Integrative Systems Biology

Integrative Systems biology advances our understanding of biological phenomena through the close collaboration between laboratory and theoretical scientists, who eventually develop mechanistic mathematical and data models that identify gaps in biological knowledge and propose hypotheses for laboratory testing. The laboratory results lead iteratively to refined models that have predictive value. The modelling may integrate phenomena at different physical scales or incorporates different branches of physics (e.g. biochemical thermo-dynamics, with fluid dynamics and materials science). The approach is analogous to that between experimental and theoretical physicists.

Key aims and expertise

Across the life sciences, with particular emphasis on Food Security, Animal Health and novel biofuels, team members apply informatics and modelling techniques:

• To reduce the time needed to achieve meaningful discoveries by integrating and filtering available data to prioritise candidates for laboratory testing. 
• To identify gaps in knowledge by making formal constructions of our understanding of biological processes and identifying where these mathematical models diverge from observed data. 
• To test hypotheses for the mechanism by which biological processes are regulated. 
• To propose new hypotheses or predictions for laboratory validation.
  This requires individuals an overlapping set of multidisciplinary expertise in the various areas of biology, statistics, computer science and applied mathematics. 

This requires individuals an overlapping set of multidisciplinary expertise in the various areas of biology, statistics, computer science and applied mathematics.

Finding the parameter values for a bacterial-growth model from lab data using Monte-Carlo Markov-Chain optimisation. The video begins by showing the original lab results, over which progressively improving growth curves have been drawn.

Current projects

• Improving Feed Conversion Efficiency in farm animals
• High-throughput analysis of cell growth data from phenotype arrays
• Quantification of promoter activity from luciferase assays 
• The effect of variation in one-carbon metabolism and epigenetics on animal health – Industrial Partnership Award see also Steegers-Theunissen et al.

Significant results

• Prioritised the pathways involved in feed conversion efficiency of animals, which has led to potential candidate genes for further intervention
• Identified key differences in 1-carbon metabolism between reproductive organisms and liver which may render gametes and embryos more susceptible to parental diet with long term implications for development and health. 
• Improved prediction of transcription factor binding sites using a non-independent energy-based analysis of multiple sequence alignments.