Plasmid conjugation for synthetic biology: tuning up the machinery.

Resumen del trabajo presentado en el 41 Congreso de la SEBBM (Sociedad Española de Bioquímica y Biología Molecular), celebrado en Santander (España) del 10 al 13 de septiembre de 2018.Biological computation is a fundamental issue in synthetic biology. Synthetic computation devices are typically based on gene regulation networks able to modulate reporter genes in response to chemical or physical inputs. This approach poses some problems, specially related to scaling the computing system: - Diffusible chemical signals are non-directional - There are few Input-regulator pairs available - Gene regulation is a stochastic process with important fluctuations associated - Output concentrations produced in a first step of computation may not match with the range of input concentrations that can be sensed by the next element in the circuit. To address these issues we propose bacterial conjugation as the mechanism for transmission of information. It is a directional, modular, and binary system of communication, the computing modules may be used as much as they are needed in a network and there are plenty of different orthogonal channels described (~50 relaxase-oriT pairs). Bacterial conjugation consists on the cut and transport of a bacterial plasmid from a bacterium to another in contact using a Type IV secretion system. Conjugative plasmids contain all the necessary genes. Plasmids lacking the secretion system, but containing an origin-of-transfer sequence (oriT) are called mobilizable. Every gene involved in the process is essential, they can be complemented in trans (allowing conditional operations and building AND gates). These essential components are exclusive for almost each conjugative plasmid. This allows us to build OR gates using equivalent components from different conjugative systems in the same plasmid. In this work we characterized conjugation of model plasmid R388 (40kb, IncW) and some synthetic constructs in E.coli as the keystone in the bacterial compute