Search

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding betweenmicrobiologists andmodelers/bioinformaticians, stemming froma lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored formicrobiologists, researchers newtomicrobiomemodeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling. [ABSTRACT FROM AUTHOR]

Background: The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. Results: BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. Conclusions: BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems. [ABSTRACT FROM AUTHOR]

The article titled "Molecular World Today and Tomorrow: Recent Trends in Biological Sciences 2.0" explores the impact of molecular techniques on scientific advancements in biological sciences. It emphasizes the role of molecular techniques in focused medication creation and disease diagnostics. The article highlights the integration of molecular phylogenetics and omics approaches to enhance our understanding of molecular mechanisms and stress responses in biological systems. The special issue features 20 papers that demonstrate the interdisciplinary nature of molecular sciences and its potential for personalized medicine and treatments. [Extracted from the article]

The SIAM Journal on Applied Mathematics has published a special section on the life sciences, highlighting the use of mathematical models and analysis in understanding biological systems. The section showcases a range of topics, including population dynamics, genetics, neuroscience, epidemics, and various physiological systems. The papers in the section focus on providing biological insights or suggesting novel experiments, and they incorporate practical issues, data-driven modeling, and uncertainty quantification. The special section reflects the growing interest and activity in the fields of mathematical and computational biology, which have been driven by real-world applications in health, medicine, and bioengineering. [Extracted from the article]

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS. [Display omitted] • Innovative Inverted Virtual Screening (IVS) pipeline. • Automated analysis of protein-ligand interactions. • Integrates Modeller, AlphaFold, Gromacs, and more. • Versatile for blind docking and site-specific targeting. • Modular design for custom research needs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Light-oxygen-voltage (LOV) domains are flavin mononucleotide-binding, blue-light receptors. • They regulate cell responses via naturally attached/engineered effector proteins. • The elucidation of their photoreaction dynamics is a major bottleneck in their study. • LOV-based tools can be designed for light-controlled processes (e.g., optogenetic tools). • Advances in time-resolved crystallography, spectroscopy, and computational science will aid in resolving the photoreaction and developing optogenetic tools. The light-oxygen-voltage (LOV) domains of phototropins emerged as essential constituents of light-sensitive proteins, helping initiate blue light-triggered responses. Moreover, these domains have been identified across all kingdoms of life. LOV domains utilize flavin nucleotides as co-factors and undergo structural rearrangements upon exposure to blue light, which activates an effector domain that executes the final output of the photoreaction. LOV domains are versatile photoreceptors that play critical roles in cellular signaling and environmental adaptation; additionally, they can noninvasively sense and control intracellular processes with high spatiotemporal precision, making them ideal candidates for use in optogenetics, where a light signal is linked to a cellular process through a photoreceptor. The ongoing development of LOV-based optogenetic tools, driven by advances in structural biology, spectroscopy, computational methods, and synthetic biology, has the potential to revolutionize the study of biological systems and enable the development of novel therapeutic strategies. [ABSTRACT FROM AUTHOR]

Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively reveals the behavior of biological systems across various spatiotemporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative structural biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in structural biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatiotemporal modeling, and provides an outlook into future directions of this field. [ABSTRACT FROM AUTHOR]