# Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria
## 1 Introduction
synthetic bacteria have many advantages over cultures composed of only one kind of bacteria, as they allow the division of tasks, spatial organization, and higher robustness to perturbations. Due to these features, microbial consortia are nowadays under study for their possible applications in many different biotechnological processes. In this paper, westudied several strategies, and therefore algorithms, oriented to the control of a synthetic microbial consortium in which different kinds of bacteria coexist in silico. As stated above, the control of microbial communities is based on an algorithm that is implemented at the 'hardware level' in the related genetic circuit. However, this hardware, i.e., the genetic circuit, is evolutionarily configured through an evolutionary algorithm designed ad hoc, which we named BAGA. In the experiments we performed, the microbial community is composed of interacting bacteria, including one strain of pathogenic bacteria and the remaining of non-pathogenic bacteria. We studied through simulation experiments the effectiveness of different strategies in order to eliminate the pathogenic bacteria from a community formed by different species or strains of synthetic bacteria. Therefore, the aim of the simulation experiments was to minimize the number of pathogenic bacteria and maximize the number of non-pathogenic bacteria that are present in a microbial consortium or community. In this paper, it is shown that by applying the BAGA evolutionary algorithm, it is possible to find the appropriate genetic circuit that leads to the eradication or progressive elimination of the number of pathogenic bacteria in a microbial consortium. Since the number of possible genetic circuits, i.e., what is known as genotype space, is discrete, the problem to be solved is a combinatorial optimization problem. In consequence, the problem will be solved using a heuristic method such as the BAGA evolutionary algorithm. In the field of synthetic biology, the creation of new programmable organisms under a bottom-up approach requires heuristic methods coming from the area of combinatorial optimization. Under this approach, the assembly of different parts of an organism in order to optimally or quasi-optimally program the living matter requires appropriate heuristic methods. In this sense, the importance of combinatorial optimization strategies in synthetic biology was recently highlighted [11]. Thus, for example, the engineering of metabolic pathways through microbial strains involves the design and sequential or combinatorial optimization [12] of genetic circuits. In turn, the appropriate regulation of the genetic circuits obtained in silico also requires the use of combinatorial optimization techniques that in the form of software [13] allow the study of the genetic circuit regulation with a computer. In this case, the regulation of a genetic circuit or network is studied either through the analysis of the parameters that affect the genetic network or through the expression of its components, i.e., the genes that define the network. In the last decade, the application of evolutionary algorithms in synthetic biology optimization, and particularly the so-called genetic algorithms, has received increased attention from researchers. For instance, by applying genetic algorithms, it is possible to design oscillator genetic circuits with specific features [14] and even genetic circuits operating as logic gates [15], e.g., AND, OR, NOT, NAND, etc. However, all these advances are achieved at the level of computer experiments and therefore in silico. The possibility of using evolutionary algorithms in the laboratory, i.e., in the _wet lab_, is so far mainly focused on directed evolution [16]. That is, the selection process proposed by [PERSON] is emulated in the laboratory through several cycles of DNA mutagenesis and selection of sequences according to a target sequence.