The Center for Quantitative Biology at Peking University

OVERVIEWIn the 21st century, scientific research is becoming more and more interdisciplinary. New discoveries are often made at the boundaries of different disciplines. Biology is entering a new era in which quantitative measurements and mathematical modeling play increasingly important roles in understanding and predicting biological behavior. Facing this challenge and opportunity, the Center for Quantitative Biology （CQB, formally the Center for Theoretical Biology） was established in 2001, with the support of the Nobel Laureate Prof. T. D. Lee and the leadership of Peking University. As part of PKU＇s strategic initiative in enhancing interdisciplinary research, CQB is dedicated to research and education at the interface between the traditional more quantitative disciplines （such as mathematics, physical sciences, engineering, computer science） and the biological sciences. CQB has a proficient teaching and research team, comprising of prominent members with outstanding achievements in various fields of physics, chemistry, life sciences, biotechnology and mathematics. Up to 2015, CQB has 16 principal investigators, including one member of the Chinese Academy of Sciences, one recipient of the ＂Thousand Talent Plan＂, two ＂Cheung Kong Scholar＂ Chair Professors, three ＂Cheung Kong Scholar＂ Visiting Professors and three recipients of the ＂Thousand Young Talent Plan＂. More than 50 graduate students and 4 postdoctoral fellows are currently enrolled in CQB.

Tuning the diffusion constant to optimize the readout of positional information of spatial concentration patterns

Positional information encoded in spatial concentration patterns is crucial for the development of multicellular organisms.However,it is still unclear how such information is affected by the physically dissipative diffusion process.Here we study one-dimensional patterning systems with analytical derivation and numerical simulations.We find that the diffusion constant of the patterning molecules exhibits a nonmonotonic effect on the readout of the positional information from the concentration patterns.Specifically,there exists an optimal diffusion constant that maximizes the positional information.Moreover,we find that the energy dissipation due to the physical diffusion imposes a fundamental upper limit on the positional information.

In situ injectable hydrogel encapsulating Mn/NO-based immune nano-activator for prevention of postoperative tumor recurrence

Postoperative tumor recurrence remains a predominant cause of treatment failure. In this study, we developed an in situ injectable hydrogel, termed MPB-NO@DOX + ATRA gel, which was locally formed within the tumor resection cavity. The MPB-NO@DOX + ATRA gel was fabricated by mixing a thrombin solution, a fibrinogen solution containing all-trans retinoic acid (ATRA), and a Mn/NO-based immune nano-activator termed MPB-NO@DOX. ATRA promoted the differentiation of cancer stem cells, inhibited cancer cell migration, and affected the polarization of tumor-associated macrophages. The outer MnO2 shell disintegrated due to its reaction with glutathione and hydrogen peroxide in the cytoplasm to release Mn2+ and produce O2, resulting in the release of doxorubicin (DOX). The released DOX entered the nucleus and destroyed DNA, and the fragmented DNA cooperated with Mn2+ to activate the cGAS-STING pathway and stimulate an anti-tumor immune response. In addition, when MPB-NO@DOX was exposed to 808 nm laser irradiation, the Fe-NO bond was broken to release NO, which downregulated the expression of PD-L1 on the surface of tumor cells and reversed the immunosuppressive tumor microenvironment. In conclusion, the MPB-NO@DOX + ATRA gel exhibited excellent anti-tumor efficacy. The results of this study demonstrated the great potential of in situ injectable hydrogels in preventing postoperative tumor recurrence.

The determinant factors for map resolutions obtained using CryoEM single particle imaging method

The CryoEM single particle structure determination method has recently received broad attention in the field of structural biology. The structures can be resolved to near-atomic resolutions after model reconstructions from a large number of CryoEM images measuring molecules in different orientations. However, the determining factors for reconstructed map resolution need to be further explored. Here, we provide a theoretical framework in conjunction with numerical simulations to gauge the influence of several key factors to CryoEM map resolutions. If the projection image quality allows orientation assignment, then the number of measured projection images and the quality of each measurement(quantified using average signal-to-noise ratio) can be combined to a single factor, which is dominant to the resolution of reconstructed maps. Furthermore, the intrinsic thermal motion of molecules has significant effects on the resolution. These effects can be quantitatively summarized with an analytical formula that provides a theoretical guideline on structure resolutions for given experimental measurements.

