Mon May 16 Topic Speaker

09.00 - 09.10 am


Jana Wolf, Sara Checa Esteban

09.10 - 09.45 am

Morphometric analysis of epithelial bladder cancer progression

Dagmar Iber,

ETH Zürich

09.45 - 10.20 am

Contextualization of Molecular

Network Models and their Application

to Cancer Biology

Thomas Sauter,

Université du Luxembourg

10.20 - 10.40 am

Coffee break

10.40 - 11.30 am

From learning patient medical journeys to modeling individual biological trajectories

Laurence Calzone &

Emmanuel Barillot,

Institut Curie, Paris

11.30 -12.30 pm

General PG meeting

Summer school,

Shared article,

Online series

12.30 pm

Break before general SBMC opening

Confirmed speakers and abstracts:

Dagmar Iber

Prof. Dr. Dagmar Iber, ETH Zürich



Morphometric analysis of epithelial bladder cancer progression

The treatment of cancer still remains a huge unmet medical need. In bladder cancer, in particular, clinical outcome is determined not only by the rate, but also by the direction of epithelial tumour cell growth. Patients with papillary carcinomas, a tumour form that grows into the bladder lumen, have a low risk of tumour progression and favourable treatment outcomes. In contrast, patients with a flat localized carcinoma in situ (CIS) are at higher risk to develop muscle-invasive cancer with a worse prognosis. We combine Single Plane Illumination Microscopy (SPIM), 3D single cell segmentation, continuum-mechanical and 3D cell-based modelling to understand the differences that lead to the two distinct directions of epithelial expansion – and thus to the two very different clinical patients’ presentations and prognoses.

Thomas Sauter

Prof. Dr. Thomas Sauter
Systems Biology group, Department of Life Sciences and Medicine, Université du Luxembourg


Contextualization of Molecular Network Models and their Application to Cancer Biology

Mathematical modelling of molecular networks allows for the discovery of knowledge at the system level. Therefore, large data sets (omics) need to be integrated with prior knowledge like interaction information or generic metabolic reconstructions into accurate and predictive models.
We have developed computational approaches to contextualize logical models of regulatory networks, as well as genome scale models of metabolic networks with additional (own) biological measurements. These approaches are based on a probabilistic description of rule-based regulatory interactions between the different molecules, respectively linear programming of the constraint based metabolic models. The resulting Matlab toolboxes allow for automatically and efficiently building and contextualizing networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes.
Applications include the signaling model-guided re-sensitization of melanoma cells, as well as the metabolic model-based drug repositioning for selectively targeting colon cancer. 

Laurence Calzone, Emmanuel Barillot

Dr. Laurence Calzone, Dr. Emmanuel Barillot
Institut Curie, Paris



From learning patient medical journeys to modeling individual biological trajectories

Recent technologies like NGS now provide masses of data which add up to the traditional medical records of each patient.
One central question in oncology is to combine these data to identify individual evolutions. Two fundamentally distinct but complementary approaches can be used to achieve this goal: reconstructing patient trajectories from the data using machine learning methods, and modeling the known (biological) mechanisms governing the progression of the disease. The first one can also provide the basis for personalising the models used in the second.
We will present examples of such approaches based on principal graph analysis and traditional clinical data for the first one, and the modeling of biological networks governing cancer progression in prostate cancer, based on stochastic Boolean modeling and agent-based approach.

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