Advancing microbial production through artificial intelligence-aided biology.

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Dynamic Clustering and Scaling Behavior of Active Particles under Confinement.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N¯âˆt-m. Simultaneously, the scaling behavior of the average cluster size S¯ is characterized by S¯âˆtm. Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system.

Accuracy meets simplicity: A constitutive model for heterogenous brain tissue.

We present a general, hyperelastic, stretch-based potential that shows promise for modeling the mechanics of brain tissue. A specific four-parameter model derived from this general potential outperforms alternative models, such as the modified Ogden model, the Gent model, Demiray model, and machine-learning models, in capturing brain tissue elasticity. Specifically, the stretch-based model achieved R2 values of 0.997, 0.992, and 0.993 (tension, compression, and shear) for the cortex, 0.995, 0.983, and 0.983 for the basal ganglia, 0.994, 0.929, and 0.970 for the corona radiata, and 0.990, 0.896, and 0.969 for the corpus callosum. This work has the potential to advance our understanding of brain tissue mechanics and provides a valuable tool to improve finite element models for the investigation of brain development, injuries, and disease.

Probe the nanoparticle-nucleus interaction via coarse-grained molecular model.

The present study reports on a computational model that systematically evaluates the effect of physical factors, including size, surface modification, and rigidity, on the nuclear uptake of nanoparticles (NPs). The NP-nucleus interaction is a crucial factor in biomedical applications such as drug delivery and cellular imaging. While experimental studies have provided evidence for the influence of size, shape, and surface modification on nuclear uptake, theoretical investigations on how these physical factors affect the entrance of NPs through the nuclear pore are lacking. Our results demonstrate that larger NPs require a higher amount of energy to enter the nucleus compared to smaller NPs. This highlights the importance of size as a critical factor in NP design for nuclear uptake. Additionally, surface modification of NPs can impact the nuclear uptake pathway, indicating the potential for tailored NP design for specific applications. Notably, our findings also reveal that the rigidity of NPs has a significant effect on the transport process. The interplay between physicochemical properties and nuclear pore is found to determine nuclear uptake efficiency. Taken together, our study provides new insights into the design of NPs for precise and controllable NP-nucleus interaction, with potential implications for the development of efficient and targeted drug delivery systems and imaging agents.

Hidden-information extraction from layered structures through terahertz imaging down to ultralow SNR.

Noninvasive inspection of layered structures has remained a long-standing challenge for time-resolved imaging techniques, where both resolution and contrast are compromised by prominent signal attenuation, interlayer reflections, and dispersion. Our method based on terahertz (THz) time-domain spectroscopy overcomes these limitations by offering fine resolution and a broadband spectrum to efficiently extract hidden structural and content information from layered structures. We exploit local symmetrical characteristics of reflected THz pulses to determine the location of each layer, and apply a statistical process in the spatiotemporal domain to enhance the image contrast. Its superior performance is evidenced by the extraction of alphabetic characters in 26-layer subwavelength papers as well as layer reconstruction and debonding inspection in the conservation of Terra-Cotta Warriors. Our method enables accurate structure reconstruction and high-contrast imaging of layered structures at ultralow signal-to-noise ratio, which holds great potential for internal inspection of cultural artifacts, electronic components, coatings, and composites with dozens of submillimeter layers.

An integrated finite element method and machine learning algorithm for brain morphology prediction.

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding, but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to expedite brain computational simulations, predict brain folding morphology, and explore the underlying folding mechanism. To do so, massive finite element method (FEM) mechanical models were run to simulate brain development using the predefined brain patch growth models with adjustable surface curvature. Then, a GAN-based machine learning model was trained and validated with these produced computational data to predict brain folding morphology given a predefined initial configuration. The results indicate that the machine learning models can predict the complex morphology of folding patterns, including 3-hinge gyral folds. The close agreement between the folding patterns observed in FEM results and those predicted by machine learning models validate the feasibility of the proposed approach, offering a promising avenue to predict the brain development with given fetal brain configurations.

Fibrin fiber deformation mechanisms: insights from phenomenological modeling to molecular details.

