Connecting and Building Collaborations between AI and Synthetic Biology Communities
2023 AAAI Fall Symposium: Artificial Intelligence for Synthetic Biology
October 25-27, 2023 | Westin Arlington Gateway | Arlington, VA, USA
We held several successful versions of this symposium at the AAAI Spring (2021, 2022) and Fall Symposia (2018, 2019) series. An article was published in Communications of the ACM based on the 2019 symposium.
This symposium continues the ongoing series to connect and build mutually beneficial collaborations between the AI and the synthetic biology communities. We are particularly interested in discussions and seek to spur actions by the community including in the following areas:
- Implications of large language models to synthetic biology,
- Ethics and safety concerns of lower barrier of entry to both AI and Synthetic Biology,
- Prospects of generative AI for synthetic biology, and
- Implications of the possibility of implementing neural network through circuits-like functions.
The symposium will include: very brief introduction to each domain to ensure the easy accessibility of attendees to both backgrounds, focused groups studying the open problems and challenges in the intersection of AI and Synthetic Biology, insightful contributed talks, and panel discussions with a wide range of speakers.
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.
August 11, 2023 August 25, 2023
Submission URL: https://easychair.org/conferences/?conf=fss230
Initial submissions are 2 page abstracts consisting of
- Challenge Overview (1 par)
- Diagram of solution (1/2 page)
- Technical Discussion of solution/results (1 page)
- Conclusion next/steps (1/2 page)
Aaron Adler (Raytheon BBN), Mohammed Ali Eslami (Netrias, LLC), Fusun Yaman (Raytheon BBN), Mingfei Sun (The University of Manchester), James Comolli (MIT Lincoln Laboratory)
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