Towards the Embedding of Artificial Intelligence into Synthetic Organisms: Engineering Intelligence in Microorganisms

Most current innovation is done using AI on a problem data to assist in some specific task at hand. However, there is little work that has been done to harness the power of gene circuits/networks and implement AI within living organisms (see below attached specific topics). This is an important matter as it endows the organisms with a decision and specific evolution capabilities, but also because the organisms, when grouped in populations, offer large-scale parallelism and a means to access massive processing power. Also, a biological AI paradigm benefits the autonomous operation of synthetic gene circuits, as it endows organisms with the potential to learn and modify their gene network function.

In this topic, the main goal is to attract research implementing some form of AI algorithm using gene circuits/networks either as implementation language or input/output data (in-vivo). Also, research involving hybrid techniques including in-silico and/or ex-vivo forms of AI in combination with gene circuit/network implementation is also of interest to this topic. Some directions in which these forms of AI may be applied are (but are not limited to):

1.- Analyze individual genetic components (promoters, transcription factors) and organize them to implement AI algorithms.

2.- Autonomous evaluation and modification of synthetic gene circuits (study genetic memory, bi-stability, positive & negative feedback) both at design time and at simulation/experiment time.

3.- Prediction of logic operations and gene circuit output.

4.- AI-enhanced directed evolution algorithms to generate novel genetic components.

5.- Implementation of classic or brand new AI algorithms in-vivo/in-silico in synthetic gene circuits.

Keywords: Synthetic biology, Artificial Intelligence, Bioinformation processing, Microbial intelligence, Cellular computing, Cell-cell communication, Non-head intelligence

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