Academic Digital Library for institutions, students, and solo learners

Discovery

Content search and filters

This is the first search surface for the seven content types. Next we will connect full-text search and metadata-specific filters.

Results 1,892

Periodicals

Advances in Prebiotic Chemistry: the potential of Analog Computing and Navier-Stokes Nernst-Planck (NPNS) Modeling in Organic Electronics Technologies (OECTs)

In this article, we attempt to make a conceptual bridge between the research in biology, pre-biotic chemistry, biomimetics, and the tools used in organic bioelectronics in terms of materials and devices. The goal is discussing how materials and devices of organic bioelectronics can be exploited and used at the interface with biology, but also how, and at what extent, they can be adapted to mimicking nature-inspired properties, herein including unconventional computing strategies. The idea is to provide new hints and solid hypotheses for designing niche experiments that could benefit from a proper interaction, even at a basic communicative level, between materials science and biotechnology. The finale long-term vision goal being the vision of collecting experimental data that may help to made a step forward toward the implementation of the transition from inanimate objects to animated beings. The mathematical model canonically considered in this work is the Navier-Stokes-Nernst-Planck (NPNS) Model which is often used to model a charged continuum system such as the organic electrochemical transistors.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

Deep learning-guided evolutionary optimization for protein design

Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of \textit{Streptococcus pneumoniae}. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \href{https://github.com/ErikHartman/bopep}{GitHub}.

Biotechnology• 2026• arXiv
Access Full TextView Details
Periodicals

Economic Rationality under Specialization: Evidence of Decision Bias in AI Agents

In the study by Chen et al. (2023) [01], the large language model GPT demonstrated economic rationality comparable to or exceeding the average human level in tasks such as budget allocation and risk preference. Building on this finding, this paper further incorporates specialized agents, such as biotechnology experts and economists, for a horizontal comparison to explore whether specialization can enhance or maintain economic rationality equivalent to that of GPT in similar decision-making scenarios. The results indicate that when agents invest more effort in specialized fields, their decision-making behavior is more prone to 'rationality shift,' specifically manifested as increased violations of GARP (Generalized Axiom of Revealed Preference), decreased CCEI (Critical Cost Efficiency Index), and more significant decision deviations under high-risk conditions. In contrast, GPT and more generalized basic agents maintain a more stable and consistent level of rationality across multiple tasks. This study reveals the inherent conflict between specialization and economic rationality, providing new insights for constructing AI decision-making systems that balance specialization and generalization across various scenarios.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

On the Structure of the Initiation and Elongation Rates that Maximize Protein Production in the Ribosome Flow Model

Translation is a crucial step in gene expression. During translation, macromolecules called ribosomes "read" the mRNA strand in a sequential manner and produce a corresponding protein. Translation is known to consume most of the cell's energy. Maximizing the protein production rate in mRNA translation, subject to the bounded biomolecullar budget, is thus an important problem in both biology and biotechnology. We consider this problem using a mathematical model for mRNA translation called the ribosome flow model (RFM). For an mRNA strand with $n$ sites the RFM includes $n$ state-variables that encode the normalized ribosomal density at each site, and $n+1$ positive parameters: the initiation rate and elongation rates along the chain. An affine constraint on these rates is used to model the bounded cellular budget. We show that for a homogeneous constraint the rates that maximize the steady-state protein production rate have a special structure. They are symmetric with respect to the middle of the chain, and monotonically increase as we move towards the center of the chain. The ribosomal densities corresponding to the optimal rates monotonically decrease along the chain. We discuss some of the biological implications of these results.

Biotechnology• 2014• arXiv
Access Full TextView Details
Periodicals

Hemodynamics of a Bileaflet Mechanical Heart Valve with Different Levels of Dysfunction

Heart disease is one of leading causes of mortality worldwide. Healthy heart valves are key for proper heart function. When these valves dysfunction, a replacement is often necessary in severe cases. The current study presents an investigation of the pulsatile blood flow through a bileaflet mechanical heart valve (BMHV) where one leaflet is healthy and can fully open and the other leaflet cannot fully open with different levels of dysfunction. To better understand the implications that a dysfunctional leaflet has on the blood flow through these valves, analysis of flow characteristics such as velocity, pressure drop, wall shear stress and vorticity profiles was performed. Results suggested that leaflet dysfunction caused increased local velocities, separation regions and wall shear stresses. For example, the maximum velocity increased from 2.53 m/s to 4.9 m/s when dysfunction increased from 0% to 100%. The pressure drop increased (by up to 300%) with dysfunctionality. Results suggested that leaflet dysfunction also caused increased wall shear stresses on the valve frame where higher stresses developed around the hinges (at 75% and 100% dysfunctions). Analysis also showed that increased dysfunctionality of one leaflet led to higher net shear forces on both the healthy and dysfunctional leaflets (by up to 200% and 600%, respectively).

