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Virtualisation of gene knockout experiments based on kinetic modelling: Escherichia coli as an algorithmic case
by Rudong Li; Guohui Ding; Yixue Li
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 10, No. 2, 2017


Abstract: Since substantial biological studies focus on molecular essentialities (e.g., gene knockout experiments), virtualisation of such studies saves experimental costs and facilitates knowledge acquisition and bio-engineering. Nevertheless, non-quantitative methods are incompetent and the traditional flux-balance analysis (FBA) is less effective at physiological rather than genome scale. In this respect, kinetic modelling can simulate bio-pathway dynamics and enhance functional characterisations of bio-molecules. However, conventional sensitivity or flux control analysis has limited predictive ability for structural perturbations like gene knockouts. Thus we hereby conceived a novel algorithm for in silico evaluating molecular essentialities based on dynamical systems. Exemplified by E. coli central carbon metabolism, our method was accurate. We found that E. coli central metabolic genes could be categorised into four types of essentiality; moreover, E. coli metabolism might be supported by a minimum of six genes. Additionally, comparing with FBA and a previous kinetic-modelling method, our method was more effective.

Online publication date: Sat, 15-Apr-2017


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