Lipid Metabolism and Comparative Genomics

Tindall, Marcus and Rougier, Jonathan and Pickersgill, Laura and Melrose, John and O’Malley, Brendan and Jones, Janette (2006) Lipid Metabolism and Comparative Genomics. European Study Group with Industry > 53rd ESGI [Manchester 21/3/2005 - 24/3/2005].

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Abstract/Summary

Unilever asked the Study Group to focus on two problems. The first concerned dysregulated lipid metabolism which is a feature of many diseases including metabolic syndrome, obesity and coronary heart disease. The Study Group was asked to develop a model of the kinetics of lipoprotein metabolism between healthy and obese states incorporating the activities of key enzymes.

The second concerned the use of comparative genomics in understanding and comparing metabolic networks in bacterium. Comparative genomics is a method to make inferences on the genome of a new organism using information of a previously charaterised organism. The first mathematical question is how one would quantify such a metabolic map in a statistical sense, in particular, where there are different levels of confidence for presense of different parts of the map. The next and most important question is how one can design a measurement strategy to maximise the confidence in the accuracy of the metabolic map.

Item Type:	Study Group Report
Study Group:	European Study Group with Industry > 53rd ESGI [Manchester 21/3/2005 - 24/3/2005]
Company Name:	Unilever Corporate Research
Industrial Sector:	Food and Drink Medical and pharmaceutical
Additional Contributors:	Broomhead, David and Gravesen, Jens and Hjorth, Poul and Ing, James and King, John and Lionheart, Bill and Mo, Eirik and Parrott, James and Wilson, Eddie
ID Code:	68
Deposited By:	Gordon White
Deposited On:	19 June 2006

Problem Statement

The Study Group participants were asked to focus on two questions relating to metabolism. The first of these concerned modelling lipoprotein metabolism such that differences in the biology of the healthy and obese states can be encompassed as well as changes in the size and composition of lipoprotein particles. The use of ordinary differential equation models to infer rate constants from experimental data was discussed and a partial differential equation model was developed to describe the dynamics of lipoprotein particle formation.

The second problem focused on understanding how certain components of well understood biological networks can help in determining the functionality of other networks, in which certain components are not so well determined. The study group participants focused on issues regarding comparative genomics to discuss this question. A number of issues were addressed including the derivation of parameter values from experimental data via a stoichiometric matrix and the importance of randomly sampling and comparing sections of known networks.

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