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Periodicals

Contributions of the Petabyte Scale Sequence Search Codeathon toward efforts to scale sequence-based searches on SRA

The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institutes of Health's (NIH) Sequence Read Archive (SRA), which is maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a rapidly growing public database that researchers use to drive scientific discovery across all domains of life. This increase in available data has great promise for pushing scientific discovery but also introduces new challenges that scientific communities need to address. As genomic datasets have grown in scale and diversity, a parade of new methods and associated software have been developed to address the challenges posed by this growth. These methodological advances are vital for maximally leveraging the power of next-generation sequencing (NGS) technologies. With the goal of laying a foundation for evaluation of methods for petabyte-scale sequence search, the Department of Energy (DOE) Office of Biological and Environmental Research (BER), the NIH Office of Data Science Strategy (ODSS), and NCBI held a virtual codeathon 'Petabyte Scale Sequence Search: Metagenomics Benchmarking Codeathon' on September 27 - Oct 1 2021, to evaluate emerging solutions in petabyte scale sequence search. The codeathon attracted experts from national laboratories, research institutions, and universities across the world to (a) develop benchmarking approaches to address challenges in conducting large-scale analyses of metagenomic data (which comprises approximately 20% of SRA), (b) identify potential applications that benefit from SRA-wide searches and the tools required to execute the search, and (c) produce community resources i.e. a public facing repository with information to rebuild and reproduce the problems addressed by each team challenge.

Biotechnology• 2025• arXiv
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Periodicals

Mapping high-growth phenotypes in the flux space of microbial metabolism

Experimental and empirical observations on cell metabolism cannot be understood as a whole without their integration into a consistent systematic framework. However, the characterization of metabolic flux phenotypes is typically reduced to the study of a single optimal state, like maximum biomass yield that is by far the most common assumption. Here we confront optimal growth solutions to the whole set of feasible flux phenotypes (FFP), which provides a benchmark to assess the likelihood of optimal and high-growth states and their agreement with experimental results. In addition, FFP maps are able to uncover metabolic behaviors, such as aerobic fermentation accompanying exponential growth on sugars at nutrient excess conditions, that are unreachable using standard models based on optimality principles. The information content of the full FFP space provides us with a map to explore and evaluate metabolic behavior and capabilities, and so it opens new avenues for biotechnological and biomedical applications.

Biotechnology• 2014• arXiv
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Periodicals

RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow

Ribonucleic acid (RNA) plays fundamental roles in biological systems, from carrying genetic information to performing enzymatic function. Understanding and designing RNA can enable novel therapeutic application and biotechnological innovation. To enhance RNA design, in this paper we introduce RiboGen, the first deep learning model to simultaneously generate RNA sequence and all-atom 3D structure. RiboGen leverages the standard Flow Matching with Discrete Flow Matching in a multimodal data representation. RiboGen is based on Euclidean Equivariant neural networks for efficiently processing and learning three-dimensional geometry. Our experiments show that RiboGen can efficiently generate chemically plausible and self-consistent RNA samples, suggesting that co-generation of sequence and structure is a competitive approach for modeling RNA.

Biotechnology• 2025• arXiv
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Periodicals

Dynamics and Control of Oscillatory Bioreactors

Bioreactors are widely used in many industries to generate a range of products using various host cells e.g., yeast, insect, and mammalian cells. Depending on the process, product, and host cell, some bioreactors exhibit sustained periodic behavior in key process variables such as metabolite concentrations, biomass, and product titer. Such dynamical behavior can arise from different mechanisms, including predator-prey dynamics, substrate inhibition, and cell sub-population synchrony. Oscillatory dynamical behavior is undesirable as it can impact downstream processes, especially in a continuous operation, and can make process operations and product quality control more challenging. This article provides an overview of oscillatory dynamics. The mechanisms that give rise to the oscillations and process control strategies for suppressing the oscillations are discussed, while providing insights that go beyond past studies. Alternative process configurations are proposed for bypassing the mechanisms that generate oscillations.

Biotechnology• 2023• arXiv
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Periodicals

Defining the boundaries: challenges and advances in identifying cells in microscopy images

Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method.

