Toward engineering biosystems with emergent collective functions

Research output: Contribution to journalArticlepeer-review

Authors

  • Thomas E. Gorochowski
  • Sabine Hauert
  • Lucia Marucci
  • Namid R. Stillman
  • T.-y. Dora Tang
  • Lucia Bandiera
  • Vittorio Bartoli
  • Daniel O. R. Dixon
  • Alex J. H. Fedorec
  • Harold Fellermann
  • Alexander G. Fletcher
  • Tim Foster
  • Luca Giuggioli
  • Antoni Matyjaszkiewicz
  • Scott Mccormick
  • Sandra Montes Olivas
  • Jonathan Naylor
  • Ana Rubio Denniss
  • Daniel Ward

Colleges, School and Institutes

Abstract

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

Bibliographic note

Funding Information: Funding. This work captured discussions between participants at the ?Multi-agent modeling meets synthetic biology? workshop held on the 16?17 May 2019 at the University of Bristol, UK and funded by BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre (Grant No. BB/L01386X/1). TG was supported by a Royal Society University Research Fellowship (Grant No. UF160357). DD and VB were supported by the University of Bristol and the EPSRC & BBSRC Centre for Doctoral Training in Synthetic Biology (Grant No. EP/L016494/1). LB was supported by EPSRC (Grant No. EP/P017134/1-CONDSYC). AF received funding from the European Research Council under the European Union?s Horizon 2020 Research and Innovation Programme (Grant No. 770835). LM was supported by the Medical Research Council (Grant No. MR/N021444/1), and the Engineering and Physical Sciences Research Council (Grant Nos. EP/R041695/1 and EP/S01876X/1). SM was supported by a Mexico Consejo Nacional de Ciencia y Tecnolog?a (CONACYT) Ph.D. scholarship. TF and J-UK are grateful to the UK National Centre for the Replacement, Refinement & Reduction of Animals in Research (NC3Rs) for funding their development of individual-based models (IBMs) for the gut environment (eGUT Grant No. NC/K000683/1 and Ph.D. training Grant No. NC/R001707/1). SH, NS, and SM received funding from the European Union?s Horizon 2020 FET Open programme (Grant No. 800983). T-YT acknowledged financial support from the MaxSynBio Consortium (jointly funded by the Federal Ministry of Education and Research, Germany and the Max Planck Society) and the MPI?CBG and the Cluster of Excellence Physics of Life of TU Dresden and EXC-1056 for funding. Publisher Copyright: © Copyright © 2020 Gorochowski, Hauert, Kreft, Marucci, Stillman, Tang, Bandiera, Bartoli, Dixon, Fedorec, Fellermann, Fletcher, Foster, Giuggioli, Matyjaszkiewicz, McCormick, Montes Olivas, Naylor, Rubio Denniss and Ward. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Article number705
Number of pages9
JournalFrontiers in Bioengineering and Biotechnology
Volume8
Publication statusPublished - 26 Jun 2020

Keywords

  • bioengineering, collectives, consortia, emergence, multi-agent modeling, multi-scale, synthetic biology, systems biology