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1.  Automated identification of pathways from quantitative genetic interaction data
We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated.
Recent developments have enabled large-scale quantitative measurement of genetic interactions (GIs) that report on the extent to which the activity of one gene is dependent on a second. It has long been recognized (Avery and Wasserman, 1992; Hartman et al, 2001; Segre et al, 2004; Tong et al, 2004; Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Costanzo et al, 2010) that functional dependencies revealed by GI data can provide rich information regarding underlying biological pathways. Further, the precise phenotypic measurements provided by quantitative GI data can provide evidence for even more detailed aspects of pathway structure, such as differentiating between full and partial dependence between two genes (Drees et al, 2005; Schuldiner et al, 2005; St Onge et al, 2007; Jonikas et al, 2009) (Figure 1A). As GI data sets become available for a range of quantitative phenotypes and organisms, such patterns will allow researchers to elucidate pathways important to a diverse set of biological processes.
We present a new method that exploits the high-quality, quantitative nature of recent GI assays to automatically reconstruct detailed multi-gene pathway structures, including the organization of a large set of genes into coherent pathways, the connectivity and ordering within each pathway, and the directionality of each relationship. We introduce activity pathway networks (APNs), which represent functional dependencies among a set of genes in the form of a network. We present an automatic method to efficiently reconstruct APNs over large sets of genes based on quantitative GI measurements. This method handles uncertainty in the data arising from noise, missing measurements, and data points with ambiguous interpretations, by performing global reasoning that combines evidence from multiple data points. In addition, because some structure choices remain uncertain even when jointly considering all measurements, our method maintains multiple likely networks, and allows computation of confidence estimates over each structure choice.
We applied our APN reconstruction method to the recent high-quality GI data set of Jonikas et al (2009), which examined the functional interaction between genes that contribute to protein folding in the ER. Specifically, Jonikas et al used the cell's endogenous sensor (the unfolded protein response), to first identify several hundred yeast genes with functions in endoplasmic reticulum folding and then systematically characterized their functional interdependencies by measuring unfolded protein response levels in double mutants. Our analysis produced an ensemble of 500 likelihood-weighted APNs over 178 genes (Figure 2).
We performed an aggregate evaluation of our results by comparing to known biological relationships between gene pairs, including participation in pathways according to the Kyoto Encyclopedia of Genes and Genomes (KEGG), correlation of chemical genomic profiles in a recent high-throughput assay (Hillenmeyer et al, 2008) and similarity of Gene Ontology (GO) annotations. In each evaluation performed, our reconstructed APNs were significantly more consistent with the known relationships than either the raw GI values or the Pearson correlation between profiles of GI values.
Importantly, our approach provides not only an improved means for defining pairs or groups of related genes, but also enables the identification of detailed multi-gene network structures. In many cases, our method successfully reconstructed known cellular pathways, including the ER-associated degradation (ERAD) pathway, and the biosynthesis of N-linked glycans, ranking them among the highest confidence structures. In-depth examination of the learned network structures indicates agreement with many known details of these pathways. In addition, quantitative analysis indicates that our learned APNs are indicative of ordering within KEGG-annotated biological pathways.
Our results also suggest several novel relationships, including placement of uncharacterized genes into pathways, and novel relationships between characterized genes. These include the dependence of the J domain chaperone JEM1 on the PDI homolog MPD1, dependence of the Ubiquitin-recycling enzyme DOA4 on N-linked glycosylation, and the dependence of the E3 Ubiquitin ligase DOA10 on the signal peptidase complex subunit SPC2. Our APNs also place the poorly characterized TPR-containing protein SGT2 upstream of the tail-anchored protein biogenesis machinery components GET3, GET4, and MDY2 (also known as GET5), suggesting that SGT2 has a function in the insertion of tail-anchored proteins into membranes. Consistent with this prediction, our experimental analysis shows that sgt2Δ cells show a defect in localization of the tail-anchored protein GFP-Sed5 from punctuate Golgi structures to a more diffuse pattern, as seen in other genes involved in this pathway.
Our results show that multi-gene, detailed pathway networks can be reconstructed from quantitative GI data, providing a concrete computational manifestation to intuitions that have traditionally accompanied the manual interpretation of such data. Ongoing technological developments in both genetics and imaging are enabling the measurement of GI data at a genome-wide scale, using high-accuracy quantitative phenotypes that relate to a range of particular biological functions. Methods based on RNAi will soon allow collection of similar data for human cell lines and other mammalian systems (Moffat et al, 2006). Thus, computational methods for analyzing GI data could have an important function in mapping pathways involved in complex biological systems including human cells.
High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.
PMCID: PMC2913392  PMID: 20531408
computational biology; genetic interaction; pathway reconstruction; probabilistic methods
2.  Insights gained from the reverse engineering of gene networks in keloid fibroblasts
Keloids are protrusive claw-like scars that have a propensity to recur even after surgery, and its molecular etiology remains elusive. The goal of reverse engineering is to infer gene networks from observational data, thus providing insight into the inner workings of a cell. However, most attempts at modeling biological networks have been done using simulated data. This study aims to highlight some of the issues involved in working with experimental data, and at the same time gain some insights into the transcriptional regulatory mechanism present in keloid fibroblasts.
Microarray data from our previous study was combined with microarray data obtained from the literature as well as new microarray data generated by our group. For the physical approach, we used the fREDUCE algorithm for correlating expression values to binding motifs. For the influence approach, we compared the Bayesian algorithm BANJO with the information theoretic method ARACNE in terms of performance in recovering known influence networks obtained from the KEGG database. In addition, we also compared the performance of different normalization methods as well as different types of gene networks.
Using the physical approach, we found consensus sequences that were active in the keloid condition, as well as some sequences that were responsive to steroids, a commonly used treatment for keloids. From the influence approach, we found that BANJO was better at recovering the gene networks compared to ARACNE and that transcriptional networks were better suited for network recovery compared to cytokine-receptor interaction networks and intracellular signaling networks. We also found that the NFKB transcriptional network that was inferred from normal fibroblast data was more accurate compared to that inferred from keloid data, suggesting a more robust network in the keloid condition.
Consensus sequences that were found from this study are possible transcription factor binding sites and could be explored for developing future keloid treatments or for improving the efficacy of current steroid treatments. We also found that the combination of the Bayesian algorithm, RMA normalization and transcriptional networks gave the best reconstruction results and this could serve as a guide for future influence approaches dealing with experimental data.
