Fitness Landscape Model Research Articles

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli.

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Ecological tradeoffs lead to complex evolutionary trajectories and sustained diversity on dynamic fitness landscapes

The course and outcome of evolution are critically determined by the fitness landscape, which maps genotype to fitness. Most theory has considered static fitness landscapes or fitness landscapes that fluctuate according to abiotic environmental changes. In the presence of biotic interactions between coexisting genotypes, the fitness landscape becomes dynamic and frequency-dependent.Here, we introduce a fitness landscape model that incorporates ecological interactions between individuals in a population. In the model, fitness is determined by individuals competing for resources according to a set of traits they possess. An individual’s genotype determines the trait values through a Rough Mount Fuji fitness landscape model, allowing for tunable epistasis (i.e. non-additive gene interaction) and trait correlations (i.e. whether there are tradeoffs or synergies in the ability to use resources). Focusing on the effects of epistasis and trait correlations, we quantify the resulting eco-evolutionary dynamics under simulated Wright–Fisher dynamics (i.e. including genetic drift, mutation, and selection under the assumption of a constant population size) on the dynamics fitness landscape in comparison with a similar, static, fitness landscape model without ecological interactions.Whereas the non-ecological model ultimately leads to the maintenance of one main genotype in the population, evolution in the ecological model can lead to the long-term coexistence of several genotypes at intermediate frequencies across much of the parameter range. Including ecological interactions increases steady-state diversity whenever the trait correlations are not too strong. However, strong epistasis can hinder coexistence, and additive genotype–phenotype maps yield the highest haplotype diversity at the steady state. Interestingly, we frequently observe long-term coexistence also in the absence of induced trade-offs in the ability to consume resources.In summary, our simulation study presents a new dynamic fitness landscape model that highlights the complex eco-evolutionary consequences of a (finite) genotype–phenotype-fitness map in the presence of biotic interactions.

Evolutionary accessibility of random and structured fitness landscapes

Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here, we discuss probabilistic models of fitness landscapes with a focus on their evolutionary accessibility, where a path in a fitness landscape is said to be accessible if the fitness values encountered along the path increase monotonically. For uncorrelated (random) landscapes with independent and identically distributed fitness values, the probability of existence of accessible paths between genotypes at a distance linear in the sequence length L becomes nonzero at a nontrivial threshold value of the fitness difference between the initial and final genotypes, which can be explicitly computed for large classes of genotype graphs. The behaviour of uncorrelated random landscapes is contrasted with landscape models that display additional, biologically motivated structural features. In particular, landscapes defined by a tradeoff between adaptation to environmental extremes have been found to display a combinatorially large number of accessible paths to all local fitness maxima. We show that this property is characteristic of a broad class of models that satisfy a certain global constraint, and provide further examples from this class.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations.

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

Ecology and demography of early Homo sapiens: a synthesis of archaeological and climatic data from eastern Africa

ABSTRACT Eastern Africa maintains a key position in debates surrounding the emergence of Homo sapiens across Africa. Extensive research in the region has revealed a rich fossil record in association with a ‘generic’ but variable Middle Stone Age (MSA) material culture, providing an important laboratory for testing hypotheses about the behavioural evolution of our species. For example, multiple archaeological studies of the eastern African MSA note a link between the distribution and density of sites, archaeological diversity and environmental conditions, with ecology and demography often cited as key drivers of cultural evolution. This article formulates new hypotheses using theoretical models of complex fitness landscapes of evolution and reviews the archaeological and climatic records of Middle-late Pleistocene eastern Africa in the light of these ideas. It proposes that evidence from eastern Africa implicates much of the region as a refugial zone within Pleistocene Africa, providing consistently suitable conditions for survival that were characterised by high and changing biodiversity, facilitating population growth and interconnectivity as well as material culture diversification. Interactions between different evolutionary processes likely resulted in the complex cultural mosaic observed across Africa, including the appearance of ‘specific’ innovations against a backdrop of more ‘generic’ MSA elements.

Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis

Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.

STUN: forward-time simulation on TUnable fitNess landscapes in recombining populations

Abstract Motivation Understanding the population genetics of complex polygenic traits during adaptation is challenging. Results Here, we implement a forward-in-time population-genetic simulator (STUN) based on Wright-Fisher dynamics. STUN is a flexible and user-friendly software package for simulating the polygenic adaptation of recombining haploid populations using either new mutations or standing genetic variation. STUN assumes that populations adapt to sudden environmental changes by undergoing selection on a new fitness landscape. With pre-implemented fitness landscape models like Rough Mount Fuji, NK, Block, additive, and House-of-Cards, users can explore the effect of different levels of epistasis (ruggedness of the fitness landscape). Custom fitness landscapes and recombination maps can also be defined. STUN empowers both experimentalists and advanced programmers to study the evolution of complex polygenic traits and to dissect the adaptation process. Availability and implementation STUN is implemented in Rust. Its source code is available at https://github.com/banklab/STUN and archived on Zenodo under doi: 10.5281/zenodo.10246377. The repository includes a link to the software’s manual and binary files for Linux, macOS and Windows.

Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia.

SummaryIntestinal homeostasis is underpinned by LGR5+ve crypt-base columnar stem cells (CBCs), but following injury, dedifferentiation results in the emergence of LGR5−ve regenerative stem cell populations (RSCs), characterized by fetal transcriptional profiles. Neoplasia hijacks regenerative signaling, so we assessed the distribution of CBCs and RSCs in mouse and human intestinal tumors. Using combined molecular-morphological analysis, we demonstrate variable expression of stem cell markers across a range of lesions. The degree of CBC-RSC admixture was associated with both epithelial mutation and microenvironmental signaling disruption and could be mapped across disease molecular subtypes. The CBC-RSC equilibrium was adaptive, with a dynamic response to acute selective pressure, and adaptability was associated with chemoresistance. We propose a fitness landscape model where individual tumors have equilibrated stem cell population distributions along a CBC-RSC phenotypic axis. Cellular plasticity is represented by position shift along this axis and is influenced by cell-intrinsic, extrinsic, and therapeutic selective pressures.

Mutational robustness changes during long-term adaptation in laboratory budding yeast populations.

As an adapting population traverses the fitness landscape, its local neighborhood (i.e., the collection of fitness effects of single-step mutations) can change shape because of interactions with mutations acquired during evolution. These changes to the distribution of fitness effects can affect both the rate of adaptation and the accumulation of deleterious mutations. However, while numerous models of fitness landscapes have been proposed in the literature, empirical data on how this distribution changes during evolution remains limited. In this study, we directly measure how the fitness landscape neighborhood changes during laboratory adaptation. Using a barcode-based mutagenesis system, we measure the fitness effects of 91 specific gene disruption mutations in genetic backgrounds spanning 8000-10,000 generations of evolution in two constant environments. We find that the mean of the distribution of fitness effects decreases in one environment, indicating a reduction in mutational robustness, but does not change in the other. We show that these distribution-level patterns result from differences in the relative frequency of certain patterns of epistasis at the level of individual mutations, including fitness-correlated and idiosyncratic epistasis.

Strategic archetypes of planning processes: Model and evidence

Complex planning processes bear the hallmarks of evolution, in that actors seek to obtain the results that serve their needs best. That is, seek to increase their fitness. As in biological evolution, there are multiple pathways toward fitness. With the rise of adaptive planning approaches, the question how such pathways can be identified in strategic planning processes becomes pivotal. To answer this question, we adapt the fitness landscape model from biology and analyze several strategic planning processes. The analysis demonstrates that one can trace how different pathways to fitness emerge, and which pathways are more successful given the circumstances. We then synthesize these pathways into six strategic archetypes of actor behavior and of interactions between actors.

