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.The quest to control evolutionary processes in areas like agriculture and medicine predates our understanding of evolution itself. Recent years have seen growing research efforts toward this goal, driven by rapid progress in quantifying genetic changes across a population 1-3 as well as a global rise in challenging problems like therapeutic drug resistance 4-6 . New approaches that have arisen in response include prospective therapies that steer evolution of pathogens toward maximized drug sensitivity 7, 8 , typically requiring multiple rounds of selective pressures and subsequent evolution under them. Since we cannot predict the exact progression of mutations that occur in the course of the treatment, the best we can hope for is to achieve control over probability distributions of evolutionary outcomes. However, our lack of precise control over the timing of these outcomes poses a major practical impediment to engineering the course of evolution. This naturally raises a question: Rather than being at the mercy of evolution's unpredictability and pace, what if we could simultaneously control the speed and the distribution of genotypes over time?Controlling an inherently stochastic process like evolution has close parallels to problems in other disciplines.Quantum information protocols crucially depend on coherent control over the time evolution of quantum states under external driving 9, 10 , in many cases requiring that a system remain in an instantaneous ground state of a time-varying Hamiltonian in applications like cold atom transport 11 and quantum adiabatic computat...
We present a model for a classical, non-singular bouncing cosmology without violation of the null energy condition (NEC). The field content is General Relativity plus a real scalar field with a canonical kinetic term and only renormalizable, polynomial-type self-interactions for the scalar field in the Jordan frame. The universe begins vacuum-energy dominated and is contracting at t=-∞. We consider a closed universe with a positive spatial curvature, which is responsible for the universe bouncing without any NEC violation. An Rϕ2 coupling between the Ricci scalar and the scalar field drives the scalar field from the initial false vacuum to the true vacuum during the bounce. The model is sub-Planckian throughout its evolution and every dimensionful parameter is below the effective-field-theory scale MP, so we expect no ghost-type or tachyonic instabilities. This model solves the horizon problem and extends co-moving particle geodesics to past infinity, resulting in a geodesically complete universe without singularities. We solve the Friedman equations and the scalar-field equation of motion numerically, and analytically under certain approximations.
The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer 1-3 , understanding how species respond to environmental perturbations like climate change 4, 5 , and developing artificial selection approaches for agriculture 6 . Great progress has been made in quantitative modeling of evolution using fitness landscapes 7-9 , allowing a degree of prediction 10 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 [11][12][13][14] . 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 15, 16 , to the development of thermotolerant crops in preparation for climate change 17 , to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules [18][19][20] .The quest to control evolutionary processes in areas like agriculture and medicine predates our understanding 1 of evolution itself. Recent years have seen growing research efforts toward this goal, driven by rapid progress in 2 quantifying genetic changes across a population 21-23 as well as a global rise in challenging problems like therapeutic 3 drug resistance 1, 3 . New approaches that have arisen in response include prospective therapies that steer evolution of 4 pathogens toward maximized drug sensitivity 15, 16 , typically requiring multiple rounds of selective pressures and 5 subsequent evolution under them. Since we cannot predict the exact progression of mutations that occur in the 6 course of the treatment, the best we can hope for is to achieve control over probability distributions of evolutionary 7 outcomes. However, our lack of precise control over the timing of these outcomes poses a major practical impediment 8 to engineering the course of evolution. This naturally raises a question: Rather than being at the mercy of evolution's 9 unpredictability and pace, what if we could simultaneously control the speed and the distribution of genotypes over 10 time? 11Controlling an inherently stochastic process like evolution has close parallels to problems in other disciplines. 12Quantum information protocols crucially depend on coherent control over the time evolution of quantum states 13 under external driving 24, 25 , in many cases requiring that a system remain in an instantaneous ground state o...
The Einstein field equations of general relativity constrain the local curvature at every point in spacetime, but say nothing about the global topology of the Universe. Cosmic microwave background anisotropies have proven to be the most powerful probe of non-trivial topology since, within ΛCDM, these anisotropies have well-characterized statistical properties, the signal is principally from a thin spherical shell centered on the observer (the last scattering surface), and space-based observations nearly cover the full sky. The most generic signature of cosmic topology in the microwave background is pairs of circles with matching temperature and polarization patterns. No such circle pairs have been seen above noise in the WMAP or Planck temperature data, implying that the shortest non-contractible loop around the Universe through our location is longer than 98.5% of the comoving diameter of the last scattering surface. We translate this generic constraint into limits on the parameters that characterize manifolds with each of the nine possible non-trivial orientable Euclidean topologies, and provide a code which computes these constraints. In all but the simplest cases, the shortest non-contractible loop in the space can avoid us, and be shorter than the diameter of the last scattering surface by a factor ranging from 2 to at least 6. This result implies that a broader range of manifolds is observationally allowed than widely appreciated. Probing these manifolds will require more subtle statistical signatures than matched circles, such as off-diagonal correlations of harmonic coefficients.
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