Biology subjects

van Aalst, M.

Publications and source records attributed to van Aalst, M..

15 recordsLinked to original sources

bioRxiv2026

From Field Photosynthesis to Genetic Architecture: Insights from the First Dedicated Photosynthesis Hackathon

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

plant biology↗

bioRxiv2026

Chilling Nights Do Not Cause Starch Over-Accumulation and Trigger a Shift in Carbon Partitioning via SPS Toward Sucrose in Arabidopsis - Differing from Acclimation to Permanent Cold

Plants acclimate to low temperatures by remodeling carbohydrate metabolism. Permanent cold at 4/5{whitebullet} C induces well-characterized cold acclimation in plants, including accumulation of starch and soluble sugars. In natural environments, however, annual plants in their vegetative phase more frequently experience chilling nights (0-6{whitebullet} C nights followed by days at least 12{whitebullet} C warmer), whose metabolic consequences remain poorly understood. We investigated Col-0 Arabidopsis thaliana exposed to one or seven chilling nights and quantified central carbohydrate metabolites, starch, photosynthetic parameters, and maximal activities of enzymes linking sucrose synthesis and cleavage. We integrated these time-series data into a biologically constrained augmented neural ordinary differential equation (ANODE) model to infer diurnal reaction-rate dynamics. Chilling nights induced strong accumulation of sucrose, glucose, and fructose, particularly after seven nights. Both measured sucrose phosphate synthase (SPS) capacity and ANODE-predicted SPS rates increased. In contrast to permanent cold acclimation, daytime starch over-accumulation was absent. Thus, lacking starch over-accumulation together with increased SPS rates, chilling nights invoke distinct carbohydrate-partitioning patterns towards sucrose, indicating a fundamentally different acclimation strategy compared to well described permanent cold.

plant biology↗

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↗

bioRxiv2025

Hackathons as essential tools in an interdisciplinary biological training - report from trainings for Sub-Saharan students

Hackathons are collaborative, fast-paced events where participants from various fields work together to solve real-world problems. They are increasingly used in higher education to foster collaboration, problem-solving and applied computational skills, yet their role in interdisciplinary biological training remains under-documented. We report on the design and implementation of two computational biology summer schools in Kenya (2022, 2023), each culminating in a hackathon that integrated biological problems, quantitative methods, and coding. Both events targeted early-career researchers from multiple sub-Saharan countries and combined intensive teaching in programming and modelling with a time-bound group challenge using authentic marine conservation and synthetic epidemiological datasets. We describe the educational design, including its grounding in project-based learning, authentic learning, and Self-Determination Theory, and we document how performance-based assessment and structured participant feedback were used to evaluate learning outcomes. We present a critical reflective account of what worked, our teaching philosophy, and how hackathons can be embedded responsibly within biological curricula. We argue that, when embedded in sustained training and supported by appropriate mentoring, hackathons provide a practical and effective way to help biologists build computational skills, communicate across disciplines, and gain confidence in shaping their own research.

scientific communication and education↗

bioRxiv2024

Rule-Based Deconstruction and Reconstruction of Diterpene Libraries: Categorizing Patterns & Unravelling the Structural Landscape

Terpenoids make up the largest class of specialized metabolites with over 180,000 reported compounds currently across all kingdoms of life. Their synthesis accentuates one of natures most choreographed enzymatic and non-reversible chemistries, leading to an extensive range of structural functionality and diversity. Current terpenoid repositories provide a seemingly endless landscape to systematically survey for information regarding structure, sourcing, and synthesis. Efforts here investigate entries for the 20-carbon diterpenoid variants and deconstruct the complex patterns into simple, categorical groups. This deconstruction approach reduces over 60,000 unique diterpenoid structures to less than 1,000 categorical structures. Furthermore, the majority of diterpene entries (over 75%) can be represented by less than 25 core skeletons. Natural diterpenoid abundance was mapped throughout the tree of life and structural diversity was correlated at an atom-and-bond resolution. Additionally, all identified core structures provide guidelines for predicting how diterpene diversity originates via the mechanisms catalyzed by diterpene synthases. Over 95% of diterpenoid structures rely on cyclization. Here a reconstructive approach is reapplied based on known biochemical rules to model the birth of compound diversity. Reconstruction enabled prediction of highly probable synthesis mechanisms for bioactive taxane-relatives, which were discovered over three decades ago. This computational synthesis validates previously identified reaction products and pathways, as well as enables predicting trajectories for synthesizing real and theoretical compounds. This deconstructive and reconstructive approach applied to the diterpene landscape provides modular, flexible, and an easy-to-use toolset for categorically simplifying otherwise complex or hidden patterns. Significance StatementWe take a deconstructive and reconstructive approach to explore the origins of the diterpene landscape. Introduction of a navigational toolset enables users to survey compound libraries in ways formerly uncharted. Their utility demonstrated here, maps out diterpene cyclization routes, critical intermediate waypoints, and guidance for how to arrive at compounds previously off-the-map. Information acquired from these tools may imply the diterpene landscape is vastly unexplored, with the plateau for discovery potentially still out of sight.

