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 genome, 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.
Selected Publications (*correspondence)
Nong W, Qu Z, Barton-Owen T, Wong YPA, Yin Yip H, Lee HT, Narayana S, Baril T, Swale T, Cao J, Chan TF, Kwan HS, Ming NS, Panagiotou G, Qian PY, Qiu JW, Yip KY, Ismail N, Pati S, John A, Tobe ST, Bendena WG, Cheung SG, Hayward A, Hui JHL (2021). Horseshoe crab genomes reveal the evolution of genes and microRNAs after three rounds of whole genome duplication. Commun Biol 4(1), 83. DOI
Mirhakkak M, Schäuble S, Klassert T, Brunke S, Brandt P, Loos D, Uribe R, de Oliveira Lino FS, Ni Y, Vylkova S, Slevogt H, Hube B, Weiss G, Sommer M, Panagiotou G* (2020). Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels. ISME Journal DOI
Seelbinder B, Chen J, Brunke S, Vazquez-Uribe R, Santhanam R, Meyer AC, de Oliveira Lino FS, Chan KF, Loos D, Imamovic L, Tsang CC, Lam RP, Sridhar S, Kang K, Hube B, Woo PCY, Sommer MOA, Panagiotou G* (2020). Antibiotics create a shift from mutualism to competition in human gut communities with a longer-lasting impact on fungi than bacteria. Microbiome 8(133). DOI
Liu Y, Wang Y, Ni Y, Lam KSL, Wang Y, Xia Z, TSE MA, Panagiotou G*, Xu A (2020). Gut microbiome fermentation determines the therapeutic efficacy of exercise on insulin resistance in individuals with prediabetes. Cell Metabolism, pii: S1550-4131(19)30608-4. DOI
Heshiki Y, Vazquez-Uribe R, Li J, Ni Y, Quainoo S, Imamovic L, Li J, Sørensen M, Chow BKC, Weiss GJ, Xu A, Sommer MOA, Panagiotou G* (2020). Predictable modulation of cancer treatment outcomes by the gut microbiota. Microbiome, Mar 5;8(1):28. DOI
Chen J, Mcllroy SE, Anand A, Baker DM, Panagiotou G* (2019). A pollution gradient contributes to the taxonomic, functional, and resistome diversity of microbial communities in marine sediments. Microbiome, 15;7(1):104. DOI
Zheng T, Li J, Ni Y, Kang K, Misiakou MA, Imamovic L, Chow BKC, Rode AA, Bytzer P, Sommer MOA, Panagiotou G* (2019). Mining, analyzing and integrating viral signals from metagenomic data. Microbiome, 19;7(1):42. DOI
Kang K Ni Y, Li J, Imamovic L, Sarkar C, Kobler MD, Heshiki Y, Zheng T, Kumari S, Wong YJC, Archna A, Wong CWM, Dingle C, Denizen S, Baker DM, Sommer MOA, Webster CJ, Panagiotou G* (2018). The Environmental Exposures and Inner- and Intercity Traffic Flows of the Metro System May Contribute to the Human Skin Microbiome and Resistome. Cell Reports. Jul 31;24(5):1190-1202.e5. DOI
Li J, Sung CY, Lee N, Ni Y, Pihlajamäki J, Panagiotou G*, El-Nezami H (2016). Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice. Proceedings of the National Academy of Sciences USA 113: e1306-15. DOI
Jensen K, Panagiotou G, Kouskoumvekaki I (2015). NutriChem: a systems chemical biology resource to explore the medicinal value of plant-based foods. Nucleic Acids Research 43 (Database issue): D940-5. DOI
Andersen MR, Vongsangnak W, Panagiotou G, Salazar MP, Lehmann L, Nielsen J (2008). A trispecies Aspergillus microarray: comparative transcriptomics of three Aspergillus species. Proceedings of the National Academy of Sciences USA 105: 4387-92. DOI
SBI is the host of the junior research group “Fungal Informatics” headed by Dr Amelia Barber. The funds are provided by the Cluster of Excellence Balance of the Microverse (EXC 2051), that is funded in the frame of Germany’s Excellence Strategy.