Metabolic PathFinding: inferring relevant pathways in biochemical networks

Metabolic PathFinding: inferring relevant pathways in biochemical networks

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes,...

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS First, a Boolean network model with noise is proposed, together with an inference algorithm for it. ...

Shortest Paths with Pairwise-Distinct Edge Labels: Finding Biochemical Pathways in Metabolic Networks

A problem studied in Systems Biology is how to find shortest paths in metabolic networks. Unfortunately, simple (i.e., graph theoretic) shortest paths do not properly reflect biochemical facts. An approach to overcome this issue is to use edge labels and search for paths with distinct labels. In this paper, we show that such biologically feasible shortest paths are hard to compute. Moreover, we...

Inferring Regulatory Pathways in E. Coli using Dynami Bayesian Networks

Finding Feasible Pathways in Metabolic Networks

Recent small-world studies of the global structure of metabolic networks have been based on the shortest-path distance. In this paper, we propose new distance measures that are based on the structure of feasible metabolic pathways between metabolites. We argue that these distances capture the complexity of the underlying biochemical processes more accurately than the shortest-path distance. To ...