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Periodicals

DNA-coated Functional Oil Droplets

Many industrial soft materials often include oil-in-water (O/W) emulsions at the core of their formulations. By using tuneable interface stabilizing agents, such emulsions can self-assemble into complex structures. DNA has been used for decades as a thermoresponsive highly specific binding agent between hard and, recently, soft colloids. Up until now, emulsion droplets functionalized with DNA had relatively low coating densities and were expensive to scale up. Here a general O/W DNA-coating method using functional non-ionic amphiphilic block copolymers, both diblock and triblock, is presented. The hydrophilic polyethylene glycol ends of the surfactants are functionalized with azides, allowing for efficient, dense and controlled coupling of dibenzocyclooctane functionalized DNA to the polymers through a strain-promoted alkyne-azide click reaction. The protocol is readily scalable due to the triblock's commercial availability. Different production methods (ultrasonication, microfluidics and membrane emulsification) are used with different oils (hexadecane and silicone oil) to produce functional droplets in various size ranges (sub-micron, $\sim 20\,\mathrm{μm}$ and $> 50\,\mathrm{μm}$), showcasing the generality of the protocol. Thermoreversible sub-micron emulsion gels, hierarchical "raspberry" droplets and controlled droplet release from a flat DNA-coated surface are demonstrated. The emulsion stability and polydispersity is evaluated using dynamic light scattering and optical microscopy. The generality and simplicity of the method opens up new applications in soft matter and biotechnological research and industrial advances.

Biotechnology• 2017• arXiv
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Periodicals

Democapsid

Capsids are the protein shells that protect the genetic material of viruses. The precise structural description of capsids informs how viruses assemble and evolve and is key to the development of antiviral targets. Most viruses form icosahedral capsids; among these, most adopt quasi-spherical shapes, and some form elongated architectures. However, elongated capsids have been understudied, despite their decoupling of width and length providing greater control over their packaging capacity, a feature of particular interest in capsid evolution and in virus-based biotechnological platforms. A key bottleneck is the lack of tools for the analysis and design of elongated viral capsids. To that end, this article introduces Democapsid as a versatile tool for generating coordinates of both quasi-spherical and elongated (and shrunk) icosahedral capsids, as well as for producing customizable graphical models and publication-quality figures. The underlying algorithm builds on the generalized geometrical theory of viral capsids and employs numerical methods to assemble capsid elements based on folding constraints. It includes parameters controlling protein tiling associated with the eight regular icosahedral lattices, elongation axes (5-fold, 3-fold, and 2-fold), sphericity, and discrete body length for prolate (extended) and oblate (shrunk) shapes. It is available as a JavaScript browser application, a Python package powering plugins for UCSF ChimeraX and Blender, and an R package for generating reproducible documents with embedded models. The code (MIT License) is available on GitHub. Democapsid will benefit both researchers and graphic designers by enabling the investigation and communication of research on viral capsids and other icosahedral compartments.

Biotechnology• 2026• arXiv
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Periodicals

Setting up and modelling of overflowing fed-batch cultures of Bacillus subtilis for the production and continuous removal of lipopeptides

This work is related to the setup of overflowing exponential fed-batch cultures (O-EFBC) derived from carbon limited EFBC dedicated to the production of mycosubtilin, an antifungal lipopeptide belonging to the iturin family. O-EFBC permits the continuous removal of the product from the bioreactor achieving a complete extraction of mycosubtilin. This paper also provides a dynamical Monod-based growth model of this process that is accurate enough to simulate the evolution of the specific growth rate and to correlate it to the mycosubtilin specific productivity. Two particular and dependant phenomena related to the foam overflow are taken into account by the model: the outgoing flow rate of a broth volume and the loss of biomass. Interestingly, the biomass concentration in the foam was found to be lower than the biomass concentration in the bioreactor relating this process to a recycling one. Parameters of this model are the growth yield on substrate and the maximal specific growth rate estimated from experiments led at feed rates of 0.062, 0.071 and 0.086 h --1. The model was extrapolated to five additional experiments carried out at feed rates of 0.008, 0.022, 0.040, 0.042 and 0.062 h --1 enabling the correlation of the mean specific growth rates with productivity results. Finally, a feed rate of 0.086 h --1 corresponding to a mean specific growth rate of 0.070 h --1 allowed a specific productivity of 1.27 mg of mycosubtilin g --1 of dried biomass h --1 .

