Our research is at the intersection of computational systems biology and systems medicine, mathematical biology, and bioinformatics. We work on the design, software development, and application of mathematical algorithms to model, simulate, and control biological systems. We place a particular emphasis on methods combining multi-omics profiles for data-driven gene regulatory networks and multi-scale Boolean mathematical modeling construction, analysis, and control.
Development and Applicaton of Network Inference Methods Much of our research work is related to the development and application of algorithms related to the different undertakings for the reverse-engineering of dynamical systems, with an emphasis on discrete dynamical systems. These undertakings include: (1) Data discretization, (2) Static Network and Dynamical Model’s Inference, (3) Validation and Benchmarking of Reverse-engineering Algorithms, (4) Model Analysis and Simulation.
Structure-Based Control of Dynamical Systems Network control has been originally developed as part of systems and control theory. While the methods developed in this area have been applied successfully to many engineered and natural systems, several factors have limited its application to large complex biological systems such as cellular signaling networks. In this project we aim at developing and applying structure-based control methods for biological systems with a particular interest on intracellular signalign networks.
Tissue-resident Macrophage Mechanisms for Pathogen Clearance The importance of tissue-resident macrophages for tissue surveillance and homeostasis is emerging. We want to gain an understading to ultimately have the ability to reprogram the mechanisms of tissue-resident macrophages for pathogen clearance and regulation of related mechanisms such as inflammation. We are collaborating with Dr. Kamal Khanna with two tissue-resident macrophage populations.
**MyD88-dependent and -independent phagosomal signals in Macrophage mediated recognition and clearance of Borrelia burgdorferi (Lyme disease spirochete) Macrophages play prominent roles in recognition and clearance of pathogens. It has been well established that TLR/MyD88 signaling enhances phagocytic efficiency in these cells. This project seeks to better understand the mechanisms behind this phagocytic effect.
Cancer Reversion: Computational Systems Biology Approaches Cancer reversion is the process by which tumorigenic cells lose their malignant phenotype. The objective of this project is to develop and apply novel computational systems biology tools and mathematical modeling to identify molecular drivers of cancer reversion, their mechanisms of action and their clinical application.