PMCID: PMC3094296  PMID: 21535890
BMC Bioinformatics  2010;11:154.
One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.
In this paper we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements concern S. cerevisiae cell cycle, E. coli SOS pathways and a recently developed network for in vivo assessment of reverse engineering algorithms. Our results are compared with ARACNE itself and with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and F-score for the network reconstruction task.
Here we report the adaptation of the ARACNE algorithm to infer gene regulatory networks from time-course data, so that, the resulting network is represented as a directed graph. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.
PMCID: PMC2862045  PMID: 20338053
4.  Reverse engineering module networks by PSO-RNN hybrid modeling
BMC Genomics  2009;10(Suppl 1):S15.
Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.
This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information.
The reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.
PMCID: PMC2709258  PMID: 19594874
5.  Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment
BMC Bioinformatics  2013;14(Suppl 14):S3.
Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting.
Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways.
Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach.
Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.
PMCID: PMC3851258  PMID: 24268022
6.  Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique
BMC Bioinformatics  2014;15(Suppl 15):S8.
To infer gene regulatory networks from time series gene profiles, two important tasks that are related to biological systems must be undertaken. One task is to determine a valid network structure that has topological properties that can influence the network dynamics profoundly. The other task is to optimize the network parameters to minimize the accumulated discrepancy between the gene expression data and the values produced by the inferred network model. Though the above two tasks must be conducted simultaneously, most existing work addresses only one of the tasks.
We propose an iterative approach that couples parameter identification and parameter optimization techniques, to address the two tasks simultaneously during network inference. This approach first identifies the most influential parameters against internal perturbations; this identification is based on sensitivity measurements. Then, a hybrid GA-PSO optimization method infers parameters in accordance with their criticalities. The proposed approach has been applied to several datasets, including subsets of the SOS DNA repair system in E. coli, the Rat central nervous system (CNS), and the protein glycosylation system of yeast S. cerevisiae. The result and analysis show that our approach can infer solutions to satisfy both the requirements of network structure and network behavior.
Network structure is an important though challenging issue to address in inferring sophisticated networks with biological details. In need of prior structural knowledge, we turn to measure parameter sensitivity instead to account for the network structure in an indirect way. By developing an integrated approach for considering both the network structure and behavior in the inference process, we can successfully infer critical gene interactions as well as valid time expression profiles.
PMCID: PMC4271569  PMID: 25474560
7.  Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference
PLoS Computational Biology  2014;10(6):e1003666.
Author Summary
We have developed a methodology that combines standard computational analysis of gene expression data with knowledge in the literature to identify pathways of gene and protein interactions. We extract the knowledge from PubMed citations using a tool (SemRep) that identifies specific relationships between genes or proteins. We string together networks of individual interactions that are found within citations that refer to the target pathways. Upon this skeleton of interactions, we calculate the weight of the interaction with the gene expression data captured over multiple time points using state-of-the-art analysis algorithms. Not surprisingly, this approach of combining prior knowledge into the analysis process significantly improves the performance of the analysis. This work is most significant as an example of how the wealth of textual data related to gene interactions can be incorporated into computational analysis, not solely to identify this type of pathway (a gene regulatory network) but for any type of similar biological problem.
PMCID: PMC4055569  PMID: 24921649
8.  Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma.
We introduce a modeling approach termed EPoC (Endogenous Perturbation analysis of Cancer), enabling the construction of global, gene-level models that causally connect gene copy number with expression in glioblastoma.On the basis of the resulting model, we predict genes that are likely to be disease-driving and validate selected predictions experimentally. We also demonstrate that further analysis of the network model by sparse singular value decomposition allows stratification of patients with glioblastoma into short-term and long-term survivors, introducing decomposed network models as a useful principle for biomarker discovery.Finally, in systematic comparisons, we demonstrate that EPoC is computationally efficient and yields more consistent results than mRNA-only methods, standard eQTL methods, and two recent multivariate methods for genotype–mRNA coupling.
Gains and losses of chromosomal material (DNA copy number aberrations; CNAs) are a characteristic feature of cancer genomes. At the level of a single locus, it is well known that increased copy number (gene amplification) typically leads to increased gene expression, whereas decreased copy number (gene deletion) leads to decreased gene expression (Pollack et al, 2002; Lee et al, 2008; Nilsson et al, 2008). However, CNAs also affect the expression of genes located outside the amplified/deleted region itself via indirect mechanisms. To fully understand the action of CNAs, it is therefore necessary to analyze their action in a network context. Toward this goal, improved computational approaches will be important, if not essential.
To determine the global effects on transcription of CNAs in the brain tumor glioblastoma, we develop EPoC (Endogenous Perturbation analysis of Cancer), a computational technique capable of inferring sparse, causal network models by combining genome-wide, paired CNA- and mRNA-level data. EPoC aims to detect disease-driving copy number aberrations and their effect on target mRNA expression, and stratify patients into long-term and short-term survivors. Technically, EPoC relates CNA perturbations to mRNA responses by matrix equations, derived from a steady-state approximation of the transcriptional network. Patient prognostic scores are obtained from singular value decompositions of the network matrix. The models are constructed by solving a large-scale, regularized regression problem.
We apply EPoC to glioblastoma data from The Cancer Genome Atlas (TCGA) consortium (186 patients). The identified CNA-driven network comprises 10 672 genes, and contains a number of copy number-altered genes that control multiple downstream genes. Highly connected hub genes include well-known oncogenes and tumor supressor genes that are frequently deleted or amplified in glioblastoma, including EGFR, PDGFRA, CDKN2A and CDKN2B, confirming a clear association between these aberrations and transcriptional variability of these brain tumors. In addition, we identify a number of hub genes that have previously not been associated with glioblastoma, including interferon alpha 1 (IFNA1), myeloid/lymphoid or mixed-lineage leukemia translocated to 10 (MLLT10, a well-known leukemia gene), glutamate decarboxylase 2 GAD2, a postulated glutamate receptor GPR158 and Necdin (NDN). Furthermore, we demonstrate that the network model contains useful information on downstream target genes (including stem cell regulators), and possible drug targets.
