On the application of statistical physics to evolutionary biology.

By N. H. Barton, J. Coe. 2009

302 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Sociology and Political Science

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Citations in the corpus

The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, background): “We expect more prevalent sequences to be more fit, consistent with expectations from simple models of evolution [23] though the precise correspondence between fitness and prevalence may have a more complicated dependence on factors such as the shape of the fitness landscape, as predicted by quasispe”
Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design (2013, unknown): “Under relatively restrictive assumptions, Sella and Hirsh have precisely derived the connection between E and fitness (Sella and Hirsh, 2005).”
The Logic of Chance: The Nature and Origin of Biological Evolution (2011, background): “It is actually possible to draw a detailed correspondence between the key variables in the two areas (Barton and Coe, 2009; Sella and Hirsh, 2005).”
Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes (2013, unknown): “But these models cannot identify which specific sites in these collectively co-evolving groups should be targeted to maximally compromise viral fitness, and how mutational escape pathways that exist even within these regions may be blocked by additional immune responses.”
The free-energy principle: a unified brain theory? (2010, background): “This means that free energy is the complement of value, and its long-term average is the complement of adaptive fitness (also known as free fitness in evolutionary biolog”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, methodology): “The Boltzmann form of P was previously derived for an evolving population in the limit where the product of the population size and mutation rate is very small (Sella and Hirsh 2005).”
Life as we know it (2013, unknown): “Indeed, free energy functionals have been invoked to describe natural selection [61].”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Examples are found in sequence analysis, where population dynamics leads to an equilibrium steady state [24, 200] and the energy can often be approximated by pairwise couplings [153, 195].”
Free-Energy Minimization and the Dark-Room Problem (2012, background): “This means that the surprise can be reduced by changing sensory input (action), predictions of that input (perception), or the model per se; through evolution to minimize free energy or maximize free-fitness (Sella and Hirsh, 2005).”
Epistasis and the Dynamics of Reversion in Molecular Evolution (2016, background): “Population-genetic model We consider a population evolving in continuous time under weak mutation on an arbitrary finite-state fitness landscape (e.g. Iwasa 1988; Sella and Hirsh 2005; McCandlish et al. 2015b).”