Category: Uncategorised

Lena Lenkeit defended her Master’s thesis titled “Design, development, and experimental verification of a framework for fully-differentiable, gradient-based optimal control of biological systems” on 23. October 2023.

This work was done in collaboration with Dr. Dr. Stefan Kallenberger who is also based at the BioQuant.

Emma Kray finished her Bachelor’s thesis titled “A model-based investigation of immune system composition upon vaccinations against influenza” in August.

Pascal Lukas defended his Master’s thesis on “Analyzing and modeling the spatio-temporal dynamics of the interferon response to Dengue virus infection” on 29. September 2023.

Both projects were done in collaboration with the group of Prof. Dr. Frederik Graw at the FAU Erlangen.

The 30th International Dynamics & Evolution of Human Viruses conference is taking place from April 19 to 22, 2023 at the German Cancer Research Center (DKFZ) and the Marriott in Heidelberg.

This meeting series was designed to promote discussion between specialists in quantitative and computational approaches in two areas in the field of virology where these are particularly important:

1. Modeling of viral and cellular dynamics 
2. Viral evolution and population genetics

We co-organised this meeting together with Frederik Graw, Emma Hodcroft, Denise Kühnert, Richard Neher. Organising institutions are the BioQuant / Heidelberg University and the School of Medicine at UC San Diego.

We provided travel grants for exceptional young researchers from abroad.

You can find more information at the conference webpage.

Johanna Daas successfully defended her Master’s thesis titled “Model workflows with the COPASI R Connector (CoRC)” on 10th June 2022.

Congratulations, Johanna!

PS It should be mentioned that Johanna had already published an article as first author in Mathematical Biosciences during her Master’s project:
J.CJ. Daas, J.D Förster and J. Pahle (2022)
Dynamic Publication Media with the COPASI R Connector (CoRC).
Mathematical Biosciences, doi:10.1016/j.mbs.2022.108822 (open access)

Pablo Naranjo successfully defended his Master’s thesis titled “Understanding the relationship between antigenic distance and susceptibility to influenza virus infection” on 19. May 2022.

Congratulations, Pablo!

Mathematical Biosciences accepted our new manuscript, titled “Dynamic Publication Media with the COPASI R Connector (CoRC)” for publication in their special issue “Dynamic Publication Media in Mathematical Biology”.

Dynamic publication media are becoming a popular tool for authors to effectively compose and publish their work. In this article we show how dynamic publication media and the COPASI R Connector (CoRC) can be combined in a natural and synergistic way to communicate (biochemical) models.

For further information please have a look at:
J.CJ. Daas, J.D Förster and J. Pahle (2022)
Dynamic Publication Media with the COPASI R Connector (CoRC).
Mathematical Biosciences, doi:10.1016/j.mbs.2022.108822 (open access)

Tim Ingenbrand defended his Master’s thesis (in Physics) titled “Chemotaxis and Memory in Escherichia Coli” successfully on 29th October 2021.

Congratulations, Tim!

Martin Zauser successfully defended his doctoral thesis titled “Communication in Plants: Analysis and Modeling of Calcium Signatures in Arabidopsis thaliana” on 26. July 2021.

Congratulations, Dr. Martin Zauser!

Martin’s thesis can be found at doi.org/10.11588/heidok.00026833.

We published an application note in Bioinformatics describing the COPASI R Connector (CoRC), our high-level application programming interface (API) that allows users to access COPASI’s (www.copasi.org) simulation and analysis capabilities from the R programming environment.

For further information please see:
J. Förster, F.T. Bergmann and J. Pahle (2021)
CoRC – the COPASI R Connector.
Bioinformatics, doi:10.1093/bioinformatics/btab033

Abstract:
In this project, we explore a data-driven modeling approach based on machine learning for predicting dynamics in biochemical systems from time-series data. Traditionally, dynamic modeling in systems biology often is a bottom-up process based on differential equations that relies on a hypothesis about the biological mechanism. However, it may take several years to map the pathway mechanisms in order to construct mathematical representations of the system. Moreover, bottom-up modeling approaches do not automatically scale in performance with more data and increase in complexity for larger systems. To solve these challenges, we blend machine learning, applied mathematics (numerical methods), and explainable AI to build a machine learning workflow that is not only superior in predictive performance on test systems, but also allows the extraction of mechanistic insights from the data. This work started as my internship at BioQuant and is now the topic of my Masters Thesis.”