“Due to the revolutionary development of genomics and systems biology, there’s a gigantic amount of biological data being generated. The real question of the 21st century is who are going to be the right kinds of groups to integrate that information in ways that best inform the biology at the earliest stages?"
Those are the words of Eric Schadt, a mathematician by training who is now Chief Scientific Officer in the next-generation sequencing company Pacific Biosciences. Schadt has made groundbreaking studies on disease pathways and genome data integration. The key to answering the above question is bioinformatics.
Obviously, systems biology requires the integration of many different types of biological data, such as genomic, proteomic, and metabolomic. Integration of different data types using tools of bioinformatics is not a trivial task, given the heterogeneity of available data across dispersed databases. However, adopting an integrative approach based on bioinformatics in the drug discovery pipeline by biotech, pharmaceutical, and diagnostics industry has become a high priority; this alternative strategy will likely help pharmaceutical companies reverse low productivity and high rates of attrition. Thus, integrative bioinformatics and network biology may offer promising solutions to overcome these major issues in the near future.
The course covers a variety of items raised by contemporary biology and medicine. Topics include: genetic and epigenetic code, organization of the genome, hierarchic and non-hierarchic regulation of the life at the cell, organ, organism and population levels, genomics, proteomics, metabolomics, evolution, genealogy, omics databases, principles of systems biology, high-throughput molecular technologies and gene diagnostics.
This course can serve as a preparation for a course on computational science in biomedicine and provides the knowledge needed to handle very large biological data sets.
Proposed literature:
Alberts, B: Molecular biology of the cell
http://www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=mboc4
http://bioinfobooks.blogspot.com/
Journal of Computational Biology
http://www.liebertpub.com/products/product.aspx?pid=31
http://www.bio.davidson.edu/projects/gcat/gcat.html
http://www.cmu.edu/bio/education/courses/03510/
http://bib.oxfordjournals.org/cgi/content/extract/bbp009v1
András Falus (born 1947) is a professor of the Semmelweis University (Budapest, Hungary) and member of Hungarian Academy of Sciences. His major research areas are immunogenomics, allergy- and onco-genomics. Recently, he has been focusing on non-coding DNA (e.g. microRNA) and microvesicles, a newly recognized form of intercellular communication. He wrote and edited nine books (with publishers like Springer, John Wiley & Sons, Karger) and published over 300 research papers with over 3,500 citations. He is the former president of Hungarian Society for Immunology. Falus was a fellow at Odense University (1980-81) andHarvard Medical School (1984-86), and a visiting professor at Osaka University (1989). He organized the first International Immunogenomics (2004) and Immunoinformatics (2006) Conference, and is a founding member of International Immunomics Society. He is editor or board member of four international scientific journals. Falus was awarded the Szechenyi price (2006), Neumann award (2006) and Semmelweis prize (2008).
The course introduces the applications of discrete mathematics in bioinformatics. Due to the very large size of biological data sets, applications of even the most powerful computers need an understanding of the underlying mathematical structure of the problems and an algorithmic way of thinking.
After a quick review of the most important discrete structures, especially graphs, the students will learn various algorithms for sequence comparison, likelihood calculations, RNA structure prediction (finding the most stable RNA structure), and other optimization problems. The course also covers genome rearrangement algorithms, which are mathematically beautiful and also very important in the age of genomics when millions of genomes are going to be sequenced.
Proposed literature:
István Miklós: Introduction to algorithms in bioinformatics
http://www.renyi.hu/~miklosi/AlgorithmsOfBioinformatics.pdf
István Miklós (born 1974) is a research fellow at the Rényi Institute, Hungarian Academy of Sciences. He graduated from Eötvös Loránd University as a mathematics teacher and biology-chemistry teacher in 1998. In the same year he started his Ph.D. studies in the field of statistical alignment under the supervision of Janos Podani. He received his Ph.D. in 2002, and took a postdoctoral position at the Department of Statistics, University of Oxford. He returned to Hungary in 2004 and has been teaching different bioinformatics courses at several universities, including the Budapest Semesters in Mathematics and Central European University since 2001. His main research fields are Markov chain Monte Carlo methods, genome rearrangement and stochastic models in bioinformatics. He has 37 peer-reviewed publications, including four book chapters. His Erdős number is 2.
