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Background: Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration., Results: Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems., Conclusion: Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

Background: To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results: In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions: In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods. [ABSTRACT FROM AUTHOR]

Background: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans. Results: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested. Conclusion: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures. [ABSTRACT FROM AUTHOR]

Background: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology., Results: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations., Conclusion: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

Background: Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results: This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions: As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-orpora.sourceforge.net/CRAFT/index.shtml. [ABSTRACT FROM AUTHOR]

Background: RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips. Methods: In this paper, we exploit the parallelism capabilities of the IBM Cell Broadband Engine to parallelize an existing Dynamic Programming (DP) algorithm for RNA secondary structure prediction. We design three different implementation strategies that exploit the inherent data, code and/or hybrid parallelism, referred to as C-Par, D-Par and H-Par, and analyze their performances. Our approach attempts to introduce parallelism in critical sections of the algorithm. We ran our experiments on SONY Play Station 3 (PS3), which is based on the IBM Cell chip. Results: Our results suggest that introducing parallelism in DP algorithm allows it to easily handle longer sequences which otherwise would consume a large amount of time in single core computers. The results further demonstrate the speed-up gain achieved in exploiting the inherent parallelism in the problem and also elicits the advantages of using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA. Conclusion: The speed-up performance reported here is promising, especially when sequence length is long. To the best of our literature survey, the work reported in this paper is probably the first-of-its-kind to utilize the IBM Cell Broadband Engine (a heterogeneous multi-core chip) to implement a DP. The results also encourage using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA to predict its secondary structure. [ABSTRACT FROM AUTHOR]

Background: Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. Results: In this paper, we present an approach based on a new learning framework, semisupervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Conclusion: Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%. [ABSTRACT FROM AUTHOR]

Gene expression profiles can show significant changes when genetically diseased cells are compared with nondiseased cells. Biological networks are often used to identify active subnetworks (ASNs) of the diseases from the expression profiles to understand the reason behind the observed changes. Current methodologies for discovering ASNs mostly use undirected PPI networks and node centric approaches. This can limit their ability to find the meaningful ASNs when using integrated networks having comprehensive information than the traditional proteinprotein interaction networks. Using appropriate scoring functions to assess both genes and their interactions may allow the discovery of better ASNs. In this paper, we present CASNet, which aims to identify better ASNs using (i) integrated interaction networks (mixed graphs), (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer programming (MIP) and show that optimal solutions may be obtained. We compare the ASNs obtained by CASNet and similar other approaches to show that CASNet can often discover more meaningful and stable regulatory ASNs. Our analysis of a breast cancer dataset finds that the positive feedback loops across 7 genes, AR, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 are conserved across the basal/triple negative subtypes in multiple datasets that could potentially explain the aggressive nature of this cancer subtype. Furthermore, comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL6 is conserved across the two subtypes. This result suggests that subtypes of different cancers can show molecular similarities indicating that the therapeutic approaches in different types of cancers may be shared. [ABSTRACT FROM AUTHOR]

Background: The normal functioning of a living cell is characterized by complex interaction networks involving many different types of molecules. Associations detected between diseases and perturbations in well-defined pathways within such interaction networks have the potential to illuminate the molecular mechanisms underlying disease progression and response to treatment. Results: In this paper, we present a computational method that compares expression profiles of genes in cancer samples to samples from normal tissues in order to detect perturbations of pre-defined pathways in the cancer. In contrast to many previous methods, our scoring function approach explicitly takes into account the interactions between the gene products in a pathway. Moreover, we compute the sub-pathway that has the highest score, as opposed to merely computing the score for the entire pathway. We use a permutation test to assess the statistical significance of the most perturbed sub-pathway. We apply our method to 20 pathways in the Netpath database and to the Global Cancer Map of gene expression in 18 cancers. We demonstrate that our method yields more sensitive results than alternatives that do not consider interactions or measure the perturbation of a pathway as a whole. We perform a sensitivity analysis to show that our approach is robust to modest changes in the input data. Our method confirms numerous well-known connections between pathways and cancers. Conclusions: Our results indicate that integrating differential gene expression with the interaction structure in a pathway is a powerful approach for detecting links between a cancer and the pathways perturbed in it. Our results also suggest that even well-studied pathways may be perturbed only partially in any given cancer. Further analysis of cancer-specific sub-pathways may shed new light on the similarities and differences between cancers. [ABSTRACT FROM AUTHOR]