Biotechnology• 2020• arXiv
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Periodicals

A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors

The mRNA optimization is critical for therapeutic and biotechnological applications, since sequence features directly govern protein expression levels and efficacy. However, current methods face significant challenges in simultaneously achieving three key objectives: (1) fidelity (preventing unintended amino acid changes), (2) computational efficiency (speed and scalability), and (3) the scope of optimization variables considered (multi-objective capability). Furthermore, existing methods often fall short of comprehensively incorporating the factors related to the mRNA lifecycle and translation process, including intrinsic mRNA sequence properties, secondary structure, translation elongation kinetics, and tRNA availability. To address these limitations, we introduce \textbf{RNop}, a novel deep learning-based method for mRNA optimization. We collect a large-scale dataset containing over 3 million sequences and design four specialized loss functions, the GPLoss, CAILoss, tAILoss, and MFELoss, which simultaneously enable explicit control over sequence fidelity while optimizing species-specific codon adaptation, tRNA availability, and desirable mRNA secondary structure features. Then, we demonstrate RNop's effectiveness through extensive in silico and in vivo experiments. RNop ensures high sequence fidelity, achieves significant computational throughput up to 47.32 sequences/s, and yields optimized mRNA sequences resulting in a significant increase in protein expression for functional proteins compared to controls. RNop surpasses current methodologies in both quantitative metrics and experimental validation, enlightening a new dawn for efficient and effective mRNA design. Code and models will be available at https://github.com/HudenJear/RPLoss.

Biotechnology• 2025• arXiv
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Periodicals

Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks

Motivation: One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one type of biomolecule as well as biological processes operating at multiple omic levels. Combining network/pathway-level information with genetic information it is possible to describe well-understood or unknown bacterial mechanisms and organism-specific dynamics. Results: Network multi-omic integration has led to the discovery of interesting oscillatory signals. Following the methodologies used in signal processing and communication engineering, a new methodology is introduced to identify and quantify the extent of the multi-omic oscillations of the signal. New signal metrics are designed to allow further biotechnological explanations and provide important clues about the oscillatory nature of the pathways and their regulatory circuits. Our algorithms designed for the analysis of multi-omic signals are tested and validated on 11 different bacteria for thousands of multi-omic signals perturbed at the network level by different experimental conditions. Information on the order of genes, codon usage, gene expression, and protein molecular weight is integrated at three different functional levels. Oscillations show interesting evidence that network-level multi-omic signals present a synchronized response to perturbations and evolutionary relations along with taxa.

Biotechnology• 2020• arXiv
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Periodicals

The Coming of Age of Nucleic Acid Vaccines during COVID-19

In the 21st century, several emergent viruses have posed a global threat. Each pathogen has emphasized the value of rapid and scalable vaccine development programs. The ongoing SARS-CoV-2 pandemic has made the importance of such efforts especially clear. New biotechnological advances in vaccinology allow for recent advances that provide only the nucleic acid building blocks of an antigen, eliminating many safety concerns. During the COVID-19 pandemic, these DNA and RNA vaccines have facilitated the development and deployment of vaccines at an unprecedented pace. This success was attributable at least in part to broader shifts in scientific research relative to prior epidemics; the genome of SARS-CoV-2 was available as early as January 2020, facilitating global efforts in the development of DNA and RNA vaccines within two weeks of the international community becoming aware of the new viral threat. Additionally, these technologies that were previously only theoretical are not only safe but also highly efficacious. Although historically a slow process, the rapid development of vaccines during the COVID-19 crisis reveals a major shift in vaccine technologies. Here, we provide historical context for the emergence of these paradigm-shifting vaccines. We describe several DNA and RNA vaccines and in terms of their efficacy, safety, and approval status. We also discuss patterns in worldwide distribution. The advances made since early 2020 provide an exceptional illustration of how rapidly vaccine development technology has advanced in the last two decades in particular and suggest a new era in vaccines against emerging pathogens.

