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Both mathematical models and biological model systems stand as tractable representations of complex biological systems or behaviors. They facilitate research and provide insights, and they can describe general rules. Models that represent biological processes or formalize general hypotheses are essential to any broad understanding. Mathematical or biological models necessarily omit details of the natural systems and thus may ultimately be "incorrect" representations. A key challenge is that tractability requires relatively simple models but simplification can result in models that are incorrect in their qualitative, broad implications if the abstracted details matter. Our paper discusses this tension, and how we can improve our inferences from models. We advocate for further efforts dedicated to model development, improvement, and acceptance by the scientific community, all of which may necessitate a more explicit discussion of the purpose and power of models. We argue that models should play a central role in reintegrating biology as a way to test our integrated understanding of how molecules, cells, organs, organisms, populations, and ecosystems function. [ABSTRACT FROM AUTHOR]

In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome. [ABSTRACT FROM AUTHOR]

Motivation: Biological networks are composed of molecular components and their interactions represented by nodes and edges, respectively, in a graph model. Based on this model, there were many studies with respect to effects of node-based mutations on the network dynamics, whereas little attention was paid to edgetic mutations so far. Results: In this paper, we defined an edgetic sensitivity measure that quantifies how likely a converging attractor is changed by edge-removal mutations in a Boolean network model. Through extensive simulations based on that measure, we found interesting properties of highly sensitive edges in both random and real signaling networks. First, the sensitive edges in random networks tend to link two end nodes both of which are susceptible to node-knockout mutations. Interestingly, it was analogous to an observation that the sensitive edges in human signaling networks are likely to connect drug-target genes. We further observed that the edgetic sensitivity predicted drug-targets better than the node-based sensitivity. In addition, the sensitive edges showed distinguished structural characteristics such as a lower connectivity, more involving feedback loops and a higher betweenness. Moreover, their gene-ontology enrichments were clearly different from the other edges. We also observed that genes incident to the highly sensitive interactions are more central by forming a considerably large connected component in human signaling networks. Finally, we validated our approach by showing that most sensitive interactions are promising edgetic drug-targets in p53 cancer and T-cell apoptosis networks. Taken together, the edgetic sensitivity is valuable to understand the complex dynamics of signaling networks. [ABSTRACT FROM AUTHOR]

This paper is motivated by a phase I-II clinical trial of a targeted agent for advanced solid tumors. We study a stylized version of this trial with the goal to determine optimal actions in each of two cycles of therapy. A design is presented that generalizes the decision-theoretic two-cycle design of Lee and others (2015. Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity. Journal of the American Statistical Association, to appear) to accommodate ordinal outcomes. Backward induction is used to jointly optimize the actions taken for each patient in each of the two cycles, with the second action accounting for the patient's cycle 1 dose and outcomes. A simulation study shows that simpler designs obtained by dichotomizing the ordinal outcomes either perform very similarly to the proposed design, or have much worse performance in some scenarios. We also compare the proposed design with the simpler approaches of optimizing the doses in each cycle separately, or ignoring the distinction between cycles 1 and 2. [ABSTRACT FROM AUTHOR]

An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (WW ) is a linear function of the unobserved true covariate (X) plus other covariates (Z) in the regression model. In this paper, we consider models for W that include interactions between X and Z. We derive the conditional distribution of X given W and Z and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models. [ABSTRACT FROM AUTHOR]

This paper presents a method for updating the average efficiency factor of an α-design after an exchange or interchange in the α-array. This allows the average efficiency factor to be used directly as the objective function in an α-design computer search algorithm. [ABSTRACT FROM PUBLISHER]

Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative modeling process, and nurture a cultural shift towards interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems. [ABSTRACT FROM AUTHOR]

The article reports on the move of the International Society for Behavioral Ecology to honor the life of biologist William Donald Hamilton in Oxford, England. It states that the Hamilton has made a lot of contributions on the field of evolutionary biology, such as the behavioral biology. The first plan of the society is to conduct an exhibition at the Eighth International Behavioral Ecology Congress in Zurich, Switzerland. It also mentions the how Hamilton developed his ideas, which is from nature while his hypothesis is in the form of rigorous mathematical models.

