Special Issue "Algorithms and Molecular Sciences"

Quicklinks

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 July 2009)

Special Issue Editors

Guest Editor
Prof. Dr. Frank Hsu (Website)

Department of Computer & Information Sciences, Fordham University, 340 John Mulcahy Hall, 441 East Fordham Road, Bronx NY 10458, USA
Interests: combinatorics, algorithms, and optimization; networking theory; computational learning theory, and informatics; biomedical information; information retrieval; information and knowledge management; data mining; multi-sensor; multi-source fusion; information technology; telecommunication infrastructure; implementation strategy
Guest Editor
Dr. Christina Schweikert (Website)

Department of Computer & Information Sciences, Fordham University, 328 John Mulcahy Hall, 441 East Fordham Road, Bronx NY 10458, USA

Keywords

  • algorithms and molecular sciences

Related Special Issue

Published Papers (19 papers)

View options order results:
result details:
Displaying articles 1-19
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle Radial Basis Function Cascade Correlation Networks
Algorithms 2009, 2(3), 1045-1068; doi:10.3390/a2031045
Received: 1 July 2009 / Revised: 31 July 2009 / Accepted: 21 August 2009 / Published: 27 August 2009
PDF Full-text (386 KB) | HTML Full-text | XML Full-text
Abstract
A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability [...] Read more.
A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Bayesian Maximum Entropy Based Algorithm for Digital X-ray Mammogram Processing
Algorithms 2009, 2(2), 850-878; doi:10.3390/a2020850
Received: 5 December 2008 / Revised: 11 May 2009 / Accepted: 28 May 2009 / Published: 9 June 2009
PDF Full-text (1016 KB)
Abstract
Basics of Bayesian statistics in inverse problems using the maximum entropy principle are summarized in connection with the restoration of positive, additive images from various types of data like X-ray digital mammograms. An efficient iterative algorithm for image restoration from large data [...] Read more.
Basics of Bayesian statistics in inverse problems using the maximum entropy principle are summarized in connection with the restoration of positive, additive images from various types of data like X-ray digital mammograms. An efficient iterative algorithm for image restoration from large data sets based on the conjugate gradient method and Lagrange multipliers in nonlinear optimization of a specific potential function was developed. The point spread function of the imaging system was determined by numerical simulations of inhomogeneous breast-like tissue with microcalcification inclusions of various opacities. The processed digital and digitized mammograms resulted superior in comparison with their raw counterparts in terms of contrast, resolution, noise, and visibility of details. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Security of the Bennett-Brassard Quantum Key Distribution Protocol against Collective Attacks
Algorithms 2009, 2(2), 790-807; doi:10.3390/a2020790
Received: 16 October 2008 / Revised: 17 April 2009 / Accepted: 7 May 2009 / Published: 3 June 2009
PDF Full-text (304 KB) | HTML Full-text | XML Full-text
Abstract
The theoretical Quantum Key-Distribution scheme of Bennett and Brassard (BB84) has been proven secure against very strong attacks including the collective attacks and the joint attacks. Though the latter are the most general attacks, collective attacks are much easier to analyze, yet, [...] Read more.
The theoretical Quantum Key-Distribution scheme of Bennett and Brassard (BB84) has been proven secure against very strong attacks including the collective attacks and the joint attacks. Though the latter are the most general attacks, collective attacks are much easier to analyze, yet, they are conjectured to be as informative to the eavesdropper. Thus, collective attacks are likely to be useful in the analysis of many theoretical and practical schemes that are still lacking a proof of security, including practical BB84 schemes. We show how powerful tools developed in previous works for proving security against the joint attack, are simplified when applied to the security of BB84 against collective attacks whilst providing the same bounds on leaked information and the same error threshold. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle SDPhound, a Mutual Information-Based Method to Investigate Specificity-Determining Positions
Algorithms 2009, 2(2), 764-789; doi:10.3390/a2020764
Received: 22 December 2008 / Revised: 12 March 2009 / Accepted: 5 May 2009 / Published: 26 May 2009
Cited by 2 | PDF Full-text (1452 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Considerable importance in molecular biophysics is attached to influencing by mutagenesis the specific properties of a protein family. The working hypothesis is that mutating residues at few selected positions can affect specificity. Statistical analysis of homologue sequences can identify putative specificity determining [...] Read more.
Considerable importance in molecular biophysics is attached to influencing by mutagenesis the specific properties of a protein family. The working hypothesis is that mutating residues at few selected positions can affect specificity. Statistical analysis of homologue sequences can identify putative specificity determining positions (SDPs) and help to shed some light on the peculiarities underlying their functional role. In this work, we present an approach to identify such positions inspired by state of the art mutual information-based SDP prediction methods. The algorithm based on this approach provides a systematic procedure to point at the relevant physical characteristics of putative SPDs and can investigate the effects of correlated mutations. The method is tested on two standard benchmarks in the field and further validated in the context of a biologically interesting problem: the multimerization of the Intrinsically Fluorescent Proteins (IFP). Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Figures

