Catabolite repression in Escherichia coli– a comparison of modelling approaches

Abstract

Research in systems biology requires experimental effort as well as theoretical attempts to elucidate the general principles of cellular dynamics and control and to help to improve molecular processes for engineering purposes or drug design. This interdisciplinary approach provides a promising method for advances in biotechnology and molecular medicine. In systems biology, quantitative experimental data and mathematical models are combined in an attempt to obtain information on the dynamics and regulatory structures of the systems. However, depending on the degree of biological knowledge and the amount of quantitative data, the models developed so far differ in their degree of granularity, starting with a simple on ⁄ off binary description of the state variables of the system and ending with fully mechanistic models. Carbohydrate uptake via the phosphoenolpyruvate-dependent phosphotransferase system (PTS) in Escherichia coli is one of the best studied biochemical networks from theoretical and experimental points of view, and has The phosphotransferase system in Escherichia coli is a transport and sensory system and, in this function, is one of the key players of catabolite repression. Mathematical modelling of signal transduction and gene expression of the enzymes involved in the transport of carbohydrates is a promising approach in biotechnology, as it offers the possibility to achieve higher production rates of desired components. In this article, the relevance of methods and approaches concerning mathematical modelling in systems biology is discussed by assessing and comparing two comprehensive mathematical models that describe catabolite repression. The focus is thereby on modular modelling with the relevant input in the central modules, the impact of quantitative model validation, the identification of control structures and the comparison of model predictions with respect to the available experimental data.Abbreviations cAMP, cyclic AMP (signalling molecule); Crp, catabolite repression protein (transcription factor); CyaA, adenylate cyclase (protein, synthesizes cAMP); dFBA, dynamic FBA (takes into account the slow dynamics of extracellular components); EI, enzyme I (protein, component of the PTS); EIIA, enzyme IIA (protein, component of the PTS, 'output' of the system as it activates the synthesis of cAMP); EIIBC (PtsG), enzyme IIBC (main membrane standing transport protein for glucose uptake); FBA, flux balance analysis (tool to determine the flux distribution in cellular networks, requires steady-state conditions); HPr, histidine-containing protein (component of the PTS); LacZ, protein of the lactose degradation pathway (b-galactodidase); Mlc, repressor protein (inhibits the synthesis of EIIBC if glucose is not present in the medium); o.d.e., ordinary differential equation (basic structure of a mathematical model, it describes the temporal changes of a component in the network, must be solved numerically); PTS, phosphotransferase system (uptake and sensory system in many bacteria, consists of several pr...

Analysis and Design of Stimulus Response Curves of E. coli

“…In [22] a detailed mathematical model similar to a model published by us [14] was presented but failed to predict genetic modifications. A comparison of modeling approaches and a presentation of the current “state of the art” on this topic can be found in [23]. To summarize, mathematical models to describe carbohydrate uptake and metabolism are available, but fail in reproducing experimental data or fail in predicting new experiments.…”

Metabolic Engineering of Corynebacterium Glutamicum Aimed at Alternative Carbon Sources and New Products

“…Coutilization is advantageous in biotechnological applications, but it is absent in other hosts e.g. E. coli [48] or B. subtilis [49], which show successive utilization of substrates, carbon catabolite repression, and diauxic growth. But, no rule without exceptions: C. glutamicum does utilize glucose before L-glutamic acid or ethanol, and acetate before ethanol [50, 51].…”

Limited by sensing - A minimal stochastic model of the lag-phase during diauxic growth

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