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Periodicals

Novel enzymes for biodegradation of polycyclic aromatic hydrocarbons: metagenomics-linked identification followed by functional analysis

Polycyclic aromatic hydrocarbons (PAHs) are highly toxic, carcinogenic substances. On soils contaminated with PAHs, crop cultivation, animal husbandry and even the survival of microflora in the soil are greatly perturbed, depending on the degree of contamination. Most microorganisms cannot tolerate PAH-contaminated soils, however, some microbial strains can adapt to these harsh conditions and survive on contaminated soils. Analysis of the metagenomes of contaminated environmental samples may lead to discovery of PAH-degrading enzymes suitable for green biotechnology methodologies ranging from biocatalysis to pollution control. In the present study, our goal was to apply a metagenomic data search to identify efficient novel enzymes in remediation of PAH-contaminated soils. The metagenomic hits were further analyzed using a set of bioinformatics tools to select protein sequences predicted to encode well-folded soluble enzymes. Three novel enzymes (two dioxygenases and one peroxidase) were cloned and used in soil remediation microcosms experiments. The novel enzymes were found to be efficient for degradation of naphthalene and phenanthrene. Adding the inorganic oxidant CaO2 further increased the degrading potential of the novel enzymes for anthracene and pyrene. We conclude that metagenome mining paired with bioinformatic predictions, structural modelling and functional assays constitutes a powerful approach towards novel enzymes for soil remediation.

Biotechnology• 2023• arXiv
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Periodicals

Coherent Control of Optogenetic Switching by Stimulated Depletion Quenching

Optogenetics is a revolutionary new field of biotechnology, achieving optical control over biological functions in living cells by genetically inserting light sensitive proteins into cellular signaling pathways. Applications of optogenetic switches are expanding rapidly, but the technique is hampered by spectral cross-talk: the broad absorption spectra of compatible biochemical chromophores limits the number of switches that can be independently controlled and restricts the dynamic range of each switch. In the present work we develop and implement a non-linear optical photoswitching capability, Stimulated Depletion Quenching (SDQ), is used to overcome spectral cross-talk by exploiting the molecules' unique dynamic response to ultrashort laser pulses. SDQ is employed to enhance the control of Cph8, a photo-reversible phytochrome based optogenetic switch designed to control gene expression in E. Coli bacteria. The Cph8 switch can not be fully converted to it's biologically inactive state ($P_{FR}$) by linear photos-witching, as spectral cross-talk causes a reverse photoswitching reaction to revert to it back to the active state ($P_{R}$). SDQ selectively halts this reverse reaction while allowing the forward reaction to proceed. The results of this proof of concept experiment lay the foundation for future experiments that will use optimal pulse shaping to further enhance control of Cph8 and enable simultaneous, multiplexed control of multiple optogenetic switches.

Biotechnology• 2018• arXiv
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Periodicals

Magnetization reversal and anisotropies in buffered transition-metal alloys thin films

Interest in planar Hall effect (PHE) sensors has re-emerged in recent years due to their promising potential for a wide range of applications, particularly in biotechnology. Sensor sensitivity can be enhanced by lowering the effective anisotropy field; however, this favors magnetic domain formation during magnetization reversal, leading to hysteretic responses. Therefore, precise control of magnetic anisotropy and magnetization reversal is essential to balance sensitivity and stability in PHE sensors. In this work, we investigate the magnetic anisotropy and magnetization reversal mechanisms of Ni-Fe- and Co-Fe-based multilayers grown on various metallic buffer layers and deposited with and without an external magnetic field, in order to evaluate the effects of the buffer layers and field-assisted deposition on the resulting magnetic anisotropy. NiFe films exhibit a dominant uniaxial anisotropy mainly determined by the applied field during growth, with an anisotropy constant of approximately $3,\mathrm{kerg,cm^{-3}}$, largely independent of the buffer layer. In contrast, the magnetic anisotropy of CoFe films is dominated by the buffer layer, resulting in a biaxial magnetic response. In particular, Ag-buffered films deposited under an external magnetic field exhibit a biaxial anisotropy with values up to $14.88,\mathrm{kerg,cm^{-3}}$. The magnetization reversal mechanism of each system was deduced from the analysis of the angular dependence of the coercive field.