Open AccessArticle A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Algorithms 2009, 2(2), 667-691; doi:10.3390/a2020667
Received: 8 January 2009 / Revised: 6 March 2009 / Accepted: 24 March 2009 / Published: 21 April 2009
Cited by 9 | PDF Full-text (311 KB) | HTML Full-text | XML Full-text
Abstract
Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML) context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable [...] Read more.
Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML) context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait locus mapping of dynamic traits within the Bayesian paradigm. Markov chain Monte Carlo techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariance matrix. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identifiability problem, enhancing the statistical inference of functional mapping. We have undertaken simulation studies to investigate the statistical behavior of Bayesian-based functional mapping and used a real example with F2 mice to validate the utilization and usefulness of the model. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Fast Structural Alignment of Biomolecules Using a Hash Table, N-Grams and String Descriptors
Algorithms 2009, 2(2), 692-709; doi:10.3390/a2020692
Received: 30 November 2008 / Revised: 8 April 2009 / Accepted: 9 April 2009 / Published: 21 April 2009
Cited by 17 | PDF Full-text (465 KB) | HTML Full-text | XML Full-text
Abstract
This work presents a generalized approach for the fast structural alignment of thousands of macromolecular structures. The method uses string representations of a macromolecular structure and a hash table that stores n-grams of a certain size for searching. To this end, macromolecular [...] Read more.
This work presents a generalized approach for the fast structural alignment of thousands of macromolecular structures. The method uses string representations of a macromolecular structure and a hash table that stores n-grams of a certain size for searching. To this end, macromolecular structure-to-string translators were implemented for protein and RNA structures. A query against the index is performed in two hierarchical steps to unite speed and precision. In the first step the query structure is translated into n-grams, and all target structures containing these n-grams are retrieved from the hash table. In the second step all corresponding n-grams of the query and each target structure are subsequently aligned, and after each alignment a score is calculated based on the matching n-grams of query and target. The extendable framework enables the user to query and structurally align thousands of protein and RNA structures on a commodity machine and is available as open source from http://lajolla.sf.net. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Pattern Recognition and Pathway Analysis with Genetic Algorithms in Mass Spectrometry Based Metabolomics
Algorithms 2009, 2(2), 638-666; doi:10.3390/a2020638
Received: 20 October 2008 / Revised: 2 February 2009 / Accepted: 26 March 2009 / Published: 3 April 2009
Cited by 11 | PDF Full-text (2169 KB) | HTML Full-text | XML Full-text
Abstract
A robust and complete workflow for metabolic profiling and data mining was described in detail. Three independent and complementary analytical techniques for metabolic profiling were applied: hydrophilic interaction chromatography (HILIC–LC–ESI–MS), reversed-phase liquid chromatography (RP–LC–ESI–MS), and gas chromatography (GC–TOF–MS) all coupled to mass [...] Read more.
A robust and complete workflow for metabolic profiling and data mining was described in detail. Three independent and complementary analytical techniques for metabolic profiling were applied: hydrophilic interaction chromatography (HILIC–LC–ESI–MS), reversed-phase liquid chromatography (RP–LC–ESI–MS), and gas chromatography (GC–TOF–MS) all coupled to mass spectrometry (MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification and PCA-DA (discriminatory analysis) were used for data mining. Genetic Algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative predictors. From thousands of peaks found in total, small subsets selected by GA were considered as highly potential predictors allowing discrimination among groups. It was found that small groups of potential top predictors selected with PCA-DA and GA are different and unique. Annotated GC–TOF–MS data generated identified feature metabolites. Metabolites putatively detected with LC–ESI–MS profiling require further elemental composition assignment with accurate mass measurement by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and structure elucidation by nuclear magnetic resonance spectroscopy (NMR). GA was also used to generate correlated networks for pathway analysis. Several case studies, comprising groups of plant samples bearing different genotypes and groups of samples of human origin, namely patients and healthy volunteers’ urine samples, demonstrated that such a workflow combining comprehensive metabolic profiling and advanced data mining techniques provides a powerful approach for pattern recognition and biomarker discovery Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle A Novel Algorithm for Macromolecular Epitope Matching
Algorithms 2009, 2(1), 498-517; doi:10.3390/a2010498
Received: 27 October 2008 / Revised: 11 January 2009 / Accepted: 25 February 2009 / Published: 11 March 2009
Cited by 3 | PDF Full-text (1685 KB) | HTML Full-text | XML Full-text
Abstract
