Connecting and Building Collaborations between AI and Synthetic Biology Communities
2021 AAAI Spring Symposium: Artificial Intelligence for Synthetic Biology
March 22–24, 2021 at Stanford University in Palo Alto, California
We held several successful versions of this symposium at the AAAI Fall Symposia series, most recently in 2019. An outcome of that symposium was an article submitted to AI Magazine centered around the conference’s theme that the attendees collaborated on after the event. Another subset of participants are working on a version of the article for the ACS SynBio journal. Our primary goal for this symposia remains the same — to begin to connect and build mutually beneficial collaborations between the AI and the synthetic biology communities. We are hopeful that the Fall Symposium success can be replicated in the Spring and bring in more of the California based synthetic biology and AI researchers. We plan to continue the working group format that we used in the 2019 symposia. We also plan to highlight research in the AI/SynBio intersection that did or could have had an impact on COVID-19.
Synthetic biology is the systematic design and engineering of biological systems. Synthetic biology holds the potential for revolutionary advances in medicine, environmental remediation, and many more. For example, some synthetic biologists are trying to develop cellular programs that will identify and kill cancer cells, while others are trying to design plants that will extract harmful pollutants like arsenic from the ground.
Many times the design of synthetic organisms occurs at a low level (e.g., DNA level) in a manual process that becomes unmanageable as the size and complexity of a design grows. This is analogous to writing a computer program in assembly language, which also becomes difficult quickly as the size of the program grows. Many of the emerging techniques and tools in synthetic biology produce large amounts of data. Understanding and processing this data provides more avenues for AI techniques to make a big impact.
Data driven modeling of biological systems also presents opportunities to apply AI techniques. Work is needed to help predict the outcome of genetic modifications, identify root causes of failure in circuits, and predict the effect of a circuit on a host organism.
Currently most organism engineering workflows have little automation and rely heavily on domain expertise, only some of which is shared in publications. Tools that support or carry out information integration and informed decision making can improve the efficiency and speed of organism engineering, and enable better results.
A broad set of AI techniques can advance the progress of synthetic biology, and help realize these goals.
Topics of interest include (but not limited to):
- Research that did or could have had an impact on COVID-19
- Machine-assisted gene circuit design
- Flexible protocol automation
- Assay interpretation and modeling
- Representation and exchange of designs
- Representation and exchange of protocols
- Data driven modeling of biological systems
The symposium will include: brief introductions to each domain to ensure it is accessible to attendees with both backgrounds; focus groups looking at some of the open problems and challenges in the intersection of AI and Synthetic Biology; contributed talks; and panel discussions. We plan to highlight research in the AI/SynBio intersection that did or could have had an impact on COVID-19.
Full papers (up to 7 pages) Presenting a problem in the synthetic biology space that AI techniques might address, and optionally a description of the technique that addresses it. Alternatively, presenting a technique from AI that is relevant for synthetic biology problems.
Abstract (300 words) outlining new or controversial views of the intersection of AI and Synthetic biology research or describing ongoing AI/Synthetic Biology research.
Submission site: https://easychair.org/conferences/?conf=sss21
Attendance logistics are pending COVID-19 travel restrictions.
Aaron Adler (BBN Technologies), Fusun Yaman (BBN Technologies), Mohammed Ali Eslami (Netrias, LLC), and Rajmonda Caceres (MIT Lincoln Laboratory).
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