The deformation mechanism of fibrin fibers has been a long-standing challenge to uncover due to the fiber's complex structure and mechanical behaviors. In this paper, a phenomenological, bilinear, force-strain model is derived to accurately reproduce the fibrin fiber force-strain curve, and then, the phenomenological model is converted to a mechanistic model using empirical relationships developed from particle simulation data. The mechanistic model assumes that the initial linear fibrin fiber force-strain response is due to entropic extension of polypeptide chains, and the final linear response is due to enthalpic extension of protofibrils. This model is the first fibrin fiber tensile force-strain equation to simultaneously (1) reproduce the bilinear force-strain curve of fibrin fibers in tension; (2) explicitly include the number of protofibrils through the fibrin fiber cross section, persistence length of [Formula: see text]-regions, and stiffness of fibrin protofibrils; and (3) make demonstrably reasonable/accurate predictions of fibrin fiber mechanics when tempered against experimental results. The model predicted that the count of protofibrils through the cross section for the analyzed fibrin fibers is between 207 and 421, the persistence length of [Formula: see text]-regions is [Formula: see text], and the stiffness of protofibrils in a deforming fiber is [Formula: see text]. The predicted [Formula: see text]-region persistence length is within the range typical of amino acid residue lengths [Formula: see text] and the predicted protofibril stiffness is shown to correspond to half-staggered protofibrils of unfolded fibrin monomers. Our analysis supports the proposition that entropic extension of [Formula: see text]-regions could be responsible for fibrin fiber's initial force-strain stiffness and suggests a structural change in fibrin protofibrils during fibrin fiber deformation. The results from the model are compared to those from five candidate deformation mechanisms reported in the literature. Our work provides (1) strong quantitative support to a deformation mechanism that was previously supported by anecdote and qualitative argument, and (2) a model for rigorously analyzing fibrin fiber force-strain data and simulating fibrin fibers in tension.

Cortex2vector: anatomical embedding of cortical folding patterns.

Current brain mapping methods highly depend on the regularity, or commonality, of anatomical structure, by forcing the same atlas to be matched to different brains. As a result, individualized structural information can be overlooked. Recently, we conceptualized a new type of cortical folding pattern called the 3-hinge gyrus (3HG), which is defined as the conjunction of gyri coming from three directions. Many studies have confirmed that 3HGs are not only widely existing on different brains, but also possess both common and individual patterns. In this work, we put further effort, based on the identified 3HGs, to establish the correspondences of individual 3HGs. We developed a learning-based embedding framework to encode individual cortical folding patterns into a group of anatomically meaningful embedding vectors (cortex2vector). Each 3HG can be represented as a combination of these embedding vectors via a set of individual specific combining coefficients. In this way, the regularity of folding pattern is encoded into the embedding vectors, while the individual variations are preserved by the multi-hop combination coefficients. Results show that the learned embeddings can simultaneously encode the commonality and individuality of cortical folding patterns, as well as robustly infer the complicated many-to-many anatomical correspondences among different brains.

A Tractable, Transferable, and Empirically Consistent Fibrous Biomaterial Model.

Stochastic modeling is a useful approach for modeling fibrous materials that attempts to recreate fibrous materials' structure using statistical data. However, several issues remain to be resolved in the stochastic modeling of fibrous materials-for example, estimating 3D fiber orientation distributions from 2D data, achieving the desired fiber tortuosity distributions, and dealing with fiber-fiber penetration. This work proposes innovative methods to (1) create a mapping from 2D fiber orientation data to 3D fiber orientation probability distributions, and vice versa; and (2) provide a means to select parameters de novo for random walks employing the popularized von Mises-Fisher distribution given that the desired tortuosity of the path is known. The proposed methods are incorporated alongside previously developed stochastic modeling techniques to simulate fiber network structures. First, fiber orientation distributions vary significantly depending on how a fibrous material is formed, and projection distortion affects the measurement of fiber orientation distributions when reported as 2D data such as histograms or polar plots. Relationships are developed to estimate 3D fiber orientation distributions from 2D data, accounting for projection distortion and the variety of orientation distributions observed in fibrous materials. We show that without correcting for projection distortion, fiber orientation distribution parameters could have errors of up to 100%. Second, in stochastic modeling, fiber tortuosity is usually treated with random walks, but no relationship is available for choosing random walk inputs to generate a desired fiber tortuosity. Relationships are also developed to relate the input parameters of von Mises-Fisher random walks to the expected tortuosity of the generated path-a necessary link to modeling fiber tortuosity distributions tractably and with empirical consistency. Using the developed relationships, we show that modeling of tortuous fibers from a distribution could be sped up by ~1200-fold and the uncertainty of selecting appropriate parameters could be eliminated. Third, randomly placing fibers in a simulation domain inevitably results in fiber-fiber penetration, and correcting this issue requires changes to the simulated fibrous material structure through non-penetration conditions. No thorough remedy can be offered here, but we statistically quantify the effects of enforcing non-penetration conditions on the fiber shape and orientation changes as well as the overall fibrous material model. This work offers tractable and transferable methods for treating fiber orientation and tortuosity that allow for empirical consistency in the stochastic modeling of fibrous materials.

