Scientific report: Training workshop interdisciplinary life sciences
- Subject Areas
- Bioinformatics, Computational Biology, Mathematical Biology, Science and Medical Education, Computational Science
- Training interdisciplinary life sciences, flowering time, angiogenesis, branching, Ordinary Differential Equations, Cellular Potts Model, network inference
- © 2014 Akudibillah et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Scientific report: Training workshop interdisciplinary life sciences. PeerJ PrePrints 2:e654v1 https://doi.org/10.7287/peerj.preprints.654v1
This preprint is the outcome of the “Training Workshop Interdisciplinary Life Sciences”, held in October 2013 in the Lorentz Center, Leiden, The Netherlands. The motivation to organize this event stems from the following considerations: The enormous progress in laboratory techniques and facilities leads to the availability of huge amounts of data at all levels of complexity (molecules, cells, tissues, organs, organisms, populations, ecosystems). Especially data at the cellular level reveal details of life processes we were unconscious of until recently. However, it becomes clear that huge amounts of data alone do not automatically lead to understanding. The data explosion in Life Sciences teaches one lesson: life processes are of a highly intricate and integrative nature. To really understand the dynamic processes in living organisms one must integrate experimental data sets in quantitative and predictive models. Only then one may hope to grasp the functioning of these complex systems and be able to convert information in understanding. In the field of physics, for instance, this strong interaction between experiment and theory is already common practice since centuries, culminating in the 20th century being called the ’Century of Physics’. In contrast to physics, the complex nature of the Life Sciences forces us to work in an interdisciplinary fashion. The necessary expertise is available, but scattered over many scientific disciplines. Only the combined efforts of biologists, chemists, mathematicians, physicists, engineers, and informaticians will lead to progress in tackling the huge challenge of understanding the complexity of life. Researchers in the Life Sciences often focus their research on a rather narrow research field. However, the majority of the upcoming generation of researchers in the Life Sciences should be trained to expand their skills, becoming able to tackle complex, multi-dimensional systems. The knowledge they have to incorporate in their research will stem from a diverse range of disciplines, So, they should be trained to integrate a broad range of modelling approaches in order to deduce quantitative, predictive and often multi-scale models from highly diverse data sets. Present curricula in the Life Sciences hardly offer this kind of training yet. This workshop intends to start filling this gap.
Three teams worked on the following open problems: 1) Modeling the influence of temperature on the Regulation of flowering time in Arabidopsis thaliana; 2) Validation of computational models of angiogenesis to experimental data; 3) Reconstructing the gene network that regulates branching in Tomato. This preprint bundles the reports of the three teams.
This preprint is the outcome of the “Training Workshop Interdisciplinary Life Sciences”, held in October 2013 in the Lorentz Center, Leiden, The Netherlands. The aim of the workshop was to bring together young Life Science researchers and to train them in modelling of biological systems. The purpose was to have participants with various backgrounds. As can be seen from the affiliations of the authors of this preprint, this goal has certainly be achieved. The didactic philosophy of the workshop was based on ’learning by doing’. This has been achieved by form multidisciplinary groups which tackled carefully selected open problems from modern Life Sciences. Each team worked together during one week and was supervised by an experienced senior scientist. The groups started with identifying the relevant system parameters and continued with putting the problem under consideration in quantitative and predictive models.