Biotechnology• 2023• arXiv
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Periodicals

Electrical Double Layer Properties of Spherical Oxide Nanoparticles

The accurate characterization of electrical double layer properties of nanoparticles is of fundamental importance for optimizing their physicochemical properties for specific biotechnological and biomedical applications. In this article, we use classical solvation density functional theory and a surface complexation model to investigate the effects of pH and nanoparticle size on the structural and electrostatic properties of an electrolyte solution surrounding a spherical silica oxide nanoparticle. The formulation has been particularly useful for identifying dominant interactions governing the ionic driving force under a variety of pH levels and nanoparticle sizes. As a result of the energetic interplay displayed between electrostatic potential, ion-ion correlation and particle crowding effects on the nanoparticle surface titration, rich, non-trivial ion density profiles and mean electrostatic potential behavior have been found.

Biotechnology• 2016• arXiv
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Periodicals

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.

Biotechnology• 2010• arXiv
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Periodicals

Atom Tunneling in the Hydroxylation Process of Taurine/$α$-Ketoglutarate Dioxygenase Identified by Quantum Mechanics/Molecular Mechanics Simulations

TauD dioxygenase is one of the most studied $α$-ketoglutarate dependent dioxygenases ($α$KGDs), involved in several biotechnological applications. We investigated the key step in the catalytic cycle of the $α$KGDs, the hydrogen transfer process, by a QM/MM approach (B3LYP/CHARMM22). Analysis of the charge and spin densities during the reaction demonstrates that a concerted mechanism takes place, where the H atom transfer happens simultaneously with the electron transfer from taurine to the Fe=O cofactor. We found quantum tunneling of the hydrogen atom to increase the rate constant by a factor of 40 at 5\textcelsius{}. As a consequence a quite high KIE value of close to 60 is obtained, which is consistent with the experimental value.

Biotechnology• 2020• arXiv
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Periodicals

Parallelism of stable traces

A parallel $d$-stable trace is a closed walk which traverses every edge of a graph exactly twice in the same direction and for every vertex $v$, there is no subset $X \subseteq N(v)$ with $1 \leq |N| \leq d$ such that every time the walk enters $v$ from $X$, it also exits to a vertex in $X$. In the past, $d$-stable traces were investigated as a mathematical model for an innovative biotechnological procedure -- self-assembling of polypeptide structures. Among other, it was proven that graphs that admit parallel $d$-stable traces are precisely Eulerian graphs with minimum degree strictly larger than $d$. In the present paper we give an alternative, purely combinatorial proof of this result.

Biotechnology• 2016• arXiv
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Periodicals

Assessing the potential of deep learning for protein-ligand docking

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein-ligand docking have recently been introduced, to date no prior works have systematically studied the behavior of the latest docking and structure prediction methods within the broadly applicable context of (1) using predicted (apo) protein structures for docking (e.g., for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (e.g., for enzyme design); and (3) having no prior knowledge of binding pockets (e.g., for generalization to unknown pockets). To enable a deeper understanding of docking methods' real-world utility, we introduce PoseBench, the first comprehensive benchmark for broadly applicable protein-ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein-ligand docking and protein-ligand structure prediction using both primary ligand and multi-ligand benchmark datasets, the latter of which we introduce for the first time to the DL community. Empirically, using PoseBench, we find that (1) DL co-folding methods generally outperform comparable conventional and DL docking baseline algorithms, yet popular methods such as AlphaFold 3 are still challenged by prediction targets with novel binding poses; (2) certain DL co-folding methods are highly sensitive to their input multiple sequence alignments, while others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting novel or multi-ligand protein targets. Code, data, tutorials, and benchmark results are available at https://github.com/BioinfoMachineLearning/PoseBench.

Biotechnology• 2024• arXiv
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Periodicals

An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings

Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. Furthermore, Tuberculosis (TB) remains a major health problem in several low- and middle-income countries and its common symptoms include fever, cough and tiredness, similarly to COVID-19. In order to help in the detection of COVID-19, we propose the extraction of deep features (DF) from chest X-ray images, a technology available in most hospitals, and their subsequent classification using machine learning methods that do not require large computational resources. We compiled a five-class dataset of X-ray chest images including a balanced number of COVID-19, viral pneumonia, bacterial pneumonia, TB, and healthy cases. We compared the performance of pipelines combining 14 individual state-of-the-art pre-trained deep networks for DF extraction with traditional machine learning classifiers. A pipeline consisting of ResNet-50 for DF computation and ensemble of subspace discriminant classifier was the best performer in the classification of the five classes, achieving a detection accuracy of 91.6+ 2.6% (accuracy + 95% Confidence Interval). Furthermore, the same pipeline achieved accuracies of 98.6+1.4% and 99.9+0.5% in simpler three-class and two-class classification problems focused on distinguishing COVID-19, TB and healthy cases; and COVID-19 and healthy images, respectively. The pipeline was computationally efficient requiring just 0.19 second to extract DF per X-ray image and 2 minutes for training a traditional classifier with more than 2000 images on a CPU machine. The results suggest the potential benefits of using our pipeline in the detection of COVID-19, particularly in resource-limited settings and it can run with limited computational resources.

