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|Title:||Derivation of a biomass proxy for dynamic analysis of whole genome metabolic models|
|Citation:||Springer Lecture Notes in Bioinformatics (LNBI), 2018, 11095 (ISBN 978-3-319-99429-1)|
|Abstract:||A whole genome metabolic model (GEM) is essentially a reconstruction of a network of enzyme-enabled chemical reactions representing the metabolism of an organism, based on information present in its genome. Such models have been designed so that ﬂux balance analysis (FBA) can be applied in order to analyse metabolism under steady state. For this purpose, a biomassfunctionisaddedtothesemodelsasanoverallindicatorofthemodel’s viability. Our objective is to develop dynamic models based on these FBA models in order to observe new and complex behaviours, including transient behaviour. There is however a major challenge in that the biomass function does not operate under dynamic simulation. An appropriate biomass function would enable the estimation under dynamic simulation of the growth of both wildtype and genetically modiﬁed bacteria under diﬀerent, possibly dynamically changing growth conditions. Using data analytics techniques, we have developed a dynamic biomass function which acts as a faithful proxy for the FBA equivalent for a reduced GEM for E. coli. This involved consolidating data for reaction rates and metabolite concentrations generated under dynamic simulation with gold standard target data for biomass obtained by steady state analysis using FBA. It also led to a number of interesting insights regarding biomass ﬂuxes for pairs of conditions. These ﬁndings were reproduced in our dynamic proxy function.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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