
Explore evolutionary dynamics through metabolism as a genotype-phenotype map, examining how collective modes in phenotype space serve as natural selection objects rather than individual genes.

Explore computational modeling techniques for understanding cell migration and chemotaxis, enhancing knowledge of cellular movement mechanisms.

Explore CellChat, an integrated method for inferring and analyzing cell-cell communication from single-cell sequencing data using systems biology and machine learning approaches.

Explore machine learning techniques for extracting causal models from scientific literature, focusing on Ras-driven cancers and systemic effects of interventions.

Explore regulatory circuitry and networks in biological systems, focusing on gene regulation mechanisms and their interconnected pathways.

Explore regulatory circuitry in genomics, uncovering mechanisms of gene expression control and cellular regulation through advanced computational analysis techniques.

Explore applications of topological data analysis in cancer research, focusing on mathematical modeling of treatments, resistance, and optimization.

Explore biological intelligence beyond neural systems, examining how cells, tissues, and subcellular components demonstrate problem-solving abilities and collective decision-making across different scales.

Explore how bacteria communicate and move collectively, focusing on P. mirabilis's identity-based signaling and its impact on bacterial communities and population-wide swarming behavior.

Explore the mathematical dynamics of cancer progression through computational analysis of fluid mechanics, biology, and critical transition points in tumor development.

Explore mathematical modeling approaches for optimizing chemotherapy dosing to minimize muscle loss while maintaining effective cancer treatment strategies.

Explore mathematical modeling approaches for T cell activation, examining the trade-offs between model interpretability and accuracy in immunological systems.

Explore how social insects use negative feedback signals in foraging behavior, examining their role in variance reduction and system robustness through stochastic dynamics analysis.

Explore groundbreaking research on how collective systems operate across different scales, from embryonic development to bioelectric patterns in brain formation, using minimal two-scale models and causal networks.

Explore pattern formation across biological scales, from subcellular structures to multicellular systems, examining the integration and emergence of complex biological organization.

Explore pattern formation across biological scales, from subcellular structures to multicellular systems, examining integrated mechanisms and hierarchical organization in living organisms.

Explore pattern formation across biological scales, from subcellular structures to multicellular systems, examining integrated mechanisms and hierarchical organization in living organisms.

Explore mathematical modeling approaches to understand hematopoietic system homeostasis and cellular clonal evolution patterns in cancer development and progression.

Explore the fundamental principles of spatial ecology through physics-based approaches, examining pattern formation and biological systems across different scales in natural environments.

Explore pattern formation across biological scales, from subcellular structures to multicellular systems, examining fundamental principles and mechanisms of biological organization.

Explore advanced concepts in biological pattern formation, focusing on self-organization mechanisms within living and synthetic cellular systems and their fundamental principles.