Many macromolecules, namely proteins, show functional substructures or epitopes defined by characteristic spatial arrangements of groups of specific atoms or residues. The identification of such substructures in a set of macromolecular 3D-structures solves an important problem in molecular biology as it allows [...] Read more.
Many macromolecules, namely proteins, show functional substructures or epitopes defined by characteristic spatial arrangements of groups of specific atoms or residues. The identification of such substructures in a set of macromolecular 3D-structures solves an important problem in molecular biology as it allows the assignment of functions to molecular moieties and thus opens the possibility of a mechanistic understanding of molecular function. We have devised an algorithm that models a functional epitope formed by a group of atoms or residues as set of points in cartesian space with associated functional properties. The algorithm searches for similar epitopes in a database of structures by an efficient multistage comparison of distance sets in the epitope and in the structures from the database. The search results in a list of optimal matches and corresponding optimal superpositions of query epitope and matching epitopes from the database. The algorithm is discussed against the background of related approaches, and it is successfully tested in three application scenarios: global match of two homologous proteins, search for an epitope on a homologous protein, and finding matching epitopes in a protein database. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Protein-Protein Interaction Analysis by Docking
Algorithms 2009, 2(1), 429-436; doi:10.3390/a2010429
Received: 1 December 2008 / Revised: 19 January 2009 / Accepted: 27 February 2009 / Published: 10 March 2009
PDF Full-text (122 KB) | HTML Full-text | XML Full-text
Abstract
Based on a protein-protein docking approach we have developed a procedure to verify or falsify protein-protein interactions that were proposed by other methods such as yeast-2-hybrid assays. Our method currently utilizes intermolecular energies but can be expanded to incorporate additional terms such [...] Read more.
Based on a protein-protein docking approach we have developed a procedure to verify or falsify protein-protein interactions that were proposed by other methods such as yeast-2-hybrid assays. Our method currently utilizes intermolecular energies but can be expanded to incorporate additional terms such as amino acid based pair-potentials. We show some early results that demonstrate the general applicability of our approach. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Resonance in Interacting Induced-Dipole Polarizing Force Fields: Application to Force-Field Derivatives
Algorithms 2009, 2(1), 437-447; doi:10.3390/a2010437
Received: 5 December 2008 / Revised: 15 January 2009 / Accepted: 27 February 2009 / Published: 10 March 2009
PDF Full-text (198 KB) | HTML Full-text | XML Full-text
Abstract
The Silberstein model of the molecular polarizability of diatomic molecules, generalized by Applequist et al. for polyatomic molecules, is analyzed. The atoms are regarded as isotropically polarizable points located at their nuclei, interacting via the fields of their induced dipoles. The [...] Read more.
The Silberstein model of the molecular polarizability of diatomic molecules, generalized by Applequist et al. for polyatomic molecules, is analyzed. The atoms are regarded as isotropically polarizable points located at their nuclei, interacting via the fields of their induced dipoles. The use of additive values for atom polarizabilities gives poor results, in some cases leading to artificial predictions of absorption bands. The molecular polarizability of methane and its derivative are computed. The agreement with experimental mean molecular polarizabilities is within 1–5%. A hypothesis is indispensable for a suitable representation of polarizability derivative. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Structural Fingerprints of Transcription Factor Binding Site Regions
Algorithms 2009, 2(1), 448-469; doi:10.3390/a2010448
Received: 4 December 2008 / Revised: 2 February 2009 / Accepted: 5 March 2009 / Published: 10 March 2009
PDF Full-text (1258 KB) | HTML Full-text | XML Full-text
Abstract
Fourier transforms are a powerful tool in the prediction of DNA sequence properties, such as the presence/absence of codons. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers. In this work we apply Fourier techniques [...] Read more.
Fourier transforms are a powerful tool in the prediction of DNA sequence properties, such as the presence/absence of codons. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers. In this work we apply Fourier techniques to the analysis of the structural properties of human chromosomes 21 and 22 and also to three sets of transcription factor binding sites within these chromosomes. We find that, for a given structural property, the structural property power spectra of chromosomes 21 and 22 are strikingly similar. We find common peaks in their power spectra for both Sp1 and p53 transcription factor binding sites. We use the power spectra as a structural fingerprint and perform similarity searching in order to find transcription factor binding site regions. This approach provides a new strategy for searching the genome data for information. Although it is difficult to understand the relationship between specific functional properties and the set of structural parameters in our database, our structural fingerprints nevertheless provide a useful tool for searching for function information in sequence data. The power spectrum fingerprints provide a simple, fast method for comparing a set of functional sequences, in this case transcription factor binding site regions, with the sequences of whole chromosomes. On its own, the power spectrum fingerprint does not find all transcription factor binding sites in a chromosome, but the results presented here show that in combination with other approaches, this technique will improve the chances of identifying functional sequences hidden in genomic data. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Exhaustive Enumeration of Kinetic Model Topologies for the Analysis of Time-Resolved RNA Folding