Biotechnology• 2022• arXiv
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Periodicals

Networks of ribosome flow models for modeling and analyzing intracellular traffic

The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The RFMIO and its variants encapsulate important properties that are relevant to modeling ribosome flow such as the possible evolution of "traffic jams" and non-homogeneous elongation rates along the mRNA molecule, and can also be used for studying additional intracellular processes such as transcription, transport, and more. Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided, using connection weights, between several inputs of other RFMIOs. We show that under quite general feedback connections the network has two important properties: (1) it admits a unique steady-state and every trajectory converges to this steady-state, and (2) the problem of how to determine the connection weights so that the network steady-state output is maximized is a convex optimization problem. These mathematical properties make these networks highly suitable as models of various phenomena: property (1) means that the behavior is predictable and ordered, and property (2) means that determining the optimal weights is numerically tractable even for large-scale networks. For the specific case of a feed-forward network of RFMIOs we prove an additional useful property, namely, that there exists a spectral representation for the network steady-state, and thus it can be determined without any numerical simulations of the dynamics. We describe the implications of these result to several fundamental biological phenomena and biotechnological objectives.

Biotechnology• 2018• arXiv
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Periodicals

Mechanical and Systems Biology of Cancer

Mechanics and biochemical signaling are both often deregulated in cancer, leading to cancer cell phenotypes that exhibit increased invasiveness, proliferation, and survival. The dynamics and interactions of cytoskeletal components control basic mechanical properties, such as cell tension, stiffness, and engagement with the extracellular environment, which can lead to extracellular matrix remodeling. Intracellular mechanics can alter signaling and transcription factors, impacting cell decision making. Additionally, signaling from soluble and mechanical factors in the extracellular environment, such as substrate stiffness and ligand density, can modulate cytoskeletal dynamics. Computational models closely integrated with experimental support, incorporating cancer-specific parameters, can provide quantitative assessments and serve as predictive tools toward dissecting the feedback between signaling and mechanics and across multiple scales and domains in tumor progression.

Biotechnology• 2018• arXiv
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Periodicals

FDH knockout and TsFDH transformation lead to enhanced growth rate of Escherichia coli

Increased Atmospheric CO2 to over 400 ppm has prompted global climate irregularities. Reducing the released CO2 from biotechnological processes could remediate these phenomena. In this study, we sought to find a solution to reduce the amount of CO2 in the process of growth and reproduction by preventing the conversion of formic acid into CO2. The (bio)chemical conversion of formic acid to CO2 is a key reaction. Therefore, we compared the growth of BL21, being a subfamily of K12, alongside two strains in which two different genes related to the formate metabolism were deleted, in complex and simple media. Experimental results were entirely consistent with metabolic predictions. Subsequently, the knockout bacteria grew more efficiently than BL21. Interestingly, TsFDH, a formate dehydrogenase with the tendency of converting CO2 to formate, increased the growth of all strains compared with cells without the TsFDH. Most mutants grew in a simple medium containing glycerol, which showed that glycerol is the preferred carbon source compared to glucose for the growth of E. coli. These results explain the reasons for the inconsistency of predictions in previous metabolic models that declared glycerol as a suitable carbon source for the growth of E. coli but failed to achieve it in practice. To conduct a more mechanistic evaluation of our observations, RNA sequencing data analysis was conducted on an E. coli RNA-seq dataset. The gene expression correlation outcome revealed the increased expression levels of several genes related to protein biosynthesis and glycerol degradation as a possible explanation of our observations.

Biotechnology• 2021• arXiv
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Periodicals

Tracking the Brownian motion of DNA-functionalized magnetic nanoparticles for conformation analysis beyond the optical resolution limit

Brownian motion provides access to hydrodynamic properties of nanoscale objects independent of their optical resolvability. Here, we present a diffusion-based approach to infer effective particle size distributions of DNA-functionalized magnetic nanoparticles (MNPs), consisting of a magnetic core and a polystyrene shell, in a regime where direct geometric sizing is limited by optical diffraction. Using multi-particle tracking microscopy, we analyze the Brownian dynamics of MNPs grafted with double-stranded DNA (dsDNA) of varying contour length under low-salt conditions. A physically motivated model is introduced that relates dsDNA contour length to an effective hydrodynamic diameter via an attenuated corona description. The measured diffusion coefficient distributions exhibit a systematic and monotonic dependence on dsDNA length in quantitative agreement with the model. While the tracked objects are predominantly dsDNA-mediated agglomerates rather than isolated nanoparticles, clustering does not obscure the length-dependent signal. Instead, the dsDNA corona determines the hydrodynamic scaling, whereas agglomeration mainly introduces an offset and distribution broadening. These results demonstrate that Brownian dynamics enables robust readout of biomolecular length scales even far below the optical resolution limit. The distribution-based approach is inherently tolerant to polydispersity and aggregation, making diffusion-based tracking a simple and promising strategy for future biotechnological and biomedical assays.

