Journal of Theoretical Biology, Nov 7, 2013, Vol.336, p.61(14)
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jtbi.2013.07.012 Byline: Christian Arnold, Peter F. Stadler, Sonja J. Prohaska Abstract: Eukaryotic histones carry a diverse set of specific chemical modifications that accumulate over the life-time of a cell and have a crucial impact on the cell state in general and the transcriptional program in particular. Replication constitutes a dramatic disruption of the chromatin states that effectively amounts to partial erasure of stored information. To preserve its epigenetic state the cell reconstructs (at least part of) the histone modifications by means of processes that are still very poorly understood. A plausible hypothesis is that the different combinations of reader and writer domains in histone-modifying enzymes implement local rewriting rules that are capable of "recomputing" the desired parental modification patterns on the basis of the partial information contained in that half of the nucleosomes that predate replication. To test whether such a mechanism is theoretically feasible, we have developed a flexible stochastic simulation system (available at http://www.bioinf.uni-leipzig.de/Software/StoChDyn) for studying the dynamics of histone modification states. The implementation is based on Gillespie's approach, i.e., it models the master equation of a detailed chemical model. It is efficient enough to use an evolutionary algorithm to find patterns across multiple cell divisions with high accuracy. We found that it is easy to evolve a system of enzymes that can maintain a particular chromatin state roughly stable, even without explicit boundary elements separating differentially modified chromatin domains. However, the success of this task depends on several previously unanticipated factors, such as the length of the initial state, the specific pattern that should be maintained, the time between replications, and chemical parameters such as enzymatic binding and dissociation rates. All these factors also influence the accumulation of errors in the wake of cell divisions. Author Affiliation: (a) Computational EvoDevo Group, Department of Computer Science, Universitat Leipzig, Hartelstra[sz]e 16-18, 04107 Leipzig, Germany (b) Interdisciplinary Center for Bioinformatics, Universitat Leipzig, Hartelstra[sz]e 16-18, 04107 Leipzig, Germany (c) Bioinformatics Group, Department of Computer Science, Universitat Leipzig, Hartelstra[sz]e 16-18, 04107 Leipzig, Germany (d) Harvard University, Department of Human Evolutionary Biology, 11 Divinity Avenue, Cambridge, MA 02138, USA (e) Max-Planck-Institute for Mathematics in the Sciences, Inselstra[sz]e 22, 04103 Leipzig, Germany (f) Fraunhofer Institut fur Zelltherapie und Immunologie Perlickstra[sz]e 1, 04103 Leipzig, Germany (g) Department of Theoretical Chemistry University of Vienna, Wahringerstra[sz]e 17, 1090 Wien, Austria (h) Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA Article History: Received 19 March 2013; Revised 2 July 2013; Accepted 15 July 2013
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