Abstract
Systems biology is the ever-growing field of integrating molecular knowledge about biological organisms into an understanding at the systems level. For this endeavour, automatic network reconstruction tools are urgently needed. In the present contribution, we show how the applicability of evolutionary algorithms to systems biology can be improved by a domain-specific representation and algorithmic extensions, especially a separation of network structure evolution from evolution of kinetic parameters. In a case study, our presented tool is applied to a model of the mitotic spindle checkpoint in the human cell cycle.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alberts, B., Johnson, A., Lewis, J.: Essential Cell Biology. Garland Publishing, New York (2003)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming, An Introduction: On The Automatic Evolution of Computer Programs And Its Applications. Morgan Kaufmann, San Francisco (1998)
Beyer, H., Schwefel, H.: Evolution strategies. Natural Computing 1, 3–52 (2002)
Burnham, K.P., Anderson, D.R.: Model selection and inference: a practical information-theoretic approach. Springer, Heidelberg (1998)
Chung, E., Chen, R.-H.: Spindle Checkpoint Requires Mad1-bound and Mad1- free Mad2. Molecular Biology of the Cell 13, 1501–1511 (2002)
Cooper, B.L., Schonbrunner, N., Krauss, G.: Biochemistry of signal transduction and regulation. Wiley-VCH, Weinheim (2001)
Deckard, A., Sauro, H.M.: Preliminary Studies on the In Silico Evolution of Biochemical Networks. ChemBioChem. 5, 1423–1431 (2004)
Fang, G.: Checkpoint Protein BubR1 Acts Synergistically with Mad2 to Inhibit Anaphase-promoting Complex. Molecular Biology of the Cell 13, 755–766 (2002)
Finney, A., Hucka, M.: Systems biology markup language: Level 2 and beyond. Biochem. Soc. Trans. 31(Pt. 6), 1472–1473 (2003)
François, P., Hakim, V.: Design of Genetic Networks With Specified Functions by Evolution in silico. PNAS 101, 580–585 (2004)
Funahashi, A., Tanimura, N., Morohashi, M., Kitano, H.: CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1, 159–162 (2003)
Ibrahim, B., Diekmann, S., Schmitt, E., Dittrich, P.: Compartmental Model of Mitotic Spindle Checkpoint Control Mechanism. BMCBioinformatic, Submitted Paper (2006)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R., Mydlowec, W., Lanza, G., Yu, J., Keane, M.A.: Automatic Synthesis of Both the Topology and Sizing of Metabolic Pathways using Genetic Programming. In: Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 57–65. Morgan Kaufmann, San Francisco (2001)
Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2002)
Machne, R., Finney, A., Muller, S., Lu, J., Widder, S., Flamm, C.: The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks. Bioinformatics 22(11), 1406–1407 (2006)
Paladugu, S.R., Chickarmane, V., Deckard, A., Frumkin, J.P., McCormack, M., Sauro, H.M.: In Silico Evolution of Functional Modules in Biochemical Networks. IEE Proceedings-Systems Biology 153(4) (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Lenser, T., Hinze, T., Ibrahim, B., Dittrich, P. (2007). Towards Evolutionary Network Reconstruction Tools for Systems Biology. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-71783-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71782-9
Online ISBN: 978-3-540-71783-6
eBook Packages: Computer ScienceComputer Science (R0)