We proceed to explore the validity of a small network region experimentally. Introducing experimental perturbations of NDN and other targets in four glioblastoma cell lines (T98G, U-87MG, U-343MG and U-373MG), we confirm several predicted mechanisms. We also demonstrate that the TCGA glioblastoma patients can be stratified into long-term and short-term survivors, using our proposed prognostic scores derived from a singular vector decomposition of the network model. Finally, we compare EPoC to existing methods for mRNA networks analysis and expression quantitative locus methods, and demonstrate that EPoC produces more consistent models between technically independent glioblastoma data sets, and that the EPoC models exhibit better overlap with known protein–protein interaction networks and pathway maps.
In summary, we conclude that large-scale integrative modeling reveals mechanistically and prognostically informative networks in human glioblastoma. Our approach operates at the gene level and our data support that individual hub genes can be identified in practice. Very large aberrations, however, cannot be fully resolved by the current modeling strategy.
DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
PMCID: PMC3101951  PMID: 21525872
cancer biology; cancer genomics; glioblastoma
9.  Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis
BMC Bioinformatics  2011;12:203.
Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.
We have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.
Relationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.
PMCID: PMC3118170  PMID: 21612636
10.  Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks
PLoS Computational Biology  2006;2(12):e169.
PMCID: PMC1761652  PMID: 17194216
11.  Integration of molecular network data reconstructs Gene Ontology
Bioinformatics  2014;30(17):i594-i600.
Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data.
Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling.
Availability and implementation: Supplementary Tables of new GO term associations and predicted gene annotations are available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC4230235  PMID: 25161252
12.  Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data
BMC Bioinformatics  2008;9:203.
Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.
The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.
The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.
PMCID: PMC2386822  PMID: 18426580
13.  Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation
PLoS Biology  2012;10(4):e1001301.
Author Summary
PMCID: PMC3317911  PMID: 22509135
14.  Computational Inference of Neural Information Flow Networks
PLoS Computational Biology  2006;2(11):e161.
One of the challenges in the area of brain research is to decipher networks describing the flow of information among communicating neurons in the form of electrophysiological signals. These networks are thought to be responsible for perceiving and learning about the environment, as well as producing behavior. Monitoring these networks is limited by the number of electrodes that can be placed in the brain of an awake animal, while inferring and reasoning about these networks is limited by the availability of appropriate computational tools. Here, Smith and Yu and colleagues begin to address these issues by implanting microelectrode arrays in the auditory pathway of freely moving songbirds and by analyzing the data using new computational tools they have designed for deciphering networks. The authors find that a dynamic Bayesian network algorithm they developed to decipher gene regulatory networks from gene expression data effectively infers putative information flow networks in the brain from microelectrode array data. The networks they infer conform to known anatomy and other biological properties of the auditory system and offer new insight into how the auditory system processes natural and synthetic sound. The authors believe that their results represent the first validated study of the inference of information flow networks in the brain.
PMCID: PMC1664702  PMID: 17121460
15.  Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress
PLoS Computational Biology  2008;4(8):e1000166.
Author Summary
PMCID: PMC2516606  PMID: 18769717
16.  The topology of the bacterial co-conserved protein network and its implications for predicting protein function
BMC Genomics  2008;9:313.
Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins.
Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the network topology, we were able to infer function for uncharacterized proteins.
Interactions networks based on co-conservation can contain information distinct from networks based on physical or other interaction types. Our study has shown co-conservation based networks to exhibit a scale free topology, as expected for biological networks. We also revealed ways that connectivity in our networks can be informative for the functional characterization of proteins.
PMCID: PMC2488357  PMID: 18590549
17.  Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations
PLoS Computational Biology  2013;9(5):e1003068.
Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs) are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL), for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML) is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL) based scheme, and the QTL-directed dependency graph (QDG) method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.
Author Summary
Deciphering the structure of gene regulatory networks is crucial for understanding gene functions and cellular dynamics, as well as system-level modeling of individual genes and cellular functions. Computational methods exploiting gene expression and other types of data generated from high-throughput experiments provide an efficient and low-cost means of inferring gene networks. Sparse structural equation models are employed to: i) integrate both gene expression and genetic perturbation data for inference of gene networks; and, ii) develop an efficient sparsity-aware inference algorithm. Computer simulations corroborate that the novel algorithm markedly outperforms state-of-the-art alternatives. The algorithm is further applied to infer a real human gene network unveiling possible interactions between several genes. Since gene networks can be perturbed not only by genetic variations but also by other means such as gene copy number changes, gene knockdown or controlled gene over-expression, this paper's method can be applied to a number of practical scenarios.
PMCID: PMC3662697  PMID: 23717196
18.  Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources
PLoS ONE  2013;8(6):e67410.
Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.
PMCID: PMC3691143  PMID: 23826291
19.  A new pairwise kernel for biological network inference with support vector machines
BMC Bioinformatics  2007;8(Suppl 10):S8.
Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.).
Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel.
The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.
PMCID: PMC2230501  PMID: 18269702
20.  System-Level Insights into the Cellular Interactome of a Non-Model Organism: Inferring, Modelling and Analysing Functional Gene Network of Soybean (Glycine max)
PLoS ONE  2014;9(11):e113907.
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN:
PMCID: PMC4244207  PMID: 25423109
21.  Identifying the genetic determinants of transcription factor activity
