Modeling the Glycolysis: An Inverse Problem Approach

We show in this paper that the metabolic chain can be supposed a potential-Hamiltonian system in which the dynamical flow can be shared between gradient dissipative and periodic conservative parts. If the chain is branched and if we know the fluxes at the extremities of each branch we can deduce information about the internal kinetics (e.g. place of allosteric and Michaelian step with respect to those of branching paths, cooperatively) from minimal additional measurements inside the black box constituted by the system. We will treat as example the glycolysis with the pentose pathway whose fluxes measurements are done at the pyruvate and pentose levels.

[1]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Udo Reichl,et al.  Simultaneous extraction of several metabolites of energy metabolism and related substances in mammalian cells: optimization using experimental design. , 2008, Analytical biochemistry.

[3]  J Demongeot,et al.  A mathematical model for storage and recall functions in plants. , 2000, Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie.

[4]  Jacques Demongeot,et al.  Liénard systems and potential-Hamiltonian decomposition: applications in biology. , 2007, Comptes rendus biologies.

[5]  Patrick Amar,et al.  Steady‐state kinetic behaviour of functioning‐dependent structures , 2006, The FEBS journal.

[6]  Jacques Demongeot,et al.  Liénard systems and potential-Hamiltonian decomposition III – applications , 2007 .

[7]  J Demongeot,et al.  Mathematical Methods for Inferring Regulatory Networks Interactions: Application to Genetic Regulation , 2004, Acta biotheoretica.

[8]  Jacques Demongeot,et al.  AN ATTEMPT TO GENERALIZE THE CONTROL COEFFICIENT CONCEPT , 1993 .

[9]  Jacques Demongeot,et al.  Storage and recall of environmental signals in a plant: modelling by use of a differential (continuous) formulation. , 2006, Comptes rendus biologies.

[10]  Reinhart Heinrich,et al.  Effect of cellular interaction on glycolytic oscillations in yeast: a theoretical investigation. , 2000, The Biochemical journal.

[11]  Jacques Demongeot,et al.  Roles of positive and negative feedback in biological systems. , 2002, Comptes rendus biologies.

[12]  Kunihiko Kaneko,et al.  Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics , 2007, PloS one.

[13]  Jacques Demongeot,et al.  High-dimensional Switches and the Modeling of Cellular Differentiation 2.2 Mathematical Models , 2022 .

[14]  Jacques Demongeot,et al.  Systemes de Lienard et decomposition potentielle-Hamiltonienne I - Methodologie Lienard systems and potential-Hamiltonian decomposition I - Methodology , 2007 .

[15]  J. Demongeot,et al.  Robustness in Regulatory Networks: A Multi-Disciplinary Approach , 2008, Acta biotheoretica.

[16]  J. Demongeot,et al.  Positive and negative feedback: striking a balance between necessary antagonists. , 2002, Journal of theoretical biology.

[17]  Eric Goles Ch.,et al.  Fixed points and maximal independent sets in AND-OR networks , 2004, Discret. Appl. Math..

[18]  J. Demongeot,et al.  Mathematical modelling in genetic networks: relationships between the genetic expression and both chromosomic breakage and positive circuits , 2000, Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering.

[19]  C. Soulé Graphic Requirements for Multistationarity , 2004, Complexus.

[20]  Eric Goles Ch.,et al.  On limit cycles of monotone functions with symmetric connection graph , 2004, Theor. Comput. Sci..

[21]  Jacques Demongeot,et al.  Mathematical modeling in genetic networks: relationships between the genetic expression and both chromosomic breakage and positive circuits , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[22]  C Reder,et al.  Metabolic control theory: a structural approach. , 1988, Journal of theoretical biology.

[23]  B. Kholodenko,et al.  Control analysis of glycolytic oscillations. , 1996, Biophysical chemistry.

[24]  Jacques Demongeot,et al.  BMP2 and BMP7 play antagonistic roles in feather induction , 2008, Development.

[25]  Fredrik H. Karlsson,et al.  Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions , 2007 .

[26]  Eric Goles Ch.,et al.  Positive and negative circuits in discrete neural networks , 2004, IEEE Transactions on Neural Networks.

[27]  Jacques Demongeot,et al.  Liénard systems and potential-Hamiltonian decomposition II – algorithm , 2007 .

[28]  Jacques Demongeot,et al.  Sigmoidicity in Allosteric Models , 1983 .

[29]  J. Demongeot,et al.  Glycolytic Oscillations: An Attempt to an “In Vitro” Reconstitution of the Higher Part of Glycolysis Modelling and Experimental Approach , 1983 .

[30]  J Ovádi,et al.  Old pathway--new concept: control of glycolysis by metabolite-modulated dynamic enzyme associations. , 1988, Trends in biochemical sciences.

[31]  H. Kacser,et al.  The control of flux. , 1995, Biochemical Society transactions.

[32]  Birger Bergersen,et al.  Effect of boundary conditions on scaling in the ''game of Life'' , 1997 .

[33]  S. Kauffman,et al.  Genetic networks with canalyzing Boolean rules are always stable. , 2004, Proceedings of the National Academy of Sciences of the United States of America.