Niall Palfreyman | PubMed Health

PubMed2005

Reverse-engineering gene-regulatory networks using evolutionary algorithms and grid computing.

OBJECTIVE: Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fully understood.However, by building computational models it is possible to gain insight into their function and operation. METHODS: Evolutionary algorithms are used to create computational models of gene-regulatory networks based on observed microarray data. These algorithms can be computationally intensive. They will be implemented within an existing grid computing infrastructure, that has been developed for data mining purposes, and which is able to deliver the required compute power. RESULTS: We discuss how models can built achieved using distributed and grid computing technology. In particular we investigate how Condor and JavaSpaces technology is suited to the requirements of our modeling approach. CONCLUSIONS: Determining network models of gene-regulatory networks using evolutionary algorithms not only requires considerable computational power, but also a modeling formalism that can explain the underlying dynamics.

Algorithms↗

PubMed2004

The construction of meaning in computational integrative biology.

It is the contention of this paper that current data mining work in bioinformatics tends to emphasize data representation to the neglect of another essential aspect of biological systems, namely dynamics. This results in a divorce of both the enterprise and the teaching of bioinformatics from its central aim of meaning-construction. The paper argues that this neglect of dynamics is rooted in an information-processing view of cognitive psychology, and needs to be complemented by a more narrative perspective which emphasizes the explanation, rather than the mere description, of observed patterns in data. This increased emphasis on explanatory narrative in the form of dynamical modeling leads to both a deeper understanding of biological information and a more invigorating approach to the teaching of bioinformatics. The paper presents a cross-curricular teaching framework for a first-year undergraduate course in bioinformatic dynamical modeling which is based around the use of narrative plots.

Communication↗