Our research is at the intersection of computational systems medicine and systems biology, mathematical biology and bioinformatics. We work on the design, software development and application of mathematical algorithms for the modeling, simulation and control of biological systems. In molecular biology the systems of our interest include gene regulatory networks and intracellular signaling networks where we aim to understand and control the cells’ intricate regulatory programs. We are focused on two main research areas:
We are interested in the development and application of computational systems biology tools to identify molecular drivers for tumor cells’ reprogramming. We are particularly interested in cancer reversion mechanisms and their therapeutic potential. We are currently focused on Breast Cancer.
We are interested in understanding the underlying cellular and molecular mechanisms responsible for the onset, maintenance, and resolution of the immune response to pathogens in vivo and in vitro. We are also interested in the role of macrophages in the tumor microenvironment. We are currenty working with BMDMs to study Lyme disease and on tissue-resident macrophages in the lung and spleen.
Cancer reversion is the process by which tumorigenic cells lose all, or a significant part of, their malignant phenotype. The objective of this project is to develop and apply novel computational systems biology tools to identify molecular drivers of cancer reversion, their mechanisms of action and their clinical application.
Triple-negative breast cancers (TNBCs) are a unique group of clinically aggressive breast cancers for which surgery and chemotherapy are the only available therapeutic modalities. Dr. Ed Liu and his lab, identified a genomic configuration called the tandem duplicator phenotype (TDP) that defines a major subgroup of TNBC. TDP tumors are initially highly sensitive to cisplatin. Despite their initial responsiveness, they develop resistance to cisplatin therapy. The challenge addressed by the proposed project is to test and validate an analysis pipeline to identify therapeutic interventions to reverse cisplatin resistance in TNBC. The objective is to apply structure-based control approaches to identify network node overrides in the form of multiple gene interventions that have the effect of preventing the development of resistance.
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.
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.
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.
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.
† indicates corresponding author, * indicates equal contribution
(Submitted for Review)
DiscreeTest is a two-step evaluation method for ranking discretization methods for time-series data
OCSANA: Optimal Combination of Interventions from Network Analysis
AlgoRun is a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows.
QuIN is a web server tool for querying and visualizing chromatin interaction networks.
REACT: Reverse Engineering Algorithm with Evolutionary Computation Tools
Our research group is involved in several education and outreach activities and programs throughout the year. Here is a list of some relevant ones.*
Dr. Vera-Licona has participated yearly by introducing attendees to Network Modeling.
Dr. Vera-Licona introduces the topic of Reverse engineering discrete dynamical systems in this graduate-level class.
Dr. Vera-Licona introduces the topic of Network Biology in this graduate-level class.
Summer Research Experience for Undergraduates (REU): Modeling and Simulation in Systems Biology (MSSB) is an NSF-sponsored research program, focused on modeling and simulation in systems biology, for U.S. citizen or permanent resident undergraduate students from around the United States and Puerto Rico.
This is a yearly summer research internship program for high school students run by the Avon High School.
This is a program for 11th and 12th grade students through the Department of Health Career Opportunity Programs at UConn Health.
This is a yearly summer research experiences for undergraduates run by the Department of Health Career Opportunity Programs at UConn Health through the Aetna Health Professions Partnership Initiative (Aetna HPPI).
Dr. Vera-Licona was the co-director and instructor in this Mathematical Biology workshop for highschool teachers.