University of Minnesota


Molecular Cell Engineering Laboratory
Department of Biomedical Engineering PI: Casim A. Sarkar
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Our laboratory uses the tools of biomolecular engineering, synthetic biology, and systems biology for two purposes: 1) to elucidate fundamental biological design principles that underlie cellular decision making and 2) to design new molecular and cellular therapeutics for diseases ranging from diabetes to cancer. We couple methods from experimental disciplines such as molecular biology, protein biochemistry, microbiology, and mammalian cell biology with computational modeling and engineering analyses to develop quantitative and predictive frameworks for the biological processes that we interrogate and design.

Elucidating biological design principles in cellular decision making

The inherent complexity and our incomplete understanding of native cell signaling networks often obfuscate the core biological design principles that govern how cells make decisions. A complementary approach to 'top-down' dissection of complex, natural signaling pathways is 'bottom-up' construction, analysis, and perturbation of minimal, well-defined signaling modules. We are applying both systems biology and synthetic biology approaches to elucidate mechanisms of signal processing and multimodal decision making in stem and progenitor cells. Through these studies we are uncovering novel modes of regulation in these natural systems, which in turn inform design strategies for rationally engineering cell behavior.

Designing new molecular and cellular therapeutics

The creation of novel molecular and cellular reagents for specific biomedical or biotechnological applications can often benefit from a quantitative, holistic perspective in order to maximize the utility of these products. We are using such systems-level approaches to overcome clinical challenges ranging from oral delivery of therapeutic proteins to eradication of tumors. When a system is sufficiently well characterized, we can employ modeling to elucidate mechanisms and design criteria; when the critical bottlenecks are less easily identified, directed evolution approaches can often be used to achieve the desired result and the outcomes of these selections can retrospectively provide deeper insight into the limitations that existed in the original system.

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