Hasil untuk "Biotechnology"

J. Zeikus, M. Jain, P. Elankovan

K. Saito, Fumio Matsuda

André Schuster, M. Schmoll

Fungi of the genus Trichoderma are soilborne, green-spored ascomycetes that can be found all over the world. They have been studied with respect to various characteristics and applications and are known as successful colonizers of their habitats, efficiently fighting their competitors. Once established, they launch their potent degradative machinery for decomposition of the often heterogeneous substrate at hand. Therefore, distribution and phylogeny, defense mechanisms, beneficial as well as deleterious interaction with hosts, enzyme production and secretion, sexual development, and response to environmental conditions such as nutrients and light have been studied in great detail with many species of this genus, thus rendering Trichoderma one of the best studied fungi with the genome of three species currently available. Efficient biocontrol strains of the genus are being developed as promising biological fungicides, and their weaponry for this function also includes secondary metabolites with potential applications as novel antibiotics. The cellulases produced by Trichoderma reesei, the biotechnological workhorse of the genus, are important industrial products, especially with respect to production of second generation biofuels from cellulosic waste. Genetic engineering not only led to significant improvements in industrial processes but also to intriguing insights into the biology of these fungi and is now complemented by the availability of a sexual cycle in T. reesei/Hypocrea jecorina, which significantly facilitates both industrial and basic research. This review aims to give a broad overview on the qualities and versatility of the best studied Trichoderma species and to highlight intriguing findings as well as promising applications.

M. Cooper

M. Famulok, J. Hartig, G. Mayer

A. Richmond

M. Gavrilescu, Y. Chisti

K. Aslan, I. Gryczynski, J. Malicka et al.

P. Gogate, Abhijeet M. Kabadi

Ana Paula Yumi Nishimura, Fernando Augusto Pedersen Voll, Nadia Krieger et al.

Kinetic models are important tools for guiding the design and optimization of lipase-catalyzed processes. These processes follow the Ping Pong bi bi mechanism, for which mechanistic kinetic equations can be derived. However, when there are several competing reactions, fully mechanistic models contain a large number of parameters, making it difficult to obtain reliable estimates, so simplified models are necessary. We present a two-step approach to developing semi-mechanistic models of such processes. The first step involves the estimation of the selectivities of the enzyme, using profiles for the reaction species plotted against the degree of reaction, while the second step involves empirical fitting to the same data, but plotted as a function of time. We demonstrate this two-step approach through four case studies based on the literature data for the lipase-catalyzed esterification of fatty acids with trimethylolpropane to produce biolubricants. The semi-mechanistic models were able to describe the data well. Our approach has the advantage of allowing selectivities to be estimated without confounding effects from phenomena such as enzyme denaturation and inhibition. It therefore provides a promising framework for developing models of enzyme-catalyzed processes that obey Ping Pong bi bi kinetics.

R. Wijffels, O. Kruse, K. Hellingwerf

Na Song, Huili Xia, Yaoru Xie et al.

Tyrosol is an important component of pharmaceuticals, nutraceuticals, and cosmetics, and their biosynthetic pathways are currently a hot research topic. d-Erythrose 4-phosphate is a key precursor for the biosynthesis of tyrosol in Saccharomyces cerevisiae. Hence, the flux of d-Erythrose 4-phosphate determined the yield of tyrosol synthesis. In this study, we first obtained an S. cerevisiae strain S19 with a tyrosol yield of 247.66 mg/L by metabolic engineering strategy. To increase the production of d-Erythrose 4-phosphate, highly active phosphoketolase BA-C was obtained by bioinformatics combined with tyrosol yield assay. The key residue sites 183, 217, and 320 were obtained by molecular docking, kinetic simulation, and tyrosol yield verification. After mutation, the highly efficient phosphoketolase BA-CHis320Met was obtained, with a 37.32 % increase in enzyme activity. The tyrosol production of strain S26 with BA-CHis320Arg increased by 43.05 % than strain S25 with BA-C and increased by 151.19 % compared with the strain S19 without phosphoketolase in a 20 L fermenter. The mining and modification of phosphoketolase will provide strong support for the de novo synthesis of aromatic compounds.

Övgü Gencer

The blue crab (Callinectes sapidus, Rathbun 1896) has become a significant source of raw materials in biotechnology and nanotechnology due to the biomaterials present in its shell. Natural polymers such as chitin and chitosan, derived from the crab's shell, are particularly noteworthy for their environmentally friendly and biologically compatible properties. These biopolymers provide an innovative alternative in the synthesis of quantum dots (QDs). Quantum dots are favored in various applications, including biomedical imaging, environmental sensors, and energy storage, due to their superior optoelectronic properties. Chitosan obtained from blue crab shells acts as both a stabilizer and a coating agent in the green synthesis of quantum dots. This process minimizes the use of toxic chemicals, thus promoting environmental sustainability. Moreover, the antimicrobial and biodegradable properties of chitosan enhance its usability in biomedical applications. For instance, biocompatible carbon-based quantum dots have shown promising results in cancer diagnostics and drug delivery systems. The synthesis of quantum dots using biomaterials is more cost-effective and environmentally friendly compared to traditional methods. Furthermore, utilizing blue crab shells as a waste material contributes to both marine ecosystem preservation and the circular economy. These synthesis methods are reported to create a significant paradigm shift in the field of sustainable technology development. In conclusion, the synthesis of quantum dots using biomaterials derived from blue crabs has the potential to reduce environmental impacts while serving advanced technological applications. This approach significantly contributes to the development of biotechnological innovations and sustainable development goals.

