Margaret J. Eppstein
ABSTRACT
The concept of "deception" in fitness landscapes was introduced in the genetic algorithm (GA) literature to characterize problems where sign epistasis can mislead a GA away from the global optimum. Evolutionary geneticists have long recognized that sign epistasis is the source of the ruggedness of fitness landscapes, and the recent availability of a growing number of empirical fitness landscapes may make it possible for evolutionary biologists to study how deception affects adaptation in a variety of organisms. However, existing definitions of deception are categorical and were developed to characterize landscapes independent of population distributions on the landscape. Here we propose two metrics that quantify deception as continuous functions of the locations of replicators on a given landscape. We develop a discrete population model to simulate within-host evolution on 19 empirical fitness landscapes of Plasmodium falciparum (the most common and deadly form of malaria) under different dosage levels of two anti-malarial drugs. We demonstrate varying levels of deception in malarial evolution, and show that the proposed metrics of deception are predictive of some important aspects of evolutionary dynamics. Our approach can be readily applied to other fitness landscapes and toward an improved understanding of the evolution of antimicrobial drug resistance.
- K. M. Brown, M. S. Costanzo, W. Xu, S. Roy, E. R. Lozovsky, and D. L. Hartl. Compensatory mutations restore fitness during the evolution of dihydrofolate reductase. Molecular Biology and Evolution, 27(12):2682--2690, 2010.Google Scholar
Cross Ref
- M. S. Costanzo, K. M. Brown, and D. L. Hartl. Fitness trade-offs in the evolution of dihydrofolate reductase and drug resistance in plasmodium falciparum. PLoS One, 6(5):e19636--e19636, 2011.Google ScholarCross Ref
- J. A. G. de Visser and J. Krug. Empirical fitness landscapes and the predictability of evolution. Nature Reviews Genetics, 2014.Google Scholar
- C. Fenseter and L. Galloway. The contribution of epistasis to the evolution of natural populations. Epistasis and the Evolutionary Process, pages 232--244, 2000.Google Scholar
- D. E. Goldberg. Simple genetic algorithms and the minimal, deceptive problem. Genetic Algorithms and Simulated Annealing, 74:88, 1987.Google Scholar
- J. Grefenstette. Deception considered harmful. Foundations of Genetic Algorithms 2, pages 75--91, 1993.Google Scholar
- J. Holland. Adaptation in artificial and natural systems. Ann Arbor: The University of Michigan Press, 1975.Google Scholar
- J. J. Juliano, K. Porter, V. Mwapasa, R. Sem, W. O. Rogers, F. Ariey, C. Wongsrichanalai, A. Read, and S. R. Meshnick. Exposing malaria in-host diversity and estimating population diversity by capture-recapture using massively parallel pyrosequencing. Proceedings of the National Academy of Sciences, 107(46):20138--20143, 2010.Google ScholarCross Ref
- L. Kallel, B. Naudts, and C. R. Reeves. Properties of fitness functions and search landscapes. In Theoretical Aspects of Evolutionary Computing, pages 175--206. Springer, 2001. Google ScholarDigital Library
- A. Le Rouzic. Estimating directional epistasis. Frontiers in Genetics, 5, 2014.Google Scholar
- E. R. Lozovsky, T. Chookajorn, K. M. Brown, M. Imwong, P. J. Shaw, S. Kamchonwongpaisan, D. E. Neafsey, D. M. Weinreich, and D. L. Hartl. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proceedings of the National Academy of Sciences, 106(29):12025--12030, 2009.Google ScholarCross Ref
- K. M. Malan and A. P. Engelbrecht. A survey of techniques for characterising fitness landscapes and some possible ways forward. Information Sciences, 241:148--163, 2013. Google ScholarDigital Library
- N. Manukyan, M. J. Eppstein and J. S Buzas. Tunably rugged landscapes with known maximum and minimum. IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2015.2454857, in press, 2016.Google Scholar
- D. C. Nwakanma, C. W. Duffy, A. Amambua-Ngwa, E. C. Oriero, K. A. Bojang, M. Pinder, C. J. Drakeley, C. J. Sutherland, P. J. Milligan, B. MacInnis, et al. Changes in malaria parasite drug resistance in an endemic population over a 25-year period with resulting genomic evidence of selection. Journal of Infectious Diseases, page jit618, 2013.Google Scholar
- C. B. Ogbunugafor, C. S. Wylie, I. Diakite, D. M. Weinreich, and D. L. Hartl. Adaptive landscape by environment interactions dictate evolutionary dynamics in models of drug resistance. PLoS Comput Biol, 12(1):e1004710, 2016.Google ScholarCross Ref
- S. Paget-McNicol and A. Saul. Mutation rates in the dihydrofolate reductase gene of plasmodium falciparum. Parasitology, 122(05):497--505, 2001.Google ScholarCross Ref
- F. J. Poelwijk, D. J. Kiviet, D. M. Weinreich, and S. J. Tans. Empirical fitness landscapes reveal accessible evolutionary paths. Nature, 445(7126):383--386, 2007.Google ScholarCross Ref
- I. G. Szendro, M. F. Schenk, J. Franke, J. Krug, and J. A. G. De Visser. Quantitative analyses of empirical fitness landscapes. Journal of Statistical Mechanics: Theory and Experiment, 2013(01):P01005, 2013.Google ScholarCross Ref
- D. M. Weinreich, N. F. Delaney, M. A. DePristo, and D. L. Hartl. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science, 312(5770):111--114, 2006.Google ScholarCross Ref
- D. M. Weinreich, Y. Lan, C. S. Wylie, and R. B. Heckendorn. Should evolutionary geneticists worry about higher-order epistasis? Current Opinion in Genetics & Development, 23(6):700--707, 2013.Google ScholarCross Ref
- D. M. Weinreich, R. A. Watson, L. Chao, and R. Harrison. Perspective: sign epistasis and genetic constraint on evolutionary trajectories. Evolution, 59(6):1165--1174, 2005.Google Scholar
- D. Whitley. Fundamental principles of deception. Foundations of Genetic Algorithms 1, pages 221--242, 1991.Google Scholar
- D. Whitley. Mk landscapes, NK landscapes, MAX-kSAT: A proof that the only challenging problems are deceptive. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pages 927--934. ACM, 2015. Google ScholarDigital Library
Index Terms
- Quantifying Deception: A Case Study in the Evolution of Antimicrobial Resistance
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