Biotechnology• 2017• arXiv
Access Full TextView Details
Periodicals

Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves

Despite the central role that antibodies play in the adaptive immune system and in biotechnology, much remains unknown about the quantitative relationship between an antibody's amino acid sequence and its antigen binding affinity. Here we describe a new experimental approach, called Tite-Seq, that is capable of measuring binding titration curves and corresponding affinities for thousands of variant antibodies in parallel. The measurement of titration curves eliminates the confounding effects of antibody expression and stability that arise in standard deep mutational scanning assays. We demonstrate Tite-Seq on the CDR1H and CDR3H regions of a well-studied scFv antibody. Our data shed light on the structural basis for antigen binding affinity and suggests a role for secondary CDR loops in establishing antibody stability. Tite-Seq fills a large gap in the ability to measure critical aspects of the adaptive immune system, and can be readily used for studying sequence-affinity landscapes in other protein systems.

Biotechnology• 2016• arXiv
Access Full TextView Details
Periodicals

Simulation of the thermocapillary assembly of a colloidal cluster during the evaporation of a liquid film in an unevenly heated cell

The control of the thermocapillary assembly of colloidal particle clusters is important for a variety of applications, including the creation of photonic crystals for microelectronics and optoelectronics, membrane formation for biotechnology, and surface cleaning for laboratory-on-chip devices. It is important to understand the main mechanisms that influence the formation of such clusters. This article considers a two-dimensional mathematical model describing the transfer of particles by a thermocapillary flow in an unevenly heated cell during the evaporation of a liquid. This gave us the opportunity to study one of the main processes that triggers the formation of a particle cluster. Whether the particle will move with the flow or stop at the heater, becoming the basis for the cluster, is determined by the ratio between gravity and the drag force. The results of numerical calculations show that, for small particle concentrations, their fraction entering the cluster decreases as the volumetric heat flux density $Q$ increases. The reason for this is an increase in the thermocapillary flow with an increase in the volumetric heat flux $Q$. It reduces the probability of particles entering the cluster.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

The Updated Genome Warehouse: Enhancing Data Value, Security, and Usability to Address Data Expansion

The Genome Warehouse (GWH), accessible at https://ngdc.cncb.ac.cn/gwh, is an extensively utilized public repository dedicated to the deposition, management and sharing of genome assembly sequences, annotations, and metadata. This paper highlights noteworthy enhancements to the GWH since the 2021 version, emphasizing substantial advancements in web interfaces for data submission, database functionality updates, and resource integration. Key updates include the reannotation of released prokaryotic genomes, mirroring of genome resources from National Center for Biotechnology Information (NCBI) GenBank and RefSeq, integration of Poxviridae sequences, implementation of an online batch submission system, enhancements to the quality control system, advanced search capabilities, and the introduction of a controlled-access mechanism for human genome data. These improvements collectively augment the ease and security of data submission and access as well as genome data value, thereby fostering heightened convenience and utility for researchers in the genomic field.

Biotechnology• 2024• arXiv
Access Full TextView Details
Periodicals

A Cyberpunk 2077 perspective on the prediction and understanding of future technology

Science fiction and video games have long served as valuable tools for envisioning and inspiring future technological advancements. This position paper investigates the potential of Cyberpunk 2077, a popular science fiction video game, to shed light on the future of technology, particularly in the areas of artificial intelligence, edge computing, augmented humans, and biotechnology. By analyzing the game's portrayal of these technologies and their implications, we aim to understand the possibilities and challenges that lie ahead. We discuss key themes such as neurolink and brain-computer interfaces, multimodal recording systems, virtual and simulated reality, digital representation of the physical world, augmented and AI-based home appliances, smart clothing, and autonomous vehicles. The paper highlights the importance of designing technologies that can coexist with existing preferences and systems, considering the uneven adoption of new technologies. Through this exploration, we emphasize the potential of science fiction and video games like Cyberpunk 2077 as tools for guiding future technological advancements and shaping public perception of emerging innovations.

