# A Microbial Screening in Silico Method for the Fitness Step Evaluation in Evolutionary Algorithms
## 1 Introduction
Bacterial screening is based on both static features, e.g., shape, Gram-staining, and dynamic traits, e.g., motility, antibiotic resistance, enzyme expression, growth rate, etc. Based on some of these traits, microbial fitness is evaluated by applying different experimental strategies. For example, fitness measures can be obtained from the Malthusian growth rate estimated from the bacterium growth curve [8], or from the cost to bacteria of being resistant to antibiotics [9]. Nevertheless, there are more elaborated methods for calculating bacterial fitness. For example, the authors of Reference [10] calculate fitness from the difference between the cost and benefit resulting from the level of expression of a protein in the Lac operon. Over the last few decades, synthetic biology [11; 12] has made it possible to design microorganisms that behave like a computer. In this paper, we show how synthetic biology could be a source of inspiration for the design of some steps in evolutionary algorithms. Nowadays, advances in synthetic biology have allowed the programming of bacteria in order for these organisms to carry out specific tasks. It is assumed that bacteria are agents [13], which, once programmed, receive a physical or chemical input, process it at a biochemical level under genetic control, and provide an output. In evolutionary computing, there are evolutionary algorithms in which a human individual carries out the evaluation and selection of individuals, i.e., solutions. These kinds of algorithms are referred to as interactive evolutionary or aesthetic selection algorithms. In this paper, we introduce a different approach. We show how a colony of synthetic bacteria or bacterial agents is able to indirectly evaluate the fitness of solutions in a genetic algorithm by building an evaluation function. The evaluation function is obtained by simulating a bacterial colony in silico, emulating the laboratory methods used in microbiology, biotechnology and synthetic biology to measure microbial fitness. Once the evaluation function is found, it is included in the code of the genetic algorithm as part of the fitness routine. In silico routines have been implemented in Gro, a cell programming language [14; 15; 16] oriented to simulation experiments in synthetic biology. The usefulness of the procedure is illustrated in two classic optimization problems. The scripts of the designed routines are available (Supplementary Material) [17] and can be easily customized to many other optimization problems. Microbial screening are laboratory methods for the detection of specific features in microorganisms, for example, bacteria. In summary, we design in silico routines simulating colonies of synthetic bacteria. Based on bacterial colonies, we emulate their homologues in vivo, conducting those experimental laboratory protocols developed to measure microbial fitness in silico. Using such procedures, which are referred to in the laboratory as screening methods, it is possible to obtain an evaluation function and then evaluate the candidate solutions in a genetic algorithm.