Abstract B027: Modeling the emergence of therapy resistance with fitness seascapes

Abstract One approach to the study of therapy resistance is through an evolutionary lens, where novel sub-types emerge through a random process and are selected for by an increased fitness. Computational models are used to understand the parameter spaces that facilitate resistance, optimize therapy regimens, and predict the evolutionary trajectory. Fitness landscapes are structures that map genotype to fitness and are commonly used in computational models of evolution. Most fitness landscape models assume the environment is constant. However, this obscures evolutionary trade-offs that depend on environmental change, i.e., where a resistant mutant trades a lower growth rate in the absence of drug in exchange for drug resistance. Furthermore, a disease agent population in a patient will never experience a constant drug concentration; the drug concentration will vary according to pharmacokinetic effects and spatial relationships to blood vessels. Here, we explored fitness seascapes as an extension to fitness landscapes. Fitness seascapes model a variable genotype-phenotype mapping. In this work, we used fitness seascapes that map genotypes to drug dose-response curves. In contrast to fitness landscapes, the fitness rank-order of genotypes changes as a function of drug concentration, representing evolutionary tradeoffs. Using a novel computational platform, we showed that modeling evolutionary tradeoffs with fitness seascapes dramatically impacted the evolutionary trajectory of a population subject to changing drug concentrations. We also showed how mutant selection windows, a commonly studied structure in evolutionary medicine, are embedded in fitness seascapes. Using an empirical fitness seascape measured in genetically engineered S. cerevisiae, we then explored the utility of fitness seascapes in two in silico experiments. In the first experiment, we subjected an evolving population to varying rates of change of drug concentration, finding that populations experiencing a lower rate of change were more likely to become resistant, while those experiencing a higher rate of change were more likely to go extinct. This aligns with similar previously published in vitro experiments. Next, we simulated patients undergoing therapy with varying levels of drug regimen nonadherence using a pharmacokinetic model. We found that a higher rate of drug regimen nonadherence was associated with a higher rate of resistance, while a higher rate of adherence was associated with a higher rate of elimination of the disease agent. Future work may reveal patterns of nonadherence that promote resistance. Here, we have demonstrated the utility of fitness seascapes in studying resistance. Our results highlight the importance of evolutionary trade-offs when modeling the emergence of resistance under varying drug concentrations. These results have implications for diseases across kingdoms, including viral and bacterial infections and cancer. In the future, fitness seascapes may be used to optimize drug dosing regimens or understand differences in metabolism in silico. Citation Format: Eshan S. King, Emily Dolson, Jacob G. Scott. Modeling the emergence of therapy resistance with fitness seascapes [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B027.

Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a

SummaryHepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the E2 glycoprotein of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear escape-resistant against both subtypes 1a and 1b, providing directions for designing HCV vaccines having cross-subtype protection.

The thermal environment at fertilization mediates adaptive potential in the sea.

Additive genetic variation for fitness at vulnerable life stages governs the adaptive potential of populations facing stressful conditions under climate change, and can depend on current conditions as well as those experienced by past stages or generations. For sexual populations, fertilization is the key stage that links one generation to the next, yet the effects of fertilization environment on the adaptive potential at the vulnerable stages that then unfold during development are rarely considered, despite climatic stress posing risks for gamete function and fertility in many taxa and external fertilizers especially. Here, we develop a simple fitness landscape model exploring the effects of environmental stress at fertilization and development on the adaptive potential in early life. We then test our model with a quantitative genetic breeding design exposing family groups of a marine external fertilizer, the tubeworm Galeolaria caespitosa, to a factorial manipulation of current and projected temperatures at fertilization and development. We find that adaptive potential in early life is substantially reduced, to the point of being no longer detectable, by genotype‐specific carryover effects of fertilization under projected warming. We interpret these results in light of our fitness landscape model, and argue that the thermal environment at fertilization deserves more attention than it currently receives when forecasting the adaptive potential of populations confronting climate change.

Global epistasis emerges from a generic model of a complex trait.

