Connecting and Building Collaborations between AI and Synthetic Biology Communities

Artificial Intelligence for Synthetic Biology

November 7–9, 2019 at the Westin Arlington Gateway in Arlington, Virginia 

We held a very successful version of this symposium at the AAAI Fall Symposia in 2018. Our primary goal remains the same – to connect and build mutually beneficial collaborations between the AI and the synthetic biology communities. This year we want to include some working groups with particular foci to foster hands-on interaction and discussion of research challenges at the intersection of synthetic biology and AI.

Synthetic biology is the systematic design and engineering of biological systems. Synthetic biology holds the potential for revolutionary advances in medicine, materials, environmental remediation, and many more. For example, some synthetic biologists are developing cellular programs to identify and kill cancer cells, while others are trying to design plants that will extract harmful pollutants like arsenic from the ground.

Often 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. Additionally, 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):

  • 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.

Submissions

Reviews have been sent, but if you still want to submit an abstract, please get in touch with us.

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=fss19

Schedule

Day / TimeActivity / TitleAuthor (s) / Speaker(s)
Thursday
9:00am - 9:15amSymposium Introduction
9:15am - 9:45amIntro Talks for AI and SynBio
9:45am - 10:00amDiscussion of Community Resources
10:00am - 10:30amTalk: Collaborative Development and Evaluation of Data-driven Biological ModelsMohammed Eslami, Hamed Eramian and Nicholas Leiby
10:30am - 11:00amCoffee Break
11:00am - 12:00pmPanel to Discuss Challenges of AI in SynBio
12:00pm - 12:30pmTalk: Using Machine Learning and Big Data to Select Smart Microbes for Generating Electricity from WastewatersCharles Zhou and Ying Zhao
12:30pm - 2:00pmLunch
2:00pm - 2:30pmTalk: The Case for A New AI+Cyber+Bio Security ParadigmWilliam Streilein and Catherine Cabrera
2:30pm - 3:00pmTalk: A Bayesian Model for Experiment Choice in Synthetic BiologyRobert P. Goldman, Puja Trivedi, Daniel Bryce, Matthew Dehaven and Alex Plotnick
3:00pm - 3:30pmTalk: AI Challenges and Applications in Detecting Engineering in Single CellsAaron Adler, Adam Abate, Joseph Collins, Benjamin Demaree, Kevin Keating, Xiangpeng Li, Thomas Mitchell, David Ruff, Allison Taggart, Shu Wang, Daniel Weisgerber, Daniel Wyschogrod, Fusun Yaman, Eric Young and Nicholas Roehner
3:30pm - 4:00pmCoffee Break
4:00pm - 4:30pmTalk: Design and Analysis of Toehold Switches via a Two-Headed Convolutional Hierarchy Model of Riboswitch Architecture (CHiMeRA)Jacqueline A. Valeri, Katherine M Collins, Bianca A. Lepe, Miguel A. Alcantar, Timothy K. Lu and Diogo M. Camacho
4:30pm - 5:30pmDiscussion Session (Breakout Groups for Data, Tools, Applications)
6:00pm - 7:00pmReception
7:00pm - ?Group outing (optional)
Friday
9:00am - 10:00amKeynote: Human vs Machine: Challenges for Applying AI in BiologyJosh Dunn, Ginkgo Bioworks
10:00am - 10:30amTalk: The DSGRN Database: Identifying Network Function with Network TopologyKonstantin Mischaikow, Chris Mischaikow, Breschine Cummins and Tomas Gedeon
10:30am - 11:00amCoffee Break
11:00am - 11:30amTalk: Convolutional Neural Networks Guide Rationally Engineered Protein StabilityRaghav Shroff, Austin Cole, Andrew Ellington and Ross Thyer
11:30am - 12:00pmTalk: Convolutional Neural Net Learns Promoter Sequence Features Driving Transcription StrengthNicholas Leiby, Ayaan Hossain and Howard M Salis
12:00pm - 12:30pmTalk: Sequence to Sequence Transfer Learning for DNA PhenotypingNiall Dalton, Peter Morales, Rajmonda Caceres, Matt Walsh, Christina Zook, Catherine Van Praagh, Nicholas Guido and Todd Thorsen
12:30pm - 2:00pmLunch
2:00pm - 2:30pmTalk: Algorithmic Optimization for Pathway Engineering and BeyondEric Young
2:30pm - 3:00pmTalk: Computational Modeling of Cell Signaling and Mutations in Pancreatic CancerCheryl Telmer, Khaled Sayed, Adam Butchy, Kara Bocan, Christof Kaltenmeier, Michael Lotze and Natasa Miskov-Zivanov
3:00pm - 3:30pmTalk: ART: A Machine Learning Automated Recommendation Tool for Guiding Synthetic BiologyHector Garcia Martin
3:30pm - 4:00pmCoffee Break
4:00pm - 4:30pmTalk: On Chemical Reaction Network Design by a Nested Evolution AlgorithmElisabeth Degrand, Mathieu Hemery and François Fages
4:30pm - 5:30pmBreakout Group Discussions
6:00pm - 7:30pmPlenary Session
Saturday
9:00am - 10:30amBreakout Group Discussions
10:30am - 11:00amCoffee Break
11:00am - 12:30pmBreakout Groups Debrief and Next Steps Discussion

Talks

Key Note: Dr. Joshua Dunn, Ginkgo Bioworks

Dr. Joshua Dunn is the Head of Design at Ginkgo Bioworks. His team of computational biologists and data scientists is tasked with developing algorithms and high-throughput experiments to answer difficult questions in synthetic biology. Joshua is also Ginkgo’s principal investigator in performance of IARPA’s FELIX program, which aims to develop machine learning approaches to identify sites of genetic engineering within unlabeled DNA sequence. Joshua has over 15 years of experience in genetics, genomics, computational biology, and synthetic biology. In his doctoral research at UCSF, he demonstrated  that non-canonical ribosomal behaviors, such as stop codon readthrough, are widespread regulatory mechanisms used throughout the tree of life to functionally expand the genetic code. Before then, he was a metabolic engineer at Microbia, Inc, where he designed heterologous pathways for biosynthesis of small molecules in industrial yeasts. His interests include genomics, RNA biology, machine learning, and automation of genetic design.

Attending

In-person registration will be available.

You can attend the symposium without submitting a paper or abstract. Registration is open: https://cvent.me/3qq59 as is the hotel reservation block.

Organizing Committee

Aaron Adler (BBN Technologies), Mohammed Ali Eslami (Netrias, LLC), Jesse Tordoff (MIT), and Rajmonda Caceres (MIT Lincoln Laboratory).

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