Machine learning-based prediction and inverse design of 2D metamaterial structures with tunable deformation-dependent Poisson's ratio.

With the aid of recent efficient and prior knowledge-free machine learning (ML) algorithms, extraordinary mechanical properties such as negative Poisson's ratio have extensively promoted the diverse designs of metamaterials with distinctive cellular structures. However, most existing ML approaches applied to the design of metamaterials are primarily based on a single property value with the assumption that the Poisson's ratio of a material is stationary, neglecting the dynamic variability of Poisson's ratio, termed deformation-dependent Poisson's ratio, during the loading process that a metamaterial other than conventional materials may experience. This paper first proposes a crystallographic symmetry-based methodology to build 2D metamaterials with complex but patterned topological structures, and then converts them into computational models suitable for molecular dynamics simulations. Then, we employ an integrated approach, consisting of molecular dynamics simulations capable of generating and collecting a large dataset for training/validation, in addition to ML algorithms (CNN and Cycle-GAN) able to predict the dynamic characteristics of Poisson's ratio and offer the inverse design of a metamaterial structure based on a target quasi-continuous Poisson's ratio-strain curve, to eventually unravel the underlying mechanism and design principles of 2D metamaterial structures with tunable Poisson's ratio. The close match between the predefined Poisson's ratio response and that from the generated structure validates the feasibility of the proposed ML model. Owing to the high efficiency and complete independence from prior knowledge, our proposed approach offers a novel and robust technique for the prediction and inverse design of metamaterial structures with tailored deformation-dependent Poisson's ratio, an unprecedented property attractive in flexible electronics, soft robotics, and nanodevices.

Gyral peaks: Novel gyral landmarks in developing macaque brains.

Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.

Mechanism Exploration of 3-Hinge Gyral Formation and Pattern Recognition.

The 3-hinge gyral folding is the conjunction of gyrus crest lines from three different orientations. Previous studies have not explored the possible mechanisms of formation of such 3-hinge gyri, which are preserved across species in primate brains. We develop a biomechanical model to mimic the formation of 3-hinge patterns on a real brain and determine how special types of 3-hinge patterns form in certain areas of the model. Our computational and experimental imaging results show that most tertiary convolutions and exact locations of 3-hinge patterns after growth and folding are unpredictable, but they help explain the consistency of locations and patterns of certain 3-hinge patterns. Growing fibers within the white matter is posited as a determining factor to affect the location and shape of these 3-hinge patterns. Even if the growing fibers do not exert strong enough forces to guide gyrification directly, they still may seed a heterogeneous growth profile that leads to the formation of 3-hinge patterns in specific locations. A minor difference in initial morphology between two growing model brains can lead to distinct numbers and locations of 3-hinge patterns after folding.

Exploring the Tunability and Dynamic Properties of MarR-PmarO Sensor System in Escherichia coli.

