Science.gov

Sample records for gene network parameters

  1. Listening to the noise: random fluctuations reveal gene network parameters.

    PubMed

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2009-01-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We show that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  2. Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

    NASA Astrophysics Data System (ADS)

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2010-03-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

  3. Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

    PubMed Central

    2014-01-01

    Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely

  4. Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae.

    PubMed

    Dahlquist, Kam D; Fitzpatrick, Ben G; Camacho, Erika T; Entzminger, Stephanie D; Wanner, Nathan C

    2015-08-01

    We investigated the dynamics of a gene regulatory network controlling the cold shock response in budding yeast, Saccharomyces cerevisiae. The medium-scale network, derived from published genome-wide location data, consists of 21 transcription factors that regulate one another through 31 directed edges. The expression levels of the individual transcription factors were modeled using mass balance ordinary differential equations with a sigmoidal production function. Each equation includes a production rate, a degradation rate, weights that denote the magnitude and type of influence of the connected transcription factors (activation or repression), and a threshold of expression. The inverse problem of determining model parameters from observed data is our primary interest. We fit the differential equation model to published microarray data using a penalized nonlinear least squares approach. Model predictions fit the experimental data well, within the 95% confidence interval. Tests of the model using randomized initial guesses and model-generated data also lend confidence to the fit. The results have revealed activation and repression relationships between the transcription factors. Sensitivity analysis indicates that the model is most sensitive to changes in the production rate parameters, weights, and thresholds of Yap1, Rox1, and Yap6, which form a densely connected core in the network. The modeling results newly suggest that Rap1, Fhl1, Msn4, Rph1, and Hsf1 play an important role in regulating the early response to cold shock in yeast. Our results demonstrate that estimation for a large number of parameters can be successfully performed for nonlinear dynamic gene regulatory networks using sparse, noisy microarray data.

  5. Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters.

    PubMed

    Moisset, P; Vaisman, D; Cintolesi, A; Urrutia, J; Rapaport, I; Andrews, B A; Asenjo, J A

    2012-09-01

    A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential

  6. Buffering in cyclic gene networks

    NASA Astrophysics Data System (ADS)

    Glyzin, S. D.; Kolesov, A. Yu.; Rozov, N. Kh.

    2016-06-01

    We consider cyclic chains of unidirectionally coupled delay differential-difference equations that are mathematical models of artificial oscillating gene networks. We establish that the buffering phenomenon is realized in these system for an appropriate choice of the parameters: any given finite number of stable periodic motions of a special type, the so-called traveling waves, coexist.

  7. Parameter estimation in tree graph metabolic networks

    PubMed Central

    Stigter, Hans; Gomez Roldan, Maria Victoria; van Eeuwijk, Fred; Hall, Robert D.; Groenenboom, Marian; Molenaar, Jaap J.

    2016-01-01

    We study the glycosylation processes that convert initially toxic substrates to nutritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis–Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree-like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings. PMID:27688960

  8. Introduction: Cancer Gene Networks.

    PubMed

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  9. Analysis of cascading failure in gene networks.

    PubMed

    Sun, Longxiao; Wang, Shudong; Li, Kaikai; Meng, Dazhi

    2012-01-01

    It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure, and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.

  10. Functional Module Analysis for Gene Coexpression Networks with Network Integration

    PubMed Central

    Zhang, Shuqin; Zhao, Hongyu

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with 3 complete subgraphs, and 11 modules with 2 complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally. PMID:26451826

  11. Reverse engineering transcriptional gene networks.

    PubMed

    Belcastro, Vincenzo; di Bernardo, Diego

    2014-01-01

    The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

  12. Reconstructing contact network parameters from viral phylogenies

    PubMed Central

    McCloskey, Rosemary M.; Liang, Richard H.; Poon, Art F.Y.

    2016-01-01

    Models of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on approximate Bayesian computation (ABC), a likelihood-free inference strategy, for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barabási-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from ten published HIV sequence datasets. This model incorporates a feature called preferential attachment (PA), whereby individuals with more existing contacts accumulate new contacts at a higher rate. On simulated data, we found that the strength of PA and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, was not estimable with ABC. We observed sub-linear PA power in all datasets, as well as higher PA power in networks of injection drug users. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics. PMID:27818787

  13. Gene networks controlling petal organogenesis.

    PubMed

    Huang, Tengbo; Irish, Vivian F

    2016-01-01

    One of the biggest unanswered questions in developmental biology is how growth is controlled. Petals are an excellent organ system for investigating growth control in plants: petals are dispensable, have a simple structure, and are largely refractory to environmental perturbations that can alter their size and shape. In recent studies, a number of genes controlling petal growth have been identified. The overall picture of how such genes function in petal organogenesis is beginning to be elucidated. This review will focus on studies using petals as a model system to explore the underlying gene networks that control organ initiation, growth, and final organ morphology.

  14. Plant Evolution: Evolving Antagonistic Gene Regulatory Networks.

    PubMed

    Cooper, Endymion D

    2016-06-20

    Developing a structurally complex phenotype requires a complex regulatory network. A new study shows how gene duplication provides a potential source of antagonistic interactions, an important component of gene regulatory networks.

  15. Order Parameters for Two-Dimensional Networks

    NASA Astrophysics Data System (ADS)

    Kaatz, Forrest; Bultheel, Adhemar; Egami, Takeshi

    2007-10-01

    We derive methods that explain how to quantify the amount of order in ``ordered'' and ``highly ordered'' porous arrays. Ordered arrays from bee honeycomb and several from the general field of nanoscience are compared. Accurate measures of the order in porous arrays are made using the discrete pair distribution function (PDF) and the Debye-Waller Factor (DWF) from 2-D discrete Fourier transforms calculated from the real-space data using MATLAB routines. An order parameter, OP3, is defined from the PDF to evaluate the total order in a given array such that an ideal network has the value of 1. When we compare PDFs of man-made arrays with that of our honeycomb we find OP3=0.399 for the honeycomb and OP3=0.572 for man's best hexagonal array. The DWF also scales with this order parameter with the least disorder from a computer-generated hexagonal array and the most disorder from a random array. An ideal hexagonal array normalizes a two-dimensional Fourier transform from which a Debye-Waller parameter is derived which describes the disorder in the arrays. An order parameter S, defined by the DWF, takes values from [0, 1] and for the analyzed man-made array is 0.90, while for the honeycomb it is 0.65. This presentation describes methods to quantify the order found in these arrays.

  16. Engineering stability in gene networks by autoregulation

    NASA Astrophysics Data System (ADS)

    Becskei, Attila; Serrano, Luis

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.

  17. Estimated hydrogeological parameters by artificial neurons network

    NASA Astrophysics Data System (ADS)

    Lin, H.; Chen, C.; Tan, Y.; Ke, K.

    2009-12-01

    In recent years, many approaches had been developed using artificial neurons network (ANN) model cooperated with Theis analytical solution to estimate the effective hydrological parameters for the homogenous and isotropic porous media, such as Lin and Chen approach [Lin and Chen, 2006] (or called the ANN approach hereafter), PC-ANN approach [Samani et al., 2008]. The above methods assumed a full superimposition of the type curve and the observed drawdown, and tried to use the first time-drawdown data as a match point to make a fine approximation of the effective parameters. However, using the first time-drawdown data or the early time-drawdown data is not always correct for the estimation of the hydrological parameters, especially for heterogeneous and anisotropic aquifers. Therefore, this paper mainly corrected the concept of superimposed plot by modifying the ANN approach and PC-ANN approach, as well as cooperating with Papadopoulos analytical solution, to estimate the transmissivities and storage coefficient for anisotropic, heterogeneous aquifers. The ANN model is trained with 4000 training sets of the well function, and tested with 1000 sets and 300 sets of synthetic time-drawdown generated from homogonous and heterogonous parameters, respectively. In-situ observation data, the time-drawdown at station Shi-Chou of the Chihuahua River alluvial fan, Taiwan, is further adopted to test the applicability and reliability of proposed methods, as well as comparing with Straight-line method and Type-curve method. Results suggested that both of the modified methods had better performance than the original ones. Using late time drawdown to optimize the effective parameters is shown better than using early-time drawdown. Additionally, results indicated that the modified ANN approach is better than the modified PC-ANN approach in terms of precision, while the efficiency of the modified PC-ANN approach is approximately three times better than the modified ANN approach.

  18. Exhaustive Search for Fuzzy Gene Networks from Microarray Data

    SciTech Connect

    Sokhansanj, B A; Fitch, J P; Quong, J N; Quong, A A

    2003-07-07

    Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.

  19. GENIES: gene network inference engine based on supervised analysis.

    PubMed

    Kotera, Masaaki; Yamanishi, Yoshihiro; Moriya, Yuki; Kanehisa, Minoru; Goto, Susumu

    2012-07-01

    Gene network inference engine based on supervised analysis (GENIES) is a web server to predict unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any 'profiles' of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene-gene similarity matrices (or 'kernels') in the tab-delimited file format. As a training data set to learn a predictive model, the users can choose either known molecular network information in the KEGG PATHWAY database or their own gene network data. The user can also select an algorithm of supervised network inference, choose various parameters in the method, and control the weights of heterogeneous data integration. The server provides the list of newly predicted gene pairs, maps the predicted gene pairs onto the associated pathway diagrams in KEGG PATHWAY and indicates candidate genes for missing enzymes in organism-specific metabolic pathways. GENIES (http://www.genome.jp/tools/genies/) is publicly available as one of the genome analysis tools in GenomeNet.

  20. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.

    PubMed

    Jiang, Xue; Zhang, Han; Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

  1. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

    PubMed Central

    Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets. PMID:28042568

  2. Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

    PubMed

    Oliver, Karen L; Lukic, Vesna; Thorne, Natalie P; Berkovic, Samuel F; Scheffer, Ingrid E; Bahlo, Melanie

    2014-01-01

    We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

  3. Evolving Robust Gene Regulatory Networks

    PubMed Central

    Noman, Nasimul; Monjo, Taku; Moscato, Pablo; Iba, Hitoshi

    2015-01-01

    Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems. PMID:25616055

  4. Inferring slowly-changing dynamic gene-regulatory networks

    PubMed Central

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with ℓ1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset. PMID:25917062

  5. Modular composition of gene transcription networks.

    PubMed

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-03-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

  6. Inversion of surface parameters using fast learning neural networks

    NASA Technical Reports Server (NTRS)

    Dawson, M. S.; Olvera, J.; Fung, A. K.; Manry, M. T.

    1992-01-01

    A neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.

  7. Gene regulatory network inference using out of equilibrium statistical mechanics

    PubMed Central

    Benecke, Arndt

    2008-01-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem. PMID:19404429

  8. Modeling of hysteresis in gene regulatory networks.

    PubMed

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  9. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    PubMed

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  10. EXAMINE: a computational approach to reconstructing gene regulatory networks.

    PubMed

    Deng, Xutao; Geng, Huimin; Ali, Hesham

    2005-08-01

    Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously.

  11. Optimization of multilayer neural network parameters for speaker recognition

    NASA Astrophysics Data System (ADS)

    Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

    2016-05-01

    This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

  12. Network Topology Reveals Key Cardiovascular Disease Genes

    PubMed Central

    Stojković, Neda; Radak, Djordje; Pržulj, Nataša

    2013-01-01

    The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs. PMID:23977067

  13. Gene Regulation Networks for Modeling Drosophila Development

    NASA Technical Reports Server (NTRS)

    Mjolsness, E.

    1999-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.

  14. Influence of choice of null network on small-world parameters of structural correlation networks.

    PubMed

    Hosseini, S M Hadi; Kesler, Shelli R

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

  15. Autonomous Boolean modeling of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Sun, Mengyang; Cheng, Xianrui

    2014-03-01

    In cases where the dynamical properties of gene regulatory networks are important, a faithful model must include three key features: a network topology; a functional response of each element to its inputs; and timing information about the transmission of signals across network links. Autonomous Boolean network (ABN) models are efficient representations of these elements and are amenable to analysis. We present an ABN model of the gene regulatory network governing cell fate specification in the early sea urchin embryo, which must generate three bands of distinct tissue types after several cell divisions, beginning from an initial condition with only two distinct cell types. Analysis of the spatial patterning problem and the dynamics of a network constructed from available experimental results reveals that a simple mechanism is at work in this case. Supported by NSF Grant DMS-10-68602

  16. Identifying genes of gene regulatory networks using formal concept analysis.

    PubMed

    Gebert, Jutta; Motameny, Susanne; Faigle, Ulrich; Forst, Christian V; Schrader, Rainer

    2008-03-01

    In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

  17. An S-System Parameter Estimation Method (SPEM) for Biological Networks

    PubMed Central

    Yang, Xinyi; Dent, Jennifer E.

    2012-01-01

    Abstract Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks. PMID:22300319

  18. Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles.

    PubMed

    Zhang, Yulin; Lv, Kebo; Wang, Shudong; Su, Jionglong; Meng, Dazhi

    2015-01-01

    Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.

  19. Combinatorial explosion in model gene networks

    NASA Astrophysics Data System (ADS)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  20. Network analysis reveals crosstalk between autophagy genes and disease genes

    PubMed Central

    Wang, Ji-Ye; Yao, Wei-Xuan; Wang, Yun; Fan, Yi-lei; Wu, Jian-Bing

    2017-01-01

    Autophagy is a protective and life-sustaining process in which cytoplasmic components are packaged into double-membrane vesicles and targeted to lysosomes for degradation. Accumulating evidence supports that autophagy is associated with several pathological conditions. However, research on the functional cross-links between autophagy and disease genes remains in its early stages. In this study, we constructed a disease-autophagy network (DAN) by integrating known disease genes, known autophagy genes and protein-protein interactions (PPI). Dissecting the topological properties of the DAN suggested that nodes that both autophagy and disease genes (inter-genes), are topologically important in the DAN structure. Next, a core network from the DAN was extracted to analyze the functional links between disease and autophagy genes. The genes in the core network were significantly enriched in multiple disease-related pathways, suggesting that autophagy genes may function in various disease processes. Of 17 disease classes, 11 significantly overlapped with autophagy genes, including cancer diseases, metabolic diseases and hematological diseases, a finding that is supported by the literatures. We also found that autophagy genes have a bridging role in the connections between pairs of disease classes. Altogether, our study provides a better understanding of the molecular mechanisms underlying human diseases and the autophagy process. PMID:28295050

  1. Modeling gene regulatory network motifs using statecharts

    PubMed Central

    2012-01-01

    Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

  2. Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.

    PubMed

    Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

    2015-07-06

    Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.

  3. Phenotypic switching in gene regulatory networks.

    PubMed

    Thomas, Philipp; Popović, Nikola; Grima, Ramon

    2014-05-13

    Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

  4. Modeling gene regulatory networks: A network simplification algorithm

    NASA Astrophysics Data System (ADS)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  5. Parameter adaptation in a simplified pulse-coupled neural network

    NASA Astrophysics Data System (ADS)

    Szekely, Geza; Lindblad, Thomas

    1999-03-01

    In a general purpose pulse coupled neural network (PCNN) algorithm the following parameters are used: 2 weight matrices, 3 time constants, 3 normalization factors and 2 further parameters. In a given application, one has to determine the near optimal parameter set to achieve the desired goal. Here a simplified PCNN is described which contains a parameter fitting part, in the least squares sense. Given input and a desired output image, the program is able to determine the optimal value of a selected PCNN parameter. This method can be extended to more general PCNN algorithms, because partial derivatives are not required for the fitting. Only the sum of squares of the differences is used.

  6. Automatic Parameter Learning for Multiple Local Network Alignment

    PubMed Central

    Novak, Antal; Do, Chuong B.; Srinivasan, Balaji S.; Batzoglou, Serafim

    2009-01-01

    Abstract We developed Græmlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Græmlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu. PMID:19645599

  7. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal ... Vasculitis Therapy as Effective as Standard Care Mutated Genes in Schizophrenia Map to Brain Networks Connect with Us Subscribe to ...

  8. Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm

    SciTech Connect

    Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana

    2008-11-06

    This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.

  9. In Silico Evolution of Gene Cooption in Pattern-Forming Gene Networks

    PubMed Central

    Spirov, Alexander V.; Sabirov, Marat A.; Holloway, David M.

    2012-01-01

    Gene recruitment or cooption occurs when a gene, which may be part of an existing gene regulatory network (GRN), comes under the control of a new regulatory system. Such re-arrangement of pre-existing networks is likely more common for increasing genomic complexity than the creation of new genes. Using evolutionary computations (EC), we investigate how cooption affects the evolvability, outgrowth and robustness of GRNs. We use a data-driven model of insect segmentation, for the fruit fly Drosophila, and evaluate fitness by robustness to maternal variability—a major constraint in biological development. We compare two mechanisms of gene cooption: a simpler one with gene Introduction and Withdrawal operators; and one in which GRN elements can be altered by transposon infection. Starting from a minimal 2-gene network, insufficient for fitting the Drosophila gene expression patterns, we find a general trend of coopting available genes into the GRN, in order to better fit the data. With the transposon mechanism, we find co-evolutionary oscillations between genes and their transposons. These oscillations may offer a new technique in EC for overcoming premature convergence. Finally, we comment on how a differential equations (in contrast to Boolean) approach is necessary for addressing realistic continuous variation in biochemical parameters. PMID:23365523

  10. Inferring gene regression networks with model trees

    PubMed Central

    2010-01-01

    Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate

  11. Gene network biological validity based on gene-gene interaction relevance.

    PubMed

    Gómez-Vela, Francisco; Díaz-Díaz, Norberto

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

  12. Gene Network Reconstruction using Global-Local Shrinkage Priors*

    PubMed Central

    Leday, Gwenaël G.R.; de Gunst, Mathisca C.M.; Kpogbezan, Gino B.; van der Vaart, Aad W.; van Wieringen, Wessel N.; van de Wiel, Mark A.

    2016-01-01

    Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival.

  13. An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

    PubMed

    Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica

    2009-01-01

    In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

  14. Summing up the noise in gene networks.

    PubMed

    Paulsson, Johan

    2004-01-29

    Random fluctuations in genetic networks are inevitable as chemical reactions are probabilistic and many genes, RNAs and proteins are present in low numbers per cell. Such 'noise' affects all life processes and has recently been measured using green fluorescent protein (GFP). Two studies show that negative feedback suppresses noise, and three others identify the sources of noise in gene expression. Here I critically analyse these studies and present a simple equation that unifies and extends both the mathematical and biological perspectives.

