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Open accessJournal ArticleDOI: 10.1103/PHYSREVX.11.011044

Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes

04 Mar 2021-Physical Review X (American Physical Society)-Vol. 11, Iss: 1, pp 011044
Abstract: Optimal control for artificial selection offers a new paradigm for directing stochastic evolution of multivariate molecular characteristics and phenotypes toward desired targets.

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Open accessJournal ArticleDOI: 10.1073/PNAS.2103398118
Abstract: The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune coevolution generates an emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host coevolution.

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Topics: Viral evolution (54%), Population (53%), Evolutionary dynamics (52%) ... show more

4 Citations


Open accessPosted ContentDOI: 10.1101/2020.10.07.330340
08 Oct 2020-bioRxiv
Abstract: Effective prophylactic vaccines usually induce the immune system to generate potent antibodies that can bind to an antigen and thus prevent it from infecting host cells. B cells produce antibodies by a Darwinian evolutionary process called affinity maturation (AM). During AM, the B cell population evolves in response to the antigen. Antibodies that bind specifically and strongly to the antigen are thus produced. Highly mutable pathogens pose a major challenge to the development of effective vaccines because antibodies that are effective against one strain of the virus may not protect against a mutant strain. Antibodies that can protect against diverse strains of a mutable pathogen are called broadly neutralizing antibodies (bnAbs). In spite of extensive experimental and computational studies that have led to important advances, an effective vaccination strategy that can generate bnAbs does not exist for any highly mutable pathogen. Here we study a minimal model of AM in different time-varying antigenic environments to explore the mechanisms underlying optimal vaccination protocols that maximize the production of bnAbs. We find that the characteristics of the time-varying Kullback-Leibler distance (KLD) between the B cell population distribution and the fitness landscape imposed by antigens is a key determinant of bnAb evolution. The optimal vaccination protocol requires a relatively low KLD in the beginning in order to increase the entropy (diversity) of the B cell population so that the surviving B cells have a high chance of evolving into bnAbs upon subsequently increasing the KLD. For a discretized two-step variation in antigenic environment, there are optimal values of the KLDs for the first and second steps. Phylogenetic tree analysis further reveals the evolutionary pathways that lead to bnAbs. The connections between our results and recent simulation studies of bnAb evolution and the general problem of evolution of generalists versus specialists are discussed.

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Topics: Population (53%), Affinity maturation (51%)

3 Citations


Open accessPosted ContentDOI: 10.1101/2021.06.13.448255
Abstract: The authors would like to thank the stimulating environment provided by the Telluride Science Research Center, where this project was conceived. M.H. acknowledges support from the U.S. National Science Foundation (NSF) under Grant No. MCB-1651650. E.I. acknowledges support from the Labex CelTisPhyBio (ANR-11-LABX-0038, ANR-10-IDEX-0001-02).

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1 Citations


Open accessPosted ContentDOI: 10.1101/2020.04.29.069179
01 May 2020-bioRxiv
Abstract: Responding to stimuli requires that organisms encode information about the external world. Not all parts of the signal are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position coordinates prove more predictive. We identify the properties of global, transferrable strategies for time-varying stimuli. For non-Markovian dynamics we explore the role of long-term memory of the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories, revealing a very different prediction strategy from motion prediction.

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1 Citations


Open accessPosted Content
Abstract: The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even further away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of co-evolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune co-evolution generates a new emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts on the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host co-evolution.