Background: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding. Methods: In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots. Results: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels. Conclusions: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots. [ABSTRACT FROM AUTHOR]

Background: Brain, heart and skeletal muscle share similar properties of excitable tissue, featuring both discrete behavior (all-or-nothing response to electrical activation) and continuous behavior (recovery to rest follows a temporal path, determined by multiple competing ion flows). Classical mathematical models of excitable cells involve complex systems of nonlinear differential equations. Such models not only impair formal analysis but also impose high computational demands on simulations, especially in large-scale 2-D and 3-D cell networks. In this paper, we show that by choosing Hybrid Automata as the modeling formalism, it is possible to construct a more abstract model of excitable cells that preserves the properties of interest while reducing the computational effort, thereby admitting the possibility of formal analysis and efficient simulation. Results: We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior. Conclusions: The CellExcite simulation framework for multicellular HA arrays exhibits significantly improved computational efficiency in large-scale simulations, thus opening the possibility for formal analysis based on HA theory. A demo of CellExcite is available at http://www.cs.sunysb.edu/∼eha/. [ABSTRACT FROM AUTHOR]

Background: Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model. Results: In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable. Conclusion: Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network. [ABSTRACT FROM AUTHOR]

Background: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. Results: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genomewide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. Conclusion: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches. [ABSTRACT FROM AUTHOR]

A report of the 12th International Conference on Systems Biology (ISB2018), 18–21 August, Guiyang, China. [ABSTRACT FROM AUTHOR]

Background: An ever increasing number of techniques are being used to find genes with similar profiles from microarray studies. Visualization of gene expression profiles can aid this process, potentially contributing to the identification of co-regulated genes and gene function as well as network development. Results: We introduce the h-Profile plot to display gene expression profiles. Thumbnail versions of plots of gene expression profiles are plotted at coordinates such that profiles of similar shape are located in the same sector, with decreasing variance towards the origin. Negatively correlated profiles can easily be identified. A new method for selecting genes with fixed periodicity, but different phase and amplitude is described and used to demonstrate the use of the plots on cell cycle data. Conclusion: Visualization tools for gene expression data are important and h-profile plots provide a timely contribution to the field. They allow the simultaneous visualization of many gene expression profiles and can be used for the identification of genes with similar or reversed profiles, the foundation step in many analyses. [ABSTRACT FROM AUTHOR]

Background: Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results: Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple "robustness score" for each of these clusters, facilitating the assessment of cluster quality. Conclusion: chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness. [ABSTRACT FROM AUTHOR]

Background: Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. Results: We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps. Conclusions: gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data. [ABSTRACT FROM AUTHOR]

Background: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). Results: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96–100%) and chromatin mark QTL (≥87–92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. Conclusions: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution). [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the technical biases in experiments, the raw scRNA-seq data usually includes large noise and makes the downstream analyses complicated. Although many methods have been proposed to impute the noisy scRNA-seq data in recent years, few of them take into account the prior associations across genes in imputation and integrate multiple types of imputation data to identify cell types. Results: We present a new framework, NetImpute, towards the identification of cell types from scRNA-seq data by integrating multiple types of biological networks. We employ a statistic method to detect the noise data items in scRNA-seq data and develop a new imputation model to estimate the real values of data noise by integrating the PPI network and gene pathways. Meanwhile, based on the data imputed by multiple types of biological networks, we propose an integrated approach to identify cell types from scRNA-seq data. Comprehensive experiments demonstrate that the proposed network-based imputation model can estimate the real values of noise data items accurately and integrating the imputation data based on multiple types of biological networks can improve the identification of cell types from scRNA-seq data. Conclusions: Incorporating the prior gene associations in biological networks can potentially help to improve the imputation of noisy scRNA-seq data and integrating multiple types of network-based imputation data can enhance the identification of cell types. The proposed NetImpute provides an open framework for incorporating multiple types of biological network data to identify cell types from scRNA-seq data. [ABSTRACT FROM AUTHOR]