A flexible,modular and versatile functional part assembly toolkit for gene cluster engineering in Streptomyces

Streptomyces has enormous potential to produce novel natural products(NPs)as it harbors a huge reservoir of uncharacterized and silent natural product biosynthetic gene clusters(BGCs).However,the lack of efficient gene cluster engineering strategies has hampered the pace of new drug discovery.Here,we developed an easy-to-use,highly flexible DNA assembly toolkit for gene cluster engineering.The DNA assembly toolkit is compatible with various DNA assembling approaches including Biobrick,Golden Gate,CATCH,yeast homologous recombination-based DNA assembly and homing endonuclease-mediated assembly.This compatibility offers great flexibility in handling multiple genetic parts or refactoring large gene clusters.To demonstrate the utility of this toolkit,we quantified a library of modular regulatory parts,and engineered a gene cluster(act)using characterized promoters that led to increased production.Overall,this work provides a powerful part assembly toolkit that can be used for natural product discovery and optimization in Streptomyces.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging,but the collection and use of its image raise significant privacy concerns.Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations.We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability.We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations,indicating that the relative positioning of facial information is a low-level biomarker of facial aging.Through visual perception tests and computational3D face verification experiments,we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines,except when only the face shape information is accessible.Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Dual-objective two-photon microscope for volumetric imaging of dense scattering biological samples by bidirectional excitation and collection

Full view observation throughout entire specimens over a prolonged period is crucial when exploring the physiological functions and system-level behaviors.Multi-photon microscopy(MPM)has been widely employed for such purposes owing to its deep penetration ability.However,the current MPM struggles with balancing the imaging depth and quality while avoiding photodamage for the exponential increasement of excitation power with the imaging depth.Here,we present a dual-objective two-photon microscope(Duo-2P),characterized by bidirectional two-photon excitation and fluorescence collection,for long-duration volumetric imaging of dense scattering samples.Duo-2P effectively doubles the imaging depth,reduces the total excitation energy by an order of magnitude for samples with a thickness five times the scattering length,and enhances the signal-to-noise ratio up to 1.4 times.Leveraging these advantages,we acquired volumetric images of a 380-μm suprachiasmatic nucleus slice for continuous 4-h recording at a rate of 1.67 s/volume,visualized the calcium activities over 4000 neurons,and uncovered their state-switching behavior.We conclude that Duo-2P provides an elegant and powerful means to overcome the fundamental depth limit while mitigating photodamages for deep tissue volumetric imaging.

AI for organic and polymer synthesis

Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the datadriven paradigm. Despite these thrilling developments, the field of artificial intelligence(AI) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between AI and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development.

Probabilistic Error Estimate for Numerical Discretization of High-Index Saddle Dynamics with Inaccurate Models

We prove probabilistic error estimates for high-index saddle dynamics with or without constraints to account for the inaccurate values of the model,which could be encountered in various scenarios such as model uncertainties or surrogate model algorithms via machine learning methods. The main contribution lies in incorporating the probabilistic error bound of the model values with the conventional error estimate methods for high-index saddle dynamics. The derived results generalize the error analysis of deterministic saddle dynamics and characterize the affect of the inaccuracy of the model on the convergence rate.

Computational design of proteins with novel structure and functions

Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence-structure-function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein-protein interactions. Challenges and future prospects of this field are also discussed.

Discretization and index-robust error analysis for constrained high-index saddle dynamics on the high-dimensional sphere

We develop and analyze numerical discretization to the constrained high-index saddle dynamics,the dynamics searching for the high-index saddle points confined on the high-dimensional unit sphere.Compared with the saddle dynamics without constraints,the constrained high-index saddle dynamics has more complex dynamical forms,and additional operations such as the retraction and vector transport are required due to the constraints,which significantly complicate the numerical scheme and the corresponding numerical analysis.Furthermore,as the existing numerical analysis results usually depend on the index of the saddle points implicitly,the proved numerical accuracy may be reduced if the index is high in many applications,which indicates the lack of robustness with respect to the index.To address these issues,we derive the error estimates for numerical discretization of the constrained high-index saddle dynamics on the high-dimensional sphere and then improve it by providing index-robust error analysis in an averaged norm by adjusting the relaxation parameters.The developed results provide mathematical support for the accuracy of numerical computations.

Condensation of STM is critical for shoot meristem maintenance and salt tolerance in Arabidopsis

The shoot meristem generates the entire shoot system and is precisely maintained throughout the life cycle under various environmental challenges.In this study,we identified a prion-like domain(PrD)in the key shoot meristem regulator SHOOT MERISTEMLESS(STM),which distinguishes STM from other related KNOX1 proteins.We demonstrated that PrD stimulates STM to form nuclear condensates,which are required for maintaining the shoot meristem.STM nuclear condensate formation is stabilized by selected PrD-containing STM-interacting BELL proteins in vitro and in vivo.Moreover,condensation of STM promotes its interaction with the Mediator complex subunit MED8 and thereby enhances its transcriptional activity.Thus,condensate formation emerges as a novel regulatory mechanism of shoot meristem functions.Furthermore,we found that the formation of STM condensates is enhanced upon salt stress,which allows enhanced salt tolerance and increased shoot branching.Our findings highlight that the transcription factor partitioning plays an important role in cell fate determination and might also act as a tunable environmental acclimation mechanism.

