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Multi-objective optimisation algorithms to predict gene expression in biological models

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Metabolic engineering is a promising biotechnology approach with an increasing demand for mathematical models for accurate design purposes. One of the main goals of metabolic engineering is to predict the best environmental condition in which a bacterium has to grow in order to reach specific optimal output values from a range of objective functions chosen by the researcher. To this end, I will show a multi-objective optimisation algorithm I have developed to search for the gene expression values that optimise multiple cellular functions in biological models.

Large biological models usually involve steady-state analyses that make it possible to predict or investigate the behaviour of the biological entity being modelled. These models are based on linear constraints and therefore are able to quickly simulate the behaviour of the organism. Flux Balance Analysis (FBA) is a common constraint-based approach for simulating biochemical networks. It is able to estimate the flow of metabolites through the network and also the growth rate of the organism.

The multi-objective optimisation is performed using a parallel optimisation algorithm based on a genetic algorithm, i.e. a robust technique inspired to the principles of evolution and natural selection. This allows to maximise two or more objectives simultaneously. The algorithm also allows to fine tune the codon usage of the bacterium in a multi-objective fashion. Coupled with gene-editing methods, this would allow to modify in vitro the bacterial genome such that the final gene expression is the one that optimises the objectives chosen.

This talk is part of the Computer Laboratory Research Students' Lectures 2014 series.

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