Biotechnology• 2026• arXiv
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Periodicals

Biased swimming cells do not disperse in pipes as tracers: a population model based on microscale behaviour

There is much current interest in modelling suspensions of algae and other micro-organisms for biotechnological exploitation, and many bioreactors are of tubular design. Using generalized Taylor dispersion theory, we develop a population-level swimming-advection-diffusion model for suspensions of micro-organisms in a vertical pipe flow. In particular, a combination of gravitational and viscous torques acting on individual cells can affect their swimming behaviour, which is termed gyrotaxis. This typically leads to local cell drift and diffusion in a suspension of cells. In a flow in a pipe, small amounts of radial drift across streamlines can have a major impact on the effective axial drift and diffusion of the cells. We present a Galerkin method to calculate the local mean swimming velocity and diffusion tensor based on local shear for arbitrary flow rates. This method is validated with asymptotic results obtained in the limits of weak and strong shear. We solve the resultant swimming-advection-diffusion equation using numerical methods for the case of imposed Poiseuille flow and investigate how the flow modifies the dispersion of active swimmers from that of passive scalars. We establish that generalized Taylor dispersion theory predicts an enhancement of gyrotactic focussing in pipe flow with increasing shear strength, in contrast to earlier models. We also show that biased swimming cells may behave very differently to passive tracers, drifting axially at up to twice the rate and diffusing much less.

Biotechnology• 2012• arXiv
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Periodicals

Role of Motility and Nutrient Availability in Drying Patterns of Algal Droplets

Sessile drying droplets in various bio-relevant systems, encompassing passive bio-colloids like DNA, proteins, and blood to active microbes, gain considerable attention due to intricate interplay among different convective flows, droplet pinning, mechanical stress, wettability, and the emergence of distinctive patterns. Chlamydomonas reinhardtii, or chlamys, is a versatile algal model employed in molecular biology research and spanning diverse biotechnological realms. While chlamys are harnessed at single-cell and population levels, their exploration in the context of drying sessile droplets remains limited. This paper illuminates the multifaceted potential of chlamys, delving into motility-nutrient interactions and their role in emergent morphological patterns. The interplay of two competing stressors -- localized nutrient scarcity and mechanical stress during drying -- is investigated. Irrespective of these stressors, the global mechanical stress fails to induce any cracks during the drying process. Interestingly, the reverse ``coffee-ring effect" is predominantly observed in the non-motile chlamys in the presence of local nutrients whereas the nutrient depletion prompts local stress in motile chlamys, culminating in cooperative aggregation and cluster formation. Furthermore, the quantitative image processing technique leverages textural statistics to classify the patterns into four classes, motile+with nutrients, motile+without nutrients, non-motile+with nutrients, and non-motile+without nutrients, with five distinct drying stages -- Droplet Deposition, Capillary Flow, Dynamic Droplet Phase, Aggregation Phase, and Dried Morphology.

Biotechnology• 2023• arXiv
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Periodicals

Stochastic Variational Inference for Structured Additive Distributional Regression

In structured additive distributional regression, the conditional distribution of the response variables given the covariate information and the vector of model parameters is modelled using a P-parametric probability density function where each parameter is modelled through a linear predictor and a bijective response function that maps the domain of the predictor into the domain of the parameter. We present a method to perform inference in structured additive distributional regression using stochastic variational inference. We propose two strategies for constructing a multivariate Gaussian variational distribution to estimate the posterior distribution of the regression coefficients. The first strategy leverages covariate information and hyperparameters to learn both the location vector and the precision matrix. The second strategy tackles the complexity challenges of the first by initially assuming independence among all smooth terms and then introducing correlations through an additional set of variational parameters. Furthermore, we present two approaches for estimating the smoothing parameters. The first treats them as free parameters and provides point estimates, while the second accounts for uncertainty by applying a variational approximation to the posterior distribution. Our model was benchmarked against state-of-the-art competitors in logistic and gamma regression simulation studies. Finally, we validated our approach by comparing its posterior estimates to those obtained using Markov Chain Monte Carlo on a dataset of patents from the biotechnology/pharmaceutics and semiconductor/computer sectors.