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Topics: Population (53%), Evolutionary dynamics (51%)

1 Citations


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63 results found


Open accessJournal ArticleDOI: 10.1111/J.1558-5646.1983.TB00236.X
Russell Lande1, Stevan J. Arnold1Institutions (1)
01 Nov 1983-Evolution
Abstract: Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et

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Topics: Stabilizing selection (72%), Selection (genetic algorithm) (67%), Natural selection (67%) ... show more

4,737 Citations


Journal ArticleDOI: 10.1126/SCIENCE.1099390
10 Sep 2004-Science
Abstract: A fraction of a genetically homogeneous microbial population may survive exposure to stress such as antibiotic treatment. Unlike resistant mutants, cells regrown from such persistent bacteria remain sensitive to the antibiotic. We investigated the persistence of single cells of Escherichia coli with the use of microfluidic devices. Persistence was linked to preexisting heterogeneity in bacterial populations because phenotypic switching occurred between normally growing cells and persister cells having reduced growth rates. Quantitative measurements led to a simple mathematical description of the persistence switch. Inherent heterogeneity of bacterial populations may be important in adaptation to fluctuating environments and in the persistence of bacterial infections.

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Topics: Phenotypic switching (54%), Population (51%)

2,410 Citations


Open accessJournal ArticleDOI: 10.1109/TCST.2005.847331
Kiam Heong Ang, G. Chong1, Yun Li1Institutions (1)
Abstract: Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements.

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Topics: PID controller (58%), Automatic control (52%), Control theory (51%)

2,074 Citations


Journal ArticleDOI: 10.1038/227520A0
George R. Price1Institutions (1)
01 Aug 1970-Nature
Abstract: THIS is a preliminary communication describing applications to genetical selection of a new mathematical treatment of selection in general.

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Topics: Selection (genetic algorithm) (67%), Price equation (55%), Covariance (51%) ... show more

1,612 Citations


Open accessJournal ArticleDOI: 10.1111/J.1558-5646.1976.TB00911.X
Russell Lande1Institutions (1)
01 Jun 1976-Evolution
Abstract: In discussions of the major features of evolution, Simpson (1953) applied population genetic models to the interpretation of the fossil record Most population genetics theory concentrates on details of the genetic system, such as gene frequencies and recombination rates, which cannot be directly observed or inferred from measurements on polygenic characters Analysis of phenotypic data, particularly fossil material, requires models which are framed as much as possible in phenotypic terms Starting from a simple formula of quantitative genetics, the methods of population genetics are used here to make a theory of the evolution of the average phenotype in a population by natural selection and random genetic drift By analogy with Wright's (1931) adaptive topography for genotypes, Simpson (1953) proposed the concept of adaptive zones for phenotypes This is an intuitive method of visualizing the dynamics of phenotypic evolution in terms of the degree of adaptation of the various phenotypes in a population, it usually being thought that natural selection increases adaptation Such qualitative ideas are used by most evolutionary biologists and the notion of adaptive zones is popular among paleontologists In the present paper, the concept of adaptive zones is clarified by the construction of an adaptive topography for the average phenotype in a population This shows that with constant fitnesses the average phenotype evolves toward the nearest adaptive zone in the phenotype space But if fitnesses are frequency-dependent the average phenotype may evolve away from an adaptive zone A method is developed for estimating the minimum selective mortality necessary to produce an observed rate of evolution In examples of the evolution of tooth characters in Tertiary mammals, these minimum selective mortalities are found to be exceedingly small In his paper on the measurement of rates of evolution, Haldane (1949) stated that "The slowness of the rate of change makes it clear that agencies other than natural selection cannot be neglected because they are extremely slow by laboratory standards or even undetectable during a human lifetime" He briefly discussed mutation pressure Random genetic drift due to finite population size is another such agency The relative importance of natural selection and random genetic drift has been debated since Wright (1931, 1932) proposed that evolution is a stochastic process Fisher (1958), for example, believed that random genetic drift is insignificant in relation to natural selection The debate continues today at a more biochemical level (Lewontin, 1974) In order to objectively evaluate the role of random genetic drift in macro-evolutionary events, it is necessary to use mathematical models to determine the rate of evolution which can occur by repeated samplings of genetic material in a finite population This paper presents a statistical test for the hypothesis of evolution by random genetic drift, contingent on the effective population size In examples from the fossil record, it is found that rates of evolution equal to or greater than those observed have a significant probability of occurring by random genetic drift

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Topics: Genetic model (62%), Genetic drift (62%), Population genetics (62%) ... show more

1,510 Citations


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