About

Bo Liu is a professor in the Department of Plant Biology at UC Davis. His research focuses on cell biology, specifically advancing knowledge of the cytoskeleton and intracellular motility in plant and fungal cells. His ongoing investigations include studying the dynamics of microtubules and actin microfilaments during plant cell division and growth, as well as exploring the functions of kinesin motor proteins in mitosis and cytokinesis. Additionally, he researches the molecular mechanisms of cytoskeleton-mediated hyphal growth in filamentous fungi. His experimental work involves organisms such as Arabidopsis thaliana, Oryza sativa (rice), Gossypium hirsutum (cotton), and Aspergillus nidulans, which serve as models for plant and fungal studies. Dr. Liu holds a B.S. in Cell Biology and Genetics and an M.S. in Cell Biology from Peking University, and a Ph.D. in Botany from the University of Georgia. His contributions aim to deepen understanding of cell structure and function, with potential implications for cancer therapies and crop improvement.

Research topics

• Computer Science
• Artificial Intelligence
• Biology
• Chemistry
• Biochemistry
• Cell biology
• Mathematics
• Operations research
• Chemical engineering
• Discrete mathematics
• Mathematical analysis
• Organic chemistry
• Programming language
• Mathematical optimization
• Algorithm
• Composite material
• Polymer chemistry
• Applied mathematics
• Materials science
• Computational biology

Selected publications

• LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

  arXiv (Cornell University) · 2023 · 84 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Large language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LLMs cannot reliably solve long-horizon planning problems. By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal, plans. In an effort to get the best of both worlds, this paper introduces LLM+P, the first framework that incorporates the strengths of classical planners into LLMs. LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language. LLM+P does so by first converting the language description into a file written in the planning domain definition language (PDDL), then leveraging classical planners to quickly find a solution, and then translating the found solution back into natural language. Along with LLM+P, we define a diverse set of different benchmark problems taken from common planning scenarios. Via a comprehensive set of experiments on these benchmark problems, we find that LLM+P is able to provide optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems.\footnote{The code and results are publicly available at https://github.com/Cranial-XIX/llm-pddl.git.

  DOI

• Oxygenated phosphatidylethanolamine navigates phagocytosis of ferroptotic cells by interacting with TLR2

  Cell Death and Differentiation · 2021 · 240 citations

  • Cell biology
  • Chemistry
  • Biology

  . Ligand fishing, lipid blotting, and cellular thermal shift assay screened and identified TLR2 as a membrane receptor that directly recognized SAPE-OOH, which was further confirmed by TLR2 inhibitors and gene silencing studies. A mouse mammary tumor model of ferroptosis verified SAPE-OOH and TLR2 as critical players in the clearance of ferroptotic cells in vivo. Taken together, this work demonstrates that SAPE-OOH on ferroptotic cell surface acts as an eat-me signal and navigates phagocytosis by targeting TLR2 on macrophages.

  DOI

• Multiple H‐Bonding Chain Extender‐Based Ultrastiff Thermoplastic Polyurethanes with Autonomous Self‐Healability, Solvent‐Free Adhesiveness, and AIE Fluorescence

  Advanced Functional Materials · 2020 · 317 citations

  • Materials science
  • Polymer chemistry
  • Composite material

  Abstract Developing an autonomous room temperature self‐healing supramolecular polyurethane (PU) with toughness and stiffness remains a great challenge. Herein, a novel concept that utilizes a T‐shaped chain extender with double amide hydrogen bonds in a side chain to extend PU prepolymers to construct highly stiff and tough supramolecular PU with integrated functions is reported. Mobile side‐chain H‐bonds afford a large flexibility to modulate the stiffness of the PUs ranging from highly stiff and tough elastomer (105.87 MPa Young's modulus, 27 kJ m −2 tearing energy), to solvent‐free hot‐melt adhesive, and coating. The dynamic side‐chain multiple H‐bonds afford an autonomous self‐healability at room temperature (25 ° C). Due to the rapid reconstruction of hydrogen bonds, this PU adhesive demonstrates a high adhesion strength, fast curing, reusability, long‐term adhesion, and excellent low‐temperature resistance. Intriguingly, the PU emits intrinsic blue fluorescence presumably owing to the aggregation‐induced emission of tertiary amine domains induced by side‐chain H‐bonds. The PU is explored as a counterfeit ink coated on the predesigned pattern, which is visible‐light invisible and UV‐light visible. This work represents a universal and facile approach to fabricate supertough supramolecular PU with tailorable functions by chain extension of PU prepolymers with multiple H‐bonding chain extenders.

