Constructing a Knowledge Base for Gene Regulatory Dynamics by Formal Concept Analysis Methods

TL;DR

This paper introduces a method using formal concept analysis to construct a knowledge base for gene regulatory dynamics, enabling reasoning over temporal dependencies in biological networks.

Contribution

It applies formal concept analysis to model gene regulatory networks and derives implications for biological processes, offering a general approach adaptable to various systems.

Findings

Successfully modeled sporulation in Bacillus subtilis

Generated a sound and complete set of implications

Method is adaptable to other biological networks

Abstract

Our aim is to build a set of rules, such that reasoning over temporal dependencies within gene regulatory networks is possible. The underlying transitions may be obtained by discretizing observed time series, or they are generated based on existing knowledge, e.g. by Boolean networks or their nondeterministic generalization. We use the mathematical discipline of formal concept analysis (FCA), which has been applied successfully in domains as knowledge representation, data mining or software engineering. By the attribute exploration algorithm, an expert or a supporting computer program is enabled to decide about the validity of a minimal set of implications and thus to construct a sound and complete knowledge base. From this all valid implications are derivable that relate to the selected properties of a set of genes. We present results of our method for the initiation of sporulation in Bacillus subtilis. However the formal structures are exhibited in a most general manner. Therefore the approach may be adapted to signal transduction or metabolic networks, as well as to discrete temporal transitions in many biological and nonbiological areas.