Hasil untuk "Biotechnology"

C. Quince, A. Walker, J. Simpson et al.

K. Liang, R. Riccò, C. Doherty et al.

Enhancing the robustness of functional biomacromolecules is a critical challenge in biotechnology, which if addressed would enhance their use in pharmaceuticals, chemical processing and biostorage. Here we report a novel method, inspired by natural biomineralization processes, which provides unprecedented protection of biomacromolecules by encapsulating them within a class of porous materials termed metal-organic frameworks. We show that proteins, enzymes and DNA rapidly induce the formation of protective metal-organic framework coatings under physiological conditions by concentrating the framework building blocks and facilitating crystallization around the biomacromolecules. The resulting biocomposite is stable under conditions that would normally decompose many biological macromolecules. For example, urease and horseradish peroxidase protected within a metal-organic framework shell are found to retain bioactivity after being treated at 80 °C and boiled in dimethylformamide (153 °C), respectively. This rapid, low-cost biomimetic mineralization process gives rise to new possibilities for the exploitation of biomacromolecules. Robust biomacromolecules could be used for a wide range of biotechnological applications. Here the authors report a biomimetic mineralization process, in which biomolecules are encapsulated within metal-organic frameworks, and their stability is subsequently increased without significant bioactivity loss.

S. Bankar, Mahesh Bule, R. Singhal et al.

E. Chi, S. Krishnan, T. Randolph et al.

W. Chan, Dustin J. Maxwell, Xiaohu Gao et al.

G. Pisano

Shimshon Belkin

This review attempts to summarize my three decades-long involvement in, and contribution to, the design, construction and testing of bioluminescent microbial sensor strains (bioreporters). With the understanding that such a document cannot be completely free of bias, the review focuses on studies from my own lab only, with almost no coverage of the parallel progress made by others in similar fields. This admittedly subjective approach by no way detracts from the achievements of countless excellent researchers who are not mentioned here, and whose contributions to the field are at least as important as that of my own. The review covers basic aspects of microbial sensor design, and then progresses to describe approaches to performance improvement of sensor strains aimed at the detection of either specific chemicals, groups of chemicals sharing similar characteristics, or global effects, such as toxicity and genotoxicity. The need for integration of live sensor cells into a compatible hardware platform is highlighted, as is the importance of long-term maintenance of the cells’ viability and activity. The use of multi-member sensors’ panels is presented as a means for enhancing the detection spectrum and sample “fingerprinting”, along with a list of different purposes to which such sensors have been put to use.

Himani Barthwal, Charu Sharma, Vijay Kumar et al.

Recently, the utilization of natural or herbal products has increased worldwide. Various naturally isolated plant products have been assessed as therapeutics for the treatment of a variety of diseases. Microbes are related to medicinal plants and have enormous potential in the context of promoting plant growth traits and producing active ingredients of therapeutic importance. Bacopa monnieri, also called ‘Brahmi’ and water hyssop, has been utilized extensively in the ayurvedic system of medicine for a long time. Phytochemical investigations of B. monnieri extracts have revealed the occurrence of several active compounds, such as bacosides, alkaloids and triterpenoids. All these active chemical ingredients act as the best memory enhancer and are also used for various illnesses. The microbially mediated production of novel secondary metabolites with key biological activities could be an alternative method to obtain bioactive ingredients. This review highlights the interactions between microbes and the medicinal plant B. monnieri, illuminating the creation of biologically active compounds with medicinal importance within the plant.

Qin Li, Yinzhu Chen, Hongmei Zhao et al.