  DOI

• Mitochondrial Sirtuin 3: New emerging biological function and therapeutic target

  Theranostics · 2020 · 463 citations

  Senior authorCorresponding
  • Cell biology
  • Computational biology
  • Chemistry

  Sirtuin 3 (SIRT3) is one of the most prominent deacetylases that can regulate acetylation levels in mitochondria, which are essential for eukaryotic life and inextricably linked to the metabolism of multiple organs. Hitherto, SIRT3 has been substantiated to be involved in almost all aspects of mitochondrial metabolism and homeostasis, protecting mitochondria from a variety of damage. Accumulating evidence has recently documented that SIRT3 is associated with many types of human diseases, including age-related diseases, cancer, heart disease and metabolic diseases, indicating that SIRT3 can be a potential therapeutic target. Here we focus on summarizing the intricate mechanisms of SIRT3 in human diseases, and recent notable advances in the field of small-molecule activators or inhibitors targeting SIRT3 as well as their potential therapeutic applications for future drug discovery.

  DOI

• GradientDICE: Rethinking Generalized Offline Estimation of Stationary\n Values

  arXiv (Cornell University) · 2020 · 35 citations

  • Computer Science
  • Mathematics
  • Mathematical optimization

  We present GradientDICE for estimating the density ratio between the state\ndistribution of the target policy and the sampling distribution in off-policy\nreinforcement learning. GradientDICE fixes several problems of GenDICE (Zhang\net al., 2020), the state-of-the-art for estimating such density ratios. Namely,\nthe optimization problem in GenDICE is not a convex-concave saddle-point\nproblem once nonlinearity in optimization variable parameterization is\nintroduced to ensure positivity, so any primal-dual algorithm is not guaranteed\nto converge or find the desired solution. However, such nonlinearity is\nessential to ensure the consistency of GenDICE even with a tabular\nrepresentation. This is a fundamental contradiction, resulting from GenDICE's\noriginal formulation of the optimization problem. In GradientDICE, we optimize\na different objective from GenDICE by using the Perron-Frobenius theorem and\neliminating GenDICE's use of divergence. Consequently, nonlinearity in\nparameterization is not necessary for GradientDICE, which is provably\nconvergent under linear function approximation.\n

  DOI

Recent grants

• Molecular Mechanisms in Abdominal Aortic Aneuysm

  NIH · $3.2M · 2010–2022

• Role of RIP3-laden extracellular vesicles in thrombosis and aortic aneurysm

  NIH · $2.0M · 2020–2026

• Vascular smooth muscle cell apoptosis in intimal hyperplasia

  NIH · $1.2M · 2015–2019

Frequent coauthors

• Zhitang Song

  623 shared

• Songlin Feng

  Shanghai Advanced Research Institute

  434 shared

• Yaya Mao

  Institute of Optics and Electronics, Chinese Academy of Sciences

  377 shared

• Liangcai Wu

  325 shared

• Xiangjun Xin

  Beijing University of Posts and Telecommunications

  301 shared

• Jianxin Ren

  296 shared

• Wei Lin

  295 shared

• Sannian Song

  Shanghai Institute of Microsystem and Information Technology

  290 shared

Awards & honors

• Annual Award and Citation Ceremony
• Storer Lectureship in the Life Sciences

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