BIOMAT 2023: Schedule
 | MONDAY June 12th | TUESDAY June 13th | WEDNESDAY June 14th | THURSDAY June 15th | FRIDAY June 16th |
9:30 10:20 | Mathematical modelling of self-organisation during embryonic development - Dagmar Iber Dagmar Iber ETH Zurich | Topological Data Analysis for Oncology - Bernadette Stolz-Pretzer École Polytechnique Fédérale de Lausanne | Bridging between higher-order mechanisms and higher-order phenomena - Giovanni Petri CENTAI Institute | TBP - Xavier Trepat Institute for Bioengineering of Catalonia | |
10:20 10:50 | |||||
10:50 11:20 | COFFEE BREAK + Group Photo | COFFEE BREAK | |||
11:20 12:10 | Mathematical modelling of self-organisation during embryonic development - Dagmar Iber Dagmar Iber ETH Zurich | TBP - Marino Arroyo Universitat Politécnica de Catalunya | TBP - Xavier Trepat Institute for Bioengineering of Catalonia | TBP - Alfonso Valencia Centro Nacional de Supercomputación de Barcelona | |
12:10 13:00 | Bridging between higher-order mechanisms and higher-order phenomena - Giovanni Petri CENTAI Institute | TBP - Marino Arroyo Universitat Politécnica de Catalunya | TBP - Jean-François Joanny College de France | TBP - Alfonso Valencia Centro Nacional de Supercomputación de Barcelona | |
13:00 15:00 | REGISTRATION 14:30-14:50 _______ WELCOME 14:50-15:00 | LUNCH | |||
15:00 15:50 | TBP - Jean-François Joanny College de France | TBP - Jean-François Joanny College de France | Bridging between higher-order mechanisms and higher-order phenomena - Giovanni Petri CENTAI Institute | Topological Data Analysis for Oncology - Bernadette Stolz-Pretzer École Polytechnique Fédérale de Lausanne | TBP - Jean-François Joanny College de France |
15:50 16:20 | Topological Data Analysis for Oncology - Bernadette Stolz-Pretzer École Polytechnique Fédérale de Lausanne | ||||
16:20 17:00 | COFFEE BREAK | COFFEE BREAK | |||
17:00 17:50 | Mathematical modelling of self-organisation during embryonic development - Dagmar Iber Dagmar Iber ETH Zurich | Bridging between higher-order mechanisms and higher-order phenomena - Giovanni Petri CENTAI Institute | The concurrence of structure and function in developing networks: An explanation for synaptic pruning - Ana Paula Millán Universidad de Granada | ||
17:50 18:40 | Mathematical modelling of self-organisation during embryonic development - Dagmar Iber Dagmar Iber ETH Zurich | Topological Data Analysis for Oncology - Bernadette Stolz-Pretzer École Polytechnique Fédérale de Lausanne | The concurrence of structure and function in developing networks: An explanation for synaptic pruning - Ana Paula Millán Universidad de Granada | ||
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Mathematical modelling of self-organisation during embryonic development
Dagmar Iber
ETH Zurich
Reproduction of complex life hinges on the reliable translation of the linear information that is contained in our DNA into complex 3D shapes and functions. In this course, I will present chemical and biophysical principles that enable this reliable self-organisation. While there is only a limited number of candidate mechanisms and at times it may be difficult to uncover any candidate mechanism, often more than one mechanism can, in principle, explain the same biological phenomenon. Careful data-based mathematical modelling is therefore important to distinguish between candidate mechanisms. In the last part, I will discuss approaches for data-based modelling and model selection.
Bridging between higher-order mechanisms and higherorder phenomena
Giovanni Petri
CENTAI Institute
In this mini-course we will link together two sides of the recent progresses in higher-order systems.
On the one hand, we will describe recent advances in dynamical systems with interactions among groups of nodes (higher-order interactions) and the novel phenomenologies that stem from them.
On the other one, we will see how recent tools from algebraic topology and multivariate information theory characterise these behaviours in real-world data.
Finally, we will describe the current attempts to infer or reconstruct the original underlying higher-order models from data.
Topological Data Analysis for Oncology
Bernadette Stolz-Pretzer
École Polytechnique Fédérale de Lausanne
Topological data analysis (TDA) is an emerging mathematical field that uses topological and geometric approaches to quantify the “shape” of data. Persistent Homology (PH), the most prominent method from TDA, captures topological invariants such as connected components, loops, and voids in data at multiple scales. The output from PH can be visualised in a barcode which can further be vectorised to enable integration with statistical and machine learning tools. In recent years, PH has been successfully applied to study many biological phenomena.
In this mini course I will introduce the mathematical concepts behind TDA and PH and show applications to both experimental data from oncology and the output from mathematical models. I will in particular demonstrate how PH allows us to quantify the effect of drugs on experimental data of vascular networks of tumours and how we can use similar approaches to stratify the parameter space of a mathematical model of tumour vasculature. I will then show how we can combine TDA and mathematical models to understand the effect of structural features of vascular networks on perfusion level and response to radiotherapy. Finally, I will present how PH can give insight into spatial relations in data and how it can complement machine learning approaches for biological data.