RESUMEN
The multienzyme complexes, pyruvate dehydrogenase and alpha-ketoglutarate dehydrogenase, involved in the central metabolism of Escherichia coli consist of multiple copies of three different enzymes, E1, E2 and E3, that cooperate to channel substrate intermediates between their active sites. The E2 components form the core of the complex, while a mixture of E1 and E3 components binds to the core. We present a random steady-state model to describe catalysis by such multienzyme complexes. At a fast time scale, the model describes the enzyme catalytic mechanisms of substrate channeling at a steady state, by polynomially approximating the analytic solution of a biochemical master equation. At a slower time scale, the structural organization of the different enzymes in the complex and their random binding/unbinding to the core is modeled using methods from equilibrium statistical mechanics. Biologically, the model describes the optimization of catalytic activity by substrate sharing over the entire enzyme complex. The resulting enzymatic models illustrate the random steady state (RSS) for modeling multienzyme complexes in metabolic pathways.
Asunto(s)
Simulación por Computador , Complejo Cetoglutarato Deshidrogenasa/química , Complejo Cetoglutarato Deshidrogenasa/metabolismo , Complejo Piruvato Deshidrogenasa/química , Complejo Piruvato Deshidrogenasa/metabolismo , Algoritmos , Biocatálisis , Dominio Catalítico , Escherichia coli/enzimología , Modelos QuímicosRESUMEN
MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/
Asunto(s)
Hipermedia , Almacenamiento y Recuperación de la Información/métodos , Metabolismo/fisiología , Modelos Biológicos , Lenguajes de Programación , Vocabulario Controlado , Sistemas de Administración de Bases de Datos , Bases de Datos Factuales , Documentación , Regulación de la Expresión Génica/fisiología , Modelos Químicos , Programas Informáticos , Diseño de Software , Terminología como AsuntoRESUMEN
A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing. Our approach is in the same spirit as a Lagrangian decomposition approach in that the row and column constraints are satisfied separately with a Lagrange multiplier used to equate the two "solutions". Due to the unavoidable symmetries in graph isomorphism (resulting in multiple global minima), we add a symmetry-breaking self-amplification term in order to obtain a permutation matrix. With the application of a fixpoint preserving algebraic transformation to both the distance measure and self-amplification terms, we obtain a Lagrangian relaxation network. The network performs minimization with respect to the Lagrange parameters and maximization with respect to the permutation matrix variables. Simulation results are shown on 100 node random graphs and for a wide range of connectivities.