An integrated model for cellular analysis

We present the MOlecular NETwork (MONET) ontology as a model to integrate data from different networks that govern cell function. To achieve this, different existing ontologies were analyzed and an integrated ontology was built in a way to make it possible to share and reuse knowledge, support interoperability between systems, and also allow the formulation of hypotheses through inferences. By studying the cell as an entity of a myriad of elements and networks of interactions, we aim to offer a means to understand the large-scale characteristics responsible for the behavior of the cell and to enable new biological insights.     

Fragment peptide library for classification and functional prediction of proteins

From protein sequence comparison data found in the literature, a library was organized using peptide fragment sequences which are common to related proteins. Each of the fragments was then examined for its occurrence in all the protein superfamilies defined by the NBRF-PIR data base. We have selected those fragment peptides that appear exclusively in one or a few superfamilies, and thus made a library of fragment peptides that characterize specific superfamilies. Such characteristic peptides are, in general, five to seven residues long and contain unusually high proportions of glycine and cysteine. This collection is a useful resource for the classification and functional prediction of protein molecules.     

The evolutionary landscape of functional model proteins.

To study the distinct influences of structure and function on evolution, we propose a minimalist model for proteins with binding pockets, called functional model proteins, based on a shifted-HP model on a two-dimensional square lattice. These model proteins are not maximally compact and contain an empty lattice site surrounded by at least three nearest neighbors, thus providing a binding pocket. Functional model proteins possess a unique native state, cooperative folding and tolerance to mutation. Due to the explicit functionality in these models (by design), we have been able to explore their fitness or evolutionary landscapes, as characterized by the size and distribution of homologous families and by the complexity of the inter-relatedness of the functional model proteins. Mindful that these minimalist models are highly idealized and two-dimensional, functional model proteins should nevertheless provide a useful means for exploring the constraints of maintaining structure and function on the evolution of proteins.     

Consistent probabilistic outputs for protein function prediction

Background:

The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellular organisms with some success, but few have been extensively tested on higher level, multicellular organisms. A recent mouse function prediction project (MouseFunc) brought together nine bioinformatics teams applying a diverse array of methodologies to mount the first large-scale effort to predict gene function in the laboratory mouse.

Results:

In this paper, we describe our contribution to this project, an ensemble framework based on the support vector machine that integrates diverse datasets in the context of the Gene Ontology hierarchy. We carry out a detailed analysis of the performance of our ensemble and provide insights into which methods work best under a variety of prediction scenarios. In addition, we applied our method to Saccharomyces cerevisiae and have experimentally confirmed functions for a novel mitochondrial protein.

Conclusion:

Our method consistently performs among the top methods in the MouseFunc evaluation. Furthermore, it exhibits good classification performance across a variety of cellular processes and functions in both a multicellular organism and a unicellular organism, indicating its ability to discover novel biology in diverse settings.

     

An integrated system for studying residue coevolution in proteins

Residue coevolution has recently emerged as an important concept, especially in the context of protein structures. While a multitude of different functions for quantifying it have been proposed, not much is known about their relative strengths and weaknesses. Also, subtle algorithmic details have discouraged implementing and comparing them. We addressed this issue by developing an integrated online system that enables comparative analyses with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods. A set of data preprocessing options are provided for improving the sensitivity and specificity of coevolution signal detection, including sequence weighting, residue grouping and the filtering of sequences, sites and site pairs. A total of more than 100 scoring variations are available. The system also provides facilities for studying the relationship between coevolution scores and inter-residue distances from a crystal structure if provided, which may help in understanding protein structures. AVAILABILITY: The system is available at http://coevolution.gersteinlab.org. The source code and JavaDoc API can also be downloaded from the web site.     

An integrated map of Arabidopsis thaliana for functional analysis of its genome sequence.

The genome of the model plant species Arabidopsis thaliana has recently been sequenced. To accelerate its current genome research, we developed a whole-genome, BAC/BIBAC-based, integrated physical, genetic, and sequence map of the A. thaliana ecotype Columbia. This new map was constructed from the clones of a new plant-transformation-competent BIBAC library and is integrated with the existing sequence map. The clones were restriction fingerprinted by DNA sequencing gel-based electrophoresis, assembled into contigs, and anchored to an existing genetic map. The map consists of 194 BAC/BIBAC contigs, spanning 126 Mb of the 130-Mb Arabidopsis genome. A total of 120 contigs, spanning 114 Mb, were anchored to the chromosomes of Arabidopsis. Accuracy of the integrated map was verified using the existing physical and sequence maps and numerous DNA markers. Integration of the new map with the sequence map has enabled gap closure of the sequence map and will facilitate functional analysis of the genome sequence. The method used here has been demonstrated to be sufficient for whole-genome physical mapping from large-insert random bacterial clones and thus is applicable to rapid development of whole-genome physical maps for other species.     

An activity coefficient model for proteins

Modeling of the properties of biochemical components is gaining increasing interest due to its potential for further application within the area of biochemical process development. Generally protein solution properties such as protein solubility are expressed through component activity coefficients which are studied here. The original UNIQUAC model is chosen for the representation of protein activity coefficients and, to the best of our knowledge, this is the first time it has been directly applied to protein solutions. Ten different protein-salt-water systems with four different proteins, serum albumin, alphacymotrypsin, beta-lactoglobulin and ovalbumin, are investigated. A root-mean-squared deviation of 0.54% is obtained for the model by comparing calculated protein activity coefficients and protein activity coefficients deduced from osmotic measurements through virial expansion. Model predictions are used to analyze the effect of salt concentrations, pH, salt types, and temperature on protein activity coefficients and also on protein solubility and demonstrate consistency with results from other references. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 65-71, 1997.     

