Computational Methodologies & Systems Biology

Computational methodologies endeavours to study, analyse and understand complex biological systems by taking a corresponding integrated systemsSystems biology and  metabolomics is the ultimate phenotyping as it opens up the possibility of studying the effect of complex mixtures. Recent advances in ‘-omics’ technologies and the development of new computational techniques and algorithms have greatly contributed to progress in metabolomics. Computational metabolomics and to highlight the similarities, differences and areas of convergence between metabolomics, genomics, proteomics and transcriptomics. Recent progress being made in four key areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS and data standards; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling. Some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS.

  • Systems biology methods to characterize biological systems
  • Knowledge discovery and data mining techniques
  • Machine learning and pattern recognition
  • Sequence motifs and alignments
  • Hidden markov model
  • Sequencing algorithms
  • Bioinformatics and cheminformatics
  • Stochastic modelling

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