Biotechnology• 2024• arXiv
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Periodicals

Physics of base-pairing dynamics in DNA

As a key molecule of Life, Deoxyribonucleic acid (DNA) is the focus of numbers of investigations with the help of biological, chemical and physical techniques. From a physical point of view, both experimental and theoretical works have brought quantitative insights into DNA base-pairing dynamics that we review in this Report, putting emphasis on theoretical developments. We discuss the dynamics at the base-pair scale and its pivotal coupling with the polymer one, with a polymerization index running from a few nucleotides to tens of kilo-bases. This includes opening and closure of short hairpins and oligomers as well as zipping and unwinding of long macromolecules. We review how different physical mechanisms are either used by Nature or utilized in biotechnological processes to separate the two intertwined DNA strands, by insisting on quantitative results. They go from thermally-assisted denaturation bubble nucleation to force- or torque- driven mechanisms. We show that the helical character of the molecule, possibly supercoiled, can play a key role in many denaturation and renaturation processes. We categorize the mechanisms according to the relative timescales associated with base-pairing and chain degrees of freedom such as bending and torsional elastic ones. In some specific situations, these chain degrees of freedom can be integrated out, and the quasi- static approximation is valid. The complex dynamics then reduces to the diffusion in a low-dimensional free-energy landscape. In contrast, some important cases of experimental interest necessarily appeal to far-from-equilibrium statistical mechanics and hydrodynamics.

Biotechnology• 2015• arXiv
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Periodicals

Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking performance are not yet known. Therefore, we present the largest publicly available and annotated dataset for MLCI, containing more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. With this dataset at hand, we generalize existing tracking metrics to incorporate relevant imaging and experiment parameters into experiment-aware metrics. These metrics reveal that current cell tracking methods crucially depend on the choice of the experiment parameters, where their performance deteriorates at high imaging intervals and large cell colonies. Thus, our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods that generalize across imaging and experiment parameters. The benchmark dataset is publicly available at https://zenodo.org/doi/10.5281/zenodo.7260136.

Biotechnology• 2024• arXiv
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Periodicals

Macro-scale Topology Optimization for Controlling Internal Shear Stress in a Porous Scaffold Bioreactor

Shear stress is an important physical factor that regulates proliferation, migration and morphogenesis. In particular, the homeostasis of blood vessels is dependent on shear stress. To mimic this process ex vivo, efforts have been made to seed scaffolds with vascular and other cell types in the presence of growth factors and under pulsatile flow conditions. However, the resulting bioreactors lack information on shear stress and flow distributions within the scaffold. Consequently, it is difficult to interpret the effects of shear stress on cell function. Such knowledge would enable researchers to improve upon cell culture protocols. Recent work has focused on optimizing the microstructural parameters of the scaffold to fine tune the shear stress. In this study, we have adopted a different approach whereby flows are redirected throughout the bioreactor along channels patterned in the porous scaffold to yield shear stress distributions that are optimized for uniformity centered on a target value. A topology optimization algorithm coupled to computational fluid dynamics simulations was devised to this end. The channel topology in the porous scaffold was varied using a combination of genetic algorithm and fuzzy logic. The method is validated by experiments using magnetic resonance imaging (MRI) readouts of the flow field.

Biotechnology• 2011• arXiv
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Periodicals

Theory of active particle penetration through a planar elastic membrane

With the rapid advent of biomedical and biotechnological innovations, a deep understanding of the nature of interaction between nanomaterials and cell membranes, tissues, and organs, has become increasingly important. Active penetration of nanoparticles through cell membranes is a fascinating phenomenon that may have important implications in various biomedical and clinical applications. Using a fully analytical theory supplemented by particle-based computer simulations, the penetration process of an active particle through a planar two-dimensional elastic membrane is studied. The membrane is modeled as a self-assembled sheet of particles, uniformly arranged on a square lattice. A coarse-grained model is introduced to describe the mutual interactions between the membrane particles. The active penetrating particle is assumed to interact sterically with the membrane particles. State diagrams are presented to fully characterize the system behavior as functions of the relevant control parameters governing the transition between different dynamical states. Three distinct scenarios are identified. These compromise trapping of the active particle, penetration through the membrane with subsequent self-healing, in addition to penetration with permanent disruption of the membrane. The latter scenario is accompanied by a partial fragmentation of the membrane and creation of a hole of a size exceeding the interaction range of the membrane components. Our analytical theory is based on a combination of a perturbative expansion technique and a discrete-to-continuum formulation. Our approach might be helpful for the prediction of the transition threshold between the trapping and penetration in real-space experiments involving motile swimming bacteria or artificial active particles.

