Potential topics that could be covered at our future training events and courses include:

Protein modelling 

We anticipate that the training course on protein modelling will cover some or all of the following areas:

  • Introduction to protein structure – structure data files and their manipulation
  • Performing calculations on 3-d structures
  • Differences between X-ray crystallographic and NMR structures
  • Protein structure analysis, homology modelling, and simulation of effects of mutagenesis
  • Mapping sequence variance onto 3-d structures
  • Cavity analysis and surface modelling – electrostatic potentials
  • Modelling of interaction surfaces
  • Prediction of 3-d structures from sequences

Bioinformatics

Over time, we hope to develop a series of courses in this area, covering discrete areas of the subject, and developing into a coherent whole. It may be appropriate to ask several groups to combine to generate this programme. Single training courses could include: 

  • Protein structure analysis, homology modelling, simulation of effects of mutagenesis (see below also)
  • Protein sequence analysis – alignments, prediction of function from sequence
  • Analysis of protein interactomes
  • Pathway modelling, mapping ‘omics’ data onto pathways
  • Analysis of image data – from gels to cells – acceptable manipulation, quantification, image analysis (from Image to proprietary analysis)

Enzymology

We anticipate that the enzymology training event will cover some or all of the following areas: 

  • Principles of biocatalysis
  • Analysis of enzyme kinetics, affinity and velocity
  • Development of a robust enzyme assay
  • Coupled enzyme systems
  • Allostery
  • Analysis of enzyme kinetic data
  • Automated enzyme assays
  • Inhibition

Metabolic flux

We anticipate that the metabolic flux training event will cover some or all of the following areas: 

  • Metabolic pathway concepts
  • Flux through pathways
  • Common metabolites and multiple routes of utilization
  • Relationship between flux and metabolite concentration
  • Analytic approaches to metabolite quantification/metabolomics
  • Use of tracers to monitor flux through metabolic systems
  • Introduction to metabolic pathway simulations

Experimental design

We anticipate that the training course on experimental design will cover some or all of the following areas: 

  • The problem of irreproducible science
  • Design of controlled experiments
  • Hypothesis falsification and outcome
  • The value of negative results
  • Power analysis, sampling strategies and study dimensions
  • Understanding data: normality, heteroscedasticity
  • The effect of the experimenter on the experimental outcome
  • The consequences of data transformation
  • The importance of ‘minimal information on an X experiment’ and of data standards
  • The myth of p=0.05
  • How to design a fully controlled experiment
  • How to test a fully designed experiment
  • The formalization of “Design of Experiments”

Data visualization

We anticipate that the data visualization training will cover some or all of the following areas:

  • The relevance of data visualization, from simple data sets to complex, multidimensional data sets
  • Coping with dynamic range and data normalization
  • The use of different graphical paradigms
  • Chartjunk
  • Developing tools for effective visualization and communication of data
  • Types of data from images to large tabular data sets
  • Communication versus distortion of data
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