Background: Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis – none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. Results: We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. Conclusions: scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney. [ABSTRACT FROM AUTHOR]

Background: Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. Results: We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. Conclusions: We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided. [ABSTRACT FROM AUTHOR]

Background: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Results: Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. Conclusions: CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere. [ABSTRACT FROM AUTHOR]

Background: Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms. Results: We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold. Conclusions: Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method. [ABSTRACT FROM AUTHOR]

With recent improvements of protocols for the assembly of transcriptional parts, synthetic biological devices can now more reliably be assembled according to a given design. The standardization of parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal design specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified design constraints are robustly satisfied. In this work we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction. The method is general and can hence be incorporated in a pipeline for the rational forward design of arbitrary devices in synthetic biology. [ABSTRACT FROM AUTHOR]

Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities. [ABSTRACT FROM AUTHOR]

Statistical model checking techniques have been shown to be effective for approximate model checking on large stochastic systems, where explicit representation of the state space is impractical. Importantly, these techniques ensure the validity of results with statistical guarantees on errors. There is an increasing interest in these classes of algorithms in computational systems biology since analysis using traditional model checking techniques does not scale well. In this context, we present two improvements to existing statistical model checking algorithms. Firstly, we construct an algorithm which removes the need of the user to define the indifference region, a critical parameter in previous sequential hypothesis testing algorithms. Secondly, we extend the algorithm to account for the case when there may be a limit on the computational resources that can be spent on verifying a property; i.e, if the original algorithm is not able to make a decision even after consuming the available amount of resources, we resort to a p-value based approach to make a decision. We demonstrate the improvements achieved by our algorithms in comparison to current algorithms first with a straightforward yet representative example, followed by a real biological model on cell fate of gustatory neurons with microRNAs. [ABSTRACT FROM AUTHOR]

The article presents a study that examines a robust automated quantification method of live cell imaging that measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. It is found that the quantitative score of the system is promising to present the cell replication level and scores images from different cell replication level.

Background: Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway crosscommunication. Results: Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation. Conclusions: Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request. [ABSTRACT FROM AUTHOR]

Background: The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL. Results: Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types. Conclusion: Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies. [ABSTRACT FROM AUTHOR]

Background: Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. Methods: We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Results: Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. Conclusion: We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of PWM (position weight matrix) motif model. [ABSTRACT FROM AUTHOR]

Background: In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations. Results: We compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameteroptimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to defaultparameter counterparts. Conclusions: Our results indicate that the preferred transformation for fluorescence channels is a parameteroptimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available. [ABSTRACT FROM AUTHOR]

Background: Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from largescale interactome data. Results: We present an algorithm, miPALM (Module Inference by Parametric Local Modularity), to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. Conclusions: miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html. [ABSTRACT FROM AUTHOR]

Background: Nanomaterials are being manufactured on a commercial scale for use in medical, diagnostic, energy, component and communications industries. However, concerns over the safety of engineered nanomaterials have surfaced. Humans can be exposed to nanomaterials in different ways such as inhalation or exposure through the integumentary system. Results: The interactions of engineered nanomaterials with primary human cells was investigated, using a systems biology approach combining gene expression microarray profiling with dynamic experimental parameters. In this experiment, primary human epidermal keratinocytes cells were exposed to several low-micron to nano-scale materials, and gene expression was profiled over both time and dose to compile a comprehensive picture of nanomaterial-cellular interactions. Very few gene-expression studies so far have dealt with both time and dose response simultaneously. Here, we propose different approaches to this kind of analysis. First, we used heat maps and multidimensional scaling (MDS) plots to visualize the dose response of nanomaterials over time. Then, in order to find out the most common patterns in gene-expression profiles, we used self-organizing maps (SOM) combined with two different criteria to determine the number of clusters. The consistency of SOM results is discussed in context of the information derived from the MDS plots. Finally, in order to identify the genes that have significantly different responses among different levels of dose of each treatment while accounting for the effect of time at the same time, we used a two-way ANOVA model, in connection with Tukey's additivity test and the Box-Cox transformation. The results are discussed in the context of the cellular responses of engineered nanomaterials. Conclusion: The analysis presented here lead to interesting and complementary conclusions about the response across time of human epidermal keratinocytes after exposure to nanomaterials. For example, we observed that gene expression for most treatments become closer to the expression of the baseline cultures as time proceeds. The genes found to be differentially-expressed are involved in a number of cellular processes, including regulation of transcription and translation, protein localization, transport, cell cycle progression, cell migration, cytoskeletal reorganization, signal transduction, and development. [ABSTRACT FROM AUTHOR]

