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

Brian Ingalls (Department of Applied Mathematics, University of Waterloo
Waterloo, Ontario, Canada) homepage
Minicourse "Introduction to Dynamic Mathematical Modelling in Systems Biology"
  • Abstract: Cellular activity is driven by complex molecular interaction networks. Systems modelling allows us to understand the behaviour of these networks, and to predict their response to perturbations. This session will introduce differential equation-based models of intracellular networks. We will address methods for model construction based on biochemical kinetics, and introduce some commonly used techniques for model investigation: phase plane, bifurcation, and sensitivity analyses. We will survey systems that cover a range of biological functions, with emphasis on intracellular signalling networks.

Lutfu Safak Yilmaz (Walhout Lab, Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, USA) homepage

Minicourse "Metabolic Network Modeling: Genome-scale Reconstruction, Flux Balance Analysis, and Applications to Caenorhabditis elegans Metabolism"
  • Abstract:  Constraint-based metabolic network modeling (MNM) allows the computational analysis of the metabolism of an organism at global scale with minimal requirements of computational performance and without model overparameterization. The initial step of MNM is the annotation of metabolic genes in the entire genome of the organism of interest, typically resulting in a network of hundreds to thousands of reactions that link metabolites. The network is then converted to a mathematical model that warrants mass balance of every compound at steady state while maximizing a biologically relevant objective, such as the rate of growth represented by the flux of a biomass assembly reaction. Fluxes in a subset of reactions are constrained to define the environmental conditions of the simulation, reaction reversibility, and known production/consumption rates. The flux distribution in the network is predicted for these defined conditions using linear programming. This method is called Flux Balance Analysis (FBA), and has been used in a variety of systems-level applications ranging from the discovery of novel pathways to the maximization of valuable metabolic products. Mathematical derivatives of FBA have been developed to integrate metabolic networks with high throughput datasets including transcriptomics, proteomics, and metabolomics. Data integration greatly constrains the generic model of an organism to predict the flux distribution in a particular metabolic state, such as stage- or tissue-specific metabolism. In this lecture series, FBA and data integration techniques will be introduced and explored using applications mostly with a recently developed metabolic model of the nematode Caenorhabditis elegans                                                                                                                             

  Maciej Dobrzynski (Systems Biology Ireland, Conway Institute Belfield, Dublin, Irelandhomepage

  • Minicourse "Introduction to modelling noise and cell-to-cell variability in signalling networks"
  • Abstract: Signalling networks exhibit complex dynamics, which allows cells to assume a wide range of phenotypes and fates. However, the outcomes may differ even across the population of genetically identical cells because networks operate in a tiny microcosm subject to thermal noise and diffusion in a crowded compartmentalised environment. Such cell-to-cell variability may be advantageous for bacteria or tumour cells to evade drug treatments but too much noise in signal processing could result in erroneous decisions, which would be detrimental for the population.
    In this mini-course I will focus on the effect of noise on cellular signal processing and cell fate determination with emphasis on phenotypic consequences of cell-to-cell variability. In particular, I will address the following:
    - Mathematical and numerical frameworks to model noisy biochemical networks with and without spatial resolution
    - Approaches to model variability on the cell population level
    - Propagation of noise in signalling networks; network structures that amplify or attenuate noise
    - Single-cell resolution measurement techniques and associated data analytics approaches for high content screening experiments.

Pedro Mendes (Director of Mendes Research Group, School of Computer Science, Manchester Institute of Biotechnology, UK.) homepage
Workshop  "Optimization and parameter estimation (with COPASI)" 
Abstract: See Copasi homepage here.