Juan Pablo Bernal Tamayo

Research Interests

Juan develops and applies dynamical models to infer gene regulatory interactions underlying cellular differentiation. By integrating high-dimensional single-cell transcriptomics and RNA-velocity inference within a Hopfield network formalism, he derives cell-type–specific interaction parameters that capture both the stability and trajectory of cell states. He developed scHopfield, a Python package that fits continuous Hopfield model parameters to infer cell-type specific regulatory interactions. Beyond this core method, Juan investigates the decomposition of Hopfield dynamics, revealing distinct mechanistic contributions to system behavior; also he analyzes small regulatory motifs within the Hopfield framework to understand their approximation limits and motif-specific dynamics. These efforts bridge mechanistic modeling and data-driven inference to generate predictive insights into cell-fate decisions and pinpoint key regulators for experimental validation.