Algorithms 2009, 2(1), 200-214; doi:10.3390/a2010200
Received: 1 December 2008 / Revised: 16 January 2009 / Accepted: 24 January 2009 / Published: 10 February 2009
Cited by 5 | PDF Full-text (1395 KB) | HTML Full-text | XML Full-text
Abstract
Unlike protein folding, the process by which a large RNA molecule adopts a functionally active conformation remains poorly understood. Chemical mapping techniques, such as Hydroxyl Radical (·OH) footprinting report on local structural changes in an RNA as it folds with single nucleotide [...] Read more.
Unlike protein folding, the process by which a large RNA molecule adopts a functionally active conformation remains poorly understood. Chemical mapping techniques, such as Hydroxyl Radical (·OH) footprinting report on local structural changes in an RNA as it folds with single nucleotide resolution. The analysis and interpretation of this kinetic data requires the identification and subsequent optimization of a kinetic model and its parameters. We detail our approach to this problem, specifically focusing on a novel strategy to overcome a factorial explosion in the number of possible models that need to be tested to identify the best fitting model. Previously, smaller systems (less than three intermediates) were computationally tractable using a distributed computing approach. However, for larger systems with three or more intermediates, the problem became computationally intractable. With our new enumeration strategy, we are able to significantly reduce the number of models that need to be tested using non-linear least squares optimization, allowing us to study systems with up to five intermediates. Furthermore, two intermediate systems can now be analyzed on a desktop computer, which eliminates the need for a distributed computing solution for most mediumsized data sets. Our new approach also allows us to study potential degeneracy in kinetic model selection, elucidating the limits of the method when working with large systems. This work establishes clear criteria for determining if experimental ·OH data is sufficient to determine the underlying kinetic model, or if other experimental modalities are required to resolve any degeneracy. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Automatic Determination of Stepsize Parameters in Monte Carlo Simulation Tested on a Bromodomain-Binding Octapeptide
Algorithms 2009, 2(1), 215-226; doi:10.3390/a2010215
Received: 30 October 2008 / Revised: 7 January 2009 / Accepted: 24 January 2009 / Published: 10 February 2009
Cited by 3 | PDF Full-text (170 KB) | HTML Full-text | XML Full-text
Abstract
The proportional integral controller, commonly used in engineering applications for process control, has been implemented for the tuning of the stepsizes in Metropolis Monte Carlo simulations. Similarly to the recent application for tuning the chemical potential parameter in grand-canonical ensemble simulation, the [...] Read more.
The proportional integral controller, commonly used in engineering applications for process control, has been implemented for the tuning of the stepsizes in Metropolis Monte Carlo simulations. Similarly to the recent application for tuning the chemical potential parameter in grand-canonical ensemble simulation, the process-control approach was found to work well for the problem of selecting the stepsize for each torsion angle that results in a targeted acceptance rate during the simulation of an octapeptide in aqueous environment. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessArticle Algorithm for Nanotubes Computer Generation with Different Configurations
Algorithms 2009, 2(1), 108-120; doi:10.3390/a2010108
Received: 28 November 2008 / Revised: 20 January 2009 / Accepted: 26 January 2009 / Published: 2 February 2009
Cited by 3 | PDF Full-text (1056 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The algorithm here described concerns generation, visualization, and modification of molecular nanostructures of single-walled nanotubes (NTs) of particular configurations (armchair, zipper, multiple zipper, zigzag, or chiral) by means of a Graphical User Interface (GUI). NTs are made from a carbon graphene sheet [...] Read more.
The algorithm here described concerns generation, visualization, and modification of molecular nanostructures of single-walled nanotubes (NTs) of particular configurations (armchair, zipper, multiple zipper, zigzag, or chiral) by means of a Graphical User Interface (GUI). NTs are made from a carbon graphene sheet created according to certain parameters defining required nanostructures. Generated NTs can easily be modified by replacing carbon atoms for nitrogen or boron, visualized and exported into a standard format useful as input to be analyzed and submitted to other applications in order to get optimized geometries and to carry out further calculations of molecular and electronic properties. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Figures