Background: Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results: To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion: We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. [ABSTRACT FROM AUTHOR]

Background: Automated microscopy technologies have led to a rapid growth in imaging data ona scale comparable to that of the genomic revolution. High throughput screens are now beingperformed to determine the localisation of all of proteins in a proteome. Closer to the bench, largeimage sets of proteins in treated and untreated cells are being captured on a daily basis todetermine function and interactions. Hence there is a need for new methodologies and protocolsto test for difference in subcellular imaging both to remove bias and enable throughput. Here weintroduce a novel method of statistical testing, and supporting software, to give a rigorous test fordifference in imaging. We also outline the key questions and steps in establishing an analysispipeline. Results: The methodology is tested on a high throughput set of images of 10 subcellularlocalisations, and it is shown that the localisations may be distinguished to a statistically significantdegree with as few as 12 images of each. Further, subtle changes in a protein's distribution betweennocodazole treated and control experiments are shown to be detectable. The effect of outlierimages is also examined and it is shown that while the significance of the test may be reduced byoutliers this may be compensated for by utilising more images. Finally, the test is compared toprevious work and shown to be more sensitive in detecting difference. The methodology has beenimplemented within the iCluster system for visualising and clustering bio-image sets. Conclusion: The aim here is to establish a methodology and protocol for testing for difference in subcellular imaging, and to provide tools to do so. While iCluster is applicable to moderate (<1000) size image sets, the statistical test is simple to implement and will readily be adapted to high throughput pipelines to provide more sensitive discrimination of difference. [ABSTRACT FROM AUTHOR]

Background: The expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of image data between samples we need to define the normal variation within distinct images of the same sample. Even with tightly controlled experimental conditions, protein expression can vary widely between cells, and because of the difficulty in viewing and comparing large image sets this might not be observed. Here we introduce a novel methodology, iCluster, for visualizing, clustering and comparing large sub-cellular localization image sets. For each member of an image set, iCluster generates statistics that have been found to be useful in distinguishing sub-cellular localization. The statistics are mapped into two or three dimensions such as to preserve distances between the statistics vectors. The complete image set is then visualized in two or three dimensions using the coordinates so determined. The result is images that are statistically similar are spatially close in the visualization allowing for easy comparison of images that are similar and distinguishment of dissimilar images into distinct clusters. Results: The methodology was tested on a set of 502 previously published images containing 10 known sub-cellular localizations. The clustering of images of like type was evaluated both by examining the classes of nearest neighbors to each image and by visual inspection. In three dimensions, 3-neighbor classification accuracy was 83.2%. Visually, each class clustered well with the majority of classes localizing to distinct regions of the space. In two dimensions, 3-neighbor classification accuracy was 68.9%, though visually clustering into classes could be readily discerned. Computational expense was found to be relatively low, and sets of up to 1400 images visualized and interacted with in real time. Conclusion: The feasibility of automated spatial layout to allow comparison and discrimination of high throughput sub-cellular imaging has been demonstrated. There are many potential applications such as image database curation, semi-automated interactive classification, outlier detection and reference image comparison. By allowing the observation of the full range of imaging data available using modern microscopes these methods will provide an invaluable tool for cell biologists. [ABSTRACT FROM AUTHOR]

Background: Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations. Results: In this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network. Conclusion: We propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods. [ABSTRACT FROM AUTHOR]

Background: Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%. Description: We have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website. Conclusion: DBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at http://www.bioinfo.tsinghua.edu.cn/DBMLoc/ index.htm. [ABSTRACT FROM AUTHOR]