Extended Nernst–Planck Equation Incorporating Partial Dehydration Effect

Novel ionic transporting phenomena emerge as nanostructures approach the molecular scale.At the sub-2 nm scale,widely used continuum equations,such as the Nernst-Planck equation,break down.Here,we extend the Nernst-Planck equation by adding a partial dehydration effect.Our model agrees with the reported ion fluxes through graphene oxide laminates with sub-2 nm interlayer spacing,outperforming previous models.We also predict that the selectivity sequences of alkali metal ions depend on the geometries of the nanostructures.Our model opens a new avenue for the investigation of the underlying mechanisms in nanofluidics at the sub-2 nm scale.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Differences in action potential propagation speed and axon initial segment plasticity between neurons from Sprague-Dawley rats and C57BL/6 mice

Action potentials(APs)in neurons are generated at the axon initial segment(AIS).AP dynamics,including initiation and propagation,are intimately associated with neuronal excitability and neurotransmitter release kinetics.Most learning and memory studies at the single-neuron level have relied on the use of animal models,most notably rodents.Here,we studied AP initiation and propagation in cultured hippocampal neurons from Sprague-Dawley(SD)rats and C57BL/6(C57)mice with genetically encoded voltage indicator(GEVI)-based voltage imaging.Our data showed that APs traveled bidirectionally in neurons from both species;forward-propagating APs(fpAPs)had a different speed than backpropagating APs(bpAPs).Additionally,we observed distinct AP propagation characteristics in AISs emerging from the somatic envelope compared to those originating from dendrites.Compared with rat neurons,mouse neurons exhibited higher bpAP speed and lower fpAP speed,more distally located ankyrin G(AnkG)in AISs,and longer Nav1.2 lengths in AISs.Moreover,during AIS plasticity,AnkG and Nav1.2 showed distal shifts in location and shorter lengths of labeled AISs in rat neurons;in mouse neurons,however,they showed a longer AnkG-labeled length and more distal Nav1.2 location.Our findings suggest that hippocampal neurons in SD rats and C57 mice may have different AP propagation speeds,different AnkG and Nav1.2 patterns in the AIS,and different AIS plasticity properties,indicating that comparisons between these species must be carefully considered.

Specific pupylation as IDEntity reporter(SPIDER)for the identification of protein-biomolecule interactions

Protein-biomolecule interactions play pivotal roles in almost all biological processes.For a biomolecule of interest,the identification of the interacting protein(s)is essential.For this need,although many assays are available,highly robust and reliable methods are always desired.By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin(SA)-biotin system,we developed the Specific Pupylation as IDEntity Reporter(SPIDER)method for identifying protein-biomolecule interactions.Using SPIDER,we validated the interactions between the known binding proteins of protein,DNA,RNA,and small molecule.We successfully applied SPIDER to construct the global protein interactome for m^(6)A and m RNA,identified a variety of uncharacterized m^(6)A binding proteins,and validated SRSF7 as a potential m^(6)A reader.We globally identified the binding proteins for lenalidomide and Cob B.Moreover,we identified SARS-CoV-2-specific receptors on the cell membrane.Overall,SPIDER is powerful and highly accessible for the study of proteinbiomolecule interactions.

Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer

Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.

Vlicrofluidics and its applications in quantitative biology

INTRODUCTIONBiology research is entering a new era when quantitative measurements are needed to advance our knowledge of the biological systems to the next level where biological behaviors can be understood, predicted and even manipulated. Microfluidics,

CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development.In this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during C.elegans embryogenesis.Based on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house dataset.Experimental results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a~1.5-min interval and covers C.elegans embryogenesis from the 4-cell to 350-cell stages.The robustness of CShaperApp is also validated with the datasets from different laboratories.Furthermore,modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future enhancements.As cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data collection.The software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.

Functional predictability of universal gene circuits in diverse microbial hosts

Although the principles of synthetic biology were initially established in model bacteria,microbial producers,extremophiles and gut microbes have now emerged as valuable prokaryotic chassis for biological engineering.Extending the host range in which designed circuits can function reliably and predictably presents a major challenge for the concept of synthetic biology to materialize.In this work,we systematically characterized the cross-species universality of two transcriptional regulatory modules—the T7 RNA polymerase activator module and the repressors module—in three non-model microbes.We found striking linear relationships in circuit activities among different organisms for both modules.Parametrized model fitting revealed host non-specific parameters defining the universality of both modules.Lastly,a genetic NOT gate and a band-pass filter circuit were constructed from these modules and tested in non-model organisms.Combined models employing host non-specific parameters were successful in quantitatively predicting circuit behaviors,underscoring the potential of universal biological parts and predictive modeling in synthetic bioengineering.