Optimal metabolic control design. 1998

F Ortega, and L Acerenza
Sección Biofisica, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay.

In a previous work [Acerenza, L. (1993). Metabolic Control Design. J. theor. Biol. 165, 63-85] we devised a procedure to design metabolic systems that respond according to pre-established patterns. This procedure includes the mandatory structural and kinetic constraints that narrow the spectrum of responses. In an evolutionary context, the structural and functional features shown during the history of the system would also be conditioned by other factors. Here we incorporate to the Metabolic Control Design procedure two additional requirements that could have influenced metabolic evolution. These are constraints that result from the adaptation to the environment (represented by independent control coefficients that take fixed values) and optimization of metabolic variables at constant total enzyme concentration. To illustrate the general strategy we consider metabolic systems consisting of r reaction steps where the variables are the fluxes, internal metabolite concentrations, enzyme concentrations and control coefficients. In our conditions the number of degrees of freedom, calculated as number of variables minus number of number of relationships, is r - 1. A detailed analysis of three particular schemes, unbranched chain of two and three steps and branch point, with simple linear rate laws is given. Novel results are obtained for the optimization of the input flux of the simple branch point. In the well studied case where there are no evolutionary constraints one of the limbs of the branch point disappears. However, for particular independent control coefficients, when we impose to the control coefficient a fixed value, the branched structure may or may not persist depending on the range to which the fixed value belongs.

UI MeSH Term Description Entries
D008433 Mathematics The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed) Mathematic
D008660 Metabolism The chemical reactions in living organisms by which energy is provided for vital processes and activities and new material is assimilated. Anabolism,Catabolism,Metabolic Concepts,Metabolic Phenomena,Metabolic Processes,Metabolic Phenomenon,Metabolic Process,Metabolism Concepts,Metabolism Phenomena,Process, Metabolic,Processes, Metabolic,Concept, Metabolic,Concept, Metabolism,Concepts, Metabolic,Concepts, Metabolism,Metabolic Concept,Metabolism Concept,Phenomena, Metabolic,Phenomena, Metabolism,Phenomenon, Metabolic
D008954 Models, Biological Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment. Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic

Related Publications

F Ortega, and L Acerenza
November 1993, Journal of theoretical biology,
F Ortega, and L Acerenza
November 1974, Journal of molecular evolution,
F Ortega, and L Acerenza
January 2002, Diabetes/metabolism research and reviews,
F Ortega, and L Acerenza
December 1989, Mathematical biosciences,
F Ortega, and L Acerenza
August 2019, PLoS computational biology,
F Ortega, and L Acerenza
January 2018, Physical review. E,
F Ortega, and L Acerenza
April 2024, Journal of biomechanical engineering,
F Ortega, and L Acerenza
October 2020, BMC bioinformatics,
F Ortega, and L Acerenza
January 2007, Progress in drug research. Fortschritte der Arzneimittelforschung. Progres des recherches pharmaceutiques,
F Ortega, and L Acerenza
November 2008, Bioinformatics (Oxford, England),
Copied contents to your clipboard!