Connecting and Building Collaborations between AI and Synthetic Biology Communities
2022 AAAI Spring Symposium: Artificial Intelligence for Synthetic Biology
March 21–22, 2022 — Virtual only
Schedule and Registration Available
The symposium will be held virtually over Zoom.
Theme : “New AI or New Data?”
Help us define what new AI and new data actually mean. Is “new” a set of constructive small tweaks/engineering, is it a new architecture, a new model? Is new data new curation methods, new instrument platforms?
We held several successful versions of this symposium at the AAAI Spring (2021) and Fall Symposia (2018, 2019) series, most recently in Spring 2021 (virtually).
We have submitted an article to Communications of the ACM (under review) based on the 2019 symposium that was co-written by that year’s attendees. The 2021 symposium was also successful, and the virtual format enabled a more geographically diverse audience.
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 2021 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):
- Developing AI techniques specifically geared towards SynBio problems
- 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.
November 15, 2021 January 21, 2022
The length of each talk will be based on the abstract / paper submission.
Full papers (up to 6 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 (1-2 pages; at least one image highly encouraged) 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=sss22
|Day / Time||Activity / Title||Author (s) / Speaker(s)|
|Monday, March 21, 2022|
|11:00a - 11:10a EDT||Welcome & Introduction|
|11:10a - 12:10p EDT||Keynote: Designing Programmable Biosensors||Prof. Elizabeth Libby, Assistant Professor of Bioengineering at Northeastern University|
|12:10p - 12:30p EDT||Break|
|12:30p - 1:00p EDT||Paper: Deep Reinforcement Learning for the Design of Optimal Experiments in Synthetic Biology||Neythen Treloar, Nathan Braniff, Chris Barnes and Brian Ingalls|
|1:00p - 1:30p EDT||Paper: Artificial Metabolic Networks: enabling neural computations with metabolic networks||Léon Faure, Bastien Mollet and Jean-Loup Faulon|
|1:30p - 1:45p EDT||Break|
|1:45p - 2:15p EDT||Paper: Antibody Representation Learning for Drug Discovery||Esther Gupta, Lin Li, John Spaeth, Leslie Shing, Tristan Bepler and Rajmonda Caceres|
|2:15p - 3:00p EDT||Panel: New AI or New Data||Geoff Baldwin, Ben Brown, Pat Langley, Jacob Beal|
|Tuesday, March 22, 2022|
|11:00a - 11:10a EDT||Welcome & CACM Article Update|
|11:10a - 12:10p EDT||Keynote: Learning the Protein Language: Evolution, Structure, and Function||Dr. Tristan Bepler, Group Leader of the Simons Machine Learning Center|
|12:10p - 12:30p EDT||Break|
|12:30p - 1:00p EDT||Paper: Protocol Modeling for High Throughput Experimentation, Data Analysis, and Replication||Robert P. Goldman, Daniel Bryce, Jacob Beal and Bryan Bartley|
|1:00p - 1:30p EDT||Paper: Agile Data Curation||Jacob Beal, Thomas Mitchell, Bryan Bartley and Nicholas Roehner|
|1:30p - 1:45p EDT||Break|
|1:45p - 2:15p EDT||Paper: TALE Writer: Design and Assembly of Custom TALE-Based Technologies||Santiago Restrepo-Castillo, Gabriel Martínez-Gálvez, Ankit Sabharwal, Bibekananda Kar, Shannon Holmberg, Ryan Cotter, Zachary Warejoncas, Karl Clark and Stephen Ekker|
|2:15p - 2:45p EDT||Paper: New Data Challenges: Thoughts from FELIX||Aaron Adler, Nic Roehner|
|2:45p - 3:00p EDT||Closing remarks|
Link to registration form: https://aaaiconf.cventevents.com/sss22
Aaron Adler (BBN Technologies), Fusun Yaman (BBN Technologies), Mohammed Ali Eslami (Netrias, LLC), and Rajmonda Caceres (MIT Lincoln Laboratory).
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