Abstract Background Phenolic acid decarboxylase (PAD) is an enzyme capable of catalyzing the nonoxidative decarboxylation of phenolic acids, yielding the corresponding 4-vinyl derivatives. This enzymatic process holds considerable promise for converting naturally abundant phenolic acid substrates into high-value compounds. Results The PAD gene from Bacillus subtilis J6 was cloned to yield the BJ6PAD enzyme, and its mutant BJ6PAD-N was generated by introducing an N-terminal substitution. Compared with BJ6PAD, BJ6PAD-N demonstrated not only higher specific enzyme activity but also increased alkaline resistance. The N-terminal region of BJ6PAD-N exhibited increased flexibility, leading to a looser structure. This change improved the catalytic efficiency for sinapic acid (SA) with bulky side chains. After its immobilization, the application potential of BJ6PAD-N was significantly enhanced, demonstrating reusability and storage stability that were superior to those of BJ6PAD. After 10 repeated uses, the residual enzyme activity remained above 80%. When stored at 4 °C for 60 days, 61.15% of the enzyme activity was retained. These characteristics are crucial for facilitating the industrial application of enzymes. Conclusions Replacing the N-terminal of phenolic acid decarboxylase BJ6PAD (resulting in BJ6PAD-N) made the enzyme structure more flexible. While this reduced substrate binding stability, it increased specific enzyme activity. Notably, the enzyme showed improved catalytic efficiency for sinapic acid, which has a bulky side chain. After being immobilized, the performance stability of the enzyme has been further enhanced.

Hedibert F. Lopes, Filippo Ascolani

Mike West is currently the Arts & Sciences Distinguished Professor Emeritus of Statistics and Decision Sciences at Duke University. Mike's research in Bayesian analysis spans multiple interlinked areas: theory and methods of dynamic models in time series analysis, foundations of inference and decision analysis, multivariate and latent structure analysis, stochastic computation and optimisation, among others. Inter-disciplinary R&D has ranged across applications in commercial forecasting, dynamic networks, finance, econometrics, signal processing, climatology, systems biology, genomics and neuroscience, among other areas. Among Mike's currently active research areas are forecasting, causal prediction and decision analysis in business, economic policy and finance, as well as in personal decision making. Mike led the development of academic statistics at Duke University from 1990-2002, and has been broadly engaged in professional leadership elsewhere. He is past president of the International Society for Bayesian Analysis (ISBA), and has served in founding roles and as board member for several professional societies, national and international centres and institutes. Recipient of numerous awards, Mike has been active in research with various companies, banks, government agencies and academic centres, co-founder of a successful biotechnology company, and board member for several financial and IT companies. He has published 4 books, several edited volumes and over 200 papers. Mike has worked with many undergraduate and Master's research students, and as of 2025 has mentored around 65 primary PhD students and postdoctoral associates who moved to academic, industrial or governmental positions involving advanced statistical and data science research.

Xiaofei Wang, Stephen Price, Chao Li

The rapid advancement of spatial transcriptomics (ST), i.e., spatial gene expressions, has made it possible to measure gene expression within original tissue, enabling us to discover molecular mechanisms. However, current ST platforms frequently suffer from low resolution, limiting the in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. However, it remains a challenge to model the interactions between histology images and gene expressions for effective ST enhancement. This study presents a cross-modal cross-content contrastive diffusion framework, called C3-Diff, for ST enhancement with histology images as guidance. In C3-Diff, we firstly analyze the deficiency of traditional contrastive learning paradigm, which is then refined to extract both modal-invariant and content-invariant features of ST maps and histology images. Further, to overcome the problem of low sequencing sensitivity in ST maps, we perform nosing-based information augmentation on the surface of feature unit hypersphere. Finally, we propose a dynamic cross-modal imputation-based training strategy to mitigate ST data scarcity. We tested C3-Diff by benchmarking its performance on four public datasets, where it achieves significant improvements over competing methods. Moreover, we evaluate C3-Diff on downstream tasks of cell type localization, gene expression correlation and single-cell-level gene expression prediction, promoting AI-enhanced biotechnology for biomedical research and clinical applications. Codes are available at https://github.com/XiaofeiWang2018/C3-Diff.

Simon Hellmann, Terrance Wilms, Stefan Streif et al.

