Modeling and Computational Biology

Research cluster V aims to develop predictive computational frameworks that connect molecular mechanisms with emergent biological properties across multiple scales, from biomolecular interactions to cellular systems, populations, and ecological communities. By integrating structural biophysics, artificial intelligence (AI), systems biology, and multi-omic data analysis, the research cluster converts complex datasets produced across the Center into testable hypotheses and predictive quantitative models that reveal the principles governing biological systems. Through this approach, the research cluster functions as a transversal computational core of the Center, linking diverse research lines through shared analytical approaches and a unified quantitative perspective.

Computational approaches developed by the research cluster support areas such as structure-guided drug discovery, microbial biotechnology, epidemiological surveillance, and health data analytics, thereby strengthening interactions with biotechnology initiatives, the healthcare system, and public institutions interested in predictive modeling and data-driven decision-making. These activities are supported by active participation in national computational infrastructures such as the National Laboratory for High Performance Computing (NLHPC), the CORFO-supported SCAI-Lab consortium, and 1000 Genomes Initiative.