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Periodicals

Biological applications of ferroelectric materials

The study and applications of ferroelectric materials in the biomedical and biotechnological fields is a novel and very promising scientific area that spans roughly one decade. However, some groups have already provided experimental proof of very interesting biological modulation when living systems are exposed to different ferroelectrics and excitation mechanisms. These materials should offer several advantages in the field of bioelectricity, such as no need of an external electric power source or circuits, scalable size of the electroactive regions, flexible and reconfigurable virtual electrodes, or fully proved biocompatibility. In this focused review we provide the underlying physics of ferroelectric activity and a recount of the research reports already published, along with some tentative biophysical mechanisms that can explain the observed results. More specifically, we focused on the biological actions of domain ferroelectrics, and ferroelectrics excited by the bulk photovoltaic effect or the pyroelectric effect. It is our goal to provide a comprehensive account of the published material so far, and to set the stage for a vigorous expansion of the field, with envisioned applications that span from cell biology and signaling to cell and tissue regeneration, antitumoral action, or cell bioengineering to name a few.

Biotechnology• 2021• arXiv
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Periodicals

In silico design, in vitro construction and in vivo application of synthetic small regulatory RNAs in bacteria

Small regulatory RNAs (sRNAs) are short non-coding RNAs in bacteria capable of post-transcriptional regulation. sRNAs have recently gained attention as tools in basic and applied sciences for example to fine-tune genetic circuits or biotechnological processes. Even though sRNAs often have a rather simple and modular structure, the design of functional synthetic sRNAs is not necessarily trivial. This protocol outlines how to use computational predictions and synthetic biology approaches to design, construct and validate synthetic sRNA functionality for their application in bacteria. The computational tool, SEEDling, matches the optimal seed region with the user-selected sRNA scaffold for repression of target mRNAs. The synthetic sRNAs are assembled using Golden Gate cloning and their functionality is subsequently validated. The protocol uses the acrA mRNA as an exemplary proof-of-concept target in Escherichia coli. Since AcrA is part of a multidrug efflux pump, acrA repression can be revealed by assessing oxacillin susceptibility in a phenotypic screen. However, in case target repression does not result in a screenable phenotype, an alternative validation of synthetic sRNA functionality based on a fluorescence reporter is described.

Biotechnology• 2023• arXiv
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Periodicals

OMICtools: a community-driven search engine for biological data analysis

With high-throughput biotechnologies generating unprecedented quantities of data, researchers are faced with the challenge of locating and comparing an exponentially growing number of programs and websites dedicated to computational biology, in order to maximize the potential of their data. OMICtools is designed to meet this need with its open-access search engine offering an easy means of locating the right tools corresponding to each researcher and their specific biological data analyses. The OMICtools website (https://OMICtools.com) centralizes more than 18,500 software tools and databases, manually classified, by a team of biocurators including many scientific experts. Key information, a direct link, and access to discussions and evaluations by the biomedical community are provided for each tool. Anyone can join the OMICtools community and create a profile page, to share their expertise and comment on tools. In addition, developers can directly upload their code versions which are registered for identification and citation in scientific publications, improving research traceability. The OMICtools community links thousands of life scientists and developers worldwide, who use this bioinformatics platform to accelerate their projects and biological data analyses.

Biotechnology• 2017• arXiv
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Periodicals

Holographic nature of critical quantum states of proteins

The Anderson metal-insulator transition is a fundamental phenomenon in condensed matter physics, describing the transition from a conducting (metallic) to a non-conducting (insulating) state driven by disorder in a material. At the critical point of the Anderson transition, wave functions exhibit multifractal behavior, and energy levels display a universal distribution, indicating non-trivial correlations in the eigenstates. Recent studies have shown that proteins, traditionally considered insulators, exhibit much higher conductivity than previously assumed. In this paper, we investigate several proteins known for their efficient electron transport properties. We compare their energy level statistics, eigenfunction correlation, and electron return probability to those expected in metallic, insulating, or critical states. Remarkably, these proteins exhibit properties of critically disordered metals in their natural state without any parameter adjustment. Their composition and geometry are self-organized into the critical state of the Anderson transition, and their fractal properties are universal and unique among critical systems. Our findings suggest that proteins' wave functions may fulfill ``holographic'' area laws, since their correlation fractal dimension is \(d_2\approx 2\).

