Biology subjects

Ebenhöh, O.

Publications and source records attributed to Ebenhöh, O..

5 recordsLinked to original sources

bioRxiv2023

Shedding Light On Blue-Green Photosynthesis: A Wavelength-Dependent Mathematical Model Of Photosynthesis In Synechocystis sp. PCC 6803

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

plant biology↗

bioRxiv2023

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗

bioRxiv2022

Stochastic modelling of a three-dimensional glycogen granule synthesis and impact of the branching enzyme

In humans, glycogen storage diseases result from metabolic inborn errors, and can lead to severe phenotypes and lethal conditions. Besides these rare diseases, glycogen is also associated to widely spread societal burdens such as diabetes. Glycogen is a branched glucose polymer synthesised and degraded by a complex set of enzymes. Over the past 50 years, the structure of glycogen has been intensively investigated. Yet, the interplay between glycogen structure and the related enzymes is still to be characterised. In this article, we develop a stochastic coarse-grained and spatially resolved model of branched polymer biosynthesis following a Gillespie algorithm. Our study largely focusses on the role of the branching enzyme, and first investigates the properties of the model with generic parameters, before comparing it to in vivo experimental data in mice. It arises that the ratio of glycogen synthase over branching enzyme activities drastically impacts the structure of the granule. We deeply investigate the mechanism of branching and parametrise it using distinct lengths. Not only do we consider various possible sets of values for these lengths, but also distinct rules to apply them. We show how combining them finely tunes glycogen macromolecular structure. Comparing the model with experimental data confirms that we can accurately reproduce glycogen chain length distributions in wild type mice. Additional granule properties obtained for this fit are also in good agreement with typically reported values in the experimental literature. Nonetheless, we find that the mechanism of branching must be more flexible than usually reported. Overall, we demonstrate that the chain length distribution is an imprint of the branching activity and mechanism. Our generic model and methods can be applied to any glycogen data set, and could in particular contribute to characterise the mechanisms responsible for glycogen storage disorders. Author summaryGlycogen is a granule-like macromolecule made of 10,000 to 50,000 glucose units arranged in linear and branched chains. It serves as energy storage in many species, including humans. Depending on physiological conditions (hormone concentrations, glucose level, etc.) glycogen granules are either synthesised or degraded. Certain metabolic disorders are associated to abnormal glycogen structures, and structural properties of glycogen might impact the dynamics of glucose release and storage. To capture the complex interplay between this dynamics and glycogen structural properties, we propose a computational model relying on the random nature of biochemical reactions. The granule is represented in three dimensions and resolved at the glucose scale. Granules are produced under the action of a complex set of enzymes, and we mostly focus on those responsible for the formation of new branches. Specifically, we study the impact of their molecular action on the granule structure. With this model, we are able to reproduce structural properties observed under certain in-vivo conditions. Our biophysical and computational approach complements experimental studies and may contribute to characterise processes responsible for glycogen related disorders.

biophysics↗

bioRxiv2021

Chlorophyll fluorescence: How the quality of information about PAM instrument parameters may affect our research

Chlorophyll a fluorescence is a powerful indicator of photosynthetic energy conversion in plants and photosynthetic microorganisms. One of the most widely used measurement techniques is Pulse Amplitude Modulation (PAM) fluorometry. Unfortunately, parameter settings of PAM instruments are often not completely described in scientific articles although their variations, however small these may seem, can influence measurements. We show the effects of parameter settings on PAM measurements. We first simulated fluorescence signals using a previously published computational model of photosynthesis. Then, we validated our findings experimentally. Our analysis demonstrates how the kinetics of non-photochemical quenching (NPQ) induction and relaxation are affected by different settings of PAM instrument parameters. Neglecting these parameters may mislead data interpretation and derived hypotheses, hamper independent validation of the results, and cause problems for mathematical formulation of underlying processes. Given the uncertainties inflicted by this neglect, we urge PAM users to provide detailed documentation of measurement protocols. Moreover, to ensure accessibility to the required information, we advocate minimum information standards that can serve both experimental and computational biologists in our efforts to advance system-wide understanding of biological processes. Such specification will enable launching a standardized database for plant and data science communities. HighlightPAM fluorometry measurement is sensitive to instrument settings and protocols. Yet, protocols are published incompletely. We urge to reach an agreement on minimal protocol information of PAM experiments to be shared publicly.

plant biology↗

bioRxiv2018

Emergent sub-population behavior uncovered with a community dynamic metabolic model of Escherichia coli diauxic growth

Microbial communities have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. The diauxic shift of Escherichia coli, growing first on glucose and, after it is exhausted, on acetate, is still today actively studied. As a fundamental example of metabolic adaptation, we are interested in understanding if diauxie in monocultures of E. coli is a coordinated and uniform metabolic shift, or rather the observable emergent result of individual sub-populations behavior. To do so we first develop a modeling framework that integrates dynamic models (ordinary differential equation systems) with structural models (metabolic networks), providing an open source modeling framework that is suitable to investigate the dynamics of microbial communities. We apply our methods to model E. coli either as having an average, unique metabolic state or as being the combination of two E. coli populations adapted to one of the two carbon sources. Our results are in strong agreement with previously published data and suggest that rather than a coordinated metabolic shift, diauxie could be the emergent pattern resulting from a survival strategy where individual cells differentiate for optimal growth on different substrates in view of environmental fluctuations. This work offers a new perspective on how to use dynamic metabolic modeling to investigate population dynamics, as the proposed approach can be easily transfered to studies on other multi-species communities as well as single cells.\n\nImportanceEscherichia coli diauxie is a fundamental example of metabolic adaptation that has not yet been completely understood, and to this aim approaches integrating experimental and theoretical biology are needed. We present a novel dynamic metabolic modeling approach that captures diauxie as an emergent property of sub-population dynamics rather than a homogeneous metabolic shift in E. coli monocultures. Without fine tuning of the parameters of E. coli core genome-scale model we obtain good agreement with published data. Our results suggest a change of paradigm in using single organism metabolic models, which can only to a certain approximation represent the average population metabolic state. We finally provide an open source modeling framework that can be applied to model multi-organism dynamics in variable environments.

systems biology↗