Biotechnology• 2026• arXiv
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Periodicals

Biocybersecurity -- A Converging Threat as an Auxiliary to War

Biodefense is the discipline of ensuring biosecurity with respect to select groups of organisms and limiting their spread. This field has increasingly been challenged by novel threats from nature that have been weaponized such as SARS, Anthrax, and similar pathogens, but has emerged victorious through collaboration of national and world health groups. However, it may come under additional stress in the 21st century as the field intersects with the cyberworld -- a world where governments have already been struggling to keep up with cyber attacks from small to state-level actors as cyberthreats have been relied on to level the playing field in international disputes. Disruptions to military logistics and economies through cyberattacks have been able to be done at a mere fraction of economic and moral costs through conventional military means, making it an increasingly tempting means of disruption. In the field of biocybersecurity (BCS), the strengths within biotechnology and cybersecurity merge, along with many of their vulnerabilities, and this could spell increased trouble for biodefense, as novel threats can be synthesized and disseminated in ways that fuse the routes of attacks seen in biosecurity and cybersecurity. Herein, we offer an exploration of how threats in the domain of biocybersecurity may emerge through less foreseen routes as it might be an attractive auxiliary to conventional war. This is done through an analysis of potential payload and delivery methods to develop notional threat vectorizations. We conclude with several paradigms through which to view BCS-based threats.

Biotechnology• 2020• arXiv
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Periodicals

Time evolution of the hierarchical networks between PubMed MeSH terms

Hierarchical organisation is a prevalent feature of many complex networks appearing in nature and society. A relating interesting, yet less studied question is how does a hierarchical network evolve over time? Here we take a data driven approach and examine the time evolution of the network between the Medical Subject Headings (MeSH) provided by the National Center for Biotechnology Information (NCBI, part of the U. S. National Library of Medicine). The network between the MeSH terms is organised into 16 different, yearly updated hierarchies such as "Anatomy", "Diseases", "Chemicals and Drugs", etc. The natural representation of these hierarchies is given by directed acyclic graphs, composed of links pointing from nodes higher in the hierarchy towards nodes in lower levels. Due to the yearly updates, the structure of these networks is subject to constant evolution: new MeSH terms can appear, terms becoming obsolete can be deleted or be merged with other terms, and also already existing parts of the network may be rewired. We examine various statistical properties of the time evolution, with a special focus on the attachment and detachment mechanisms of the links, and find a few general features that are characteristic for all MeSH hierarchies. According to the results, the hierarchies investigated display an interesting interplay between non-uniform preference with respect to multiple different topological and hierarchical properties.

Biotechnology• 2019• arXiv
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Periodicals

EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics

Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or protein binding pocket design often fall short in capturing the dynamic and complex nature of enzyme-substrate interactions, particularly in catalytic processes. To address the challenges, we introduce EnzymeFlow, a generative model that employs flow matching with hierarchical pre-training and enzyme-reaction co-evolution to generate catalytic pockets for specific substrates and catalytic reactions. Additionally, we introduce a large-scale, curated, and validated dataset of enzyme-reaction pairs, specifically designed for the catalytic pocket generation task, comprising a total of $328,192$ pairs. By incorporating evolutionary dynamics and reaction-specific adaptations, EnzymeFlow becomes a powerful model for designing enzyme pockets, which is capable of catalyzing a wide range of biochemical reactions. Experiments on the new dataset demonstrate the model's effectiveness in designing high-quality, functional enzyme catalytic pockets, paving the way for advancements in enzyme engineering and synthetic biology. We provide EnzymeFlow code at https://github.com/WillHua127/EnzymeFlow with notebook demonstration at https://github.com/WillHua127/EnzymeFlow/blob/main/enzymeflow_demo.ipynb.

Biotechnology• 2024• arXiv
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Periodicals

Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and Evaluation

Proteins play essential roles in nature, from catalyzing biochemical reactions to binding specific targets. Advances in protein engineering have the potential to revolutionize biotechnology and healthcare by designing proteins with tailored properties. Machine learning and generative models have transformed protein design by enabling the exploration of vast sequence-function landscapes. Here, we introduce Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot), a framework that iteratively combines autoregressive protein generation with quantitative structure-property relationship models for fine-tuned optimization. By integrating diverse protein descriptors, SAGE-Prot enhances key properties, including binding affinity, thermal stability, enzymatic activity, and solubility. We demonstrate its effectiveness by optimizing GB1 for binding affinity and thermal stability and TEM-1 for enzymatic activity and solubility. Leveraging curriculum learning, SAGE-Prot adapts rapidly to increasingly complex design objectives, building on past successes. Experimental validation demonstrated that SAGE-Prot-generated proteins substantially outperformed their wild-type counterparts, achieving up to a 17-fold increase in beta-lactamase activity, underscoring SAGE-Prot's potential to tackle critical challenges in protein engineering. As generative models continue to evolve, approaches like SAGE-Prot will be indispensable for advancing rational protein design.