In many applications of biotechnology, measurements are available at different sampling rates, e.g., due to online sensors and offline lab analysis. Offline measurements typically involve time delays that may be unknown a priori due to the underlying laboratory procedures. This multirate (MR) setting poses a challenge to Kalman filtering, where conventionally measurement data is assumed to be available on an equidistant time grid and without delays. This tutorial paper derives the MR version of an extended Kalman filter (EKF) based on sample state augmentation, and applies it to the anaerobic digestion (AD) process in a simulative agricultural setting. The performance of the MR-EKF is investigated for various scenarios including varying delay lengths, measurement noise levels, plant-model mismatch (PMM), and initial state error. Provided with an adequate tuning, the MR-EKF can reliably estimate the process state and, thus, appropriately fuse the delayed offline measurements and smooth the noisy online measurements. Because of the sample state augmentation approach, the delay length of offline measurements does not critically effect the performance of the state estimation, provided that observability is not lost during the delays. Poor state initialization and PMM affect convergence more than measurement noise levels. Furthermore, selecting an appropriate tuning was found to be critically important for successful application of the MR-EKF for which a systematic approach is presented. This tutorial provides implementation guidance for practitioners seeking to successfully apply state estimation for multirate systems. Thus, it contributes to the development of demand-driven operation of biogas plants, which may aid in stabilizing a renewable electricity grid.

Mohammad Usman Altam, Md Imtiaz Habib, Tuan Hoang

Retrieval-Augmented Generation (RAG) represents a transformative approach within natural language processing (NLP), combining neural information retrieval with generative language modeling to enhance both contextual accuracy and factual reliability of responses. Unlike conventional Large Language Models (LLMs), which are constrained by static training corpora, RAG-powered systems dynamically integrate domain-specific external knowledge sources, thereby overcoming temporal and disciplinary limitations. In this study, we present the design and evaluation of a RAG-enabled system tailored for Mycophyto, with a focus on advancing agricultural applications related to arbuscular mycorrhizal fungi (AMF). These fungi play a critical role in sustainable agriculture by enhancing nutrient acquisition, improving plant resilience under abiotic and biotic stresses, and contributing to soil health. Our system operationalizes a dual-layered strategy: (i) semantic retrieval and augmentation of domain-specific content from agronomy and biotechnology corpora using vector embeddings, and (ii) structured data extraction to capture predefined experimental metadata such as inoculation methods, spore densities, soil parameters, and yield outcomes. This hybrid approach ensures that generated responses are not only semantically aligned but also supported by structured experimental evidence. To support scalability, embeddings are stored in a high-performance vector database, allowing near real-time retrieval from an evolving literature base. Empirical evaluation demonstrates that the proposed pipeline retrieves and synthesizes highly relevant information regarding AMF interactions with crop systems, such as tomato (Solanum lycopersicum). The framework underscores the potential of AI-driven knowledge discovery to accelerate agroecological innovation and enhance decision-making in sustainable farming systems.

Fiona Y. Wang, Di Sheng Lee, David L. Kaplan et al.

Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative methods, such as protein language models (PLMs) and diffusion-based architectures, often require extensive fine-tuning, task-specific data, or model reconfiguration to support objective-directed design, thereby limiting their flexibility and scalability. To overcome these limitations, we present a decentralized, agent-based framework inspired by swarm intelligence for de novo protein design. In this approach, multiple large language model (LLM) agents operate in parallel, each assigned to a specific residue position. These agents iteratively propose context-aware mutations by integrating design objectives, local neighborhood interactions, and memory and feedback from previous iterations. This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences without reliance on motif scaffolds or multiple sequence alignments, validated with experiments on proteins with alpha helix and coil structures. Through analyses of residue conservation, structure-based metrics, and sequence convergence and embeddings, we demonstrate that the framework exhibits emergent behaviors and effective navigation of the protein fitness landscape. Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training, offering a generalizable and adaptable solution for protein design. Beyond proteins, the approach lays the groundwork for collective LLM-driven design across biomolecular systems and other scientific discovery tasks.

