The Computational Systems Biology Laboratory at USC develops mechanistic models of biological processes and utilizes the models to:
  • gain insight into the dynamics and regulation of biological systems
  • synthesize and interpret experimental and clinical observations
  • provide a quantitative framework to test biological hypotheses
  • support the development of novel therapeutics for pathological conditions
We perform experimental studies to obtain quantitative measurements needed to construct computational models that increase our understanding of specific biological processes. We also collaborate closely with experimental and clinical researchers. These fruitful collaborations enable experimental testing of the model predictions.

Current projects:

The main projects in the CSBL are focused on applying computational modeling to study angiogenesis, metabolism, and immunotherapy. Current projects study how these processes are exploited in cancer. The biochemical networks that regulate these processes involve numerous cell types, molecular species, and signaling pathways, and the dynamics occur on multiple timescales. Therefore, a systems biology approach, including experiment-based computational modeling, is required to understand these complex processes and their interconnectedness in cancer. Models can simulate biological processes under pathological conditions and predict interventions that restore normal physiology. Additionally, the models can identify which tumors will respond favorably to a particular therapy, aiding in the development and optimization of effective therapeutics.

Specific projects are described below.

Systems biology approach to understand immune cell activation
Cancer immunotherapy has seen great success in recent years; however, its efficacy is still limited, especially in solid tumors, because we do not fully understand the complex interactions between tumor cells and immune cells in the tumor microenvironment. We use computational modeling to predict the dynamics of immune cell activation and to study interactions between tumor and immune cells. We have particularly studied T cells engineered with chimeric antigen receptors (CARs), Natural Killer cells, and macrophages. The goal of this research is to generate novel mechanistic insight into immune cell activation and predict the effects of immunotherapeutic strategies.

NIH NRSA F31 (2015-2018; Co-Sponsor: Finley);
Center for Computational Modeling of Cancer (2020-present);
NIH NCI  (2022 - present; multi-PI: Finley, Graham)

Model of metabolic phenotypes in cancer
Quantitative, dynamic models of the metabolic phenotypes observed in cancer can be used to develop therapies that inhibit tumor metabolism. The development of  an experiment-based, validated computational model of metabolism in various cancer types will provide a more in-depth understanding of the dynamics of altered tumor metabolism, which drives cancer progression. Our long-term goal is to understand the cellular metabolism of tumors and support the development of novel cancer therapies that inhibit aberrant tumor metabolism.

Rose Hills Research Fellowship and USC Provost's Office (2015-17; PI: Finley);
NIH NCI (2018 - present; multi-PI: Finley, Macklin, Mumenthaler)

Model of pro- and anti-angiogenic factors
Computational modeling is needed to understand the complexity and interconnected nature of the angiogenesis signaling pathways and predict the effect of anti-angiogenic strategies. A quantitative understanding of the relative levels of angiogenic factors under pathological conditions would aid in the development of therapeutic agents that target these factors. Thus, the long-term goal of this project is to investigate the angiogenic balance in cancer and identify effective therapies that target promoters and inhibitors of angiogenesis.

NSF CAREER Award (2016-22, PI: Finley)
American Cancer Society Research Scholar Grant (2017-22, PI: Finley)

Studying pancreatic beta cells:

Our research group is collaborating with experimental and clinical researchers to study beta cells.

  • Integrating imaging and systems biology models This is a collaboration with Professor Scott Fraser at USC. We are combining high-resolution live-cell microscopy and computational modeling to study an important signaling pathway in pancreatic beta cells: prolactin-induced JAK-STAT signal transduction, which plays a role in beta cell proliferation.
  • Signaling pathways that regulate pancreatic beta cell regeneration This is a collaboration with Professor Senta Georgia from Children's Hospital of Los Angeles. We are constructing a predictive mathematical model to investigate the dynamic cell signaling that mediates regeneration of beta cells.
  • Computational model of pancreatic beta cell metabolism We used mathematical modeling to study metabolic pathways in beta cells. This is part of the Pancreatic Beta Cell (PBC) Consortium. We are collaborating with Professors Nicholas Graham and Scott Fraser, who are collecting experimental data needed to construct the model.