Abstract
Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models.
Highlights
Understanding the way in which individual components interact in a biological network is a major goal of systems biology [1]
We demonstrate the application of our extended Structural kinetic modeling (SKM) approach to a detailed model of the autocatalytic Calvin-Benson cycle (CBC), which is the main pathway in plant cells for the fixation of atmospheric CO2 to produce energy-rich biomolecules
We used the kinetic model by Laisk et al (2009) [17] as a reference for network stoichiometry and steady-state information. This model was chosen, because a recent study by Arnold and Nikoloski (2011) [10] based on a slightly smaller predecessor model [18] showed that its predicted steady state agrees well with experimental measurements. It includes the reactions of the CBC, starch and sucrose metabolism, adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) generation, parts of the cytosolic glycolysis and gluconeogenesis pathway, and an entry point to amino acid metabolism via alanine production
Summary
Understanding the way in which individual components interact in a biological network is a major goal of systems biology [1]. The prediction of a system’s response to internal or external perturbations, as well as the identification of components that play a major role in this response, requires mathematical modeling [2]. Approaches for mathematical modeling of metabolic networks can be subdivided into (1) structural modeling and (2) kinetic modeling. Kinetic modeling allows the analysis of the dynamic properties of the network and is not restricted to steady states. This approach relies on detailed knowledge about all enzymatic rate laws and kinetic parameters in the system, which are often difficult to obtain experimentally
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
