Identification of minimal metabolic pathway models consistent with phenotypic data

• Autor(a) principal: Soons, Zita
• Data de Publicação: 2011
• Outros Autores: Ferreira, Eugénio C., Rocha, I.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/16695
• topic: Elementary modes; Generating vectors; Controlled random search; Model reduction; Metabolism; Escherichia coli; Science & Technology

The cellular network of metabolic reactions, together with constraints of (ir)reversibility of enzymes, determines the space of all possible steady-state phenotypes. Analysis of large metabolic models, however, is not feasible in real-time and identification of a smaller model without loss of accuracy is desirable for model-based bioprocess optimization and control. To this end, we propose two search algorithms for systematic identification of a subset of pathways that match the observed cellular phenotype relevant for a particular process condition. Central carbon metabolism of Escherichia coli was used as a case-study together with three phenotypic datasets obtained from the literature. The first search method is based on ranking pathways and the second is a controlled random search (CRS) algorithm. Since we wish to obtain a biologically realistic subset of pathways, the objective function to be minimized is a trade-off between the error and investment costs. We found that the CRS outperforms the ranking algorithm, as it is less likely to fall into local minima. In addition, we compared two pathway analysis methods (elementary modes versus generating vectors) in terms of modelling accuracy and computational intensity. We conclude that generating vectors have preference over elementary modes to describe a particular phenotype. Overall, the original model containing 433 generating vectors or 2706 elementary modes could be reduced to a system of one to three pathways giving a good correlation with the measured datasets. We consider this work as a first step towards the use of detailed metabolic models to improve real-time optimization, monitoring, and control of biological processes.