Biology subjects

Citak, T.

Publications and source records attributed to Citak, T..

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

Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy

High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular "magic bullet" but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An "IMOme" score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.

cancer biology↗

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↗