Genome-wide messenger RNA expression levels are highly heritable. However, the molecular mechanisms underlying this heritability are poorly understood.The influence of trans-acting polymorphisms is often mediated by changes in the regulatory activity of one or more sequence-specific transcription factors (TFs). We use a method that exploits prior information about the DNA-binding specificity of each TF to estimate its genotype-specific regulatory activity. To this end, we perform linear regression of genotype-specific differential mRNA expression on TF-specific promoter-binding affinity.Treating inferred TF activity as a quantitative trait and mapping it across a panel of segregants from an experimental genetic cross allows us to identify trans-acting loci (‘aQTLs') whose allelic variation modulates the TF. A few of these aQTL regions contain the gene encoding the TF itself; several others contain a gene whose protein product is known to interact with the TF.Our method is strictly causal, as it only uses sequence-based features as predictors. Application to budding yeast demonstrates a dramatic increase in statistical power, compared with existing methods, to detect locus-TF associations and trans-acting loci. Our aQTL mapping strategy also succeeds in mouse.
Genetic sequence variation naturally perturbs mRNA expression levels in the cell. In recent years, analysis of parallel genotyping and expression profiling data for segregants from genetic crosses between parental strains has revealed that mRNA expression levels are highly heritable. Expression quantitative trait loci (eQTLs), whose allelic variation regulates the expression level of individual genes, have successfully been identified (Brem et al, 2002; Schadt et al, 2003). The molecular mechanisms underlying the heritability of mRNA expression are poorly understood. However, they are likely to involve mediation by transcription factors (TFs). We present a new transcription-factor-centric method that greatly increases our ability to understand what drives the genetic variation in mRNA expression (Figure 1). Our method identifies genomic loci (‘aQTLs') whose allelic variation modulates the protein-level activity of specific TFs. To map aQTLs, we integrate genotyping and expression profiling data with quantitative prior information about DNA-binding specificity of transcription factors in the form of position-specific affinity matrices (Bussemaker et al, 2007). We applied our method in two different organisms: budding yeast and mouse.
In our approach, the inferred TF activity is explicitly treated as a quantitative trait, and genetically mapped. The decrease of ‘phenotype space' from that of all genes (in the eQTL approach) to that of all TFs (in our aQTL approach) increases the statistical power to detect trans-acting loci in two distinct ways. First, as each inferred TF activity is derived from a large number of genes, it is far less noisy than mRNA levels of individual genes. Second, the number of trait/marker combinations that needs to be tested for statistical significance in parallel is roughly two orders of magnitude smaller than for eQTLs. We identified a total of 103 locus-TF associations, a more than six-fold improvement over the 17 locus-TF associations identified by several existing methods (Brem et al, 2002; Yvert et al, 2003; Lee et al, 2006; Smith and Kruglyak, 2008; Zhu et al, 2008). The total number of distinct genomic loci identified as an aQTL equals 31, which includes 11 of the 13 previously identified eQTL hotspots (Smith and Kruglyak, 2008).
To better understand the mechanisms underlying the identified genetic linkages, we examined the genes within each aQTL region. First, we found four ‘local' aQTLs, which encompass the gene encoding the TF itself. This includes the known polymorphism in the HAP1 gene (Brem et al, 2002), but also novel predictions of trans-acting polymorphisms in RFX1, STB5, and HAP4. Second, using high-throughput protein–protein interaction data, we identified putative causal genes for several aQTLs. For example, we predict that a polymorphism in the cyclin-dependent kinase CDC28 antagonistically modulates the functionally distinct cell cycle regulators Fkh1 and Fkh2. In this and other cases, our approach naturally accounts for post-translational modulation of TF activity at the protein level.
We validated our ability to predict locus-TF associations in yeast using gene expression profiles of allele replacement strains from a previous study (Smith and Kruglyak, 2008). Chromosome 15 contains an aQTL whose allelic status influences the activity of no fewer than 30 distinct TFs. This locus includes IRA2, which controls intracellular cAMP levels. We used the gene expression profile of IRA2 replacement strains to confirm that the polymorphism within IRA2 indeed modulates a subset of the TFs whose activity was predicted to link to this locus, and no other TFs.
Application of our approach to mouse data identified an aQTL modulating the activity of a specific TF in liver cells. We identified an aQTL on mouse chromosome 7 for Zscan4, a transcription factor containing four zinc finger domains and a SCAN domain. Even though we could not detect a candidate causal gene for Zscan4p because of lack of information about the mouse genome, our result demonstrates that our method also works in higher eukaryotes.
In summary, aQTL mapping has a greatly improved sensitivity to detect molecular mechanisms underlying the heritability of gene expression. The successful application of our approach to yeast and mouse data underscores the value of explicitly treating the inferred TF activity as a quantitative trait for increasing statistical power of detecting trans-acting loci. Furthermore, our method is computationally efficient, and easily applicable to any other organism whenever prior information about the DNA-binding specificity of TFs is available.
Analysis of parallel genotyping and expression profiling data has shown that mRNA expression levels are highly heritable. Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans-acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). We present a method that exploits prior knowledge about the in vitro DNA-binding specificity of a TF in order to map the loci (‘aQTLs') whose inheritance modulates its protein-level regulatory activity. Genome-wide regression of differential mRNA expression on predicted promoter affinity is used to estimate segregant-specific TF activity, which is subsequently mapped as a quantitative phenotype. In budding yeast, our method identifies six times as many locus-TF associations and more than twice as many trans-acting loci as all existing methods combined. Application to mouse data from an F2 intercross identified an aQTL on chromosome VII modulating the activity of Zscan4 in liver cells. Our method has greatly improved statistical power over existing methods, is mechanism based, strictly causal, computationally efficient, and generally applicable.
PMCID: PMC2964119  PMID: 20865005
gene expression; gene regulatory networks; genetic variation; quantitative trait loci; transcription factors
22.  Inferring microRNA and transcription factor regulatory networks in heterogeneous data
BMC Bioinformatics  2013;14:92.
Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.
We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.
We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT.
We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
PMCID: PMC3636059  PMID: 23497388
23.  Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration
PLoS ONE  2010;5(4):e10268.
Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.
Results and Principal Findings
We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.
We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.
PMCID: PMC2858157  PMID: 20422009
24.  An integrative approach to inferring biologically meaningful gene modules
BMC Systems Biology  2011;5:117.
PMCID: PMC3156758  PMID: 21791051
25.  Sets2Networks: network inference from repeated observations of sets
BMC Systems Biology  2012;6:89.
The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated co-occurrence of entities in related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research. Hence, such methods would be of great utility and value.
Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically execute the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics, integrate data from Chip-seq and loss-of-function/gain-of-function followed by expression data to infer a network of associations between pluripotency regulators, extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA’s Adverse Events Reporting Systems (AERS), and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at
The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.
PMCID: PMC3443648  PMID: 22824380

