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
Department of Computer & Information Sciences, Fordham University, 340 John Mulcahy Hall, 441 East Fordham Road, Bronx NY 10458, USA
Website: http://www.cis.fordham.edu/faculty/Frank-Hsu.html
E-Mail: hsu@cis.fordham.edu
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
Department of Computer & Information Sciences, Fordham University, 328 John Mulcahy Hall, 441 East Fordham Road, Bronx NY 10458, USA
Website: http://www.cis.fordham.edu/faculty/Christina-Schweikert.html
E-Mail: cschweikert@cis.fordham.edu
Special Issue Information
Related Special Issue: Algorithms and Molecular Sciences in the International Journal of Molecular Sciences
Submission
All papers should be submitted to algorithms@mdpi.com. To be published continuously until the deadline and papers will be listed together at the special issue website.
Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by MDPI.
Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first three volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections) for papers submitted before 31 December 2010.
Keywords
- algorithms and molecular sciences
Published Papers (19 papers)
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Received: 23 October 2008; in revised form: 23 November 2008 / Accepted: 5 December 2008 / Published: 18 December 2008
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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 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.
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Received: 30 November 2008; in revised form: 8 January 2009 / Accepted: 20 January 2009 / Published: 22 January 2009
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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 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.
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Received: 28 November 2008; in revised form: 20 January 2009 / Accepted: 26 January 2009 / Published: 2 February 2009
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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 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.
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Received: 1 December 2008; in revised form: 16 January 2009 / Accepted: 24 January 2009 / Published: 10 February 2009
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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 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.
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Received: 30 October 2008; in revised form: 7 January 2009 / Accepted: 24 January 2009 / Published: 10 February 2009
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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 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.
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Received: 24 November 2008; in revised form: 6 January 2009 / Accepted: 26 February 2009 / Published: 4 March 2009
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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 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.
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Received: 1 December 2008; in revised form: 19 January 2009 / Accepted: 27 February 2009 / Published: 10 March 2009
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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 as amino acid based pair-potentials. We show some early results that demonstrate the general applicability of our approach.
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Received: 5 December 2008; in revised form: 15 January 2009 / Accepted: 27 February 2009 / Published: 10 March 2009
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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 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.
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Received: 4 December 2008; in revised form: 2 February 2009 / Accepted: 5 March 2009 / Published: 10 March 2009
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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 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.
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Received: 27 October 2008; in revised form: 11 January 2009 / Accepted: 25 February 2009 / Published: 11 March 2009
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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 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.
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Received: 6 January 2009 / Accepted: 17 March 2009 / Published: 24 March 2009
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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 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.
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Received: 20 October 2008; in revised form: 2 February 2009 / Accepted: 26 March 2009 / Published: 3 April 2009
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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 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
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Received: 8 January 2009; in revised form: 6 March 2009 / Accepted: 24 March 2009 / Published: 21 April 2009
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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 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.
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Received: 30 November 2008; in revised form: 8 April 2009 / Accepted: 9 April 2009 / Published: 21 April 2009
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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 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.
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Received: 22 December 2008; in revised form: 12 March 2009 / Accepted: 5 May 2009 / Published: 26 May 2009
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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 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).
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Received: 16 October 2008; in revised form: 17 April 2009 / Accepted: 7 May 2009 / Published: 3 June 2009
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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, 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.
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Received: 5 December 2008; in revised form: 11 May 2009 / Accepted: 28 May 2009 / Published: 9 June 2009
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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 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.
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Received: 1 July 2009; in revised form: 31 July 2009 / Accepted: 21 August 2009 / Published: 27 August 2009
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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 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.
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Received: 15 July 2009; in revised form: 20 August 2009 / Accepted: 10 September 2009 / Published: 16 September 2009
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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 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.
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Last update: 19 January 2010
