Biology subjects

Ebenhoeh, O.

Publications and source records attributed to Ebenhoeh, O..

26 records · Page 2Linked to original sources

bioRxiv2020

moped - A Python package for metabolic modelling and topological analysis

Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, amongst others, differential equation based modeling of metabolic systems, constraint based modeling and topological analysis of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present the Python package moped which serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and path-way/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, topological analysis and export for constraint-based analyses.

bioinformatics↗

bioRxiv2020

Constructing and analysing dynamic models withn modelbasev1.0 - a software update

BackgroundComputational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired. Results and DiscussionWe provide here the update on the development of modelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps, modelbase v1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format using modelbase. ConclusionWith the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

systems biology↗

bioRxiv2019

An integrative approach to investigate natural variation in the accumulation of aliphatic glucosinolates in Arabidopsis thaliana

Glucosinolates are a fascinating class of specialised metabolites found in the plants of Brassicacea family. The variation in glucosinolate composition across different Arabidopsis ecotypes could be a result of allelic compositions at different biosynthetic loci. The contribution of methylthioalkylmalate synthase (MAM) genes to diversity of glucosinolate profiles across different Arabidopsis ecotypes has been confirmed by genetic analyses. Different MAM isoforms utilise different chain-elongated substrates for glucosinolate biosynthesis causing thus a variation in chain lengths across different Arabidopsis ecotypes. To further investigate the relationship between the genotype and the associated metabolic phenotype, we studied the diversity of genes and enzymes of glucosinolate biosynthesis. Using Shannon entropy as a measure we revealed that several genes of the pathway show a clear derivation from the expected behaviour, either accumulating non-synonymous SNPs or showing signs of purifying selection. We found that the genotype-phenotype relationship is much more complicated than inferred from the diversity of MAM synthases. We conclude therefore, that the ON/OFF feature of key QTLs is not enough to elucidate the diversity of glucosinolates across different Arabidopsis thaliana ecotypes and that glucosinolate profiles are determined also through the polymorphic residues along the coding regions of multiple metabolic genes.

plant biology↗

bioRxiv2018

Balancing energy supply during photosynthesis - a theoretical perspective

The photosynthetic electron transport chain (PETC) provides energy and redox equivalents for carbon fixation by the Calvin-Benson-Bassham (CBB) cycle. Both of these processes have been thoroughly investigated and the underlying molecular mechanisms are well known. However, it is far from understood by which mechanisms it is ensured that energy and redox supply by photosynthesis matches the demand of the downstream processes. Here, we deliver a theoretical analysis to quantitatively study the supply-demand regulation in photosynthesis. For this, we connect two previously developed models, one describing the PETC, originally developed to study non-photochemical quenching, and one providing a dynamic description of the photosynthetic carbon fixation in C3 plants, the CBB Cycle. The merged model explains how a tight regulation of supply and demand reactions leads to efficient carbon fixation. The model further illustrates that a stand-by mode is necessary in the dark to ensure that the carbon fixation cycle can be restarted after dark-light transitions, and it supports hypotheses, which reactions are responsible to generate such mode in vivo.

systems biology↗

bioRxiv2018

Building mathematical models of biological systems with modelbase, a Python package for semi-automatic ODE assembly and construction of isotope-specific models

The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods to define reactions and their rates. Based on the rates and stoichiometries, the system of differential equations is assembled automatically. modelbase minimises the constraints imposed on the user, allowing for easy and dynamic access to all variables, including derived ones, in a convenient manner. A simple incorporation of algebraic equations is, for example, convenient to study systems with rapid equilibrium or quasi steady-state approximations. Moreover, modelbase provides construction methods that automatically build all isotope-specific versions of a particular reaction, making it a convenient tool to analyse non-steady state isotope-labelling experiments.

systems biology↗

bioRxiv2017

The importance of the photosynthetic Gibbs effect in the elucidation of the Calvin-Benson-Bassham Cycle

The photosynthetic carbon reduction cycle, or Calvin-Benson-Bassham Cycle, is now contained in every standard biochemistry textbook. Although the cycle was already proposed in 1954, it is still subject of intense research, and even the structure of the cycle, i.e. the exact series of reactions, is still under debate. The controversy about the cycles structure was fuelled by the findings of Gibbs and Kandler in 1956 and 1957, when they observed that radioactive 14CO2 was dynamically incorporated in hexoses in a very atypical and asymmetrical way, a phenomenon later termed the photosynthetic Gibbs effect. Now, it is widely accepted that the photosynthetic Gibbs effect is not in contradiction to the reaction scheme proposed by Calvin, Benson and Bassham, but the arguments given have been largely qualitative and hand-waving. To fully appreciate the controversy and to understand the difficulties in interpreting the Gibbs effect, it is illustrative to illuminate the history of the discovery of the Calvin-Benson-Bassham Cycle. We here give an account of central scientific advances and discoveries, which were essential prerequisites for the elucidation of the cycle. Placing the historic discoveries in the context of the modern textbook pathway scheme illustrates the complexity of the cycle and demonstrates why especially dynamic labelling experiments are far from easy to interpret. We conclude by arguing that only mathematical models based on a sound theory are capable of resolving conflicting interpretations and providing a consistent quantitative explanation.

biochemistry↗

bioRxiv2016

Dynamical modelling of the heat shock response in Chlamydomonas reinhardtii

Global warming is exposing plants to more frequent heat stress, with consequent crop yield reduction. Organisms exposed to large temperature increases protect themselves typically with a heat shock response (HSR). To study the HSR in photosynthetic organisms we present here a data driven mathematical model describing the dynamics of the HSR in the model organism Chlamydomonas reinhartii. Temperature variations are sensed by the accumulation of unfolded proteins, which activates the synthesis of heat shock proteins (HSP) mediated by the heat shock transcription factor HSF1. Our dynamical model employs a system of ordinary differential equations mostly based on mass-action kinetics to study the time evolution of the involved species. The signalling network is inferred from data in the literature, and the multiple experimental data-sets available are used to calibrate the model, which allows to reproduce their qualitative behaviour. With this model we show the ability of the system to adapt to temperatures higher than usual during heat shocks longer than three hours by shifting to a new steady state. We study how the steady state concentrations depend on the temperature at which the steady state is reached. We systematically investigate how the accumulation of HSPs depends on the combination of temperature and duration of the heat shock. We finally investigate the system response to a smooth variation in temperature simulating a hot day.

plant biology↗

bioRxiv2016

Dynamics of the bacterial community associated with Phaeodactylum tricornutum cultures

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesise industrially-relevant molecules. Large-scale monocultures are prone to bio-contamination, however, little is known about the identity of the invading organisms. To gain insight into the bacterial community associated with diatoms, we translated the complexity of a natural system into reproducible experiments where we investigated the microbiome of P. tricornutum cultures. The results revealed a dynamic bacterial community that changed over time and in differing media conditions. We propose a network of putative interactions between P. tricornutum and the main bacterial factions, which is translated into a set of ordinary differential equations constituting a computational dynamic model. The proposed mathematical model is able to capture the population dynamics, further supporting the hypothesised interactions. The interdisciplinary approach implemented provides a framework for understanding the dynamics of diatom-associated microbial communities, and provides a foundation for further systematic investigations of host-microbe interactions.

ecology↗