Our group aims to transform large-scale molecular data into actionable biomedical knowledge. We develop and apply computational approaches to model complex biological systems, with a particular focus on understanding how cells respond to therapeutic perturbations, process environmental signals, and communicate with their surroundings.
To achieve this, we generate and analyze high-dimensional molecular readouts, including multi-omics data, and integrate them with structured biological prior knowledge. By combining scalable experimental workflows with advanced computational frameworks, we seek to uncover mechanisms, generate testable hypotheses, and build predictive models that can inform translational research and therapeutic decision-making.
More information can be found at: https://www.garridolab.org
Lines of Research
The experimental component of our group focuses on scalable LC-MS/MS-based proteomics and phosphoproteomics workflows. These approaches enable us to capture molecular snapshots of biological samples under basal and perturbed conditions, providing quantitative insight into signaling pathways, cellular states, and treatment-induced molecular responses.
A central part of our work is the development and application of computational methods to extract actionable hypotheses from omics data. We integrate large-scale molecular readouts with structured biological knowledge, such as pathways, regulatory networks, and functional annotations, to improve biological interpretation and prioritize hypotheses for experimental follow-up.
Our expertise spans multiple molecular layers, including genomics, transcriptomics, proteomics, phosphoproteomics, and metabolomics. We also develop integrative methodologies that connect these layers to provide a more comprehensive view of biological systems and disease mechanisms.
The long-term goal of our group is to build accurate, scalable, and interpretable computational models that can simulate perturbations in biological systems. By leveraging artificial intelligence approaches, we aim to learn from the growing landscape of perturbational datasets and predict how cells and tissues respond to drugs, genetic alterations, and environmental changes. These models are intended to support mechanism-driven discovery, guide experimental design, and contribute to the development of more precise translational and therapeutic strategies.
Key Words
- Systems Biology
- Computational Biology
- Bioinformatics
- Multi-omics
- Proteomics
- Cellular Signaling
