PT  - JOURNAL ARTICLE
AU  - Jonathan Sakkos
AU  - Joe Weaver
AU  - Connor Robertson
AU  - Bowen Li
AU  - Denis Taniguchi
AU  - Ketan Maheshwari
AU  - Daniel Ducat
AU  - Paolo Zuliani
AU  - Andrew Stephen McGough
AU  - Tom Curtis
AU  - Miguel Fuentes-Cabrera
TI  - Investigating the growth of an engineered strain of Cyanobacteria with an Agent-Based Model and a Recurrent Neural Network
AID  - 10.1101/2021.10.11.463942
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.10.11.463942
4099  - http://biorxiv.org/content/early/2021/10/12/2021.10.11.463942.short
4100  - http://biorxiv.org/content/early/2021/10/12/2021.10.11.463942.full
AB  - A computational framework combining Agent-Based Models (ABMs) and Deep Learning techniques was developed to help design microbial communities that convert light and CO2 into useful bioproducts. An ABM that accounts for CO2, light, sucrose export rate and cell-to-cell mechanical interactions was used to investigate the growth of an engineered sucrose-exporting strain of Synechococcus elongatus PCC 7942. The ABM simulations produced population curves and synthetic images of colony growth. The curves and the images were analyzed, and growth was correlated to nutrients availability and colonies’ initial spatial distribution. To speed up the ABM simulations, a metamodel based on a Recurrent Neural Network, RNN, was trained on the synthetic images of growth. This metamodel successfully reproduced the population curves and the images of growth at a lower computational cost. The computational framework presented here paves the road towards designing microbial communities containing sucrose-exporting Synechococcus elongatus PCC 7942 by exploring the solution space in silico first.Competing Interest StatementThe authors have declared no competing interest.