Main Speakers/Courses

The meeting will be focused on short courses, of two or three one-hour lectures each, given by invited distinguished researchers, which are supplemented by contributed short talks by other participants and posters of case studies.

Main Speakers

• Dirk Fey, (homepage) School Of Medicine, Systems Biology Ireland
  

• Minicourse "Dynamic modelling cell-to-cell and patient-to-patient heterogeneity", or "When modelling meets data"
• Abstract: Signal transduction networks process the cell’s intra- and extracellular cues, drive vital cell-fate decisions, and are often dysregulated in cancer and other diseases. Despite their importance, the dynamic responses of these signalling pathways can be highly variable on both the cellular and the patient level. Here I will present examples of what we can learn by taking this variability into account using a semi-deterministic modelling approach based on ordinary differential equations
  Lecture 1: Introduction to reaction kinetic modelling and parameter estimation.
  Lecture 2: Dynamic modelling of cell-to-cell signalling heterogeneity and cell fate.
  Lecture 3: Patient-specific modelling of cancer signalling dynamics and patient survival.
  

• John Tyson, (homepage) Tyson Lab Computacional Cell Biology, Virgina Tech, USA.
  

• Minicourse "Network Dynamics and Cell Physiology"
• Abstract:The physiological traits of a living cell are governed by complex networks of interacting genes, proteins and metabolites. These networks exhibit remarkable "information processing" power, on the basis of their ability to make binary decisions (coexisting stable steady states) and to differentiate in time and space (limit cycle oscillations and spatial pattern formation). In this series of three lectures we will focus on a few examples of such information-processing systems, particularly related to cancer (cell growth, division. stress responses and programmed death), on the underlying gene/protein regulatory networks that govern these processes, on realistic mathematical models of the dynamical properties of these networks, and on the novel biological insights delivered by these models.
  

• Montagud Arnau, (homepage) Institut Curie, Paris, France  
  

• Minicourse "Use of computational methods for logical modelling of biological networks deregulated in diseases"
• Abstract:
  In these series of lectures, we will work on a pipeline   of computational tools that performs a series of analyses to increase a model's usefulness. We will start by analysing the structure of the network of interactions and we will translate this network into a mathematical object, specifically a logical model. Also, we will study how robustness analyses can be applied to it. Next, we will see how to assign to each solution of the model a probability and how to identify genetic interactions using the mutant phenotypes probabilities. Finally, we will relate the model to data: how the data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A mathematical model of initiation of the metastatic process will be used as a transversal example for all these analyses.
  
  A step by step tutorial will be provided and all models, tools and scripts are provided on an accompanying website.
  
• References:
  
  Barillot E, Calzone L, Hupe P, Vert J-P, Zinovyev A. Computational Systems Biology of Cancer. 1st ed. Boca Raton, FL: CRC Press; 2012. Chapter 7: Mathematical modelling applied to cancer cell biology.
  
  Cohen DPA, Martignetti L, Robine S, Barillot E, Zinovyev A, Calzone L. Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration. PLoS Comput Biol. 2015;11: e1004571. doi:10.1371/journal.pcbi.1004571 

•  Walter Kolch, (homepage) Director Of Conway Institute, Systems Biology Ireland
  

• Minicourse  "When models meet biology"
• Abstract: Modern biology is technology and data driven. We never had so many and so sophisticated tools to analyse biological systems in order to study everything from the basic processes of life to complex diseases. However, we also now facing the bottleneck that data generation has outpaced data interpretation. Thus, the next grand challenge in biology is making sense of data. I will address these issues in a series of three lectures.
  Lecture 1: Why does biology need modelling and what can we explain by modelling? This presentation will be a short survey of biological data generation and interpretation with a view to derive some general principles of how we best can deploy mathematical and computational modelling to explain biological processes.
  Lecture 2:  Using network reconstruction and modelling to improve cancer therapies. This lecture will bring practical examples for how modelling can be used to identify drug targets in cancer.
  Lecture 3: Integration of multiple omics data. Here, I will present a strategy for the integration of different omics datasets using RAS oncogene signalling as example. 

• Claudine Chaouiya, (homepage) (IGC, Gulbenkian, Lisbon)

• Plenary talk "Uncovering basins of attraction in asynchronous Boolean models, application to assess genetic alterations in cancer networks"
• Abstract: Boolean models permit retrieving salient dynamical properties of regulatory and signalling networks. In these discrete dynamical systems, it is particularly relevant to identify the model attractors and their reachability properties. Indeed, attractors denote the long term behaviours of the modelled regulatory networks and are thus associated with cellular fates. Due to the combinatorial explosion of the size of the state space, these properties are often difficult to study, leading to a wealth of theoretical and computational developments. While many methods have been proposed to identify attractors (stable states or cyclical attractors) and/or to check their reachability from specific initial conditions, the problem of identifying their basins of attraction (set of states leading to an attractor) has not received much attention; it has been mostly addressed by exhaustive and demanding searches.
  Here, we focus on asynchronous Boolean models that are non deterministic, and are considered biologically more realistic than synchronous, deterministic models. Evidently, exhaustive searches in asynchronous dynamics are even less tractable for large networks than in the synchronous setting. Moreover, different attractors may be reachable from the same state, following concurrent trajectories. This leads to the notion of weak and strong basins of attraction, the later being the set of states that inevitably lead to a given attractor. We also define the interior and exterior boundaries of strong basins, which would correspond to a notion of separatrix adapted to the discrete framework.
  We have recently proposed the consideration of reverse models to uncover basins of attraction. I will show that we can properly define the model whose dynamics is exactly the reverse (asynchronous) dynamics of a given Boolean model. I will further discuss the properties of this reverse Boolean model and why it can help uncovering the basins of attraction of the original model.
  Finally, relying on published network models of cell fate decision in cancer cells, I will illustrate how genetic alterations in a cancer cell can modulate the basins of attraction to favor e.g. proliferation at the expense of apoptosis or growth arrest.