Preparatory Workshops

The optional Preparatory Workshops (B or MC, 1 week) provide cross-training for scientists with weaknesses in specific areas of their individual educational backgrounds. The workshops will prepare participants for the Core Course by reviewing fundamental concepts and theory, as well as conduct wet/dry bench experiments to build key practical skills. Course attendees are strongly encouraged to select one of the workshops to ensure they have the required foundational skills for the Core Course.
Foundational Paradigms in Biology (recommended for non-biologists)
    • mousehistologyOverview of Cell Signaling & Developmental Pathways
    • Nucleic acid biology (cloning strategies, DNA/RNA isolation, in situ hybridization probe synthesis, QT-PCR, next generation sequencing technologies)
    • Protein biochemistry (protein expression/
    • purification, enzyme kinetics, Western blots)
    • Cell Biology (cell culture techniques, transfection protocols, cell based reporter assays, cell labelling studies)
    • Animal models (Xenopus, C. elegans and Drosophila, microdissection/ manipulation)
    • Microscopy (basic principles, fluorescence and confocal instrumentation).



Mathematical & Computational Biology (recommended for biologists)
    • Overview of calculus, ordinary differential equations (ODEs), initial value problems, numerical methods. For further details click here.
    • Mathematica software (notation, implementation for biological problems)
    • MatLab software (notation, graphing & modeling)
    • Biostatistics - focus on stochastic and probabilistic methods for systems biology. Specific topics will include linear models, linear regression, principle component analysis, support vector machines, bayesian networks, stochastic processes, chemical reaction models, and diffusion approximation.

Systems Biology Core Course

The Core Course will provide a didactic lecture series on essential components and themes frequently encountered in Systems Biology research related to morphogenesis & spatial dynamics.
Core Course Lecture Topics
    • hydra blackSystems Biology - introduction and perspective to the field
    • Molecular Signaling Systems (MAP Kinase pathways)
    • Spatial Dynamics of Signal Transduction Pathways (Yeast mating)
    • Computation & Modeling of Dynamical Systems
    • Cellerator/Mathematica: tools for model construction
    • Dynamical Behaviors: multistability, oscillation, ultrasensitivity etc.
    • Bioinformatics of Gene Regulation & Gene Networks
    • Developmental Gene Networks
    • Agent Based Modeling
    • Fluorescence Confocal Microscopy
    • cellsAdvanced Techniques in Dynamic Fluorescence Imaging
    • Advanced Image Analyses
    • Morphogen Gradients & Patterning
    • Reaction-Diffusion Models
    • Stochastics
    • Systems Biology of Stem Cells
    • Growth Control
    • Modeling Cell Cycle Behaviors
Laboratory Practicals
Lecture themes will be illustrated through hands-on experimention in selected model systems to provide practical skills in wet-bench data acquisition, computation and modeling. Lab practicals will include:
    • Dynamics of molecular signaling networks (MAP Kinase pathways)
    • Spatial organization of BMP signaling in murine embryonic stem (mES) cell cultures
    • C. elegans germ line patterning and morphogenesis
    • Drosophila wing disk morphogen gradients: patterning and performance analysis
    • Image analysis for quantitative dynamic spatial information in advanced confocal microscopy techniques, including Fluorescence Correlation Spectroscopy (FCS), Raster Scanning Image Correlation Spectroscopy (RICS) and Number & Brightness (N&B) analyses
    • and cutting edge high-content bioinformatic tools/applications for genome & transcriptome datasets, including NanoString technology in analyzing developmental gene regulatory networks.
gonadPractical skills acquired will include:
    • Experimental design
    • techniques for molecular & genetic perturbations
    • of biological systems
    • Acquisition of quantitative data & parameter estimates
    • Quantitative fluorescence biomarker visualization
    • Confocal microscopy (acquisition/image analysis)
    • Network reconstruction from bioinformatic data sources
    • (e.g. NanoString datasets)
    • Use of computational software (Mathematica/MatLab
    • and other dedicated analytical software packages) for:
    • Construction of systems biology models
    • Modeling and simulation techniques (concepts and applications)