Research

Research Mission

I co-lead the KERMIT research unit together with Bernard De Baets, where I am responsible for the computational modelling research line. KERMIT’s mission is to harness mathematics and computation to unravel life’s complexities, optimize biological functions, and drive innovation in biodesign and decision-making under uncertainty.

In our subgroup, we focus on non-conventional computing for understanding biological systems, including stochastic and differential programming, evolutionary computing, and other related approaches. Though we have an interest in various application fields within the applied biological sciences, we have plant growth and synthetic biology as our key focuses.

Poster showing computational modelling research at KERMIT — Computational Modelling at KERMIT

Research Themes

Computational Modelling for Synthetic Biology

We develop AI-driven methods to design and optimize biological parts and systems. This includes predicting promoter strength, designing proteins, and exploring the open-ended nature of biological innovation.

Machine Learning for Ecological Networks

Using optimal transportation theory and maximum entropy principles, we model how species interact in ecological networks — predicting pollination, host-parasite, and food web patterns.

Hyperdimensional Computing for Biological Data

We pioneer the use of hyperdimensional computing as a fast, robust, and interpretable paradigm for analyzing biological sequences, molecular data, and ecological patterns.

Plant Growth Modelling

Combining differential equations, sensor data, and machine learning to model and predict plant growth dynamics under varying environmental conditions.

Quality-Diversity & Evolutionary Algorithms

We adapt quality-diversity algorithms from evolutionary computation to discover diverse, high-performing solutions in biological design and optimization problems.

Key Publications

Open-endedness in synthetic biology: A route to continual innovation for biological design

M. Stock, T. E. Gorochowski

Science Advances, 10, eadi3621 (2024)

Paper

Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data

M. Stock, W. Van Criekinge, D. Boeckaerts, P. Dewulf, S. Taelman, M. Van Haeverbeke, B. De Baets

PLOS Computational Biology, 20, e1012426 (2024)

Paper

Quality-diversity methods for the modern data scientist

M. Stock, D. Van Hauwermeiren, B. De Baets, S. Taelman, D. Marzougui, M. Van Haeverbeke

WIREs Computational Statistics, 17, e70047 (2025)

Paper

Prediction of Klebsiella phage-host specificity at the strain level

D. Boeckaerts, M. Stock, C. Ferriol-González, J. Oteo-Iglesias, R. Sanjuán, P. Domingo-Calap, B. De Baets, Y. Briers

Nature Communications, 15, 4355 (2024)

Paper

Optimal transportation theory for species interaction networks

M. Stock, T. Poisot, B. De Baets

Ecology and Evolution, 11, 3841–3855 (2021)

Paper

Plant science in the age of simulation intelligence

M. Stock, O. Pieters, T. De Swaef, F. Wyffels

Frontiers in Plant Science, 14, 1299208 (2024)

Paper

For a complete list, see Google Scholar or the UGent Academic Bibliography.

Open Positions

If you are interested in a PhD or postdoc position in computational modelling for biological systems, please get in touch.

I also supervise MSc thesis projects — see the Teaching page.

