FANGZHOU XIAO was born in 1993 in Puyang, Henan, and moved with family to Shanghai during middle school. During highschool (2008-2011 at No. 2 High School Attached to East China Normal University), he was exposed to scientific research in biology, especially microbiology, plant science, and analytical chemistry. This made him fascinated with the power of biological organisms, and wondered maybe one day we can design and manufacture biological machines just like cars and computers. This led him to the emerging field of synthetic biology during his undergrad studies towards a Bachelor’s degree in bioengineering and mathematics with highest distinction at Washington University in St Louis (2012-2016). Exploring the possible paths to make biomachine design a reality, he did research projects in genetic engineering, computational biology, machine learning, biophysics, and biomathematics. This led him to believe that for synthetic biology to become a mature engineering field that design and manufacture biomachines, a major obstacle lacking adequate attention is the need for a reliable theoretical foundation, like Newton’s laws and Lagrangian mechanics for mechanical machines. A particular question perplexing him was how both robust reliability and perfect precision in biological systems can arise out of random and noisy interactions of molecules. “The answer is feedback!” he was told during PhD interviews. So he was convinced that control theory is a core piece of the puzzle for biomachine design, and in 2016 went on to study for a PhD in bioengineering mentored by John C Doyle, “a living legend in the field of robust control.” He graduated in summer 2022, with a thesis laying a foundational theory for analysis and design of biomachines, from metabolism to bioregulatory circuits in cells. He went on to a postdoc at Suckjoon Jun lab at University of California, San Diego, to learn about bacterial physiology and how the theoretical foundation can extend to microbial growth and survival. In Jan 2024, he took on the unique opportunity to start the BioMachine Architecture and Control (BMAC) lab at Westlake University as an assistant professor to incubate technological infrastructure enabling a bio-industrial revolution. The short term goal is to (1) make the theoretical foundation reach industrial maturity with computer-aided design (CAD) tools that facilitate design-test-build cycles in metabolic engineering; (2) widely apply the theoretical foundation to demonstrate its power, including metabolic engineering and systems biology; and (3) grow it to capture cellular physiology, microbial populations and communities, and multicellular sensing and communication.

BioMachine Architecture and Control (BMAC) lab aspires to push for the mature engineering of complex biomachines that fully realizes the unique potential of biotechnology, namely biomachines that adapt, survive, grow, and dominate.

BMAC lab aims to prepare the bio-industry for an industrial revolution of its own, not as a sub-industry or a technique in chemistry, medicine, materials, or health care, but as a mature industry with its own unique capabilities that enable other industries. In order to do so, design and manufacture processes of biomachines need to be scaled up so that they become routine, rather than risky, exploratory, and requiring experienced craftsmanship. Looking at other mature engineering disciplines, this scaling up requires not only theoretical understanding and reliable manufacturing of biological parts and components, but also a systems theory to understand how to put parts together and what can and cannot be achieved. Examples of systems theory from other engineering disciplines are Turing machines for computers, information channel for communication networks, linear input output systems for electrical circuits, and thermodynamics for heat engines.

Believing that the bioindustry urgently needs a systems theory of its own, BMAC lab has developed a foundational systems theory with two cornerstone results, combining mathematics, control theory, and bioengineering. One cornerstone is reaction order polyhedra (ROP), a holistic method to analyze bioregulation that fully generalizes the century-old Michaelis-Menten formula. ROP can reveal all possible behaviors of a biocircuit, therefore can understand why a circuit fail, discover necessary and sufficient conditions to achieve a function, and analyze complex bioregulations in combinatorial or highly dynamic scenarios. The other cornerstone is flux exponent control (FEC), a constraint-based method to model large-scale dynamic metabolism overcoming steady-state assumptions in flux balance analysis. Employing a radically new approach to model dynamic metabolism as optimal control problems, FEC can capture fast metabolism dynamics on the timescale of seconds to hours that are previously inaccessible. FEC can predict dynamic features emerging directly from metabolic network stoichiometry, e.g. glycolytic oscillations and cell growth arrest from shocks.

BMAC lab's next step is to scale-up this systems theory foundation by developing large-scale computations and applying them to problems in metabolic engineering, microbial physiology and communities, and systems biology. The goal is to speed up the design-test-build cycle and obtain control principles and design laws, so that this systems theory can reach the maturity of industrial production. This requires efforts combining several fronts, from mathematical foundation and computational analysis and design tools, to experimental and theoretical studies of specific biological contexts such as combinatorial regulation in eukaryotic sensing and cell fate decisions, cell survival and growth in rapidly fluctuating environments, and species invasion in microbial communities.

1. F. Xiao, “Biocontrol of Biomolecular Systems: Polyhedral Constraints on Binding's Regulation of Catalysis from Biocircuits to Metabolism”, PhD Thesis, Caltech, 2022

2. F. Xiao, J. Li, and J. C. Doyle, “Flux exponent control predicts microbial metabolism dynamics from network structure”, in 2023 American Control Conference (ACC), 2023

3. F. Xiao, M. Khammash, and J. C. Doyle, “Stability and control of biomolecular circuits through structure”, in 2021 Annual American Control Conference (ACC)

★ Best student paper finalist, 5 out of 800+ papers.

4. V. Galstyan, K. Husain, F. Xiao, A. Murugan, and R. Phillips, “Proofreading through spatial gradients”, eLife, 2020.

5. J. P. Marken*, F. Xiao*, and R. M. Murray, “A geometric and structural approach to the analysis and design of biological circuit dynamics: A theory tailored for synthetic biology”, bioRxiv, 2020.

6. N. Olsman, A.-A. Baetica, F. Xiao, Y. P. Leong, R. M. Murray, and J. C. Doyle, “Hard limits and performance tradeoffs in a class of antithetic integral feedback networks”, Cell Systems, 2019.

7. N. Olsman, F. Xiao, and J. C. Doyle, “Architectural principles for characterizing the performance of antithetic integral feedback networks”, iScience, 2019.

8. F. Xiao, M. Fang, J. Yan, and J. C. Doyle, “Coupled reaction networks for noise suppression”, in 2019 American Control Conference (ACC), 2019, pp. 1547–1554.

9. F. Xiao and J. Doyle, “Robust perfect adaptation in biomolecular reaction networks”, in IEEE Conference on Decision and Control (CDC), 2018, pp. 4345–4352.