Effect of substrate competition in kinetic models of metabolic networks

Abstract

a b s t r a c tSubstrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.

Model of Tryptophan Metabolism, Readily Scalable Using Tissue-specific Gene Expression Data

“…1). We recently demonstrated that such competition can affect both steady state concentrations and the dynamic behavior of metabolic systems (28). Initial simulations with the tryptophan metabolic model confirmed that the calculated fluxes were affected by the inhibition as expected.…”

“…Equation 4 can be extended to any finite number of competitors as shown mathematically by Chou and Talaly (27). For the reversible transport of tryptophan and L-kynurenine we used a rate law for two competing substrates based on steady state assumptions (28). For the approach using purified enzyme activities and gene expression data, V max in Equation 4 was substituted with F ϫ mRNA ϫ k cat as in Equation 3.…”

Development of a mechanistic biokinetic model for hepatic bile acid handling to predict possible cholestatic effects of drugs

“…Our final assumption includes competitive inhibition of bile acids for both the uptake and efflux transporters using the adapted formula for enzymes (Asante-Appiah and Chan, 1996;Martinez-Irujo et al, 1998;Pocklington and Jeffery, 1969;Schäuble et al, 2013). In addition, the drugs we choose as model drugs, glibenclamide and cyclosporin A, are both competitive inhibitors of BSEP.…”

Stochastic modeling of biochemical systems with multistep reactions using state-dependent time delay

“…The next question is whether the state-dependent time delay is able to approximate multistep reactions with varied reaction rates with good accuracy. Here we discuss a system that is a simplified representation of the pathway for aliphatic glucosinolate biosynthesis11. This system considers the chain elongation process as a series of sequential events.…”

“…Simultaneously research works have also been conducted to explore the conditions and assumptions of these simplified models in order to obtain accurate simulations67. Among them, one of the important biological processes is multistep reactions that has implication in a wide range of biochemical processes, including synthesis of mRNA for a series of strands of DNA, protein synthesis when ribosome reading a series of codons of mRNA8, signaling transduction for the activation of a sequence of kinases from growth factor receptor to transcriptional factors9, degradation of polymeric carbohydrates and synthesis of metabolic1011, cancer initiation that can be regarded as a series of gene mutations12, as well as telomere shortening process13. Although a number of mathematical models in recent years have been designed to describe these multistep pathways accurately, due to the complex nature of molecular networks, more sophisticated models are needed to simplify the multistep reaction processes.…”