Seminar: Computational Models in Biology
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Seminar: Computational Models in Biology

Students will present papers that address modelling and data analysis in the context of biology. We will talk about discrete- and continuous-time dynamics, both stochastic and deterministic. We will see, though the review of literature, how to apply such models to understand and study ecology, evolution, genetics, epidemiology, and behaviour.

הסטודנטים יציגו מאמרים שעוסקים במידול מערכות וניתוח נתונים בהקשרים ביולוגיים. נדבר על מודלים דיסקטיים ורציפים בזמן, אקראיים ודטרמניסטים. נראה, אגב סקירת הספרות, כיצד להשתמש במודלים על מנת להבין ולחקור אקולוגיה, אבולוציה, גנטיקה, אפידמיולוגיה, והתנהגות.

  • Elective graduate course
  • Spring 2019
  • Prerequisites: quantitative background is required, biology background is not required
  • Grading: seminar presentation (in English), participation in discussions


Models in Biology

Evolution: Generation and transmission of variations

  • Rivoire O, Leibler S (2014) A model for the generation and transmission of variations in evolution. Proc Natl Acad Sci USA 111:E1940–E1949
  • Altenberg L, Liberman U, Feldman MW (2017) Unified reduction principle for the evolution of mutation, migration, and recombination. Proc Natl Acad Sci USA 114:E2392– E2400.

Evolution: Classics

  • Haldane J, Jayakar S (1963) Polymorphism due to selection of varying direction. J Genet 58:237–242
  • Kimura, M. and Maruyama, T. (1966), The mutational load with epistatic gene interactions in fitness., Genetics 54(6), 1337–51
  • Haigh, J. (1978), The accumulation of deleterious genes in a population - mullers ratchet, Theoretical Population Biology 14(2), 251–267.
  • Maynard Smith J, Haigh J (1974) The hitch-hiking effect of a favourable gene. Genet Res 23(1):23–35.
  • Patwa Z, Wahl LM (2008) The fixation probability of beneficial mutations. J R Soc Interface 5(28):1279–89.
  • Kimura M, Ohta T (1969) The Average Number of Generations until Fixation of a Mutant Gene in a Finite Population. Genetics 61(3):763–71.
  • Eshel I (1981) On the survival probability of a slightly advantageous mutant gene with a general distribution of progeny size - a branching process model. J Math Biol 12(3):355–362.
  • Gillespie JH (1983) A simple stochastic gene substitution model. Theor Popul Biol 23(2):202–215.

Ecology: Classics

  • Charnov EL (1976) Optimal foraging, the marginal value theorem. Theor Popul Biol 9(2):129–36.
  • Tilman D (1977) Resource Competition between Plankton Algae: An Experimental and Theoretical Approach. Ecology 58(2):338.

Ecology: Community structure

  • Friedman J, Higgins LM, Gore J (2016) Community structure follows simple assembly rules in microbial microcosms. bioRxiv:67926.

Evolution: Mutation rate

  • Tenaillon O, Le Nagard H, Godelle B, Taddei F (2000) Mutators and sex in bacteria: conflict between adaptive strategies. Proc Natl Acad Sci U S A 97(19):10465–70.
  • Heo M, Shakhnovich EI (2010) Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response. PLoS Comput Biol 6(3):e1000710.

Evolution: Learning

  • Wakano JY, Aoki K, Feldman MW (2004) Evolution of social learning: A mathematical analysis. Theor Popul Biol 66:249–258.
  • Rogers AR (1988) Does biology constrain culture? Am Anthropol 90:819-831.

Evolution: phenotypic switching

  • Kussell E, Leibler S (2005) Phenotypic diversity, population growth, and information in fluctuating environments. Science 309:2075–2078.
  • Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat Genet 40(4):471–5.

Evolution: Sex

  • de Visser JAGM, Park S-C, Krug J (2009) Exploring the effect of sex on empirical fitness landscapes. Am Nat 174 Suppl(July 2009):S15-30.
  • Levin BR, Cornejo OE (2009) The population and evolutionary dynamics of homologous gene recombination in bacterial populations. PLoS Genet 5(8):e1000601.

Evolution: Cooperation

  • Obolski U, Lewin-Epstein O, Even-Tov E, Ram Y, Hadany L (2017) With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing. BMC Evol Biol 17(1):143.

Evolution: Analysis of empirical data

  • Szendro IG, Schenk MF, Franke J, Krug J, de Visser JAGM (2013) Quantitative analyses of empirical fitness landscapes. J Stat Mech Theory Exp 2013(1):P01005.
  • Bank C, Hietpas RT, Wong A, Bolon DNA, Jensen JD (2014) A Bayesian MCMC Approach To Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments. Genetics 196(3):1–35.
  • Gordo I, Dionisio F (2005) Nonequilibrium model for estimating parameters of deleterious mutations. Phys Rev E 71(3):18–21.
  • Trindade S, Perfeito L, Gordo I (2010) Rate and effects of spontaneous mutations that affect fitness in mutator Escherichia coli. Philos Trans R Soc B Biol Sci 365(1544):1177–1186.
  • Moura De Sousa JA, Campos PRAA, Gordo I (2013) An ABC Method for Estimating the Rate and Distribution of Effects of Beneficial Mutations. Genome Biol Evol 5(5):794–806.
  • An Approximate Markov Model for the Wright–Fisher Diffusion and Its Application to Time Series Data

Ecology: Social netowrks

  • Ilany A, Akçay E (2016) Social inheritance can explain the structure of animal social networks. Nat Commun 7(May). doi:10.1038/ncomms12084.


  • Bonten MJ, Austin DJ, Lipsitch M (2001) Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Clin Infect Dis 33(10):1739–46.
  • Tanaka MM, Francis AR, Luciani F, Sisson SA (2006) Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data. 1520(July):1511–1520.
  • Extension of previous: Sisson SA, Fan Y, Tanaka MM (2007) Sequential Monte Carlo without likelihoods. Proc Natl Acad Sci 104(6):1760–1765.