The goal of relative survival methodology is to compare the survival experience of a cohort with that of the background population. Most often an additive excess hazard model is employed, which assumes that each persons hazard is a sum of 2 components—the population hazard obtained from life tables and an excess hazard attributable to the specific condition. Usually covariate effects on the excess hazard are assumed to have a proportional hazards structure with parametrically modelled baseline. In this paper, we introduce a new fitting procedure using the expectation–maximization algorithm, treating the cause of death as missing data. The method requires no assumptions about the baseline excess hazard thus reducing the risk of bias through misspecification. It accommodates the possibility of knowledge of cause of death for some patients, and as a side effect, the method yields an estimate of the ratio between the excess and the population hazard for each subject. More importantly, it estimates the baseline excess hazard flexibly with no additional degrees of freedom spent. Finally, it is a generalization of the Cox model, meaning that all the wealth of options in existing software for the Cox model can be used in relative survival. The method is applied to a data set on survival after myocardial infarction, where it shows how a particular form of the hazard function could be missed using the existing methods. [ABSTRACT FROM AUTHOR]

Multiparameter evidence synthesis is becoming widely used as a way of combining evidence from multiple and often disparate sources of information concerning a number of parameters. Synthesizing data in one encompassing model allows propagation of evidence and learning. We demonstrate the use of such an approach in estimating the number of people infected with the hepatitis C virus (HCV) in England and Wales. Data are obtained from seroprevalence studies conducted in different subpopulations. Each subpopulation is modeled as a composition of 3 main HCV risk groups (current injecting drug users (IDUs), ex-IDUs, and non-IDUs). Further, data obtained on the prevalence (size) of each risk group provide an estimate of the prevalence of HCV in the whole population. We simultaneously estimate all model parameters through the use of Bayesian Markov chain Monte Carlo techniques. The main emphasis of this paper is the assessment of evidence consistency and investigation of the main drivers for model inferences. We consider a cross-validation technique to reveal data conflict and leverage when each data source is in turn removed from the model. [ABSTRACT FROM AUTHOR]

Abstract: Batesian mimicry is characterized by phenotypic convergence between an unpalatable model and a palatable mimic. However, because convergent evolution may arise via alternative evolutionary mechanisms, putative examples of Batesian mimicry must be rigorously tested. Here, we used artificial butterfly facsimiles (N = 4000) to test the prediction that (1) palatable Limenitis lorquini butterflies should experience reduced predation when in sympatry with their putative model, Adelpha californica, (2) protection from predation on L. lorquini should erode outside of the geographical range of the model, and (3) mimetic color pattern traits are more variable in allopatry, consistent with relaxed selection for mimicry. We find support for these predictions, implying that this convergence is the result of selection for Batesian mimicry. Additionally, we conducted mark–recapture studies to examine the effect of mimicry and found that mimics survive significantly longer at sites where the model is abundant. Finally, in contrast to theoretical predictions, we found evidence that the Batesian model (A. californica) is protected from predation outside of its geographic range. We discuss these results considering the ongoing hybridization between L. lorquini and its sister species, L. weidemeyerii, and growing evidence that selection for mimicry predictably leads to a reduction in gene flow between nascent species. [ABSTRACT FROM AUTHOR]

Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials. [ABSTRACT FROM AUTHOR]

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves. [ABSTRACT FROM AUTHOR]

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach. [ABSTRACT FROM AUTHOR]

High-throughput time course expression profiles have been available in the last decade due to developments in measurement techniques and devices. Functional data analysis, which treats smoothed curves instead of originally observed discrete data, is effective for the time course expression profiles in terms of dimension reduction, robustness, and applicability to data measured at small and irregularly spaced time points. However, the statistical method of differential analysis for time course expression profiles has not been well established. We propose a functional logistic model based on elastic net regularization (F-Logistic) in order to identify the genes with dynamic alterations in case/control study. We employ a mixed model as a smoothing method to obtain functional data; then F-Logistic is applied to time course profiles measured at small and irregularly spaced time points. We evaluate the performance of F-Logistic in comparison with another functional data approach, i.e. functional ANOVA test (F-ANOVA), by applying the methods to real and synthetic time course data sets. The real data sets consist of the time course gene expression profiles for long-term effects of recombinant interferon β on disease progression in multiple sclerosis. F-Logistic distinguishes dynamic alterations, which cannot be found by competitive approaches such as F-ANOVA, in case/control study based on time course expression profiles. F-Logistic is effective for time-dependent biomarker detection, diagnosis, and therapy. [ABSTRACT FROM AUTHOR]

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. [ABSTRACT FROM AUTHOR]