Biotechnology• 2025• arXiv
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Periodicals

GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information

While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; (2) GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; (3) Different types of errors are enriched in different tasks, providing valuable insights for future improvements.

Biotechnology• 2023• arXiv
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Periodicals

An analysis of the evolution of science-technology linkage in biomedicine

Demonstrating the practical value of public research has been an important subject in science policy. Here we present a detailed study on the evolution of the citation linkage between life science related patents and biomedical research over a 37-year period. Our analysis relies on a newly-created dataset that systematically links millions of non-patent references to biomedical papers. We find a large disparity in the volume of science linkage among technology sectors, with biotechnology and drug patents dominating it. The linkage has been growing exponentially over a long period of time, doubling every 2.9 years. The U.S. has been the largest producer of cited science for years, receiving nearly half of the citations. More than half of citations goes to universities. We use a new paper-level indicator to quantify to what extent a paper is basic research or clinical medicine. We find that the cited papers are likely to be basic research, yet a significant portion of papers cited in patents that are related to FDA-approved drugs are clinical research. The U.S. National Institute of Health continues to be an important funder of cited science. For the majority of companies, more than half of citations in their patents are authored by public research. Taken together, these results indicate a continuous linkage of public science to private sector inventions.

Biotechnology• 2019• arXiv
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Periodicals

Monte Carlo simulation of particle size separation in evaporating bi-dispersed colloidal droplets on hydrophilic substrates

Colloidal droplets are used in a variety of practical applications. Some of these applications require particles of different sizes. These include medical diagnostic methods, the creation of photonic crystals, the formation of supraparticles, and the production of membranes for biotechnology. A series of earlier experiments had shown the possibility of particle separation near the contact line, dependent upon their size. A mathematical model has been developed to describe this process. Bi-dispersed colloidal droplets evaporating on a hydrophilic substrate are taken into consideration. A particle monolayer is formed near the periphery of such droplets due to the small value of the contact angle. The shape of the resulting deposit is associated with the coffee ring effect. The model takes into account both particle diffusion and transfers caused by capillary flow due to liquid evaporation. Monte Carlo simulations of such particle dynamics have been performed at several values of particle concentration in the colloidal solution. The numerical results agree with the experimental observations, in which small particles accumulate nearer to the contact line than do the large particles. However, the particles do not actually reach the contact line, but accumulate at a small distance from it. The reason for this is the surface tension acting on the particles in areas where the thickness of the liquid layer is comparable to the particle size. Indeed, the same mechanism affects the observed separation of the small and large particles.

Biotechnology• 2021• arXiv
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Periodicals

A method for site-specifically tethering the enzyme urease to DNA origami with sustained activity

Attaching enzymes to nanostructures has proven useful to the study of enzyme functionality under controlled conditions and has led to new technologies. Often, the utility and interest of enzyme-tethered nanostructures lie in how the enzymatic activity is affected by how the enzymes are arranged in space. Therefore, being able to conjugate enzymes to nanostructures while preserving the enzymatic activity is essential. In this paper, we present a method to conjugate single-stranded DNA to the enzyme urease while maintaining enzymatic activity. We show evidence of successful conjugation and quantify the variables that affect the conjugation yield. We also show that the enzymatic activity is unchanged after conjugation compared to the enzyme in its native state. Finally, we demonstrate the tethering of urease to nanostructures made using DNA origami with high site-specificity. Decorating nanostructures with enzymatically-active urease may prove to be useful in studying, or even utilizing, the functionality of urease in disciplines ranging from biotechnology to soft-matter physics. The techniques we present in this paper will enable researchers across these fields to modify enzymes without disrupting their functionality, thus allowing for more insightful studies into their behavior and utility.

