The main topic of this edition is Cancer Modeling. This subject will be covered from three points of view: mathematical, computing and biology.

Area: Mathematics

Prof. Luigi Preziosi
Politecnico di Torino, Italy.
Abstract: Multiscale Developments of Cellular Potts Models and Individual Cell-based Models in Cancer Modeling. All biological phenomena emerge from an intricate interconnection of multiple processes occurring at different levels of organization: namely, at the molecular, the cellular and the tissue level. These natural levels can approximately be connected to a microscopic, mesoscopic, and macroscopic scale, respectively. The microscopic scale refers to those processes that occur at the subcellular level, such as DNA synthesis and duplication, gene dynamics, activation of receptors, transduction of chemical signals and diffusion of ions. The mesoscopic scale, on the other hand, can refer to cell-level phenomena, such as adhesive interactions between cells or between cells and ECM components, cell duplication and death and cell motion. The macroscopic scale finally corresponds to those processes that are typical of multicellular behavior, such as population dynamics, tissue mechanics and organ growth and development. One of the most widespread hybrid approaches, that is particularly suitable for cancer modeling and other biological problems, is the Cellular Potts Model, a stochastic Monte Carlo method based on energy minimization principles.
The scope of the series of lectures is to present some innovative multiscale extensions of the Cellular Potts models and of Individual Cell-based models. In particular, we focus on ways to integrate and interface the standard method with detailed descriptions of microscopic dynamics located not only in the external space but also within the simulated elements. We aim therefore to introduce some nested characteristics in the basic hybrid environment, that realistically reproduce the multiscale organization typical of biological development, where the individual behavior is driven by the constant interplay between different levels of description.

Area: Computer Science

Dr. Pietro Liò
University of Cambridge, UK.
Abstract: Medicine is moving from reacting to a disease to a proactive P4 medicine: personalized, predictive, preventive and participatory medicine. A difficult step is to bridge the actual distance between biomedical research and clinical practice. I would like to focus on the challenges in data analysis and modeling in cancer bioinformatics. In particular I would like to discuss the following issues:
  • Multi omics (1 lecture): Expensive and complex data are gathered and analysed in a rather simple way that completely misses the opportunity to uncover combinations of predictive and meaningful profiles among the omics data. Novel methodological frameworks, beyond single datasets, should integrate multilevel omics data to bring biological understanding to the next level. "Super-Meta" methods combining multilevel data across populations need to be developed. Omics may include HI-C, epigenetic, gene expression and sequence data; they are not independent each other. I will provide details of the algorithms and the software
  • Multi scale modeling (1 lecture): A disease manifests first as a dysfunction at the cell level and is then translated at the tissue level due to a change in the cell response. Here I am considering tgb-beta as a coupling factors for modeling breast cancer at different scales (from molecules to tissues).
  • Multi morbidities (1 lecture): Comorbidity addresses the occurrence of different medical conditions or diseases, usually complex and often chronic ones, in the same patient. I am addressing bone diseases as a secondary effect of several types of cancers.
  • Multi objective optimisation (1 lecture): If there is time I will show use multi objective optimisation to investigate how energetic factors enters the tissue dynamics.

Area: Biology

Dr. Francesca Ciccarelli
IEO-IFOM Milano, Italy.
Abstract: In this series of lectures I will discuss the recent advances in on our understanding of cancer genetics and evolution. I will start by reviewing the accumulating evidence of cancer heterogeneity in terms of acquired genetic mutations and genomic rearrangements. I will then describe the impact of these novel results on our modeling of cancer networks. In the last lectures, I will focus on the current attempts of using large-scale genomics data for rebuilding tumor evolution and how this is changing also anti-cancer therapeutic approaches.