Sowmya S Sundaram, Rafael S. Gonçalves, Mark A Musen

Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves using CEDAR templates to guide GPT-4 in accurately correcting and refining metadata entries in bulk, resulting in significant improvements in metadata retrieval performance, especially in recall -- the proportion of relevant datasets retrieved from the total relevant datasets available. Using the BioSample and GEO repositories maintained by the National Center for Biotechnology Information (NCBI), we demonstrate that retrieval of datasets whose metadata are altered by GPT-4 when provided with CEDAR templates (GPT-4+CEDAR) is substantially better than retrieval of datasets whose metadata are in their original state and that of datasets whose metadata are altered using GPT-4 with only data-dictionary guidance (GPT-4+DD). The average recall increases dramatically, from 17.65\% with baseline raw metadata to 62.87\% with GPT-4+CEDAR. Furthermore, we evaluate the robustness of our approach by comparing GPT-4 against other large language models, including LLaMA-3 and MedLLaMA2, demonstrating consistent performance advantages for GPT-4+CEDAR. These results underscore the transformative potential of combining advanced language models with symbolic models of standardized metadata structures for more effective and reliable data retrieval, thus accelerating scientific discoveries and data-driven research.

Ellen Piercy, Xinyang Sun, Peter R Ellis et al.

Anaerobic digestion (AD) offers a sustainable biotechnology to recover resources from carbon-rich wastewater, such as food-processing wastewater. Despite crude wastewater characterisation, the impact of detailed chemical fingerprinting on AD remains underexplored. This study investigated the influence of fermentation-wastewater composition and operational parameters on AD over time to identify critical factors influencing reactor biodiversity and performance. Eighteen reactors were operated under various operational conditions using mycoprotein fermentation wastewater. Detailed chemical analysis fingerprinted the molecules in the fermentation wastewater throughout AD including sugars, sugar alcohols and volatile fatty acids (VFAs). Sequencing revealed distinct microbiome profiles linked to temperature and reactor configuration, with mesophilic conditions supporting a more diverse and densely connected microbiome. Significant elevations in Methanomassiliicoccus were correlated to high butyric acid concentrations and decreased biogas production, further elucidating the role of this newly discovered methanogen. Dissimilarity analysis demonstrated the importance of individual molecules on microbiome diversity, highlighting the need for detailed chemical fingerprinting in AD studies of microbial trends. Machine learning (ML) models predicting reactor performance achieved high accuracy based on operational parameters and microbial taxonomy. Operational parameters had the most substantial influence on chemical oxygen demand removal, whilst Oscillibacter and two Clostridium sp. were highlighted as key factors in biogas production. By integrating detailed chemical and biological fingerprinting with ML models this research presents a novel approach to advance our understanding of AD microbial ecology, offering insights for industrial applications of sustainable waste-to-energy systems.

Zhenqiao Song, Ramith Hettiarachchi, Chuan Li et al.

The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by scarce protein-ligand complex data. To circumvent this data bottleneck, we leverage the abundant natural language descriptions characterizing protein-ligand interactions. Here, we introduce InstructPro, a family of generative models that design proteins following the guidance of natural language instructions and ligand formulas. InstructPro produces protein sequences consistent with specified function descriptions and ligand targets. To enable training and evaluation, we develop InstructProBench, a large-scale dataset of 9.6 million (function description, ligand, protein) triples. We train two model variants -- InstructPro-1B and InstructPro-3B -- that substantially outperform strong baselines. InstructPro-1B achieves an AlphaFold3 ipTM of 0.918 and a binding affinity of -8.764 on seen ligands, while maintaining robust performance in a zero-shot setting with scores of 0.869 and -6.713, respectively. These results are accompanied by novelty scores of 70.1% and 68.8%, underscoring the model's ability to generalize beyond the training set. Furthermore, the model yields a superior binding free energy of -20.9 kcal/mol and an average of 5.82 intermolecular hydrogen bonds, validating its proficiency in designing high-affinity ligand-binding proteins. Notably, scaling to InstructPro-3B further improves the zero-shot ipTM to 0.882, binding affinity to -6.797, and binding free energy to -25.8 kcal/mol, demonstrating clear performance gains associated with increased model capacity. These findings highlight the power of natural language-guided generative models to mitigate the data bottlenecks in traditional structure-based methods, significantly broadening the scope of de novo protein design.

Julien Guerrero, Ekaterina Maevskaia, Chafik Ghayor et al.