--- title: "Research" toc: true aliases: - /research/ --- ## Research Mission I co-lead the [KERMIT](https://kermit.ugent.be/) research unit together with Bernard De Baets, where I am responsible for the computational modelling research line. KERMIT's mission is to harness mathematics and computation to unravel life's complexities, optimize biological functions, and drive innovation in biodesign and decision-making under uncertainty. In our subgroup, we focus on non-conventional computing for understanding biological systems, including stochastic and differential programming, evolutionary computing, and other related approaches. Though we have an interest in various application fields within the applied biological sciences, we have **plant growth** and **synthetic biology** as our key focuses. ![Computational Modelling at KERMIT](images/kermit-compmod.png){fig-alt="Poster showing computational modelling research at KERMIT" width="100%"} ## Research Themes ### Computational Modelling for Synthetic Biology We develop AI-driven methods to design and optimize biological parts and systems. This includes predicting promoter strength, designing proteins, and exploring the open-ended nature of biological innovation. ### Machine Learning for Ecological Networks Using optimal transportation theory and maximum entropy principles, we model how species interact in ecological networks — predicting pollination, host-parasite, and food web patterns. ### Hyperdimensional Computing for Biological Data We pioneer the use of hyperdimensional computing as a fast, robust, and interpretable paradigm for analyzing biological sequences, molecular data, and ecological patterns. ### Plant Growth Modelling Combining differential equations, sensor data, and machine learning to model and predict plant growth dynamics under varying environmental conditions. ### Quality-Diversity & Evolutionary Algorithms We adapt quality-diversity algorithms from evolutionary computation to discover diverse, high-performing solutions in biological design and optimization problems. ## Key Publications ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-openended.png){.pub-thumb} ::::: {.pub-card-text} [**Open-endedness in synthetic biology: A route to continual innovation for biological design**]{.pub-title} [M. Stock, T. E. Gorochowski]{.pub-authors} [Science Advances, 10, eadi3621 (2024)]{.pub-venue} ::: {.pub-links} [Paper](https://www.science.org/doi/10.1126/sciadv.adi3621) ::: ::::: :::: ::: ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-hdc.png){.pub-thumb} ::::: {.pub-card-text} [**Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data**]{.pub-title} [M. Stock, W. Van Criekinge, D. Boeckaerts, P. Dewulf, S. Taelman, M. Van Haeverbeke, B. De Baets]{.pub-authors} [PLOS Computational Biology, 20, e1012426 (2024)]{.pub-venue} ::: {.pub-links} [Paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012426) ::: ::::: :::: ::: ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-qd.png){.pub-thumb} ::::: {.pub-card-text} [**Quality-diversity methods for the modern data scientist**]{.pub-title} [M. Stock, D. Van Hauwermeiren, B. De Baets, S. Taelman, D. Marzougui, M. Van Haeverbeke]{.pub-authors} [WIREs Computational Statistics, 17, e70047 (2025)]{.pub-venue} ::: {.pub-links} [Paper](https://doi.org/10.1002/wics.70047) ::: ::::: :::: ::: ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-phage.png){.pub-thumb} ::::: {.pub-card-text} [**Prediction of Klebsiella phage-host specificity at the strain level**]{.pub-title} [D. Boeckaerts, M. Stock, C. Ferriol-González, J. Oteo-Iglesias, R. Sanjuán, P. Domingo-Calap, B. De Baets, Y. Briers]{.pub-authors} [Nature Communications, 15, 4355 (2024)]{.pub-venue} ::: {.pub-links} [Paper](https://doi.org/10.1038/s41467-024-48675-6) ::: ::::: :::: ::: ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-ot.png){.pub-thumb} ::::: {.pub-card-text} [**Optimal transportation theory for species interaction networks**]{.pub-title} [M. Stock, T. Poisot, B. De Baets]{.pub-authors} [Ecology and Evolution, 11, 3841–3855 (2021)]{.pub-venue} ::: {.pub-links} [Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.7254) ::: ::::: :::: ::: ::: {.pub-card} :::: {.pub-card-inner} ![](images/pub-plant.png){.pub-thumb} ::::: {.pub-card-text} [**Plant science in the age of simulation intelligence**]{.pub-title} [M. Stock, O. Pieters, T. De Swaef, F. Wyffels]{.pub-authors} [Frontiers in Plant Science, 14, 1299208 (2024)]{.pub-venue} ::: {.pub-links} [Paper](https://www.frontiersin.org/articles/10.3389/fpls.2023.1299208/full) ::: ::::: :::: ::: For a complete list, see [Google Scholar](https://scholar.google.be/citations?user=qR_LXM8AAAAJ&hl=en) or the [UGent Academic Bibliography](https://biblio.ugent.be/person/802001370792). ## Open Positions If you are interested in a PhD or postdoc position in computational modelling for biological systems, please [get in touch](mailto:michiel.stock@ugent.be). I also supervise MSc thesis projects — see the [Teaching](teaching.qmd) page.