Background: Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copynumbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion). Results: We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97.71%). Moreover, although the known copynumbers could be well recovered in simulated cancer samples with more than 70% cancer cells (and less than 30% normal cells), we demonstrate that including the mixture proportion in the HMM increases the accuracy of the method. Finally, the method is tested on HapMap samples and on bladder and prostate cancer samples. Conclusion: The HMM method developed here uses the genotype calls of germline DNA and the allelic SNP intensities from the tumour DNA to estimate allelic copy-numbers (including changes) in the tumour. It differentiates between different events like uniparental disomy and allelic imbalances. Moreover, the HMM can estimate the mixture proportion, and thus inform about the purity of the tumour sample. [ABSTRACT FROM AUTHOR]

Background: Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease. Results: We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters. Conclusion: We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest. [ABSTRACT FROM AUTHOR]

Background: An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties. Results: We propose a procedure to estimate the evolution of the metabolic fluxes that is structured as follows: 1) measure the concentrations of extracellular species and biomass, 2) convert this data to measured fluxes and 3) estimate the non-measured fluxes using the Flux Spectrum Approach, a variant of Metabolic Flux Analysis that overcomes the difficulties mentioned above without assuming optimal behaviour. We apply the procedure to a real problem taken from the literature: estimate the metabolic fluxes during a cultivation of CHO cells in batch mode. We show that it provides a reliable and rich estimation of the non-measured fluxes, thanks to considering measurements uncertainty and reversibility constraints. We also demonstrate that this procedure can estimate the non-measured fluxes even when there is a lack of measurable species. In addition, it offers a new method to deal with inconsistency. Conclusion: This work introduces a procedure to estimate time-varying metabolic fluxes that copes with the insufficiency of measured species and with its intrinsic uncertainty. The procedure can be used as an off-line analysis of previously collected data, providing an insight into the dynamic behaviour of the organism. It can be also profitable to the on-line monitoring of a running process, mitigating the traditional lack of reliable on-line sensors in industrial environments. [ABSTRACT FROM AUTHOR]

Background: A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level. Results: We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in in vitro translation system that a viable protein can be autonomously assembled. Conclusion: Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function. [ABSTRACT FROM AUTHOR]

Background: Experimental studies of gene expression have identified some of the individual molecular components and elementary reactions that comprise and control cellular behavior. Given our current understanding of gene expression, and the goals of biotechnology research, both scientists and engineers would benefit from detailed simulators that can explicitly compute genome-wide expression levels as a function of individual molecular events, including the activities and interactions of molecules on DNA at single base pair resolution. However, for practical reasons including computational tractability, available simulators have not been able to represent genome-scale models of gene expression at this level of detail. Results: Here we develop a simulator, TABASCO http://openwetware.org/wiki/TABASCO, which enables the precise representation of individual molecules and events in gene expression for genome-scale systems. We use a single molecule computational engine to track individual molecules interacting with and along nucleic acid polymers at single base resolution. Tabasco uses logical rules to automatically update and delimit the set of species and reactions that comprise a system during simulation, thereby avoiding the need for a priori specification of all possible combinations of molecules and reaction events. We confirm that single molecule, base-pair resolved simulation using TABASCO (Tabasco) can accurately compute gene expression dynamics and, moving beyond previous simulators, provide for the direct representation of intermolecular events such as polymerase collisions and promoter occlusion. We demonstrate the computational capacity of Tabasco by simulating the entirety of gene expression during bacteriophage T7 infection; for reference, the 39,937 base pair T7 genome encodes 56 genes that are transcribed by two types of RNA polymerases active across 22 promoters. Conclusion: Tabasco enables genome-scale simulation of transcription and translation at individual molecule and single base-pair resolution. By directly representing the position and activity of individual molecules on DNA, Tabasco can directly test the effects of detailed molecular processes on system-wide gene expression. Tabasco would also be useful for studying the complex regulatory mechanisms controlling eukaryotic gene expression. The computational engine underlying Tabasco could also be adapted to represent other types of processive systems in which individual reaction events are organized across a single spatial dimension (e.g., polysaccharide synthesis). [ABSTRACT FROM AUTHOR]