  15. Reverse engineering of gene regulatory network using restricted gene expression programming.

    PubMed

    Yang, Bin; Liu, Sanrong; Zhang, Wei

    2016-10-01

    Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

  16. Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

    PubMed

    Li, Xumeng; Feltus, Frank A; Sun, Xiaoqian; Wang, James Z; Luo, Feng

    2011-10-01

    Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.

  17. Autonomous Boolean modelling of developmental gene regulatory networks

    PubMed Central

    Cheng, Xianrui; Sun, Mengyang; Socolar, Joshua E. S.

    2013-01-01

    During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development. PMID:23034351

  18. On Bayesian Network Classifiers with Reduced Precision Parameters.

    PubMed

    Tschiatschek, Sebastian; Pernkopf, Franz

    2015-04-01

    Bayesian network classifier (BNCs) are typically implemented on nowadays desktop computers. However, many real world applications require classifier implementation on embedded or low power systems. Aspects for this purpose have not been studied rigorously. We partly close this gap by analyzing reduced precision implementations of BNCs. In detail, we investigate the quantization of the parameters of BNCs with discrete valued nodes including the implications on the classification rate (CR). We derive worst-case and probabilistic bounds on the CR for different bit-widths. These bounds are evaluated on several benchmark datasets. Furthermore, we compare the classification performance and the robustness of BNCs with generatively and discriminatively optimized parameters, i.e. parameters optimized for high data likelihood and parameters optimized for classification, with respect to parameter quantization. Generatively optimized parameters are more robust for very low bit-widths, i.e. less classifications change because of quantization. However, classification performance is better for discriminatively optimized parameters for all but very low bit-widths. Additionally, we perform analysis for margin-optimized tree augmented network (TAN) structures which outperform generatively optimized TAN structures in terms of CR and robustness.

  19. Additive Functions in Boolean Models of Gene Regulatory Network Modules

    PubMed Central

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H.; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  20. Additive functions in boolean models of gene regulatory network modules.

    PubMed

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  1. GenePANDA—a novel network-based gene prioritizing tool for complex diseases

    PubMed Central

    Yin, Tianshu; Chen, Shu; Wu, Xiaohui; Tian, Weidong

    2017-01-01

    Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS. PMID:28252032

  2. Generation of oscillating gene regulatory network motifs

    NASA Astrophysics Data System (ADS)

    van Dorp, M.; Lannoo, B.; Carlon, E.

    2013-07-01

    Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

  3. Field measurements and neural network modeling of water quality parameters

    NASA Astrophysics Data System (ADS)

    Qishlaqi, Afishin; Kordian, Sediqeh; Parsaie, Abbas

    2017-01-01

    Rivers are one of the main resources for water supplying the agricultural, industrial, and urban use; therefore, unremitting surveying the quality of them is necessary. Recently, artificial neural networks have been proposed as a powerful tool for modeling and predicting the water quality parameters in natural streams. In this paper, to predict water quality parameters of Tireh River located at South West of Iran, a multilayer neural network model (MLP) was developed. The T.D.S, Ec, pH, HCO3, Cl, Na, So4, Mg, and Ca as main parameters of water quality parameters were measured and predicted using the MLP model. The architecture of the proposed MLP model included two hidden layers that at the first and second hidden layers, eight and six neurons were considered. The tangent sigmoid and pure-line functions were selected as transfer function for the neurons in hidden and output layers, respectively. The results showed that the MLP model has suitable performance to predict water quality parameters of Tireh River. For assessing the performance of the MLP model in the water quality prediction along the studied area, in addition to existing sampling stations, another 14 stations along were considered by authors. Evaluating the performance of developed MLP model to map relation between the water quality parameters along the studied area showed that it has suitable accuracy and minimum correlation between the results of MLP model and measured data was 0.85.

  4. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  5. Hybrid stochastic simplifications for multiscale gene networks

    PubMed Central

    Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

    2009-01-01

    Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

  6. Extraction of exposure parameters by using neural networks

    NASA Astrophysics Data System (ADS)

    Jeon, Kyoung-Ah; Kim, Hyoung-Hee; Yoo, Ji-Yong; Park, Jun-Taek; Oh, Hye-Keun

    2003-06-01

    Dill"s ABC parameters are key parameters for the simulation of photolithography patterning. The exposure parameters of each resist should be exactly known to simulate the desired pattern. In ordinary extracting methods of Dill"s ABC parameters, the changed refractive index and the absorption coefficient of photoresist are needed during exposure process. Generally, these methods are not easy to be applied in a normal fab because of a difficulty of in-situ measuring. An empirical E0 (dose-to-clear) swing curve is used to extract ABC exposure parameters previously by our group. Dill"s ABC parameters are not independent from each other and different values of them would cause the dose to clear swing curve variation. By using the known relationship of ABC parameters, the experimental swing curves are to be matched with the simulated ones in order to extract the parameters. But sometimes this method is not easy in matching the procedure and performing simulation. This procedure would take much time for matching between the experimental data and the simulation by the naked eyes, and also the simulations are performed over and over again for different conditions. In this paper, Dill"s ABC parameters were extracted by applying the values, which are quantitatively determined by measuring the mean value, period, slope, and amplitude of the swing curve, to the neural network algorithm. As a result, Dill"s ABC parameters were able to rapidly and accurately extracted with some of the quantified values of the swing curve. This method of extracting the exposure parameters can be used in a normal fab so that any engineer can easily obtain the exposure parameters and apply them to the simulation tools.

  7. Discovering Study-Specific Gene Regulatory Networks

    PubMed Central

    Bo, Valeria; Curtis, Tanya; Lysenko, Artem; Saqi, Mansoor; Swift, Stephen; Tucker, Allan

    2014-01-01

    Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets. PMID:25191999

  8. Local network parameters can affect inter-network phase lags in central pattern generators.

    PubMed

    Jones, S R; Kopell, N

    2006-01-01

    Weakly coupled phase oscillators and strongly coupled relaxation oscillators have different mechanisms for creating stable phase lags. Many oscillations in central pattern generators combine features of each type of coupling: local networks composed of strongly coupled relaxation oscillators are weakly coupled to similar local networks. This paper analyzes the phase lags produced by this combination of mechanisms and shows how the parameters of a local network, such as the decay time of inhibition, can affect the phase lags between the local networks. The analysis is motivated by the crayfish central pattern generator used for swimming, and uses techniques from geometrical singular perturbation theory.

  9. Pathway network inference from gene expression data

    PubMed Central

    2014-01-01

    Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889

  10. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

    PubMed Central

    Kaltenbacher, Barbara; Hasenauer, Jan

    2017-01-01

    Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

  11. Improving gene regulatory network inference using network topology information.

    PubMed

    Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P

    2015-09-01

    Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

  12. Efficient parameter sensitivity computation for spatially extended reaction networks

    NASA Astrophysics Data System (ADS)

    Lester, C.; Yates, C. A.; Baker, R. E.

    2017-01-01

    Reaction-diffusion models are widely used to study spatially extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on the stochastic models of spatially extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.

  13. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

    PubMed Central

    Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

    2006-01-01

    A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

  14. Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics

    NASA Astrophysics Data System (ADS)

    Ronen, Michal; Rosenberg, Revital; Shraiman, Boris I.; Alon, Uri

    2002-08-01

    A basic challenge in systems biology is to understand the dynamical behavior of gene regulation networks. Current approaches aim at determining the network structure based on genomic-scale data. However, the network connectivity alone is not sufficient to define its dynamics; one needs to also specify the kinetic parameters for the regulation reactions. Here, we ask whether effective kinetic parameters can be assigned to a transcriptional network based on expression data. We present a combined experimental and theoretical approach based on accurate high temporal-resolution measurement of promoter activities from living cells by using green fluorescent protein (GFP) reporter plasmids. We present algorithms that use these data to assign effective kinetic parameters within a mathematical model of the network. To demonstrate this, we employ a well defined network, the SOS DNA repair system of Escherichia coli. We find a strikingly detailed temporal program of expression that correlates with the functional role of the SOS genes and is driven by a hierarchy of effective kinetic parameter strengths for the various promoters. The calculated parameters can be used to determine the kinetics of all SOS genes given the expression profile of just one representative, allowing a significant reduction in complexity. The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data. This finding opens the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.

  15. Differential network analysis from cross-platform gene expression data

    PubMed Central

    Zhang, Xiao-Fei; Ou-Yang, Le; Zhao, Xing-Ming; Yan, Hong

    2016-01-01

    Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes. PMID:27677586

  16. Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm.

    PubMed

    Tsai, Jinn-Tsong; Chou, Jyh-Horng; Liu, Tung-Kuan

    2006-01-01

    In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. The HTGA approach is a method of combining the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently enhance the genetic algorithms. Therefore, the HTGA approach can be more robust, statistically sound, and quickly convergent. First, the authors evaluate the performance of the presented HTGA approach by studying some global numerical optimization problems. Then, the presented HTGA approach is effectively applied to solve three examples on forecasting the sunspot numbers, tuning the associative memory, and solving the XOR problem. The numbers of hidden nodes and the links of the feedforward neural network are chosen by increasing them from small numbers until the learning performance is good enough. As a result, a partially connected feedforward neural network can be obtained after tuning. This implies that the cost of implementation of the neural network can be reduced. In these studied problems of tuning both network structure and parameters of a feedforward neural network, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed GA-based approaches. The computational experiments show that the presented HTGA approach can obtain better results than the existing method reported recently in the literature.

  17. Paper-based Synthetic Gene Networks

    PubMed Central

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  18. Paper-based synthetic gene networks.

    PubMed

    Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

    2014-11-06

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

  19. Estimating Gene Regulatory Networks with pandaR.

    PubMed

    Schlauch, Daniel; Paulson, Joseph N; Young, Albert; Glass, Kimberly; Quackenbush, John

    2017-03-11

    PANDA (Passing Attributes betweenNetworks forData Assimilation) is a gene regulatory network inference method that begins with amodel of transcription factor-target gene interactions and usesmessage passing to update the network model given available transcriptomic and protein-protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks.

  20. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

    PubMed Central

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case. PMID:27298752

  1. Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization

    PubMed Central

    Shim, Jung Eun; Hwang, Sohyun; Lee, Insuk

    2015-01-01

    A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity. PMID:26091506

  2. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

    PubMed

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

    2016-06-02

    The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

  3. Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

    PubMed Central

    Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

    2013-01-01

    In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

  4. Biomarker Gene Signature Discovery Integrating Network Knowledge

    PubMed Central

    Cun, Yupeng; Fröhlich, Holger

    2012-01-01

    Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches. PMID:24832044

  5. A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

    PubMed

    Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-12-22

    Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

  6. NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities

    PubMed Central

    da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D.; Correia, Cristina; Li, Hu

    2016-01-01

    The sequential chain of interactions altering the binary state of a biomolecule represents the ‘information flow’ within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein–protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes—network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. PMID:26975659

  7. Mechanistically Consistent Reduced Models of Synthetic Gene Networks

    PubMed Central

    Mier-y-Terán-Romero, Luis; Silber, Mary; Hatzimanikatis, Vassily

    2013-01-01

    Designing genetic networks with desired functionalities requires an accurate mathematical framework that accounts for the essential mechanistic details of the system. Here, we formulate a time-delay model of protein translation and mRNA degradation by systematically reducing a detailed mechanistic model that explicitly accounts for the ribosomal dynamics and the cleaving of mRNA by endonucleases. We exploit various technical and conceptual advantages that our time-delay model offers over the mechanistic model to probe the behavior of a self-repressing gene over wide regions of parameter space. We show that a heuristic time-delay model of protein synthesis of a commonly used form yields a notably different prediction for the parameter region where sustained oscillations occur. This suggests that such heuristics can lead to erroneous results. The functional forms that arise from our systematic reduction can be used for every system that involves transcription and translation and they could replace the commonly used heuristic time-delay models for these processes. The results from our analysis have important implications for the design of synthetic gene networks and stress that such design must be guided by a combination of heuristic models and mechanistic models that include all relevant details of the process. PMID:23663853

  8. Variability of multifractal parameters in an urban precipitation monitoring network

    NASA Astrophysics Data System (ADS)

    Licznar, Paweł; De Michele, Carlo; Dżugaj, Dagmara; Niesobska, Maria

    2014-05-01

    Precipitation especially over urban areas is considered a highly non-linear process, with wide variability over a broad range of temporal and spatial scales. Despite obvious limitations of rainfall gauges location at urban sites, rainfall monitoring by gauge networks is a standard solution of urban hydrology. Often urban precipitation gauge networks are formed by modern electronic gauges and connected to control units of centralized urban drainage systems. Precipitation data, recorded online through these gauge networks, are used in so called Real-Time-Control (RTC) systems for the development of optimal strategies of urban drainage outflows management. As a matter of fact, the operation of RTC systems is motivated mainly by the urge of reducing the severity of urban floods and combined sewerage overflows, but at the same time, it creates new valuable precipitation data sources. The variability of precipitation process could be achieved by investigating multifractal behavior displayed by the temporal structure of precipitation data. There are multiply scientific communications concerning multifractal properties of point-rainfall data from different worldwide locations. However, very little is known about the close variability of multifractal parameters among closely located gauges, at the distances of single kilometers. Having this in mind, here we assess the variability of multifractal parameters among gauges of the urban precipitation monitoring network in Warsaw, Poland. We base our analysis on the set of 1-minute rainfall time series recorded in the period 2008-2011 by 25 electronic weighing type gauges deployed around the city by the Municipal Water Supply and Sewerage Company in Warsaw as a part of local RTC system. The presence of scale invariance and multifractal properties in the precipitation process was investigated with spectral analysis, functional box counting method and studying the probability distributions and statistical moments of the rainfall

  9. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

    PubMed

    Tamada, Yoshinori; Imoto, Seiya; Araki, Hiromitsu; Nagasaki, Masao; Print, Cristin; Charnock-Jones, D Stephen; Miyano, Satoru

    2011-01-01

    We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

  10. Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

    PubMed Central

    2014-01-01

    Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel

  11. Network rewiring is an important mechanism of gene essentiality change

    PubMed Central

    Kim, Jinho; Kim, Inhae; Han, Seong Kyu; Bowie, James U.; Kim, Sanguk

    2012-01-01

    Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality. PMID:23198090

  12. An efficient automated parameter tuning framework for spiking neural networks.

    PubMed

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  13. COXPRESdb: a database of coexpressed gene networks in mammals.

    PubMed

    Obayashi, Takeshi; Hayashi, Shinpei; Shibaoka, Masayuki; Saeki, Motoshi; Ohta, Hiroyuki; Kinoshita, Kengo

    2008-01-01

    A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein-protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species.

  14. Enhancing debris flow modeling parameters integrating Bayesian networks

    NASA Astrophysics Data System (ADS)

    Graf, C.; Stoffel, M.; Grêt-Regamey, A.

    2009-04-01

    Applied debris-flow modeling requires suitably constraint input parameter sets. Depending on the used model, there is a series of parameters to define before running the model. Normally, the data base describing the event, the initiation conditions, the flow behavior, the deposition process and mainly the potential range of possible debris flow events in a certain torrent is limited. There are only some scarce places in the world, where we fortunately can find valuable data sets describing event history of debris flow channels delivering information on spatial and temporal distribution of former flow paths and deposition zones. Tree-ring records in combination with detailed geomorphic mapping for instance provide such data sets over a long time span. Considering the significant loss potential associated with debris-flow disasters, it is crucial that decisions made in regard to hazard mitigation are based on a consistent assessment of the risks. This in turn necessitates a proper assessment of the uncertainties involved in the modeling of the debris-flow frequencies and intensities, the possible run out extent, as well as the estimations of the damage potential. In this study, we link a Bayesian network to a Geographic Information System in order to assess debris-flow risk. We identify the major sources of uncertainty and show the potential of Bayesian inference techniques to improve the debris-flow model. We model the flow paths and deposition zones of a highly active debris-flow channel in the Swiss Alps using the numerical 2-D model RAMMS. Because uncertainties in run-out areas cause large changes in risk estimations, we use the data of flow path and deposition zone information of reconstructed debris-flow events derived from dendrogeomorphological analysis covering more than 400 years to update the input parameters of the RAMMS model. The probabilistic model, which consistently incorporates this available information, can serve as a basis for spatial risk

  15. Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhu, Shijia; Wang, Yadong

    2015-12-01

    Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

  16. A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

    PubMed Central

    Li, Jianhua; Lin, Xiaoyan; Teng, Yueyang; Qi, Shouliang; Xiao, Dayu; Zhang, Jianying; Kang, Yan

    2016-01-01

    Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary. PMID:27415759

  17. Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

    NASA Technical Reports Server (NTRS)

    Mjolsness, Eric

    2000-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

  18. Gene Networks Underlying Chronic Sleep Deprivation in Drosophila

    DTIC Science & Technology

    2014-06-15

    SECURITY CLASSIFICATION OF: Studies of the gene network affected by sleep deprivation and stress in the fruit fly Drosophila have revealed the...transduction pathways are affected. Subseuqent tests of mutants in these pathways demonstrated a strong effect on sleep maintenance. Further...15-Apr-2009 14-Apr-2013 Approved for Public Release; Distribution Unlimited Gene Networks Underlying Chronic Sleep Deprivation in Drosophila The

  19. The Max-Min High-Order Dynamic Bayesian Network for Learning Gene Regulatory Networks with Time-Delayed Regulations.

    PubMed

    Li, Yifeng; Chen, Haifen; Zheng, Jie; Ngom, Alioune

    2016-01-01

    Accurately reconstructing gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationships between genes. We applied sensitivity analysis on our model to study the performance variation along different parameter settings. The result provides hints on the setting of parameters of MMHO-DBN.

  20. Gene Coexpression Network Topology of Cardiac Development, Hypertrophy, and Failure

    PubMed Central

    Dewey, Frederick E.; Perez, Marco V.; Wheeler, Matthew T.; Watt, Clifton; Spin, Joshua; Langfelder, Peter; Horvath, Stephen; Hannenhalli, Sridhar; Cappola, Thomas P.; Ashley, Euan A.