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Price-To-Research Ratio - PRR

DEFINITION of 'Price-To-Research Ratio - PRR'

A measure of the relationship between a company's market capitalization and its research and development (R&D) expenses. The price-to-research ratio is calculated by dividing a company's market value by its last 12 months' worth of R&D expenditures. PRR is very important in research-based businesses such as pharmaceutical companies, software companies, hardware companies and consumer products companies. Without spending on scientific and technological work, these firms cannot generate new products, processes or services and will not grow, increase their market share or improve their profitability.

BREAKING DOWN 'Price-To-Research Ratio - PRR'

In an industry heavily dependent on R&D, the price-to-research ratio is an important indicator of a company's ability to generate profits. Increasing earnings cannot tell the whole story because a company can increase earnings by slashing R&D expenses, which may stifle long-term growth and profitability. However, a favorable price-to-research ratio does not guarantee the success of future product innovations, nor does a large amount of R&D spending guarantee future profits. What really matters is how effectively the company is employing its R&D dollars. Also, the appropriate level of R&D spending varies by industry and depends on the company's development stage.

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BETTY SPINNER enrolled in Nassau County's Republican Party when she was 21. That was 56 years ago.

This year, after a lifetime as a Republican, she decided she was so fed up with the Republican Party that she marched into the Board of Elections office in Mineola and re-registered herself, this time as a Democrat.

''The irresponsibleness of the Republican Party made me really furious,'' said Mrs. Spinner, of Oceanside. ''If I ran my household the way they're running this county I would have lost everything I own. I'm 77. Why should I have to now begin to pay off their debt?''

So thorough a convert is she that she now volunteers five days a week at party headquarters -- answering phones, stuffing envelopes, whatever is asked of her.

Mrs. Spinner is, of course, the Democratic Party's favorite example. But even though the Board of Elections does not keep a tally of those who switch parties, officials estimated that there have been hundreds, perhaps thousands, of Republicans turning Democrat in recent years. Their shared disaffection with the Republican Party is one of the main reasons that Republican Party enrollment has declined significantly in Nassau County. Just 10 years ago, Republicans made up nearly 49 percent of the registered voters in the county, but that figure has dropped to 42 percent, while the number of enrolled Democrats has grown from 30 to 33 percent.

The numbers in Suffolk County, another Republican bastion, have seen a similar shift, with the percentage of Republican voters dropping from 44 to 39 while the percentage of Democrats has grown from 25 to 28. For decades, the ratio of Republicans to Democrats in Nassau County was roughly three to two and even higher in Suffolk County at two to one. But over the years, the Republican advantage has narrowed and is coming ever closer to parity.

Politicians and political analysts say the change is big enough to seriously challenge decades of Republican supremacy in both counties. Now, instead of relying on their party machines to get their voters to the polls and maybe counting on some Conservative Party voters, the Republicans in both counties are more likely to have to make inroads elsewhere. As the Republican and Democratic parties start wooing support from members of other minor parties like the Independence, Working Families and Right to Life parties, they imbue the smaller parties with newfound power.

Some voters, like Mrs. Spinner, switched in anger over Republican stewardship during Nassau County's plunge toward insolvency or recent scandals that embroiled the Suffolk County Republican Party. But political analysts and party leaders said there were a variety of other reasons for the Island's shifting party enrollment figures:

*Much of the decline likely resulted from population changes, with Republican retirees leaving Long Island for warmer climates and an influx of new voters who tend to be younger, more diverse and more inclined to align with Democrats.

*The ''motor voter'' registration system, which encourages people to register when they get a new driver's license or vehicle registration, also has probably brought in younger people who never voted before but are familiar enough with local politics to decide they do not want to be affiliated with the Republicans.

*The influence of the conservative right at the national level of Republican politics may have turned off many Long Islanders, who have traditionally been more moderate.

In both counties, while the Republican Party saw significant drops in enrollment, the Democrat Party picked up a large portion, but not all of, the decrease. The Independence and Working Families parties soaked up some of the new enrollees. Those who chose not to register with any party also grew in Nassau, going from 18 percent to nearly 21 percent, although that same figure in Suffolk declined slightly from 27 to 26 percent.

Howard A. Scarrow, a professor of political science at the State University of New York at Stony Brook, said these shifts show that ''Long Island is getting more people coming in who are not part of the traditional view of the suburbs.'' He noted that the latest census figures showed significant increases in the number of Hispanics, Asians and blacks. ''A lot of these people are coming from the city, and a lot are immigrants,'' he said. ''And when they look for the party that is more favorable to their point of view, more often it is the Democrats.''

While Long Island's growth in the 1950's and 1960's was also attributed to an influx of city dwellers who were Democrats, many of those people opted to become Republicans when they moved here because the Republicans represented the party in power. ''Everybody knew that if you wanted your kid to get a job as a lifeguard in the summer, you better be a Republican,'' said Bradford W. O'Hearn, a Democratic political consultant. The people in that earlier generation were also familiar with machine politics since the Democratic Party ruled the city, he said, but today's new city transplants ''tend to be liberal and idealistic and they don't have the same context of coming out of a strong Democratic or Republican machine area.''


A Christian-owned bakery in Northern Ireland may face legal action, after being informed that it broke the law by refusing to bake a cake for a customer, based on the management’s opposition to gay rights.

A branch manager has vowed to “take a stand” against instructions to stop discriminating, and compared supporting same-sex marriage to making a cake with pornographic images on it.

Asher’s Baking Company has been told by the Equality Commission that broke the law by discriminating against a gay rights activist, when it refused to make a cake carrying the words “support gay marriage,” reports the Belfast Telegraph.

