- Neda Bagheri of Northwestern University
- A Genomics@JHU Seminar
- When: December 08, 2015, 10:00
- Where: The Barber Conference Room at Charles Commons
10 E 33rd Street
Baltimore, MD 21218
- Light refreshments served at 9:30am
In the past decade, emerging technologies have offered increasingly high throughput data with greater resolution to investigate cellular responses. To gain insight from dynamic gene expression, transcription factor activity, phospho-signaling or other data, improved computational strategies to analyze, integrate, and predict complex biological function must be developed. We employ a variety of inference and modeling algorithms to investigate the temporal and multifunctional evolution of various cellular responses. By developing predictive models that are informed by new experimental tools, we aim to resolve regulatory pathways responsible for complex biological response and cell fate decisions. In this manner, we can generate informed hypotheses on the mechanism of action of potential drug candidates and gain insight for improved efficacy/specificity of treatment strategies, providing a unique opportunity to predict and modulate biological responses.
Neda Bagheri earned a doctorate in Electrical Engineering from the University of California Santa Barbara. Her interest in computational and systems biology was piqued by the fact that, not surprisingly, many of the principles (i.e., regulatory motifs) commonly employed in control theory and dynamical systems are intrinsic to biology. As a result, she pursued a postdoc in Biological Engineering at MIT, where she worked closely with experimentalists and a clinical oncologist to investigate cancer and immune cell signaling. Now, as a member of the faculty in Chemical & Biological Engineering at Northwestern University, Neda continues her mission to integrate experimental data with novel computational strategies to elucidate complex intra-cellular dynamics and inter-cellular regulation. Neda Bagheri’s long-term goal is to resolve signal and information processing in complex regulatory networks, and identify control policies to effectively modulate biological function.