Biosystems

 

Systems Biology and Bioinformatics

Gene reg net Inferring gene regulatory networks in Arabidopsis

With increasing amounts of gene expression microarray data available, it has become possible to apply machine learning approaches to reverse engineering gene regulatory networks from gene expression data. Information about this can be found on Chris Needham's page introducing his work on 'Learning gene regulatory networks in Arabidopsis'.

Network inference for dynamic time series data

Network models are a powerful tool for understanding the dynamics of biological systems. However, deriving the best network model from measurements of system behaviour is a difficult, underdetermined problem. James Watson is researching algorithms for inferring the most likely network descriptions given a dataset. A network model of the budding yeast cell cycle is currently being used as a test suite. The research is undertaken in collaboration with Netta Cohen, David Irons (University of Sheffield), and Chris Needham. This study is funded by the AMORPH project.

Microscopic models of gene expression

Margaritis Voliotis's current research focuses on microscopic (molecular-level) models of gene expression and gene regulation. He is particularly interested in the origins, manifestations and consequences of stochasticity in such systems. He is also interested in the organizational principles behind the structure, dynamics and function of biological systems and processes, and how such principles might have been shaped by evolution.

Voliotis, Margaritis; Cohen, Netta; Molina-Paris, Carmen; Liverpool, Tanniemola. Fluctuations, pauses and backtracking in DNA transcription. Biophysical Journal, vol. 94, pp. 334-348. 2008. ( abstract ) ( PDF ).

Bacterial adaptations to stress

In collaboration with Biologists and Computer Scientists in Leeds, Sheffield and York, the MICROSYST network has been formed, including a number of members of the group. The aim of this systems biology project is to elucidate the mechanisms by which bacteria (Escherichia coli, Propionibacterium acnes and Neisseria species) adapt to changing and stressful environments.

Previous Related Activities

Protein function prediction

A number of bioinformatics applications have been developed within the group. A number of these were developed during a recent BBSRC project on Protein Function Prediction using uncertainty, worked on by Chris Needham and Andy Bulpitt. Topics include the prediction of protein-protein interfaces, predicting the functional effects of mutations and prediction of protein function into Gene Ontology classes.

Earlier work related to proteins (surface matching and structure analysis) can be found here.

Modelling the major evolutionary transitions

John Bryden undertook research which looked at an enigma in the major evolutionary transitions: the problem of why an individual would invest resources for some genetic stake in an offspring rather than invest in its own clonal offspring. The problem is tackled with models of resource allocation strategy, studying invasion of different strategies done with computer simulation models. The results are relevant to Artificial Life, Social Evolution and Adaptive Dynamics.

Compositional structures in non-coding DNA

How are nucleotides distributed along genomes? Is there a functional significance to the organization of bases along noncoding DNA, and do compositional structures reflect fundamental laws underlying evolutionary processes? Several decades ago, a series of seminar experiments uncovered surprisingly long sequences of DNA across a variety of species that have a remarkably homogeneous composition of bases. On the basis of these results, the ``isochore'' theory was proposed to account for the structure of the genomes of all warm-blooded vertebrates. In 2001, the sequencing of the complete human genome paved the way for a sequence-based reinspection of the isochore theory, which has been the focus of our work.

Cohen, N., Dagan, T., Stone L. and Graur, D. GC Composition of the Human Genome: In Search of Isochores. Molecular Biology and Evolution 22(5), 1260-1272 (2005). ( abstract ) ( PDF ).