# Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria
## 5 Discussion
Whether in a consortium or in a biofilm, cell-to-cell communication between bacteria represents today a subject of study of great interest. Nowadays, it is thought that cell-to-cell communication based on molecular-level mechanisms is the origin of the social intelligence found in bacteria. In addition to quorum sensing, other forms of communication such as chemotactic signaling or plasmid exchange [31] explain how bacteria self-organize in highly structured communities. These bacterial communities are a form of organization that allows microorganisms to acquire an identity, the recognition of other colonies, etc. Our work includes an evolutionary algorithm that we named BAGA and that is based on the communication mechanism between bacteria known as quorum sensing. In the future, the design of evolutionary algorithms with other forms of communication between bacteria different from quorum sensing will result in simulations with which it will be possible to study in silico relevant issues, for instance, the development of biofilms, the design of cellular patterns with practical purposes [32], etc. Many of the ideas described in this paper will be in the future the basis for the use of microbial communities in biotechnology. Today, synthetic biology under the influence of ideas coming from the fields of engineering and computer science [33; 34; 35; 36] has developed the tools to enable the implementation in a real laboratory--the wet lab--of many proposals modeled and simulated in silico, as is the case of this work. These bacteria will be responsible for executing in vivo sophisticated algorithms designed to solve crucial problems such as the biodegradation of environmental pollutants [37], the design of probiotics [38], or even the design of microbial agents capable of inducing the regression of a cancerous tumor [39]. The present work was limited to the design of a meta-model and its in silico simulation. However, the possibility that in the future, other research groups will adopt our meta-model for its implementation in real bacteria in the wet lab is not remote. Living microorganisms such as bacteria used in synthetic biology are fully functional with biological sensing, production, and containment devices. Bacteria include sensors, regulators, memory circuits, and switch-off triggers [36]. That is, bacteria have many advantages by interacting directly with the human body: they are programmable to perform specific tasks, respond to environmental stimuli, and are more effective in the treatment of complex and multifactorial diseases [40]. For example, there are already real synthetic bacteria capable of breaking down the excess phenylalanine responsible for phenylketonuria [1], an inherited disease that over time can lead to serious health problems. In [1], other examples are also described of treatments known as bacotherapy, i.e., treatments based on the administration to the patient of synthetic bacteria like those we studied in silico in this work. At present, and in a similar way to is happening with generative artificial intelligence, synthetic biology has opened an ethical debate about genetic modifications and the use of synthetic organisms for therapeutic purposes. It is now urgent to study at a social and philosophical level the potential risks of the design and creation of new life forms, evaluating their possible risks and benefits [41]. One of these potential risks could be the possibility of synthetic bacteria escaping human control and becoming integrated in nature, competing with and even eliminating their wild counterparts. Indeed, as synthetic bacteria share physiological principles of non-synthetic bacteria, a colony of synthetic bacteria could increase in size according to the exponential or Malthusian model. That is, in certain environments and due to their greater adaptability or fitness [42], the Malthusian parameter or growth rate would increase, growing exponentially the number of synthetic bacteria.