Abstract
A central question in cell and developmental biology is how signaling pathways maintain specificity and avoid erroneous cross-talk so that distinct signals produce the appropriate changes. In this paper, a model system of the yeast mating, invasive growth and stress-responsive mitogen activated protein kinase (MAPK) cascades for scaffolding-mediated is developed. Optimization with respect to the mutual specificity of this model system is performed by a high performance multi-objective evolutionary algorithm (HPMOEA) based on the principles of the minimal free energy in thermodynamics. The results are good agreement with published experimental data. (1) Scaffold proteins can enhance specificity in cell signaling when different pathways share common components; (2) The mutual specificity could be accomplished by a selectively-activated scaffold that had a relatively high value of dissociation constant and reasonably small values of leakage rates; (3) When Pareto-optimal mutual specificity is achieved, the coefficients, deactivation rates reach fastest, association and leakage rates reach slowest.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Elion, E.A., Qi, M., Chen, W.: Signal Transduction: Signaling Specificity in Yeast. Science 307, 687–688 (2005)
Komarova, N.L., Zou, X., Nie, Q., Bardwell, L.: A Theoretical Framework For Specificit In Cell Signaling, Molecular Systems Biology, report (2005)
Schwartz, M.A., Madhani, H.D.: Principles of MAP Kinase Signaling Specificity In Saccharomyces Cerevisiae. Annu. Rev. Genet. 38, 725–748 (2004)
Flatauer, L.J., Zadeh, S.F., Bardwell, L.: Mitogen-activated protein kinases with distinct requirements for Ste5 scaffolding influence signaling specificity in Saccharomyces cerevisiae. Mol. Cell. Biol. 25, 1793–1803 (2005)
Zou, X., Liu, M., Kang, L., He, J.: A High Performance Multi-objective Evolutionary Algorithm Based on the Principle of Thermodynamics. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 922–931. Springer, Heidelberg (2004)
Zitzler, E., Thiele, L., Laumanns, M., et al.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7, 117–132 (2003)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm:NSGA. IEEE Transaction on Evolutionary Computation 6, 182–197 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zou, X., Chen, Y., Pan, Z. (2006). Modeling and Optimization of the Specificity in Cell Signaling Pathways Based on a High Performance Multi-objective Evolutionary Algorithm. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_97
Download citation
DOI: https://doi.org/10.1007/11903697_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)