title: Synthetic associative learning in engineered multicellular consortia.
abstract: Associative learning (AL) is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was recognized early as a general trait of complex multicellular organisms but is also found in 'simpler' ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell, and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display AL responses to two classes of stimuli and displaying long- and short-term memory (i.e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.
authors.affiliation: ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Spain.|Institut de Biologia Evolutiva, CSIC-UPF, Passeig Maritim de la Barceloneta, 37, 08003 Barcelona, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Macia
authors.affiliation: ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Spain.|Institut de Biologia Evolutiva, CSIC-UPF, Passeig Maritim de la Barceloneta, 37, 08003 Barcelona, Spain.
authors.forename: Blai
authors.initials: B
authors.lastname: Vidiella
authors.affiliation: ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Spain ricard.sole@upf.edu.|Institut de Biologia Evolutiva, CSIC-UPF, Passeig Maritim de la Barceloneta, 37, 08003 Barcelona, Spain.|Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
authors.forename: Ricard V
authors.identifier: 0000-0001-6974-1008
authors.initials: RV
authors.lastname: Solé
mesh_terms: D001245:Association Learning; D003198:Computer Simulation; D004926:Escherichia coli / Q000502:physiology; D006801:Humans; D064307:Microbiota; D016571:Neural Networks, Computer; D058615:Synthetic Biology / Q000379:methods*
keywords: adaptation; associative learning; cellular circuits; neural systems; synthetic biology