pp 1-38 | Cite as

Predicting Tumor Growth and Ligand Dependence from mRNA by Combining Machine Learning with Mechanistic Modeling

  • Helge Hass
  • Andreas RaueEmail author
Part of the Methods in Pharmacology and Toxicology book series


For successful treatment of cancer patients, it is crucial to identify subgroups that respond to certain types of targeted therapy. A key element for tumor growth is the abundance of receptors and binding of growth factors, which can be diminished via therapeutic antibodies. Here, a mechanistic signaling network model is linked to patient-specific ribonucleic acid sequencing data (RNAseq), enabling the prediction of individuals susceptible to a particular medication. The mechanistic model comprises multiple receptors and their dimerization, and is calibrated using time-resolved in-vitro data. Further, the model is combined with in-vitro cell viability measurements via a machine learning algorithm and ultimately applied to patient-derived data to predict ligand dependence of tumors. For this purpose, RNA sequencing data are exploited to constrain model parameters and generalize model response. Mathematical modeling of signal transduction is used as a mediator, performing a non-trivial transformation of initial protein expression levels and ligand conditions to cell-type specific response. Thereby, it allows for bridging the gap between studies of signal transduction on a short time scale and cell fate decisions in the long term, potentially aiding in drug development, patient stratification, and prediction of tumor response. This chapter is based on work previously published in Hass et al. (NPJ Syst Biol Appl 3(1):27, 2017) and Hass (Quantifying cell biology: mechanistic dynamic modeling of receptor crosstalk. PhD thesis, Albert-Ludwigs-Universität Freiburg, 2017).


Cancer cell lines Computational biology Decision trees Growth response Machine learning Mechanistic modeling Prediction of responses Receptor tyrosine kinase Signaling pathways 



We thank Tim Heinemann, Jeffrey Kearns, Sergio Iadevaia, Yasmin Hasham-bhoy-Ramsay, and Tim Maiwald for their constructive feedback and proof reading the manuscript.


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Authors and Affiliations

  1. 1.Institute of PhysicsUniversity of FreiburgFreiburgGermany
  2. 2.Merrimack Pharmaceuticals, Inc.CambridgeUSA

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