Computational Systems Biochemistry Group

The metabolic system of a cell has redundancy, which means that the same metabolic function can be realized with a number of alternative paths. This redundancy is very important for the stability of the metabolic system. For the stoichiometric analysis, these alternative paths are equivalent. However, in reality, these paths may have hugely different characteristics and importance.

The aim of this project is to make available the most basic characteristic for constrained optimization, the cellular effort to constitute the enzymatic capacity to realize the flux through the path.

Flux optimization based on the maximization of the cellular output by fixed substrate supply rate (FBA, biomass maximization) has no way to express the difference between alternative paths. Thus, the physiologically dominant alternative is only predicted by chance.

An improvement has been the principle of flux-minimization [4]. Here, alternative paths are (effectively) distinguished by the number of enzymes required in the path. The problem persists if alternative paths consisting of the same number of reactions (which is frequently the case). Furthermore, the longer path may be the primary one because the involved enzymes are small proteins with a low degradation rate and high cytalytic activity, thus, leading to the wrong result.

For the objective function in flux minimization, every reaction is weighted with the same weight (except for backward reactions). This concept can be modified in a very straightforward way by adjusting the weight according to the enzymatic effort.

How to quantify the effort to constitute a unit of enzymatic capacity? The following parameters represent the enzymatic costs:

the energy requirement to synthesize the enzyme protein, roughly proportional to the number of amino acid residues (proportional)
the catalytic activity (reversed proportional)
the degradation rate of the protein (proportional)

Enzyme cost minimization is implemented in FASIMU [5] (FASIMU website).

Researchers

Christine Richter
Dr. Andreas Hoppe

References

[1] Hoppe A, Richter C, Holzhütter HG. (2011) Enzyme maintenance effort as criterion for the characterization of alternative pathways and length distribution of isofunctional enzymes. BioSystems, in print.
[2] Hoppe A, Richter C, Holzhütter HG. Are enzyme costs minimized in evolution? Enzyme size, efficiency and turnover as parameters for advanced flux-balance models. Poster at te ICSB 2010 [pdf]
[3] Richter C. (2009) Kosten und Effizienz von Enzymen als zusätzliche Bedingung für die Flussverteilung in metabolischen Netzwerken. Diplomarbeit, Humboldt-Universität zu Berlin [pdf]
[4] Holzhütter HG. (2004) The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem., 271(14), 2905-22. [PubMed]
[5] Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter HG. (2011) FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics., 12(1):28. [PubMed]

		Influence of Enzyme Costs on Fluxes in Metabolic Networks
Home Research VirtualLiver People Publications Software Positions Events Contact		The metabolic system of a cell has redundancy, which means that the same metabolic function can be realized with a number of alternative paths. This redundancy is very important for the stability of the metabolic system. For the stoichiometric analysis, these alternative paths are equivalent. However, in reality, these paths may have hugely different characteristics and importance. The aim of this project is to make available the most basic characteristic for constrained optimization, the cellular effort to constitute the enzymatic capacity to realize the flux through the path. Flux optimization based on the maximization of the cellular output by fixed substrate supply rate (FBA, biomass maximization) has no way to express the difference between alternative paths. Thus, the physiologically dominant alternative is only predicted by chance. An improvement has been the principle of flux-minimization [4]. Here, alternative paths are (effectively) distinguished by the number of enzymes required in the path. The problem persists if alternative paths consisting of the same number of reactions (which is frequently the case). Furthermore, the longer path may be the primary one because the involved enzymes are small proteins with a low degradation rate and high cytalytic activity, thus, leading to the wrong result. For the objective function in flux minimization, every reaction is weighted with the same weight (except for backward reactions). This concept can be modified in a very straightforward way by adjusting the weight according to the enzymatic effort. How to quantify the effort to constitute a unit of enzymatic capacity? The following parameters represent the enzymatic costs: the energy requirement to synthesize the enzyme protein, roughly proportional to the number of amino acid residues (proportional) the catalytic activity (reversed proportional) the degradation rate of the protein (proportional) Enzyme cost minimization is implemented in FASIMU [5] (FASIMU website). Researchers Christine Richter Dr. Andreas Hoppe References [1] Hoppe A, Richter C, Holzhütter HG. (2011) Enzyme maintenance effort as criterion for the characterization of alternative pathways and length distribution of isofunctional enzymes. BioSystems, in print. [2] Hoppe A, Richter C, Holzhütter HG. Are enzyme costs minimized in evolution? Enzyme size, efficiency and turnover as parameters for advanced flux-balance models. Poster at te ICSB 2010 [pdf] [3] Richter C. (2009) Kosten und Effizienz von Enzymen als zusätzliche Bedingung für die Flussverteilung in metabolischen Netzwerken. Diplomarbeit, Humboldt-Universität zu Berlin [pdf] [4] Holzhütter HG. (2004) The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur. J. Biochem., 271(14), 2905-22. [PubMed] [5] Hoppe A, Hoffmann S, Gerasch A, Gille C, Holzhütter HG. (2011) FASIMU: flexible software for flux-balance computation series in large metabolic networks. BMC Bioinformatics., 12(1):28. [PubMed]