Overordnede kursusmål
Provide the student with the computational skills needed to survie
in data-rich cell factory engineering projects.
Læringsmål
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
- To use the Unix shell for working with files and directories,
pipes and filters, loops, shell scripts, and searching.
- Use Python and R for data analysis and task automation,
including the import of libraries, reading and plotting of data,
selection and filtering of data, writing of conditional statements
and functions, and debugging.
- Utilize basic version control of data and programming code with
Git.
- Import, manipulate, and simulate genome-scale metabolic
models.
- Calculate theoretical maximum yields for different products,
host organisms, and feed
- Predict gene knockout strategies.
- Compute over-expression and down-regulation targets.
- Enumerate heterologous pathways and assess their thermodynamic
feasibility.
- Integrate transcriptomics (and other types of omics) data into
genome-scale metabolic models.
- Simulate batch growth using dynamic flux balance
analysis.
Kursusindhold
With data generation becoming evermore easy in biology, life
scientists and engineers are increasingly facing challenges in
analyzing data in their line of work. For example, simple
bioinformatics tasks can become a huge drain on scientists’ time as
they repetitively copy and paste information into web interfaces
instead of running batch operations.
Furthermore, with genetic manipulations becoming evermore easy to
carry out, qualifications demanded of cell factory engineers will
shift away from the ability to construct strains towards analysis
of data and design of new strains, a trend that is only going to be
accelerated with DNA synthesis costs plummeting and lab automation
becoming more widely available. Therefore, it will be essential
that the next generation of PhD students in cell factory
engineering are trained in computational tools needed for data
analysis and strain design.
As a first step towards this goal, two courses (two days each) will
be held back to back:
1. A Software Carpentry course that covers basic computational
skills and specifically targets life scientists (task
automatization, command line usage, programming with Python, data
analysis with R, version control etc.). Since 1998, Software
Carpentry has been teaching basic lab skills for research computing
to scientists and engineers and course materials have continuously
been adapted and tailored to their problems and needs.
2. Taking advantage of the materials covered in (1), a
computational cell factory design course that covers basic
genome-scale metabolic modeling and in silico strain design methods
will be held for people involved in strain engineering projects or
metabolic research in general.
Litteraturhenvisninger
http://software-carpentry.org/lessons/
Lewis, N. E., Nagarajan, H., & Palsson, B. Ø. (2012).
Constraining the metabolic genotype-phenotype relationship using a
phylogeny of in silico methods. Nature Reviews Microbiology, 10(4),
291–305.
http://doi.org/10.1038/nrmicro2737
Bordbar, A., Monk, J. M., King, Z. A., & Palsson, B. Ø. (2014).
Constraint-based models predict metabolic and associated cellular
functions. Nature Reviews Genetics, 15(2), 107–120.
http://doi.org/10.1038/nrg3643
Maia, P., Rocha, M., & Rocha, I. (2016). In Silico
Constraint-Based Strain Optimization Methods: the Quest for Optimal
Cell Factories. Microbiology and Molecular Biology Reviews : MMBR,
80(1), 45–67.
http://doi.org/10.1128/MMBR.00014-15Sidst opdateret
23. februar, 2018