Epistasis between mutations can make adaptation contingent on evolutionary history. Yet despite widespread 'microscopic' epistasis between the mutations involved, microbial evolution experiments show consistent patterns of fitness increase between replicate lines. Recent work shows that this consistency is driven in part by global patterns of diminishing-returns and increasing-costs epistasis, which make mutations systematically less beneficial (or more deleterious) on fitter genetic backgrounds. However, the origin of this 'global' epistasis remains unknown. Here, we show that diminishing-returns and increasing-costs epistasis emerge generically as a consequence of pervasive microscopic epistasis. Our model predicts a specific quantitative relationship between the magnitude of global epistasis and the stochastic effects of microscopic epistasis, which we confirm by reanalyzing existing data. We further show that the distribution of fitness effects takes on a universal form when epistasis is widespread and introduce a novel fitness landscape model to show how phenotypic evolution can be repeatable despite sequence-level stochasticity.

Coupling relationships and synergistic mechanisms between technology management capability and technological capability in product innovation: a simulation study

ABSTRACT This paper focuses on the dynamic co-evolution of technology management capability and technological capability in the system of product innovation. It is motivated by the increasing attention that product innovation has received recently, as it is considered one of the most important factors that can bring about competitive advantages for firms to compete with their rivals in the current competitive business environment. Framing the system of product innovation as a complex adaptive system and focusing on the roles of technology management capability and technological capability in the system, the most suitable synergistic mechanisms between technology management capability and technological capability that can make the system reach the highest fitness under their diverse coupling relationships are identified by means of NK fitness landscape model. The simulation results show that there are matches between the coupling relationships among the elements of technology management capability and technological capability and their synergistic mechanisms in the system of product innovation. This paper provides a better understanding of the relationships between technology management capability and technological capability and managerial implications for firms to innovate products successfully and promptly and thus improve their competitive advantages.

Innovation Search Scope, Technological Complexity, and Environmental Turbulence: A N-K Simulation

This paper discusses the effects of different innovation search scopes on the performance of the N-K fitness landscape model with a focus on its contextual factors of technology complexity and environmental turbulence. Results show that the medium-level search scope has a significantly better outcome than the low-level search scope, especially when the technological complexity is high, while the high-level search scope would not provide a statistically significant advantage. After introducing the turbulent range and rapidity into the N-K model, we extend the model into a dynamic one to simulate better the real turbulent business world. The results of the simulation in dynamic landscapes show that the higher degree of environmental turbulence causes a higher search scope to become more valuable.

Transparency in policy making: A complexity view

The literature on transparency in participatory policy making is flourishing. With the increased digitization of our world, recent work suggests that the digitally-enabled relationships of how policy makers and citizens observe each other may transform policy making in a fundamental way. In this paper, we use complexity theory to examine how digitally-enabled transparency affects the effectiveness of policy making in aligning citizens with the policy goal to improve collective human welfare. We map Kauffman's NKC fitness landscape model, a generalizable theory of co-evolutionary complexity, to the phenomenon of transparent policy making in order to explain how transparency as an enabling generative mechanism encourages citizens to align with the policy goal without exercising central control. In our framework, citizens are agents who co-evolve by adapting to information available in their citizen landscapes. These landscapes represent the citizens' decision context, which policy makers observe and modify throughout an iterative policy cycle. In our study we identify three types of transparencies that relate to three properties of the citizens' decision context: (1) individual decision interdependence; (2) decision bias; and (3) collective decision interdependence. Using conceptual modeling, a form of inquiry combining formal representation with empirical sense making in three policy domains (e-health, smart transportation, and smart energy), we articulate and empirically validate two generative mechanisms that explain transparency effects for each of the three transparencies: (1) orchestration via iterative landscape “tuning” to reduce ambiguity and simplify citizens' alignment with the policy goal; and (2) social learning via information sharing, a co-evolutionary social “nudge” that encourages citizens to be more open to behavioral changes. Our findings have implications for the literature on transparency in participatory policy making as well as the literature on complexity in public policy and public administration.