    2011-01-01

    Background Network analysis techniques allow a more accurate reflection of underlying systems biology to be realized than traditional unidimensional molecular biology approaches. Here, using gene coexpression network analysis, we define the gene expression network topology of cardiac hypertrophy and failure and the extent of recapitulation of fetal gene expression programs in failing and hypertrophied adult myocardium. Methods and Results We assembled all myocardial transcript data in the Gene Expression Omnibus (n = 1617). Since hierarchical analysis revealed species had primacy over disease clustering, we focused this analysis on the most complete (murine) dataset (n = 478). Using gene coexpression network analysis, we derived functional modules, regulatory mediators and higher order topological relationships between genes and identified 50 gene co-expression modules in developing myocardium that were not present in normal adult tissue. We found that known gene expression markers of myocardial adaptation were members of upregulated modules but not hub genes. We identified ZIC2 as a novel transcription factor associated with coexpression modules common to developing and failing myocardium. Of 50 fetal gene co-expression modules, three (6%) were reproduced in hypertrophied myocardium and seven (14%) were reproduced in failing myocardium. One fetal module was common to both failing and hypertrophied myocardium. Conclusions Network modeling allows systems analysis of cardiovascular development and disease. While we did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, our analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation. PMID:21127201

  1. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  2. Efficient reverse-engineering of a developmental gene regulatory network.

    PubMed

    Crombach, Anton; Wotton, Karl R; Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  3. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    PubMed Central

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  4. On the robustness of complex heterogeneous gene expression networks.

    PubMed

    Gómez-Gardeñes, Jesús; Moreno, Yamir; Floría, Luis M

    2005-04-01

    We analyze a continuous gene expression model on the underlying topology of a complex heterogeneous network. Numerical simulations aimed at studying the chaotic and periodic dynamics of the model are performed. The results clearly indicate that there is a region in which the dynamical and structural complexity of the system avoid chaotic attractors. However, contrary to what has been reported for Random Boolean Networks, the chaotic phase cannot be completely suppressed, which has important bearings on network robustness and gene expression modeling.

  5. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine. PMID:25364745

  6. Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-01-01

    Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

  7. Parameter identification and synchronization for uncertain network group with different structures

    NASA Astrophysics Data System (ADS)

    Li, Chengren; Lü, Ling; Sun, Ying; Wang, Ying; Wang, Wenjun; Sun, Ao

    2016-09-01

    We design a novel synchronization technique to research the synchronization of network group constituted of uncertain networks with different structures. Based on Lyapunov theorem, the selection principles of the control inputs and the parameter identification laws of the networks are determined, and synchronization conditions of the network group are obtained. Some numerical simulations are provided to verify the correctness and effectiveness of the synchronization technique. We find that the network number, the number of network nodes and network connections indeed will not affect the stability of synchronization of network group.

  8. Uncover disease genes by maximizing information flow in the phenome–interactome network

    PubMed Central

    Chen, Yong; Jiang, Tao; Jiang, Rui

    2011-01-01

    Motivation: Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein–protein interactions, no combinatorial approaches have been proposed in the literature. Results: We propose the first combinatorial approach for prioritizing candidate genes. We first construct a phenome–interactome network by integrating the given phenotype similarity profile, protein–protein interaction network and associations between diseases and genes. Then, we introduce a computational method called MAXIF to maximize the information flow in this network for uncovering genes that underlie diseases. We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated. We demonstrate the competitive performance of our method through a comparison with two existing state-of-the-art methods, and we analyze the robustness of our method with respect to the parameters involved. As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma. Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes. Contact: ruijiang@tsinghua.edu.cn PMID:21685067

  9. Approaches for recognizing disease genes based on network.

    PubMed

    Zou, Quan; Li, Jinjin; Wang, Chunyu; Zeng, Xiangxiang

    2014-01-01

    Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

  10. Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

    NASA Astrophysics Data System (ADS)

    Sengupta, A.; Ranjan, P.

    2002-07-01

    Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double

  11. [Study on the automatic parameters identification of water pipe network model].

    PubMed

    Jia, Hai-Feng; Zhao, Qi-Feng

    2010-01-01

    Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved.

  12. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    PubMed

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  13. Evolvability and hierarchy in rewired bacterial gene networks

    PubMed Central

    Isalan, Mark; Lemerle, Caroline; Michalodimitrakis, Konstantinos; Beltrao, Pedro; Horn, Carsten; Raineri, Emanuele; Garriga-Canut, Mireia; Serrano, Luis

    2009-01-01

    Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage. PMID:18421347

  14. Analysis of bHLH coding genes using gene co-expression network approach.

    PubMed

    Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.

  15. Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering

    PubMed Central

    Aibar, Sara; Fontanillo, Celia; Droste, Conrad; De Las Rivas, Javier

    2015-01-01

    Summary: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use. Availability and implementation: FGNet is available in Bioconductor, including a tutorial. URL: http://bioconductor.org/packages/release/bioc/html/FGNet.html Contact: jrivas@usal.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25600944

  16. GINI: From ISH Images to Gene Interaction Networks

    PubMed Central

    Puniyani, Kriti; Xing, Eric P.

    2013-01-01

    Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and

  17. Reverse engineering of gene regulatory networks: a comparative study.

    PubMed

    Hache, Hendrik; Lehrach, Hans; Herwig, Ralf

    2009-01-01

    Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  18. Time-Delayed Models of Gene Regulatory Networks

    PubMed Central

    Parmar, K.; Blyuss, K. B.; Kyrychko, Y. N.; Hogan, S. J.

    2015-01-01

    We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems. PMID:26576197

  19. Variable neighborhood search for reverse engineering of gene regulatory networks.

    PubMed

    Nicholson, Charles; Goodwin, Leslie; Clark, Corey

    2017-01-01

    A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

  20. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  1. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks.

    PubMed

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-06-01

    The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes' evolutionary properties. Slowly evolving ("cold"), old genes tend to interact with each other, as do rapidly evolving ("hot"), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN's community structures and its genes' evolutionary properties provide new perspectives for understanding evolutionary genetics.

  2. A Maize Gene Regulatory Network for Phenolic Metabolism.

    PubMed

    Yang, Fan; Li, Wei; Jiang, Nan; Yu, Haidong; Morohashi, Kengo; Ouma, Wilberforce Zachary; Morales-Mantilla, Daniel E; Gomez-Cano, Fabio Andres; Mukundi, Eric; Prada-Salcedo, Luis Daniel; Velazquez, Roberto Alers; Valentin, Jasmin; Mejía-Guerra, Maria Katherine; Gray, John; Doseff, Andrea I; Grotewold, Erich

    2017-03-06

    The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

  3. Analysis of Gene Sets Based on the Underlying Regulatory Network

    PubMed Central

    Michailidis, George

    2009-01-01

    Abstract Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast. PMID:19254181

  4. Phenotype accessibility and noise in random threshold gene regulatory networks.

    PubMed

    Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W

    2014-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes.

  5. Identifying gene regulatory network rewiring using latent differential graphical models

    PubMed Central

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-01-01

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions. PMID:27378774

  6. Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles

    PubMed Central

    2015-01-01

    Background Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes. Results We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype. Conclusions The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The

  7. Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing

    PubMed Central

    Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem

    2013-01-01

    This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability. PMID:23737768

  8. Gene duplication models for directed networks with limits on growth

    NASA Astrophysics Data System (ADS)

    Enemark, Jakob; Sneppen, Kim

    2007-11-01

    Background: Duplication of genes is important for evolution of molecular networks. Many authors have therefore considered gene duplication as a driving force in shaping the topology of molecular networks. In particular it has been noted that growth via duplication would act as an implicit means of preferential attachment, and thereby provide the observed broad degree distributions of molecular networks. Results: We extend current models of gene duplication and rewiring by including directions and the fact that molecular networks are not a result of unidirectional growth. We introduce upstream sites and downstream shapes to quantify potential links during duplication and rewiring. We find that this in itself generates the observed scaling of transcription factors for genome sites in prokaryotes. The dynamical model can generate a scale-free degree distribution, p(k)\\propto 1/k^{\\gamma } , with exponent γ = 1 in the non-growing case, and with γ>1 when the network is growing. Conclusions: We find that duplication of genes followed by substantial recombination of upstream regions could generate features of genetic regulatory networks. Our steady state degree distribution is however too broad to be consistent with data, thereby suggesting that selective pruning acts as a main additional constraint on duplicated genes. Our analysis shows that gene duplication can only be a main cause for the observed broad degree distributions if there are also substantial recombinations between upstream regions of genes.

  9. microRNA and gene networks in human laryngeal cancer

    PubMed Central

    ZHANG, FENGYU; XU, ZHIWEN; WANG, KUNHAO; SUN, LINLIN; LIU, GENGHE; HAN, BAIXU

    2015-01-01

    Genes and microRNAs (miRNAs) are considered to be key biological factors in human carcinogenesis. To date, considerable data have been obtained regarding genes and miRNAs in cancer; however, the regulatory mechanisms associated with the genes and miRNAs in cancer have yet to be fully elucidated. The aim of the present study was to use the key genes and miRNAs associated with laryngeal cancer (LC) to construct three regulatory networks (differentially expressed, LC-related and global). A network topology of the development of LC, involving 10 differentially expressed miRNAs and 55 differentially expressed genes, was obtained. These genes exhibited multiple identities, including target genes of miRNA, transcription factors (TFs) and host genes. The key regulatory interactions were determined by comparing the similarities and differences among the three networks. The nodes and pathways in LC, as well as the association between each pair of factors within the networks, such as TFs and miRNA, miRNA and target genes and miRNA and its host gene, were discussed. The mechanisms of LC involved certain key pathways featuring self-adaptation regulation and nodes without direct predecessors or successors. The findings of the present study have further elucidated the pathogenesis of LC and are likely to be beneficial for future research into LC. PMID:26668624

  10. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    PubMed

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  11. Systems Approaches to Identifying Gene Regulatory Networks in Plants

    PubMed Central

    Long, Terri A.; Brady, Siobhan M.; Benfey, Philip N.

    2009-01-01

    Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed. PMID:18616425

  12. Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

    PubMed

    Zak, Daniel E; Gonye, Gregory E; Schwaber, James S; Doyle, Francis J

    2003-11-01

    Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

  13. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

    PubMed Central

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-01-01

    The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics. PMID:27359334

  14. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

    PubMed Central

    2012-01-01

    Background Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis

  15. Inferring gene correlation networks from transcription factor binding sites.

    PubMed

    Mahdevar, Ghasem; Nowzari-Dalini, Abbas; Sadeghi, Mehdi

    2013-01-01

    Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

  16. Dynamics of gene regulatory networks with cell division cycle

    NASA Astrophysics Data System (ADS)

    Chen, Luonan; Wang, Ruiqi; Kobayashi, Tetsuya J.; Aihara, Kazuyuki

    2004-07-01

    This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA , in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac,tetR , and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

  17. Fused Regression for Multi-source Gene Regulatory Network Inference

    PubMed Central

    Lam, Kari Y.; Westrick, Zachary M.; Müller, Christian L.; Christiaen, Lionel; Bonneau, Richard

    2016-01-01

    Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms. PMID:27923054

  18. Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes.

    PubMed

    D'Antonio, Matteo; Pendino, Vera; Sinha, Shruti; Ciccarelli, Francesca D

    2012-01-01

    The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each cancer gene, NCG 3.0 provides a summary of the gene features and the cross-reference to other databases. In addition, it describes duplicability, evolutionary origin, orthology, network properties, interaction partners, microRNA regulation and functional roles of cancer genes and of all genes that are related to them. This integrated network of information can be used to better characterize cancer genes in the context of the system in which they act. The data can also be used to identify novel candidates that share the same properties of known cancer genes and may therefore play a similar role in cancer. NCG 3.0 is freely available at http://bio.ifom-ieo-campus.it/ncg.

  19. Two different modes of oscillation in a gene transcription regulatory network with interlinked positive and negative feedback loops

    NASA Astrophysics Data System (ADS)

    Karmakar, Rajesh

    2016-12-01

    We study the oscillatory behavior of a gene regulatory network with interlinked positive and negative feedback loop. The frequency and amplitude are two important properties of oscillation. The studied network produces two different modes of oscillation. In one mode (mode-I), frequency of oscillation remains constant over a wide range of amplitude and in the other mode (mode-II) the amplitude of oscillation remains constant over a wide range of frequency. Our study reproduces both features of oscillations in a single gene regulatory network and shows that the negative plus positive feedback loops in gene regulatory network offer additional advantage. We identified the key parameters/variables responsible for different modes of oscillation. The network is flexible in switching between different modes by choosing appropriately the required parameters/variables.

  20. Topological origin of global attractors in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhang, YunJun; Ouyang, Qi; Geng, Zhi

    2015-02-01

    Fixed-point attractors with global stability manifest themselves in a number of gene regulatory networks. This property indicates the stability of regulatory networks against small state perturbations and is closely related to other complex dynamics. In this paper, we aim to reveal the core modules in regulatory networks that determine their global attractors and the relationship between these core modules and other motifs. This work has been done via three steps. Firstly, inspired by the signal transmission in the regulation process, we extract the model of chain-like network from regulation networks. We propose a module of "ideal transmission chain (ITC)", which is proved sufficient and necessary (under certain condition) to form a global fixed-point in the context of chain-like network. Secondly, by examining two well-studied regulatory networks (i.e., the cell-cycle regulatory networks of Budding yeast and Fission yeast), we identify the ideal modules in true regulation networks and demonstrate that the modules have a superior contribution to network stability (quantified by the relative size of the biggest attraction basin). Thirdly, in these two regulation networks, we find that the double negative feedback loops, which are the key motifs of forming bistability in regulation, are connected to these core modules with high network stability. These results have shed new light on the connection between the topological feature and the dynamic property of regulatory networks.

  1. GENIUS: web server to predict local gene networks and key genes for biological functions.

    PubMed

    Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

    2016-12-19

    GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.

  2. On-line Parameter Estimation of Time-varying Systems by Radial Basis Function Networks

    NASA Astrophysics Data System (ADS)

    Kobayashi, Yasuhide; Tanaka, Shinichi; Okita, Tsuyoshi

    This paper proposes a new on-line parameter estimation method with radial basis function networks for time-varying linear discrete-time systems. The time-varying parameters of the system are expressed with the radial basis function networks. These parameters are estimated by the nonlinear optimization technique, and the setting rules of the initial values in the optimization are proposed. The system parameters are usually unknown because they are changed by the circumstance conditions. Then, it is reasonable that the structures of the radial basis function networks are regulated according to the change of parameters. The minimum description length criterion studied in the encoding theory is applied to select the network structures. It is demonstrated in digital simulation that the proposed on-line estimation method succeeded to reduce the computaion time extremely, for time-varying parameters system.

  3. Reverse engineering gene regulatory network from microarray data using linear time-variant model

    PubMed Central

    2010-01-01

    Background Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm. Results To assess the potency of the proposed work, a well known nonlinear synthetic network has been used. The reconstruction method has inferred this synthetic network topology and the associated regulatory parameters with high accuracy from both the noise-free and noisy time-series data. For validation purposes, the proposed approach is also applied to the simulated expression dataset of cAMP oscillations in Dictyostelium discoideum and has proved it's strength in finding the correct regulations. The strength of this work has also been verified by analyzing the real expression dataset of SOS DNA repair system in Escherichia coli and it has succeeded in finding more correct and reasonable regulations as compared to various existing works. Conclusion By the proposed approach, the gene interaction networks have been inferred in an efficient manner from both the synthetic, simulated cAMP oscillation expression data and real expression data. The computational time of this approach is also considerably smaller, which makes it to be more suitable for larger network reconstruction. Thus the proposed approach can serve as an initiate for the future researches regarding the associated area. PMID:20122231

  4. Inference of gene regulatory networks from genome-wide knockout fitness data

    PubMed Central

    Wang, Liming; Wang, Xiaodong; Arkin, Adam P.; Samoilov, Michael S.

    2013-01-01

    Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis. Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/ Contact: wangx@ee.columbia.edu or mssamoilov@lbl.gov Supplementary information

  5. Gene regulation: hacking the network on a sugar high.

    PubMed

    Ellis, Tom; Wang, Xiao; Collins, James J

    2008-04-11

    In a recent issue of Molecular Cell, Kaplan et al. (2008) determine the input functions for 19 E. coli sugar-utilization genes by using a two-dimensional high-throughput approach. The resulting input-function map reveals that gene network regulation follows non-Boolean, and often nonmonotonic, logic.

  6. Gene-based and semantic structure of the Gene Ontology as a complex network

    NASA Astrophysics Data System (ADS)

    Coronnello, Claudia; Tumminello, Michele; Miccichè, Salvatore

    2016-09-01

    The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics.

  7. Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

    PubMed Central

    CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

    2014-01-01

    Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

  8. Gene regulatory network clustering for graph layout based on microarray gene expression data.

    PubMed

    Kojima, Kaname; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

    2010-01-01

    We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization. We apply the proposed model to the time series data from a synthetically generated network and verified the effectiveness of the proposed model. The proposed model is also applied the time series microarray data from HeLa cell. Detected gene module information gives the great help on drawing the estimated gene network.

  9. Optimal finite horizon control in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  10. Identification of cancer-related genes and motifs in the human gene regulatory network.

    PubMed

    Carson, Matthew B; Gu, Jianlei; Yu, Guangjun; Lu, Hui

    2015-08-01

    The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes. Potential mechanisms of these motifs and positions are discussed. These results could be used to identify other disease- and cancer-related genes and could also suggest mechanisms for how these genes relate to co-occurring diseases.

  11. Beyond Genomics: Studying Evolution with Gene Coexpression Networks.

    PubMed

    Ruprecht, Colin; Vaid, Neha; Proost, Sebastian; Persson, Staffan; Mutwil, Marek

    2017-04-01

    Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.

  12. Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

    PubMed Central

    de Franciscis, Sebastiano; Caravagna, Giulio; Mauri, Giancarlo; d’Onofrio, Alberto

    2016-01-01

    Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting. PMID:27256916

  13. Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif.

    PubMed

    de Franciscis, Sebastiano; Caravagna, Giulio; Mauri, Giancarlo; d'Onofrio, Alberto

    2016-06-03

    Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting.

