Computational Systems Biology of Cancer
Tumor cells live in a complex environment communicating with various non-cancerous cells including normal origin, immune, stromal, vascular, and many other cell types. Due to the stochastic genetic mutations and non-genetic modifications, tumor cells themselves also exhibit intratumoral heterogeneity that influences the tumor behaviour. Therefore, traditional bulk-level and single-omics profiling of the tumor is far from a realistic representation of the tumor characteristics.
State of the art single-cell multi-omics technologies can reveal cell biology with inimitable spatiotemporal resolution. However, appreciation of the value in this large amount of data requires advanced statistical methods that can capture the complexity at a systems level.
Our research focus:
The main focus in our lab is the study of tumor evolution (See the Figure 1). Unleashing the analytical capacity of multi-omics data at high spatiotemporal resolutions down to single cells level and at a systems scale is emphasised.
We aim in our lab to understand different aspects of cancer biology on a systems level. This includes malfunctioning of signalling networks, tumor evolution, stromal remodelling, immune system suppression, as well as the treatment response.
We employ advanced statistical models such as Machine learning (ML), which are tools to automatically find patterns in data, to appreciate the complex biology of the cancer.
Most important publications
Cartolano M*, Abedpour N*, Achter V, Yang TP, Ackermann S, Fischer M, Peifer M., CaMuS: simultaneous fitting and de novo imputation of cancer mutational signature. Scientific reports 2020; 10 (1), 1-10. *equal contribution
Roider T, Seufert J, Uvarovskii A, Frauhammer F, Bordas M, Abedpour N, Stolarczyk M, Mallm JP, Herbst SA, Bruch PM, Balke-Want H, Hundemer M, Rippe K, Goeppert B, Seiffert M, Brors B, Mechtersheimer G, Zenz T, Peifer M, Chapuy B, Schlesner M, Müller-Tidow C, Fröhling S, Huber W, Anders S, Dietrich S., Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nature Cell Biology 2020 Jun 15.
Zhao L, Abedpour N, Blum C, Kolkhof P, Beller M, Kollmann M, Capriotti E, Predicting gene expression level in E. coli from mRNA sequence information. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2019; 1-8.
Herling CD*, Abedpour N*, Weiss J, Schmitt A, Jachimowicz RD, Merkel O, Cartolano M, Oberbeck S, Mayer P, Berg V, Thomalla D, Kutsch N, Stiefelhagen M, Cramer P, Wendtner CM, Persigehl T, Saleh A, Altmüller J, Nürnberg P, Pallasch C, Achter V, Lang U, Eichhorst B, Castiglione R, Schäfer SC, Büttner R, Kreuzer KA, Reinhardt HC, Hallek M, Frenzel LP, Peifer M. Clonal dynamics towards the development of venetoclax resistance in chronic lymphocytic leukemia. Nature communications 2018; 9 (1), 727. *equal contribution
- Abedpour N* and Kollmann M. Resource Constrained Flux Balance Analysis predicts Selective Pressure on the Global Structure of Metabolic Networks. BMC Systems Biology 2015; 9, 88. *Corresponding Author