From heterogeneous data of biological systems to quantitative predictive models

Nicole RADDE

Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany


   Experimental techniques to monitor cellular processes on a molecular level have rapidly developed and improved in the last decades, providing large amounts of data on different scales and of different nature. The integration of these data into computational models is today a major challenge in systems biology. Appropriate methods for data pre-processing, model-based experiment design, model calibration and uncertainty quantification are necessary on the way to build predictive models and to improve the quality of in silico experiments.

   Here I will exemplarily show some of these challenges on two particular projects within my research group. In the first example, we use published data on the mitogen-activated protein kinase (MAPK) signaling pathway in PC12 cell lines in order to investigate context-dependent responses of this pathway to different growth factors [1]. We use statistical approaches for model calibration, which allow consistent uncertainty quantification from noise in experimental data to variances in model predictions for any quantity of interest. Our study reveals a new mechanism termed quasi-bistability that might play a role in cellular decision processes.

   The second project was done in collaboration with partners from Cell Biology. Using single-cell time-lapse microscopy data on the spindle assembly checkpoint efficiency for different fission yeast strains [2], we built a finite mixture modeling framework that is able to integrate data from various experiments and to handle right- and interval-censored data. Here we were able to generate biologically insightful hypotheses about the appearance of subpopulation structures under different experimental conditions [3]. Moreover, the integration of censored data into models constitutes several problems and challenges that are also interesting from a theoretical viewpoint, and we review some of them in the presentation.



1. Jensch A, Thomaseth C, and Radde N (2017). Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines. BMC Syst Biol 11:11.
2. Heinrich S, Geissen E-M, Kamenz J, Trautmann S, Widmer C, Drewe P, Hauf S (2013). Determinants of robustness in spindle assembly checkpoint signaling. Nat Cell Biol 15, 1328-1339.
3. Geissen E-M, Hasenauer J, Heinrich S, Hauf S, Theis FJ, Radde N (2016). MEMO: multi-experiment mixture model analysis of censored data. Bioinformatics 32(16):2464-72.