Biology subjects

Barker-Clarke, R. J.

Publications and source records attributed to Barker-Clarke, R. J..

2 recordsLinked to original sources

bioRxiv2023

Masking, maintenance and mimicry: the interplay of cell-intrinsic and cell-extrinsic effects in evolutionary games

The temporal evolution of mutating pathogens in disease contexts arises from both the intrinsic properties of each subpopulation and the interactions among them, yet experimental inference often neglects the latter. Drug development studies commonly estimate selective advantages by comparing growth rates in monoculture, and in vitro monoculture dose-response curves are frequently used to justify or halt further investigation of a drug. Although many ecological models distinguish intrinsic from interaction-dependent growth rates, we show that simpler evolutionary game theory (EGT) frameworks can also be used to disentangle these contributions. We present a game-theoretic reparameterization of the replicator equation payoff matrix that separates intrinsic effects from interaction-driven contributions to frequency-dependent fitness. We also introduce an interaction-selection plot that facilitates the interpretation of the relative importance of between-population interactions compared with intrinsic evolutionary trade-offs. Using this framework, we map how interactions can mask, mirror, maintain, or mimic frequency-independent selection. We derive analytical conditions for these behaviors in both deterministic (replicator equation) and stochastic (Fokker-Planck-Kolmogorov) models, showing that simple conditions persist when mutation and noise are introduced. We validate these predictions using Wright-Fisher simulations. Applying our framework to published microbial and cancer co-culture data, we find that real systems span regimes dominated by either autonomous selection or interaction-driven effects, with interactions sometimes reversing or neutralizing frequency-independent fitness differences. Together, our results show that frequency-dependent effects can shape evolutionary dynamics in subtle and non-obvious ways, highlighting the importance of accounting for interactions when inferring fitness and predicting evolutionary outcomes.

evolutionary biology↗

bioRxiv2022

gtexture: Haralick texture analysis for graphs and its application to biological networks

ObjectiveThe calculation of texture features, such as those derived by Haralick et al., has been traditionally limited to 2D-imaging data. We present the novel derivation of an extension to these texture features that can be applied to graphs and networks and set out to illustrate the potential of these metrics for use in cancer informatics. ApproachWe extend the pixel-based calculation of texture and generate analogous novel metrics for graphs and networks. The graph structures in question must have ordered or continuous node weights/attributes. To demonstrate the utility of these metrics in cancer biology, we demonstrate these metrics can distinguish different fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks with both simulated and publicly available experimental gene expression data. Main ResultsWe demonstrate that texture features are informative of graph structure and analyse their sensitivity to discretization parameters and node label noise. We demonstrate that graph texture varies across multiple network types including fitness landscapes and large protein interaction networks with experimental expression data. We show the ability of these texture metrics, calculated on specific protein interaction subnetworks, to classify cell line expression by lineage, generating classifiers with 82% and 89% accuracy. SignificanceGraph texture features are a novel second order graph metric that can distinguish cancer types and topologies of evolutionary landscapes. It appears that no similar metrics currently exist and thus we open up the potential derivation of more metrics for the classification and analysis of network-structured data. This may be particularly useful in the complex setting of cancer, where large graph and network structures underlie the omics data generated. Network-based data underlies drug discovery, drug response prediction and single-cell dynamics and thus these metrics provide an additional tool in tackling these problems in cancer.

bioinformatics↗