Software agents in molecular computational biology

Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.     

Genomics and computational molecular biology

There has been a dramatic increase in the number of completely sequenced bacterial genomes during the past two years as a result of the efforts both of public genome agencies and the pharmaceutical industry. The availability of completely sequenced genomes permits more systematic analyses of genes, evolution and genome function than was otherwise possible. Using computational methods - which are used to identify genes and their functions including statistics, sequence similarity, motifs, profiles, protein folds and probabilistic models - it is possible to develop characteristic genome signatures, assign functions to genes, identify pathogenic genes, identify metabolic pathways, develop diagnostic probes and discover potential drug-binding sites. All of these directions are critical to understanding bacterial growth, pathogenicity and host-pathogen interactions.     

Cellware--a multi-algorithmic software for computational systems biology

The intracellular environment of a cell hosts a wide variety of enzymatic reactions, diffusion events, molecular binding, polymerization and metabolic channeling. To transform these biological events into a computational framework, distinct modeling strategies are required. While currently no tool is capable of capturing all these events, progress is being made to create an integrated environment for the modeling community. To address this niche requirement, Cellware has been developed to offer a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. AVAILABILITY: The software is available for free and can be downloaded from http://www.bii.a-star.edu.sg/sbg/cellware     

Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.     

Algorithms in nature: the convergence of systems biology and computational thinking

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high‐level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.     

The Virtual Cell: a software environment for computational cell biology

The newly emerging field of computational cell biology requires software tools that address the needs of a broad community of scientists. Cell biological processes are controlled by an interacting set of biochemical and electrophysiological events that are distributed within complex cellular structures. Computational modeling is familiar to researchers in fields such as molecular structure, neurobiology and metabolic pathway engineering, and is rapidly emerging in the area of gene expression. Although some of these established modeling approaches can be adapted to address problems of interest to cell biologists, relatively few software development efforts have been directed at the field as a whole. The Virtual Cell is a computational environment designed for cell biologists as well as for mathematical biologists and bioengineers. It serves to aid the construction of cell biological models and the generation of simulations from them. The system enables the formulation of both compartmental and spatial models, the latter with either idealized or experimentally derived geometries of one, two or three dimensions.     

DNA and the revolutions of molecular evolution, computational biology, and bioinformatics.

The discovery of the structure of DNA was a necessary prerequisite for determining the sequence of DNA molecules. Technological advances have now made it possible to sequence DNA rapidly and has resulted in public databases with over 30 billion nucleotides of known sequence. The analysis of these data has lead to new fields of science and to amazing advances in our understanding of evolution.     

Electrophoretic gel image analysis software for the molecular biology laboratory

We present GelReader 1.0, a microcomputer program designed to make precision, digital analysis of one-dimensional electrophoretic gels accessible to the molecular biology laboratory of modest means. Images of electrophoretic gels are digitized via a desktop flatbed scanner from instant photographs, autoradiograms or chromogenically stained blotting media. GelReader is then invoked to locate lanes and bands and generate a report of molecular weights of unknowns, based on specified sets of standards. Frequently used standards can be stored in the program. Lanes and bands can be added or removed, based upon users' subjective preferences. A unique lane histogram feature facilitates precise manual addition of bands missed by the software. Image enhancement features include palette manipulation, histogram equalization, shadowing and magnification. The user interface strikes a balance between program autonomy and user intervention, in recognition of the variability in electrophoretic gel quality and users' analytical needs.     

DAMBE: software package for data analysis in molecular biology and evolution

DAMBE (data analysis in molecular biology and evolution) is an integrated software package for converting, manipulating, statistically and graphically describing, and analyzing molecular sequence data with a user-friendly Windows 95/98/2000/NT interface. DAMBE is free and can be downloaded from http://web.hku.hk/~xxia/software/software.htm. The current version is 4.0.36.     

Parameterized complexity analysis in computational biology

Many computational problems in biology involve par–ametersfor which a small range of values cover important applications.We argue that for many problems in this setting, parameterizedcomputational complexity rather than NP-completeness is theappropriate tool for studying apparent intractability. At issuein the theory of parameter–ized complexity is whethera problem can be solved in time O(n)for each fixed parametervalue, where a is a constant independent of the parameter. Inaddition to surveying this complexity framework, we describea new result for the Longest Common Subsequence problem. Inparticular, we show that the problem is hard for W[t] for allI when parameterized by the number of strings and the size ofthe alphabet. Lower bounds on the complexity of this basic combinatorialproblem imply lower bounds on more general sequence alignmentand consensus discovery problems. We also describe a numberof open problems pertaining to the parameterized complexityof problems in computational biology where small parameter valuesare important     

多分DNA病毒分子生物学研究进展

本文从基因组特点、生理功能、起源进化及应用前景等方面综述了近年来有关多分DNA病毒分子生物学的研究进展。     

The Nucleic Acid Blot Analyzer II. ANALYZE, an image analysis software package for molecular biology

The Nucleic Acid Blot Analyzer, a new instrument providing high-speed imaging of 32P labeled nucleic acids, captures, stores and presents images in digital form, thus lending itself to rapid data handling and analysis as well as replacing conventional X-ray film autoradiography for many applications. A software package called ANALYZE has been specifically designed for the instrument in order to provide automatic or semi-automatic analysis for molecular biological techniques. The software includes image display manipulation, quantitative and positional analysis, as well as file maintenance utilities. The specific application of the software/hardware to various techniques is presented.