Over the last 15 years, genome-scale metabolic models (GEMs) have been reconstructed for human and model animals, such as mouse and rat, to systematically understand metabolism, simulate multicellular or multi-tissue interplay, understand human diseases, and guide cell factory design for biopharmaceutical protein production. Here, we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation. Then, we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals, together with their applications. We describe how model develops from H. sapiens to other animals and from generic purpose to precise context-specific simulation. The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals. We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation. We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.

Creating a man-made life in the laboratory is one of life science’s most intriguing yet challenging problems. Advances in synthetic biology and related theories, particularly those related to the origin of life, have laid the groundwork for further exploration and understanding in this field of artificial life or man-made life. But there remains a wealth of quantitative mathe-matical models and tools that have yet to be applied to this area. In this paper, we review the two main approaches often employed in the field of man-made life: the top-down approach that reduces the complexity of extant and existing living systems and the bottom-up approach that integrates well-defined components, by introducing the theoretical basis, recent advances, and their limitations. We then argue for another possible approach, namely “bottom-up from the origin of life”: Starting with the establishment of auto-catalytic chemical reaction networks that employ physical boundaries as the initial compartments, then designing directed evolutionary systems, with the expectation that independent compartments will eventually emerge so that the system becomes free-living. This approach is actually analogous to the process of how life originated. With this paper, we aim to stimulate the interest of synthetic biologists and experimentalists to consider a more theoretical perspective, and to promote the communication between the origin of life community and the synthetic man-made life community.

The prediction of molecular properties is a crucial task in the field of drug discovery. Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery. In recent years, iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction. Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering. In this review, we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction, including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models, as well as state-of-the-art deep learning strategies such as 3D pre-train, contrastive learning, multi-task learning, transfer learning, and meta-learning. We also point out some critical issues such as lack of datasets, low information utilization, and lack of specificity for diseases.

Electroactive microorganisms (EAMs) could utilize extracellular electron transfer (EET) pathways to exchange electrons and energy with their external surroundings. Conductive cytochrome proteins and nanowires play crucial roles in controlling electron transfer rate from cytosol to extracellular electrode. Many previous studies elucidated how the c-type cytochrome proteins and conductive nanowires are synthesized, assembled, and engineered to manipulate the EET rate, and quantified the kinetic processes of electron generation and EET. Here, we firstly overview the electron transfer pathways of EAMs and quantify the kinetic parameters that dictating intracellular electron production and EET. Secondly, we systematically review the structure, conductivity mechanisms, and engineering strategies to manipulate conductive cytochromes and nanowire in EAMs. Lastly, we outlook potential directions for future research in cytochromes and conductive nanowires for enhanced electron transfer. This article reviews the quantitative kinetics of intracellular electron production and EET, and the contribution of engineered c-type cytochromes and conductive nanowire in enhancing the EET rate, which lay the foundation for enhancing electron transfer capacity of EAMs.

The causative pathogen of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an enveloped virus assembled by a lipid envelope and multiple structural proteins. In this study, by integrating experimental data, structural modeling, as well as coarse-grained and all-atom molecular dynamics simulations, we constructed multiscale models of SARS-CoV-2. Our 500-ns coarse-grained simulation of the intact virion allowed us to investigate the dynamic behavior of the membrane-embedded proteins and the surrounding lipid molecules in situ. Our results indicated that the membrane-embedded proteins are highly dynamic, and certain types of lipids exhibit various binding preferences to specific sites of the membrane-embedded proteins. The equilibrated virion model was transformed into atomic resolution, which provided a 3D structure for scientific demonstration and can serve as a framework for future exascale all-atom molecular dynamics (MD) simulations. A short all-atom molecular dynamics simulation of 255 ps was conducted as a preliminary test for large-scale simulations of this complex system.

Gene regulatory network (GRN) inference from gene expression data is a significant approach to understanding aspects of the biological system. Compared with generalized correlation-based methods, causality-inspired ones seem more rational to infer regulatory relationships. We propose GRINCD, a novel GRN inference framework empowered by graph representation learning and causal asymmetric learning, considering both linear and non-linear regulatory relationships. First, high-quality representation of each gene is generated using graph neural network. Then, we apply the additive noise model to predict the causal regulation of each regulator-target pair. Additionally, we design two channels and finally assemble them for robust prediction. Through comprehensive comparisons of our framework with state-of-the-art methods based on different principles on numerous datasets of diverse types and scales, the experimental results show that our framework achieves superior or comparable performance under various evaluation metrics. Our work provides a new clue for constructing GRNs, and our proposed framework GRINCD also shows potential in identifying key factors affecting cancer development.

The information on host–microbe interactions contained in the operational taxonomic unit (OTU) abundance table can serve as a clue to understanding the biological traits of OTUs and samples. Some studies have inferred the taxonomies or functions of OTUs by constructing co-occurrence networks, but co-occurrence networks can only encompass a small fraction of all OTUs due to the high sparsity of the OTU table. There is a lack of studies that intensively explore and use the information on sample-OTU interactions. This study constructed a sample-OTU heterogeneous information network and represented the nodes in the network through the heterogeneous graph embedding method to form the OTU space and sample space. Taking advantage of the represented OTU and sample vectors combined with the original OTU abundance information, an Integrated Model of Embedded Taxonomies and Abundance (IMETA) was proposed for predicting sample attributes, such as phenotypes and individual diet habits. Both the OTU space and sample space contain reasonable biological or medical semantic information, and the IMETA using embedded OTU and sample vectors can have stable and good performance in the sample classification tasks. This suggests that the embedding representation based on the sample-OTU heterogeneous information network can provide more useful information for understanding microbiome samples. This study conducted quantified representations of the biological characteristics within the OTUs and samples, which is a good attempt to increase the utilization rate of information in the OTU abundance table, and it promotes a deeper understanding of the underlying knowledge of human microbiome.