For Current Issue

To all CSI  Members, Please read through the Abstracts given below and send your suggestions by clicking on the " Your Suggestions" button as to which articles you would like to read in complete in E-Visions. Send the name of article and number in your answer.

Computers Are from Mars, Organisms Are from Venus

Biology and computer science share a natural affinity. Physicist Erwin Schrödinger envisioned life as ana periodic crystal, observing that the organizing structure of life is neither completely regular, like a pure crystal, nor completely chaotic and without structure, like dust in the wind. Perhaps this is why biological information has never satisfactorily yielded to classical mathematical analysis.

Machine computations combine elegant algorithms with brute force calculations— which seems a reasonable approach to this a periodic structure. Like-wise, computing seeks to create a machine that can flexibly solve diverse problems. In nature, such plastic problem solving resides uniquely in the domain of organic matter. Thus, examining how organisms solve problems can lead to new computation- and algorithm-development approaches that devour the problems that are so easy to approach using a computer, yet so difficult to tackle in the laboratory.

The Blueprint for Life?

One of the greatest scientific discoveries of the twentieth century is the structure of DNA and how it encodes proteins. Current genome projects, especially the Human Genome Project, have sparked interest in the information encoded in DNA, which is often referred to as "the blueprint for life, "implying that it contains all the information needed to create life. But this interpretation ignores the complex interactions between DNA and its cellular environment— interactions that regulate and control the spatial and temporal patterns of gene expression. 

Moreover, the particulars of many cellular structures seem not to be encoded in DNA, and they are never created from scratch—rather, each cell inherits templates for these structures from its parent cell. Thus, it is not clear that DNA directly or indirectly encodes all life processes, casting doubt on the belief that we can understand them solely by studying DNA sequences.

Genome Sequence Assembly: Algorithms and Issues

Ultimately, genome sequencing seeks to provide an organism’s complete DNA sequence. Automation of DNA sequencing allowed scientists to decode entire genomes and gave birth to genomics, the analytic and comparative study of genomes. Although genomes can include billions of nucleotides, the chemical reactions researchers use to decode the DNA are accurate for only about 600 to 700 nucleotides at a time.

The DNA reads that sequencing produces must then be assembled into a complete picture of the genome. Errors and certain DNA characteristics complicate assembly. Resolving these problems entails an additional and costly finishing phase that involves extensive human intervention. Assembly programs can dramatically reduce this cost by taking into account additional information obtained during finishing. Algorithms that can assemble millions of DNA fragments into gene sequences underlie the current revolution in biotechnology, helping researchers build the growing database of complete genomes.

Toward New Software for Computational Phylogenetics 

Bernard M.E. Moret, Li-San Wang, and Tandy Warnow

Systematic study how a group of genes or organisms evolved. These biologists now have set their sights on the Tree of Life challenge: to reconstruct the evolutionary history of all known living organisms. A typical phylogenetic reconstruction starts with biomolecular data, such as DNA sequences for modern organisms, and builds a tree, or phylogeny, for these sequences that represents a hypothesized evolutionary history. Finding the best tree for a data set can be a computationally intensive problem.

Phylogenetic software for mapping the Tree of Life will require entirely new approaches in statistical models of evolution, high-performance implementations, and data analysis and visualization. The authors have developed polynomial algorithmic techniques for use in their research addressing phylogeny reconstruction from biomolecular sequences, focusing on the accuracy of reconstructions and the use of simulations. 

BioSig: An Imaging Bioinformatic System for Studying Phenomics

Bahram Parvin, Qing Yang, Gerald Fontenay, and Mary Helen Barcellos-Hoff

Using genomic information to understand complex organisms requires comprehensive knowledge of the dynamics of phenotype generation and maintenance. A phenotype results from selective expression of the genome, creating a history of the cell and its response to the extracellular environment. Defining cell phenomes requires tracking the kinetics and quantities of multiple constituent proteins, their cellular context, and their morphological features in large populations. The BioSig imaging bioinformatic sys-tem for characterizing phenomics answers these challenges. 

The BioSig approach to microscopy and quantitative image analysis helps to build a more detailed picture of the signaling that occurs between cells as a response to exogenous stimulus such as radiation or as a consequence of endogenous pro-grams leading to biological functions. A r t The system provides a data model for capturing experimental annotations and variables, computational techniques for summarizing large numbers of images, and a distributed architecture that facilitates distant collaboration. 

A Random Walk Down the Genomes: DNA Evolution in Valis

Salvatore Paxia, Archisman Rudra, Yi

A better understanding of biology will come through information-theoretic studies of genomes that provide insights into DNA’s role in governing metabolic and regulatory pathways. Understanding the evolutionary processes that act on these "codes of life" requires the ability to analyze vast amounts of continually generated genomic data. Researchers in the emerging bioinfor matics discipline require more complex mechanisms to investigate the full ensemble of available biological facts. To meet this challenge, New York University’s Bioinformatics Group is creating a computational environment called Valis — the vast active living intelligent system. Valis is designed to solve the immediate genomic and proteomic problems that the biological community currently faces, while remaining flexible enough to adapt to the maturing bioinformatics field.