Transcriptional factor-based biosensors (TFBs) have been widely used in dynamic pathway control or high-throughput screening. Here, we systematically explored the tunability of a salicylic acid responsive regulator MarR from Escherichia coli aiming to explore its engineering potential. The effect of endogenous MarR in E. coli on the MarR-PmarO biosensor system was investigated. Furthermore, to investigate the function of marO binding boxes in this biosensor system, a series of hybrid promoters were constructed by placing the marO binding boxes in the strong constitutive pL promoter. The engineered hybrid promoters became responsive to MarR and salicylic acid. To further study the influence of each nucleotide in the marO box on MarR binding, we employed dynamic modeling to simulate the interaction and binding energy between each nucleotide in the marO boxes with the corresponding residues on MarR. Guided by the results of the simulation, we introduced mutations to key positions on the hybrid promoters and investigated corresponding dynamic performance. Two promoter variants I12AII4T and I12AII14T that exhibited improved responsive strengths and shifted dynamic ranges were obtained, which can be beneficial for future metabolic engineering research.

Geometrical nonlinear elasticity of axon under tension: A coarse-grained computational study.

Axon bundles cross-linked by microtubule (MT) associate proteins and bounded by a shell skeleton are critical for normal function of neurons. Understanding effects of the complexly geometrical parameters on their mechanical properties can help gain a biomechanical perspective on the neurological functions of axons and thus brain disorders caused by the structural failure of axons. Here, the tensile mechanical properties of MT bundles cross-linked by tau proteins are investigated by systematically tuning MT length, axonal cross-section radius, and tau protein spacing in a bead-spring coarse-grained model. Our results indicate that the stress-strain curves of axons can be divided into two regimes, a nonlinear elastic regime dominated by rigid-body like inter-MT sliding, and a linear elastic regime dominated by affine deformation of both tau proteins and MTs. From the energetic analyses, first, the tau proteins dominate the mechanical performance of axons under tension. In the nonlinear regime, tau proteins undergo a rigid-body like rotating motion rather than elongating, whereas in the nonlinear elastic regime, tau proteins undergo a flexible elongating deformation along the MT axis. Second, as the average spacing between adjacent tau proteins along the MT axial direction increases from 25 to 125 nm, the Young's modulus of axon experiences a linear decrease whereas with the average space varying from 125 to 175 nm, and later reaches a plateau value with a stable fluctuation. Third, the increment of the cross-section radius of the MT bundle leads to a decrease in Young's modulus of axon, which is possibly attributed to the decrease in MT numbers per cross section. Overall, our research findings offer a new perspective into understanding the effects of geometrical parameters on the mechanics of MT bundles as well as serving as a theoretical basis for the development of artificial MT complexes potentially toward medical applications.

Marmoset Brain ISH Data Revealed Molecular Difference Between Cortical Folding Patterns.

Literature studies have demonstrated the structural, connectional, and functional differences between cortical folding patterns in mammalian brains, such as convex and concave patterns. However, the molecular underpinning of such convex/concave differences remains largely unknown. Thanks to public access to a recently released set of marmoset whole-brain in situ hybridization data by RIKEN, Japan; this data's accessibility empowers us to improve our understanding of the organization, regulation, and function of genes and their relation to macroscale metrics of brains. In this work, magnetic resonance imaging and diffusion tensor imaging macroscale neuroimaging data in this dataset were used to delineate convex/concave patterns in marmoset and to examine their structural features. Machine learning and visualization tools were employed to investigate the possible transcriptome difference between cortical convex and concave patterns. Experimental results demonstrated that a collection of genes is differentially expressed in convex and concave patterns, and their expression profiles can robustly characterize and differentiate the two folding patterns. More importantly, neuroscientific interpretations of these differentially expressed genes, as well as axonal guidance pathway analysis and gene enrichment analysis, offer novel understanding of structural and functional differences between cortical folding patterns in different regions from a molecular perspective.

Mechanism of Coupling Nanoparticle Stiffness with Shape for Endocytosis: From Rodlike Penetration to Wormlike Wriggling.

