Cancer Reversion: Computational Systems Biology Approaches

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.

Structure-based Control to Reverse Cisplatin Resistance in Triple Negative Breast Cancer

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.

Development of Novel 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.

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.

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.


Paola Vera-Licona

Team Leader

Lauren Marazzi

MD/PhD Student

Maddie Gastonguay

Honors Program Student

Catherine Qiu

Pre-med Student

Leelakrishna Channa

Pre-med Student

Luis Sordo-Vieira

Postdoctoral Fellow

Erin Boggess

Undergrad/REU summer 2016

Mark Nwokocha

Undergrad/2016, Dep Health Career Opportunity

Tiffany Jann

Undergrad/REU summer 2016

Vishal Shah

High school intern, summer 2017

Christopher Tseng

Undergrad/REU summer 2015

Shichao Wang

Undergrad/REU summer 2015

Recent Publications

More Publications

† indicates corresponding author, * indicates equal contribution

(2017). Chromatin interaction networks revealed unique connectivity patterns of broad H3K4me3 domains and super enhancers in 3D chromatin. Scientific Reports. Volume 7, Article number: 14466.

PDF Code

(2017). CD169+ marginal zone macrophages orchestrate innate immune responses to bacterial infection. Science Immunology. Vol. 2, Issue 16 (article featured on the cover).

PDF Project

(2017). The Minimal Hitting Set Generation Problem: Algorithms and Computation. SIAM Journal on Discrete Mathematics. 2017; 31(1):63–100.

Preprint PDF Code Project AlgoRunContainer

(2016). AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms. Bioinformatics, Volume 32, Issue 15, 1 August 2016, Pages 2396–2398.

PDF Code AlgoRunWebsite

(2016). QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks. PLoS Comput Biol. 2016 Jun 23;12(6):e1004809. PubMed PMID: 27336171; PubMed Central PMCID: PMC4919057.

PDF Code QuinWebServer


(Submitted for Review)

(2018). Benchmarking Time-Series Data Discretization on Inference Methods. bioRxiv 378620

Preprint Project Please check that the `projects` parameter in the front matter of content in `content/publication/` refers to an existing filename (without .md extension) of a published project.



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

Education & Outreach

Our research group is involved in several education and outreach activities and programs throughout the year. Here is a list of some relevant ones.

Big Genomic Data Skills Training for Professors (CT)

Dr. Vera-Licona has participated yearly by introducing attendees to Network Modeling.

MEDS 6455: Introduction to Systems Biology

Dr. Vera-Licona introduces the topic of Reverse engineering discrete dynamical systems in this graduate-level class.

MEDS6498: Topics in Bioinformatics and Computational Biology

Dr. Vera-Licona introduces the topic of Network Biology in this graduate-level class.

NSF Summer Research Experience for Undergraduates

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.

Avon High School Internship Program

This is a yearly summer research internship program for high school students run by the Avon High School.

UConn Health Junior & Senior Doctors Academy Program

This is a program for 11th and 12th grade students through the Department of Health Career Opportunity Programs at UConn Health.

UConn Health Summer Research Fellowship Program

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).

Model Your Genes the Mathematical Way II

Dr. Vera-Licona was the co-director and instructor in this Mathematical Biology workshop for highschool teachers.


The Vera-Licona Research Group