  14. Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

    NASA Astrophysics Data System (ADS)

    de Franciscis, Sebastiano; Caravagna, Giulio; Mauri, Giancarlo; D’Onofrio, Alberto

    2016-06-01

    Gene switching dynamics is a major source of randomness in genetic networks, also in the case of large concentrations of the transcription factors. In this work, we consider a common network motif - the positive feedback of a transcription factor on its own synthesis - and assess its response to extrinsic noises perturbing gene deactivation in a variety of settings where the network might operate. These settings are representative of distinct cellular types, abundance of transcription factors and ratio between gene switching and protein synthesis rates. By investigating noise-induced transitions among the different network operative states, our results suggest that gene switching rates are key parameters to shape network response to external perturbations, and that such response depends on the particular biological setting, i.e. the characteristic time scales and protein abundance. These results might have implications on our understanding of irreversible transitions for noise-related phenomena such as cellular differentiation. In addition these evidences suggest to adopt the appropriate mathematical model of the network in order to analyze the system consistently to the reference biological setting.

  15. Parameter space exploration within dynamic simulations of signaling networks.

    PubMed

    De Ambrosi, Cristina; Barla, Annalisa; Tortolina, Lorenzo; Castagnino, Nicoletta; Pesenti, Raffaele; Verri, Alessandro; Ballestrero, Alberto; Patrone, Franco; Parodi, Silvio

    2013-02-01

    We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

  16. An extensive network of coupling among gene expression machines.

    PubMed

    Maniatis, Tom; Reed, Robin

    2002-04-04

    Gene expression in eukaryotes requires several multi-component cellular machines. Each machine carries out a separate step in the gene expression pathway, which includes transcription, several pre-messenger RNA processing steps and the export of mature mRNA to the cytoplasm. Recent studies lead to the view that, in contrast to a simple linear assembly line, a complex and extensively coupled network has evolved to coordinate the activities of the gene expression machines. The extensive coupling is consistent with a model in which the machines are tethered to each other to form 'gene expression factories' that maximize the efficiency and specificity of each step in gene expression.

  17. Replaying the evolutionary tape: biomimetic reverse engineering of gene networks.

    PubMed

    Marbach, Daniel; Mattiussi, Claudio; Floreano, Dario

    2009-03-01

    In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse-engineering procedure, thus biasing the search toward biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. We apply the biomimetic method to a gold-standard dataset from an in vivo gene network. The obtained results won a reverse engineering competition of the second DREAM conference (Dialogue on Reverse Engineering Assessments and Methods 2007, New York, NY).

  18. Identification of marginal causal relationships in gene networks from observational and interventional expression data.

    PubMed

    Monneret, Gilles; Jaffrézic, Florence; Rau, Andrea; Zerjal, Tatiana; Nuel, Grégory

    2017-01-01

    Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub).

  19. Identification of marginal causal relationships in gene networks from observational and interventional expression data

    PubMed Central

    Monneret, Gilles; Jaffrézic, Florence; Rau, Andrea; Zerjal, Tatiana; Nuel, Grégory

    2017-01-01

    Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub). PMID:28301504

  20. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

    PubMed

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-07-12

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

  1. Linear control theory for gene network modeling.

    PubMed

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  2. A complex network analysis of hypertension-related genes

    NASA Astrophysics Data System (ADS)

    Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei

    2014-01-01

    In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.

  3. Gene network and pathway generation and analysis: Editorial

    SciTech Connect

    Zhao, Zhongming; Sanfilippo, Antonio P.; Huang, Kun

    2011-02-18

    The past decade has witnessed an exponential growth of biological data including genomic sequences, gene annotations, expression and regulation, and protein-protein interactions. A key aim in the post-genome era is to systematically catalogue gene networks and pathways in a dynamic living cell and apply them to study diseases and phenotypes. To promote the research in systems biology and its application to disease studies, we organized a workshop focusing on the reconstruction and analysis of gene networks and pathways in any organisms from high-throughput data collected through techniques such as microarray analysis and RNA-Seq.

  4. Identification of Gene Networks: An Approach Based on Mathematical Modeling

    DTIC Science & Technology

    2014-08-21

    applied the sensitivity method to a five-gene subnetwork of Escherichia coli and obtained promising preliminary experimental results. (a) Papers... Escherichia coli : ompR, flhC, flhD, flgA, and flgC. The regulatory interactions among these genes have been previously discovered and are part of the...Network Identification. (Submitted). 3 Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR

  5. Gene regulatory networks modelling using a dynamic evolutionary hybrid

    PubMed Central

    2010-01-01

    Background Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. Results The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes. Conclusions The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/. PMID:20298548

  6. Analyzing and constraining signaling networks: parameter estimation for the user.

    PubMed

    Geier, Florian; Fengos, Georgios; Felizzi, Federico; Iber, Dagmar

    2012-01-01

    The behavior of most dynamical models not only depends on the wiring but also on the kind and strength of interactions which are reflected in the parameter values of the model. The predictive value of mathematical models therefore critically hinges on the quality of the parameter estimates. Constraining a dynamical model by an appropriate parameterization follows a 3-step process. In an initial step, it is important to evaluate the sensitivity of the parameters of the model with respect to the model output of interest. This analysis points at the identifiability of model parameters and can guide the design of experiments. In the second step, the actual fitting needs to be carried out. This step requires special care as, on the one hand, noisy as well as partial observations can corrupt the identification of system parameters. On the other hand, the solution of the dynamical system usually depends in a highly nonlinear fashion on its parameters and, as a consequence, parameter estimation procedures get easily trapped in local optima. Therefore any useful parameter estimation procedure has to be robust and efficient with respect to both challenges. In the final step, it is important to access the validity of the optimized model. A number of reviews have been published on the subject. A good, nontechnical overview is provided by Jaqaman and Danuser (Nat Rev Mol Cell Biol 7(11):813-819, 2006) and a classical introduction, focussing on the algorithmic side, is given in Press (Numerical recipes: The art of scientific computing, Cambridge University Press, 3rd edn., 2007, Chapters 10 and 15). We will focus on the practical issues related to parameter estimation and use a model of the TGFβ-signaling pathway as an educative example. Corresponding parameter estimation software and models based on MATLAB code can be downloaded from the authors's web page ( http://www.bsse.ethz.ch/cobi ).

  7. The incorporation of epigenetics in artificial gene regulatory networks.

    PubMed

    Turner, Alexander P; Lones, Michael A; Fuente, Luis A; Stepney, Susan; Caves, Leo S D; Tyrrell, Andy M

    2013-05-01

    Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

  8. Development of a synthetic gene network to modulate gene expression by mechanical forces

    PubMed Central

    Kis, Zoltán; Rodin, Tania; Zafar, Asma; Lai, Zhangxing; Freke, Grace; Fleck, Oliver; Del Rio Hernandez, Armando; Towhidi, Leila; Pedrigi, Ryan M.; Homma, Takayuki; Krams, Rob

    2016-01-01

    The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude. PMID:27404994

  9. A Synthesis Method of Gene Networks Having Cyclic Expression Pattern Sequences by Network Learning

    NASA Astrophysics Data System (ADS)

    Mori, Yoshihiro; Kuroe, Yasuaki

    Recently, synthesis of gene networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene networks, and it could be the first step in controlling living cells. There exist several periodic phenomena in cells, e.g. circadian rhythm. These phenomena are considered to be generated by gene networks. We have already proposed synthesis method of gene networks based on gene expression. The method is applicable to synthesizing gene networks possessing the desired cyclic expression pattern sequences. It ensures that realized expression pattern sequences are periodic, however, it does not ensure that their corresponding solution trajectories are periodic, which might bring that their oscillations are not persistent. In this paper, in order to resolve the problem we propose a synthesis method of gene networks possessing the desired cyclic expression pattern sequences together with their corresponding solution trajectories being periodic. In the proposed method the persistent oscillations of the solution trajectories are realized by specifying passing points of them.

  10. microRNA and gene networks in human pancreatic cancer

    PubMed Central

    ZHU, MINGHUI; XU, ZHIWEN; WANG, KUNHAO; WANG, NING; LI, YANG

    2013-01-01

    To date, scientists have obtained a substantial amount of knowledge with regard to genes and microRNAs (miRNAs) in pancreatic cancer (PC). However, deciphering the regulatory mechanism of these genes and miRNAs remains difficult. In the present study, three regulatory networks consisting of a differentially-expressed network, a related network and a global network, were constructed in order to identify the mechanisms and certain key miRNA and gene pathways in PC. The interactions between transcription factors (TFs) and miRNAs, miRNAs and target genes and an miRNA and its host gene were investigated. The present study compared and analyzed the similarities and differences between the three networks in order to distinguish the key pathways. Certain pathways involving the differentially-expressed genes and miRNAs demonstrated specific features. TP53 and hsa-miR-125b were observed to form a self-adaptation association. A further 16 significant differentially-expressed miRNAs were obtained and it was observed that an miRNA and its host gene exhibit specific features in PC, for example, hsa-miR-196a-1 and its host gene, HOXB7, form a self-adaptation association. The differentially-expressed network partially illuminated the mechanism of PC. The present study provides comprehensive data that is associated with PC and may aid future studies in obtaining pertinent data results with regards to PC. In the future, an improved understanding of PC may be obtained through an increased knowledge of the occurrence, mechanism, improvement, metastasis and treatment of the disease. PMID:24137477

  11. Hidden hysteresis – population dynamics can obscure gene network dynamics

    PubMed Central

    2013-01-01

    Background Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit. Results The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change. Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture. Conclusions These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application. PMID:23800122

  12. Solution of the quasispecies model for an arbitrary gene network

    NASA Astrophysics Data System (ADS)

    Tannenbaum, Emmanuel; Shakhnovich, Eugene I.

    2004-08-01

    In this paper, we study the equilibrium behavior of Eigen’s quasispecies equations for an arbitrary gene network. We consider a genome consisting of N genes, so that the full genome sequence σ may be written as σ=σ1σ2⋯σN , where σi are sequences of individual genes. We assume a single fitness peak model for each gene, so that gene i has some “master” sequence σi,0 for which it is functioning. The fitness landscape is then determined by which genes in the genome are functioning and which are not. The equilibrium behavior of this model may be solved in the limit of infinite sequence length. The central result is that, instead of a single error catastrophe, the model exhibits a series of localization to delocalization transitions, which we term an “error cascade.” As the mutation rate is increased, the selective advantage for maintaining functional copies of certain genes in the network disappears, and the population distribution delocalizes over the corresponding sequence spaces. The network goes through a series of such transitions, as more and more genes become inactivated, until eventually delocalization occurs over the entire genome space, resulting in a final error catastrophe. This model provides a criterion for determining the conditions under which certain genes in a genome will lose functionality due to genetic drift. It also provides insight into the response of gene networks to mutagens. In particular, it suggests an approach for determining the relative importance of various genes to the fitness of an organism, in a more accurate manner than the standard “deletion set” method. The results in this paper also have implications for mutational robustness and what C.O. Wilke termed “survival of the flattest.”

  13. Modularity and evolutionary constraints in a baculovirus gene regulatory network

    PubMed Central

    2013-01-01

    Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Results We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. Conclusions Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates

  14. Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

    PubMed Central

    Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

    2014-01-01

    Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

  15. Multiscale Embedded Gene Co-expression Network Analysis

    PubMed Central

    Song, Won-Min; Zhang, Bin

    2015-01-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

  16. Learning gene regulatory networks from next generation sequencing data.

    PubMed

    Jia, Bochao; Xu, Suwa; Xiao, Guanghua; Lamba, Vishal; Liang, Faming

    2017-03-10

    In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network. In this article, we propose a random effect model-based transformation to continuize NGS data and then we transform the continuized data to Gaussian via a semiparametric transformation and apply an equivalent partial correlation selection method to reconstruct gene regulatory networks. The proposed method is consistent. The numerical results indicate that the proposed method can lead to much more accurate inference of gene regulatory networks than the local Poisson graphical model and other existing methods. The proposed data-continuized transformation fills the theoretical gap for how to transform discrete data to continuous data and facilitates NGS data analysis. The proposed data-continuized transformation also makes it feasible to integrate different types of data, such as microarray and RNA-seq data, in reconstruction of gene regulatory networks.

  17. Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

    PubMed

    Wolen, Aaron R; Miles, Michael F

    2012-01-01

    For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

  18. Gene-network inference by message passing

    NASA Astrophysics Data System (ADS)

    Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

    2008-01-01

    The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

  19. Stochastic S-system modeling of gene regulatory network.

    PubMed

    Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob

    2015-10-01

    Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.

  20. BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks.

    PubMed

    Richiardi, Jonas; Altmann, Andre; Milazzo, Anna-Clare; Chang, Catie; Chakravarty, M Mallar; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Bromberg, Uli; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Lemaître, Hervé; Mann, Karl F; Martinot, Jean-Luc; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Spanagel, Rainer; Ströhle, Andreas; Schumann, Gunter; Hawrylycz, Mike; Poline, Jean-Baptiste; Greicius, Michael D

    2015-06-12

    During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.

  1. Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

    PubMed

    Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

    2014-10-01

    To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general.

  2. Inferring orthologous gene regulatory networks using interspecies data fusion

    PubMed Central

    Penfold, Christopher A.; Millar, Jonathan B. A.; Wild, David L.

    2015-01-01

    Motivation: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved ‘hypernetwork’. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression. Results: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase. Availability and implementation: MATLAB code is available from http://go.warwick.ac.uk/systemsbiology/software/. Contact: d.l.wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics

  3. Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

    PubMed

    Han, Hyun Wook; Ohn, Jung Hun; Moon, Jisook; Kim, Ju Han

    2013-11-01

    Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10(-12), P < 2 × 10(-4)) and a high codon adaptation index (P < 2 × 10(-16), P < 2 × 10(-8)), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

  4. Inference and validation of predictive gene networks from biomedical literature and gene expression data.

    PubMed

    Olsen, Catharina; Fleming, Kathleen; Prendergast, Niall; Rubio, Renee; Emmert-Streib, Frank; Bontempi, Gianluca; Haibe-Kains, Benjamin; Quackenbush, John

    2014-01-01

    Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

  5. Wisdom of crowds for robust gene network inference

    PubMed Central

    Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

    2012-01-01

    Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662

  6. Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

    PubMed Central

    2012-01-01

    Background Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis. Methods We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis. Results Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters. Conclusions We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis. PMID:22536863

  7. Parameter estimates in binary black hole collisions using neural networks

    NASA Astrophysics Data System (ADS)

    Carrillo, M.; Gracia-Linares, M.; González, J. A.; Guzmán, F. S.

    2016-10-01

    We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given gravitational wave (GW) strains. In this analysis, the ANN is trained with a sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GWs generated also with simulations for given mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the interpolation and extrapolation regimes. We present the results for noise free signals and signals contaminated with Gaussian noise, in order to foresee the dependence of the method accuracy in terms of the signal to noise ratio.

  8. DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks.

    PubMed

    Tse, Margaret J; Chu, Brian K; Roy, Mahua; Read, Elizabeth L

    2015-10-20

    Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.

  9. Visualizing Gene - Interactions within the Rice and Maize Network

    NASA Astrophysics Data System (ADS)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  10. Portrait of Candida Species Biofilm Regulatory Network Genes.

    PubMed

    Araújo, Daniela; Henriques, Mariana; Silva, Sónia

    2017-01-01

    Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis.

  11. Linear fuzzy gene network models obtained from microarray data by exhaustive search

    PubMed Central

    Sokhansanj, Bahrad A; Fitch, J Patrick; Quong, Judy N; Quong, Andrew A

    2004-01-01

    Background Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. Results We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. Conclusion Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems. PMID:15304201

  12. The values of the parameters of some multilayer distributed RC null networks

    NASA Technical Reports Server (NTRS)

    Huelsman, L. P.; Raghunath, S.

    1974-01-01

    In this correspondence, the values of the parameters of some multilayer distributed RC notch networks are determined, and the usually accepted values are shown to be in error. The magnitude of the error is illustrated by graphs of the frequency response of the networks.

  13. Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

    DTIC Science & Technology

    2015-07-01

    theoretical analyses of missing data problems, which utilize a graphical representa- tion, called the missingness graph. In the case of MCAR and MAR ...missing at random assumption ( MAR ), but also for a broad class of data that is not MAR . Their analysis is based on a graphical representation for...data. For the cases of both MCAR and MAR data, where the missingness graph need not be explicit, we start by deriving the closed-form parameter

  14. Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model

    PubMed Central

    Alberton, André Luís; Di Maggio, Jimena Andrea; Estrada, Vanina Gisela; Díaz, María Soledad; Secchi, Argimiro Resende

    2015-01-01

    This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available. PMID:25654103

  15. PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data.

    PubMed

    Guan, Daogang; Shao, Jiaofang; Zhao, Zhongying; Wang, Panwen; Qin, Jing; Deng, Youping; Boheler, Kenneth R; Wang, Junwen; Yan, Bin

    2014-07-01

    Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein-protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.

  16. Beyond antioxidant genes in the ancient NRF2 regulatory network

    PubMed Central

    Lacher, Sarah E.; Lee, Joslynn S.; Wang, Xuting; Campbell, Michelle R.; Bell, Douglas A.; Slattery, Matthew

    2016-01-01

    NRF2, a basic leucine zipper transcription factor encoded by the gene NFE2L2, is a master regulator of the transcriptional response to oxidative stress. NRF2 is structurally and functionally conserved from insects to humans, and it heterodimerizes with the small MAF transcription factors to bind a consensus DNA sequence (the antioxidant response element, or ARE) and regulate gene expression. We have used genome-wide chromatin immunoprecipitation (ChIP-seq) and gene expression data to identify direct NRF2 target genes in Drosophila and humans. These data have allowed us to construct the deeply conserved ancient NRF2 regulatory network – target genes that are conserved from Drosophila to human. The ancient network consists of canonical antioxidant genes, as well as genes related to proteasomal pathways, metabolism, and a number of less expected genes. We have also used enhancer reporter assays and electrophoretic mobility shift assays to confirm NRF2-mediated regulation of ARE (antioxidant response element) activity at a number of these novel target genes. Interestingly, the ancient network also highlights a prominent negative feedback loop; this, combined with the finding that and NRF2-mediated regulatory output is tightly linked to the quality of the ARE it is targeting, suggests that precise regulation of nuclear NRF2 concentration is necessary to achieve proper quantitative regulation of distinct gene sets. Together, these findings highlight the importance of balance in the NRF2-ARE pathway, and indicate that NRF2-mediated regulation of xenobiotic metabolism, glucose metabolism, and proteostasis have been central to this pathway since its inception. PMID:26163000

  17. Random matrix analysis of localization properties of gene coexpression network.

    PubMed

    Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen

    2010-04-01

    We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.