Biotechnology• 2024• arXiv
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Periodicals

Notes on stochastic (bio)-logic gates: the role of allosteric cooperativity

Recent experimental breakthroughs have finally allowed to implement in-vitro reaction kinetics (the so called {\em enzyme based logic}) which code for two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the stochastic OR (and NOR). This accomplishment, together with the already-known single-input gates (performing as YES and NOT), provides a logic base and paves the way to the development of powerful biotechnological devices. The investigation of this field would enormously benefit from a self-consistent, predictive, theoretical framework. Here we formulate a complete statistical mechanical description of the Monod-Wyman-Changeaux allosteric model for both single and double ligand systems, with the purpose of exploring their practical capabilities to express logical operators and/or perform logical operations. Mixing statistical mechanics with logics, and quantitatively our findings with the available biochemical data, we successfully revise the concept of cooperativity (and anti-cooperativity) for allosteric systems, with particular emphasis on its computational capabilities, the related ranges and scaling of the involved parameters and its differences with classical cooperativity (and anti-cooperativity).

Biotechnology• 2014• arXiv
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Periodicals

Spin-Dependent Electron Transport through Bacterial Cell Surface Multiheme Electron Conduits

Multiheme cytochromes, located on the bacterial cell surface, function as long-distance (> 10 nm) electron conduits linking intracellular reactions to external surfaces. This extracellular electron transfer process, which allows microorganisms to gain energy by respiring solid redox-active minerals, also facilitates the wiring of cells to electrodes. While recent studies suggested that a chiral induced spin selectivity effect is linked to efficient electron transmission through biomolecules, this phenomenon has not been investigated in the extracellular electron conduits. Using magnetic conductive probe atomic force microscopy, Hall voltage measurements, and spin-dependent electrochemistry of the decaheme cytochromes MtrF and OmcA from the metal-reducing bacterium Shewanella oneidensis MR-1, we show that electron transport through these extracellular conduits is spin-selective. Our study has implications for understanding how spin-dependent interactions and magnetic fields may control electron transport across biotic-abiotic interfaces in both natural and biotechnological systems. Electron

Biotechnology• 2026• arXiv
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Periodicals

Stabilization by Harvesting in Age-Structured Trophic Networks

We propose and analyze a nonlinear age-structured multi-species model that serves as a unifying framework for ecological and biotechnological systems in complex environments (microbial communities, bioreactors, and others). The formulation incorporates nonlocal intra- and interspecific interactions modulated by environmental covariates. Under general assumptions on mortality, reproduction rates, and interaction kernels, we establish existence, uniqueness, and positivity of solutions. We illustrate the model's practical relevance along two lines. First, multi-species examples, notably a non-transitive (cyclic) competition model, for which we show that, under the model assumptions, a control applied to a single species can achieve global stabilization of the system. Second, the population dynamics of malaria-vector mosquitoes, for which we develop two control strategies (biological and genetic). In the biological-control scenario, we prove global asymptotic stability of the aquatic compartment by constructing an explicit Lyapunov function. Numerical simulations validate the theoretical results and compare the effectiveness of the proposed strategies in reducing vector density and malaria transmission.

Biotechnology• 2025• arXiv
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Periodicals

How high-resolution agent-based models can improve fundamental insights in tissue development and cell culturing methods

The fundamental understanding of how cells physically interact with each other and their environment is key to understanding their organisation in living tissues. Over the past decades several computational methods have been developed to decipher emergent multi-cellular behaviors. In particular agent-based (or cell-based) models that consider the individual cell as basic modeling unit tracked in space and time enjoy increasing interest across scientific communities. In this article we explore a particular class of cell-based models, so-called Deformable Cell Models (DCMs), that allow to simulate the biophysics of the cell with high realism. After situating this model among other model types, We give an overview of past and recent DCM developments and discuss new simulation results of several applications covering in-vitro and in-vivo systems. Our goal is to demonstrate how such models can generate quantitative added value in biological and biotechnological problems.

Biotechnology• 2026• arXiv
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Periodicals

Opportunities for artificial intelligence in advancing precision medicine

Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays, libraries of medical images, or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

Biotechnology• 2019• arXiv
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Periodicals

A Review on the Applications of Transformer-based language models for Nucleotide Sequence Analysis

In recent times, Transformer-based language models are making quite an impact in the field of natural language processing. As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for various applications in bioinformatics. In this regard, this paper introduces the major developments of Transformer-based models in the recent past in the context of nucleotide sequences. We have reviewed and analysed a large number of application-based papers on this subject, giving evidence of the main characterizing features and to different approaches that may be adopted to customize such powerful computational machines. We have also provided a structured description of the functioning of Transformers, that may enable even first time users to grab the essence of such complex architectures. We believe this review will help the scientific community in understanding the various applications of Transformer-based language models to nucleotide sequences. This work will motivate the readers to build on these methodologies to tackle also various other problems in the field of bioinformatics.

Biotechnology• 2024• arXiv
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Periodicals

Controlling false discoveries in high-dimensional situations: Boosting with stability selection

Modern biotechnologies often result in high-dimensional data sets with much more variables than observations (n $\ll$ p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provides insights into the usefulness of this combination. Limitations are discussed and guidance on the specification and tuning of stability selection is given. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given. The results will be used to detect differentially expressed phenotype measurements in patients with autism spectrum disorders. All methods are implemented in the freely available R package stabs.