In a video posted by the bakery, Daniel McArthur, the general manager of the branch describes that a request was made on a Friday for a cake carrying a picture of Sesame Street characters Bert and Ernie, the words “support gay marriage”, and the logo of the Queerspace charity. By Monday, the request had been refused.

He Notes that the bakery was founded by, and is still run by Christians, and that Northern Ireland currently does not allow same-sex marriage, saying marriage “has not been redefined” there.

McArthur goes on to say that other requests, including pornographic images, and swear words, are also refused by the bakery.

The bakery was given seven days to respond to the letter with how it planned to recompense the customer. McArthur, however, said the company would “take a stand” against it.

“We thought that this order was at odds with our beliefs. It certainly was in contradiction of what the Bible teaches,” he said. “I feel if we don’t take a stand with this case, then how can we stand up against it further down the line?”

Colin Hart, director of the Christian Institute, said: “All the McArthurs want is to run their bakery according to their Christian beliefs. There won’t be many situations where they need to turn down an order but this is obviously one of them. No-one should be forced to use their creative skills to promote a cause which goes against their consciences.“

Northern Ireland remains the only place in the UK where same-sex marriage is not available, or soon-to-be available.


id,summary,reporter,owner,description,type,status,component,version,severity,resolution,keywords,cc,stage,has_patch,needs_docs,needs_tests,needs_better_patch,easy,ui_ux 16381,"Specify a folder for the 404.html, 505.html files",anonymous,nobody,I think it would be a good idea if we had a setting in the setting.py file so we can change the location of the error pages. I know we could do this with handler404 but i think a setting would be better.,New feature,closed,HTTP handling,1.3,Normal,wontfix,,,Unreviewed,0,0,0,0,0,0


Re: Laser Satellite Comms

At 07:24 AM 3/1/99 , Bob Bruninga wrote:
>some potential as a laser reflector for optical comms. ...
>    Although the ability to actually detect a laser at amateur power

I did some quick calculations, and the results say that this is just completely impossible.  Perhaps I did something wrong.  I often get such a calculation wrong on the first try.  Please double check my calcs and let me know if I screwed up.

When a point light source on the earth reflects off of a 1" mirror and back to earth, the spot of light on the earth will be 2" in diameter.  (Presuming that the path up and down are equal length -- a reasonable first order assumption.  Different path geometry will of course produce a different result.)

Now, the "disco ball" in space is rotating at 1RPM, or 6 degrees/second.  When a disco ball rotates 6 deg/sec, the spot of light on the disco wall rotates at twice that rate, or 12 degrees/second.  Because the ball is 205mi from earth, the spot on the earth's surface travels 2pi x 205mi / 360 degrees =  3.6 miles for every degree.  12 degrees/second x 3.6mi/degree = 43 miles/second.  (I assumed a particular geometry here.  Sometimes the path is longer, which makes the spot move even faster.  I also ignored the orbital motion.)

Now, we have a 2" diameter spot of light travelling at 43 miles/second on the earth's surface.  How long will I be illuminated by this spot?

2" / (63360 inch/mile) / (43 miles/second) = 7x10^-7 second.

By this calculation, a flash from a point light source on earth will last less than a microsecond.  That's a damn short flash.  Communication using such flashes seems impossible.

A flash of reflected sunlight will last a lot longer, because the sun is so big.  My back-of-the-envelope calc comes out 40 milliseconds.

If the source on earth is larger than a point, the reflected spot will also be larger, and thus last longer, but still not enough to be useful.

On top of this, I would guess that there's an awful link budget problem and laser aiming problem.

Communication just seems completely impossible using amateur equipment.

Even with professional equipment, I don't see any use for this satellite.  The whole project seems like a waste of government money.

If you want to see flashes of sunlight reflected from satellites, you can look at Iridium flares.


Schoolchildren will soon get to meet this Burrowing Owl.

Little Dobby, hatched from his egg on August 8 and is being hand reared by owner Martin Southcombe.

Mr Southcombe, who runs Celebrity Reptiles, an animal encounter company that takes exotic animals into schools, is looking forward to showing schoolchildren in Croydon Dobby’s hunting methods.

Burrowing owls spend much of their life at ground level and reach a maximum height of 38 centimetres.


man delivering bottled water

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Enjoy the convenience of having a delivery of bottled water direct to your home or office in Crystal Lake. Bottle capacity includes half liter personal single serves, three gallon and five gallon, with the two larger sizes being convenient for a delivery to your Crystal Lake home or business office.

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Old-time banjo players love to employ a variety of different banjo tunings to play songs in different keys. With each new tuning, unique combinations of open and fretted strings emerge, resulting in new sounds and playing possibilities.

It's amazing how these tunings can create different emotions and moods in both players and listeners. Using G tuning as a starting point, you put the banjo in C tuning by lowering the pitch of your 4th string the equivalent of two frets from D to C and raising the pitch of your 2nd string from B to C. Your 1st, 3rd, and 5th strings are the same in C and G tuning.

If your banjo is already in G tuning, lowering the 4th string from a D to a C is easy. Here's a step-by-step guide:

  1. Attach your electronic tuner to the banjo headstock.

  2. Turn the peg on the 4th string in a counterclockwise direction until your electronic tuner indicates that you've lowered the pitch the equivalent of two frets from a D to a C.

  3. Double-check your 4th string tuning.

    Fret the 4th string at the 7th fret and compare the sound of this string with the open 3rd string. If both strings are in tune, your electronic tuner should indicate a G note.

Next, follow these simple steps to raise the 2nd string from a B to a C:

  1. Tighten the 2nd string peg by moving it in a counterclockwise motion until your tuner indicates that the string is sounding a C note, a change in pitch of one fret.

  2. Double-check your 2nd string tuning.

    Fret the 2nd string at the 2nd fret to compare the sound of this string with the open 1st string. If both strings are in tune, your tuner should indicate a D note.

As you turn each peg to retune, try striking the string with any right-hand finger to hear whether the pitch is going in the desired direction. By using your ear in addition to your electronic tuner, you refine your tuning ability, which is a very important skill in old-time banjo.

