Biology subjects

Dolson, E.

Publications and source records attributed to Dolson, E..

6 recordsLinked to original sources

bioRxiv2025

Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells

Drug resistance in cancer is shaped not only by evolutionary processes but also by eco-evolutionary interactions between tumor subpopulations. These interactions can support the persistence of resistant cells even in the absence of treatment, undermining standard aggressive therapies and motivating drug holiday-based approaches that leverage ecological dynamics. A key challenge in implementing such strategies is efficiently identifying interaction between drug-sensitive and drug-resistant subpopulations. Evolutionary game theory provides a framework for characterizing these interactions. We investigate whether spatial patterns in single time-point images of cell populations can reveal the underlying game theoretic interactions between sensitive and resistant cells. To achieve this goal, we develop an agent-based model in which cell reproduction is governed by local game-theoretic interactions. We compute a suite of spatial statistics on single time-point images from the agent-based model under a range of games being played between cells. We quantify the informativeness of each spatial statistic and demonstrate that a simple machine learning model can classify the type of game being played. Our findings suggest that spatial structure contains sufficient information to infer ecological interactions. This work represents a step toward clinically viable tools for identifying cell-cell interactions in tumors, supporting the development of ecologically informed cancer therapies. Author summaryDrug resistance is a major challenge in cancer treatment, often leading to relapse despite initially successful therapy. While mutations are a key driver, ecological interactions between drug-sensitive and drug-resistant cells also play a critical role. These interactions are complex and dynamic, and few molecular biomarkers exist, making them difficult to study and account for in treatment planning. We use evolutionary game theory, a framework for quantifying interactions between cells, to investigate whether it is possible to infer these interactions using just a single time-point image of the cells. We develop an agent-based model where cells reproduce based on local interactions and quantify the resulting patterns in how cells are distributed across space using a suite of spatial statistics. We find that specific interaction types produce distinct spatial patterns that are evident in these metrics, and we train a simple machine learning model to classify the interaction type based on the metrics. Our results suggest that spatial data alone can offer valuable insights into tumor dynamics, potentially enabling more informed and adaptable cancer treatments based on eco-evolutionary principles.

cancer biology↗

bioRxiv2022

Fitness seascapes facilitate the prediction of therapy resistance under time-varying selection

Pharmacokinetic (PK) and pharmacodynamic (PD) modeling of host-pathogen interactions has enhanced our understanding of drug resistance. However, how combinations of drug resistance mutations impact dose-response curves remains underappreciated in PK-PD studies. The fitness seascape model addresses this by extending the fitness landscape model to map genotypes to dose-response functions, enabling the study of evolution under fluctuating drug concentrations. Here, we present an empirical fitness seascape in E. coli harboring all combinations of four drug resistance mutations. Incorporating these data into PK-PD simulations of antibiotic treatment, we find that higher mutation supply increases the probability of resistance, and early adherence to the drug regimen is critical. In vitro studies further support the finding that the second dose in a drug regimen is important for preventing resistance. This work represents the first application of an empirical fitness seascape in computational PK-PD studies, revealing novel insights into drug resistance.

evolutionary biology↗

bioRxiv2022

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask if parent selection algorithms--procedures for choosing promising progenitors--from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top-10% selection). We found that multi-objective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multi-objective selection procedures into the laboratory. Additionally, our findings suggest that more sophisticated artificial selection methods from evolutionary computation should also be evaluated for use in directed microbial evolution.

evolutionary biology↗

bioRxiv2019

Controlling the speed and trajectory of evolution with counterdiabatic driving

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.

evolutionary biology↗

bioRxiv2017

Spatial resource heterogeneity creates local hotspots of evolutionary potential

Do local conditions influence evolutions ability to produce new traits? Biological data demonstrate that evolutionary processes can be profoundly influenced by local conditions. However, the evolution of novel traits has not been addressed in this context, owing in part to the challenges of performing the necessary experiments with natural organisms. We conduct in silico experiments with the Avida Digital Evolution Platform to address this question. We created eight different spatially heterogeneous environments and ran 100 replicates in each. Within each environment, we examined the distribution of locations where nine different focal traits first evolved. Using spatial statistics methods, we identified regions within each environment that had significantly elevated probabilities of containing the first organism with a given trait (i.e. hotspots of evolutionary potential). Having demonstrated the presence of many such hotspots, we explored three potential mechanisms that could drive the formation of these patterns: proximity of specific resources, variation in local diversity, and variation in the sequence of locations the members of an evolutionary lineage occupy. Resource proximity and local diversity appear to have minimal explanatory power. Lineage paths through space, however, show some promising preliminary trends. If we can understand the processes that create evolutionary hotspots, we will be able to craft environments that are more effective at evolving targeted traits. This capability would be useful both to evolutionary computation, and to efforts to guide biological evolution.

evolutionary biology↗

bioRxiv2017

Spatial resource heterogeneity increases diversity and evolutionary potential

Spatial heterogeneity is believed to be an evolutionary driver of biodiversity. Variability in the distribution of resource patches can allow an environment to support a wider variety of phenotypes for selection to act upon at the ecosystem level, which may lead to more species. However, the generality of this principle has not been thoroughly tested, as the relevant adaptive dynamics occur on evolutionary timescales. We overcame this challenge by performing experiments on populations of digital organisms in the Avida Digital Evolution Platform, in which we investigated the impact of spatial resource heterogeneity on phenotypic diversity. Since an important benefit of diversity may be increased evolutionary potential, we also tracked the probability of a complex trait evolving in the context of various levels of spatial heterogeneity. We found that spatial entropy and phenotypic diversity have a strong positive correlation and this relationship is consistent across various spatial configurations. Diversity also increases evolutionary potential, but has a much smaller impact than other components of environmental composition. The most important of these components was the mean number of resources present in locations across the environment, likely owing to the importance of building blocks for the evolution of complex features. These results suggest that a general relationship exists between spatial heterogeneity and diversity, beyond the specific ecosystems and timescales in which it has previously been studied. By examining this relationship in the context of phenotypic evolution, we advance a mechanistic understanding of the resulting dynamics. Moreover, our results suggest that the likelihood of evolving various traits can be impacted by the spatial configuration of patches in which these traits are advantageous. These findings have implications for both evolutionary biology and evolutionary computation, as generating and maintaining diversity is critical to all forms of evolution.

ecology↗