Journal Information
ISSN / EISSN : 1872-8324 / 1872-8324
Published by: Elsevier BV (10.1016)
Total articles ≅ 4,492
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Published: 19 January 2022
In nature, bacteria exhibit a limited repertoire of behaviors in response to environmental changes. Synthetic biology has now opened up the possibility of programming cells or unicellular organisms in order to enable them to perform certain tasks, which would allow the programming of 'intelligent' bacteria. Many of the theoretical ideas that Liberman proposed last century, for example his seminal idea that a cell is a computer, are now being put into practice with bacterial colonies in both wet and in silico experiments.These bacteria may one day be used to solve a wide range of problems whose solution requires their adaptation to external changes either within a bioreactor, organ or tissue of a patient or through the design of microbial-synthetic consortia oriented to their use in bioprocesses to produce medicines, biofuels or biomaterials. In this work, we show the possibility of programming synthetic bacteria with a previously trained perceptron neural network. First, we illustrate how a colony of bacteria endowed with a perceptron is able to solve an optimization problem in silico. Secondly, we study by means of in silico simulations how a perceptron can be applied to program behaviors in bacteria leading to social interactions and to the formation of complex communities that in the future would be useful in biotechnology. Finally, we go a step further, and study how the above perceptron designed to program bacterial behavior is implemented in a genetic circuit designed for this purpose. Once the genetic circuit was obtained, it was engineered into a plasmid.
, Dmitry Grigoriev
Published: 10 January 2022
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Published: 4 January 2022
Membrane systems are a very powerful computational modelling formalism inspired by the internal organisation of living cells. Modelling of membrane systems is challenged by composing many structurally similar components, which may result in very large models. Furthermore, some components may suffer from a lack of precise kinetic parameters. FPNC (coloured fuzzy Petri nets) combine coloured Petri nets with fuzzy kinetic parameters, and thus offer an approach to address these challenges. In this paper, we use FPNC to model and simulate membrane systems which are enriched by fuzzy kinetic parameters. We also introduce a methodology and workflow utilising FPNC for modelling and simulating general biological systems which have to cope with incomplete knowledge of their kinetic data.
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