Network analysis is emerging from niche status in computational biology and could soon jump to the forefront of the field, according to speakers at the Institute for Systems Biology’s fourth international symposium held in Seattle this April. This year’s meeting, entitled, “Computational Challenges in Systems Biology,” focused on what many see as the key enabling tool in merging systems analysis with biological research — algorithmic methods for mapping the “digital information” encoded by the genome onto biological networks.
Lee Hood, president of ISB, kicked off the conference with an overview of the considerable computational hurdles that systems biology faces, but singled out network analysis as the heart of the emerging field. To demonstrate the importance of a network-based approach to analyzing biological data, he used a slide comparing a road map of the United States with a map of national flight patterns. Just as the flight map is a better way to visualize which cities are the most important, network representations will help identify those biomolecules that act as subcellular “hubs,” he said.
Bernhard Palsson, professor of engineering at the University of California, San Diego, described computational biology as moving from a “one-dimensional” focus on “component enumeration” — the gathering of genes, proteins, and other molecules — to the “two-dimensional” approach of network reconstruction. Naturally, this view of the field leads to three- and even four-dimensional approaches, which Palsson described as reconstructing the complete cellular architecture and accounting for the dynamic nature of cellular processes, respectively.
Richard Karp of the University of California, Berkeley, conceded that the use of interaction networks to study systems behavior is limited, because they capture only a “snapshot” of static — and temporary — connections. But he noted that computational systems biology is still constrained by the type of data that is available to analyze.
US Patent 6,882,990. Methods of identifying biological patterns using multiple data sets. Inventors: Stephen Barnhill, Isabelle Guyon, Jason Weston. Assignee: Biowulf Technologies. Issued: April 19, 2005.
Covers systems and methods for identifying biological patterns in data using multiple support vector machines. Training data for a learning machine is pre-processed in order to add meaning thereto. The invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.
US Patent 6,882,742. Method and apparatus for providing a bioinformatics database. Inventors: David Balaban, Arun Aggarwal. Assignee: Affymetrix.
Protects a system and method for organizing information relating to polymer probe array chips, including oligonucleotide array chips, using a database model that organizes information relating to sample preparation, chip layout, application of samples to chips, scanning of chips, expression analysis of chip results, etc.
GenBank 147.0 is now available from the National Center for Biotechnology Information at ftp://ftp.ncbi.nih.gov. The release contains 48,235,738,567 base pairs and 44,202,133 entries. Uncompressed, the 144.0 flat files require about 168 GB for the sequence files only.
SGI has signed two new life science customers — AstraZeneca’s research team in Mölndal, Sweden, and the Bioinformatics Research Group at the University of Ulster in Coleraine, Northern Ireland — for its Altix high-performance computing products.
IBM has signed an eight-year, $402 million healthcare IT agreement with the University of Pittsburgh Medical Center that will build on UPMC’s current electronic health record strategy.
Compugen has extended its relationship with Novartis into systems biology, in a project that will focus on modeling signal transduction pathways, using transcription factor binding information, expression profiles, and other data.
After several years of gradually de-emphasizing the genomics information business upon which it was launched, Celera Genomics has officially pulled the plug on its commercial database efforts.
Mount Sinai Hospital’s Blueprint Initiative has added SMID-Genomes — a collection of more than 9.4 million predicted interactions between small molecules and protein binding sites for more than 1,500 organisms — to its catalog of bioinformatics resources.
The National Foundation for Cancer Research, Oxford University, the NFCR Center for Targeted Cancer Therapies, and TGEN announced the launch of a new peer-to-peer computing project to develop drugs to treat pancreatic cancer.