Johanna Daas successfully defended her Master’s thesis titled “Model workflows with the COPASI R Connector (CoRC)” on 10th June 2022.
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)
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)
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.
Kevin Siswandi, a Master student in our group, gave a talk in the BioQuant Internal Seminar series on 16. April 2020. Due to the Corona virus pandemic the seminar was exclusively held online.
In his talk Kevin presented an AI project he has worked on last semester in our group with the title “Predicting the dynamics of biochemical systems from time-series multi-omics data“. Please see Kevin’s abstract below for further details.
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.”
A new book chapter titled “Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices” with co-author Jürgen Pahle was published as part of the book “Yeast Systems Biology“, edited by Stephen G. Oliver and Juan I. Castrillo (pages 285-314).
On 16. August 2019, a LG Global Challenger team from South Korea visited our group. The team, consisting of the students Alvin Choi, Wooyoung Jo, Heeryung Heo, and Yoojin Bang from the Korea Advanced Institute of Science and Technology, (KAIST), was on their two-week tour through a handful of research labs all around Europe.
The team was most interested in the various modelling methods currently used for biological processes in the human body, and our modelling software COPASI. We talked about some of the challenges the field is facing now, and discussed about what technologies and services might be available in the future. They wanted to find out how far we are from reliably modelling whole organs, to assist the development of artificial organs in the future.
The LG Global Challenger program competitively selects teams of students, who are then funded to travel the world and visit academic groups in order to talk with them about research questions that the teams chose for themselves to work on.