Biotechnology• 2019• arXiv
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Periodicals

epiGPTope: A machine learning-based epitope generator and classifier

Epitopes are short antigenic peptide sequences which are recognized by antibodies or immune cell receptors. These are central to the development of immunotherapies, vaccines, and diagnostics. However, the rational design of synthetic epitope libraries is challenging due to the large combinatorial sequence space, $20^n$ combinations for linear epitopes of n amino acids, making screening and testing unfeasible, even with high throughput experimental techniques. In this study, we present a large language model, epiGPTope, pre-trained on protein data and specifically fine-tuned on linear epitopes, which for the first time can directly generate novel epitope-like sequences, which are found to possess statistical properties analogous to the ones of known epitopes. This generative approach can be used to prepare libraries of epitope candidate sequences. We further train statistical classifiers to predict whether an epitope sequence is of bacterial or viral origin, thus narrowing the candidate library and increasing the likelihood of identifying specific epitopes. We propose that such combination of generative and predictive models can be of assistance in epitope discovery. The approach uses only primary amino acid sequences of linear epitopes, bypassing the need for a geometric framework or hand-crafted features of the sequences. By developing a method to create biologically feasible sequences, we anticipate faster and more cost-effective generation and screening of synthetic epitopes, with relevant applications in the development of new biotechnologies.

Biotechnology• 2025• arXiv
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Periodicals

A review of DNA restriction-free overlapping sequence cloning techniques for synthetic biology

DNA cloning methods are fundamental tools in molecular biology, synthetic biology, and genetic engineering that enable precise DNA manipulation for various scientific and biotechnological applications. This review systematically summarizes the major restriction-free overlapping sequence cloning (RFOSC) techniques currently used in synthetic biology and examines their development, efficiency, practicality, and specific applications. In vitro methods, including Gibson Assembly, Circular Polymerase Extension Cloning (CPEC), Polymerase Incomplete Primer Extension (PIPE), Overlap Extension Cloning (OEC), Flap Endonuclease Cloning (FEN-Cloning), and commercially available techniques such as TOPO and In-Fusion, have been discussed alongside hybrid approaches such as Ligation-Independent Cloning (LIC), Sequence-Independent Cloning (SLIC), and T5 Exonuclease-Dependent Assembly (TEDA). Additionally, in vivo methods leveraging host recombination machinery, including Yeast Homologous Recombination (YHR), In Vivo Assembly (IVA), Transformation-Associated Recombination (TAR), and innovative approaches such as Multiple-Round In Vivo Site-Specific Assembly (MISSA) and Phage Enzyme-Assisted Direct Assembly (PEDA), are critically evaluated. The review highlights that method selection should consider the scale, complexity, cost, and specific needs of individual research projects, noting that no single technique is universally optimal. Future trends suggest the increased integration of enzymatic efficiency, host versatility, and automation, broadening the accessibility and capabilities of DNA assembly technologies.

Biotechnology• 2025• arXiv
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Periodicals

Programmed Internal Reconfigurations in a 3D-Printed Auxetic Metamaterial Enable Fluidic Control for a Vertically Stacked Valve Array

Microfluidic valves play a key role within microfluidic systems by regulating fluid flow through distinct microchannels, enabling many advanced applications in medical diagnostics, lab-on-chips, and laboratory automation. While microfluidic systems are often limited to planar structures, 3D printing enables new capabilities to generate complex designs for fluidic circuits with higher densities and integrated components. However, the control of fluids within 3D structures presents several difficulties, making it challenging to scale effectively and many fluidic devices are still often restricted to quasi-planar structures. Incorporating mechanical metamaterials that exhibit spatially adjustable mechanical properties into microfluidic systems provides an opportunity to address these challenges. Here, we have performed systematic computational and experimental characterization of a modified auxetic structure to generate a modular metamaterial for an active device that allows us to directly regulate flow through integrated, multiplexed fluidic channels "one-at-a-time," in a manner that is highly scalable. We present a design algorithm so that this architecture can be extended to arbitrary geometries, and we expect that by incorporation of mechanical metamaterial designs into 3D printed fluidic systems, which themselves are readily expandible to any complex geometries, will enable new biotechnological and biomedical applications of 3D printed devices.