Additive manufacturing has emerged as a transformative tool in biomedical engineering, offering precise control over scaffold design for bone tissue engineering and regenerative medicine. While much attention has been focused on optimizing pore-based scaffold architectures, filament-based microarchitectures remain relatively understudied, despite the fact that the majority of 3D-printers generate filament-based structures. Here, we investigated the influence of filament characteristics on bone regeneration outcomes using a lithography-based additive manufacturing approach. Three distinct filament-based scaffolds (Fil050, Fil083, and Fil125) identical in macroporosity and transparency, crafted from tri-calcium phosphate (TCP) with varying filament thicknesses and distance, were evaluated in a rabbit model of bone augmentation and non-critical calvarial defect. Additionally, two scaffold types differing in filament directionality (Fil and FilG) were compared to elucidate optimal design parameters. Distance of bone ingrowth and percentage of regenerated area within scaffolds were measured by histomorphometric analysis. Our findings reveal filaments of 0.50 mm as the most effective filament-based scaffold, demonstrating superior bone ingrowth and bony regenerated area compared to larger size filament (i.e., 0.83 mm and 1.25 mm scaffolds). Optimized directionality of filaments can overcome the reduced performance of larger filaments. This study advances our understanding of microarchitecture’s role in bone tissue engineering and holds significant implications for clinical practice, paving the way for the development of highly tailored, patient-specific bone substitutes with enhanced efficacy.

Andre de Carvalho, Robson Bonidia, Jude Dzevela Kong et al.

Infectious diseases, transmitted directly or indirectly, are among the leading causes of epidemics and pandemics. Consequently, several open challenges exist in predicting epidemic outbreaks, detecting variants, tracing contacts, discovering new drugs, and fighting misinformation. Artificial Intelligence (AI) can provide tools to deal with these scenarios, demonstrating promising results in the fight against the COVID-19 pandemic. AI is becoming increasingly integrated into various aspects of society. However, ensuring that AI benefits are distributed equitably and that they are used responsibly is crucial. Multiple countries are creating regulations to address these concerns, but the borderless nature of AI requires global cooperation to define regulatory and guideline consensus. Considering this, The Global South AI for Pandemic & Epidemic Preparedness & Response Network (AI4PEP) has developed an initiative comprising 16 projects across 16 countries in the Global South, seeking to strengthen equitable and responsive public health systems that leverage Southern-led responsible AI solutions to improve prevention, preparedness, and response to emerging and re-emerging infectious disease outbreaks. This opinion introduces our branches in Latin American and Caribbean (LAC) countries and discusses AI governance in LAC in the light of biotechnology. Our network in LAC has high potential to help fight infectious diseases, particularly in low- and middle-income countries, generating opportunities for the widespread use of AI techniques to improve the health and well-being of their communities.

Damilola Oshunyinka

The pharmaceutical Research and development (R&D) process is lengthy and costly, taking nearly a decade to bring a new drug to the market. However, advancements in biotechnology, computational methods, and machine learning algorithms have the potential to revolutionize drug discovery, speeding up the process and improving patient outcomes. The COVID-19 pandemic has further accelerated and deepened the recognition of the potential of these techniques, especially in the areas of drug repurposing and efficacy predictions. Meanwhile, non-small molecule therapeutic modalities such as cell therapies, monoclonal antibodies, and RNA interference (RNAi) technology have gained importance due to their ability to target specific disease pathways and/or patient populations. In the field of RNAi, many experiments have been carried out to design and select highly efficient siRNAs. However, the established patterns for efficient siRNAs are sometimes contradictory and unable to consistently determine the most potent siRNA molecules against a target mRNA. Thus, this paper focuses on developing machine learning models based on the cheminformatics representation of the nucleotide composition (i.e. AUTGC) of siRNA to predict their potency and aid the selection of the most efficient siRNAs for further development. The PLS (Partial Least Square) and SVR (Support Vector Regression) machine learning models built in this work outperformed previously published models. These models can help in predicting siRNA potency and aid in selecting the best siRNA molecules for experimental validation and further clinical development. The study has demonstrated the potential of AI/machine learning models to help expedite siRNA-based drug discovery including the discovery of potent siRNAs against SARS-CoV-2.