Background: Information resources on the World Wide Web play an indispensable role in modern biology. But integrating data from multiple sources is often encumbered by the need to reformat data files, convert between naming systems, or perform ongoing maintenance of local copies of public databases. Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data. Results: The Firegoose, an extension to the Mozilla Firefox web browser, enables data transfer between web sites and desktop tools. As a component of the Gaggle integration framework, Firegoose can also exchange data with Cytoscape, the R statistical package, Multiexperiment Viewer (MeV), and several other popular desktop software tools. Firegoose adds the capability to easily use local data to query KEGG, EMBL STRING, DAVID, and other widely-used bioinformatics web sites. Query results from these web sites can be transferred to desktop tools for further analysis with a few clicks. Firegoose acquires data from the web by screen scraping, microformats, embedded XML, or web services. We define a microformat, which allows structured information compatible with the Gaggle to be embedded in HTML documents. We demonstrate the capabilities of this software by performing an analysis of the genes activated in the microbe Halobacterium salinarum NRC-1 in response to anaerobic environments. Starting with microarray data, we explore functions of differentially expressed genes by combining data from several public web resources and construct an integrated view of the cellular processes involved. Conclusion: The Firegoose incorporates Mozilla Firefox into the Gaggle environment and enables interactive sharing of data between diverse web resources and desktop software tools without maintaining local copies. Additional web sites can be incorporated easily into the framework using the scripting platform of the Firefox browser. Performing data integration in the browser allows the excellent search and navigation capabilities of the browser to be used in combination with powerful desktop tools. [ABSTRACT FROM AUTHOR]

Background: Cell specific gene expression is largely regulated by different combinations of transcription factors that bind cis-elements in the upstream promoter sequence. However, experimental detection of cis-elements is difficult, expensive, and time-consuming. This provides a motivation for developing bioinformatic methods to identify cis-elements that could prioritize future experimental studies. Here, we use motif discovery algorithms to predict transcription factor binding sites involved in regulating the differences between murine rod and cone photoreceptor populations. Results: To identify highly conserved motifs enriched in promoters that drive expression in either rod or cone photoreceptors, we assembled a set of murine rod-specific, cone-specific, and nonphotoreceptor background promoter sequences. These sets were used as input to a newly devised motif discovery algorithm called Iterative Alignment/Modular Motif Selection (IAMMS). Using IAMMS, we predicted 34 motifs that may contribute to rod-specific (19 motifs) or cone-specific (15 motifs) expression patterns. Of these, 16 rod- and 12 cone-specific motifs were found in clusters near the transcription start site. New findings include the observation that cone promoters tend to contain TATA boxes, while rod promoters tend to be TATA-less (exempting Rho and Cnga1). Additionally, we identify putative sites for IL-6 effectors (in rods) and RXR family members (in cones) that can explain experimental data showing changes to cell-fate by activating these signaling pathways during rod/cone development. Two of the predicted motifs (NRE and ROP2) have been confirmed experimentally to be involved in cell-specific expression patterns. We provide a full database of predictions as additional data that may contain further valuable information. IAMMS predictions are compared with existing motif discovery algorithms, DME and BioProspector. We find that over 60% of IAMMS predictions are confirmed by at least one other motif discovery algorithm. Conclusion: We predict novel, putative cis-elements enriched in the promoter of rod-specific or cone-specific genes. These are candidate binding sites for transcription factors involved in maintaining functional differences between rod and cone photoreceptor populations. [ABSTRACT FROM AUTHOR]

Background: Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell. Results: To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell. We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and we have compared the dynamics of complex formation in a two and a three dimensional lattice model. Conclusion: ProtNet is a cellular automaton model, where each protein molecule or complex is explicitly represented and where simple interaction rules are applied to populations of discrete particles. This tool can be used to simulate the dynamics of protein interactions in the cell. [ABSTRACT FROM AUTHOR]

