Detalle de: Characterization of diseases from multicellularity

A comprehensive integrative systems biology approach to understanding disease requires addressing both the multi-scale dimensions (from chromatin dynamics to cell packing geometry) and the multicellular organization (tissues, organs and bacterial aggregates) of biological systems. Our goal is to create scalable modeling frameworks using quantitative data, linking molecular biophysics to cellular population dynamics to create digital twins of biological systems that help to evaluate system responses to disturbances and design targeted interventions.

Describe. We study various biological scenarios including tissue tropism in infections, cell-specific responses to viral infections, mechanobiology in tissues, spinal cord injury regeneration, bacterial behavior under antibiotics, and plant infectious ecology. By generating single-cell multi-omics and imaging data, we aim to characterize these systems at molecular, cellular, and tissue levels to identify factors associated with perturbations like climate, mechanical forces, drug treatments, and wound healing.

Predict. We aim to develop and integrate models across time and space scales into cohesive frameworks optimized for HPC, tailored to specific cell types and spatial locations. By integrating diverse omics and imaging data and validating through exploratory techniques and optimization, we perform perturbation and sensitivity analyses to identify parameters affecting deviations from expected behaviors, with the goal of uncovering biomarkers and intervention points to induce desired responses..

Apply. Laboratory experiments to validate modeling predictions include using microfluidics to alter cellular environments and testing time-dependent perturbations, as well as micropatterning multicellular environments to assess spatial-dependent perturbations.. After refining models and confirming outcomes, further predictions and interventions are developed based on the validated results.