Biotechnology• 2023• arXiv
Access Full TextView Details
Periodicals

A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 1: Halocarbons

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.

Biotechnology• 2024• arXiv
Access Full TextView Details
Periodicals

Machine learning in bioprocess development: From promise to practice

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.

Biotechnology• 2022• arXiv
Access Full TextView Details
Periodicals

DNA Calorimetric Force Spectroscopy at Single Base Pair Resolution

DNA hybridization is a fundamental reaction with wide-ranging applications in biotechnology. The nearest-neighbor (NN) model provides the most reliable description of the energetics of duplex formation. Most DNA thermodynamics studies have been done in melting experiments in bulk, of limited resolution due to ensemble averaging. In contrast, single-molecule methods have reached the maturity to derive DNA thermodynamics with unprecedented accuracy. We combine single-DNA mechanical unzipping experiments using a temperature jump optical trap with machine learning methods and derive the temperature-dependent DNA energy parameters of the NN model. In particular, we measure the previously unknown ten heat-capacity change parameters $ΔC_p$, relevant for thermodynamical predictions throughout the DNA stability range. Calorimetric force spectroscopy establishes a groundbreaking methodology to accurately study nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid structures.

Biotechnology• 2024• arXiv
Access Full TextView Details
Periodicals

Democratising Knowledge Representation with BioCypher

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to non-specialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://biocypher.org) frees up valuable developer time; we encourage further development and usage by the community.

Biotechnology• 2022• arXiv
Access Full TextView Details
Periodicals

ProtiGeno: a prokaryotic short gene finder using protein language models

Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology. Although the current gene finders are highly sensitive in finding long genes, their sensitivity decreases noticeably in finding shorter genes (<180 nts). The culprit is insufficient annotated gene data to identify distinguishing features in short open reading frames (ORFs). We develop a deep learning-based method called ProtiGeno, specifically targeting short prokaryotic genes using a protein language model trained on millions of evolved proteins. In systematic large-scale experiments on 4,288 prokaryotic genomes, we demonstrate that ProtiGeno predicts short coding and noncoding genes with higher accuracy and recall than the current state-of-the-art gene finders. We discuss the predictive features of ProtiGeno and possible limitations by visualizing the three-dimensional structure of the predicted short genes. Data, codes, and models are available at https://github.com/tonytu16/protigeno.

Biotechnology• 2023• arXiv
Access Full TextView Details
Periodicals

An Assembly-Line Mechanism for In-Vitro Encapsulation of Fragmented Cargo in Virus-Like Particles

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.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety

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.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives

Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate their effectiveness in accelerating enzyme discovery. Despite notable progress, current approaches remain limited by data scarcity, model generalizability, and interpretability. We discuss emerging strategies to overcome these challenges, including multi-task learning, integration of multi-modal data, and explainable AI. Together, these developments establish ML-guided enzyme mining as a scalable and predictive framework for uncovering novel biocatalysts, with broad applications in biocatalysis, biotechnology, and synthetic biology.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

Nano-gap electrode dielectrophoresis for tether-free trapping and interferometric-scattering detection of single 20 nm particles

Accurate detection and characterization of nanoparticles within confined spaces is crucial for applications ranging from nanofluidics to biotechnology. We present a novel approach that combines interferometric scattering (iSCAT) detection with trapping by dielectrophoresis (DEP) to achieve label-free detection of nanoparticles that are trapped and/or actuated between nano-gap electrodes. DEP utilizes the interaction between the induced dipole of the particle and the applied electric field to create a trapping potential. We demonstrate our method by trapping and label-free detection of down to 20 nm polystyrene nanoparticles. Additionally, we demonstrate that the signal-to-noise ratio of our detection can be boosted up to 20-fold by periodic actuation of the nanoparticle in the trap. This is done by a digital lock-in detection scheme on the modulated scattering signal. Our method holds promise for various applications, including assembly of nanoparticles, single-particle property analysis, and nanofluidic devices.

