Author
Benjamin Kuznets-Speck
Other affiliations: Case Western Reserve University
Bio: Benjamin Kuznets-Speck is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Population & Non-equilibrium thermodynamics. The author has an hindex of 5, co-authored 11 publications receiving 71 citations. Previous affiliations of Benjamin Kuznets-Speck include Case Western Reserve University.
Papers
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TL;DR: In this paper, the authors use counter-diabatic driving to control the behaviour of quantum states for applications like quantum computing and manipulating ultracold atoms, and show how a set of external control parameters (that is, varying drug concentrations and types, temperature and nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval.
Abstract: The pace and unpredictability of evolution are critically relevant in a variety of modern challenges, such as combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modelling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context—counterdiabatic driving to control the behaviour of quantum states for applications like quantum computing and manipulating ultracold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (that is, varying drug concentrations and types, temperature and nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules. The unpredictability of evolution makes it difficult to deal with drug resistance because over the course of a treatment there may be mutations that we cannot predict. The authors propose to use quantum methods to control the speed and distribution of potential evolutionary outcomes.
55 citations
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TL;DR: The authors propose to use quantum methods to control the speed and distribution of potential evolutionary outcomes, using counterdiabatic driving toControl the behaviour of quantum states for applications like quantum computing and manipulating ultracold atoms.
Abstract: The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context: counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.
33 citations
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TL;DR: Stochastic thermodynamics is employed to build a framework which can be used to gain mechanistic insight into transitions far from equilibrium, and shows that under general conditions, there is a basic speed limit relating the typical excess heat dissipated throughout a transition and the rate amplification achievable.
Abstract: Complex systems can convert energy imparted by nonequilibrium forces to regulate how quickly they transition between long-lived states. While such behavior is ubiquitous in natural and synthetic systems, currently there is no general framework to relate the enhancement of a transition rate to the energy dissipated or to bound the enhancement achievable for a given energy expenditure. We employ recent advances in stochastic thermodynamics to build such a framework, which can be used to gain mechanistic insight into transitions far from equilibrium. We show that under general conditions, there is a basic speed limit relating the typical excess heat dissipated throughout a transition and the rate amplification achievable. We illustrate this tradeoff in canonical examples of diffusive barrier crossings in systems driven with autonomous and deterministic external forcing protocols. In both cases, we find that our speed limit tightly constrains the rate enhancement.
17 citations
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TL;DR: This work shows how a set of external control parameters can guide the probability distribution of genotypes in a population along a specified path and time interval, allowing empirical optimization of evolutionary speed and trajectories.
Abstract: The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context: counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.
9 citations
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TL;DR: In this article, the authors acknowledge support from the U.S. National Science Foundation (NSF) under Grant No. MCB-1651650 and the Labex CelTisPhyBioBioNitrome(ANR-11-LABX-0038, ANR-10-IDEX-0001-02).
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).
8 citations
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TL;DR: This volume of the Annual Reviews of Physical Chemistry (ARPC67) once again brings out the sheer breadth of contemporary physical chemistry research as mentioned in this paper, as is evident from the various articles that range from processes inside a cell to tracking the motion of electrons.
Abstract: This volume of the Annual Reviews of Physical Chemistry (ARPC67) once again brings out the sheer breadth of contemporary physical chemistry research. The so-called ‘middle kingdom’ is indeed rather vast – as is eminently clear from the various articles that range from processes happening inside a cell to tracking the motion of electrons. ARPC67 has a good balance between theory and experiments, applied and fundamental, representing frontline research on gas, condensed and solid phase systems.
156 citations
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01 Jan 1949TL;DR: In this article, the Fourier series is used for generalized harmonic analysis and Coherency matrices, and the realizability of Filters is discussed in the complex domain.
Abstract: This chapter contains sections titled: 1.00 Fourier Series, 1.01 Orthogonal Functions, 1.02 The Fourier Integral, 1.04 More on the Fourier Integral; Realizability of Filters, 1.1 Generalized Harmonic Analysis, 1.18 Discrete Arrays and Their Spectra, 1.2 Multiple Harmonic Analysis and Coherency Matrices, 1.3 Smoothing Problems, 1.4 Ergodic Theory, 1.5 Brownian Motion, 1.6 Poisson Distributions, 1.7 Harmonic Analysis in the Complex Domain
145 citations
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TL;DR: The results imply that mutator phenotypes are less effective in larger asexual populations, and have consequences for the advantages (or disadvantages) of sex via the Fisher–Muller effect.
Abstract: When beneficial mutations are rare, they accumulate by a series of selective sweeps. But when they are common, many beneficial mutations will occur before any can fix, so there will be many different mutant lineages in the population concurrently. In an asexual population, these different mutant lineages interfere and not all can fix simultaneously. In addition, further beneficial mutations can accumulate in mutant lineages while these are still a minority of the population. In this paper, we analyze the dynamics of such multiple mutations and the interplay between multiple mutations and interference between clones. These result in substantial variation in fitness accumulating within a single asexual population. The amount of variation is determined by a balance between selection, which destroys variation, and beneficial mutations, which create more. The behavior depends in a subtle way on the population parameters: the population size, the beneficial mutation rate, and the distribution of the fitness increments of the potential beneficial mutations. The mutation-selection balance leads to a continually evolving population with a steady-state fitness variation. This variation increases logarithmically with both population size and mutation rate and sets the rate at which the population accumulates beneficial mutations, which thus also grows only logarithmically with population size and mutation rate. These results imply that mutator phenotypes are less effective in larger asexual populations. They also have consequences for the advantages (or disadvantages) of sex via the Fisher-Muller effect; these are discussed briefly.
64 citations
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Harvard University1, University of Groningen2, Yale University3, Santa Fe Institute4, Purdue University5, Arizona State University6, University of Nebraska–Lincoln7, Okinawa Institute of Science and Technology8, Max Planck Society9, Princeton University10, Aix-Marseille University11, Stanford University12, SLAC National Accelerator Laboratory13, Icahn School of Medicine at Mount Sinai14, Tata Institute of Fundamental Research15, National Centre for Biological Sciences16, University of California, Berkeley17, Stony Brook University18, Massachusetts Institute of Technology19
TL;DR: Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints as discussed by the authors.
Abstract: Cells are the basic units of all living matter which harness the flow of energy to drive the processes of life. While the biochemical networks involved in energy transduction are well-characterized, the energetic costs and constraints for specific cellular processes remain largely unknown. In particular, what are the energy budgets of cells? What are the constraints and limits energy flows impose on cellular processes? Do cells operate near these limits, and if so how do energetic constraints impact cellular functions? Physics has provided many tools to study nonequilibrium systems and to define physical limits, but applying these tools to cell biology remains a challenge. Physical bioenergetics, which resides at the interface of nonequilibrium physics, energy metabolism, and cell biology, seeks to understand how much energy cells are using, how they partition this energy between different cellular processes, and the associated energetic constraints. Here we review recent advances and discuss open questions and challenges in physical bioenergetics.
35 citations