Computational Biology CB_J0007

Title : Reproducible computational biology experiments with SED-ML-The Simulation Experiment Description Markup Language
Author : Dagmar Waltemath, Richard Adams, Frank T Bergmann, Michael Hucka, Fedor Kolpakov, Andrew K Miller, Ion I Moraru, David Nickerson, Sven Sahle, Jacky L Snoep and Nicolas Le Novère
Year Publish : 2011
Place of Publish: BioMed Central Ltd
Abstract :

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

Computational Biology CB_J0006

Title :  Algorithm engineering for optimal graph bipartization
Author : Falk Hüffner
Year Publish : 2005
Place of Publish: Springer Berlin / Heidelberg
Abstract :

We examine exact algorithms for the NP-complete GRAPH BIPARTIZATION problem that asks for a minimum set of vertices to delete from a graph to make it bipartite. Based on the “iterative compression” method recently introduced by Reed, Smith, and Vetta, we present new algorithms and experimental results. The worst-case time complexity is improved from O(3 k • kmn) to O(3 k • mn), where n is the number of vertices, m is the number of edges, and k is the number of vertices to delete. Our best algorithm can solve all problems from a testbed from computational biology within minutes, whereas established methods are only able to solve about half of the problems within reasonable time.

Computational Biology CB_J0005

Title :  An adaptive and iterative algorithm for refining multiple sequence alignment
Author : Yi Wang, Kuo-Bin Li
Year Publish : 2004
Place of Publish: Elsevier Ltd
Abstract :

Multiple sequence alignment is a basic tool in computational genomics. The art of multiple sequence alignment is about placing gaps. This paper presents a heuristic algorithm that improves multiple protein sequences alignment iteratively. A consistency-based objective function is used to evaluate the candidate moves. During the iterative optimization, well-aligned regions can be detected and kept intact. Columns of gaps will be inserted to assist the algorithm to escape from local optimal alignments. The algorithm has been evaluated using the BAliBASE benchmark alignment database. Results show that the performance of the algorithm does not depend on initial or seed alignments much. Given a perfect consistency library, the algorithm is able to produce alignments that are close to the global optimum. We demonstrate that the algorithm is able to refine alignments produced by other software, including ClustalW, SAGA and T-COFFEE. The program is available upon request.

Computational Biology CB_J0003

Title : Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum
Author : Kuang Lin, Dirk Husmeier, Frank Dondelinger, Claus D. Mayer, Hui Liu, Leighton Prichard, George P. C. Salmond, Ian K. Toth and Paul R. J. Birch
Year Publish : 2010
Place of Publish: Springer Berlin / Heidelberg
Abstract :

The objective of the project reported in the present chapter was the reverse
engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the ...

Computational Biology CB_J0004

Title :  The IUPS human physiome project
Author : Peter Hunter, Peter Robbins and Denis Noble
Year Publish : 2002
Place of Publish: Springer Berlin / Heidelberg
Abstract :

The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. Here we discuss the background and goals of the project, the problems of modelling across multiple spatial and temporal scales, and the development of model ontologies and markup languages at all levels of biological function

Computational Biology CB_J0002

Title : On the Average Sequence Complexity
Author : Svante Janson, Stefano Lonardi and Wojciech Szpankowski
Year Publish : 2004
Place of Publish: Springer Berlin / Heidelberg
Abstract :

In this paper we study the average behavior of the number of distinct substrings in a text of size n over an alphabet of cardinality k. This quantity is called the complexity index and it captures the “richness of the language” used in a sequence. For example, sequences with low complexity index contain a large number of repeated substrings and they eventually become periodic (e.g., tandem repeats in a DNA sequence). In order to identify unusually low- or high-complexity strings one needs to determine how far are the complexities of the strings under study from the average or maximum string complexity. While the maximum string complexity was studied quite extensively in the past, to the best of our knowledge there are no results concerning the average complexity. We first prove that for a sequence generated by a mixing model (which includes Markov sources) the average complexity is asymptotically equal to n 2/2 which coincides with the maximum string complexity. However, for memoryless source we establish a more precise result, namely the average string complexity is n 2/2–nlog k n+(1+(1–?)/ln k +? k (log k n)+o(1))n where??0.577 and ? k (x) is a periodic function with a small amplitude for small alphabet size.

Computational Biology CB_J0001

Title : A bibliography on learning causal networks of gene interactions
Author : F Markowetz
Year Publish : 2005
Place of Publish:
URL :
Abstract :