Metaheuristics (MH) are Artificial Intelligence procedures that frequently rely on evolution. MH approximate difficult problem solutions, but are computationally costly as they explore large solution spaces. This work pursues to lay the foundations of general mappings for implementing MH using Synthetic Biology constructs in cell colonies. Two advantages of this approach are: harnessing large scale parallelism capability of cell colonies and, using existing cell processes to implement basic dynamics defined in computational versions. We propose a framework that maps MH elements to synthetic circuits in growing cell colonies to replicate MH behavior in cell colonies. Cell-cell communication mechanisms such as quorum sensing (QS), bacterial conjugation, and environmental signals map to evolution operators in MH techniques to adapt to growing colonies. As a proof-of-concept, we implemented the workflow associated to the framework: automated MH simulation generators for the gro simulator and two classes of algorithms (Simple Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits. Implementation tests show that synthetic counterparts mimicking MH are automatically produced, but also that cell colony parallelism speeds up the execution in terms of generations. Furthermore, we show an example of how our framework is extended by implementing a different computational model: The Cellular Automaton.

Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH are used to approximate the solution to difficult problems but require a large amount of computational resources. Cell colonies harboring synthetic distributed circuits using intercell communication offer a direction for tackling this problem, as they process information in a massively parallel fashion. In this work, we propose a framework that maps MH elements to synthetic circuits in growing cell colonies. The framework relies on cell-cell communication mechanisms such as quorum sensing (QS) and bacterial conjugation. As a proofof-concept, we also implemented the workflow associated to the framework, and tested the execution of two specific MH (Genetic Algorithms and Simulated Annealing) encoded as synthetic 2 circuits on the gro simulator. Furthermore, we show an example of how our framework can be extended by implementing another kind of computational model: The Cellular Automaton. This work seeks to lay the foundations of mappings for implementing AI algorithms in a general manner using Synthetic Biology constructs in cell colonies. MAIN TEXTEvolution is a key element involved in all microbiology processes. It is the process that drives organism adaptation to better survive and thrive in their surrounding environment. This process occurs at a genetic level, involving mainly genetic recombination and mutation. The genetic diversity produced by evolution is studied and used as inspiration in computational methods such as Evolutionary Algorithms (EAs) 1,2 . These algorithms are generally used for approximating solutions to optimization problems. Since evolution is a standard occurring process, it is natural to relate EAs to microbiology experiments, and more specifically, to Synthetic Biology constructs. This relationship has already been addressed by Directed Evolution 3,4 . However, the control level of Directed Evolution is not as specific as the one reached in EAs. Furthermore, several other computational methods can be translated to Synthetic Biology constructs that emulate their operation. Metaheuristic procedures (MH) 5-7 are a larger class of procedures that contain EAs.Inspiration upon which these techniques are designed range from metallurgy processes 8-10 through bird flock movement patterns [11][12][13] , and ant colony food foraging 14,15 . A general mapping, relating Synthetic Biology constructs to MH elements can be proposed such that any procedure of that class can be modeled as a synthetic circuit. This is due to MH sharing common elements and similarities that can be generalized.