Motivation: Recent exome-sequencing studies have successfully identified disease-causing sequence variants for several rare monogenic diseases by examining variants common to a group of patients. However, the current data analysis strategies are only insufficiently able to deal with confounding factors such as genetic heterogeneity, incomplete penetrance, individuals lacking data and involvement of several genes.Results: We introduce BioGranat-IG, an analysis strategy that incorporates the information contained in biological networks to the analysis of exome-sequencing data. To identify genes that may have a disease-causing role, we label all nodes of the network according to the individuals that are carrying a sequence variant and subsequently identify small subnetworks linked to all or most individuals. Using simulated exome-sequencing data, we demonstrate that BioGranat-IG is able to recover the genes responsible for two diseases known to be caused by variants in an underlying complex. We also examine the performance of BioGranat-IG under various conditions likely to be faced by the user, and show that its network-based approach is more powerful than a set-cover-based approach.Availability: We implemented our methods in Java as BioGranat-IG, a bundle within our BioGranat graph analysis and visualization tool (www.biogranat.org).Contact: thomas.schlitt@genetics.kcl.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Ideological language is widespread in theoretical biology. Evolutionary game theory has been defended as a worldview and a leap of faith, and sexual selection theory has been criticized for what it posits as basic to biological nature. Views such as these encourage the impression of ideological rifts in the field. I advocate an alternative interpretation, whereby many disagreements between different camps of biologists merely reflect methodological differences. This interpretation provides a more accurate and more optimistic account of the state of play in the field of biology. It also helps account for biologists' tendency to embrace ideological positions. [ABSTRACT FROM AUTHOR]

Motivation: Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology. Results: A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by non-linear stochastic game method directly, the Takagi–Sugeno (T–S) fuzzy model is proposed to approximate the non-linear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method. Availability: http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf Contact: bschen@ee.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: The biological pathway exchange language (BioPAX) and the systems biology markup language (SBML) belong to the most popular modeling and data exchange languages in systems biology. The focus of SBML is quantitative modeling and dynamic simulation of models, whereas the BioPAX specification concentrates mainly on visualization and qualitative analysis of pathway maps. BioPAX describes reactions and relations. In contrast, SBML core exclusively describes quantitative processes such as reactions. With the SBML qualitative models extension (qual), it has recently also become possible to describe relations in SBML. Before the development of SBML qual, relations could not be properly translated into SBML. Until now, there exists no BioPAX to SBML converter that is fully capable of translating both reactions and relations.Results: The entire nature pathway interaction database has been converted from BioPAX (Level 2 and Level 3) into SBML (Level 3 Version 1) including both reactions and relations by using the new qual extension package. Additionally, we present the new webtool BioPAX2SBML for further BioPAX to SBML conversions. Compared with previous conversion tools, BioPAX2SBML is more comprehensive, more robust and more exact.Availability: BioPAX2SBML is freely available at http://webservices.cs.uni-tuebingen.de/ and the complete collection of the PID models is available at http://www.cogsys.cs.uni-tuebingen.de/downloads/Qualitative-Models/.Contact: finja.buechel@uni-tuebingen.deSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean–variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis. [ABSTRACT FROM PUBLISHER]