Match fitness landscapes for macromolecular interaction networks: Selection for translational accuracy and rate can displace tRNA-binding interfaces of non-cognate aminoacyl-tRNA synthetases

Advances in structural biology of aminoacyl-tRNA synthetases (aaRSs) have revealed incredible diversity in how aaRSs bind their tRNA substrates. The causes of this diversity remain mysterious. We developed a new class of highly rugged fitness landscape models called match landscapes, through which genes encode the assortative interactions of their gene products through the complementarity and identifiability of their structural features. We used results from coding theory to prove bounds and equalities on fitness in match landscapes assuming additive interaction energies, macroscopic aminoacylation kinetics including proofreading, site-specific modifiers of interaction, and selection for translational accuracy in multiple, perfectly encoded site-types. Using genotypes based on extended Hamming codes we show that over a wide array of interface sizes and numbers of encoded cognate pairs, selection for translational accuracy alone is insufficient to displace the tRNA-binding interfaces of aaRSs. Yet, under combined selection for translational accuracy and rate, site-specific modifiers are selected to adaptively displace the tRNA-binding interfaces of non-cognate aaRS–tRNA pairs. We describe a remarkable correspondence between the lengths of perfect RNA (quaternary) codes and the modal sizes of small non-coding RNA families.

How to use fitness landscape models for the analysis of collective decision-making: a case of theory-transfer and its limitations

There is considerable correspondence between theories and models used in biology and the social sciences. One type of model that is in use in both biology and the social sciences is the fitness landscape model. The properties of the fitness landscape model have been applied rather freely in the social domain. This is partly due to the versatility of the model, but it is also due to the difficulties of transferring a model to another domain. We will demonstrate that in order to transfer the biological fitness landscape model to the social science it needs to be substantially modified. We argue that the syntactic structure of the model can remain unaltered, whilst the semantic dimension requires considerable modification in order to fit the specific phenomena in the social sciences. We will first discuss the origin as well as the basic properties of the model. Subsequently, we will demonstrate the considerations and modifications pertaining to such a transfer by showing how and why we altered the model to analyse collective decision-making processes. We will demonstrate that the properties of the target domain allow for a transfer of the syntactic structure but don’t tolerate the semantic transfer.

Semantic and syntactic transfer of fitness landscape models to the analysis of collective and public decision making processes

textabstractWe set out to develop a method and research technique that could unite both modelling and case-based observations in order to analyse collective decision-making processes. Following Abbott’s (2001) recommendation regarding social processes, we have defined collective decision-making as an uninterrupted and non-directional process that is structured in sequences or lineages of events. To structure these processes, we re-modelled the basic components of Kauffman’s (1993) NK-model. We converted N to ‘problem and solution definitions’ (PSDs) and K to ‘connectedness’ between actors (c_score). An important modification is that we consider NK to be a dependent configuration; i.e., K entails both content and process. Fitness is defined as the probability of an actor achieving (elements of) its PSD as a result of its adaptive moves in relation to the adaptive moves of others. The model is put to the test in four different studies: (1) 20 years of decision-making in planning, building and servicing HSL-Zuid high-speed railways in the Netherlands; (2) the strategic search process of villages and cantons in the Gotthard region of Switzerland; (3) the redevelopment of a football stadium and the surrounding area in south Rotterdam, the Netherlands; and (4) the rise and fall of the Airport RailLink in Bangkok, Thailand. From these studies, we derived six archetypes in collection decision-making, subdivided into actor archetypes and interaction archetypes. For the actor archetypes, behavioural consistency is not just a trait for the actor but also affects the space of possibilities and/or behaviours of other actors. The interactions of individual actors combine to produce self-propagating dynamics that drive the further evolution of the collective decision-making process. The fitness field model enables researchers to investigate the various dimensions of the collective decision-making process – ranging from individual strategies and actions to variation, selection and retention of contents, from interactions to fitness gains and losses, and back again.