Bioinformatics and Systems Biology

Objectives

In B&BS is necessary to integrate research in experimental biology, with large-scale experimental data, with the construction, simulation and optimization of computational models. The following specific objectives have been defined:

  • Enumerate the main analytical methodologies used to quantify transcriptome, proteome and metabolome.
  • Identify key biological databases and apply sequence analysis tools to infer unknown gene functions.
  • Understand the basic principles of phylogenetics.
  • Identify methodologies for representing mathematically complex cellular processes.
  • Apply tools to genomic scale data analysis and strategy design for its integration with cellular models.
  • Apply algorithms to metabolic engineering and drug target identification tasks using mathematical models of cellular systems.

Program

  1. Analysis of omics data (transcriptomic, proteomic and metabolomic).
  2. Sequence Analysis.
  3. Notions of sequence alignment
  4. Notions of phylogeny.
  5. Stoichiometric cell models.
  6. Dynamic cell models.
  7. Application of some concepts in the elaboration of programs in MATLAB.

Bibliography

  1. Baxevanis, A; Ouellette, B. (2005). Bioinformatics – A practical guide to the analysis of genes and proteins, Wiley (3rd ed)
  2. Nielsen, J.; Villadsen, J.; Lidén, G. (2002). Bioreaction Engineering Principles, Kluwer Academic (2nd ed.)
  3. Klipp et al. (2005). Systems Biology in Practice, Wiley-VCH
  4. Edwards, J.S. et al. (2002). Metabolic modelling of microbes: the flux-balance approach. Environmental Microbiology 4, 133-140
  5. Dias, O., Rocha, M., Ferreira, E. C., & Rocha, I. (2018). Reconstructing high-quality large-scale metabolic models with merlin. In Methods in Molecular Biology (Vol. 1716, pp. 1–36).
  6. 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.
  7. Rocha, M., & Ferreira, P. G. (n.d.). Bioinformatics algorithms: design and implementation in Python.

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