This course provides an introduction to Bioinformatics. We define this field as the principles and computational methods aiming at the upgrade of the information content of the large volume of biological data generated by genome sequencing, as well as cellwide measurements of gene expression (DNA microarrays), protein profiles (proteomics), metabolites and metabolic fluxes. Additionally, bioinformatics is concerned with whole organism data, especially human physiological variable measurements including organ function assessments, hormone levels, blood flow, neuronal activity etc., that characterize normal and pathophysiology. The overall goal of this data upgrade process is to elucidate cell function and physiology from a comprehensive set of measurements as opposed to using single markers of cellular function. Fundamentals from systems theory will be presented to define modeling philosophies and simulation methodologies for the integration of genomic and physiological data in the analysis of complex biological processes, e.g. genetic regulatory networks and metabolic pathways. Various computational methods will address a broad spectrum of problems in functional genomics and cell physiology, including; analysis of sequences, (alignment, homology discovery, gene annotation), gene clustering, pattern recognition/discovery in large-scale expression data, elucidation of genetic regulatory circuits, analysis of metabolic networks and signal transduction pathways. Applications of bioinformatics to metabolic engineering, drug design, and biotechnology will be also discussed.
[1]
Dan Gusfield.
Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology
,
1997
.
[2]
김삼묘,et al.
“Bioinformatics” 특집을 내면서
,
2000
.
[3]
Pavel A. Pevzner,et al.
Computational molecular biology : an algorithmic approach
,
2000
.
[4]
L. K. Buehler,et al.
Bioinformatics Basics: Applications in Biological Science and Medicine
,
1999
.
[5]
L. Wackett.
An annotated selection of World Wide Web sites relevant to the topics in Microbial Biotechnology
,
2013,
Microbial biotechnology.
[6]
Pierre Baldi,et al.
Bioinformatics - the machine learning approach (2. ed.)
,
2000
.
[7]
Sean R. Eddy,et al.
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
,
1998
.
[8]
A. Baxevanis,et al.
A Practical Guide to the Analysis of Genes and Proteins
,
1998
.