Elements of Computational Systems Biology

8

SUPERVISED INFERENCE OF METABOLIC NETWORKS FROM THE INTEGRATION OF GENOMIC DATA AND CHEMICAL INFORMATION

Yoshihiro Yamanishi

Mines ParisTech - Institut Curie - Inserm U900, Paris, France

8.1 INTRODUCTION

Most biological functions involve the coordinated actions of many biomolecules such as genes, proteins, and chemical compounds, and the complexity of living systems arises as a result of such interactions. It is, therefore, important to understand the biological systems through the analysis of the relationships amongst biomolecules. The biological system can be represented by a network of proteins by using graph representation, with proteins as nodes and their functional interactions as edges. Examples of such biological networks include metabolic network, protein–protein interaction network, gene regulatory network, and signaling network. A grand challenge in recent bioinformatics and systems biology is to computationally predict such biological networks from genomic and molecular information for practical applications. Recent sequence projects and development in biotechnology have contributed to an increasing amount of high-throughput genomic data for biomolecules and their interactions, including amino acid sequences, gene expression data, yeast two-hybrid data, and several more. These data are useful sources from which we can computationally infer various biological networks [1–4].

Figure 8.1 An example of the metabolic pathway in the KEGG pathway database. One box indicates ...