Motivation: Building an accurate alignment of a large set of distantly related protein structures is still very challenging.Results: This article presents a novel method 3DCOMB that can generate a multiple structure alignment (MSA) with not only as many conserved cores as possible, but also high-quality pairwise alignments. 3DCOMB is unique in that it makes use of both local and global structure environments, combined by a statistical learning method, to accurately identify highly similar fragment blocks (HSFBs) among all proteins to be aligned. By extending the alignments of these HSFBs, 3DCOMB can quickly generate an accurate MSA without using progressive alignment. 3DCOMB significantly excels others in aligning distantly related proteins. 3DCOMB can also generate correct alignments for functionally similar regions among proteins of very different structures while many other MSA tools fail. 3DCOMB is useful for many real-world applications. In particular, it enables us to find out that there is still large improvement room for multiple template homology modeling while several other MSA tools fail to do so.Availability: 3DCOMB is available at http://ttic.uchicago.edu/~jinbo/software.htm.Contact: jinboxu@gmail.comSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.Results: We propose a statistical modeling framework that is based on experimental designs with at least two controls profiled throughout the experiment, and a normalization and variance estimation procedure with linear mixed-effects models. We evaluate the framework using three comprehensive screens of Saccharomyces cerevisiae, which involve 4940 single-gene knock-out haploid mutants, 1127 single-gene knock-out diploid mutants and 5798 single-gene overexpression haploid strains. We show that the proposed approach (i) can be used in conjunction with practical experimental designs; (ii) allows extensions to alternative experimental workflows; (iii) enables a sensitive discovery of biologically meaningful changes; and (iv) strongly outperforms the existing noise reduction procedures.Availability: All experimental datasets are publicly available at www.ionomicshub.org. The R package HTSmix is available at http://www.stat.purdue.edu/~ovitek/HTSmix.html.Contact: ovitek@stat.purdue.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Motivation: Oscillating signals produced by biological systems have shapes, described by their Fourier spectra, that can potentially reveal the mechanisms that generate them. Extracting this information from measured signals is interesting for the validation of theoretical models, discovery and classification of interaction types, and for optimal experiment design.Results: An automated workflow is described for the analysis of oscillating signals. A software package is developed to match signal shapes to hundreds of a priori viable model structures defined by a class of first-order differential equations. The package computes parameter values for each model by exploiting the mode decomposition of oscillating signals and formulating the matching problem in terms of systems of simultaneous polynomial equations. On the basis of the computed parameter values, the software returns a list of models consistent with the data. In validation tests with synthetic datasets, it not only shortlists those model structures used to generate the data but also shows that excellent fits can sometimes be achieved with alternative equations. The listing of all consistent equations is indicative of how further invalidation might be achieved with additional information. When applied to data from a microarray experiment on mice, the procedure finds several candidate model structures to describe interactions related to the circadian rhythm. This shows that experimental data on oscillators is indeed rich in information about gene regulation mechanisms.Availability: The software package is available at http://babylone.ulb.ac.be/autoosc/.Contact: tkonopka@ulb.ac.beSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Motivation: Theoretical models of biological networks are valuable tools in evolutionary inference. Theoretical models based on gene duplication and divergence provide biologically plausible evolutionary mechanics. Similarities found between empirical networks and their theoretically generated counterpart are considered evidence of the role modeled mechanics play in biological evolution. However, the method by which these models are parameterized can lead to questions about the validity of the inferences. Selecting parameter values in order to produce a particular topological value obfuscates the possibility that the model may produce a similar topology for a large range of parameter values. Alternately, a model may produce a large range of topologies, allowing (incorrect) parameter values to produce a valid topology from an otherwise flawed model. In order to lend biological credence to the modeled evolutionary mechanics, parameter values should be derived from the empirical data. Furthermore, recent work indicates that the timing and fate of gene duplications are critical to proper derivation of these parameters.Results: We present a methodology for deriving evolutionary rates from empirical data that is used to parameterize duplication and divergence models of protein interaction network evolution. Our method avoids shortcomings of previous methods, which failed to consider the effect of subsequent duplications. From our parameter values, we find that concurrent and existing existing duplication and divergence models are insufficient for modeling protein interaction network evolution. We introduce a model enhancement based on heritable interaction sites on the surface of a protein and find that it more closely reflects the high clustering found in the empirical network.Contact: Debra@Colorado.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Quality assessment of protein structures is an important part of experimental structure validation and plays a crucial role in protein structure prediction, where the predicted models may contain substantial errors. Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the absolute quality of an individual protein model has received little attention in the field. However, reliable absolute quality estimates are crucial to assess the suitability of a model for specific biomedical applications.Results: In this work, we present a new absolute measure for the quality of protein models, which provides an estimate of the ‘degree of nativeness’ of the structural features observed in a model and describes the likelihood that a given model is of comparable quality to experimental structures. Model quality estimates based on the QMEAN scoring function were normalized with respect to the number of interactions. The resulting scoring function is independent of the size of the protein and may therefore be used to assess both monomers and entire oligomeric assemblies. Model quality scores for individual models are then expressed as ‘Z-scores’ in comparison to scores obtained for high-resolution crystal structures. We demonstrate the ability of the newly introduced QMEAN Z-score to detect experimentally solved protein structures containing significant errors, as well as to evaluate theoretical protein models.In a comprehensive QMEAN Z-score analysis of all experimental structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other. Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an estimate of protein stability.Availability: The Z-score calculation has been integrated in the QMEAN server available at: http://swissmodel.expasy.org/qmean.Contact: torsten.schwede@unibas.chSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: An increasing number of discrete mathematical models are being published in Systems Biology, ranging from Boolean network models to logical models and Petri nets. They are used to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation. [ABSTRACT FROM PUBLISHER]

When dispersal options are limited and encounters with relatives are likely, individuals need to recognize and avoid mating with kin to avoid the fitness costs of close inbreeding. New Zealand robins and saddlebacks are genetically monogamous and possess life-history traits that predict they should show zero tolerance of close inbreeding. However, of 11 population-years of pedigree data, there was evidence of inbreeding avoidance in only 1 year. We also found no indication that incestuous pairings were avoided or that individuals were choosing genetically dissimilar mates based on microsatellite DNA analysis. Furthermore, a review of the literature revealed that inbreeding avoidance via kin recognition is common in cooperatively breeding birds, but pair-breeding birds such as robins and saddlebacks mate randomly with respect to relatedness. A model that incorporates encounter rates with close kin for various degrees of mate-searching effort shows that inbreeding avoidance is beneficial at intermediate to high levels of encounter rates with close kin (as found in cooperative breeders), but that random mating is more beneficial at low or extremely high encounter rates. We conclude that random mating normally results in such low rates of close inbreeding that it exerts negligible selection pressure to evolve kin recognition. Consequently, many threatened species are unlikely to have a natural “built-in” mechanism for avoiding close inbreeding, and the assumption of random mating built into many population viability models may be appropriate. [ABSTRACT FROM AUTHOR]