Biotechnology• 2024• arXiv
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Periodicals

Digital Biosensing By Foundry-Fabricated Graphene Sensors

Biomedical and environmental testing currently require access to highly specialized facilities or extensive training. Current biochemical tests require complex reagents to achieve a simple human readable result such as a color change. This results in low-information tests, with high reagent costs. To maximize information from testing while minimizing costs, biotechnology should leverage complex analysis enabled by advanced portable computing power and use simplified reagents. Nanotechnologists have created proof-of-concept devices to move diagnosis and monitoring from large core labs to hand-held, easy-to-use personal devices, but none have achieved large-scale manufacturability. We demonstrate the first economical nanoelectronics produced in a commercial foundry. The mass-manufacture of these graphene-based digital biosensors is achieved by successfully integrating delicate graphene material into standard electronic high-volume production processes. We describe critical manufacturing parameters and the results of biosensing measurements that establish graphene biosensors as a portable, low-cost method to gain precise data without labeled, complex reagents. The low power and resource usage of these biosensors enable individuals to gain personal, immediate control over precise biological and environmental data.

Biotechnology• 2018• arXiv
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Periodicals

Analytical method for parameterizing the random profile components of nanosurfaces imaged by atomic force microscopy

The functional properties of many technological surfaces in biotechnology, electronics, and mechanical engineering depend to a large degree on the individual features of their nanoscale surface texture, which in turn are a function of the surface manufacturing process. Among these features, the surface irregularities and self-similarity structures at different spatial scales, especially in the range of 1 to 100 nm, are of high importance because they greatly affect the surface interaction forces acting at a nanoscale distance. An analytical method for parameterizing the surface irregularities and their correlations in nanosurfaces imaged by atomic force microscopy (AFM) is proposed. In this method, flicker noise spectroscopy - a statistical physics approach - is used to develop six nanometrological parameters characterizing the high-frequency contributions of jump- and spike-like irregularities into the surface texture. These contributions reflect the stochastic processes of anomalous diffusion and inertial effects, respectively, in the process of surface manufacturing. The AFM images of the texture of corrosion-resistant magnetite coatings formed on low-carbon steel in hot nitrate solutions with coating growth promoters at different temperatures are analyzed. It is shown that the parameters characterizing surface spikiness are able to quantify the effect of process temperature on the corrosion resistance of the coatings. It is suggested that these parameters can be used for predicting and characterizing the corrosion-resistant properties of magnetite coatings.

Biotechnology• 2010• arXiv
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Periodicals

Fluctuation relations to calculate protein redox potentials from molecular dynamics simulations

The tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are post-processed into estimates for the redox potentials in an efficient manner. This new method, denoted MD+CB, is tested on a de novo four-helix bundle heme protein (the m4D2 `maquette') and five designed mutants, including some mutants characterized experimentally in this work. The MD+CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD+CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD+CB approach is straightforwardly transferable to standard equilibrium MD simulations, and holds promise for redox protein engineering and design applications.

Biotechnology• 2023• arXiv
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Periodicals

Langevin Navier-Stokes simulation of protoplasmic streaming by 2D MAC method

We study protoplasmic streaming in plant cells such as chara brauni by simplifying the flow field to a two-dimensional Couette flow with Brownian random motion inside parallel plates. Protoplasmic streaming is receiving a lot of attention in many areas, such as agriculture-technology and biotechnology. The plant size depends on the velocity of streaming and the driving force originating in molecular motors. Therefore, it is interesting to study detailed information on the velocity of streaming. Recently, experimentally observed peaks in the velocity distribution have been simulated by a 2D Langevin Navier-Stokes (LNS) equation for vortex and flow function. However, to simulate actual 3D flows, we have to use the NS equation for velocity, which, in the case of 2D flows, is not always equivalent to that for vorticity and stream function. In this paper, we report that a 2D LNS equation for velocity and pressure successfully simulates protoplasmic streaming by comparing the results with the experimental data and those obtained by 2D LNS simulations for vortex and flow function. Moreover, a dimensional analysis clarifies the dependence of numerical results on the strength $D$ of Brownian random force and physical parameters such as kinematic viscosity and cell size. We find from this analysis how the peak position in normalized velocity distribution moves depending on these parameters.

