Biology subjects

Nies, T.

Publications and source records attributed to Nies, T..

7 recordsLinked to original sources

bioRxiv2026

GreenSloth: a curated database and executable platform for mechanistic photosynthesis models

Mechanistic models of photosynthesis have expanded substantially over the past decades, covering processes from light reactions to carbon fixation. However, these models remain fragmented across the literature, inconsistently implemented, and difficult to reproduce or reuse, limiting their adoption beyond the research group that developed them. Here, we present GreenSloth, a freely accessible web-based database of 22 published mechanistic photosynthesis models, reimplemented as standardized, executable Python objects within MxlPy, an open-source framework for mechanistic biological modeling. Although the database is primarily designed for dynamic mechanistic models formulated as ordinary differential equations, the current implementation also includes the fields most widely cited steady-state mechanistic model and its variants. GreenSloth provides a structured environment for model discovery, comparison, and reuse, addressing reproducibility challenges in the field and enabling integration into emerging hybrid modeling approaches. It is also interactive: each model runs directly in the browser, with no installation, environment setup, or programming required. The resource is openly accessible and designed for long-term community maintenance, hoping to position itself as foundational infrastructure for the photosynthesis modeling community. Database URLhttps://greensloth.rwth-aachen.de/

systems biology↗

bioRxiv2025

MxlPy - Python Package for Mechanistic Learning in Life Science

SummaryRecent advances in artificial intelligence have accelerated the adoption of ML in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that combines mechanistic modelling with ML to deliver explainable, data-informed solutions. MxlPy facilitates mechanistic learning, an emerging approach that integrates the transparency of mathematical models with the flexibility of data-driven methods. By streamlining tasks such as data integration, model formulation, output analysis, and surrogate modelling, MxlPy enhances the modelling experience without sacrificing interpretability. Designed for both computational biologists and interdisciplinary researchers, it supports the development of accurate, efficient, and explainable models, making it a valuable tool for advancing bioinformatics, systems biology, and biomedical research. AvailabilityMxlPy source code is freely available at https://github.com/Computational-Biology-Aachen/MxlPy. The full documentation with features and examples can be found here https://computational-biology-aachen.github.io/MxlPy.

systems biology↗

bioRxiv2023

Microbial pathway thermodynamics: structural models unveil anabolic and catabolic processes

The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genomescale metabolic model. Interestingly, both approaches are rather successful to characterise and predict the expected product yield. Over the years, especially macrochemical equations have been extensively characterised in terms of their thermodynamic properties. However, a common challenge when characterising microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions.

systems biology↗

bioRxiv2023

A mathematical model of photoinhibition: exploring the impact of quenching processes

Plants are constantly exposed to changing environments, sometimes leading to extreme conditions and stress. For example, sudden exposure to high light leads to excess absorbed light energy, causing reactive oxygen species (ROS) formation. ROS damage the photosynthetic machinery, particularly the D1 protein in photosystem II (PSII), which therefore needs to be continuously repaired and replaced. The effect of the damage inflicted by high light is a prolonged decrease in photosynthetic efficiency. Hence, it is not surprising that photoinhibition has been subject to numerous experimental studies investigating its effects in the context of crop productivity. However, it has become apparent that classical measures of photoinhibition, i.e., changes in the chlorophyll fluorescence parameter Fv/Fm, are not only determined by the loss of PSII core function but also by processes such as energy transfer and quenching. Mathematical models can help dissect the influences on such fluorescence signals and quantify the contributions of various interacting mechanisms. We present a mathematical model with a dynamic description of the photosynthetic electron transport chain (PETC), non-photochemical quenching, and photoinhibition. With our model, we investigate the interconnection between quenching, photoprotection, and fluorescence using simulations and experimental data. We found that different energy-dissipating properties of intact and damaged PSIIs, as well as energy transfer between PSIIs, are critical components that need to be included in the model to ensure a satisfactory fit to the experimental data. We envisage that our model provides a framework for future investigations of photoinhibition dynamics and its importance for plant growth and yield.

plant biology↗

bioRxiv2023

What controls carbon sequestration in plants under which conditions?

Plants use photosynthesis to harvest sunlight and convert the solar energy into chemical energy, which is then used to reduce atmospheric carbon dioxide into organic molecules. This process forms the basis of all life on Earth, and stands at the beginning of the food chain which feeds the world population. Not surprisingly, many research efforts are currently ongoing aiming at improving plant growth and crop yield, and several of these activities directly target the photosynthetic pathways. Metabolic Control Analysis (MCA) shows that, in general, the control over a metabolic flux, such as carbon fixation, is distributed among several steps and highly dependent on the external conditions. Therefore, the concept of a single rate-limiting step is hardly ever applicable, and as a consequence, any strategy relying on improving a single molecular process in a complex metabolic system is bound to fail to yield the expected results. In photosynthesis, reports on which processes exert the highest control over carbon fixation are contradictory. This refers to both, the photosynthetic light reactions harvesting photons, and the dark reactions of the CalvinBenson-Bassham Cycle (CBB cycle). Here, we employ a recently developed mathematical model, which describes photosynthesis as an interacting supply-demand system, to systematically study how external conditions affect the control over carbon fixation fluxes.

systems biology↗

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

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↗