Agent-based modeling (ABM), also termed ‘Individual-based modeling (IBM)’, is a computational approach that simulates the interactions of autonomous entities (agents, or individual cells) with each other and their local environment to predict higher level emergent patterns. A literature-derived rule set governs the actions of each individual agent. While this technique has been widely used in the ecological and social sciences, it has only recently been applied in biomedical research. The purpose of this review is to provide an introduction to ABM as it has been used to study complex multi-cell biological phenomena, underscore the importance of coupling models with experimental work, and outline future challenges for the ABM field and its application to biomedical research. We highlight a number of published examples of ABM, focusing on work that has combined experimental with ABM analyses and how this pairing produces new understanding. We conclude with suggestions for moving forward with this parallel approach. [ABSTRACT FROM AUTHOR]

One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes. [ABSTRACT FROM AUTHOR]

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short branch lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed. [Bayesian; phylogenetics; polytomy; reversible-jump MCMC; star tree; unresolved.] [ABSTRACT FROM AUTHOR]

Patients at the same stage of a chronic disease may have had different rates of disease progression. The authors developed a mathematical modeling approach that allows reconstructing and comparing populations in terms of the disease progression rates of their participants when the disease onset and progression rates are unknown for individual patients. Human immunodeficiency virus 1 infection was used as an example. Both published and hypothetical models were used to describe the human immunodeficiency virus 1 epidemic (epidemic heterogeneity) and incubation and survival functions for different disease stages (individual heterogeneity). Reconstructions of populations with late disease (e.g., acquired immunodeficiency syndrome patients) show a marked predominance of rapid progressors, unless the incidence of new infections has been decreasing for a long time. Rapid progressors would also predominate in populations of acute seroconverters, unless diagnosis is based on repeated serologic screening rather than symptoms. Populations of patients who have not progressed beyond an early stage of the disease (e.g., patients with CD4 cell counts >500/μl) tend to overrepresent slow progressors, especially if the epidemic has been decreasing for a long time. With this approach, one can assess whether the target population of a clinical trial is comparable with other patient populations at different places and times. Epidemic and individual diversity may even affect trial results if patients with different progression rates experience different benefits from a treatment. By modeling the targeted populations in trials of early versus deferred antiretroviral treatment, the authors observed larger treatment benefits in trials in which rapid progressors probably predominated, compared with trials of slow progressors. Am J Epidemiol 1996;144:1074-85. [ABSTRACT FROM AUTHOR]

Raup's model of shell growth, now in standard use, is operational only for strictly conispiral shells. The pertinence of evolutionary interpretations of the distribution of existing shells in the morphospace defined by Raup's parameters is questioned. A simple, more general model accounts for the non-isometric growth of many shells. Some aspects of the distribution of existing shells in the morphospace derived from the new model are discussed. [ABSTRACT FROM AUTHOR]

The author reflects on the advances offered by Integrative Biology on the understanding of biological phenomena, through the close collaborative efforts of laboratory and theoretical scientists which develop mechanistic mathematical models that leads to hypotheses for laboratory testing. He argues that it differs form Systems Biology where he refers to the various dimensions where models spans in different scales. An overview of new areas and challenges for Bioinformatics is offered.

Motivation: For the efficient application of multi-agent systems to spatial and functional modeling of tissues flexible and intuitive modeling tools are needed, which allow the graphical specification of cellular behavior in a tissue context without presuming specialized programming skills. Results: We developed a graphical modeling system for multi-agent based simulation of tissue homeostasis. An editor allows the intuitive and hierarchically structured specification of cellular behavior. The models are then automatically compiled into highly efficient source code and dynamically linked to an interactive graphical simulation environment. The system allows the quantitative analysis of the morphological and functional tissue properties emerging from the cell behavioral model. We demonstrate the relevance of the approach using a recently published model of epidermal homeostasis as well as a series of cell-cycle models. Availability: The complete software is available in binary executables for MS-Windows and Linux at tiga.uni-hd.de Contact: niels.grabe@bioquant.uni-heidelberg.de [ABSTRACT FROM AUTHOR]