Open AccessArticle Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels
Algorithms 2008, 1(2), 183-200; doi:10.3390/a1020183
Received: 23 October 2008 / Revised: 23 November 2008 / Accepted: 5 December 2008 / Published: 18 December 2008
Cited by 5 | PDF Full-text (312 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A new algorithm for divisive hierarchical clustering of chemical compounds based on 2D structural fragments is suggested. The algorithm is deterministic, and given a random ordering of the input, will always give the same clustering and can process a database up to [...] Read more.
A new algorithm for divisive hierarchical clustering of chemical compounds based on 2D structural fragments is suggested. The algorithm is deterministic, and given a random ordering of the input, will always give the same clustering and can process a database up to 2 million records on a standard PC. The algorithm was used for classification of 1,183 antibiotics mixed with 999,994 random chemical structures. Similarity threshold, at which best separation of active and non active compounds took place, was estimated as 0.6. 85.7% of the antibiotics were successfully classified at this threshold with 0.4% of inaccurate compounds. A .sdf file was created with the probe molecules for clustering of external databases. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Figures

Review

Jump to: Research

Open AccessReview Algorithm for the Analysis of Tryptophan Fluorescence Spectra and Their Correlation with Protein Structural Parameters
Algorithms 2009, 2(3), 1155-1176; doi:10.3390/a2031155
Received: 15 July 2009 / Revised: 20 August 2009 / Accepted: 10 September 2009 / Published: 16 September 2009
Cited by 7 | PDF Full-text (173 KB) | HTML Full-text | XML Full-text
Abstract
The fluorescence properties of tryptophan residues are sensitive to the microenvironment of fluorophores in proteins. Therefore, fluorescence characteristics are widely used to study structural transitions in proteins. However, the decoding of the structural information from spectroscopic data is challenging. Here we present [...] Read more.
The fluorescence properties of tryptophan residues are sensitive to the microenvironment of fluorophores in proteins. Therefore, fluorescence characteristics are widely used to study structural transitions in proteins. However, the decoding of the structural information from spectroscopic data is challenging. Here we present a review of approaches developed for the decomposition of multi-component protein tryptophan fluorescence spectra and correlation of these spectral parameters with protein structural properties. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessReview Recent Advances in the Computational Discovery of Transcription Factor Binding Sites
Algorithms 2009, 2(1), 582-605; doi:10.3390/a2010582
Received: 6 January 2009 / Accepted: 17 March 2009 / Published: 24 March 2009
Cited by 11 | PDF Full-text (241 KB) | HTML Full-text | XML Full-text
Abstract
The discovery of gene regulatory elements requires the synergism between computational and experimental techniques in order to reveal the underlying regulatory mechanisms that drive gene expression in response to external cues and signals. Utilizing the large amount of high-throughput experimental data, constantly [...] Read more.
The discovery of gene regulatory elements requires the synergism between computational and experimental techniques in order to reveal the underlying regulatory mechanisms that drive gene expression in response to external cues and signals. Utilizing the large amount of high-throughput experimental data, constantly growing in recent years, researchers have attempted to decipher the patterns which are hidden in the genomic sequences. These patterns, called motifs, are potential binding sites to transcription factors which are hypothesized to be the main regulators of the transcription process. Consequently, precise detection of these elements is required and thus a large number of computational approaches have been developed