Our lab operates at the interface of computational systems biology, systems medicine, mathematical biology, and bioinformatics, with a strong focus on developing AI-augmented computational tools for analyzing and controlling complex biological systems. We design and implement innovative algorithms to model, simulate, and reprogram cellular behaviors. Our research emphasizes data-driven discovery and the integration of multi-modal omics data, with the goal of transforming how we identify therapeutic targets and understand cellular decision-making.
Network Inference, Simulation, and Analysis of Gene Regulatory and Signaling Systems. We develop and apply algorithms to reverse-engineer gene regulatory and signaling networks from multi-platform high-throughput data, with an emphasis on discrete dynamical models such as Boolean networks. Our pipeline includes:
Data discretization and preprocessing
Static network inference
Dynamical model construction
Simulation and perturbation analysis
Model verification and validation
These models allow us to explore how network topology and system’s dynamics govern cellular function and how interventions may alter fate trajectories.
Computational Strategies for Target Discovery in Cell Reprogramming. We are pioneering computational approaches to identify and prioritize reprogramming targets —genes or molecular regulators that, when perturbed, can drive a cell from one state to another. This work is grounded in control theory, network dynamics, and AI-based prioritization, and is applicable to diverse areas such as cancer biology, regenerative medicine, immunology, and aging. We recognize that reprogramming is governed not only by intracellular network architecture but also by dynamic interactions with the microenvironment, for which we develop multiscale models to reflect this complexity.
Building the Cancer Reversion Atlas. Cancer reversion refers to the process by which malignant cells are reprogrammed to lose their tumorigenic properties and return to a more stable, non-cancerous phenotype. In this initiative—aligned with our computational platform—we combine mechanistic modeling, control theory-based approaches and AI-assisted data mining and prioritization to uncover molecular drivers of reversion across cancer types. Our long-term vision is to establish a Cancer Reversion Atlas, a computational resource cataloging the reversion potential of various tumors and the most promising targets for therapeutic intervention.