Motivation: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. Results: We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. Availability: An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Contact: andreas.raue@me.com Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The ligamental apparatus of Thracia comprises external and internal resilient components, as well as a calcified ossicle termed the lithodesma. All these elements have hitherto been considered portions of a single unit and their allometry and functional morphology interpreted as such. However, analyses of the fine structure of the hinge of a growth series of Thracia phaseolina using conventional light, confocal and scanning electron microscopy revealed that, instead, external and internal components comprise distinct, independent layers of fibrous ligament, each formed at different ontogenetic stages. The internal ligament, with a lithodesma along its mid-sagittal sector, comprises the sole early juvenile ligament of the species and its main function is to force the shell valves open, not align them as had been previously suggested. The external (parivincular) ligament only appears at a shell length of c. 2.5 mm, but grows faster than the internal ligament to become the main component of the hinge of adults. Two- and three-parameter power functions (simple and full allometric equations) were fitted to measurements of the different ligament parts, and the performance and appropriateness of these models in describing the allometry of each of the studied traits are evaluated. Use of the full allometric model is recommended whenever the body size (or other standard) at which a trait of interest arises lies within or close to the limits of the sampled range. [ABSTRACT FROM AUTHOR]

Has the recent US decline in coronary heart disease (CHD) mortality increased life expectancy? The authors estimated the number of life-years gained from CHD treatments and changes in the prevalence of cardiovascular disease risk factors for the US population between 1980 and 2000. The previously validated IMPACT model was used to integrate data on numbers of CHD patients, treatment uptake, treatment effectiveness, population risk factor trends, and median survival among US adults. There were 308,900 fewer CHD deaths in 2000 among Americans aged 25–84 years than if 1980 mortality rates had applied. These 308,900 fewer deaths represented approximately 3,147,800 life-years gained (sensitivity analysis range, 2,448,900–3,744,900). Treatments for patients accounted for approximately 1,092,400 (751,700–1,387,000) life-years gained, whereas changes in the prevalence of population risk factors accounted for a gain of 2,055,500 (1,697,200–2,346,300) life-years. However, the 2,770,500 life-years gained through decreased levels of smoking, cholesterol, blood pressure, and physical inactivity were diminished by a loss of 715,000 life-years attributable to increased rates of obesity and diabetes. Therefore, modest reductions in the prevalence of several major cardiovascular disease risk factors accounted for more than twice as many life-years gained as did treatments. Unfortunately, these gains were partially offset by substantial increases in obesity and diabetes. [ABSTRACT FROM AUTHOR]

Motivation: Picking peaks from experimental NMR spectra is a key unsolved problem for automated NMR protein structure determination. Such a process is a prerequisite for resonance assignment, nuclear overhauser enhancement (NOE) distance restraint assignment, and structure calculation tasks. Manual or semi-automatic peak picking, which is currently the prominent way used in NMR labs, is tedious, time consuming and costly. Results: We introduce new ideas, including noise-level estimation, component forming and sub-division, singular value decomposition (SVD)-based peak picking and peak pruning and refinement. PICKY is developed as an automated peak picking method. Different from the previous research on peak picking, we provide a systematic study of the proposed method. PICKY is tested on 32 real 2D and 3D spectra of eight target proteins, and achieves an average of 88% recall and 74% precision. PICKY is efficient. It takes PICKY on average 15.7 s to process an NMR spectrum. More important than these numbers, PICKY actually works in practice. We feed peak lists generated by PICKY to IPASS for resonance assignment, feed IPASS assignment to SPARTA for fragments generation, and feed SPARTA fragments to FALCON for structure calculation. This results in high-resolution structures of several proteins, for example, TM1112, at 1.25 Å. Availability: PICKY is available upon request. The peak lists of PICKY can be easily loaded by SPARKY to enable a better interactive strategy for rapid peak picking. Contact: mli@uwaterloo.ca [ABSTRACT FROM AUTHOR]

Motivation: Most current research in network evolution focuses on networks that follow a Duplication Attachment model where the network is only allowed to grow. The evolution of metabolic networks, however, is characterized by gain as well as loss of reactions. It would be desirable to have a biologically relevant model of network evolution that could be used to calculate the likelihood of homologous metabolic networks. Results: We describe metabolic network evolution as a discrete space continuous time Markov process and introduce a neighbor-dependent model for the evolution of metabolic networks where the rates with which reactions are added or removed depend on the fraction of neighboring reactions present in the network. We also present a Gibbs sampler for estimating the parameters of evolution without exploring the whole search space by iteratively sampling from the conditional distributions of the paths and parameters. A Metropolis–Hastings algorithm for sampling paths between two networks and calculating the likelihood of evolution is also presented. The sampler is used to estimate the parameters of evolution of metabolic networks in the genus Pseudomonas. Availability: An implementation of the Gibbs sampler in Java is available at http://www.stats.ox.ac.uk/∼mithani/networkGibbs/ Contact: mithani@stats.ox.ac.uk Supplementary information: Supplementary data are available at the Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Our focus has been on detecting topological properties that are rare in real proteins, but occur more frequently in models generated by protein structure prediction methods such as Rosetta. We previously created the Knotfind algorithm, successfully decreasing the frequency of knotted Rosetta models during CASP6. We observed an additional class of knot-like loops that appeared to be equally un-protein-like and yet do not contain a mathematical knot. These topological features are commonly referred to as slip-knots and are caused by the same mechanisms that result in knotted models. Slip-knots are undetectable by the original Knotfind algorithm. We have generalized our algorithm to detect them, and analyzed CASP6 models built using the Rosetta loop modeling method. Results: After analyzing known protein structures in the PDB, we found that slip-knots do occur in certain proteins, but are rare and fall into a small number of specific classes. Our group used this new Pokefind algorithm to distinguish between these rare real slip-knots and the numerous classes of slip-knots that we discovered in Rosetta models and models submitted by the various CASP7 servers. The goal of this work is to improve future models created by protein structure prediction methods. Both algorithms are able to detect un-protein-like features that current metrics such as GDT are unable to identify, so these topological filters can also be used as additional assessment tools. Contact: firas@u.washington.edu [ABSTRACT FROM AUTHOR]

