(4ho) Automating Systems Engineering of Smart and Eco-friendly Synthetic Microbes

Synthetic microbes hold the promise to revolutionize medicine, agriculture, and environmental science with the potential to control and regulate microbiome ecosystems from within the community. However, the application of this technology in real life is currently under scrutiny because of its potential ecological footprints. As a solution, the synthetic microbes need to be both smart and eco-friendly. A smart synthetic microbe should produce system output molecules only when and where it needs to; to be eco-friendly, it needs to have minimal long-lasting impacts on its acting environments. This described synthetic microbe is a dual-scale feedback-controlled dynamical system, which is a challenge for systems design. My long-term goal is to fulfill the promise of synthetic microbes in various real-life applications to improve human life qualities. My research plan is designed to achieve two overall objectives: (i) Solve the systems design problem for smart and eco-friendly microbes; (ii) create an automation tool to aid future systems engineering in synthetic biology. Throughout my plan, I will take a data-driven modeling and model-driven implementation approach to achieve my objectives. Specifically, I will pursue the following aims:

1. Machine learning aided, data-driven development of effective multiscale dynamical models.
2. Development of new system identification methods with data type layering and configuration updates.
3. Engineering a smart and eco-friendly synthetic cross-feeder with a model-driven approach.

Upon completing my proposed work, I will create a computational-experimental platform for synthetic microbe engineering. Future synthetic microbial engineering works will have tools to address the potential ecological impacts of synthetic microbes on their working environments based on this platform.

Training Background:

My Ph.D. studies were conducted under the advisory of Prof. Julius Lucks and Prof. Jeffrey Varner at Cornell University, focusing on dynamical model developments and experimental implementation of regulatory sRNA-based synthetic circuits. My postdoctoral work in the Murray lab at Caltech tackles the dynamical control strategies of synthetic microbes. In my latest published work, I implemented layered control in the synthetic biological context of living cells and validated that layered control overcomes the robustness-efficiency performance trade-off in this context.

My training in chemical engineering, dynamical modeling, experimental synthetic biology, and control theory has uniquely prepared me to lead an interdisciplinary group to study the complex multiscale dynamics of synthetic microbes. As a modeler, I understand how synthetic networks behave as a dynamical system and how to design the systems to achieve robust performance; as an experimentalist, I am experienced in designing relevant experiments for model training and constructing synthetic circuits to realize the model guided designs. My hybrid background results from the integration of my three advisors’ expertise in synthetic biology, systems biology, and control theory. This interdisciplinary training path founded my quantitative and systems approach in synthetic biology and my experimental data-driven approach in systems modeling.

Teaching Interests:

My teaching experience includes serving as an Adjunct Assistant Professor of Biology at Harvey Mudd College and as an iGEM instructor at Northwestern University. From these two teaching roles, I’ve gained teaching experiences in interdisciplinary learning for more advanced students and various active learning strategies for beginner classes. I am prepared to teach synthetic biology, systems biology, and various chemical engineering core classes at the introductory, intermediate, and advanced levels, including transport, dynamical system and control, and numerical methods. I would welcome the opportunity to develop new courses according to departmental needs, especially on control theory in synthetic biology.