Controlling the speed and trajectory of evolution with counterdiabatic driving

Abstract

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...

Extreme Antagonism Arising from Gene-Environment Interactions

“…Finally, while here we have focused on pairwise gene-environment interactions, this study can also inform the development of methods to optimize the scheduling of drug treatments (43,33,36), and to identify higher order antagonistic interactions (47,97), in which the antagonism emerges between three or more genes and/or drugs. We anticipate that applications and generalizations of our approach will contribute to countering antibiotic resistance and advance our understanding of the role of antagonism in cellular networks (98) of multiple organisms.…”

“…Determining the prevalence of extreme antagonism would address a fundamental question in biology concerning the nature of possible interactions between genes and environmental factors. Furthermore, implementing such environmental factors using antibiotic stressors would offer a pathway to design new antibiotic combinations that exhibit collateral sensitivity, as discussed below, because the action of one antibiotic disfavors the acquisition of resistance to another (30,11,(31)(32)(33)(34)(35)(36).…”

Persistence as an optimal hedging strategy

“…3, we simulate a persister producing bacteria colony under antibiotic treatment, demonstrating the distinct advantage persisters afford bacteria (10, 14, 17). Our stochastic model of persister production can be used to improve the efficacy of antimicrobial therapies using optimal control from an optimal treatment perspective (52, 57–59). Furthermore, recent experimental and mathematical work examines the potential of so-called evolutionarily informed therapy or drug sequencing (53, 54, 60), where a sequence of drugs is administered to sequentially induce susceptibility and overcome drug resistance.…”

Modeling collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

“…A subpopulation having a same fitness value is assumed to be homogeneous in terms of how it responds to drug exposure. And, while it has been shown that fitness can often change as a function of drug dose, so called seascapes [31], and that this can be useful for control, we do not consider that possibility here [32].…”