Biotechnology• 2024• arXiv
Access Full TextView Details
Periodicals

Diffusion-influenced reactions in a hollow nano-reactor with a circular hole

Hollow nanostructures are paid increasing attention in many nanotechnology-related communities in view of their numerous applications in chemistry and biotechnology, e.g. as smart nanoreactors or drug-delivery systems. In this paper we consider irreversible, diffusion-influenced reactions occurring within a hollow spherical cavity endowed with a circular hole on its surface. Importantly, our model is not limited to small sizes of the aperture. In our scheme, reactants can freely diffuse inside and outside the cavity through the hole, and react at a spherical boundary of given size encapsulated in the chamber and endowed with a given intrinsic rate constant. We work out the solution of the above problem, enabling one to compute the reaction rate constant to any desired accuracy. Remarkably, we show that, in the case of narrow holes, the rate constant is extremely well-approximated by a simple formula that can be derived on the basis of simple physical arguments and that can be readily employed to analyze experimental data.

Biotechnology• 2014• arXiv
Access Full TextView Details
Periodicals

Selecting Differential Splicing Methods: Practical Considerations

Alternative splicing is crucial in gene regulation, with significant implications in clinical settings and biotechnology. This review article compiles bioinformatics RNA-seq tools for investigating differential splicing; offering a detailed examination of their statistical methods, case applications, and benefits. A total of 22 tools are categorised by their statistical family (parametric, non-parametric, and probabilistic) and level of analysis (transcript, exon, and event). The central challenges in quantifying alternative splicing include correct splice site identification and accurate isoform deconvolution of transcripts. Benchmarking studies show no consensus on tool performance, revealing considerable variability across different scenarios. Tools with high citation frequency and continued developer maintenance, such as DEXSeq and rMATS, are recommended for prospective researchers. To aid in tool selection, a guide schematic is proposed based on variations in data input and the required level of analysis. Additionally, advancements in long-read RNA sequencing are expected to drive the evolution of differential splicing tools, reducing the need for isoform deconvolution and prompting further innovation.

Biotechnology• 2024• arXiv
Access Full TextView Details
Periodicals

Reproducibility of Large Eddy Simulations for mixing in stirred tank reactors

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.

Biotechnology• 2025• arXiv
Access Full TextView Details
Periodicals

Orthotropic Piezoelectricity in 2D Nanocellulose

The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present I\b{eta}-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D I\b{eta}-nanocellulose piezoelectric response, ~pm V-1, ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies.

Biotechnology• 2017• arXiv
Access Full TextView Details
Periodicals

DNA Nucleobase Interaction Driven Electronic and Optical Fingerprints in Gallium Selenide Monolayer for DNA Sequencing Devices

The interaction of DNA nucleobases with monolayer GaSe has been studied with in DFT framework using vdW functional. We found that nucleobases are physisorbed on the GaSe monolayer. The order of binding energy per atom is C > T > G > A. The room temperature recovery time estimated to be maximum of 113.88 micro sec. for T+GaSe indicting reusability of the GaSe based devices. The modulation in the electronic structures of GaSe has been clearly captured within the simulated STM measurements. We also demonstrate quantum capacitance as a key parameter for sensing applications. Furthermore, in optical properties, electron energy loss (EEL) spectra show red shift in photon energy on nucleobase adsorption in UV region. In nutshell, GaSe monolayer exhibit anisotropic optical response in UV-region which can be highly beneficial for developing polarized optical sensors. Our results demonstrate that GaSe monolayer can be utilized to fabricate reusable DNA sequencing devices for biotechnology and medical science.

Biotechnology• 2021• arXiv
Access Full TextView Details
Periodicals

Diffusion towards a nanoforest of absorbing pillars

Spiky coatings (also known as nanoforests or Fakir-like surfaces) have found many applications in chemical physics, material sciences and biotechnology, such as superhydrophobic materials, filtration and sensing systems, selective protein separation, to name but a few. In this paper, we provide a systematic study of steady-state diffusion towards a periodic array of absorbing cylindrical pillars protruding from a flat base. We approximate a periodic cell of this system by a circular tube containing a single pillar, derive an exact solution of the underlying Laplace equation, and deduce a simple yet exact representation for the total flux of particles onto the pillar. The dependence of this flux on the geometric parameters of the model is thoroughly analyzed. In particular, we investigate several asymptotic regimes such as a thin pillar limit, a disk-like pillar, and an infinitely long pillar. Our study sheds a light onto the trapping efficiency of spiky coatings and reveals the roles of pillar anisotropy and diffusional screening.

Biotechnology• 2022• arXiv
Access Full TextView Details
Previous1...1415161718...79Next