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COLLOQUIUM-Empowering Interdisciplinary Learning and Research with Open-Source Tools for Computational Biology

Add to Calendar 02/13/2023 11:00 02/13/2023 12:00 America/Los_Angeles COLLOQUIUM-Empowering Interdisciplinary Learning and Research with Open-Source Tools for Computational Biology


Abstract: Can we scale up biological circuit designs using CAD tools like we do for digital circuits? How would computer science education change if that were the case? Computational tools in research and education have immense potential to unlock new discoveries and provide a more engaging and inclusive learning experience. For bioengineering research, designing and analyzing biological systems at a larger scale is limited by the heuristic tuning of components to work in specific conditions. To address this, I will present scalable computational tools for (1) writing formal system specifications using assume-guarantee contracts, (2) building mathematical models from graph descriptions, and (3) learning from data using Bayesian inference. With compelling examples from the field of bioengineering, I will introduce a full-stack pipeline of modeling, analysis, and parameter learning aimed at guiding the modeling and design of genetic circuits. In the second part of the talk, I will highlight the educational impact of these tools, showcasing how they can drive a more inclusive, active, and project-based learning in computer science classes.

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TBD


Abstract: Can we scale up biological circuit designs using CAD tools like we do for digital circuits? How would computer science education change if that were the case? Computational tools in research and education have immense potential to unlock new discoveries and provide a more engaging and inclusive learning experience. For bioengineering research, designing and analyzing biological systems at a larger scale is limited by the heuristic tuning of components to work in specific conditions. To address this, I will present scalable computational tools for (1) writing formal system specifications using assume-guarantee contracts, (2) building mathematical models from graph descriptions, and (3) learning from data using Bayesian inference. With compelling examples from the field of bioengineering, I will introduce a full-stack pipeline of modeling, analysis, and parameter learning aimed at guiding the modeling and design of genetic circuits. In the second part of the talk, I will highlight the educational impact of these tools, showcasing how they can drive a more inclusive, active, and project-based learning in computer science classes.

Type
Colloquium
Target Audience
Students
Admission
Free
Registration Required
No
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