Biotechnology• 2020• arXiv
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Periodicals

The university-industry knowledge relationship: Analyzing patents and the science base of technologies

Via the Internet, information scientists can obtain cost-free access to large databases in the hidden or deep web. These databases are often structured far more than the Internet domains themselves. The patent database of the U.S. Patent and Trade Office is used in this study to examine the science base of patents in terms of the literature references in these patents. University-based patents at the global level are compared with results when using the national economy of the Netherlands as a system of reference. Methods for accessing the on-line databases and for the visualization of the results are specified. The conclusion is that 'biotechnology' has historically generated a model for theorizing about university-industry relations that cannot easily be generalized to other sectors and disciplines.

Biotechnology• 2009• arXiv
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Periodicals

Bond-order correlation energies for small Si-containing molecules compared with ab initio results from low-order Moller-Plesset perturbation theory

The present study of small molecules containing silicon has been motivated by (a) the considerable interest being shown currently in the kinetics and reactivity of such molecules, and (b) the biotechnological potential of silicon-derivate surfaces as substrates in the adsorption of, for instance, amino acids and proteins. Therefore, we have studied by (i) a semi-empirical approach and (ii) an ab initio procedure employing low-order Moller-Plesset perturbation theory, the molecular correlation energies of some neutral closed and open shell silicon-containing molecules in the series SiXnYm. Procedure (i) is shown to have particular merit for the correlation of the ionic members studied in the above series, while the ab initio procedures employed come into their own for neutral species.

Biotechnology• 2004• arXiv
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Periodicals

InstructPro: Natural Language Guided Ligand-Binding Protein Design

The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by scarce protein-ligand complex data. To circumvent this data bottleneck, we leverage the abundant natural language descriptions characterizing protein-ligand interactions. Here, we introduce InstructPro, a family of generative models that design proteins following the guidance of natural language instructions and ligand formulas. InstructPro produces protein sequences consistent with specified function descriptions and ligand targets. To enable training and evaluation, we develop InstructProBench, a large-scale dataset of 9.6 million (function description, ligand, protein) triples. We train two model variants -- InstructPro-1B and InstructPro-3B -- that substantially outperform strong baselines. InstructPro-1B achieves an AlphaFold3 ipTM of 0.918 and a binding affinity of -8.764 on seen ligands, while maintaining robust performance in a zero-shot setting with scores of 0.869 and -6.713, respectively. These results are accompanied by novelty scores of 70.1% and 68.8%, underscoring the model's ability to generalize beyond the training set. Furthermore, the model yields a superior binding free energy of -20.9 kcal/mol and an average of 5.82 intermolecular hydrogen bonds, validating its proficiency in designing high-affinity ligand-binding proteins. Notably, scaling to InstructPro-3B further improves the zero-shot ipTM to 0.882, binding affinity to -6.797, and binding free energy to -25.8 kcal/mol, demonstrating clear performance gains associated with increased model capacity. These findings highlight the power of natural language-guided generative models to mitigate the data bottlenecks in traditional structure-based methods, significantly broadening the scope of de novo protein design.

Biotechnology• 2025• arXiv
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Periodicals

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

Biotechnology• 2015• arXiv
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Periodicals

CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs

Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequencing. Machine learning models can infer previously unobserved biomedical relationships and characterize biomedical entities in these graphs, but existing knowledge graph embedding methods and their continual learning extensions either assume static graph structure or fail to exploit multimodal information under evolving data distributions. They also apply uniform regularization across all model parameters, ignoring that different modalities may exhibit distinct forgetting dynamics as the graph evolves. We propose the Continual Multimodal Knowledge Graph Learner (CMKL), a CL framework for biomedical KGs that natively encodes structure, text, and molecules, fuses them through a Mixture-of-Experts (MoE) router, and protects previously learned knowledge with standard EWC regularization and a K-means-diverse multimodal replay buffer. We evaluate CMKL on a 129K-entity biomedical continual benchmark with 10 tasks. On continual biomedical entity classification, CMKL reaches AP 0.591 versus 0.370 for the strongest structural baseline, a 60% gain that is driven by access to multimodal features and preserved across the sequence with near-zero forgetting (AF 0.008). On continual relationship prediction, CMKL reaches AP $0.062$, matching Naive Sequential and EWC (0.058) within seed noise and outperforming Joint Training (0.047, p=0.045) and LKGE (0.039). A frozen-text ablation reaches AP 0.136, more than double any jointly trained model, yet that signal is unreachable by margin-ranking gradients: the greedy-modality asymmetry lives at the representation level, not the fusion level, and MoE routing manages it by suppressing the unreachable modality without forcing it through a learned bottleneck. Code: github.com/yradwan147/cmkl-neurips2026

