Lauffenburger:Neda Bagheri

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Neda Bagheri (BE postdoctoral) Email CV


  • 2007, MS/PhD, Department of Electrical Engineering, UCSB
    • Advisor: Prof. Francis J. Doyle III
    • Doctoral thesis: Phase as a performance metric for the analysis, control, and model development of circadian gene networks.
  • 2002, BS, Department of Electrical Engineering, UCSB


Research Aim (2010-present): Computational analysis of dynamic cytokine signaling responses by individual T cells to resolve and predict complex immune function.

Research Collaborator: Professor J. Christopher Love, Department of Chemical Engineering, Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139.


The immune system coordinates a diverse T cell response 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 [1], 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 statistical and systems analysis 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.

[1] Qing Han et al., Lab Chip, 2010, 10: 1391-1400.




Research Aim (2008-2010): Development of experimentally-driven quantitative models to optimize oncolytic adenovirus cancer treatment.

Research Collaborators: 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.


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 CAR is down-regulated in highly malignant cells, hindering ONYX-015’s ability to infect. Pharmaceutical intervention into the Raf-MEK-ERK pathway via the CI1040 MEK inhibitor 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 the data-driven modeling of cancer cells subject to CI1040 and ONYX-015, 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 the model to include relevant sub-cellular events that better characterizes both the mechanistic and 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 growth.


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