A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

Unsupervised reduction of random noise in complex data by a row-specific, sorted principal component-guided method

<p>Abstract</p> <p>Background</p> <p>Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but it tends to remove small features as well as random noise.</p> <p>Results</p> <p>We interpreted the PCs as a mere signal-rich coordinate system and sorted the squared PC-coordinates of each row in descending order. The sorted squared PC-coordinates were compared with the distribution of the ordered squared random noise, and PC-coordinates for insignificant contributions were treated as random noise and nullified. The processed data were transformed back to the initial coordinates as noise-reduced data. To increase the sensitivity of signal capture and reduce the effects of stochastic noise, this procedure was applied to multiple small subsets of rows randomly sampled from a large data set, and the results corresponding to each row of the data set from multiple subsets were averaged. We call this procedure Row-specific, Sorted PRincipal component-guided Noise Reduction (RSPR-NR). Robust performance of RSPR-NR, measured by noise reduction and retention of small features, was demonstrated using simulated data sets. Furthermore, when applied to an actual expression profile data set, RSPR-NR preferentially increased the correlations between genes that share the same Gene Ontology terms, strongly suggesting reduction of random noise in the data set.</p> <p>Conclusion</p> <p>RSPR-NR is a robust random noise reduction method that retains small features well. It should be useful in improving the quality of large biological data sets.</p

The grinch who stole wisdom

Dr. Seuss is wise. How the Grinch Stole Christmas (Seuss, 1957) could serve as a parable for our time. It can also be seen as a roadmap for the development of contemplative wisdom. The abiding popularity of How the Grinch Stole Christmas additionally suggests that contemplative wisdom is more readily available to ordinary people, even children, than is normally thought. This matters because from the point of view of contemplatives in any of the world's philosophies or religions, people are confused about wisdom. The content of the nascent field of wisdom studies, they might say, is largely not wisdom at all but rather what it's like to live in a particular kind of prison cell, a well appointed cell perhaps, but not a place that makes possible either personal satisfaction or deep problem solving. I believe that what the contemplative traditions have to say is important; they offer a different orientation to what personal wisdom is, how to develop it, and how to use it in the world than is presently contained in either our popular culture or our sciences. In order to illustrate this I will examine, in some detail, one contemplative path within Buddhism. Buddhism is particularly useful in this respect because its practices are nontheistic and thus avoid many of the cultural landmines associated with the contemplative aspects of Western religions

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

A Pilot study of the Sharing Risk Information Tool (ShaRIT) for Families with Hereditary Breast and Ovarian Cancer Syndrome

<p>Abstract</p> <p>Background</p> <p>Individuals who carry deleterious BRCA mutations face significantly elevated risks of breast, ovarian, and other cancers. These individuals are also responsible for informing relatives of their increased risk for carrying the family BRCA mutation. Few interventions have been developed to facilitate this family communication process.</p> <p>Methods</p> <p>We developed the Sharing Risk Information Tool (ShaRIT), a personalized educational intervention, to support BRCA carriers as they discuss BRCA positive results and their implications with relatives. We conducted a pilot study of 19 BRCA carriers identified through the University of California San Francisco Cancer Risk Program. Our study had two aims: 1) to assess the feasibility and acceptability of ShaRIT, and 2) describe characteristics associated with increased family communication and BRCA testing. Participants in our study were divided into two groups: those who had not received ShaRIT as part of their genetic counseling protocol (control group, n = 10) and those who received ShaRIT (n = 9).</p> <p>Results</p> <p>All 9 women who received ShaRIT reported that it was a useful resource. Characteristics associated with increased sharing and testing included: female gender, degree of relationship, and frequency of communication. Increased pedigree knowledge showed a trend toward higher rates of sharing.</p> <p>Conclusions</p> <p>Both participants and genetic counselors considered ShaRIT a well-received, comprehensive tool for disseminating individual risk information and clinical care guidelines to Hereditary Breast and Ovarian Cancer Syndrome families. Because of this, ShaRIT has been incorporated as standard of care at our institution. In the future we hope to evaluate the effects of ShaRIT on family communication and family testing in larger populations of BRCA positive families.</p

Postherpetic Neuralgia: Role of Gabapentin and Other Treatment Modalities

Postherpetic neuralgia (PHN) is a chronic and painful condition that may occur after a herpes zoster infection. The frequency of PHN after untreated zoster varies widely. Age is the most important risk factor for development of PHN. The condition occurs in an estimated 50% of patients older than 50 years. The pain of PHN can be severe and debilitating and is frequently associated with allodynia. Although in most patients pain remits within the first year, it may persist for a lifetime. Tricyclic antidepressants (TCAs), topical agents, opioids, and gabapentin, a structural Γ-amino butyric acid (GABA) analogue, are the only agents that have demonstrated efficacy in randomized clinical trials for treatment of both the shooting and the burning form of pain associated with PHN. TCAs are among the most commonly used classes of agents for treating PHN and are effective in a significant proportion of patients. However, various adverse events can limit treatment. These side effects tend to be more acute in the elderly, the population most likely to suffer from PHN. Topical agents have led to mild to moderate improvement in patients with PHN but are usually ineffective as monotherapy for this condition. Until recently, carbamazepine was the only antiepileptic drug evaluated for the treatment of PHN. Over the past few years, however, gabapentin has received increasing attention as a useful treatment for neuropathic pain. Gabapentin lacks significant drug-drug interactions and has a favorable safety profile, which makes it particularly useful for treatment of PHN.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65597/1/j.1528-1157.1999.tb00933.x.pd

Can we derive an 'exchange rate' between descriptive and preference-based outcome measures for stroke? Results from the transfer to utility (TTU) technique

<p>Abstract</p> <p>Background</p> <p>Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke.</p> <p>Methods</p> <p>We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke.</p> <p>Results</p> <p>Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale.</p> <p>Conclusion</p> <p>While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation.</p

Identification of Yeast Transcriptional Regulation Networks Using Multivariate Random Forests

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes

Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder

<p>Abstract</p> <p>Background</p> <p>Survival analysis methods such as the Kaplan-Meier method, log-rank test, and Cox proportional hazards regression (Cox regression) are commonly used to analyze data from randomized withdrawal studies in patients with major depressive disorder. However, unfortunately, such common methods may be inappropriate when a long-term censored relapse-free time appears in data as the methods assume that if complete follow-up were possible for all individuals, each would eventually experience the event of interest.</p> <p>Methods</p> <p>In this paper, to analyse data including such a long-term censored relapse-free time, we discuss a semi-parametric cure regression (Cox cure regression), which combines a logistic formulation for the probability of occurrence of an event with a Cox proportional hazards specification for the time of occurrence of the event. In specifying the treatment's effect on disease-free survival, we consider the fraction of long-term survivors and the risks associated with a relapse of the disease. In addition, we develop a tree-based method for the time to event data to identify groups of patients with differing prognoses (cure survival CART). Although analysis methods typically adapt the log-rank statistic for recursive partitioning procedures, the method applied here used a likelihood ratio (LR) test statistic from a fitting of cure survival regression assuming exponential and Weibull distributions for the latency time of relapse.</p> <p>Results</p> <p>The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder.</p> <p>Conclusions</p> <p>We concluded that Cox cure regression reveals facts on who may be cured, and how the treatment and other factors effect on the cured incidence and on the relapse time of uncured patients, and that cure survival CART output provides easily understandable and interpretable information, useful both in identifying groups of patients with differing prognoses and in utilizing Cox cure regression models leading to meaningful interpretations.</p

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure