Abstract
The distribution of mutational effects on fitness (DMEF) is of fundamental importance for many questions in biology. Previously, wet-lab experiments and population genetic methods have been used to infer the sizes of effects of mutations. Both approaches have important limitations. Here we propose a new framework for estimating the DMEF by constructing fitness correlates in molecular systems biology models. This new framework can complement the other approaches in estimating small effects on fitness. We present a notation for the various DMEs that can be present in a molecular systems biology model. Then we apply this new framework to a simple circadian clock model and estimate various DMEs in that system. Circadian clocks are responsible for the daily rhythms of activity in a wide range of organisms. Mutations in the corresponding genes can have large effects on fitness by changing survival or fecundity. We define potential fitness correlates, describe methods for automatically measuring them from simulations and implement a simple clock using the Gillespie stochastic simulation algorithm within StochKit. We determine what fraction of examined mutations with small effects on the rates of the reactions involved in this system are advantageous or deleterious for emerging features of the system like a fitness correlate, cycle length and cycle amplitude. We find that the DME can depend on the wild type reference used in its construction. Analyzing many models with our new approach will open up a third source of information about the distribution of mutational effects, one of the fundamental quantities that shape life.
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References
Eyre-Walker, A., Keightley, P.D.: The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007)
Loewe, L., Charlesworth, B.: Inferring the distribution of mutational effects on fitness in Drosophila. Biology Letters 2, 426–430 (2006)
Keightley, P.D., Eyre-Walker, A.: Joint inference of the distribution of fitness effects of deleterious mutations and population demography based on nucleotide polymorphism frequencies. Genetics 177, 2251–2261 (2007)
Martin, G., Lenormand, T.: A general multivariate extension of Fisher’s geometrical model and the distribution of mutation fitness effects across species. Evolution 60, 893–907 (2006)
Kitano, H.: Towards a theory of biological robustness. Mol. Syst. Biol. 3, 137 (2007)
Kitano, H.: A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Disc. 6, 202–210 (2007)
Brommer, J.E.: The evolution of fitness in life-history theory. Biol. Rev. Camb. Philos. Soc. 75, 377–404 (2000)
Stearns, S.C.: The evolution of life histories. Oxford University Press, Oxford (1992)
Rust, M.J., Markson, J.S., Lane, W.S., Fisher, D.S., O’Shea, E.K.: Ordered phosphorylation governs oscillation of a three-protein circadian clock. Science 318, 809–812 (2007)
Panda, S., Hogenesch, J.B., Kay, S.A.: Circadian rhythms from flies to human. Nature 417, 329–335 (2002)
Brunner, M., Káldi, K.: Interlocked feedback loops of the circadian clock of Neurospora crassa. Mol. Microbiol. 68(2), 255–262 (2008)
Gjuvsland, A.B., Plahte, E., Omholt, S.W.: Threshold-dominated regulation hides genetic variation in gene expression networks. BMC Syst. Biol. 1, 57 (2007)
Efron, B., Tibshirani, R.D.: An introduction to the bootstrap. Chapman & Hall, New York (1993)
Leloup, J.C., Gonze, D., Goldbeter, A.: Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora. J. Biol. Rhythms 14(6), 433–448 (1999)
Goodwin, B.C.: Oscillatory behavior in enzymatic control processes. Adv. Enzyme Regul. 3, 425–438 (1965)
Gonze, D., Halloy, J., Goldbeter, A.: Deterministic versus stochastic models for circadian rhythms. J. Biol. Phys. 28, 637–653 (2002)
Bundschuh, R., Hayot, F., Jayaprakash, C.: Fluctuations and Slow Variables in Genetic Networks. Biophys. J. 84, 1606–1615 (2003)
Arkin, A.P., Rao, C.V.: Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm. J. Chem. Phys. 11, 4999–5010 (2003)
Cao, Y., Gillespie, D.T., Petzold, L.: Accelerated Stochastic Simulation of the Stiff Enzyme-Substrate Reaction. J. Chem. Phys. 123(14), 144917–144929 (2005)
Cao, Y., Gillespie, D.T., Petzold, L.: Adaptive explicit-implicit tau-leaping method with automatic tau selection. J. Chem. Phys. 126, 224101 (2007)
Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)
Bradley, J.T., Thorne, T.: Stochastic Process Algebra models of a Circadian Clock. In: Nicol, D.M., Priami, C., Nielson, H.R., Uhrmacher, A.M. (eds.) Simulation and Verification of Dynamic Systems, Dagstuhl Seminar Proceedings, Dagstuhl, Germany (2006), http://drops.dagstuhl.de/opus/volltexte/2006/705
Stenico, M.: Modelling molecular systems with discrete concentration levels in the context of process algebra PEPA: Stochastic and deterministic interpretations. MSc.Thesis, University of Trento (2006)
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Loewe, L., Hillston, J. (2008). The Distribution of Mutational Effects on Fitness in a Simple Circadian Clock. In: Heiner, M., Uhrmacher, A.M. (eds) Computational Methods in Systems Biology. CMSB 2008. Lecture Notes in Computer Science(), vol 5307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88562-7_14
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DOI: https://doi.org/10.1007/978-3-540-88562-7_14
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