to support the de novo identification of TFBSs. Even though novel approaches are continuously proposed and almost all have reported some success in yeast and other lower organisms, in higher organisms the problem still remains a challenge. In this paper, we therefore review the recent developments in computational methods for transcription factor binding site prediction. We start with a brief review of the basic approaches for binding site representation and promoter identification, then discuss the techniques to locate physical TFBSs, identify functional binding sites using orthologous information, and infer functional TFBSs within some context defined by additional prior knowledge. Finally, we briefly explore the opportunities for expanding these approaches towards the computational identification of transcriptional regulatory networks. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessReview Genetic Algorithms in Application to the Geometry Optimization of Nanoparticles
Algorithms 2009, 2(1), 410-428; doi:10.3390/a2010410
Received: 24 November 2008 / Revised: 6 January 2009 / Accepted: 26 February 2009 / Published: 4 March 2009
Cited by 9 | PDF Full-text (597 KB) | HTML Full-text | XML Full-text
Abstract
Applications of genetic algorithms to the global geometry optimization problem of nanoparticles are reviewed. Genetic operations are investigated and importance of phenotype genetic operations, considering the geometry of nanoparticles, are mentioned. Other efficiency improving developments such as floating point representation and local [...] Read more.
Applications of genetic algorithms to the global geometry optimization problem of nanoparticles are reviewed. Genetic operations are investigated and importance of phenotype genetic operations, considering the geometry of nanoparticles, are mentioned. Other efficiency improving developments such as floating point representation and local relaxation are described broadly. Parallelization issues are also considered and a recent parallel working single parent Lamarckian genetic algorithm is reviewed with applications on carbon clusters and SiGe core-shell structures. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)
Open AccessReview On the Reconstruction of Three-dimensional Protein Structures from Contact Maps
Algorithms 2009, 2(1), 76-92; doi:10.3390/a2010076
Received: 30 November 2008 / Revised: 8 January 2009 / Accepted: 20 January 2009 / Published: 22 January 2009
Cited by 4 | PDF Full-text (240 KB) | HTML Full-text | XML Full-text
Abstract
The problem of protein structure prediction is one of the long-standing goals of Computational Biology. Although we are still not able to provide first principle solutions, several shortcuts have been discovered to compute the protein three-dimensional structure when similar protein sequences are [...] Read more.
The problem of protein structure prediction is one of the long-standing goals of Computational Biology. Although we are still not able to provide first principle solutions, several shortcuts have been discovered to compute the protein three-dimensional structure when similar protein sequences are available (by means of comparative modeling and remote homology detection). Nonetheless, these approaches can assign structures only to a fraction of proteins in genomes and ab-initio methods are still needed. One relevant step of ab-initio prediction methods is the reconstruction of the protein structures starting from inter-protein residue contacts. In this paper we review the methods developed so far to accomplish the reconstruction task in order to highlight their differences and similarities. The different approaches are fully described and their reported performances, together with their computational complexity, are also discussed. Full article
(This article belongs to the Special Issue Algorithms and Molecular Sciences)

Journal Contact

MDPI AG
Algorithms Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
algorithms@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Algorithms
Back to Top