  18. A network of genes regulated by light in cyanobacteria.

    PubMed

    Aurora, Rajeev; Hihara, Yukako; Singh, Abhay K; Pakrasi, Himadri B

    2007-01-01

    Oxygenic photosynthetic organisms require light for their growth and development. However, exposure to high light is detrimental to them. Using time series microarray data from a model cyanobacterium, Synechocystis 6803 transferred from low to high light, we generated a gene co-expression network. The network has twelve sub-networks connected hierarchically, each consisting of an interconnected hub-and-spoke architecture. Within each sub-network, edges formed between genes that recapitulate known pathways. Analysis of the expression profiles shows that the cells undergo a phase transition 6-hours post-shift to high light, characterized by core sub-network. The core sub-network is enriched in proteins that (putatively) bind Fe-S clusters and proteins that mediate iron and sulfate homeostasis. At the center of this core is a sulfate permease, suggesting sulfate is rate limiting for cells grown in high light. To validate this novel finding, we demonstrate the limited ability of cell growth in sulfate-depleted medium in high light. This study highlights how understanding the organization of the networks can provide insights into the coordination of physiologic responses.

  19. An algebra-based method for inferring gene regulatory networks

    PubMed Central

    2014-01-01

    Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also

  20. A gene regulatory network controlling the embryonic specification of endoderm.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2011-05-29

    Specification of endoderm is the prerequisite for gut formation in the embryogenesis of bilaterian organisms. Modern lineage labelling studies have shown that in the sea urchin embryo model system, descendants of the veg1 and veg2 cell lineages produce the endoderm, and that the veg2 lineage also gives rise to mesodermal cell types. It is known that Wnt/β-catenin signalling is required for endoderm specification and Delta/Notch signalling is required for mesoderm specification. Some direct cis-regulatory targets of these signals have been found and various phenomenological patterns of gene expression have been observed in the pre-gastrular endomesoderm. However, no comprehensive, causal explanation of endoderm specification has been conceived for sea urchins, nor for any other deuterostome. Here we propose a model, on the basis of the underlying genomic control system, that provides such an explanation, built at several levels of biological organization. The hardwired core of the control system consists of the cis-regulatory apparatus of endodermal regulatory genes, which determine the relationship between the inputs to which these genes are exposed and their outputs. The architecture of the network circuitry controlling the dynamic process of endoderm specification then explains, at the system level, a sequence of developmental logic operations, which generate the biological process. The control system initiates non-interacting endodermal and mesodermal gene regulatory networks in veg2-derived cells and extinguishes the endodermal gene regulatory network in mesodermal precursors. It also generates a cross-regulatory network that specifies future anterior endoderm in veg2 descendants and institutes a distinct network specifying posterior endoderm in veg1-derived cells. The network model provides an explanatory framework that relates endoderm specification to the genomic regulatory code.

  1. Geometry on the parameter space of the belief propagation algorithm on Bayesian networks

    NASA Astrophysics Data System (ADS)

    Watanabe, Yodai

    2006-01-01

    This Letter considers a geometrical structure on the parameter space of the belief propagation algorithm on Bayesian networks. The statistical manifold of posterior distributions is introduced, and the expression for the information metric on the manifold is derived. The expression is used to construct a cost function which can be regarded as a measure of the distance in the parameter space.

  2. Optimal Parameter Determination for Tritiated Water Storage in Polyacrylic Networks

    SciTech Connect

    Postolache, C.; Matei, Lidia; Georgescu, Rodica; Ionita, Gh.

    2005-07-15

    Due to the remarkable capacity of water retaining, croslinked polyacrylic acids (PAA) represent an interesting alternative for tritiated water trapping. The study was developed on radiolytical processes in PAA:HTO systems derivated from irradiation of polymeric network by disintegration of tritium atoms from HTO. The aim of these studies is the identification of polymeric structures and optimal storage conditions.Sol and gel fractions were determinated by radiometrical methods using PAA labeled with 14-C at carboxylic groups and T at main chains of the polymer. Simulation of radiolytical processes was realized using {gamma} radiation field emitted by a irradiation source of 60-Co which ensures a maximum of absorbed dose rate of 3 kGy/h. Self-radiolytical effects were investigated using labeled PAA in HTO with great radioactive concentration (37-185 GBq/mL). The experiment suggests as optimum for HTO storage as tritium liquid wastes a 1:30 PAA:HTO swelling degree at 18.5-37 MBqL. HTO radioactive concentration.RES studies of radiolytical processes were also realized on dry polyacrylic acid (PAA) and polyacrylic based hydrogels irradiated and determined at 77 K. In the study we observed the effect of swelling capacity of hydrogel o the formation of free radicals.

  3. Appraisal of artificial neural network for forecasting of economic parameters

    NASA Astrophysics Data System (ADS)

    Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

    2017-01-01

    The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

  4. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks

    PubMed Central

    Martin, Alberto J. M.; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S.; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). PMID:27695050

  5. Graphlet Based Metrics for the Comparison of Gene Regulatory Networks.

    PubMed

    Martin, Alberto J M; Dominguez, Calixto; Contreras-Riquelme, Sebastián; Holmes, David S; Perez-Acle, Tomas

    2016-01-01

    Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto).

  6. Prediction of compressibility parameters of the soils using artificial neural network.

    PubMed

    Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

    2016-01-01

    The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

  7. Sensitivity of the active fracture model parameter to fracture network orientation and injection scenarios

    NASA Astrophysics Data System (ADS)

    Başağaoğlu, Hakan; Succi, Sauro; Manepally, Chandrika; Fedors, Randall; Wyrick, Danielle Y.

    2009-09-01

    Active fractures refer to the portions of unsaturated, connected fractures that actively conduct water. The active fracture model parameter accounts for the reduction in the number of fractures carrying water and in the fracture-matrix interface area in field-scale simulations of flow and transport in unsaturated fractured rocks. One example includes the numerical analyses of the fault test results at the Yucca Mountain site, Nevada (USA). In such applications, the active fracture model parameter is commonly used as a calibration parameter without relating it to fracture network orientations and infiltration rates. A two-dimensional, multiphase lattice-Boltzmann model was used in this study to investigate the sensitivity of the active fracture model parameter to fracture network orientation and injection scenarios for an unsaturated, variable dipping, and geometrically simple fracture network. The active fracture model parameter differed by as much as 0.11-0.44 when the effects of fracture network orientation, injection rate, and injection mode were included in the simulations. Hence, the numerical results suggest that the sensitivity of the active fracture model parameter to fracture network orientation, injection rates, and injection modes should be explored at the field-scale to strengthen the technical basis and range of applicability of the active fracture model.

  8. Detecting Essential Proteins Based on Network Topology, Gene Expression Data and Gene Ontology Information.

    PubMed

    Zhang, Wei; Xu, Jia; Li, Yuanyuan; Zou, Xiufen

    2016-10-07

    The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles and GO information. To evaluate the performance of the TEO method, we compare it with five other methods (degree, betweenness, NC, Pec, CowEWC) in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

  9. Network analysis of genes and their association with diseases.

    PubMed

    Kontou, Panagiota I; Pavlopoulou, Athanasia; Dimou, Niki L; Pavlopoulos, Georgios A; Bagos, Pantelis G

    2016-09-15

    A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

  10. Using SPEEDES to simulate the blue gene interconnect network

    NASA Technical Reports Server (NTRS)

    Springer, P.; Upchurch, E.

    2003-01-01

    JPL and the Center for Advanced Computer Architecture (CACR) is conducting application and simulation analyses of BG/L in order to establish a range of effectiveness for the Blue Gene/L MPP architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution.

  11. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Head blight caused by Fusarium graminearum (Fg) is a major limiting factor of wheat production with both yield loss and mycotoxin contamination. Here we report a model for global Fg gene regulatory networks (GRNs) inferred from a large collection of transcriptomic data using a machine-learning appro...

  12. A gene network engineering platform for lactic acid bacteria.

    PubMed

    Kong, Wentao; Kapuganti, Venkata S; Lu, Ting

    2016-02-29

    Recent developments in synthetic biology have positioned lactic acid bacteria (LAB) as a major class of cellular chassis for applications. To achieve the full potential of LAB, one fundamental prerequisite is the capacity for rapid engineering of complex gene networks, such as natural biosynthetic pathways and multicomponent synthetic circuits, into which cellular functions are encoded. Here, we present a synthetic biology platform for rapid construction and optimization of large-scale gene networks in LAB. The platform involves a copy-controlled shuttle for hosting target networks and two associated strategies that enable efficient genetic editing and phenotypic validation. By using a nisin biosynthesis pathway and its variants as examples, we demonstrated multiplex, continuous editing of small DNA parts, such as ribosome-binding sites, as well as efficient manipulation of large building blocks such as genes and operons. To showcase the platform, we applied it to expand the phenotypic diversity of the nisin pathway by quickly generating a library of 63 pathway variants. We further demonstrated its utility by altering the regulatory topology of the nisin pathway for constitutive bacteriocin biosynthesis. This work demonstrates the feasibility of rapid and advanced engineering of gene networks in LAB, fostering their applications in biomedicine and other areas.

  13. Integration of molecular network data reconstructs Gene Ontology

    PubMed Central

    Gligorijević, Vladimir; Janjić, Vuk; Pržulj, Nataša

    2014-01-01

    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

  14. Mean field analysis of a spatial stochastic model of a gene regulatory network.

    PubMed

    Sturrock, M; Murray, P J; Matzavinos, A; Chaplain, M A J

    2015-10-01

    A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products. In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.

  15. A gene network simulator to assess reverse engineering algorithms.

    PubMed

    Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

    2009-03-01

    In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

  16. Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks.

    PubMed

    Ben-Tabou de-Leon, Smadar

    2016-01-01

    Developmental gene regulatory networks robustly control the timely activation of regulatory and differentiation genes. The structure of these networks underlies their capacity to buffer intrinsic and extrinsic noise and maintain embryonic morphology. Here I illustrate how the use of specific architectures by the sea urchin developmental regulatory networks enables the robust control of cell fate decisions. The Wnt-βcatenin signaling pathway patterns the primary embryonic axis while the BMP signaling pathway patterns the secondary embryonic axis in the sea urchin embryo and across bilateria. Interestingly, in the sea urchin in both cases, the signaling pathway that defines the axis controls directly the expression of a set of downstream regulatory genes. I propose that this direct activation of a set of regulatory genes enables a uniform regulatory response and a clear cut cell fate decision in the endoderm and in the dorsal ectoderm. The specification of the mesodermal pigment cell lineage is activated by Delta signaling that initiates a triple positive feedback loop that locks down the pigment specification state. I propose that the use of compound positive feedback circuitry provides the endodermal cells enough time to turn off mesodermal genes and ensures correct mesoderm vs. endoderm fate decision. Thus, I argue that understanding the control properties of repeatedly used regulatory architectures illuminates their role in embryogenesis and provides possible explanations to their resistance to evolutionary change.

  17. Gap Gene Regulatory Dynamics Evolve along a Genotype Network

    PubMed Central

    Crombach, Anton; Wotton, Karl R.; Jiménez-Guri, Eva; Jaeger, Johannes

    2016-01-01

    Developmental gene networks implement the dynamic regulatory mechanisms that pattern and shape the organism. Over evolutionary time, the wiring of these networks changes, yet the patterning outcome is often preserved, a phenomenon known as “system drift.” System drift is illustrated by the gap gene network—involved in segmental patterning—in dipteran insects. In the classic model organism Drosophila melanogaster and the nonmodel scuttle fly Megaselia abdita, early activation and placement of gap gene expression domains show significant quantitative differences, yet the final patterning output of the system is essentially identical in both species. In this detailed modeling analysis of system drift, we use gene circuits which are fit to quantitative gap gene expression data in M. abdita and compare them with an equivalent set of models from D. melanogaster. The results of this comparative analysis show precisely how compensatory regulatory mechanisms achieve equivalent final patterns in both species. We discuss the larger implications of the work in terms of “genotype networks” and the ways in which the structure of regulatory networks can influence patterns of evolutionary change (evolvability). PMID:26796549

  18. Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.

    PubMed

    García-Alonso, Luz; Alonso, Roberto; Vidal, Enrique; Amadoz, Alicia; de María, Alejandro; Minguez, Pablo; Medina, Ignacio; Dopazo, Joaquín

    2012-11-01

    Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

  19. Analysing gene regulatory networks by both constraint programming and model-checking.

    PubMed

    Fromentin, Jonathan; Comet, Jean-Paul; Le Gall, Pascale; Roux, Olivier

    2007-01-01

    In this article, we propose a formal method to analyse gene regulatory networks (GRN). The dynamics of such systems is often described by an ordinary differential equation system, but has also been abstracted into a discrete transition system. This modeling depends on parameters for which different values are possible. Each instantiation of these parameters defines a possible dynamics and verification tools can be used to select the tuples of values which lead to dynamics consistent with known behaviours. GRN are so complex that their discrete modeling gives a number of possible dynamics exponential in function of the GRN's size (number of genes and interactions). In this paper, we propose to use constraint programming and CTL formal language to determine the set of all dynamics consistent with the known behavioral properties without enumerating all of them. This approach allows us therefore to minimize the computation time necessary for the research of these parameters.

  20. 3D visualization of gene clusters and networks

    NASA Astrophysics Data System (ADS)

    Zhang, Leishi; Sheng, Weiguo; Liu, Xiaohui

    2005-03-01

    In this paper, we try to provide a global view of DNA microarray gene expression data analysis and modeling process by combining novel and effective visualization techniques with data mining algorithms. An integrated framework has been proposed to model and visualize short, high-dimensional gene expression data. The framework reduces the dimensionality of variables before applying appropriate temporal modeling method. Prototype has been built using Java3D to visualize the framework. The prototype takes gene expression data as input, clusters the genes, displays the clustering results using a novel graph layout algorithm, models individual gene clusters using Dynamic Bayesian Network and then visualizes the modeling results using simple but effective visualization techniques.

  1. Floral Morphogenesis: Stochastic Explorations of a Gene Network Epigenetic Landscape

    PubMed Central

    Aldana, Maximino; Benítez, Mariana; Cortes-Poza, Yuriria; Espinosa-Soto, Carlos; Hartasánchez, Diego A.; Lotto, R. Beau; Malkin, David; Escalera Santos, Gerardo J.; Padilla-Longoria, Pablo

    2008-01-01

    In contrast to the classical view of development as a preprogrammed and deterministic process, recent studies have demonstrated that stochastic perturbations of highly non-linear systems may underlie the emergence and stability of biological patterns. Herein, we address the question of whether noise contributes to the generation of the stereotypical temporal pattern in gene expression during flower development. We modeled the regulatory network of organ identity genes in the Arabidopsis thaliana flower as a stochastic system. This network has previously been shown to converge to ten fixed-point attractors, each with gene expression arrays that characterize inflorescence cells and primordial cells of sepals, petals, stamens, and carpels. The network used is binary, and the logical rules that govern its dynamics are grounded in experimental evidence. We introduced different levels of uncertainty in the updating rules of the network. Interestingly, for a level of noise of around 0.5–10%, the system exhibited a sequence of transitions among attractors that mimics the sequence of gene activation configurations observed in real flowers. We also implemented the gene regulatory network as a continuous system using the Glass model of differential equations, that can be considered as a first approximation of kinetic-reaction equations, but which are not necessarily equivalent to the Boolean model. Interestingly, the Glass dynamics recover a temporal sequence of attractors, that is qualitatively similar, although not identical, to that obtained using the Boolean model. Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model. Therefore, our model provides a novel explanation for the emergence and robustness of the ubiquitous temporal pattern of floral organ specification. It also constitutes a new approach to understanding morphogenesis, providing predictions on the population dynamics of cells with different

  2. Computational gene network study on antibiotic resistance genes of Acinetobacter baumannii.

    PubMed

    Anitha, P; Anbarasu, Anand; Ramaiah, Sudha

    2014-05-01

    Multi Drug Resistance (MDR) in Acinetobacter baumannii is one of the major threats for emerging nosocomial infections in hospital environment. Multidrug-resistance in A. baumannii may be due to the implementation of multi-combination resistance mechanisms such as β-lactamase synthesis, Penicillin-Binding Proteins (PBPs) changes, alteration in porin proteins and in efflux pumps against various existing classes of antibiotics. Multiple antibiotic resistance genes are involved in MDR. These resistance genes are transferred through plasmids, which are responsible for the dissemination of antibiotic resistance among Acinetobacter spp. In addition, these resistance genes may also have a tendency to interact with each other or with their gene products. Therefore, it becomes necessary to understand the impact of these interactions in antibiotic resistance mechanism. Hence, our study focuses on protein and gene network analysis on various resistance genes, to elucidate the role of the interacting proteins and to study their functional contribution towards antibiotic resistance. From the search tool for the retrieval of interacting gene/protein (STRING), a total of 168 functional partners for 15 resistance genes were extracted based on the confidence scoring system. The network study was then followed up with functional clustering of associated partners using molecular complex detection (MCODE). Later, we selected eight efficient clusters based on score. Interestingly, the associated protein we identified from the network possessed greater functional similarity with known resistance genes. This network-based approach on resistance genes of A. baumannii could help in identifying new genes/proteins and provide clues on their association in antibiotic resistance.

  3. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  4. A gene regulatory network armature for T-lymphocyte specification

    SciTech Connect

    Fung, Elizabeth-sharon

    2008-01-01

    Choice of a T-lymphoid fate by hematopoietic progenitor cells depends on sustained Notch-Delta signaling combined with tightly-regulated activities of multiple transcription factors. To dissect the regulatory network connections that mediate this process, we have used high-resolution analysis of regulatory gene expression trajectories from the beginning to the end of specification; tests of the short-term Notchdependence of these gene expression changes; and perturbation analyses of the effects of overexpression of two essential transcription factors, namely PU.l and GATA-3. Quantitative expression measurements of >50 transcription factor and marker genes have been used to derive the principal components of regulatory change through which T-cell precursors progress from primitive multipotency to T-lineage commitment. Distinct parts of the path reveal separate contributions of Notch signaling, GATA-3 activity, and downregulation of PU.l. Using BioTapestry, the results have been assembled into a draft gene regulatory network for the specification of T-cell precursors and the choice of T as opposed to myeloid dendritic or mast-cell fates. This network also accommodates effects of E proteins and mutual repression circuits of Gfil against Egr-2 and of TCF-l against PU.l as proposed elsewhere, but requires additional functions that remain unidentified. Distinctive features of this network structure include the intense dose-dependence of GATA-3 effects; the gene-specific modulation of PU.l activity based on Notch activity; the lack of direct opposition between PU.l and GATA-3; and the need for a distinct, late-acting repressive function or functions to extinguish stem and progenitor-derived regulatory gene expression.