Nanoparticle (NP)-mediated therapies are promising tools for the treatment of a wide range of diseases, including stroke and cancer, due to the outstanding performance they have shown for specifically targeting diseased sites. Importantly, the coupling of stiffness and shape of NPs has a significant influence on transportation via blood flow and internalization by targeted cells. Nevertheless, the underlying mechanism of this coupling effect on the endocytosis of NPs remains largely unexplored, resulting from a lack of clear measurement of stiffness for NPs in experiments, as well as the complexity of the endocytosis process. To overcome the above challenges, coarse-grained simulations, which can provide abundant nanoscale details and precise control of mechanical properties of NPs, were implemented to study the stiffness and shape dependence of the endocytosis of spherocylindrical NPs. To understand the coupling effect between shape and stiffness of NPs for membrane wrapping, coarse-grained molecular dynamics (CGMD) models with explicit bond, area, volume, and bending stiffness control were constructed for spherocylindrical NPs with identical volumes but different aspect ratios (ARs) ranging from 1.3 to 11.0. Results indicate that the endocytosis time of NPs increases as the aspect ratio increases due to both the increasing surface area and decreasing wrapping rate resulting from the decreasing contact perimeter. Moreover, soft and long NPs with AR = 11.0 exhibit wormlike wiggling in contrast to rodlike penetration of the stiff, enlarging the contact area and facilitating the endocytosis process. In addition, three types of NP fates are differentiated: full endocytosis, full endocytosis with membrane damage, and partial endocytosis with membrane damage. Among those patterns, damages/defects on the membrane can promote wrapping of NPs, although extra time is needed to close the defect after endocytosis. In summary, our results help gain a deeper understanding of the underlying mechanism of endocytosis of NPs with respect to geometry and particle stiffness, providing a useful guideline for designs of nanoparticles that can be implemented in next-generation nanoparticle-assisted therapy.

Twining plant inspired pneumatic soft robotic spiral gripper with a fiber optic twisting sensor.

The field of soft robotics has been significantly advanced with the recent developments of pneumatic techniques, soft materials, and high-precision motion control. While comprehensive motions can be achieved by sophisticated soft robots, multiple coordinated pneumatic controls are usually required to achieve even the simplest motions. Furthermore, most soft robotics are lacking the ability to sense the environment and provide feedback to the pneumatic control system. In this work, we design a twining plant inspired soft-robotic spiral gripper that requires only one single pneumatic control to perform the twining motion and to firmly hold onto a target object. The soft-robotic spiral gripper has an embedded high-birefringence fiber optic twisting sensor to provide critical information, including twining angle, presence of external perturbation, and physical parameter of the target object. Furthermore, finite element analyses (FEA) in parametric studies of the spiral gripper are performed for module design optimization. The unique single pneumatic channel design enables easy manipulation of the soft spiral gripper with a maximum of 540° twining angle and allows a firm grip of a target object as small as 1-mm in diameter. The embedded fiber optic sensor provides useful information of the target object as well as the twining angle of the soft robotic spiral gripper with high twining angle sensitivity of 0.03nm. The unique fiber-optic sensor embedded single-channel pneumatic spiral gripper that is made from non-toxic silicone rubber allows parallel and soft gripping of elongated objects located in a confined area, which is an essential building block for twining and twisting motions in soft robot.

Cortical 3-hinges could serve as hubs in cortico-cortical connective network.

Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as 'connector' hubs rather than 'provincial' hubs and they account for a dominant proportion of nodes in the high-level 'backbone' of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.

Interplay of Nanoparticle Rigidity and Its Translocation Ability through Cell Membrane.

Understanding the endocytic process of nanoparticles (NPs) with different mechanical rigidities is critical to develop effective drug delivery vectors. Here, we perform experiments, coarse-grained molecular dynamics simulations, and theoretical analyses to investigate the role of NPs' mechanical rigidity in the cellular endocytic process. Experiments based on two types of engineered Au NPs that have similar properties but different rigidities are performed in order to investigate their cellular uptake efficiencies, and it has been found that the more rigid NPs can achieve a higher cellular uptake efficiency. Simulation results confirm that rigid NPs can achieve full internalization by forming a complete double-layer endosome coating, while relatively soft NPs can only reach 40% surface coverage by membrane lipids. Simulation results capture an intriguing translocation of multiple NPs with different rigidities in a cooperative manner where the NPs' mechanical rigidities regulate their translocation efficiencies. We find that theoretically rigid NPs require less energy to overcome the energy barrier for membrane internalization than soft NPs do, which is in good agreement with experiment and simulation results. This synergetic study offers useful insight into the design principle of a general NP-based drug delivery vector as well as the promising biomedical application of NP-based medicine.