P20_00765 - Aprovechar la evolución mediante la inge

Our society faces huge biomedical challenges such as cancer, antibiotic resistance, and unbalance microbiomes. We all know evolution is the problem, but only a few scientists think ecology might be the solution (e.g., the human microbiome is a vast ecosystem— akin to a rainforest or a grassland—and to control it, we must understand what its mem- bers do and how they compete and cooperate with each other). Yet, the coupling between evolution and ecology through the interdisciplinary framework of complex networks has not been implemented into medicine yet. We urgently need to develop a general approach to anticipate and control the evolution of asexual populations of tumor cells, pathogens and entire microbial communities. Building on my previous research, I propose to com- bine mathematical models with digital evolution (i.e., an evolving computational sys- tem characterized by patterns of organization akin to those of natural systems) to find general principles that can help researchers working in the lab to fight against hu- man diseases. Specifically, I will first design and run evolutionary experiments using self-replicating computer programs—digital organisms—that interact, mutate and evolve within a user defined computational environment. Second, I will develop mathematical models to formalize hypothesis on the role of interactions among organisms in shaping the evolutionary trajectories of populations. Lastly, I will test the developed theory with data obtained from the experiments. The idea of controlling evolution to fight disease could eventually be translated from the computer to the lab. This added value will reinforce the potential for applied research, and will be highly relevant for our regional industrial tissue. Certainly, this proposal is a unique opportunity for an interdisciplinary profile to make a shift from basic research in ecology and evolutionary biology to applied research in biomedicine.