Ayesha Amjad, Irina Tsvetkova, Lena G. Lowry et al.

The ability of virus shells to encapsulate a wide range of functional cargoes, especially multiple cargoes - siRNAs, enzymes, and chromophores - has made them an essential tool in biotechnology for advancing drug delivery applications and developing innovative new materials. Here we present a mechanistic study of the processes and pathways that lead to multiple cargo encapsulation in the co-assembly of virus shell proteins with ligand-coated nanoparticles. Based on the structural identification of different intermediates, enabled by the contrast in electron microscopy provided by the metal nanoparticles that play the cargo role, we find that multiple cargo encapsulation occurs by self-assembly via a specific ``assembly line'' pathway that is different from previously described \emph{in vitro} assembly mechanisms of virus-like particles (VLP). The emerging model explains observations that are potentially important for delivery applications, for instance, the pronounced nanoparticle size selectivity.

Yuhao Wang, Keyan Ding, Kehua Feng et al.

Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and denovo design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibility or evade immune responses. These concerns underscore critical biosafety and ethical challenges. To address these issues, we propose a Knowledge-guided Preference Optimization (KPO) framework that integrates prior knowledge via a Protein Safety Knowledge Graph. This framework utilizes an efficient graph pruning strategy to identify preferred sequences and employs reinforcement learning to minimize the risk of generating harmful proteins. Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality, offering a robust safety assurance framework for applying generative models in biotechnology.

Alireza Abbaszadeh, Armita Shahlai

CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.

Cees Haringa, Ryan Rautenbach, Héctor Maldonado de Léon et al.

CFD simulations are widely used to quantify mixing performance of stirred tanks, for various applications in chemical engineering and biotechnology. Due to advances in GPU computing, more and more often these simulations make use of Large Eddy Simulations (LES), which explicitly simulate the dynamics of large-scale turbulence. Although these simulations are fully deterministic and hence theoretically reproducible, small numerical variations induced by round-off errors combined with differences in distribution and order of operations in parallel computing lead to separation of trajectories, i.e. different flowfield evolutions and different mixing times between repeat simulations, even on the same architecture. We investigate the impact of repeat simulations on the mixing time distribution observed in a $30 \liter$ stirred vessel with two commercial CFD packages, and compare to experimental variability. While the distribution between simulations and experiments is in very good agreement, we do conclude confidence intervals should be reported for CFD simulations of mixing.

Kyung-Nam Kang, Hayoung Park

Hyunjin Shim

Expanding genetic codes from natural standard nucleotides to artificial non-standard nucleotides marks a significant advancement in synthetic biology, with profound implications for biotechnology and medicine. Decoding the biological information encoded in these non-standard nucleotides presents new challenges, as traditional sequencing technologies are unable to recognize or interpret novel base pairings. In this perspective, we explore the potential of nanopore sequencing, which is uniquely suited to decipher both standard and non-standard nucleotides by directly measuring the biophysical properties of nucleic acids. Nanopore technology offers real-time, long-read sequencing without the need for amplification or synthesis, making it particularly advantageous for expanded genetic systems like Artificially Expanded Genetic Information Systems (AEGIS). We discuss how the adaptability of nanopore sequencing and advancements in data processing can unlock the potential of these synthetic genomes and open new frontiers in understanding and utilizing expanded genetic codes.

Daria Grigorash, Simon Müller, Patrice Paricaud et al.

The COSMO-RS (Conductor-like Screening Model for Real Solvents) is a predictive thermodynamic model that has found diverse applications in various domains like chemical engineering, environmental chemistry, nanotechnology, material science, and biotechnology. Its core concept involves calculating the screening charge density on the surface of each molecule and letting these surface patches interact with each other to calculate thermodynamic properties. In this study, we aim to enhance the performance of the open-source implementation openCOSMO-RS by incorporating dispersive interactions between the paired segments. Several parametrizations were systematically evaluated through the extensive regression analysis using a comprehensive database of Vapor-Liquid Equilibrium (VLE), Liquid-Liquid Equilibrium (LLE) and Infinite Dilution Activity Coefficients (IDACs). Furthermore, the influence of different combinatorial terms on the model performance was investigated. Our findings indicate that incorporating dispersive interactions significantly improves the accuracy of phase equilibrium predictions for halocarbons and refrigerant mixtures.