A scalable pipeline for designing reconfigurable organisms

14 de January de 2020
A scalable pipeline for designing reconfigurable organismsDescarga
Fig. 1.Designing and manufacturing reconfigurable organisms. A behavioral goal (e.g., maximize displacement), along with structural building blocks [here, con-tractile (red) and passive (cyan) voxels], are supplied to an evolutionaryalgorithm. The algorithm evolves an initially random population and returns the best designthat was found. The algorithm is rerun 99 times starting with different random populations, generating a diversity of performant designs in silico (A;SI Appendix,section S5).

Abstract

“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”.

Author contributions: S.K., D.B., M.L., and J.B. designed research; S.K. and D.B. performedresearch; S.K. and D.B. analyzed data; and S.K., D.B., M.L., and J.B. wrote the paper. The authors declare no competing interest. This article is a PNAS Direct Submission. This open access article is distributed under Creative Commons Attribution License 4.0(CC BY). Data deposition: The source code necessary for reproducing the computational results re-ported in this paper can be found at GitHub: (https://github.com/skriegman/reconfigurable_organisms).1S. K. and D. B. contributed equally to this work. 2 To whom correspondence may be addressed. Email: josh.bongard@uvm.edu. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1910837117/-/DCSupplemental

Bibliography

Sam Kriegman, Douglas Blackiston, Michael Levin, Josh Bongard Proceedings of the National Academy of Sciences Jan 2020, 201910837; DOI: 10.1073/pnas.1910837117

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