A Boolean Approach for Disentangling the Roles of Submodules to the Global Properties of a Biomodel

• Autorzy: Czeizler E. , Mizera A. , Petre I.
• Języki publikacji: EN

Abstrakty

EN

To disentangle the numerical contribution of modules to the system-level behavior of a given biomodel, one often considers knock-out mutant models, investigating the change in the model behavior when modules are systematically included and excluded from the model architecture in all possible ways. We propose in this paper a Boolean approach for extracting conclusions about the role of each module from the systematic comparison of the numerical behavior of all knock-out mutants. We associate a Boolean variable to each module, expressing when the module is included in the architecture and when it is not. We can then express the satisfiability of system-level properties of the full model, such as efficiency, or economical use of resources, in terms of a Boolean formula expressing in a compact way which model architectures, i.e., which combinations of modules, give rise to the desired property. We demonstrate this method on a recently proposed computational model for the heat shock response in eukaryotes. We describe the contribution of each of its three feedback loops towards achieving an economical and effective heat shock response.

Słowa kluczowe

EN

model comparison; Boolean approach; feedback loops

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2012
• Tom: Vol. 116, nr 1-4
• Strony: 51--63
• Opis fizyczny: Bibliogr. 21 poz., tab., wykr.

Twórcy

autor

Czeizler E.

autor

Mizera A.

autor

Petre I.

• Department of Information Technologies, Abo Akademi University FIN-20520 Turku, Finland,

Bibliografia

• [1] Chaves, M., Albert, R., Sontag, E. D.: Robustness and fragility of Boolean models for genetic regulatory networks, J Theor Biol, 235, 2005, 431-49.
• [2] Chen, W. W., Schoeberl, B., Jasper, P. J., Niepel, M., Nielsen, U. B., Lauffenburger, D. A., Sorger, P. K.: Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data, Molecular Systems Biology, 5, 2009, 239.
• [3] Czeizler, E., Czeizler, E., Back, R.-J., Petre, I.: Control strategies for the regulation of the eukaryotic heat shock response, in: Lecture Notes in Bioinformatics (P. Degano, R. Gorrieri, Eds.), vol. 5688, Springer, Berlin, Heidelberg, 2009, 111-125.
• [4] Czeizler, E., Mizera, A., Petre, I.: A Boolean Approach for Disentangling the Numerical Contribution of Modules to the System-Level Behavior of a Biomodel, Technical Report 997, Turku Centre for Computer Science, http://tucs.fi/research/publication-view/?pub_id=tCzMiPe11a, January 2011.
• [5] El-Samad, H., Kurata, H., Doyle, J. C., Gross, C. A., Khammash, M.: Surviving heat shock: Control strategies for robustness and performance, Proc Natl Acad Sci USA, 102, 2005, 2736-2741.
• [6] Gillespie, D. T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J Comput Phys, 22, 1976, 403-434.
• [7] Gillespie, D. T.: Exact stochastic simulation of coupled chemical reactions, J Phys Chem, 81, 1977, 2340-2361.
• [8] Guldberg, C. M., Waage, P.: Studies Concerning Affinity, C. M. Forhandlinger: Videnskabs-Selskabet I Christiana, 35, 1864.
• [9] Guldberg, C. M., Waage, P.: Concerning Chemical Affinity, Erdmann's Journal fr Practische Chemie, 127, 1879, 69-114.
• [10] Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.: Copasi - a COmplex PAthway SImulator, Bioinformatics, 22, 2006, 3067-3074.
• [11] Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random Boolean network models and the yeast transcriptional network, Proc Natl Acad Sci USA, 100, 2003, 14796-14799.
• [12] Kervizic, G., Corcos, L.: Dynamical modeling of the cholesterol regulatory pathway with Boolean networks, BMC Systems Biology, 2, 2008, 99.
• [13] Kline, M. P., Morimoto, R. I.: Repression of the heat shock factor 1 transcriptional activation domain is modulated by constitutive phosphorylation, Molecular and Cellular Biology, 17, 1997, 2107-2115.
• [14] Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems Biology in Practice, WILEY-VCH Verlag GmbH & Co, KGaA, Weinheim, 2005.
• [15] Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems Biology in Practice. Concepts, Implementation and Application, Wiley-VCH, Weinheim, 2005.
• [16] Mizera, A., Czeizler, E., Petre, I.: Methods for biochemical model decomposition and quantitative submodel comparison, Israel J Chem, 51(1), 2011, 151-164.
• [17] Petre, I., Mizera, A., Hyder, C. L., Mikhailov, A., Eriksson, J. E., Sistonen, L., Back, R.-J.: A simple massaction model for the eukaryotic heat shock response and its mathematical validation, Natural Computing, 10(1), 2011, 595-612.
• [18] Savageau, M. A.: Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions, J Theoret Biol, 25, 1969, 365-369.
• [19] Savageau, M. A.: Biochemical systems analysis: II. The steady-state solutions for an n-pool system using a power-law approximation., J Theoret Biol, 25, 1969, 370-379.
• [20] Savageau, M. A.: The behavior of intact biochemical control systems, Curr. Top. Cell. Reg., 6, 1972, 63-130.
• [21] Shmulevich, I., Dougherty, R., Zhang, W.: From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks, Proc IEEE, 90, 2002, 1778-1792.