Systems Immunology
Computational analysis of dynamic cytokine signaling by immune cells.
Research Aim 1: Classify, resolve, and predict complex immune function.
Research Aim 2: Quantify the emergent network behavior governing population dynamics.
Research Aim 3: Characterize synchrony and circadian regulation of the immune response.
Research Aim 4: Develop computational methods to (i) analyze novel multi-dimensional data from isolated single- and multiple-cells, and (ii) predict the dynamic response of these cells with respect to specific stimulatory inputs and perturbations.
The immune system comprises a complex network of integrated cells that offer innate and adaptive immunity. T cells are a fundamental part of the adaptive immune system as they coordinate diverse responses upon antigenic stimulation to protect the host from disease. This response involves the release of many cytokines with various immunomodulatory functions. The efficacy of the immune response depends, in part, on the types of cytokines secreted by activated T cells and their corresponding kinetic profiles. In order to resolve and predict the contribution of specific T cell subsets to an immunological response, we need to quantitatively measure and computationally analyze the large diversity of cells that comprise the human immune system.
Through serial microengraving [Han et al., Lab Chip 2010], the Love Lab is able to quantify single- and multiple-cell cytokine secretion dynamics, offering a unique multi-dimensional approach to investigate functional differences specific to immunophenotypes. By combining quantitative experimental measurements with systems analysis and control theory tools, we can ascertain the contribution of specific cytokine signaling to overall immune function and investigate the regulatory mechanisms involved in the differentiation and proliferation of immune cells. As a result, we are able to better investigate, understand, and predict the nonlinear signaling dynamics that govern qualitatively different T-cell performance. Continued studies may offer insight to design more effective and personalized treatment strategies.
Collaboration: Professor J. Christopher Love, Department of Chemical Engineering, Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139.
Combination Virotherapy for Cancer
Enhance the efficacy of combination and virus-based cancer treatment.
Research Aim 1: Predict the impact of subcellular processes on cell-level dynamics through multi-scale modeling.
Research Aim 2: Investigate the emergent behavior that governs the population level dynamics of a heterogeneous 3D virus-host cancer environment.
Research Aim 3: Characterize the impact of circadian regulation and identify control strategies that optimize the efficacy of treatment.
Replication-selective adenoviruses, such as ONYX-015, replicate in cells containing certain mutations, motivating their use in targeted gene therapy. Although ONYX-015 is designed to preferentially target cancer cells, experimental data suggests that the corresponding receptor, CAR, is down-regulated in highly malignant cells, hindering ONYX-015’s ability to infect cancer cells. Pharmaceutical intervention into the Raf-MEK-ERK pathway via MEK inhibition has shown to counter this effect by up-regulating CAR expression. MEK inhibition, however, causes G1 cell cycle arrest thereby stunting viral replication and consequently virus-induced cancer cell death. Through computational modeling of cancer cells subject to MEK inhibition and ONYX-015 infection, we aim to characterize and predict system dynamics, providing a means to optimize the efficacy of oncolytic adenovirus cancer treatment by manipulating the timing of drug treatment and infection. Preliminary studies have supported a population based (cellular level) deterministic model that highlights sub-cellular virus-host dynamics as components necessary for accurate and predictive simulations. Thus, we aim to refine existing models to include relevant (i) sub-cellular events (ii) heterogeneity, (iii) spatial dynamics, and (iv) circadian regulation to better characterize both the mechanistic and highly complex dynamic behavior of adenovirus cancer treatment. Pending successful test of model predictions, our goal is to elucidate optimization strategies that could offer practical and effective means for minimizing cancer.
Acknowledgement/Collaboration: Experimental data was generated by Dr. Marisa Shiina and Professor W. Michael Korn at the Division of Gastroenterology and Medical Hematology/Oncology, UCSF Helen Diller Family Comprehensive Cancer Center, Department of Medicine, San Francisco, CA 94115-1705.