4th Leibniz MMS Days
March 20 - March 22, 2019
Kühlungsborn
Sessions on Systems biology and genetics
This sessions focus on mathematical, statistical, and biophysical models for solving biological and medical problems. Often, large data sets of e.g., gene expression, genomic variants, or time series are analyzed. Models have to reflect and accommodate uncertainties typical for biological systems, and often draw upon dynamical systems theory, stochastics and probability, as well as control theory. Applications may span scales ranging from GWAS studies in medicine and agriculture, over population models, data and image analysis in cell biology and medicine, to single-cell studies.
Figure: Cell biology is characterized by millions of interacting biomolecule species that form highly complex networks. Understanding important phenomena such as cancer therefore present formidable challenges. Systems biology and systems genetics represent avenues of research that apply biophysical and statistical models to describe and analyze biological network phenomena. Leveraging the large amounts of data generated by modern methods in molecular biology, such as next-generation sequencing, recurring structures in cell biological networks have been identified, such as bow-tie motifs (A). Statistical models (B) or dynamical models incorporating causal mechanisms can be built and simulated or solved to be compared with experimental data (C).
Illustration © 2015 Friedlander et al., Creative Commons Attribution License. Friedlander T, Mayo AE, Tlusty T, Alon U (2015) Evolution of Bow-Tie Architectures in Biology. PLOS Comput Biol 11: e1004055. doi:10.1371/journal.pcbi.1004055