A scalable pipeline for designing reconfigurable organisms

By Sam Kriegman, Douglas Blackiston, Michael Levin, Josh C. Bongard. Proceedings of the National Academy of Sciences of the United States of America, 2020

395 citations recorded by the source corpus.

Significance Most technologies are made from steel, concrete, chemicals, and plastics, which degrade over time and can produce harmful ecological and health side effects. It would thus be useful to build technologies using self-renewing and biocompatible materials, of which the ideal candidates are living systems themselves. Thus, we here present a method that designs completely biological machines from the ground up: computers automatically design new machines in simulation, and the best designs are then built by combining together different biological tissues. This suggests others may use this approach to design a variety of living machines to safely deliver drugs inside the human body, help with environmental remediation, or further broaden our understanding of the diverse forms and functions life may adopt. Living systems are more robust, diverse, complex, and supportive of human life than any technology yet created. However, our ability to create novel lifeforms is currently limited to varying existing organisms or bioengineering organoids in vitro. Here we show a scalable pipeline for creating functional novel lifeforms: AI methods automatically design diverse candidate lifeforms in silico to perform some desired function, and transferable designs are then created using a cell-based construction toolkit to realize living systems with the predicted behaviors. Although some steps in this pipeline still require manual intervention, complete automation in future would pave the way to designing and deploying unique, bespoke living systems for a wide range of functions.

Subfields: Biomedical Engineering, Condensed Matter Physics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/fc93ba5dfdb5e8b3da1e83a49b514305142fe0ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

  1. Risto Miikkulainen
  2. Maxim Bazhenov
  3. Zsolt Kira
  4. Marco Dorigo
  5. Guy Theraulaz
  6. J. Krichmar
  7. Jeff Clune
  8. Vito Trianni

In Condensed Matter Physics

  1. Jeff Clune

In Mechanical Engineering

  1. Jeff Clune
  2. Sebastian Risi
  3. Josh C. Bongard
  4. Marco Dorigo
  5. Vito Trianni
  6. Risto Miikkulainen
  7. J. Krichmar
  8. Andrea Soltoggio

Citations in the corpus