to the eects of small sample size and the stability of the solution. We engage in a systematic investigation of these issues. As a starting point for further research we introduce the - network. It is a small bayesian network model (5 nodes with three states) in which a parameter controls the conditional probability distributions of the nodes. With data sampled from this model, we evaluate the eects of dierent sample sizes and of perturbation data on the reconstruction of the original network topology. Bayesian networks 1 De nition A Bayesian network is a graph-based representation

Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their application to the analysis of various types of genomic data, from genomic markers to gene expression data. The examples will highlight the potential of this methodology but also the current limitations and we will describe new research directions that hold the promise to make Bayesian networks a fundamental tool for genome data

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The challenge with todays microarray experiments is to infer biological conclusions from them. There are two crucial difficulties to be surmounted in this challenge:(1) A lack of suitable biological repository that can be easily integrated into computational algorithms. (2) Contemporary algorithms used to analyze microarray data are unable to draw consistent biological results from diverse datasets of the same disease. To deal with the first difficulty, we believe a core database that unifies available biological repositories is important. Towards this end, we create a unified biological database from three popular biological repositories (KEGG, Ingenuity and Wikipathways). This database provides computer scientists the flexibility of easily integrating biological information using simple API calls or SQL queries. To deal with the second difficulty of deriving consistent biological results from the experiments, we first conceptualize the notion of “subnetworks”, which refers to a