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

    PubMed Central

    Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

    2012-01-01

    AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

  6. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

    DOE PAGES

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; ...

    2015-02-14

    Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstratemore » that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.« less

  7. Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

    SciTech Connect

    Peng, Jiajie; Uygun, Sahra; Kim, Taehyong; Wang, Yadong; Rhee, Seung Y.; Chen, Jin

    2015-02-14

    Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited.

  8. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    PubMed

    Narang, Vipin; Ramli, Muhamad Azfar; Singhal, Amit; Kumar, Pavanish; de Libero, Gennaro; Poidinger, Michael; Monterola, Christopher

    2015-01-01

    Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

  9. Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

    PubMed Central

    Lim, Dajeong; Kim, Nam-Kuk; Lee, Seung-Hwan; Park, Hye-Sun; Cho, Yong-Min; Chai, Han-Ha; Kim, Heebal

    2014-01-01

    Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7) using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60) and dihydropyrimidine dehydrogenase (DPYD) are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness. PMID:24624372

  10. Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks.

    PubMed

    Espinosa-Soto, C

    2016-11-01

    Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross-regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild-type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self-sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.

  11. Toxic Diatom Aldehydes Affect Defence Gene Networks in Sea Urchins

    PubMed Central

    Varrella, Stefano; Ruocco, Nadia; Ianora, Adrianna; Bentley, Matt G.; Costantini, Maria

    2016-01-01

    Marine organisms possess a series of cellular strategies to counteract the negative effects of toxic compounds, including the massive reorganization of gene expression networks. Here we report the modulated dose-dependent response of activated genes by diatom polyunsaturated aldehydes (PUAs) in the sea urchin Paracentrotus lividus. PUAs are secondary metabolites deriving from the oxidation of fatty acids, inducing deleterious effects on the reproduction and development of planktonic and benthic organisms that feed on these unicellular algae and with anti-cancer activity. Our previous results showed that PUAs target several genes, implicated in different functional processes in this sea urchin. Using interactomic Ingenuity Pathway Analysis we now show that the genes targeted by PUAs are correlated with four HUB genes, NF-κB, p53, δ-2-catenin and HIF1A, which have not been previously reported for P. lividus. We propose a working model describing hypothetical pathways potentially involved in toxic aldehyde stress response in sea urchins. This represents the first report on gene networks affected by PUAs, opening new perspectives in understanding the cellular mechanisms underlying the response of benthic organisms to diatom exposure. PMID:26914213

  12. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    PubMed Central

    2012-01-01

    Background Various computational models have been of interest due to their use in the modelling of gene regulatory networks (GRNs). As a logical model, probabilistic Boolean networks (PBNs) consider molecular and genetic noise, so the study of PBNs provides significant insights into the understanding of the dynamics of GRNs. This will ultimately lead to advances in developing therapeutic methods that intervene in the process of disease development and progression. The applications of PBNs, however, are hindered by the complexities involved in the computation of the state transition matrix and the steady-state distribution of a PBN. For a PBN with n genes and N Boolean networks, the complexity to compute the state transition matrix is O(nN22n) or O(nN2n) for a sparse matrix. Results This paper presents a novel implementation of PBNs based on the notions of stochastic logic and stochastic computation. This stochastic implementation of a PBN is referred to as a stochastic Boolean network (SBN). An SBN provides an accurate and efficient simulation of a PBN without and with random gene perturbation. The state transition matrix is computed in an SBN with a complexity of O(nL2n), where L is a factor related to the stochastic sequence length. Since the minimum sequence length required for obtaining an evaluation accuracy approximately increases in a polynomial order with the number of genes, n, and the number of Boolean networks, N, usually increases exponentially with n, L is typically smaller than N, especially in a network with a large number of genes. Hence, the computational efficiency of an SBN is primarily limited by the number of genes, but not directly by the total possible number of Boolean networks. Furthermore, a time-frame expanded SBN enables an efficient analysis of the steady-state distribution of a PBN. These findings are supported by the simulation results of a simplified p53 network, several randomly generated networks and a network inferred from a T

  13. Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

    PubMed

    Karadeniz, İlknur; Hur, Junguk; He, Yongqun; Özgür, Arzucan

    2015-01-01

    Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host-pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene-gene interactions from the abstracts of articles in PubMed. The gene-gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene-gene interactions demonstrates that host-pathogen gene-gene interactions occur at experimental conditions which can be ontologically represented. Our

  14. Innovation and robustness in complex regulatory gene networks

    PubMed Central

    Ciliberti, S.; Martin, O. C.; Wagner, A.

    2007-01-01

    The history of life involves countless evolutionary innovations, a steady stream of ingenuity that has been flowing for more than 3 billion years. Very little is known about the principles of biological organization that allow such innovation. Here, we examine these principles for evolutionary innovation in gene expression patterns. To this end, we study a model for the transcriptional regulation networks that are at the heart of embryonic development. A genotype corresponds to a regulatory network of a given topology, and a phenotype corresponds to a steady-state gene expression pattern. Networks with the same phenotype form a connected graph in genotype space, where two networks are immediate neighbors if they differ by one regulatory interaction. We show that an evolutionary search on this graph can reach genotypes that are as different from each other as if they were chosen at random in genotype space, allowing evolutionary access to different kinds of innovation while staying close to a viable phenotype. Thus, although robustness to mutations may hinder innovation in the short term, we conclude that long-term innovation in gene expression patterns can only emerge in the presence of the robustness caused by connected genotype graphs. PMID:17690244

  15. Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization

    PubMed Central

    2013-01-01

    Background Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters. PMID:24176044

  16. Online in-situ estimation of network parameters under intermittent excitation conditions

    NASA Astrophysics Data System (ADS)

    Taylor, Jason Ashley

    2008-10-01

    Online in-situ estimation of network parameters is a potential tool to evaluate electrical network and conductor health. The integration of the physics-based models with stochastic models can provide important diagnostic and prognostic information. Correct diagnoses and prognoses using the model-based techniques therefore depend on accurate estimations of the physical parameters. As artificial excitation of the modeled dynamics is not always possible for in-situ applications, the information necessary to make accurate estimations can be intermittent over time. Continuous online estimation and tracking of physics-based parameters using recursive least-squares with directional forgetting is proposed to account for the intermittency in the excitation. This method makes optimal use of the available information while still allowing the solution to following time-varying parameter changes. Computationally efficient statistical inference measures are also provided to gauge the confidence of each parameter estimate. Additionally, identification requirements of the methods and multiple network and conductor models are determined. Finally, the method is shown to be effective in estimating and tracking parameter changes in both the DC and AC networks as well as both time and frequency domain models.

  17. Prioritization of Susceptibility Genes for Ectopic Pregnancy by Gene Network Analysis.

    PubMed

    Liu, Ji-Long; Zhao, Miao

    2016-02-01

    Ectopic pregnancy is a very dangerous complication of pregnancy, affecting 1%-2% of all reported pregnancies. Due to ethical constraints on human biopsies and the lack of suitable animal models, there has been little success in identifying functionally important genes in the pathogenesis of ectopic pregnancy. In the present study, we developed a random walk-based computational method named TM-rank to prioritize ectopic pregnancy-related genes based on text mining data and gene network information. Using a defined threshold value, we identified five top-ranked genes: VEGFA (vascular endothelial growth factor A), IL8 (interleukin 8), IL6 (interleukin 6), ESR1 (estrogen receptor 1) and EGFR (epidermal growth factor receptor). These genes are promising candidate genes that can serve as useful diagnostic biomarkers and therapeutic targets. Our approach represents a novel strategy for prioritizing disease susceptibility genes.

  18. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    PubMed Central

    Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong

    2007-01-01

    Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the

  19. Toward an orofacial gene regulatory network.

    PubMed

    Kousa, Youssef A; Schutte, Brian C

    2016-03-01

    Orofacial clefting is a common birth defect with significant morbidity. A panoply of candidate genes have been discovered through synergy of animal models and human genetics. Among these, variants in interferon regulatory factor 6 (IRF6) cause syndromic orofacial clefting and contribute risk toward isolated cleft lip and palate (1/700 live births). Rare variants in IRF6 can lead to Van der Woude syndrome (1/35,000 live births) and popliteal pterygium syndrome (1/300,000 live births). Furthermore, IRF6 regulates GRHL3 and rare variants in this downstream target can also lead to Van der Woude syndrome. In addition, a common variant (rs642961) in the IRF6 locus is found in 30% of the world's population and contributes risk for isolated orofacial clefting. Biochemical studies revealed that rs642961 abrogates one of four AP-2alpha binding sites. Like IRF6 and GRHL3, rare variants in TFAP2A can also lead to syndromic orofacial clefting with lip pits (branchio-oculo-facial syndrome). The literature suggests that AP-2alpha, IRF6 and GRHL3 are part of a pathway that is essential for lip and palate development. In addition to updating the pathways, players and pursuits, this review will highlight some of the current questions in the study of orofacial clefting.

  20. Evolution of the mammalian embryonic pluripotency gene regulatory network

    PubMed Central

    Fernandez-Tresguerres, Beatriz; Cañon, Susana; Rayon, Teresa; Pernaute, Barbara; Crespo, Miguel; Torroja, Carlos; Manzanares, Miguel

    2010-01-01

    Embryonic pluripotency in the mouse is established and maintained by a gene-regulatory network under the control of a core set of transcription factors that include octamer-binding protein 4 (Oct4; official name POU domain, class 5, transcription factor 1, Pou5f1), sex-determining region Y (SRY)-box containing gene 2 (Sox2), and homeobox protein Nanog. Although this network is largely conserved in eutherian mammals, very little information is available regarding its evolutionary conservation in other vertebrates. We have compared the embryonic pluripotency networks in mouse and chick by means of expression analysis in the pregastrulation chicken embryo, genomic comparisons, and functional assays of pluripotency-related regulatory elements in ES cells and blastocysts. We find that multiple components of the network are either novel to mammals or have acquired novel expression domains in early developmental stages of the mouse. We also find that the downstream action of the mouse core pluripotency factors is mediated largely by genomic sequence elements nonconserved with chick. In the case of Sox2 and Fgf4, we find that elements driving expression in embryonic pluripotent cells have evolved by a small number of nucleotide changes that create novel binding sites for core factors. Our results show that the network in charge of embryonic pluripotency is an evolutionary novelty of mammals that is related to the comparatively extended period during which mammalian embryonic cells need to be maintained in an undetermined state before engaging in early differentiation events. PMID:21048080

  1. Dynamic Gene Regulatory Networks of Human Myeloid Differentiation.

    PubMed

    Ramirez, Ricardo N; El-Ali, Nicole C; Mager, Mikayla Anne; Wyman, Dana; Conesa, Ana; Mortazavi, Ali

    2017-03-27

    The reconstruction of gene regulatory networks underlying cell differentiation from high-throughput gene expression and chromatin data remains a challenge. Here, we derive dynamic gene regulatory networks for human myeloid differentiation using a 5-day time series of RNA-seq and ATAC-seq data. We profile HL-60 promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. We find a rapid response in the expression of key transcription factors and lineage markers that only regulate a subset of their targets at a given time, which is followed by chromatin accessibility changes that occur later along with further gene expression changes. We observe differences between promyelocyte- and monocyte-derived macrophages at both the transcriptional and chromatin landscape level, despite using the same differentiation stimulus, which suggest that the path taken by cells in the differentiation landscape defines their end cell state. More generally, our approach of combining neighboring time points and replicates to achieve greater sequencing depth can efficiently infer footprint-based regulatory networks from long series data.

  2. Gene regulation networks generate diverse pigmentation patterns in plants.

    PubMed

    Albert, Nick W; Davies, Kevin M; Schwinn, Kathy E

    2014-01-01

    The diversity of pigmentation patterns observed in plants occurs due to the spatial distribution and accumulation of colored compounds, which may also be associated with structural changes to the tissue. Anthocyanins are flavonoids that provide red/purple/blue coloration to plants, often forming complex patterns such as spots, stripes, and vein-associated pigmentation, particularly in flowers. These patterns are determined by the activity of MYB-bHLH-WDR (MBW) transcription factor complexes, which activate the anthocyanin biosynthesis genes, resulting in anthocyanin pigment accumulation. Recently, we established that the MBW complex controlling anthocyanin synthesis acts within a gene regulation network that is conserved within at least the Eudicots. This network involves hierarchy, reinforcement, and feedback mechanisms that allow for stringent and responsive regulation of the anthocyanin biosynthesis genes. The gene network and mobile nature of the WDR and R3-MYB proteins provide exciting new opportunities to explore the basis of pigmentation patterning, and to investigate the evolutionary history of the MBW components in land plants.

  3. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

    PubMed Central

    Parikshak, Neelroop N.; Gandal, Michael J.; Geschwind, Daniel H.

    2015-01-01

    Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology. PMID:26149713

  4. The use of gene interaction networks to improve the identification of cancer driver genes

    PubMed Central

    Walkins, Kheston; Tripathi, Vrijesh; John, Melford

    2017-01-01

    Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes. PMID:28149674

  5. The use of gene interaction networks to improve the identification of cancer driver genes.

    PubMed

    Ramsahai, Emilie; Walkins, Kheston; Tripathi, Vrijesh; John, Melford

    2017-01-01

    Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  6. An Arabidopsis gene regulatory network for secondary cell wall synthesis

    SciTech Connect

    Taylor-Teeples, M.; Lin, L.; de Lucas, M.; Turco, G.; Toal, T. W.; Gaudinier, A.; Young, N. F.; Trabucco, G. M.; Veling, M. T.; Lamothe, R.; Handakumbura, P. P.; Xiong, G.; Wang, C.; Corwin, J.; Tsoukalas, A.; Zhang, L.; Ware, D.; Pauly, M.; Kliebenstein, D. J.; Dehesh, K.; Tagkopoulos, I.; Breton, G.; Pruneda-Paz, J. L.; Ahnert, S. E.; Kay, S. A.; Hazen, S. P.; Brady, S. M.

    2014-12-24

    The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. In this paper, we present a protein–DNA network between Arabidopsis thaliana transcription factors and secondary cell wall metabolic genes with gene expression regulated by a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. Finally, these interactions will serve as a foundation for understanding the regulation of a complex, integral plant component.

  7. Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach

    PubMed Central

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown. PMID:24058904

  8. Parameter estimation in spiking neural networks: a reverse-engineering approach.

    PubMed

    Rostro-Gonzalez, H; Cessac, B; Vieville, T

    2012-04-01

    This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

  9. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    PubMed

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.

  10. Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

    PubMed Central

    2013-01-01

    Background Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types. Results The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15. Conclusions For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF

  11. Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia

    PubMed Central

    Cosgrove, Elissa J.; Zhou, Yingchun; Gardner, Timothy S.; Kolaczyk, Eric D.

    2008-01-01

    Motivation: DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation. Results: Our proposed SSEM–Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of perturbations in both simulated datasets and microarray compendia. In simulated data, for two different network types, and over a wide range of signal-to-noise ratios, our algorithm demonstrated a 167% increase in sensitivity on average for the top 100 ranked genes, compared with the next best method. Our method also performed well in identifying targets of genetic perturbations in microarray compendia, with up to a 24% improvement in sensitivity on average for the top 100 ranked genes. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles. Availability: Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso). Contact: kolaczyk@math.bu.edu Supplementary information: Supplementary Data are available at Bioinformatics on line. PMID:18779235

  12. An evaluation of neural networks for identification of system parameters in reactor noise signals

    SciTech Connect

    Miller, L.F.

    1991-12-31

    Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms.

  13. Evolutionary Signatures amongst Disease Genes Permit Novel Methods for Gene Prioritization and Construction of Informative Gene-Based Networks

    PubMed Central

    Priedigkeit, Nolan; Wolfe, Nicholas; Clark, Nathan L.

    2015-01-01

    Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC), is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting “disease map” network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks. PMID:25679399

  14. Reconstruction of Gene Networks of Iron Response in Shewanella oneidensis

    SciTech Connect

    Yang, Yunfeng; Harris, Daniel P; Luo, Feng; Joachimiak, Marcin; Wu, Liyou; Dehal, Paramvir; Jacobsen, Janet; Yang, Zamin Koo; Gao, Haichun; Arkin, Adam; Palumbo, Anthony Vito; Zhou, Jizhong

    2009-01-01

    It is of great interest to study the iron response of the -proteobacterium Shewanella oneidensis since it possesses a high content of iron and is capable of utilizing iron for anaerobic respiration. We report here that the iron response in S. oneidensis is a rapid process. To gain more insights into the bacterial response to iron, temporal gene expression profiles were examined for iron depletion and repletion, resulting in identification of iron-responsive biological pathways in a gene co-expression network. Iron acquisition systems, including genes unique to S. oneidensis, were rapidly and strongly induced by iron depletion, and repressed by iron repletion. Some were required for iron depletion, as exemplified by the mutational analysis of the putative siderophore biosynthesis protein SO3032. Unexpectedly, a number of genes related to anaerobic energy metabolism were repressed by iron depletion and induced by repletion, which might be due to the iron storage potential of their protein products. Other iron-responsive biological pathways include protein degradation, aerobic energy metabolism and protein synthesis. Furthermore, sequence motifs enriched in gene clusters as well as their corresponding DNA-binding proteins (Fur, CRP and RpoH) were identified, resulting in a regulatory network of iron response in S. oneidensis. Together, this work provides an overview of iron response and reveals novel features in S. oneidensis, including Shewanella-specific iron acquisition systems, and suggests the intimate relationship between anaerobic energy metabolism and iron response.

