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Explore the historical evolution of quantitative and physical biology through key milestones, from ancient developments to foundational concepts that shaped our understanding of living systems.
Explore how phase separation and wet-dry cycles influence chemical processes in early life formation, focusing on sequence selection and molecular evolution in prebiotic conditions.
Explore dynamical systems theory and its application to collective motion modeling, focusing on how active sensing influences group behavior in biological systems.
Explore the fascinating dynamics of motile cilia and their collective behavior through advanced statistical physics, focusing on local beating properties and emergent patterns.
Explore mathematical approaches to understanding movement patterns in animal ecology and cell biology, integrating quantitative methods with conceptual frameworks for advanced biological analysis.
Explore mathematical modeling approaches for collective movement in animal ecology and cell biology through research experiences at the Isaac Newton Institute.
Explore a physicist's perspective on genome modeling, examining chromatin folding mechanisms, 3D structural ensembles, and the role of neural networks in predicting chromosomal organization.
Explore how mechanical forces in cellular environments influence DNA organization and chromatin dynamics, with insights into disease progression and potential therapeutic approaches.
Discover how machine learning models non-Newtonian fluid dynamics, bridging micro and macro scales through innovative tensor derivatives and polymer configurations for enhanced molecular fidelity.
Explore groundbreaking research on active particles, from liquid crystal skyrmions to nanoswimmers and ultrasound-powered microspinners, examining their unique dynamics and applications in soft matter physics.
Explore mathematical modeling of spatial movement in structured populations, focusing on applications in early atherosclerosis and the intersection of animal ecology with cell biology.
Explore the mathematical modeling of ant search behavior, examining non-Markovian processes and indirect coordination mechanisms in collective insect movement patterns and decision-making.
Explore deep learning applications in analyzing wound healing mechanisms through Drosophila research, focusing on cell behavior patterns, division dynamics, and tissue regeneration processes.
Explore the fascinating intersection of statistical physics and immune system behavior, examining how T-cells and B-cells adapt to pathogens through mathematical modeling and evolutionary processes.
Explore how entropy and population size influence evolution, challenging traditional adaptationist views through biophysical models and examining their impact on protein stability and speciation rates.
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