The Oliveira Lab investigates the emergence of microbial community complexity and their underlying interactions in varying environmental contexts and studies microbial community design principles and metrics to help build novel collective behaviors. We hope that breakthroughs in computational and synthetic biology methods will accelerate our knowledge of the links between genetic sequences and intercellular communication to study and engineer the spatiotemporal behavior of biological networks.

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One important goal is to develop innovative ways to shift microbiomes' metabolic state after interacting with desired external stimuli, e.g., heavy metals. Nevertheless, the increasing demand for testing and combining multiple cell types, mechanisms, topologies, and conditions requires the development of novel experimental methods.

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Our work explores novel ways to probe these interactions using massively parallel microscopy, microfluidic screening techniques, and control theory principles. These techniques use genetic engineering advancements, single-cell microscopy data and machine learning, dynamical modeling, and design optimization.