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests.     

An integrated approach to the prediction of domain-domain interactions

Background  

The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.     

An integrative probabilistic model for identification of structural variation in sequencing data

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.     

Hidden markov model for the prediction of transmembrane proteins using MATLAB

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. AVAILABILITY: The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.     

Purdue ionomics information management system. An integrated functional genomics platform

The advent of high-throughput phenotyping technologies has created a deluge of information that is difficult to deal with without the appropriate data management tools. These data management tools should integrate defined workflow controls for genomic-scale data acquisition and validation, data storage and retrieval, and data analysis, indexed around the genomic information of the organism of interest. To maximize the impact of these large datasets, it is critical that they are rapidly disseminated to the broader research community, allowing open access for data mining and discovery. We describe here a system that incorporates such functionalities developed around the Purdue University high-throughput ionomics phenotyping platform. The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high-throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories. PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including ethyl methanesulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations. PiiMS is accessible at www.purdue.edu/dp/ionomics.     

An integrated electromechanical-growth heart model for simulating cardiac therapies

An emerging class of models has been developed in recent years to predict cardiac growth and remodeling (G&R). We recently developed a cardiac G&R constitutive model that predicts remodeling in response to elevated hemodynamics loading, and a subsequent reversal of the remodeling process when the loading is reduced. Here, we describe the integration of this G&R model to an existing strongly coupled electromechanical model of the heart. A separation of timescale between growth deformation and elastic deformation was invoked in this integrated electromechanical-growth heart model. To test our model, we applied the G&R scheme to simulate the effects of myocardial infarction in a realistic left ventricular (LV) geometry using the finite element method. We also simulate the effects of a novel therapy that is based on alteration of the infarct mechanical properties. We show that our proposed model is able to predict key features that are consistent with experiments. Specifically, we show that the presence of a non-contractile infarct leads to a dilation of the left ventricle that results in a rightward shift of the pressure volume loop. Our model also predicts that G&R is attenuated by a reduction in LV dilation when the infarct stiffness is increased.     

An integrated scheduling and control model for multi-mode projects

In today’s highly competitive uncertain project environments, it is of crucial importance to develop analytical models and algorithms to schedule and control project activities so that the deviations from the project objectives are minimized. This paper addresses the integrated scheduling and control in multi-mode project environments. We propose an optimization model that models the dynamic behavior of projects and integrates optimal control into a practically relevant project scheduling problem. From the scheduling perspective, we address the discrete time/cost trade-off problem, whereas an optimal control formulation is used to capture the effect of project control. Moreover, we develop a solution algorithm for two particular instances of the optimal project control. This algorithm combines a tabu search strategy and nonlinear programming. It is applied to a large scale test bed and its efficiency is tested by means of computational experiments. To the best of our knowledge, this research is the first application of optimal control theory to multi-mode project networks. The models and algorithms developed in this research are targeted as a support tool for project managers in both scheduling and deciding on the timing and quantity of control activities.     

An integrated physical and biological model for anaerobic lagoons

A computational fluid dynamics (CFD) model that integrates physical and biological processes for anaerobic lagoons is presented. In the model development, turbulence is represented using a transition k-ω model, heat conduction and solar radiation are included in the thermal model, biological oxygen demand (BOD) reduction is characterized by first-order kinetics, and methane yield rate is expressed as a linear function of temperature. A test of the model applicability is conducted in a covered lagoon digester operated under tropical climate conditions. The commercial CFD software, ANSYS-Fluent, is employed to solve the integrated model. The simulation procedures include solving fluid flow and heat transfer, predicting local resident time based on the converged flow fields, and calculating the BOD reduction and methane production. The simulated results show that monthly methane production varies insignificantly, but the time to achieve a 99% BOD reduction in January is much longer than that in July.     

An all-atom force field for tertiary structure prediction of helical proteins

We have developed an all-atom free-energy force field (PFF01) for protein tertiary structure prediction. PFF01 is based on physical interactions and was parameterized using experimental structures of a family of proteins believed to span a wide variety of possible folds. It contains empirical, although sequence-independent terms for hydrogen bonding. Its solvent-accessible surface area solvent model was first fit to transfer energies of small peptides. The parameters of the solvent model were then further optimized to stabilize the native structure of a single protein, the autonomously folding villin headpiece, against competing low-energy decoys. Here we validate the force field for five nonhomologous helical proteins with 20-60 amino acids. For each protein, decoys with 2-3 A backbone root mean-square deviation and correct experimental Cbeta-Cbeta distance constraints emerge as those with the lowest energy.     

An empirical approach for structure-based prediction of carbohydrate-binding sites on proteins

A computer program system was developed to predict carbohydrate-binding sites on three-dimensional (3D) protein structures. The programs search for binding sites by referring to the empirical rules derived from the known 3D structures of carbohydrate-protein complexes. A total of 80 non-redundant carbohydrate-protein complex structures were selected from the Protein Data Bank for the empirical rule construction. The performance of the prediction system was tested on 50 known complex structures to determine whether the system could detect the known binding sites. The known monosaccharide-binding sites were detected among the best three predictions in 59% of the cases, which covered 69% of the polysaccharide-binding sites in the target proteins, when the performance was evaluated by the overlap between residue patches of predicted and known binding sites.