Background: Spatio-temporal dynamics within cells can now be visualized at appropriate resolution, due to the advances in molecular imaging technologies. Even single-particle tracking (SPT) and single fluorophore video imaging (SFVI) are now being applied to observation of molecular-level dynamics. However, little is known concerning how molecular-level dynamics affect properties at the cellular level. Results: We propose an algorithm designed for three-dimensional simulation of the reactiondiffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space, with activation energies determining the rates of these chemical reactions at each interaction. This energy-based model can include the cellular membrane, membranes of other organelles, and cytoskeleton. The simulation algorithm was tested for a reversible enzyme reaction model and its validity was confirmed. Snapshot images taken from simulated molecular interactions on the cell-surface revealed clustering domains (size ~0.2 μm) associated with rafts. Sample trajectories of raft constructs exhibited "hop diffusion". These domains corralled the diffusive motion of membrane proteins. Conclusion: These findings demonstrate that our approach is promising for modelling the localization properties of biological phenomena. [ABSTRACT FROM AUTHOR]

Background: Cell culture systems are useful in studying toxicological effects of chemicals such as Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), however little is known as to how accurately isolated cells reflect responses of intact organs. In this work, we compare transcriptional responses in livers of Sprague-Dawley rats and primary hepatocyte cells after exposure to RDX to determine how faithfully the in vitro model system reflects in vivo responses. Results: Expression patterns were found to be markedly different between liver tissue and primary cell cultures before exposure to RDX. Liver gene expression was enriched in processes important in toxicology such as metabolism of amino acids, lipids, aromatic compounds, and drugs when compared to cells. Transcriptional responses in cells exposed to 7.5, 15, or 30 mg/L RDX for 24 and 48 hours were different from those of livers isolated from rats 24 hours after exposure to 12, 24, or 48 mg/Kg RDX. Most of the differentially expressed genes identified across conditions and treatments could be attributed to differences between cells and tissue. Some similarity was observed in RDX effects on gene expression between tissue and cells, but also significant differences that appear to reflect the state of the cell or tissue examined. Conclusion: Liver tissue and primary cells express different suites of genes that suggest they have fundamental differences in their cell physiology. Expression effects related to RDX exposure in cells reflected a fraction of liver responses indicating that care must be taken in extrapolating from primary cells to whole animal organ toxicity effects. [ABSTRACT FROM AUTHOR]

Background: The Thymidine kinase (Tk) mutants generated from the widely used L5178Y mouse lymphoma assay fall into two categories, small colony and large colony. Cells from the large colonies grow at a normal rate while cells from the small colonies grow slowerthan normal. The relative proportion of large and small colonies after mutagen treatment is associated with a mutagen's ability to induce point mutations and/or chromosomal mutations. The molecular distinction between large and small colony mutants, however, is not clear. Results: To gain insights into the underlying mechanisms responsible for the mutant colony phenotype, microarray gene expression analysis was carried out on 4 small and 4 large colony Tk mutant samples. NCTR-fabricated long-oligonucleotide microarrays of 20,000 mouse genes were used in a two-color reference design experiment. The data were analyzed within ArrayTrack software that was developed at the NCTR. Principal component analysis and hierarchical clustering of the gene expression profiles showed that the samples were clearly separated into two groups based on their colony size phenotypes. The Welch T-test was used for determining significant changes in gene expression between the large and small colony groups and 90 genes whose expression was significantly altered were identified (p < 0.01; fold change > 1.5). Using Ingenuity Pathways Analysis (IPA), 50 out of the 90 significant genes were found in the IPA database and mapped to four networks associated with cell growth. Eleven percent of the 90 significant genes were located on chromosome 11 where the Tk gene resides while only 5.6% of the genes on the microarrays mapped to chromosome 11. All of the chromosome 11 significant genes were expressed at a higher level in the small colony mutants compared to the large colony mutants. Also, most of the significant genes located on chromosome 11 were disproportionally concentrated on the distal end of chromosome 11 where the Tk mutations occurred. Conclusion: The results indicate that microarray analysis can define cellular phenotypes and identify genes that are related to the colony size phenotypes. The findings suggest that genes in the DNA segment altered by the Tk mutations were significantly up-regulated in the small colony mutants, but not in the large colony mutants, leading to differential expression of a set of growth regulation genes that are related to cell apoptosis and other cellular functions related to the restriction of cell growth. [ABSTRACT FROM AUTHOR]

Background: Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments. Results: We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells. Conclusion: We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC® and TRANSPATH®). The corresponding software and databases are available at http://www.gene-regulation.com. [ABSTRACT FROM AUTHOR]