Development of predictive systems-level models in medical and industrial biotechnology
It is often said that biological systems, such as cells, are 'complex systems'. A popular notion of complex systems is of one of a very large number of simple and identical elements interacting to produce 'complex' behaviors. However, the reality of biological systems is somewhat different: large numbers of functionally diverse, and frequently multifunctional sets of elements interact selectively and nonlinearly to produce coherent rather than complex behaviors. To understand the complexity of biological systems, our group integrates experimental and computational research — in other words we apply a systems biology approach. Systems biology, through pragmatic modeling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on.
The main objective of our Systems biology and Bioinformatics (SBI) unit is to discover new molecular mechanisms using an iterative cycle that starts with experimental data, followed by data analysis and data integration to determine correlations between concentrations of molecules, and ends with the formulation of hypotheses concerning co- and inter-regulation of groups of those molecules. These hypotheses then predict new correlations, which can be tested in new rounds of experiments or by further biochemical analyses. The major strengths of our approach are that it is potentially complete (i.e. genome-wide) and that it addresses the transcriptome, proteome, metabolome and fluxome. SBI works on addressing questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and bioengineering. SBI has two distinct branches: knowledge discovery, or data-mining, which extracts the hidden patterns from huge quantities of experimental data, forming hypotheses as a result; and simulation-based analysis, which tests hypotheses with in silico experiments, providing predictions to be tested by in vitro and in vivo studies.
The research group PiDOMICS is integrated into the structural unit Systems Biology and Bioinformatics. The group develops bioinformatic tools for the processing of high-throughput data and for the detection of new diagnostic and therapeutic biomarkers using invasive aspergillosis as a model. The results of the research group enable new possibilities for application in commercial bioinformatic services and novel biomarker-based diagnostic products - also with a possible utilization for other diseases.
The research group PiDOMICS is funded by the Free State of Thuringia with means of the European Social Fund.