Biotechnology• 2026• arXiv
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Periodicals

Phototgyrotactic concentration of a population of swimming microalgae across a porous layer

The light environment controls the swimming of microalgae through a light-seeking and avoiding behaviour, which is known as phototaxis. In this work, we exploit phototaxis to control the migration and concentration of populations of the soil microalga {\it Chlamydomonas reinhardtii}. By imaging a suspension of these microalgae in a cuvette illuminated from above by blue light, we study how phototaxis changes the stability of the suspension and demonstrate how a thin, porous layer at the top of the cuvette prevents phototaxing microalgae from sinking, leading to the up-concentration of the microalgae in the region above the porous layer. We discuss the potential implications of our findings for microalgae in biotechnological applications and the natural environment.

Biotechnology• 2021• arXiv
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Periodicals

Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs for many applications in biology (protein expression), biotechnology (synthesis of substances), and medicine.

Biotechnology• 2015• arXiv
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Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.

Biotechnology• 2017• arXiv
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Periodicals

Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development

AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstraction: a Company World Model, defined as a persistent asset-to-value state representation with transition models, explicit value functions, planning, and updating across scientific, regulatory, BD, commercial, financial, and execution constraints. We introduce a dry-lab benchmark for testing whether AI-agent organizations should mimic departments or operate around such a world model. The benchmark contains 45 retrospective public-information decision cases with strict time cutoffs, hidden outcomes, common schemas, automatic scoring, and blinded pairwise judging. We compare human-org-mimic, stronger human-org-mimic-plus, AI-native asset-centric, and AI-native value-conversion architectures. The value-conversion architecture is a prompt-level approximation of a Company World Model: a Live Asset Value Record updated by Deal, Approval, Revenue, and Investment Arbiter loops. Under a success function defined by external BD, regulatory approval and launch, and revenue discipline, it achieved the highest automatic value-conversion score and was strongly preferred over the original baselines by value-specific blinded judges. Stress tests narrowed the claim: a stronger human baseline remained competitive, and a neutral judge did not show robust value-conversion dominance. Codex-only mechanistic ablations suggest that Revenue Room, Deal Room, and Approval Room carry useful work under the target objective. The central finding is objective-sensitive: departments may remain useful governance views, but the core AI-native operating primitive should be a shared, predictive asset-to-value state rather than a static human org chart. The study is dry-lab only and does not establish real-world drug success, clinical benefit, or revenue prediction accuracy.

Biotechnology• 2026• arXiv
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Periodicals

Deterministic access to global viral sequence data enables robust agentic scientific discovery

Public viral genome resources such as the National Center for Biotechnology Information (NCBI) Virus database are central to outbreak response, evolutionary analysis, vaccine design, and genomic surveillance. Yet many high-value retrieval workflows remain optimized for interactive use rather than deterministic, reproducible programmatic interfaces. This creates a challenge for Large Language Model (LLM)-based scientific agents, where errors in metadata interpretation, filtering logic, or retrieval can propagate into incorrect datasets. To evaluate agentic viral data retrieval, we built VirBench, a manually curated benchmark of 120 queries spanning diverse pathogens, taxonomic levels, and metadata filters. When autonomous AI systems, including Biomni, Claude, GPT, and Edison Analysis, were tasked with these queries without a dedicated retrieval layer, performance varied widely: mean accuracy ranged from 16.9% for Claude Sonnet 4 to 91.3% for GPT-5.5, with newer frontier models showing progress but residual errors remaining consequential. To address this, we built gget virus, a deterministic query framework that formalizes NCBI Virus-style filtering as a reproducible programmatic system. By staging retrieval, applying metadata constraints before sequence download, and retrieving structured GenBank records, gget virus reduces data transfer by more than 98% for high-volume queries while preserving exact-match semantics. Instructing autonomous AI systems to use gget virus increased accuracy to at least 90.0% across all evaluated systems and up to 99.7% for GPT-5.5, improved response stability to 0.92-1.00, reduced error magnitude, and generally decreased runtime and tool calls. Together, this work establishes deterministic data access as critical infrastructure for reliable agentic science and provides a reproducible retrieval layer for robust human- and AI-driven viral genomics workflows.