Biotechnology• 2021• arXiv
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Periodicals

RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

Biotechnology• 2022• arXiv
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Periodicals

3D Nanofabrication inside rapid prototyped microfluidic channels showcased by wet-spinning of single micrometre fibres

Microfluidics is an established multidisciplinary research domain with widespread applications in the fields of medicine, biotechnology and engineering. Conventional production methods of microfluidic chips have been limited to planar structures, preventing the exploitation of truly three-dimensional architectures for applications such as multi-phase droplet preparation or wet-phase fibre spinning. Here the challenge of nanofabrication inside a microfluidic chip is tackled for the showcase of a spider-inspired spinneret. Multiphoton lithography, an additive manufacturing method, was used to produce free-form microfluidic masters, subsequently replicated by soft lithography. Into the resulting microfluidic device, a threedimensional spider-inspired spinneret was directly fabricated in-chip via multiphoton lithography. Applying this unprecedented fabrication strategy, the to date smallest printed spinneret nozzle is produced. This spinneret resides tightly sealed, connecting it to the macroscopic world. Its functionality is demonstrated by wet-spinning of single-digit micron fibres through a polyacrylonitrile coagulation process induced by a water sheath layer. The methodology developed here demonstrates fabrication strategies to interface complex architectures into classical microfluidic platforms. Using multiphoton lithography for in-chip fabrication adopts a high spatial resolution technology for improving geometry and thus flow control inside microfluidic chips. The showcased fabrication methodology is generic and will be applicable to multiple challenges in fluid control and beyond.

Biotechnology• 2018• arXiv
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Periodicals

Luminescence from ZAIS quantum dots: radiative or nonradiative

Quantum dots have found applications across the semiconductor and biotechnology industries, improving energy efficiency, color purity, and imaging capability. However, the standard quantum dot relies on a heavy metal architecture, which has undesirable environmental impacts. Thus, alternative material systems are desirable. One strategy is the ZnS-AgInS2 (ZAIS) quantum dot which has a metal dichalcogenide chemical structure. Unlike heavy metal quantum dots, these nanoparticles luminesce even when defects are present, and their emission wavelength is tuned by varying the relative composition. Past works have linked the source of this luminescence to a defect-assisted transition recombination process, but the precise mechanism is still unclear. In this work, we investigate the physics of this defect-assisted transition by systematically varying the concentration of defects through a simple quick-cool thermal annealing process which provides control over the crystalline disorder of the nanoparticle. Using a combination of ultraviolet photoelectron spectroscopy and fluorescence measurements, information about the nature of the defect-assisted transition is obtained. In contrast to previous work, we find that the ZAIS quantum dot luminescence has an electronic-based rather than defect-assisted transition mechanism.

Biotechnology• 2021• arXiv
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Periodicals

Oriented suspension mechanics with applications to flow linear dichrosim spectroscopy and pathogen detection

Flow linear dichroism is a biophysical spectroscopic technique that exploits the shear-induced alignment of elongated particles in suspension. Motivated by the broad aim of optimising the sensitivity of this technique, and more specifically by a handheld synthetic biotechnology prototype for waterborne-pathogen detection, a model of steady and oscillating pressure-driven channel flow and orientation dynamics of a suspension of slender microscopic fibres is developed. The model couples the Fokker-Planck equation for Brownian suspensions with the narrow channel flow equations, the latter modified to incorporate mechanical anisotropy induced by the particles. The linear dichroism signal is estimated through integrating the perpendicular components of the distribution function via an appropriate formula which takes the bi-axial nature of the orientation into account. For the specific application of pathogen detection via binding of M13 bacteriophage, it is found that increases in the channel depth are more significant in improving the linear dichroism signal than increases in the channel width. Increasing channel depth to $2~$mm and pressure gradient to $5 \times 10^4~$Pa/m essentially maximises the alignment. Oscillating flow can produce nearly equal alignment to steady flow at appropriate frequencies, which has significant potential practical value in the analysis of small sample volumes.