bioinformatics↗

bioRxiv2024

Alternatives to photorespiration: A systems-level analysis reveals mechanisms of enhanced plant productivity

Photorespiration causes a significant decrease in crop yield due to mitochondrial decarboxylation. Alternative pathways (APs) have been designed to relocate the decarboxylating step or even fix additional carbon. To improve the success of transferring those engineered APs from model species to crops we must understand how they will interact with metabolism and how the plant physiology affects their performance. Here we used multiple mathematical modelling techniques to analyse and compare existing AP designs. We show that carbon-fixing APs are the most promising candidates to replace native photorespiration in major crop species. Our results demonstrate the different metabolic routes that APs employ to increase yield and which plant physiology can profit most from them. We anticipate our results to guide the design of new APs and to help improve existing ones. Codehttps://gitlab.com/gain4crops/2024-paper

plant biology↗

bioRxiv2023

dismo: building and analysing discrete spatial ODE models

Summarydismo is a Python package for building and analysing discrete spatial models based on ordinary differential equations. Its primary purpose is to allow arbitrarily complex internal and transport processes to easily be mapped over multiple different regular grids. For this it features one, two and threedimensional layouts, with standard and non-standard (e.g. hexagonal or triangular) grids. Availability and implementationhttps://gitlab.com/qtb-hhu/dismo Contactoliver.ebenhoeh@hhu.de

systems 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↗

bioRxiv2022

Approximating carbon fixation - how important is the Calvin-Benson cycle steady-state assumption?

Plants use light energy to produce ATP and redox equivalents for metabolism. Since during the course of a day plants are exposed to constantly fluctuating light, the supply of ATP and redox equivalents is also fluctuating. Further, if the metabolism cannot use all of the supplied energy, the excess absorbed energy can damage the plant in the form of reactive oxygen species. It is thus reasonable to assume that the metabolism downstream of the energy supply is dynamic and as being capable of dampening sudden spikes in supply is advantageous, it is further reasonable to assume that the immediate downstream metabolism is flexible as well. A flexible metabolism exposed to a fluctuating input is unlikely to be in metabolic steady-state, yet a lot of mathematical models for carbon fixation assume one for the Calvin-Benson-Bassham (CBB) cycle. Here we present an analysis of the validity of this assumption by progressively simplifying an existing model of photosynthesis and carbon fixation.

plant biology↗

bioRxiv2022

Bioenergetics of the secondary metabolites production in photosynthetic glandular trichomes

Several commercially important secondary metabolites are produced and accumulated in high amounts by glandular trichomes, giving the prospect of using them as metabolic cell factories. Due to extremely high metabolic fluxes through glandular trichomes, previous research focused on how such flows are achieved. The question regarding their bioenergetics became even more interesting with the discovery of photosynthetic activity in some glandular trichomes. Despite recent advances, how primary metabolism contributes to the high metabolic fluxes in glandular trichomes is still not fully elucidated. Using computational methods and available multi-omics data, we first developed a quantitative framework to investigate the possible role of photosynthetic energy supply in terpenoid production and next tested experimentally the simulation-driven hypothesis. With this work, we provide the first reconstruction of specialised metabolism in Type-VI photosynthetic glandular trichomes of Solanum lycopersicum. Our model predicted that increasing light intensities results in a shift of carbon partitioning from catabolic to anabolic reactions driven by the energy availability of the cell. Moreover, we show the benefit of shifting between isoprenoid pathways under different light regimes, leading to a production of different classes of terpenes. Our computational predictions were confirmed in vivo, demonstrating a significant increase in production of monoterpenoids while the sesquiterpenes remained unchanged under higher light intensities. The outcomes of this research provide quantitative measures to assess the beneficial role of chloroplast in glandular trichomes for enhanced production of secondary metabolites and can guide the design of new experiments that aim at modulating terpenoid production. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/510054v2_ufig1.gif" ALT="Figure 1"> View larger version (9K): org.highwire.dtl.DTLVardef@2ec6f4org.highwire.dtl.DTLVardef@107d0c1org.highwire.dtl.DTLVardef@1a545corg.highwire.dtl.DTLVardef@98747b_HPS_FORMAT_FIGEXP M_FIG C_FIG Please check the journals author guidelines for whether a graphical abstract, key points, new findings, or other items are required for display in the Table of Contents.

plant biology↗

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

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↗

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↗