Metabolic network structure determines key aspects of functionality and regulation

Abstract

The relationship between structure, function and regulation in complex cellular networks is a still largely open question. Systems biology aims to explain this relationship by combining experimental and theoretical approaches. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness or metabolic phenotype, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.

Diverse metabolic model parameters generate similar methionine cycle dynamics

“…A series of recent modeling studies have made major strides towards identifying topological features of bionetworks that give rise to emergent properties such as the ability to sense and respond to environmental signals, maintain metabolic homeostasis, and form complex developmental patterns, all in a manner that is robust to environmental and genetic noise (Barkai and Leibler, 1997;Bhartiya et al, 2006;Brandman et al, 2005;Kollmann et al, 2005;Meir et al, 2002;Stelling et al, 2002;von Dassow et al, 2000;Wagner, 2005). A complement to understanding the topological features of bionetworks that lead to their qualitative properties is finding the precise parameter values which lead to specific quantitative phenotypes.…”

Theoretical Description of Metabolism Using Queueing Theory

“…Such approach in general does not introduce new biophysical processes, but is fully probabilistic in its origin and therefore should be described by the laws of probability. Its quantitative description may, in principle, be accomplished by any of the approaches currently developed in literature and cited in the introductory section, e.g., flux-based models (Grimbs et al 2007;Aldridge et al 2006;Stelling et al 2002) or Petri net analysis (Chaouiya 2007;Peleg et al 2002). However, the Pachinko approach introduces the incoming flow of metabolites in general case as a flow of discrete particles, which may create queues near the Pins and Holes and result in deviation from the common mass action law which is most often used in order to quantify the metabolic network by means of kinetic equations (see for example Wagner and Fell 2001;Aldridge et al 2006;Covert et al 2001).…”

“…Theoretical description of metabolism in complex multicellular system in terms of metabolites' distribution is extremely difficult task as it requires detailed knowledge of all metabolic pathways. Numerous approaches for analytical description of metabolism have so far been developed, all of them grounded on the use of concrete metabolic picture, e.g., the flux-based approaches (see Grimbs et al 2007; Aldridge et al 2006;Stelling et al 2002), metabolic network analysis (Jeong et al 2000;Lima-Mendez and Helden 2009), Petri net analysis (Chaouiya 2007;Peleg et al 2002), stochastic approach (De Jong 2002, entropy approach (see Veselkov et al 2010) and others.…”

Dissipation and maintenance of stable states in an enzymatic system: Analysis and simulation

“…Therefore, an metabolic network is an integrative and open system, its study needs to define a boundary at which materials are transported into or out of the network [1]. By analysing a network and its boundary fluxes as a whole, constraint-based approach has emerged as a useful tool for analysis of the integrated functions of the network [2][3][4][5][6][7][8][9][10]. Broadly speaking, constraint-based approach analyses the possible flux distributions under the constraints of stoichiometry, thermodynamics and kinetics, and links them with possible phenotypic outcomes.…”