Engineering Molecules, Mineralization and Magnetism in Biology by Directed Evolution and Computation

Abstract

The intersection of synthetic biology with physics and computer science generates rich opportunities for both advancing our understanding of biological entities and systems as well as engineering biology to address a variety of scientific or societal challenges. In this thesis, I describe my efforts, along with my colleagues across disciplines, to explore the boundaries of synthetic biology with physics and computer science to engineer molecules, materials, and magnetism in the biological context. In Chapter 2, I will demonstrate applying structural insights of a key metabolic protein in microbes toward targeted mutagenesis, enabling tailoring of the production of microbial biofuel molecules applicable as renewable energy supplies. In Chapter 3, I will discuss the application of directed evolution toward engineering inorganic nanomaterial synthesis, as well as their applications with cells. Specifically I will show that using the ubiquitous and important iron storage enzyme ferritin as template, random mutagenesis and magnetic selection could lead to more efficient iron sequestration and magnetic phenotype for cells with potential applications in noninvasive magnetic imaging and manipulation in biology. The directed evolution approach here is suitable due to lack of predictive understanding between properties of the protein complex such as its molecular composition and structure, with the properties of the functional, inorganic nanoparticle products. Lastly in Chapter 4, I will introduce a computational approach toward engineering with limited prior knowledge by employing deep, artificial neural networks to learn directly from abundant protein sequence data, making accurate property predictions on new, unannotated proteins that can be validated by experiment. In the future, combining tools and ideas across disciplines as well as the core strategies of rational design, directed evolution, and computational prediction could accelerate our progress in engineering and understanding of biology.