Motivation: Analysis of array comparative genomic hybridization (aCGH) data for recurrent DNA copy number alterations from a cohort of patients can yield distinct sets of molecular signatures or profiles. This can be due to the presence of heterogeneous cancer subtypes within a supposedly homogeneous population. Results: We propose a novel statistical method for automatically detecting such subtypes or clusters. Our approach is model based: each cluster is defined in terms of a sparse profile, which contains the locations of unusually frequent alterations. The profile is represented as a hidden Markov model. Samples are assigned to clusters based on their similarity to the clusters profile. We simultaneously infer the cluster assignments and the cluster profiles using an expectation maximization-like algorithm. We show, using a realistic simulation study, that our method is significantly more accurate than standard clustering techniques. We then apply our method to two clinical datasets. In particular, we examine previously reported aCGH data from a cohort of 106 follicular lymphoma patients, and discover clusters that are known to correspond to clinically relevant subgroups. In addition, we examine a cohort of 92 diffuse large B-cell lymphoma patients, and discover previously unreported clusters of biological interest which have inspired followup clinical research on an independent cohort. Availability: Software and synthetic datasets are available at http://www.cs.ubc.ca/∼sshah/acgh as part of the CNA-HMMer package. Contact: sshah@bccrc.ca Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: The XML-based Systems Biology Markup Language (SBML) has emerged as a standard for storage, communication and interchange of models in systems biology. As a machine-readable format XML is difficult for humans to read and understand. Many tools are available that visualize the reaction pathways stored in SBML files, but many components, e.g. unit declarations, complex kinetic equations or links to MIRIAM resources, are often not made visible in these diagrams. For a broader understanding of the models, support in scientific writing and error detection, a human-readable report of the complete model is needed. We present SBML2LaTEX, a Java-based stand-alone program to fill this gap. A convenient web service allows users to directly convert SBML to various formats, including DVI, LaTEX and PDF, and provides many settings for customization. Availability: Source code, documentation and a web service are freely available at http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX. Contact:andreas.draeger@uni-tuebingen.de Supplementary information:Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

: Background and Aims The framework provided by an emergence model was used: (1) for phenotyping germination and heterotrophic growth of Medicago truncatula in relation to two major environmental factors, temperature and water potential; and (2) to evaluate the extent of genetic differences in emergence-model parameters. : Methods Eight cultivars and natural accessions of M. trunculata were studied. Germination was recorded from 5 to 30 °C and from 0 to −0·75 MPa, and seedling growth from 10 to 20 °C. : Key Results Thermal time to reach 50 % germination was very short (15 °Cd) and almost stable between genotypes, while base temperature (2–3 °C) and base water potential for germination (−0·7 to −1·3 MPa) varied between genotypes. Only 35 °Cd after germination were required to reach 30 mm hypocotyl length with significant differences among genotypes. Base temperature for elongation varied from 5·5 to 7·5 °C. Low temperatures induced a general shortening of the seedling, with some genotypes more responsive than others. No relationship with initial seed mass or seed reserve distribution was observed, which might have explained differences between genotypes and the effects of low temperatures. : Conclusions The study provides a set of reference values for M. trunculata users. The use of the ecophysiological model allows comparison of these values between such non-crop species and other crops. It has enabled phenotypic variability in response to environmental conditions related to the emergence process to be identified. The model will allow simulation of emergence differences between genotypes in a range of environments using these parameter values. Genomic tools available for the model species M. trunculata will make it possible to analyse the genetic and molecular determinants of these differences. [ABSTRACT FROM AUTHOR]

Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes. Results: We successfully constructed a three-endophenotype model for Taiwanese hypertensive males with high identification accuracy. Of the three endophenotypes, one is strongly protective, another is weakly protective and the third is highly correlated with developing young-onset male hypertension. Sixteen of the involved 101 genes were highly and consistently influential to the endophenotypes. Identification of SLC4A5, SLC5A10 and LDOC1 indicated that sodium/bicarbonate transport, sodium/glucose transport and cell-proliferation regulation may play important upstream roles and identification of BNIP1, APOBEC3F and LDOC1 suggested that apoptosis, innate immune response and cell-proliferation regulation may play important downstream roles in hypertension. The involved genes not only provide insights into the mechanism of hypertension but should also be considered in future gene mapping endeavors. Availability: Microarray data and test program are available at http://ms.iis.sinica.edu.tw/microarray/index.htm Contact: pan@ibms.sinica.edu.tw or hsu@iis.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Helix–helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. Availability: http://bio-cluster.iis.sinica.edu.tw/TMhit/ Contact: tsung@iis.sinica.edu.tw Supplementary information:Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent distributions that are a class of symmetric and long-tailed distributions and are able to accommodate residual outliers. Subsequently, we developed a Bayesian robust analysis strategy for dissecting genetic architecture of quantitative traits and for mapping genome-wide interacting QTLs in line crosses. Results: Through computer simulations, we showed that our strategy had a similar power for QTL detection compared with traditional methods assuming normal-distributed traits, but had a substantially increased power for non-normal phenotypes. When this strategy was applied to a group of traits associated with physical/chemical characteristics and quality in rice, more main and epistatic QTLs were detected than traditional Bayesian model analyses under the normal assumption. Contact: runqingyang@sjtu.edu.cn; dengh@umkc.edu Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS. Results: We suggest a procedure to model instrument and workflow-specific noise behaviour of iTRAQ™ reporter ions that can provide regulatory information during automated peptide sequencing by LC-MS/MS. The established mathematical model representatively predicts possible variations of iTRAQ™ reporter ions in an MS data-dependent manner. The model can be utilized to calculate the robustness of regulatory information systematically at the peptide level in so-called bottom-up proteome approaches. It allows to determine the best fitting regulation factor and in addition to calculate the probability of alternative regulations. The result can be visualized as likelihood curves summarizing both the quantity and quality of regulatory information. Likelihood curves basically can be calculated from all peptides belonging to different regions of proteins if they are detected in LC-MS/MS experiments. Therefore, this approach renders excellent opportunities to detect and statistically validate dynamic post-translational modifications usually affecting only particular regions of the whole protein. The detection of known phosphorylation events at protein kinases served as a first proof of concept in this study and underscores the potential for noise models in quantitative proteomics. Contact: lothar.jaensch@helmholtz-hzi.de; f.klawonn@fh-wolfenbuettel.de Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Bright field imaging of biological samples stained with antibodies and/or special stains provides a rapid protocol for visualizing various macromolecules. However, this method of sample staining and imaging is rarely employed for direct quantitative analysis due to variations in sample fixations, ambiguities introduced by color composition and the limited dynamic range of imaging instruments. We demonstrate that, through the decomposition of color signals, staining can be scored on a cell-by-cell basis. We have applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two distinct populations. Initially, nuclear regions are segmented through conversion of color images into gray scale, and detection of dark elliptic features. Subsequently, the strength of staining is quantified by a color decomposition model that is optimized by a graph cut algorithm. In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected. Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation. Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance. contact: hchang@lbl.gov [ABSTRACT FROM AUTHOR]

Motivation: MicroRNAs miRNAs play important roles in gene regulation and are regarded as key components in gene regulatory pathways. Systematically understanding functional roles of miRNAs is essential to define core transcriptional units regulating key biological processes. Here, we propose a method based on the probabilistic graphical model to identify the regulatory modules of miRNAs and the core regulatory motifs involved in their ability to regulate gene expression. Results: We applied our method to datasets of different sources from Arabidopsis consisting of miRNA-target pair information, upstream sequences of miRNAs, transcriptional regulatory motifs and gene expression profiles. The graphical model used in this study can efficiently capture the relationship between miRNAs and diverse conditions such as various developmental processes, thus allowing us to detect functionally correlated miRNA regulatory modules involved in specific biological processes. Furthermore, this approach can reveal core transcriptional elements associated with their miRNAs. The proposed method found clusters of miRNAs, as well as putative regulators controlling the expression of miRNAs, which were highly related to diverse developmental processes of Arabidopsis. Consequently, our method can provide hypothetical miRNA regulatory circuits for functional testing that represent transcriptional events of miRNAs and transcriptional factors involved in gene regulatory pathways. Contact: zf25@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]