  15. Ethanol modulation of gene networks: implications for alcoholism.

    PubMed

    Farris, Sean P; Miles, Michael F

    2012-01-01

    Alcoholism is a complex disease caused by a confluence of environmental and genetic factors influencing multiple brain pathways to produce a variety of behavioral sequelae, including addiction. Genetic factors contribute to over 50% of the risk for alcoholism and recent evidence points to a large number of genes with small effect sizes as the likely molecular basis for this disease. Recent progress in genomics (microarrays or RNA-Seq) and genetics has led to the identification of a large number of potential candidate genes influencing ethanol behaviors or alcoholism itself. To organize this complex information, investigators have begun to focus on the contribution of gene networks, rather than individual genes, for various ethanol-induced behaviors in animal models or behavioral endophenotypes comprising alcoholism. This chapter reviews some of the methods used for constructing gene networks from genomic data and some of the recent progress made in applying such approaches to the study of the neurobiology of ethanol. We show that rapid technology development in gathering genomic data, together with sophisticated experimental design and a growing collection of analysis tools are producing novel insights for understanding the molecular basis of alcoholism and that such approaches promise new opportunities for therapeutic development.

  16. Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm.

    PubMed

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

    2016-06-01

    The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

  17. The Efficacy of Galaxy Shape Parameters in Photometric Redshift Estimation: A Neural Network Approach

    SciTech Connect

    Singal, J.; Shmakova, M.; Gerke, B.; Griffith, R.L.; Lotz, J.; /NOAO, Tucson

    2011-05-20

    We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data in an unbiased way, can be a useful estimator of the additional information contained in extra parameters, such as those describing morphology, if the input data are treated on an equal footing. We show that certain principal components of the morphology information are correlated with galaxy type. However, we find that for the data used the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here. The inclusion of these parameters may result in a trade-off between extra information and additional noise, with the additional noise becoming more dominant as more parameters are added.

  18. Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Günaydın, O.

    2009-03-01

    This study presents the application of different methods (simple-multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations ( r 2 = 0.70-0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.

  19. Vertebrate segmentation: from cyclic gene networks to scoliosis.

    PubMed

    Pourquié, Olivier

    2011-05-27

    One of the most striking features of the human vertebral column is its periodic organization along the anterior-posterior axis. This pattern is established when segments of vertebrates, called somites, bud off at a defined pace from the anterior tip of the embryo's presomitic mesoderm (PSM). To trigger this rhythmic production of somites, three major signaling pathways--Notch, Wnt/β-catenin, and fibroblast growth factor (FGF)--integrate into a molecular network that generates a traveling wave of gene expression along the embryonic axis, called the "segmentation clock." Recent systems approaches have begun identifying specific signaling circuits within the network that set the pace of the oscillations, synchronize gene expression cycles in neighboring cells, and contribute to the robustness and bilateral symmetry of somite formation. These findings establish a new model for vertebrate segmentation and provide a conceptual framework to explain human diseases of the spine, such as congenital scoliosis.

  20. Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks

    PubMed Central

    Shin, Jeewoen; MacCarthy, Thomas

    2015-01-01

    Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability—the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon “whack-a-mole” sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance. PMID:26451700

  1. Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer.

    PubMed

    Mine, Karina L; Shulzhenko, Natalia; Yambartsev, Anatoly; Rochman, Mark; Sanson, Gerdine F O; Lando, Malin; Varma, Sudhir; Skinner, Jeff; Volfovsky, Natalia; Deng, Tao; Brenna, Sylvia M F; Carvalho, Carmen R N; Ribalta, Julisa C L; Bustin, Michael; Matzinger, Polly; Silva, Ismael D C G; Lyng, Heidi; Gerbase-DeLima, Maria; Morgun, Andrey

    2013-01-01

    Although human papillomavirus was identified as an aetiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major subnetworks upregulated in tumour samples. These genes are located within the same regions as chromosomal amplifications, most frequently on 3q. We propose a model in which selected chromosomal gains drive activation of antiviral genes contributing to episomal virus elimination, which synergizes with cell cycle dysregulation. These findings may help to explain the paradox of episomal human papillomavirus decline in women with invasive cancer who were previously unable to clear the virus.

  2. Systems Biology Approach to Identify Gene Network Signatures for Colorectal Cancer

    PubMed Central

    Sonachalam, Madhankumar; Shen, Jeffrey; Huang, Hui; Wu, Xiaogang

    2012-01-01

    In this work, we integrated prior knowledge from gene signatures and protein interactions with gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from gene expression microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets. First, we used GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly available up-to-date gene set databases – Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compared the enriched gene sets through enrichment score, false-discovery rate, and nominal p-value. Third, we constructed an integrated protein–protein interaction (PPI) network through connecting these enriched genes by high-quality interactions from a human annotated and predicted protein interaction database, with a confidence score labeled for each interaction. Finally, we mapped differential gene expressions onto the constructed network to build a comprehensive network model containing visualized transcriptome and proteome data. The results show that although MSigDB has more CRC-relevant gene sets than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide a more complete view for discovering gene network signatures. We also found several important sub-network signatures for CRC, such as TP53 sub-network, PCNA sub-network, and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response, respectively. PMID:22629282

  3. Analyzing stationary states of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2006-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  4. Analyzing stationary States of gene regulatory network using petri nets.

    PubMed

    Gambin, Anna; Lasota, Sławomir; Rutkowski, Michał

    2011-01-01

    We introduce and formally define the notion of a stationary state for Petri nets. We also propose a fully automatic method for finding such states. The procedure makes use of the Presburger arithmetic to describe all the stationary states. Finally we apply this novel approach to find stationary states of a gene regulatory network describing the flower morphogenesis of A. thaliana. This shows that the proposed method can be successfully applied in the study of biological systems.

  5. Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

    PubMed

    Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

    2011-01-01

    High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as

  6. Drug target prioritization by perturbed gene expression and network information

    PubMed Central

    Isik, Zerrin; Baldow, Christoph; Cannistraci, Carlo Vittorio; Schroeder, Michael

    2015-01-01

    Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects. PMID:26615774

  7. Phase transitions in the evolution of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Skanata, Antun; Kussell, Edo

    The role of gene regulatory networks is to respond to environmental conditions and optimize growth of the cell. A typical example is found in bacteria, where metabolic genes are activated in response to nutrient availability, and are subsequently turned off to conserve energy when their specific substrates are depleted. However, in fluctuating environmental conditions, regulatory networks could experience strong evolutionary pressures not only to turn the right genes on and off, but also to respond optimally under a wide spectrum of fluctuation timescales. The outcome of evolution is predicted by the long-term growth rate, which differentiates between optimal strategies. Here we present an analytic computation of the long-term growth rate in randomly fluctuating environments, by using mean-field and higher order expansion in the environmental history. We find that optimal strategies correspond to distinct regions in the phase space of fluctuations, separated by first and second order phase transitions. The statistics of environmental randomness are shown to dictate the possible evolutionary modes, which either change the structure of the regulatory network abruptly, or gradually modify and tune the interactions between its components.

  8. Functional divergence and convergence between the transcript network and gene network in lung adenocarcinoma

    PubMed Central

    Hsu, Min-Kung; Pan, Chia-Lin; Chen, Feng-Chi

    2016-01-01

    Introduction Alternative RNA splicing is a critical regulatory mechanism during tumorigenesis. However, previous oncological studies mainly focused on the splicing of individual genes. Whether and how transcript isoforms are coordinated to affect cellular functions remain underexplored. Also of great interest is how the splicing regulome cooperates with the transcription regulome to facilitate tumorigenesis. The answers to these questions are of fundamental importance to cancer biology. Results Here, we report a comparative study between the transcript-based network (TN) and the gene-based network (GN) derived from the transcriptomes of paired tumor–normal tissues from 77 lung adenocarcinoma patients. We demonstrate that the two networks differ significantly from each other in terms of patient clustering and the number and functions of network modules. Interestingly, the majority (89.5%) of multi-transcript genes have their transcript isoforms distributed in at least two TN modules, suggesting regulatory and functional divergences between transcript isoforms. Furthermore, TN and GN modules share onlŷ50%–60% of their biological functions. TN thus appears to constitute a regulatory layer separate from GN. Nevertheless, our results indicate that functional convergence and divergence both occur between TN and GN, implying complex interactions between the two regulatory layers. Finally, we report that the expression profiles of module members in both TN and GN shift dramatically yet concordantly during tumorigenesis. The mechanisms underlying this coordinated shifting remain unclear yet are worth further explorations. Conclusion We show that in lung adenocarcinoma, transcript isoforms per se are coordinately regulated to conduct biological functions not conveyed by the network of genes. However, the two networks may interact closely with each other by sharing the same or related biological functions. Unraveling the effects and mechanisms of such interactions will

  9. Shaping protein distributions in stochastic self-regulated gene expression networks

    NASA Astrophysics Data System (ADS)

    Pájaro, Manuel; Alonso, Antonio A.; Vázquez, Carlos

    2015-09-01

    In this work, we study connections between dynamic behavior and network parameters, for self-regulatory networks. To that aim, a method to compute the regions in the space of parameters that sustain bimodal or binary protein distributions has been developed. Such regions are indicative of stochastic dynamics manifested either as transitions between absence and presence of protein or between two positive protein levels. The method is based on the continuous approximation of the chemical master equation, unlike other approaches that make use of a deterministic description, which as will be shown can be misleading. We find that bimodal behavior is a ubiquitous phenomenon in cooperative gene expression networks under positive feedback. It appears for any range of transcription and translation rate constants whenever leakage remains below a critical threshold. Above such a threshold, the region in the parameters space which sustains bimodality persists, although restricted to low transcription and high translation rate constants. Remarkably, such a threshold is independent of the transcription or translation rates or the proportion of an active or inactive promoter and depends only on the level of cooperativity. The proposed method can be employed to identify bimodal or binary distributions leading to stochastic dynamics with specific switching properties, by searching inside the parameter regions that sustain such behavior.

  10. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

    PubMed Central

    Sayyed-Ahmad, Abdallah; Tuncay, Kagan; Ortoleva, Peter J

    2007-01-01

    Background Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. Results Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. Conclusion Multiplex time series data can be used for the construction of the network of

  11. A novel analytical characterization for short-term plasticity parameters in spiking neural networks.

    PubMed

    O'Brien, Michael J; Thibeault, Corey M; Srinivasa, Narayan

    2014-01-01

    Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.

  12. Inferring transcription factor collaborations in gene regulatory networks

    PubMed Central

    2014-01-01

    Background Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation. Results We model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones. Conclusions mTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM. PMID:24565025

  13. Detection of driver pathways using mutated gene network in cancer.

    PubMed

    Li, Feng; Gao, Lin; Ma, Xiaoke; Yang, Xiaofei

    2016-06-21

    Distinguishing driver pathways has been extensively studied because they are critical for understanding the development and molecular mechanisms of cancers. Most existing methods for driver pathways are based on high coverage as well as high mutual exclusivity, with the underlying assumption that mutations are exclusive. However, in many cases, mutated driver genes in the same pathways are not strictly mutually exclusive. Based on this observation, we propose an index for quantifying mutual exclusivity between gene pairs. Then, we construct a mutated gene network for detecting driver pathways by integrating the proposed index and coverage. The detection of driver pathways on the mutated gene network consists of two steps: raw pathways are obtained using a CPM method, and the final driver pathways are selected using a strict testing strategy. We apply this method to glioblastoma and breast cancers and find that our method is more accurate than state-of-the-art methods in terms of enrichment of KEGG pathways. Furthermore, the detected driver pathways intersect with well-known pathways with moderate exclusivity, which cannot be discovered using the existing algorithms. In conclusion, the proposed method provides an effective way to investigate driver pathways in cancers.

  14. Response of the parameters of a neural network to pseudoperiodic time series

    NASA Astrophysics Data System (ADS)

    Zhao, Yi; Weng, Tongfeng; Small, Michael

    2014-02-01

    We propose a representation plane constructed from parameters of a multilayer neural network, with the aim of characterizing the dynamical character of a learned time series. We find that fluctuation of this plane reveals distinct features of the time series. Specifically, a periodic representation plane corresponds to a periodic time series, even when contaminated with strong observational noise or dynamical noise. We present a theoretical explanation for how the neural network training algorithm adjusts parameters of this representation plane and thereby encodes the specific characteristics of the underlying system. This ability, which is intrinsic to the architecture of the neural network, can be employed to distinguish the chaotic time series from periodic counterparts. It provides a new path toward identifying the dynamics of pseudoperiodic time series. Furthermore, we extract statistics from the representation plane to quantify its character. We then validate this idea with various numerical data generated by the known periodic and chaotic dynamics and experimentally recorded human electrocardiogram data.

  15. Parameter estimation of breast tumour using dynamic neural network from thermal pattern.

    PubMed

    Saniei, Elham; Setayeshi, Saeed; Akbari, Mohammad Esmaeil; Navid, Mitra

    2016-11-01

    This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN). Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position) from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients) were promising in terms of the model's potential for retrieving tumour parameters.

  16. Differentially expressed genes and gene networks involved in pig ovarian follicular atresia.

    PubMed

    Terenina, Elena; Fabre, Stephane; Bonnet, Agnès; Monniaux, Danielle; Robert-Granié, Christèle; SanCristobal, Magali; Sarry, Julien; Vignoles, Florence; Gondret, Florence; Monget, Philippe; Tosser-Klopp, Gwenola

    2017-02-01

    Ovarian folliculogenesis corresponds to the development of follicles leading to either ovulation or degeneration, this latter process being called atresia. Even if atresia involves apoptosis, its mechanism is not well understood. The objective of this study was to analyze global gene expression in pig granulosa cells of ovarian follicles during atresia. The transcriptome analysis was performed on a 9,216 cDNA microarray to identify gene networks and candidate genes involved in pig ovarian follicular atresia. We found 1,684 significantly regulated genes to be differentially regulated between small healthy follicles and small atretic follicles. Among them, 287 genes had a fold-change higher than two between the two follicle groups. Eleven genes (DKK3, GADD45A, CAMTA2, CCDC80, DAPK2, ECSIT, MSMB, NUPR1, RUNX2, SAMD4A, and ZNF628) having a fold-change higher than five between groups could likely serve as markers of follicular atresia. Moreover, automatic confrontation of deregulated genes with literature data highlighted 93 genes as regulatory candidates of pig granulosa cell atresia. Among these genes known to be inhibitors of apoptosis, stimulators of apoptosis, or tumor suppressors INHBB, HNF4, CLU, different interleukins (IL5, IL24), TNF-associated receptor (TNFR1), and cytochrome-c oxidase (COX) were suggested as playing an important role in porcine atresia. The present study also enlists key upstream regulators in follicle atresia based on our results and on a literature review. The novel gene candidates and gene networks identified in the current study lead to a better understanding of the molecular regulation of ovarian follicular atresia.

  17. Inference of Gene Regulatory Networks Based on a Universal Minimum Description Length

    PubMed Central

    2008-01-01

    The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements. PMID:18437238

  18. Gene regulatory networks governing haematopoietic stem cell development and identity.

    PubMed

    Pimanda, John E; Göttgens, Berthold

    2010-01-01

    Development can be viewed as a dynamic progression through regulatory states which characterise the various cell types within a given differentiation cascade. To understand the progression of regulatory states that define the origin and subsequent development of haematopoietic stem cells, the first imperative is to understand the ontogeny of haematopoiesis. We are fortunate that the ontogeny of blood development is one of the best characterized mammalian developmental systems. However, the field is still in its infancy with regard to the reconstruction of gene regulatory networks and their interactions with cell signalling cascades that drive a mesodermal progenitor to adopt the identity of a haematopoietic stem cell and beyond. Nevertheless, a framework to dissect these networks and comprehend the logic of its circuitry does exist and although they may not as yet be available, a sense for the tools that will be required to achieve this aim is also emerging. In this review we cover the fundamentals of network architecture, methods used to reconstruct networks, current knowledge of haematopoietic and related transcriptional networks, current challenges and future outlook.

  19. On a fundamental structure of gene networks in living cells.

    PubMed

    Kravchenko-Balasha, Nataly; Levitzki, Alexander; Goldstein, Andrew; Rotter, Varda; Gross, A; Remacle, F; Levine, R D

    2012-03-20

    Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

  20. Network Security via Biometric Recognition of Patterns of Gene Expression

    NASA Technical Reports Server (NTRS)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time expression and assay of gene expression products.

  1. Network Security via Biometric Recognition of Patterns of Gene Expression

    NASA Technical Reports Server (NTRS)

    Shaw, Harry C.

    2016-01-01

    Molecular biology provides the ability to implement forms of information and network security completely outside the bounds of legacy security protocols and algorithms. This paper addresses an approach which instantiates the power of gene expression for security. Molecular biology provides a rich source of gene expression and regulation mechanisms, which can be adopted to use in the information and electronic communication domains. Conventional security protocols are becoming increasingly vulnerable due to more intensive, highly capable attacks on the underlying mathematics of cryptography. Security protocols are being undermined by social engineering and substandard implementations by IT (Information Technology) organizations. Molecular biology can provide countermeasures to these weak points with the current security approaches. Future advances in instruments for analyzing assays will also enable this protocol to advance from one of cryptographic algorithms to an integrated system of cryptographic algorithms and real-time assays of gene expression products.

  2. Inference of gene regulatory networks from time series by Tsallis entropy

    PubMed Central

    2011-01-01

    Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence

  3. Elements of an algorithm for optimizing a parameter-structural neural network

    NASA Astrophysics Data System (ADS)

    Mrówczyńska, Maria

    2016-06-01

    The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

  4. Topology association analysis in weighted protein interaction network for gene prioritization

    NASA Astrophysics Data System (ADS)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  5. Passivity analysis of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters.

    PubMed

    Ali, M Syed; Rani, M Esther

    2015-01-01

    This paper investigates the problem of robust passivity of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters. To reflect most of the dynamical behaviors of the system, both parameter uncertainties and stochastic disturbances are considered; stochastic disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov functional method, the Itô differential rule, and matrix analysis techniques, we establish a sufficient criterion such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic neural network is robustly passive in the sense of expectation. A delay-dependent stability condition is formulated, in which the restriction of the derivative of the time-varying delay should be less than 1 is removed. The derived criteria are expressed in terms of linear matrix inequalities that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.

  6. Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression.