Biotechnology• 2023• arXiv
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From Stoop to Squat: A comprehensive analysis of lumbar loading among different lifting styles

Lifting up objects from the floor has been identified as a risk factor for low back pain, whereby a flexed spine during lifting is often associated with producing higher loads in the lumbar spine. Even though recent biomechanical studies challenge these assumptions, conclusive evidence is still lacking. This study therefore aimed at comparing lumbar loads among different lifting styles using a comprehensive state-of-the-art motion capture-driven musculoskeletal modeling approach. Thirty healthy pain-free individuals were enrolled in this study and asked to repetitively lift a 15 kg-box by applying 1) a freestyle, 2) a squat and 3) a stoop lifting technique. Whole-body kinematics were recorded using an optical motion capture system and used to drive a full-body musculoskeletal model including a detailed thoracolumbar spine. Compressive, shear and total loads were calculated based on a static optimization approach and expressed as factor body weight (BW). In addition, lumbar lordosis angles and total lifting time were calculated. All parameters were compared among the lifting styles using a repeated measures design. For all lumbar segments, stoop lifting showed significantly lower compressive and total loads (-0.3 to -1.0BW) when compared to freestyle and squat lifting. Stoop lifting produced higher shear loads (+0.1 to +0.8BW) in the segments T12/L1 to L4/L5, but lower loads in L5/S1 (-0.2 to -0.4BW). Peak compressive and total loads during squat lifting occurred approximately 30% earlier in the lifting cycle compared to stoop lifting. Stoop lifting showed larger lumbar lordosis range of motion (35.9+/-10.1°) than freestyle (24.2+/-7.3°) and squat (25.1+/-8.2°) lifting. Lifting time differed significantly with freestyle being executed the fastest (4.6+/-0.7s), followed by squat (4.9+/-0.7s) and stoop (5.9+/-1.1s).

Biotechnology• 2021• arXiv
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Uncovering Biological Network Function via Graphlet Degree Signatures

Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins aggregate to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines. We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method groups topologically similar proteins under this measure in a PPI network and shows that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research.

Biotechnology• 2008• arXiv
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Enzyme Similarity Networks

There is a crescent use of enzymes in multiple industries and sciences, ranging from materials and fuel synthesis to pharmaceutical and food production. Their applicability in this variety of fields depends not only on their biochemical function but also on their physicochemical properties. In the present work, we describe how the coincidence methodology can be employed to construct similarity networks of seventy well-studied enzymes of the Glycoside Hydrolase Family 13 and to identify communities of physicochemically related enzymes. More specifically, each of the selected enzymes is mapped into a network node, while the links between pairs of enzymes are determined by the coincidence similarity between selected physicochemical features of interest. The obtained networks have modularity and number of isolated nodes optimized respectively to two parameters involved in the coincidence methodology, resulting in highly modular networks. In order to investigate the effect of the considered physicochemical features on the enzymes relationships, the coincidence-based method also is applied to create a meta-network, in which the enzymes similarity networks obtained by the combination of every possible feature becomes nodes of a feature combination network, and the coincidence similarity between those networks defines the respective links. The obtained feature combination network systematically and comprehensively indicates the impact of the selected physicochemical features on enzyme similarity. Several interesting results are reported and discussed, including the identification of subgroups of enzymes with similar physicochemical features within catalytical classes, providing important information for the selection and design of enzymes for targeted biotechnological applications.

Biotechnology• 2022• arXiv
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Quantitative elemental imaging in eukaryotic algae

All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved with how these elements are utilized, for what purpose and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable its operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supply or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging allowing subcellular resolution, with X-ray fluorescence microscopy (XFM) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.

Biotechnology• 2022• arXiv
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