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Bottom-up approaches are becoming increasingly popular for studying multicellular self-organization and development. In contrast to the classic top-down approach, where parts of the organization/developmental process are broken to understand the process, the goal is to build the process to understand it. For example, synthetic circuits have been built to understand how cell-cell communication and differential adhesion can drive multicellular development. The majority of current bottom-up efforts focus on using activatory circuits to engineer and understand development, but efforts with inhibitory circuits have been minimal. Yet, inhibitory circuits are ubiquitous and vital to native developmental processes. Thus, inhibitory circuits are a crucial yet poorly studied facet of bottom-up multicellular development. To demonstrate the potential of inhibitory circuits for building and developing multicellular structures, several synthetic inhibitory circuits that combine engineered cell-cell communication and differential adhesion were designed, and then examined for synthetic development capability using a previously validated in silico framework. These designed inhibitory circuits can build a variety of patterned, self-organized structures and even morphological oscillations. These results support that inhibitory circuits can be powerful tools for building, studying, and understanding developmental processes.

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., (© 2024. The Author(s).)

The expanding field of synthetic biology (synbio) supports new opportunities in the design of targeted bioproducts or modified microorganisms. However, this rapid development of synbio products raises concerns surrounding the potential risks of modified microorganisms contaminating unintended environments. These potential invasion risks require new bioinformatic tools to inform the design phase. EcoGenoRisk is a newly constructed computational risk assessment tool for invasiveness that aims to predict where synbio microorganisms may establish a population by screening for habitats of genetically similar microorganisms. The first module of the tool identifies genetically similar microorganisms and potential ecological relationships such as competition, mutualism, and inhibition. In total, 520 archaeal and 32,828 bacterial complete assembly genomes were analyzed to test the specificity and accuracy of the tool as well as to characterize the enzymatic profiles of different taxonomic lineages. Additionally, ecological relationships were analyzed to determine which would result in the greatest potential overlap between shared functional profiles. Notably, competition displayed the significantly highest overlap of shared functions between compared genomes. Overall, EcoGenoRisk is a flexible software pipeline that assists environmental risk assessors to query large databases of known microorganisms and prioritize follow-up bench scale studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. [ABSTRACT FROM AUTHOR]

Abstract 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.

Oxidation–reduction post-translational modifications (redox-PTMs) are chemical alterations to amino acids of proteins. Redox-PTMs participate in the regulation of protein conformation, localization and function, acting as signalling effectors that impact many essential biochemical processes in the cells. Crucially, the dysregulation of redox-PTMs of proteins has been implicated in the pathophysiology of numerous human diseases, including neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. This review aims to highlight the current gaps in knowledge in the field of redox-PTMs biology and to explore new methodological advances in proteomics and computational modelling that will pave the way for a better understanding of the role and therapeutic potential of redox-PTMs of proteins in neurodegenerative diseases. Here, we summarize the main types of redox-PTMs of proteins while providing examples of their occurrence in neurodegenerative diseases and an overview of the state-of-the-art methods used for their detection. We explore the potential of novel computational modelling approaches as essential tools to obtain insights into the precise role of redox-PTMs in regulating protein structure and function. We also discuss the complex crosstalk between various PTMs that occur in living cells. Finally, we argue that redox-PTMs of proteins could be used in the future as diagnosis and prognosis biomarkers for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. Results: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. Conclusion: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community. [ABSTRACT FROM AUTHOR]

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1 , fol2 , fol3 , abz1 , and abz2 , or a combined knockout of hfd1 , ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△ fol3), EK5 (OA07△ abz1), and EK6 (OA07△ abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1 , abz2 , and at acetyl-CoA carboxylase-encoding gene acc1 , to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L−1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design. • We developed a semi-rational strain design method for PNP overproduction in microbes. • The flux optimization algorithm and gene circuit design approach were jointly used. • The 3 constructed S. cerevisiae variants were sustainably efficient in OA production. • Strain R_3A with the self-regulated suppression of fol3 improved OA production to 1.23 g L−1. • Illustration of novel ideas to compromise host cell growth and the heterologous production. [ABSTRACT FROM AUTHOR]

Generative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state-of-the-art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state-of-the-art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures Software > Molecular Modeling [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the International Journal of Molecular Sciences titled "State-of-the-Art Molecular Plant Sciences in Japan." The issue focuses on the impact of climate change on food production and the efforts of plant scientists in Japan to develop resilient crops. The articles cover various topics such as stress tolerance, plant metabolism, plant-microbe interactions, and the use of omics techniques in agricultural research. The research presented in the issue aims to improve food production and understand the interaction between plants and their environment. [Extracted from the article]

Motivation The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue. Results We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability. Availability and implementation https://github.com/KIT-MBS/pycofitness. [ABSTRACT FROM AUTHOR]

Nowadays, interdisciplinary fields between Artificial Life, artificial intelligence, computational biology, and synthetic biology are increasingly emerging into public view. It is necessary to reconsider the relations between the material body, identity, the natural world, and the concept of life. Art is known to pave the way to exploring and conveying new possibilities. This survey provides a literature review on recent works of Artificial Life in visual art during the past 40 years, specifically in the computational and software domain. Having proposed a set of criteria and a taxonomy, we briefly analyze representative artworks of different categories. We aim to provide a systematic overview of how artists are understanding nature and creating new life with modern technology. [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]

Cordyceps militaris , widely recognized as a medicinal and edible mushroom in East Asia, contains a variety of bioactive compounds, including cordycepin (COR), pentostatin (PTN) and other high-value compounds. This review explores the potential of developing C. militaris as a cell factory for the production of high-value chemicals and nutrients. This review comprehensively summarizes the fermentation advantages, metabolic networks, expression elements, and genome editing tools specific to C. militaris and discusses the challenges and barriers to further research on C. militaris across various fields, including computational biology, existing DNA elements, and genome editing approaches. This review aims to describe specific and promising opportunities for the in-depth study and development of C. militaris as a new chassis cell. Additionally, to increase the practicability of this review, examples of the construction of cell factories are provided, and promising strategies for synthetic biology development are illustrated. • C. militaris is a medicinal and edible mushroom. • C. militaris has the potential and possibility to be built as a cell factory. • Computational and advanced CRISPR technique were applied in C. militaris. • Metabolic engineering instructs in producing high-value compounds. • Synthetic biology facilitates the development of cell factory construction. [ABSTRACT FROM AUTHOR]

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations. • The latest advances in large scale biomolecular simulation make it a powerful tool for both post-fact rationalisation and prediction of biomolecular phenomena. • The largest systems can enable paradigm shifting simulation capabilities, but only when the software and workflow capabilities are optimized to fully utilise the system. • Properly deployed, AI enables significant improvements in the speed / accuracy ratio through powerful predictive models, intelligent steering and faster, highly accurate, potentials. • Quantum computing is an emerging paradigm which has the potential to further revolutionise the field, although larger high-fidelity systems will be required to fulfil this potential. [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]