Biotechnology• 2019• arXiv
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Periodicals

Machine-Learning Driven Drug Repurposing for COVID-19

The integration of machine learning methods into bioinformatics provides particular benefits in identifying how therapeutics effective in one context might have utility in an unknown clinical context or against a novel pathology. We aim to discover the underlying associations between viral proteins and antiviral therapeutics that are effective against them by employing neural network models. Using the National Center for Biotechnology Information virus protein database and the DrugVirus database, which provides a comprehensive report of broad-spectrum antiviral agents (BSAAs) and viruses they inhibit, we trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs. Model training excluded SARS-CoV-2 proteins and included only Phases II, III, IV and Approved level drugs. Using sequences for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained models produces outputs of tentative safe-in-human antiviral candidates for treating COVID-19. Our results suggest multiple drug candidates, some of which complement recent findings from noteworthy clinical studies. Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viruses.

Biotechnology• 2020• arXiv
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Periodicals

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community.

Biotechnology• 2022• arXiv
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Periodicals

Patents as Knowledge Artifacts: An Information Science Perspective on Global Innovation

In an age of fast-paced technological change, patents have evolved into not only legal mechanisms of intellectual property, but also structured storage containers of knowledge full of metadata, categories, and formal innovation. This chapter proposes to reframe patents in the context of information science, by focusing on patents as knowledge artifacts, and by seeing patents as fundamentally tied to the global movement of scientific and technological knowledge. With a focus on three areas, the inventions of AIs, biotech patents, and international competition with patents, this work considers how new technologies are challenging traditional notions of inventorship, access, and moral accountability.The chapter provides a critical analysis of AI's implications for patent authorship and prior art searches, ownership issues arising from proprietary claims in biotechnology to ethical dilemmas, and the problem of using patents for strategic advantage in a global context of innovation competition. In this analysis, the chapter identified the importance of organizing information, creating metadata standards about originality, implementing retrieval systems to access previous works, and ethical contemplation about patenting unseen relationships in innovation ecosystems. Ultimately, the chapter called for a collaborative, transparent, and ethically-based approach in managing knowledge in the patenting environment highlighting the role for information professionals and policy to contribute to access equity in innovation.

Biotechnology• 2025• arXiv
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FDTD Computation of Human Eye Exposure to Ultra-wideband Electromagnetic Pulses

With an increase in the application of ultra-wideband (UWB) electromagnetic pulses in the communications industry, radar, biotechnology and medicine, comes an interest in UWB exposure safety standards. Despite an increase of the scientific research on bioeffects of exposure to non-ionizing UWB pulses, characterization of those effects is far from complete. A numerical computational approach, such as a finite-difference time domain (FDTD) method, is required to visualize and understand the complexity of broadband electromagnetic interactions. The FDTD method has almost no limits in the description of the geometrical and dispersive properties of the simulated material, it is numerically robust and appropriate for current computer technology. In this paper, a complete calculation of exposure of the human eye to UWB electromagnetic pulses in the frequency range of 3.1-10.6, 22-29, and 57-64 GHz is performed. Computation in this frequency range required a geometrical resolution of the eye of $\rm 0.1 \: mm$ and an arbitrary precision in the description of its dielectric properties in terms of the Debye model. New results show that the interaction of UWB pulses with the eye tissues exhibits the same properties as the interaction of the continuous electromagnetic waves (CW) with the frequencies from the pulse's frequency spectrum. It is also shown that under the same exposure conditions the exposure to UWB pulses is from one to many orders of magnitude safer than the exposure to CW.

Biotechnology• 2007• arXiv
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Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence (AI) Guided Microassembly

s miniaturization of electrical and mechanical components used in modern technology progresses, there is an increasing need for high-throughput and low-cost micro-scale assembly techniques. Many current micro-assembly methods are serial in nature, resulting in unfeasibly low throughput. Additionally, the need for increasingly smaller tools to pick and place individual microparts makes these methods cost prohibitive. Alternatively, parallel self-assembly or directed-assembly techniques can be employed by utilizing forces dominant at the micro and nano scales such as electro-kinetic, thermal, and capillary forces. However, these forces are governed by complex equations and often act on microparts simultaneously and competitively, making modeling and simulation difficult. The research in this paper presents a novel phenomenological approach to directed micro-assembly through the use of artificial intelligence to correlate micro-particle movement via dielectrophoretic and electro-osmotic forces in response to varying frequency of an applied non-uniform electric field. This research serves as a proof of concept of the application of artificial intelligence to create high yield low-cost micro-assembly techniques, which will prove useful in a variety of fields including micro-electrical-mechanical systems (MEMS), biotechnology, and tissue engineering.

Biotechnology• 2021• arXiv
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