Biotechnology• 2014• arXiv
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Periodicals

Preference-based Antibody Expression Ranking: Scaling with Large-scale Weak Supervision

Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose a unified preference-based learning framework that integrates scarce quantitative expression data with large-scale weak positive supervision from immunization data. We adapt Direct Preference Optimization (DPO) to protein language models by introducing a union-masked log-likelihood approximation and IMGT-based alignment, enabling efficient training on variable-length sequences. Evaluating on a diverse internal dataset of 1254 labeled sequences and 4 million unlabeled camelid-derived antibodies, we show that our method consistently outperforms baselines on most metrics. Our results demonstrate that preference learning can effectively learn from weak supervision, providing a scalable solution for antibody expressibility optimization in data-constrained settings. Project page: https://kisoji-biotechnology-inc.github.io/Preference-Expression-Ranking/.

Biotechnology• 2026• arXiv
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Periodicals

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms competing methods and can identify new associations. Furthermore, through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problems.

Biotechnology• 2020• arXiv
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Periodicals

DuctApe: a suite for the analysis and correlation of genomic and OmnilogTM Phenotype Microarray data

Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLogTM Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on genephenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

Biotechnology• 2013• arXiv
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Periodicals

Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3

Cyanobacteriochromes are bi-stable photoreceptor proteins with desirable photochemical properties for biotechnological applications such as optogenetics or fluorescence microscopy. Here, we investigated Slr1393-g3, a cyanobacteriochrome that reversibly photo-switches between a red-absorbing (Pr) and green-absorbing (Pg) form. We applied advanced IR spectroscopic methods to track the sequence of intermediates during the photocycle over many orders in magnitude in time. In the conversion from Pg to Pr, we have revealed a new intermediate which precedes the Pr formation by using transient IR spectroscopy. In addition, stationary and transient 2D~IR experiments measured the vibrational couplings between different groups of the chromophore and the protein during these intermediate states. Anharmonic QM/MM calculations predict spectra in close-to-quantitative agreement with experimental 2D~IR spectra of the initial and the final state of the photocycle. They facilitate the assignment of the IR spectra and provide an atomistic insight into the coupling mechanism. This serves as a basis for the interpretation of the spectroscopic results and suggests structural changes of the intermediates along the photocycle.

Biotechnology• 2023• arXiv
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Periodicals

Numerical Modeling of Oxygen Diffusion in Tissue Spheroids Undergoing Fusion Using Functional Representation and Finite Volumes

A three-dimensional cell culture called a spheroid serves as a foundational entity in a wide variety of modern tissue engineering applications, including 3D-bioprinting and preclinical drug testing. Lack of oxygen within tissue spheroids hinders metabolism of cells and eventually leads to cell death. Prevention of necrosis is crucial to success of tissue engineering methods and such prevention requires estimation of cell viability in the spheroid. We propose a novel approach for numerical modeling of diffusion in tissue spheroids during their fusion. The approach is based on numerical solutions of partial differential equations and the application of Functional Representations (FRep) framework for geometric modeling. We present modeling of oxygen diffusion based on meshes derived from the geometry of fusing spheroids, a method for selecting optimal spheroid size, and several statistics for estimating cellular viability. Our findings provide insights into oxygen diffusion in three-dimensional cell cultures thus improving the robustness of biotechnological methods that employ tissue spheroids.

Biotechnology• 2025• arXiv
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Periodicals

Thermo-optic modulator with ultra-high extinction ratio for low-loss silicon nitride integrated photonics

Extremely low-loss silicon nitride integrated circuits is a potential platform for a growing number of frontier applications in quantum technologies, high-performance and analog computing, nonlinear optics, light detection and ranging (LiDAR), and biotechnologies. However, efficient optical modulation with a wide frequency response, high contrast, low power and scalable manufacturing remains one of the key challenges for silicon nitride integrated photonics. Here, we propose an integrated thermo-optic phase shifter with isolation trenches operating in the C-band. The fabricated thermo-optic modulator capable to achieve a $π$-phase shift shift at a power consumption of 65 mW, bandwidth of 12 kHz, and extinction ratio (ER) over 80 dB. Moreover, we systematically demonstrate its compatibility with low-loss silicon nitride photonic integrated circuits with microring resonators exibiting an average quality factor more than $5.9 \times 10^{6}$, which correspond to propagation loss of 0.058 dB/cm.

