Quantitative Research Of The Regulatory Constraints On Metabolic COBRA Model

In Systems Biology, the main biological processes of the cell are expressed in the form of biochemical networks, such as transcriptional regulatory networks, metabolic networks, signal transduction networks, and so on. With the developments of the high-throughput technologies, researchers have made great progresses on the reconstruction of metabolic networks. At the same time, the COBRA framework based on the constraints provides a standardized way for the reconstruction of metabolic networks.Many constraints limit the growth of cell. When metabolic networks are reconstructed, these constraints should be considered so that the network model can be more accurate. Unlike other constraints like the physical and chemical constraints, regulatory constraints are change from time to time. So how to integrate regulatory constraints in metabolic networks is a hot research topic nowadays. So far, such research in this area is focus on the integration of the Boolean transcriptional regulatory network and the COBRA model of metabolic network. Due to the characteristics of Boolean networks, the influence of the regulatory network on the metabolic network is qualitative. The qualitative value cannot be sufficiently accurate, which makes the reconstruction of the metabolic networks cannot be close enough to the reality of biological systems.How to make the regulation constraints as accurate as possible? In this paper, with the help of high-throughput data, we turn the qualitative regulatory constraints into quantitative ones. And then we integrate these qualitative constraints to the metabolic networks.In this paper, we use Escherichia coli as the experimental bacterial. Two kinds of experiments, lethal experiments and growth phenotype prediction experiments are done to demonstrate the integration of the quantitative regulatory constraints to metabolic network is more close to the biological systems than the qualitative one.