Park Research Lab
330 Lindy Claiborne Boggs Center for Energy and Biotechnology
6823 St. Charles Ave.
New Orleans, LA 70118
© Park Research Lab 2026
Research Overview
Tumor cells vary in treatment sensitivity and phenotype. This cell-to-cell heterogeneity plays a major role in disease progression and drug resistance. Concomitantly, cancer stem cells and tumor cell plasticity, i.e., their ability to change certain cellular characteristics and behavior independent of changes to a cell’s genetic code, are key contributors to tumor progression and recurrence. Intrinsic (e.g., gene expression regulation) and extrinsic (e.g., cell-to-cell interactions) factors drive tumor cells into treatment-resistant states. By applying computational and experimental methodologies, our group seeks to determine non-genetic mechanisms that drive the dynamics of tumor cells and tumor microenvironment cell types that contribute to the emergence of drug-resistance in tumor cells. Our research currently focuses on the following areas of interest:
Tumor cells vary in treatment sensitivity and phenotype. This cell-to-cell heterogeneity plays a major role in disease progression and drug resistance. Concomitantly, cancer stem cells and tumor cell plasticity, i.e., their ability to change certain cellular characteristics and behavior independent of changes to a cell’s genetic code, are key contributors to tumor progression and recurrence. Intrinsic (e.g., gene expression regulation) and extrinsic (e.g., cell-to-cell interactions) factors drive tumor cells into treatment-resistant states. By applying computational and experimental methodologies, our group seeks to determine non-genetic mechanisms that drive the dynamics of tumor cells and tumor microenvironment cell types that contribute to the emergence of drug-resistance in tumor cells. Our research currently focuses on the following areas of interest:
Gene-regulatory networks driving cancer stem-cell dynamics
We use single-cell multi-omic data to characterize the molecular responses of tumor cells to drug treatment to infer and test gene regulatory networks driving phenotypic responses and cell-state transitions.
Objective: To identify gene regulatory network topologies and network properties that contribute to cell state plasticity.
Purpose: Phenotypic shifts that occur in tumor cells lead to varying responses and ultimately drug efficacy, i.e., ability to completely eradicate a tumor cell population. Understanding how gene network topologies support these shifts can provide insights into novel mechanisms on how to prevent cells from evading drug treatment effects.
Challenge: Gene regulatory networks are inferred from highly noisy data, particularly when dealing with data at the single-cell level. The increasing number of data modalities (e.g., RNA-seq, ATAC-seq, spatial transcriptomics) add a greater degree of complexity in our efforts to reconcile genotype to phenotype.
Vision: To develop and refine models and methodologies that integrate multi-modal data at the single-cell level to reveal gene regulatory network mechanisms that drive cell-level phenotypic changes and cellular dynamics.
Cell-cell networks driving phenotypic changes
We will develop cell-cell network models and identify gene expression programs across cell types that drive state transitions in the context of interactions between GSC and immune cells (macrophages) to reveal how such interactions foster and sustain drug-resistance in GSCs.
Objective: To determine cell interaction network states driving cell-state transitions and support stability of drug-resistant cell states (phenotypes).
Purpose: The tumor microenvironment (TME) and the cell-cell interactions occurring between the tumor and TME have pro-tumor effects and support tumor growth and therapy evasion. Elucidating specific cell-cell interactions and how those interactions may change over time would reveal targetable mechanisms that may perturb these pro-tumor effects.
Challenge: The TME is complex and dynamic, making it difficult to deconvolute cell-cell interactions having pro-tumor impacts. As multi-modal data on tumors and the surrounding TME become increasingly availability, a challenge becomes integrating these data and interpreting these data appropriately. Here we are taking a bottom-up approach to focus on GBM stem-cell, immune cell (macrophage, the predominant immune cell in the TME) interactions to investigate how these cell types interact with one another and elucidate the cell-cell interactions through which these key cell types influence one another.
Vision: To develop quantitative models of cell type interactions (gene expression and protein interactions) between GBM stem cells and macrophages that drive cell-state transitions and provide a quantitative model that can be used to inform on plausible targets that would perturb macrophage reprogramming and/or pro-tumor effects.
Mathematical models of cell-state transitions and dynamics
We will develop quantitative models that characterize the transcriptional regulatory network and corresponding network dynamics that contribute to cell state transitions and cellular plasticity in cancer stem cells and constituent components of the tumor microenvironment.
Objective: To identify gene regulatory network topologies and network properties that contribute to cell state plasticity.
Purpose: Phenotypic shifts that occur in tumor cells lead to varying responses and ultimately drug efficacy, i.e., ability to completely eradicate a tumor cell population. Understanding how gene network topologies support these shifts can provide insights into novel mechanisms on how to prevent cells from evading drug treatment effects.
Challenge: Gene regulatory networks are inferred from highly noisy data, particularly when dealing with data at the single-cell level. The increasing number of data modalities (e.g., RNA-seq, ATAC-seq, spatial transcriptomics) add a greater degree of complexity in our efforts to reconcile genotype to phenotype.
Vision: To develop and refine models and methodologies that integrate multi-modal data at the single-cell level to reveal gene regulatory network mechanisms that drive cell-level phenotypic changes and cellular dynamics.