Publications
The listed publications are published by AI4b.io members or associated members. Please contact us through the contact form when you have questions about the content or are interested to further discuss the contents of a publication.
All publications
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Jaxkineticmodel: Neural ordinary differential equations inspired parameterization of kinetic models | 2025
Motivation: Metabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale kinetic models. Recent work on neural ODEs has shown the potential for modeling time-series data using neural networks, and many methodological developments in this field can similarly be applied to kinetic models.
Results: We have implemented a simulation and training framework for Systems Biology Markup Language (SBML) models using JAX/Diffrax, which we named jaxkineticmodel. JAX allows for automatic differentiation and just-in-time compilation capabilities to speed up the parameterization of kinetic models, while also allowing for hybridizing kinetic models with neural networks. We show the robust capabilities of training kinetic models using this framework on a large collection of SBML models with different degrees of prior information on parameter initialization. We furthermore showcase the training framework implementation on a complex model of glycolysis. Finally, we show an example of hybridizing kinetic model with a neural network if a reaction mechanism is unknown. These results show that our framework can be used to fit large metabolic kinetic models efficiently and provides a strong platform for modeling biological systems.
Implementation: Implementation of jaxkineticmodel is available as a Python package at https://github.com/AbeelLab/jaxkineticmodel.
Publication year: 2025 -
Learning From Scenarios for Stochastic Repairable Scheduling | 2024
When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed. Historical realizations of the stochastic processing times are available. We show how existing decision-focused learning techniques based on stochastic smoothing can be adapted to this scheduling problem. We include an extensive experimental evaluation to investigate in which situations decision-focused learning outperforms the state of the art, i.e., scenario-based stochastic optimization.
Publication year: 2024Location: Open publication on Springer. -
Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Eng. | 2023
Publication year: 2023Location: Open publication on Pubmed.
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Rolling Horizon Simulation Optimization For A Multi-Objective Biomanufacturing Scheduling | 2023
Publication year: 2023Authors: Mathijs de Weerdt , Alessandro Nati , Eva Christoupoulou , David M.J. Tax , Esteban Freydell , Kim van den HoutenLocation: Open publication.
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Unveiling microbial biomarkers of ruminant methane emission through machine learning | 2023
Publication year: 2023Location: Open publication on Frontiers.
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ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology | 2023
The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.
Publication year: 2023Authors: Meng Lu , Charles N Christensen , Jana Marie Weber , Tasuku Konno , Nino F Läubli , Katharina M Scherer , Edward Avezov , Pietro Lio , Alexei A Lapkin , Gabriele S Kaminski Schierle , Clemens F KaminskiLocation: Open publication on Nature Methods. - An analysis of organism lifelines in an industrial bioreactor using Lattice-Boltzmann CFD | 2022
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Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework | 2022
Publication year: 2022Location: Open publication on ACM Digital Library.
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Dynamic Scenario Reduction for Simulation Based Optimization Under Uncertainty | 2022
Publication year: 2022Location: Open publication.
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Autonomous experimentation systems for materials development: A community perspective | 2021
Publication year: 2021Authors: Simon Billinge , Tonio Buonassisi , Ian Foster , Carla P. Gomes , Chiwoo Park , John M. Gregoire , Brian De Cost , Apurva Mehta , A. Gilad Kusne , Joseph Montoya , Jason Hattrick-Simpers , Elsa Olivetti , Keith A.Brown , Eric Stach , Kristofer G. Reyes , Joshua SchrierLocation: Open publication on Science Direct.
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Next-Generation Experimentation with Self-Driving Laboratories | 2019
Publication year: 2019Location: Open publication on CellPress.
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From industrial fermentor to CFD-guided downscaling: what have we learned? | 2018
Publication year: 2018Location: Open publication.
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Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization | 2018
Publication year: 2018Authors: Wenjun Tang , Guan Wang , Amit T. Deshmukh , Wouter A. van Winden , Ju Chu , Walter M. van Gulik , Joseph, J. Heijnen , Robert F. Mudde , Cees Haringa , Henk J. NoormanLocation: Open publication.