Biotechnology• 2026• arXiv
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Periodicals

Mechanics of Tunable Helices and Geometric Frustration in Biomimetic Seashells

Helical structures are ubiquitous in nature and engineering, ranging from DNA molecules to plant tendrils, from sea snail shells to nanoribbons. While the helical shapes in natural and engineered systems often exhibit nearly uniform radius and pitch, helical shell structures with changing radius and pitch, such as seashells and some plant tendrils, adds to the variety of this family of aesthetic beauty. Here we develop a comprehensive theoretical framework for tunable helical morphologies, and report the first biomimetic seashell-like structure resulting from mechanics of geometric frustration. In previous studies, the total potential energy is everywhere minimized when the system achieves equilibrium. In this work, however, the local energy minimization cannot be realized because of the geometric incompatibility, and hence the whole system deforms into a shape with a global energy minimum whereby the energy in each segment may not necessarily be locally optimized. This novel approach can be applied to develop materials and devices of tunable geometries with a range of applications in nano/biotechnology.

Biotechnology• 2013• arXiv
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Periodicals

EH-DPD: a Dissipative Particle Dynamics approach to Electro-Hydrodynamics

Electrohydrodynamics is crucial in many nanofluidic and biotechnological applications. In such small scales, the complexity due to the coupling of fluid dynamics with the dynamics of ions is increased by the relevance of thermal fluctuations. Here, we present a mesoscale method based on the Dissipative Particle Dynamics (DPD) model of the fluid. Two scalar quantities, corresponding to the number of positive and negative ions carried by each DPD particle, are added to the standard DPD formulation. We introduced a general framework that, given the definition of the free-energy of the DPD particle, allows to derive a fluctuation-dissipation relation and the expression for ionic fluxes between the DPD particles. This provides a link between the dynamics of the system and its equilibrium properties. The model is then validated simulating a planar electroosmotic flow for the cases of overlapping and non overlapping electric double layers. It is shown that using a Van der Waals equation of state the effect of ionic finite size can be accounted, leading to significant effects on the concentration and velocity profiles with respect to the ideal solution case.

Biotechnology• 2022• arXiv
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Periodicals

Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design

Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple-objectives like process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<~10% instead of 41.7%) using 5 or 4 shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in form of a decision tool, e.g. for the choice of an optimal and robust seed train design or for further optimization tasks within process development.

Biotechnology• 2022• arXiv
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Periodicals

Comparative Analysis of Machine Learning Algorithms for Predicting On-Target and Off-Target Effects of CRISPR-Cas13d for gene editing

CRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing. Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency enables us to design specific single guide RNAs (sgRNAs) that help locate the desired RNA target positions. In this study, we compared the performance of multiple machine learning algorithms in predicting these effects using a reported dataset. Our results show that Catboost is the most accurate model with high sensitivity. This finding represents a significant advancement in our understanding of how to chose modeling methods to deal with RNA sequence feaatures effictivelys. Furthermore, our approach can potentially be applied to other CRISPR systems and genetic engineering techniques. Overall, this work has important implications for developing safer and more effective gene therapies and biotechnological applications.

Biotechnology• 2023• arXiv
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Periodicals

The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization

Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Objectives: A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Methods: The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results: VBO is an open, community-driven ontology representing over 19,500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance: The adoption of VBO as a source of standard breed names in databases and veterinary electronic health records can enhance veterinary data interoperability and computability.

Biotechnology• 2024• arXiv
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Hydrogel-based Bio-nanomachine Transmitters for Bacterial Molecular Communications

Bacterial quorum sensing can be engineered with a view to the design of biotechnological applications based on their intrinsic role as a means of communication. We propose the creation of a positive feedback loop that will promote the emission of a superfolded green fluorescence protein from a bacterial population that will flow through hydrogel, which is used to encapsulate the cells. These engineered cells are heretofore referred to as bio-nanomachine transmitters and we show that for lower values of diffusion coefficient, a higher molecular output signal power can be produced, which supports the use of engineered bacteria contained within hydrogels for molecular communications systems. In addition, our wet lab results show the propagation of the molecular output signal, proving the feasibility of engineering a positive feedback loop to create a bio-nanomachine transmitter that can be used for biosensing applications.

Biotechnology• 2020• arXiv
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Periodicals

Banana skin: a novel material for a low-cost production of laccase

Laccases (benzenodiol: oxygen oxidoreductases; EC 1.10.3.2) are multicopper oxidases of wide substrate specificity mainly found in white-rot fungi, which are the only microorganisms able to degrade the whole wood components, but they are also expressed in bacteria and higher plants. Laccases are used currently in biotechnological processes because this enzyme oxidizes both phenolic and non-phenolic lignin-related compounds as well as highly recalcitrant environmental pollutants. In this work banana skin has been selected as a supporting material for laccase produntion because of its high content in carbohydrates, which due to their organic nature are easily metabolized by the fungus. In addition, its content in ascorbic acid exerts an inhibitory effect against bacteria. The activity of the produced laccase is tested in decoloration studies.

Biotechnology• 2008• arXiv
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