Our research centers on exploring how evolutionary biology may help us make sense, predict, and engineer microbial communities. To this end, we employ a combination of laboratory experiments, computer simulations, and mathematical modeling. Our laboratory is highly interdisciplinary and quantitative in nature, and our students and postdocs typically combine theory and experiment in their projects, acquiring a broad range of technical skills across the wet-lab / dry-lab divide. They also get exposed to a broad scientific intellectual background in ecology, evolution and systems biology. Our current lines of work are described below.

1) Engineering microbial consortia from the top-down. For millennia, selective breeding has allowed us to artificially select crops and domestic animals. In more recent times, the idea of directed evolution has been extended to engineering biological systems at or below the organismal level: from enzymes and RNA molecules to genetic circuits, metabolic networks, and microbial strains. Can these approaches be also extended to engineering biological systems above the organismal level of organization, such as microbial consortia? Our laboratory is exploring how one may do this successfully (Chang et al 2021, Nature Ecol Evol, Sanchez et al 2021 Ann Rev Biophys, Chang et al 2020 Evolution). We seek to develop empirical methods, grounded on ecological and evolutionary theory, that may be then adapted by applied biotechnologists in their particular settings. To fund the work in this research line, we have been awarded a project from the Spanish Agencia Estatal de Investigacion, MICROBREED: Extending evolutionary engineering to microbial consortia.

2) Predictively linking microbial community composition and community function. Microbial communities provide us with countless ecological services that are essential for the health of the biosphere and to preserve life as we know it. They also carry out a vast and growing number of important functions in biotechnology, from food production to the synthesis of biofuels and other economically critical molecules. These community functions are determined by community composition, i.e. by which genotypes are found in each community and their abundance. If we wish to engineer microbial consortia to optimize the functions they provide, it is key that we predictively and quantitatively link community composition and function. This is a hard problem, that cannot be solved empirically due to the astronomical number of potential consortia one may form even with a modest number of potential candidate genotypes. Microscopic models are exceedingly difficult to build, due to the complex web of interactions that are involved. Our laboratory is exploring whether ideas from the theory of fitness landscapes in genetics might help us solve this problem and produce predictive and quantitative models of community function from its composition. (Sanchez-Gorostiaga et al 2019 PLoS Biol, Lino et al 2021 Nature Comm, Sanchez 2019 Cell Systems, Sanchez et al 2023 Cell Systems, Diaz-Colunga et al 2023 Phil Trans Roy Soc). To fund the work in this research line, we have been awarded an ERC Consolidator Grant.