This is the GitHub organization for the Computational Systems Biology department of the RPTU Kaiserslautern.
You can read more about the following topics on our website:
The capability of biological systems to respond to environmental changes is realized by a complex dynamic adjustment of the interplay between genes, proteins and metabolites. For a deeper understanding at the systems level, we need to study the structure and dynamics of cellular and organismal functions rather than the characteristics of isolated parts of a cell or an organism.
We are committed to the success of FAIR (Findable, Accessible, Interoperable, and Reusable) and open research data. Research data possess immense value, which, when combined with tomorrow's technologies such as machine and deep learning, can unlock answers to questions we cannot even conceive today. Therefore, the flexible contextualization of research data with machine-actionable metadata is essential to advance modern science.
We draw inspiration from the open-source software community's success, developing approaches that enable the biological community to collaboratively build a community-wide FAIR research data resource. Our research group participates in the National Research Data Infrastructure (NFDI) initiative with the DataPLANT project, which empowers plant researchers to engage in a thriving RDM ecosystem without barriers.
Modern technologies now allow researchers to simultaneously study biological organisms and processes across various molecular layers and in diverse biological contexts. To leverage this wealth of data, we are developing advanced methods for both supervised and unsupervised analysis of multi-modal omics data using cutting-edge machine learning and statistical modeling techniques. As part of fslabs.org, we contribute to the open-source data analysis library environment, ensuring that our innovations are accessible to the broader research community. Our methods enable comprehensive, data-driven integration and analysis of data derived from multiple omics technologies and varied biological scenarios, thereby enhancing our understanding of complex biological systems.
Acclimation responses involve complex interactions among genes, proteins, and metabolites, resulting in both general and specific elements. Previously, we focused on extracting response-specific structural models from complex 'omics data using functional constraint aggregation and network topology inference.
