Computational Biology @ UC

Topics Discussed in the Courses

Cell Signaling and Metabolism

Monday, 4th September & Tuesday, 5th September

  • Cell signaling I

    • From extracellular signal to cellular response.
    • Studying cell surface receptors and signal transduction pathways.
    • G protein-coupled receptors.
  • From genes to protein

    • Transcriptional control of gene expression.
    • Post-transcriptional gene control.
  • Cell signaling II

    • The cytoskeleton: cell structure and movement.
    • Metabolism

Tools for Modeling Biological Systems: a “hands-on” course

Monday, 4th September & Tuesday, 5th September

  • Mathematical models of intra-cellular processes

    • Quantitative mathematical descriptions of coupled reactions by ordinary differential equations (ODEs)
    • Michaelis Menten kinetics, Hill kinetics, stochastic terms and other common variations of ODEs used in the literature
  • Computational modeling of Biological systems 

    • Introduction to computational modeling and coding in MatLab.
    • Practical examples of modeling intra-cellular processes
  • Mathematical models of extra-cellular processes

    • Quantitative mathematical descriptions of diffusion and chemotaxis through partial differential equations (PDEs)
    • Computational implementation

Data Science - application to Biology

Monday, 4th September

  • Basic Concepts of Machine-learning (ML)

  • Unsupervised learning

    • Clustering (k-­means and hierarchical)
    • Dimensionality reduction (Principal Component Analysis)
  • Supervised Learning/ Classification

    • Generative vs discriminative models
    • Model validation (bias variance trade-­off and learning curves)
    • Classifiers (Naive Bayes, KNN, Linear SVM)
  • Ensemble methods (Boosting, Bagging, and Random Forests)

  • Application of ML to genomics and proteomics

Modeling Proteins and Membranes: folding and aggregation

Wednesday, 6th September & Thursday, 7th September

  • Foundations of molecular dynamics
  • Molecular dynamics for lipid bilayers
  • Molecular dynamics for proteins
  • Studying protein folding and aggregation
  • Protein-Ligand interactions and  Drug Discovery

Transcriptomics to Characterize Cellular Mechanisms

Wednesday, 6th September Thursday, 7th September

  • Next-generation sequencing technologies (NGS)
  • RNA, transcriptomes and RNA
  • Experimental design for RNA-Seq
  • RNA-Seq data processing
  • RNA-Seq data analysis methods
  • Differential expression and functional analysis
  • Analysis of time-series data
  • Reconstruction of signaling networks

Modeling Metabolic and Signaling Pathways

Thursday, 7th September Friday, 8th September

  • Graphical representation of regulatory networks
  • From diagrams to equations
  • Relating concentrations to process rates 
  • Handling compartments
  • Studying steady state behavior
  • Simulating dynamics
  • Taming complexity
  • The dynamic bestiary

Mathematical Models of Age-Related Diseases

Friday, 8th September

  • Modeling physiological systems using continuous and discrete methods.

  • Exemplification of the methods by analysing tumor growth, blood vessel formation and vessel irrigation in pathological settings.

    • Angiogenesis and tumor growth using cell based models.
    • Coupling cell signalling to matrix elasticity to regulate vessel morphology.
    • Quantifying blood flow and hypoxia in pathological vascular network.
    • Models of drug release in the context of glaucoma.