When you change the tension of one string by raising or lowering its pitch, don't be surprised to find that the adjacent strings on your banjo have now unfortunately gone out of tune. No worries. If that's the case, simply touch up the other strings so that your 3rd and 5th strings are tuned to G and the 1st string to D.

Then double-check the 4th and 2nd strings to make sure that they're still holding the desired pitch.

From the 5th string to the 1st string, here's a summary of the pitches that you need for double C tuning:

5th string: G (highest-pitched string)
4th string: C (lowest-pitched string)
3rd string: G
2nd string: C
1st string: D

For double C tuning, as well as the other tunings featured in this section, you can hear this demonstrated in "Soldier's Joy" in Double C Tuning before the song is played. As a companion to your own ear and your electronic tuner, listening to this demo is a great way to get your banjo into the different tunings you need.

You're not cheating if you use an electronic tuner to help you retune the banjo. At an old-time jam session, almost all musicians use tuners as aids to their ears, so you can too!

Now that you're in double C tuning, you're ready to play “Soldier's Joy,” one of the oldest tunes in all old-time music — and maybe the most frequently played. As shown here, this is a straightforward arrangement that focuses on a clear presentation of the melody using basic clawhammer techniques.

“Soldier's Joy,” using double C tuning.
“Soldier's Joy,” using double C tuning.

Also, check out the video Clawhammer Banjo: Double C Tuning and "Soldier's Joy".


Evolution News and Views
Intelligent Design NEWS

The Latest Buzz on Bumblebees

When it comes to finding food, bumblebees don't bumble very often. They are well equipped to detect the best flowers to pollinate. Recent research shows two more unexpected ways they avoid costly errors.

That buzzing of bees that we hear sounds almost electric. Even though it is produced by rapidly flapping wings, scientists at the University of Bristol found that electrical cues indeed play a measurable role in bee foraging. Publishing in Science, they announced an intriguing discovery:

The paper reads like a classic example of good controlled experiment: hypothesis, experimental design, control. First, the four scientists had to measure electric fields on natural bees, natural flowers, and in the ambient air, to establish a baseline. Did you know most insect pollinators are positively charged, and most flowers are negatively charged? That's not all. By dusting six different species of flowers with colored electrostatic powder, they found distinctive species-specific patterns of electric fields on the petals, stamens and ovaries, almost like signals telling the bees, "It's right in here!"

To test the hypothesis that floral electric fields function as cues, they built an apparatus with artificially charged structures ("E-flowers"). They trained 51 bumblebees "to fly into a Faraday pail that contained a sucrose reward." With surprising accuracy, the bees learned to fly to the charged E-flowers instead of the ones in the electrical ground state, even thought there were identical in all other respects. But when the charged flowers were discharged, the bees could no longer discriminate between the sucrose-loaded ones and the empty ones. The researchers also found that the bees could learn to associate a specific pattern of electric field with the reward.

This showed that the bumblebees were able to detect electric field patterns and use that information to find the reward, but was it the only cue? To study the effect of multi-modal cues, the team combined the electric cue with color cues: one color tasted good, another bad. It took most bees 35 visits to achieve 80% success. When the electric field was added, most found them much faster, in just 24 visits. This showed that a combination of cues improves foraging efficiency. In conclusion, they said:

Our results show that electric field constitutes a floral cue. Contributing to a varied floral display aimed at pollinator senses, electric fields act to improve both speed and accuracy with which bees learn and discriminate rewarding resources. As such, electric field sensing constitutes a potentially important sensory modality, which should be considered alongside vision and olfaction. The ubiquity of electric fields in nature and their integration into the bees' sensory ecology suggest that E-fields play a thus far unappreciated role in plant-insect interactions. The present study raises the possibility of reciprocal information transfer between plants and pollinators at time scales of milliseconds to seconds, much faster than previously described alterations in floral scent, color, or humidity. The remarkably accurate discrimination and learning of color patterns by bees was revealed by both laboratory and field training experiments. Similarly, the present laboratory study reveals that floral electric fields occur in patterns and that they can be perceived. Hence, our study provides a framework for exploring the function and adaptive value of the perception of weak electric fields by bees in nature.
Meanwhile researchers at Queen Mary University of London have found that bumblebees employ a simple form of logic. They can learn from watching their neighbors' hit rates to decide what flowers to visit. In experiments with laboratory "flight arenas," bees "actively avoided" flowers that other bees found distasteful.

The researchers claimed it's simply logical for a bee, or any other animal, to learn from others:

Despite their tiny brains, bees are smart enough to pick out the most attractive flowers by watching other bees and learning from their behaviour. By using simple logic, they see which coloured flowers are the most popular, and conclude that those of the same colour must also contain lots of energy-rich nectar.

"Learning where to find nectar by watching others seems fantastically complex for a tiny bee, but it's something that almost any animal could do, in the right circumstances," says Dr Elli Leadbeater from ZSL's Institute for Zoology.....

This suggests that other species, not just bees, may also use this logical process when learning from others.

Try formulating that as a syllogism. Major premise: My neighbors will devote energy to the best food sources. Minor premise: That colored flower is popular. Conclusion: The popular flowers must have the best food sources.

This kind of deductive logic requires the ability to form associations. It also presupposes a good memory. That's a lot to pack into a tiny bee brain.

Nor is that all. Electric field detectors, olfactory senses, and some 6,000 facets of their compound eyes all have to send information to the brain, where the information must be integrated with memory to make reliable decisions. Bumblebees are pretty complex creatures.

Neither article mentions evolution, confirming again that evolutionary theory is often superfluous to scientific research. Critics of ID might retort, "Well, that doesn't mean this was intelligent design research!" Doesn't it?

True, there is also no mention of specified complexity, irreducible complexity, or the design filter. There is no reference to major ID books. But Dembski, Meyer and other leading lights of the intelligent-design community have said that one of their goals was to formally describe the way that human beings reach design conclusions from the evidence, according to rigorous mathematical principles. One doesn't need to use explicit ID language to do that. Design detection is largely an intuitive logic we all use every day; ID helps make it robust.

