Towards dynamic genome-scale models

被引:9
|
作者
Gilbert, David [1 ]
Heiner, Monika [2 ]
Jayaweera, Yasoda [3 ]
Rohr, Christian [4 ]
机构
[1] Brunel Univ London, Dept Comp Sci, Comp, Uxbridge, Middx, England
[2] Brandenburg Tech Univ Cottbus, Dept Comp Sci, Comp Sci, Cottbus, Germany
[3] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England
[4] Brandenburg Tech Univ Cottbus, Dept Comp Sci, Cottbus, Germany
关键词
whole-genome-scale metabolic models; formal analysis; scalability; approximative stochastic simulation; model checking; reaction profiling; clustering; data analytics; delta leaping; subsystems behaviour; model-based design; TIME-SERIES; SYSTEMS;
D O I
10.1093/bib/bbx096
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The analysis of the dynamic behaviour of genome-scale models of metabolism(GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large-up to whole genome in size-and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.
引用
收藏
页码:1167 / 1180
页数:14
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