Connecting and Building Collaborations between AI and Synthetic Biology Communities
2021 AAAI Spring Symposium: Artificial Intelligence for Synthetic Biology
March 22–24, 2021 — Online
We held several successful versions of this symposium at the AAAI Fall Symposia series, most recently in 2019 (2018, 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.
Submissions
Deadline: January 18, 2021 February 15, 2021
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=sss21
Theme & Schedule
Due to COVID-19, the symposium will be held online.
Theme: “Delivering on the promise: How to go from idea to impact?”
We want to move the field out of one-of systems that work on restricted setups to products that deliver the potential of Synthetic Biology and Artificial Intelligence. Specifically, COVID-19 was the first real test for this field. In trying to rapidly analyze, build tests and vaccines, the field faced constraints that are usually dismissed while doing research, such as regulations, uncertainty and rapid mutations, and productization.
We will examine questions such as: “What do these fields need to rapidly deliver and develop solutions.”
The schedule will include panels, keynotes, and paper presentations, running roughly from 11am ET to 3:30pm ET to accommodate as many timezones as possible. More details are below.
The symposium will be held over Zoom: the link will be sent to registered participants.
We will write a workshop report as one outcome of the meeting.
Schedule
| Day / Time | Activity / Title | Author (s) / Speaker(s) |
|---|---|---|
| Monday, March 22, 2021 | ||
| 11:00am – 11:15am ET | Welcome & Symposium Goals | |
| 11:15am – 11:35am ET | Talk: Automated Biodesign Engineering by Abductive Meta-Interpretive Learning | Wang-Zhou Dai, Liam Hallett, Stephen Muggleton, and Geoff Baldwin |
| 11:35am – 11:50am ET | Community Introductions | |
| 11:50am – 12:05pm ET | Reviewing AI4Synbio 2019 | Mohammed Eslami |
| 12:05pm – 12:45pm ET | AI4Synbio during the COVID-19 era (open session) | Mohammed Eslami |
| 12:45pm – 01:15pm ET | Lunch Break | |
| 01:15pm – 01:35pm ET | Talk: Machine learning methods to predict binding of SARS-CoV-2 | Sara Capponi, Shangying Wang, and Simone Bianco |
| 01:35pm – 01:55pm ET | Talk: Levels of autonomy in synthetic biology engineering | Jacob Beal and Miles Rogers |
| 01:55pm – 02:15pm ET | Talk: Genetic perceptron network implemented in bacteria consortia for classification of chemical patterns | Ximing Li and Ramez Daniel |
| 02:15pm – 02:30pm ET | Break | |
| Tuesday, March 23, 2021 | ||
| 11:00am – 11:15am ET | Community Introductions | |
| 11:15am – 12:15pm ET | Keynote 1 | Pamela Silver, Harvard |
| 12:15pm – 12:40pm ET | Lunch Break | |
| 12:40pm – 01:00pm ET | Talk: Mind the Gap: From Prototype to Production: Insights from Development of a COVID-19 Diagnostic | Aaron Adler and Bryan Bartley |
| 01:00pm – 01:20pm ET | Talk: Learning Interpretable Models of Genotype-Phenotype Landscapes without Sacrificing Predictive Power | Peter Tonner, Abe Pressman, and David Ross |
| 01:20pm – 01:40pm ET | Talk: Error-guided Search for Part Assignment in Genetic Circuits | Tammy Qiu and Swapnil Bhatia |
| 01:40pm – 02:00pm ET | Talk: Reverse Engineering Design of Experiments for Review (REDOER) | Bryan Bartley, Jacob Beal, and Mark Weston |
| 02:00pm – 02:15pm ET | Break | |
| 2:15pm – 3:30pm ET | Panel | Šeila Selimović, BARDA; Peter Carr, MITLL; Hector Munoz-Avila, NSF; Vanessa Varaljay, AFRL |
| Wednesday, March 24, 2021 | ||
| 11:00am – 11:15am ET | Community Introductions | |
| 11:15am – 12:15pm ET | Symposium Report & Planning | |
| 12:15pm – 01:00pm ET | Lunch Break | |
| 01:00pm – 02:00pm ET | Keynote 2: Using Synthetic Biology to Diagnose and Decipher Disease Biology | Tim Lu, MIT |
Registration
Link to registration form: https://aaaiconf.cventevents.com/sss21
We are pleased to announce that we have a small scholarship fund available for up to 5 students to join the conference free of charge! If you are interested, please apply here.
Organizing Committee
Aaron Adler (BBN Technologies), Fusun Yaman (BBN Technologies), Mohammed Ali Eslami (Netrias, LLC), and Rajmonda Caceres (MIT Lincoln Laboratory).
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