Biology subjects

Appold, N.

Publications and source records attributed to Appold, N..

3 recordsLinked to original sources

bioRxiv2025

Spatio-temporal resource landscapes govern the confinement and escape of therapy-resistant mutants in structured populations

1AbstractsThe evolution of therapy resistance in structured populations such as biofilms and solid tumours is shaped by emergent spatial organization, with pro-found consequences for evolution-based therapies. However, how treatment reshapes these patterns remains poorly understood. Here we show that intermittent treatment pulses transiently reconfigure the resource landscape, reorganize spatial growth zones, and can enable resistant mutants to escape spatial confinement and drive therapy failure. We introduce a spatial evolution assay in which populations expand from single, genetically tailored yeast cells, enabling quantitative tracking of the full spatiotemporal trajectories of continually emerging resistant mutants under intermittent treatment. By integrating these observations with a mechanistically interpretable \textit{in silico} model in a real-to-sim-to-real loop, we identify an effective phase transition in schedule space that defines an optimal balance between population control and sustained resistance confinement, which we validate experimentally. Together, our results establish resource-mediated spatial confinement as a central organizing principle of resistance evolution and provide a mechanistic foundation for spatially informed, evolution-based therapies.

biophysics↗

bioRxiv2025

GRAPEVINE: A Reinforcement Failing Framework for AI-Guided Discovery of Hidden Spatial Dynamics in Adaptive Tumor Therapy

Artificial intelligence is revolutionizing scientific discovery in medicine, with reinforcement learning (RL) emerging as a promising tool for optimizing therapeutic strategies. Yet applying RL to complex scenarios such as therapy dynamics in solid tumors is constrained by the challenge of constructing training environments that are both computationally efficient and mechanistically interpretable. Here we introduce Reinforcement Failing, an AI-guided, human-in-the-loop discovery framework that shifts the focus from agent policy optimization to the refinement of the training environment itself. By combining multi-fidelity RL with group-relative performance evaluation across agent cohorts, Reinforcement Failing systematically reveals emergent mechanisms that first-principles models overlook. We apply this framework to adaptive therapy in solid tumors, which seeks to delay resistance-mediated treatment failure. In this setting, Reinforcement Failing uncovered a coupling between the mechanically driven collective motion of cells and spatially-heterogeneous proliferation that strongly influences therapy outcomes. Incorporating these emergent physical mechanisms into an augmented training environment improved cross-environment therapeutic performance and exposed potential pitfalls in translation. More broadly, these findings position Reinforcement Failing as a powerful artificial scientific discovery framework, capable of deciphering high-complexity processes at the interface of physics, machine learning, and medicine.

biophysics↗

bioRxiv2022

Evolutionary rescue is promoted in compact cellular populations

Mutation-mediated drug resistance is one of the primary causes for the failure of modern antibiotic or chemotherapeutic treatment. Yet, in the absence of treatment many drug resistance mutations are associated with a fitness cost and therefore subject to purifying selection. While, in principle, resistant subclones can escape purifying selection via subsequent compensatory mutations, current models predict such evolutionary rescue events to be exceedingly unlikely. Here, we show that the probability of evolutionary rescue, and the resulting long-term persistence of drug resistant subclones, is dramatically increased in dense microbial populations via an inflation-selection balance that stabilizes the less-fit intermediate state. Tracking the entire evolutionary trajectory of fluorescence-augmented "synthetic mutations" in expanding yeast colonies, we trace the origin of this balance to the opposing forces of radial population growth and a clone-width-dependent weakening of selection pressures, inherent to crowded populations. Additionally conducting agent-based simulations of tumor growth, we corroborate the fundamental nature of the observed effects and demonstrate the potential impact on drug resistance evolution in cancer. The described phenomena should be considered when predicting the evolutionary dynamics of any sufficiently dense cellular populations, including pathogenic microbial biofilms and solid tumors, and their response to therapeutic interventions. Our experimental approach could be extended to systematically study rates of specific evolutionary trajectories, giving quantitative access to the evolution of complex adaptations.

biophysics↗