Connecting and Building Collaborations between AI and Synthetic Biology Communities

★ We’re happy to announce that the next symposium will be held as part of the AAAI Spring Symposia 2022. ★

We held several successful versions of this symposium:

Our primary goal for this symposia is to begin to connect and build mutually beneficial collaborations between the AI and the synthetic biology communities.

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, for example:

  • 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