Horizon2020 project

CanPathPro - a platform for predictive cancer pathway modelling

The advancement of omics technologies as well as growing amounts of public datasets provide the basis for large scale mechanistic modelling of biological systems. Mechanistic models help to gain a mechanistic understanding of biological processes instead of providing mere statistical correlations. Such mechanistic models can be used for in-silico experiments to, for example, predict the outcome of a pharmaceutical intervention, which is an important tool for personalised medicine as well as for identifying novel drug targets.

However, the parameterisation of such large scale models is computationally very demanding and currently not yet feasible. Furthermore, improved visualisation methods are required to analyse and interpret simulation results and parameter estimates. To this end, CanPathPro aims to generate and integrate novel algorithms and protocols for the large scale predictive modelling of cancer pathways.

Together with our project partners, we will:

  • refine hybrid stochastic-deterministic global optimisation methods for maximum likelihood model parameter estimation
  • develop methods for the integrated visualisation of the underlying heterogeneous experimental data, model predictions, and statistical measures
  • integrate these tools into an easy-to-use platform for the predictive modelling of cancer signalling
  • generate hypotheses which will - in close collaboration with experimentalists - be tested in vivo to validate or refine the model

CanPathPro is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 686282.

Collaboration partners: Alacris Theranostics, PHENOMIN-ICS - Institut Clinique de la Souris, Stichting Het Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, Leibniz-Institut für Alternsforschung - Fritz-Lipmann-institut e.V., Agencia Estatal Consejo Superior de Investigaciones Cientificas, Biognosys AG, Simula Research Laboratory AS, FINOVATIS SAS.

Project website:

DFG Project

Simulation-based parameter optimisation and uncertainty analysis methods for reaction-diffusion-advection equations

Reaction-diffusion-advection equations are used in many fields of engineering and natural sciences to model spatio-temporal processes. As the parameters of these mathematical models are often unknown, they have to be determined from the available experimental data. Here, the first step is usually to employ optimisation to determine the parameter values yielding the best match of the model prediction and the experimental data. In the second step, the uncertainty of these parameter values is analysed to determine the predictive power of the model. In both steps constraint optimisation problems have to be solved. For this reliable optimisation algorithms are required which converge robustly. Available methods however fail to meet these reliability requirements for a variety of models. Accordingly, we will:

  • develop a novel simulation-based optimisation and profile likelihood calculation method for reaction-diffusion-advection equations, which exploiting the problem structure
  • evaluate the simulation-based methods to several biological problems, including lateral line formation in zebrafish 
  • implement a user-friendly MATLAB toolbox

The project is funded by the German Research Foundation (Grant no. 629352).

Collaboration partners: Barbara KaltenbacherHernán Schier-Lopéz

BMBF Project

SYS-Stomach - Identification of predictive response and resistance factors to targeted therapy in gastric cancer

Gastric cancer was estimated to be the fifth most common cancer and third leading cause of death from cancer worldwide. Treatment options for gastric cancer patients include surgery, chemotherapy and radiation therapy. However, the overall survival rate remains unsatisfactory and new treatment options are urgently required. Novel drugs targeting members of a family of receptor tyrosine kinases including HER2 and epidermal growth factor receptor (EGFR) have shown mixed success in clinical trials. 

In this project a systematic molecular and phenotypic analysis of a panel of gastric cancer cell lines will be performed. From these data we derive mechanistic and statistical models for use in patient stratification. In our subproject, we will:

  • analyse the relation of molecular and phenotypic properties of gastric cancer cell lines
  • reconstruct the signalling pathways of cetuximab and trastuzumab
  • validate the reconstructed signalling pathways and phenotype link based upon MALDI imaging MS patient samples

The project is funded by the Federal Ministry of Education and Research (grant no. 01ZX1310B).

Project website:

Postdoctorial Fellowship Program (PFP) Project

Mathematical methods for the model-based integration of single-cell data

The number of experimental techniques used to address a single biological question increases steadily. Nowadays, experimental devices providing single cell time-lapse data (e.g., fluorescent microscopy), single cell snapshot data (e.g., flow cytometry, mass cytometry and single-cell PCR), and population average data (e.g., Western blotting, quantitative PCR and different omics technologies) are often used jointly. Furthermore, many experiments are performed under a variety of different experimental conditions. The analysis of the resulting vast amount of data and data types becomes more and more important. It is obvious that the simultaneous analysis of all available data enables a deeper understanding of the process compared to the independent analysis of the individual datasets. In particular, if many experiments are performed in which only a few properties are assessed – this is common in studies with primary cells, tissues or organisms – the reconstruction of the high-dimensional distribution of properties is of interest.

The aim of the proposed project is to develop model-based data integration methods enabling the simultaneous analysis of different data types, namely single-cell time lapse, population snapshot and population average data. In particular, we will:

  • establish a class of mechanistic mathematical models whose predictions can be compared with a wide range of single-cell data types
  • develop methods to estimate the parameters of the afore-introduced population model 
  • develop methods to analyze the models and to reanalyze single-cell data using the model
  • apply the method to a series of datasets

The project is funded by the Helmholtz Zentrum München via the Postdoctoral Fellowship Program.

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