Scientists, especially, are trained to figure out how things work. These articles are in the "how it works" category, not the "where did it come from" category. As Paul Nelson says in the upcoming film from Illustra Media on bird flight, "It if works, it's not by accident."

We see bumblebees with highly specialized, multi-modal equipment for finding nectar. Regardless of the researchers' personal beliefs about the origins of complex life, they were focused on complex functions that work. If something works, and if the research has no need of unguided processes to explain it, then yes: it's de facto intelligent-design research.

Image credit: John W. Coke/Flickr.


Elements in the Human Body

We all know about the different elements (types of atoms) found in the universe. What many people arenâ€™t aware of, however, is how these elements are utilized by the human body. The following table will inform you how much of the body (by mass) is composed of each element.

Percentage of Body WeightElementUsage
65%OxygenThis element is obviously the most important element in the human body. Oxygen atoms are present in water, which is the compound most common in the body, and other compounds that make up tissues. It is also found in the blood and lungs due to respiration.
18.6%CarbonCarbon is found in every organic molecule in the body, as well as the waste product of respiration (carbon dioxide). It is typically ingested in food that is eaten.
9.7%HydrogenHydrogen is found in all water molecules in the body as well as many other compounds making up the various tissues.
3.2%NitrogenNitrogen is very common in proteins and organic compounds. It is also present in the lungs due to its abundance in the atmosphere.
1.8%CalciumCalcium is a primary component of the skeletal system, including the teeth. It is also found in the nervous system, muscles, and the blood.
1.0%PhosphorusThis element is common in the bones and teeth, as well as nucleic acids.
0.4%PotassiumPotassium is found in the muscles, nerves, and certain tissues.
0.2%SodiumSodium is excreted in sweat, but is also found in muscles and nerves.
0.2%ChlorineChlorine is present in the skin and facilitates water absorption by the cells.
0.06%MagnesiumMagnesium serves as a cofactor for various enzymes in the body.
0.04%SulfurSulfur is present in many amino acids and proteins.
0.007%IronIron is found mostly in the blood since it facilitates the transportation of oxygen.
0.0002%IodineIodine is found in certain hormones in the thyroid gland.

There are many other elements found in the human body, but their presence is largely insignificant when compared to these other elements.

Return to the Physiology Articles Section

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Numerical Aperture

Numerical Aperture (also termed Object-Side Aperture) is a value (often symbolized by the abbreviation NA) originally defined by Abbe for microscope objectives and condensers. It is given by the simple expression:

Numerical Aperture (NA) = n × sin(µ) or n × sin(α)

Note: Many authors use the variable µ to designate the one-half angular aperture while others employ the more common term α, and in some instances, θ.

In the numerical aperture equation, n represents the refractive index of the medium between the objective front lens and the specimen, and µ or α is the one-half angular aperture of the objective.

The numerical aperture of a microscope objective is a measure of its ability to gather light and resolve fine specimen detail at a fixed object distance. Image-forming light waves pass through the specimen and enter the objective in an inverted cone as illustrated in Figure 1 (above). A longitudinal slice of this cone of light reveals the angular aperture, a value that is determined by the focal length of the objective.

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Numerical Aperture Light Cones

In practice, it is difficult to achieve numerical aperture values above 0.95 with dry objectives. Figure 1 illustrates a series of light cones derived from objectives of varying focal length and numerical aperture. As the light cones grow larger, the angular aperture (α) increases from 7° to 60°, with a resulting increase in the numerical aperture from 0.12 to 0.87, nearing the limit when air is utilized as the imaging medium. Higher numerical apertures can be obtained by increasing the imaging medium refractive index (n) between the specimen and the objective front lens (see Figure 2). Microscope objectives are available in a variety of immersion-media types including water (refractive index = 1.33), glycerin (refractive index = 1.47), and oil (refractive index = 1.51). As shown in Figure 2, oil immersion objectives (green) have higher numerical apertures than water (blue) or air (dry; red). The numerical aperture of an objective is also dependent, to a certain degree, upon the amount of correction for optical aberration.

Highly corrected objectives tend to have much larger numerical apertures for the respective magnification as illustrated in Figure 2 (compare Apochromat to Fluorite and Achromat) and Table 1 below. If we take a series of typical 10x objectives as an example, we see that for flat-field corrected plan objectives, numerical aperture increases correspond to enhanced correction for chromatic and spherical aberration: plan achromat, N.A. = 0.25; plan fluorite, N.A. = 0.30; and plan apochromat, N.A. = 0.45 (Table 1). Figure 2 also includes non-Plan corrected objectives lenses within each category of Achromat, Fluor and Apochromat.

This feature of increasing numerical aperture across an increasing optical correction factor in a series of objectives of similar magnification holds true throughout the range of magnifications as shown in Table 1 and Figure 2. Most manufacturers strive to ensure that their objectives have the highest correction and numerical aperture that is possible for each class of objective.

Numerical Aperture versus Optical Correction

Magnification Plan Achromat
Plan Fluorite
Plan Apochromat
0.5x 0.025 n/a n/a
1x 0.04 n/a n/a
2x 0.06 n/a 0.10
4x 0.10 0.13 0.20
10x 0.25 0.30 0.45
20x 0.40 0.50 0.75
40x 0.65 0.75 0.95
40x (oil) n/a 1.30 1.00
60x 0.75 0.85 0.95
60x (oil) n/a n/a 1.40
100x (oil) 1.25 1.30 1.40
150x n/a n/a 0.90

Table 1

A majority of objectives in the magnification range between 60x and 100x (and higher) are designed for use with immersion oil. By examining the numerical aperture equation presented above, we find that the highest theoretical numerical aperture obtainable with common immersion oils is 1.51 (when sin (α) = 1). In practice, however, most oil immersion objectives have a maximum numerical aperture of 1.4, with the most common numerical apertures ranging from 1.0 to 1.35.

Note: Unless specified by image-side, the term numerical aperture refers to the object side.

Contributing Author