Biotechnology• 2026• arXiv
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Periodicals

Automated multi-dataset INST $^{13}$C metabolic flux analysis at microliter scale reveals robust fluxes but variable metabolite pools in $Corynebacterium~glutamicum$

Isotopically non-stationary metabolic flux analysis (INST $^{13}$C-MFA) provides unique insights into cellular physiology but is typically limited by low throughput and high experimental costs. Here, we present a miniaturized and automated workflow that integrates transient isotope labeling experiments with advanced computational modeling to enable parallel INST $^{13}$C-MFA at microliter scale. The approach is demonstrated for an evolved $Corynebacterium~glutamicum$ strain capable of efficient growth on ethanol, a substrate for which isotopically stationary $^{13}$C-MFA is inherently limited due to low labeling diversity. Using robotic liquid handling, rapid hot isopropanol quenching, and LC-QToF-MS-based analytics, highly informative datasets were generated from parallel 48-well experiments with different ethanol tracers. Multi-dataset INST $^{13}$C-MFA unlocked joint estimation of intracellular fluxes and metabolite pool sizes and significantly improved flux precision compared to single-dataset analyses. While net fluxes were robust across datasets, pool size estimates exhibited variability and did not converge under joint inference, highlighting a fundamental methodological difference to single-dataset INST $^{13}$C-MFA. The resulting multi-dataset flux map reveals a central role of the glyoxylate shunt during growth on ethanol, consistent with metabolic adaption to C2-based substrate utilization. Overall, this work demonstrates that automated multi-dataset INST $^{13}$C-MFA is technically feasible and provides high-quality flux analysis at a fraction of the cost of conventional lab-scale bioreactor-based approaches. The presented workflow establishes a scalable framework for high-throughput quantitative fluxomics in microbial biotechnology and supports integration into iterative strain engineering and biofoundry pipelines.

Biotechnology• 2026• arXiv
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Study on the Concept and Development of a Mobile Incubator

Creating the best possible conditions is essential for proper cell growth. Incubators, a type of biotechnological instrument, are used to simulate this condition and maintain the cells within them. The processes involved in creating a mobile incubator, which are essential for monitoring a cell culture's physiological parameters, are outlined in this article. The goal is to keep image-taking during cell development from compromising data accuracy. The cell culture is prone to contamination once it has been removed from the incubation environment for further monitoring. The proposed approach allows for on-the-go monitoring of the cell culture. Moreover, it enables constant monitoring.

Biotechnology• 2022• arXiv
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Fragmentation and aggregation of cyanobacterial colonies

Fluid flow has a major effect on the aggregation and fragmentation of bacterial colonies. Yet, a generic framework to understand and predict how hydrodynamics affects colony size remains elusive. This study investigates how fluid flow affects the formation and maintenance of large colonial structures in cyanobacteria, using an experimental technique that precisely controls hydrodynamic conditions. We performed experiments on laboratory cultures and lake samples of the cyanobacterium Microcystis, while their colony size distribution was measured simultaneously by direct microscopic imaging. We demonstrate that EPS-embedded cells formed by cell division exhibit significant mechanical resistance to shear forces. However, at elevated hydrodynamic stress levels (exceeding those typically generated by surface wind mixing) these colonies experience fragmentation through an erosion process. We also show that single cells can aggregate into small colonies due to fluid flow. However, the structural integrity of these flow-induced colonies is weaker than that of colonies formed by cell division. We provide a mathematical analysis to support the experiments and demonstrate that a population model with two categories of colonies describes the measured size distributions. Our results shed light on the specific conditions wherein flow-induced fragmentation and aggregation of cyanobacteria are decisive and indicate that colony formation under natural conditions is mainly driven by cell division, although flow-induced aggregation could play a role in dense bloom events. These findings can be used to improve prediction models and mitigation strategies for toxic cyanobacterial blooms and also offer potential applications in other areas such as algal biotechnology or medical settings where the dynamics of biological aggregates play a significant role.

Biotechnology• 2024• arXiv
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