About

Dr. Huimin Zhao is the Steven L. Miller Chair of chemical and biomolecular engineering, and a professor of chemistry, biochemistry, biophysics, and bioengineering at the University of Illinois at Urbana-Champaign (UIUC). He received his B.S. degree in Biology from the University of Science and Technology of China in 1992 and his Ph.D. in Chemistry from the California Institute of Technology in 1998 under the guidance of Nobel Laureate Dr. Frances Arnold. Prior to joining UIUC in 2000, he was a project leader at the Industrial Biotechnology Laboratory of the Dow Chemical Company, and he was promoted to full professor in 2008. Dr. Zhao has authored and co-authored over 480 research articles and over 30 issued and pending patent applications, with several licensed by industry. His research focuses on developing and applying synthetic biology, machine learning, and laboratory automation tools to address challenges in human health and energy, as well as investigating fundamental aspects of enzyme catalysis, cell metabolism, and gene regulation. He is also the director of NSF AI Research Institute for Molecule Synthesis, NSF iBioFoundry, and NSF Global Center for Biofoundry Applications.

Research topics

• Computer Science
• Biology
• Genetics
• Computational biology
• Artificial Intelligence
• Cell biology
• Machine Learning
• Chemistry
• Organic chemistry
• Anatomy
• Materials science
• Combinatorial chemistry
• Stereochemistry
• Molecular biology
• Biochemistry
• Engineering
• Biochemical engineering

Selected publications

• The sound of silence: Transgene silencing in mammalian cell engineering

  Cell Systems · 2022 · 189 citations

  • Biology
  • Cell biology
  • Computational biology
  DOI

• Promoter-proximal CTCF binding promotes distal enhancer-dependent gene activation

  Nature Structural & Molecular Biology · 2021 · 295 citations

  • Biology
  • Cell biology
  • Molecular biology
  DOI

• Directed Evolution: Methodologies and Applications

  Chemical Reviews · 2021 · 678 citations

  Senior authorCorresponding
  • Computer Science
  • Computational biology
  • Artificial Intelligence

  Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.

  DOI

• TALEN outperforms Cas9 in editing heterochromatin target sites

  Nature Communications · 2021 · 96 citations

  Senior authorCorresponding
  • Computer Science
  • Computational biology
  • Biology

  Genome editing critically relies on selective recognition of target sites. However, despite recent progress, the underlying search mechanism of genome-editing proteins is not fully understood in the context of cellular chromatin environments. Here, we use single-molecule imaging in live cells to directly study the behavior of CRISPR/Cas9 and TALEN. Our single-molecule imaging of genome-editing proteins reveals that Cas9 is less efficient in heterochromatin than TALEN because Cas9 becomes encumbered by local searches on non-specific sites in these regions. We find up to a fivefold increase in editing efficiency for TALEN compared to Cas9 in heterochromatin regions. Overall, our results show that Cas9 and TALEN use a combination of 3-D and local searches to identify target sites, and the nanoscopic granularity of local search determines the editing outcomes of the genome-editing proteins. Taken together, our results suggest that TALEN is a more efficient gene-editing tool than Cas9 for applications in heterochromatin.

  PublisherOA PDFDOI

• ARHGEF3 Regulates Skeletal Muscle Regeneration and Strength through Autophagy

  Cell Reports · 2021 · 37 citations

  • Cell biology
  • Biology
  • Chemistry

  Skeletal muscle regeneration after injury is essential for maintaining muscle function throughout aging. ARHGEF3, a RhoA/B-specific GEF, negatively regulates myoblast differentiation through Akt signaling independently of its GEF activity in vitro. Here, we report ARHGEF3's role in skeletal muscle regeneration revealed by ARHGEF3-KO mice. These mice exhibit indiscernible phenotype under basal conditions. Upon acute injury, however, ARHGEF3 deficiency enhances the mass/fiber size and function of regenerating muscles in both young and regeneration-defective middle-aged mice. Surprisingly, these effects occur independently of Akt but via the GEF activity of ARHGEF3. Consistently, overexpression of ARHGEF3 inhibits muscle regeneration in a Rho-associated kinase-dependent manner. We further show that ARHGEF3 KO promotes muscle regeneration through activation of autophagy, a process that is also critical for maintaining muscle strength. Accordingly, ARHGEF3 depletion in old mice prevents muscle weakness by restoring autophagy. Taken together, our findings identify a link between ARHGEF3 and autophagy-related muscle pathophysiology.

  PublisherOA PDFDOI

• Biosystems Design by Machine Learning

  ACS Synthetic Biology · 2020 · 134 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

  DOI

• DNA punch cards for storing data on native DNA sequences via enzymatic nicking

  Nature Communications · 2020 · 117 citations

  • Computer Science
  • Computer Science
  • Computational biology

  Synthetic DNA-based data storage systems have received significant attention due to the promise of ultrahigh storage density and long-term stability. However, all known platforms suffer from high cost, read-write latency and error-rates that render them noncompetitive with modern storage devices. One means to avoid the above problems is using readily available native DNA. As the sequence content of native DNA is fixed, one can modify the topology instead to encode information. Here, we introduce DNA punch cards, a macromolecular storage mechanism in which data is written in the form of nicks at predetermined positions on the backbone of native double-stranded DNA. The platform accommodates parallel nicking on orthogonal DNA fragments and enzymatic toehold creation that enables single-bit random-access and in-memory computations. We use Pyrococcus furiosus Argonaute to punch files into the PCR products of Escherichia coli genomic DNA and accurately reconstruct the encoded data through high-throughput sequencing and read alignment.

  DOI

• Photoenzymatic enantioselective intermolecular radical hydroalkylation

  Nature · 2020 · 348 citations

  Senior authorCorresponding
  • Chemistry
  • Combinatorial chemistry
  • Stereochemistry
  DOI

Recent grants

• A Next-Generation Scalable Platform to Discover Antimicrobials of Ribosomal Origin

  NIH · $5.3M · 2019–2029

• CAREER: Bimolecular Engineering via Directed Evolution

  NSF · $400k · 2004–2009

• Molecule Maker Lab Institute (MMLI): An AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing

  NSF · $19.8M · 2020–2026

• NIH Grant R21HL089418

  NIH · $411k · 2011

• NIH Grant P01GM077596

  NIH · $31.1M · 2018

Frequent coauthors

• Ee Lui Ang

  Agency for Science, Technology and Research

  175 shared

• Yifeng Wei

  Agency for Science, Technology and Research

  89 shared

• Yan Zhang

  Chinese Academy of Medical Sciences & Peking Union Medical College

  51 shared

• Jiazhang Lian

  Zhejiang University

  47 shared

• Yee Hwee Lim

  Agency for Science, Technology and Research

  41 shared

• Tong Si

  Shenzhen Institutes of Advanced Technology

  36 shared

• Shuobo Shi

  Beijing University of Chemical Technology

  34 shared

• Mingfeng Cao

  University of Illinois Urbana-Champaign

  28 shared

Labs

• Zhao GroupPI

Education

• Ph.D., Chemistry

  California Institute of Technology

  1998

Awards & honors

• SIMB Charles Scott Award
• AIChE Daniel I.C. Wang Award
• AIChE FP&B Division Award
• ECI Enzyme Engineering Award
• ACS Marvin Johnson Award

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