Biology subjects

Kratochvil, M.

Publications and source records attributed to Kratochvil, M..

9 recordsLinked to original sources

bioRxiv2026

Multi-omic data fusion reveals the in vivo enzyme kinetics of Vibrio natriegens at the genome-scale

Vibrio natriegens is a halophilic, Gram-negative marine bacterium that is increasingly used in metabolic engineering applications due to its fast growth rate. In sparse minimal medium the organism has a doubling time of 25 minutes, which is about twice as fast as Escherichia coli under similar conditions. Given that its protein density is similarly constrained to that of E. coli, this necessitates that its metabolic enzymes are able to catalyze flux at a higher rate to sustain its metabolism. In this work, we measure the apparent turnover numbers of metabolically active enzymes in V. natriegens under a variety of growth conditions. The apparent turnover numbers of V. natriegens enzymes were measured in vivo by conducting coupled quantitative proteomics and 13C metabolic flux analysis experiments under seven different carbon source conditions in sparse minimal medium. A high quality genome-scale metabolic model was constructed and curated using additional experimental data. This model was extended with enzyme constraints, and subsequently used to find kinetic parameters that minimize the difference between model predictions and experimental observations. This model guided data fusion approach enabled the estimation of 357 apparent turnover numbers for metabolically active enzymes in V. natriegens. Our results reveal that the metabolic enzymes of V. natriegens are in median 14-fold faster than those of E. coli under similar conditions. Moreover, we show that machine learning generated turnover number estimates substantially underestimate the kinetics of V. natriegens. Our turnover number estimates were used to parameterize multiple condition dependent enzyme constrained flux balance analysis models of V. natriegens, which improved their predictive accuracy compared to the machine learning parameterisation. The combined experimental-computational approach employed here sheds light on the mechanism V. natriegens uses to accelerate its growth. This approach can also be extended to other bacteria, increasing the availability of in vivo measured enzyme turnover numbers, and improving the predictive accuracy of enzyme constrained metabolic models of other microbes.

systems biology↗

bioRxiv2025

Exploring the Role of PEDOT Electrodes in Electrostimulation of vascular endothelial cells

The potential of electrical stimulation for cell control in tissue engineering remains largely underestimated, as does the use of organic semiconductors. The tunable physico-chemical properties of organic semiconductors offer advantages over commonly used inert metals. This study investigates the effects of pulsed electrostimulation and electrode materials, gold and poly(3,4-ethylenedioxythiophene) (PEDOT), on the physiological functionality of human vascular endothelial cells. A novel electrostimulation platform incorporating gold or PEDOT electrodes was developed and characterized for electrical performance. Human umbilical vein endothelial cells were cultured on this platform, and the effects of electrostimulation and electrode material were assessed through morphological analysis, nitric oxide (NO) production, and expression of key endothelial marker genes. PEDOT electrodes produced higher electrical current during electrostimulation. Interestingly, cell morphology, including elongation and alignment, showed minimal changes under electrostimulation. NO production, a key marker of vascular health, was enhanced by electrostimulation, with PEDOT electrodes showing a trend toward greater NO accumulation than gold. Gene expression analysis revealed material- and stimulation-specific trends: electrostimulation generally upregulated KLF2, KLF4, and CYP1B1 on PEDOT electrodes but suppressed their expression on gold electrodes. These findings suggest that PEDOT electrodes, with their enhanced electrochemical properties and ability to support endothelial functionality, provide a safe and efficient platform for endothelial cell electrostimulation. This study advances understanding of the interplay between material properties and electrostimulation and highlights PEDOT as a promising candidate for vascular tissue engineering and regenerative medicine.

cell biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2024

MaBoSS for HPC environments: Implementations of the continuous time Boolean model simulator for large CPUclusters and GPU accelerators

Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. We present two new developments in the continuous time Boolean simulators: Ma-BoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.

bioinformatics↗

bioRxiv2022

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

1Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

bioengineering↗

bioRxiv2021

COBREXA.jl: constraint-based reconstruction and exascale analysis

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. Availability and implementationhttps://doi.org/10.17881/ZKCR-BT30. Contactchristophe.trefois@uni.lu, wei.gu@uni.lu

systems biology↗

bioRxiv2020

GigaSOM.jl: High-perfomance clustering and visualization of huge cytometry datasets

BackgroundThe amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow to easily generate data with hundreds of millions of single-cell data points with more than 40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to down-sample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. ResultsWe present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality-reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community, and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. ConclusionsGigaSOM.jl facilitates utilization of the commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from an massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. Key pointsO_LIGigaSOM.jl improves the applicability of FlowSOM-style single-cell cytometry data analysis by increasing the acceptable dataset size to billions of single cells. C_LIO_LISignificant speedup over current methods is achieved by distributed processing and utilization of efficient algorithms. C_LIO_LIGigaSOM.jl package includes support for fast visualization of multidimensional data. C_LI

bioinformatics↗

bioRxiv2020

Sustained Release of IL-2 Using an Injectable Hydrogel Prevents Autoimmune Diabetes

Interleukin 2 (IL-2) is a promising therapy for autoimmune type 1 diabetes (T1D), but the short half-life in vivo (less than 6 minutes) limits effective tissue exposure to IL-2. Tissue exposure is required for the tolerogenic effects of IL-2. We have developed an injectable hydrogel that incorporates heparin polymers to enable the sustained release of IL-2. This platform uses clinical grade and commercially available materials, including collagen, hyaluronan, and heparin, to deliver IL-2 by slowly degrading and releasing IL-2 over a two-week period in vivo. We find that heparin potentiates the activity of IL-2 and IL-2-mediated expansion of Foxp3+ regulatory T cell (Treg). Hydrogel-mediated IL-2 release showed a reduction of CD4+ and CD8+ T cells and an increase of FoxP3+ Treg in the lymph nodes of injected mice. Moreover, in the Non-Obese Diabetic (NOD) mouse model of T1D once-weekly administration of IL-2 hydrogels prevented diabetes onset as efficiently as 3x weekly repeated injections of soluble IL-2. Together these data suggest that heparin-containing hydrogels may have benefit in delivering low-dose IL-2 and promoting immune tolerance in autoimmune diabetes.

immunology↗

bioRxiv2018

Rapid single-cell cytometry data visualization with EmbedSOM

Efficient unbiased data analysis is a major challenge for laboratories handling large cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analyses by providing high-performance visualization of complex single cell distributions within cellular populations and their transition states. The algorithm is designed for linear scaling and speed suitable for interactive analyses of millions of cells without downsampling. At the same time, the visualization quality is competitive with current state-of-art algorithms. We demonstrate the properties of EmbedSOM on workflows that improve two essential types of analyses: The native ability of EmbedSOM to align population positions in embedding is used for comparative analysis of multi-sample data, and the connection to FlowSOM is exploited for simplifying the supervised hierarchical dissection of cell populations. Additionally, we discuss the visualization of the trajectories between cellular states facilitated by the local linearity of the embedding.

bioinformatics↗