    PubMed

    Zhang, Shao-Wu; Shao, Dong-Dong; Zhang, Song-Yao; Wang, Yi-Bin

    2014-06-01

    The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for

  7. Evolution of a core gene network for skeletogenesis in chordates.

    PubMed

    Hecht, Jochen; Stricker, Sigmar; Wiecha, Ulrike; Stiege, Asita; Panopoulou, Georgia; Podsiadlowski, Lars; Poustka, Albert J; Dieterich, Christoph; Ehrich, Siegfried; Suvorova, Julia; Mundlos, Stefan; Seitz, Volkhard

    2008-03-21

    The skeleton is one of the most important features for the reconstruction of vertebrate phylogeny but few data are available to understand its molecular origin. In mammals the Runt genes are central regulators of skeletogenesis. Runx2 was shown to be essential for osteoblast differentiation, tooth development, and bone formation. Both Runx2 and Runx3 are essential for chondrocyte maturation. Furthermore, Runx2 directly regulates Indian hedgehog expression, a master coordinator of skeletal development. To clarify the correlation of Runt gene evolution and the emergence of cartilage and bone in vertebrates, we cloned the Runt genes from hagfish as representative of jawless fish (MgRunxA, MgRunxB) and from dogfish as representative of jawed cartilaginous fish (ScRunx1-3). According to our phylogenetic reconstruction the stem species of chordates harboured a single Runt gene and thereafter Runt locus duplications occurred during early vertebrate evolution. All newly isolated Runt genes were expressed in cartilage according to quantitative PCR. In situ hybridisation confirmed high MgRunxA expression in hard cartilage of hagfish. In dogfish ScRunx2 and ScRunx3 were expressed in embryonal cartilage whereas all three Runt genes were detected in teeth and placoid scales. In cephalochordates (lancelets) Runt, Hedgehog and SoxE were strongly expressed in the gill bars and expression of Runt and Hedgehog was found in endo- as well as ectodermal cells. Furthermore we demonstrate that the lancelet Runt protein binds to Runt binding sites in the lancelet Hedgehog promoter and regulates its activity. Together, these results suggest that Runt and Hedgehog were part of a core gene network for cartilage formation, which was already active in the gill bars of the common ancestor of cephalochordates and vertebrates and diversified after Runt duplications had occurred during vertebrate evolution. The similarities in expression patterns of Runt genes support the view that teeth and

  8. Cerebellar network plasticity: from genes to fast oscillation.

    PubMed

    Cheron, G; Servais, L; Dan, B

    2008-04-22

    The role of the cerebellum has been increasingly recognized not only in motor control but in sensory, cognitive and emotional learning and regulation. Purkinje cells, being the sole output from the cerebellar cortex, occupy an integrative position in this network. Plasticity at this level is known to critically involve calcium signaling. In the last few years, electrophysiological study of genetically engineered mice has demonstrated the topical role of several genes encoding calcium-binding proteins (calretinin, calbindin, parvalbumin). Specific inactivation of these genes results in the emergence of a fast network oscillation (ca. 160 Hz) throughout the cerebellar cortex in alert animals, associated with ataxia. This oscillation is produced by synchronization of Purkinje cells along the parallel fiber beam. It behaves as an electrophysiological arrest rhythm, being blocked by sensorimotor stimulation. Pharmacological manipulations showed that the oscillation is blocked by GABA(A) and NMDA antagonists as well as gap junction blockers. This cerebellar network oscillation has also been documented in mouse models of human conditions with complex developmental cerebellar dysfunction, such as Angelman syndrome and fetal alcohol syndrome. Recent evidence suggests a relationship between fast oscillation and cerebellar long term depression (LTD). This may have major implications for future therapeutic targeting.

  9. Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions.

    PubMed

    Ammar, Amine; Cueto, Elías; Chinesta, Francisco

    2012-09-01

    The numerical solution of the chemical master equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task owing to its complexity, exponentially growing with the number of species involved. Although most of the existing techniques rely on the use of Monte Carlo-like techniques, we present here a new technique based on the approximation of the unknown variable (the probability of having a particular chemical state) in terms of a finite sum of separable functions. In this framework, the complexity of the CME grows only linearly with the number of state space dimensions. This technique generalizes the so-called Hartree approximation, by using terms as needed in the finite sums decomposition for ensuring convergence. But noteworthy, the ease of the approximation allows for an easy treatment of unknown parameters (as is frequently the case when modeling gene regulatory networks, for instance). These unknown parameters can be considered as new space dimensions. In this way, the proposed method provides solutions for any value of the unknown parameters (within some interval of arbitrary size) in one execution of the program.

  10. Robust dynamic balance of AP-1 transcription factors in a neuronal gene regulatory network

    PubMed Central

    2010-01-01

    Background The octapeptide Angiotensin II is a key hormone that acts via its receptor AT1R in the brainstem to modulate the blood pressure control circuits and thus plays a central role in the cardiac and respiratory homeostasis. This modulation occurs via activation of a complex network of signaling proteins and transcription factors, leading to changes in levels of key genes and proteins. AT1R initiated activity in the nucleus tractus solitarius (NTS), which regulates blood pressure, has been the subject of extensive molecular analysis. But the adaptive network interactions in the NTS response to AT1R, plausibly related to the development of hypertension, are not understood. Results We developed and analyzed a mathematical model of AT1R-activated signaling kinases and a downstream gene regulatory network, with structural basis in our transcriptomic data analysis and literature. To our knowledge, our report presents the first computational model of this key regulatory network. Our simulations and analysis reveal a dynamic balance among distinct dimers of the AP-1 family of transcription factors. We investigated the robustness of this behavior to simultaneous perturbations in the network parameters using a novel multivariate approach that integrates global sensitivity analysis with decision-tree methods. Our analysis implicates a subset of Fos and Jun dependent mechanisms, with dynamic sensitivities shifting from Fos-regulating kinase (FRK)-mediated processes to those downstream of c-Jun N-terminal kinase (JNK). Decision-tree analysis indicated that while there may be a large combinatorial functional space feasible for neuronal states and parameters, the network behavior is constrained to a small set of AP-1 response profiles. Many of the paths through the combinatorial parameter space lead to a dynamic balance of AP-1 dimer forms, yielding a robust AP-1 response counteracting the biological variability. Conclusions Based on the simulation and analysis results, we

  11. Protein networks identify novel symbiogenetic genes resulting from plastid endosymbiosis

    PubMed Central

    Méheust, Raphaël; Zelzion, Ehud; Bhattacharya, Debashish; Lopez, Philippe; Bapteste, Eric

    2016-01-01

    The integration of foreign genetic information is central to the evolution of eukaryotes, as has been demonstrated for the origin of the Calvin cycle and of the heme and carotenoid biosynthesis pathways in algae and plants. For photosynthetic lineages, this coordination involved three genomes of divergent phylogenetic origins (the nucleus, plastid, and mitochondrion). Major hurdles overcome by the ancestor of these lineages were harnessing the oxygen-evolving organelle, optimizing the use of light, and stabilizing the partnership between the plastid endosymbiont and host through retargeting of proteins to the nascent organelle. Here we used protein similarity networks that can disentangle reticulate gene histories to explore how these significant challenges were met. We discovered a previously hidden component of algal and plant nuclear genomes that originated from the plastid endosymbiont: symbiogenetic genes (S genes). These composite proteins, exclusive to photosynthetic eukaryotes, encode a cyanobacterium-derived domain fused to one of cyanobacterial or another prokaryotic origin and have emerged multiple, independent times during evolution. Transcriptome data demonstrate the existence and expression of S genes across a wide swath of algae and plants, and functional data indicate their involvement in tolerance to oxidative stress, phototropism, and adaptation to nitrogen limitation. Our research demonstrates the “recycling” of genetic information by photosynthetic eukaryotes to generate novel composite genes, many of which function in plastid maintenance. PMID:26976593

  12. Protein networks identify novel symbiogenetic genes resulting from plastid endosymbiosis.

    PubMed

    Méheust, Raphaël; Zelzion, Ehud; Bhattacharya, Debashish; Lopez, Philippe; Bapteste, Eric

    2016-03-29

    The integration of foreign genetic information is central to the evolution of eukaryotes, as has been demonstrated for the origin of the Calvin cycle and of the heme and carotenoid biosynthesis pathways in algae and plants. For photosynthetic lineages, this coordination involved three genomes of divergent phylogenetic origins (the nucleus, plastid, and mitochondrion). Major hurdles overcome by the ancestor of these lineages were harnessing the oxygen-evolving organelle, optimizing the use of light, and stabilizing the partnership between the plastid endosymbiont and host through retargeting of proteins to the nascent organelle. Here we used protein similarity networks that can disentangle reticulate gene histories to explore how these significant challenges were met. We discovered a previously hidden component of algal and plant nuclear genomes that originated from the plastid endosymbiont: symbiogenetic genes (S genes). These composite proteins, exclusive to photosynthetic eukaryotes, encode a cyanobacterium-derived domain fused to one of cyanobacterial or another prokaryotic origin and have emerged multiple, independent times during evolution. Transcriptome data demonstrate the existence and expression of S genes across a wide swath of algae and plants, and functional data indicate their involvement in tolerance to oxidative stress, phototropism, and adaptation to nitrogen limitation. Our research demonstrates the "recycling" of genetic information by photosynthetic eukaryotes to generate novel composite genes, many of which function in plastid maintenance.

  13. Complex Dynamic Behavior in Simple Gene Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Santillán Zerón, Moisés

    2007-02-01

    Knowing the complete genome of a given species is just a piece of the puzzle. To fully unveil the systems behavior of an organism, an organ, or even a single cell, we need to understand the underlying gene regulatory dynamics. Given the complexity of the whole system, the ultimate goal is unattainable for the moment. But perhaps, by analyzing the most simple genetic systems, we may be able to develop the mathematical techniques and procedures required to tackle more complex genetic networks in the near future. In the present work, the techniques for developing mathematical models of simple bacterial gene networks, like the tryptophan and lactose operons are introduced. Despite all of the underlying assumptions, such models can provide valuable information regarding gene regulation dynamics. Here, we pay special attention to robustness as an emergent property. These notes are organized as follows. In the first section, the long historical relation between mathematics, physics, and biology is briefly reviewed. Recently, the multidisciplinary work in biology has received great attention in the form of systems biology. The main concepts of this novel science are discussed in the second section. A very slim introduction to the essential concepts of molecular biology is given in the third section. In the fourth section, a brief introduction to chemical kinetics is presented. Finally, in the fifth section, a mathematical model for the lactose operon is developed and analyzed..

  14. Using Morpholinos to Probe Gene Networks in Sea Urchin.

    PubMed

    Materna, Stefan C

    2017-01-01

    The control processes that underlie the progression of development can be summarized in maps of gene regulatory networks (GRNs). A critical step in their assembly is the systematic perturbation of network candidates. In sea urchins the most important method for interfering with expression in a gene-specific way is application of morpholino antisense oligonucleotides (MOs). MOs act by binding to their sequence complement in transcripts resulting in a block in translation or a change in splicing and thus result in a loss of function. Despite the tremendous success of this technology, recent comparisons to mutants generated by genome editing have led to renewed criticism and challenged its reliability. As with all methods based on sequence recognition, MOs are prone to off-target binding that may result in phenotypes that are erroneously ascribed to the loss of the intended target. However, the slow progression of development in sea urchins has enabled extremely detailed studies of gene activity in the embryo. This wealth of knowledge paired with the simplicity of the sea urchin embryo enables careful analysis of MO phenotypes through a variety of methods that do not rely on terminal phenotypes. This article summarizes the use of MOs in probing GRNs and the steps that should be taken to assure their specificity.

  15. Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing.

    PubMed

    Belcastro, Vincenzo; Gregoretti, Francesco; Siciliano, Velia; Santoro, Michele; D'Angelo, Giovanni; Oliva, Gennaro; di Bernardo, Diego

    2012-01-01

    Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.

  16. Extracting S-parameters of bilateral electro-optic network for lightwave component analyzer calibration

    NASA Astrophysics Data System (ADS)

    Frolov, D.; Levchenko, A.; Korotkov, K.

    2015-11-01

    A new method for extracting E/O and O/E S-parameters of a bilateral electro-optic network (BEON) is theoretically proposed. It is based on measuring reflection coefficients from three optical loads: an absorber and two mirrors. This technique includes two series of reflections measurements: first when loads are connected to optical port of BEON directly and second when loads are connected in series with optical waveguide of fixed length. Using two BEONs and this calibration technique allows to make calibrated lightwave measurements with a standard microwave network analyzer without using additional electro-optical equipment such as lightwave component analyzer or optical heterodyne techniques.

  17. Quantitative assessment of gene expression network module-validation methods.

    PubMed

    Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

    2015-10-16

    Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

  18. Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

    PubMed

    Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina

    2015-01-01

    Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.

  19. Algebraic model checking for Boolean gene regulatory networks.

    PubMed

    Tran, Quoc-Nam

    2011-01-01

    We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

  20. Stochastic models and numerical algorithms for a class of regulatory gene networks.

    PubMed

    Fournier, Thomas; Gabriel, Jean-Pierre; Pasquier, Jerôme; Mazza, Christian; Galbete, José; Mermod, Nicolas

    2009-08-01

    Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

  1. Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes.

    PubMed

    Wuttke, Daniel; Connor, Richard; Vora, Chintan; Craig, Thomas; Li, Yang; Wood, Shona; Vasieva, Olga; Shmookler Reis, Robert; Tang, Fusheng; de Magalhães, João Pedro

    2012-01-01

    Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR-essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR-essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR-essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR-essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR-induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led

  2. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

    PubMed

    Liu, Zhi-Ping

    2015-02-01

    Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

  3. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

    PubMed Central

    Liu, Zhi-Ping

    2015-01-01

    Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented. PMID:25937810

  4. Use of Network Analysis to Establish Neurosurgical Parameters in Gliomas and Epilepsy

    PubMed Central

    MAESAWA, Satoshi; BAGARINAO, Epifanio; FUJII, Masazumi; FUTAMURA, Miyako; WAKABAYASHI, Toshihiko

    2016-01-01

    Cutting-edge neuroimaging technologies can facilitate preoperative evaluation in various neurosurgical settings. Surgery for gliomas and epilepsy requires precise localization for resection due to the need to preserve (or perhaps improve) higher cognitive functions. Accordingly, a hodological approach should be taken that considers subcortical networks as well as cortical functions within various functional domains. Resting state functional magnetic resonance imaging (fMRI) has the potential to provide new insights that are valuable for this approach. In this review, we describe recent developments in network analysis using resting state fMRI related to factors in glioma and epilepsy surgery: the identification of functionally dominant areas, evaluation of cognitive function by alteration of resting state networks (RSNs), glioma grading, and epileptic focus detection. One particular challenge that is close to realization is using fMRI for the identification of sensorimotor- and language-dominant areas during a task-free resting state. Various RSNs representative of the default mode network demonstrated at least some alterations in both patient groups, which correlated with behavioral changes including cognition, memory, and attention, and the development of psychosis. Still challenging is the detection of epileptic foci and propagation pathways when using only network analysis with resting state fMRI; however, a combined method with simultaneous electroencephalography has produced promising results. Consequently, network analysis is expected to continue to advance as neuroimaging technology improves in the next decade, and preoperative evaluation for neurosurgical parameters through these techniques should improve parallel with them. PMID:26923836

  5. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    NASA Technical Reports Server (NTRS)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  6. Use of Network Analysis to Establish Neurosurgical Parameters in Gliomas and Epilepsy.

    PubMed

    Maesawa, Satoshi; Bagarinao, Epifanio; Fujii, Masazumi; Futamura, Miyako; Wakabayashi, Toshihiko

    2016-01-01

    Cutting-edge neuroimaging technologies can facilitate preoperative evaluation in various neurosurgical settings. Surgery for gliomas and epilepsy requires precise localization for resection due to the need to preserve (or perhaps improve) higher cognitive functions. Accordingly, a hodological approach should be taken that considers subcortical networks as well as cortical functions within various functional domains. Resting state functional magnetic resonance imaging (fMRI) has the potential to provide new insights that are valuable for this approach. In this review, we describe recent developments in network analysis using resting state fMRI related to factors in glioma and epilepsy surgery: the identification of functionally dominant areas, evaluation of cognitive function by alteration of resting state networks (RSNs), glioma grading, and epileptic focus detection. One particular challenge that is close to realization is using fMRI for the identification of sensorimotor- and language-dominant areas during a task-free resting state. Various RSNs representative of the default mode network demonstrated at least some alterations in both patient groups, which correlated with behavioral changes including cognition, memory, and attention, and the development of psychosis. Still challenging is the detection of epileptic foci and propagation pathways when using only network analysis with resting state fMRI; however, a combined method with simultaneous electroencephalography has produced promising results. Consequently, network analysis is expected to continue to advance as neuroimaging technology improves in the next decade, and preoperative evaluation for neurosurgical parameters through these techniques should improve parallel with them.

  7. Relative stability of network states in Boolean network models of gene regulation in development.

    PubMed

    Zhou, Joseph Xu; Samal, Areejit; d'Hérouël, Aymeric Fouquier; Price, Nathan D; Huang, Sui

    2016-01-01

    Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.

  8. A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

    PubMed Central

    Kerkhofs, Johan; Geris, Liesbet

    2015-01-01

    Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

  9. Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction.

    PubMed

    Negarestani, A; Setayeshi, S; Ghannadi-Maragheh, M; Akashe, B

    2002-01-01

    A layered neural network (LNN) has been employed to estimate the radon concentration in soil related to the environmental parameters. This technique can find any functional relationship between the radon concentration and the environmental parameters. Analysis of the data obtained from a site in Thailand indicates that this approach is able to differentiate time variation of radon concentration caused by environmental parameters from those arising by anomaly phenomena in the earth (e.g. earthquake). This method is compared with a linear computational technique based on impulse responses from multivariable time series. It is indicated that the proposed method can give a better estimation of radon variations related to environmental parameters that may have a non-linear effect on the radon concentration in soil, such as rainfall.

  10. A Gene Regulatory Network Model for Cell-Fate Determination during Arabidopsis thaliana Flower Development That Is Robust and Recovers Experimental Gene Expression ProfilesW⃞

    PubMed Central

    Espinosa-Soto, Carlos; Padilla-Longoria, Pablo; Alvarez-Buylla, Elena R.

    2004-01-01

    Flowers are icons in developmental studies of complex structures. The vast majority of 250,000 angiosperm plant species have flowers with a conserved organ plan bearing sepals, petals, stamens, and carpels in the center. The combinatorial model for the activity of the so-called ABC homeotic floral genes has guided extensive experimental studies in Arabidopsis thaliana and many other plant species. However, a mechanistic and dynamical explanation for the ABC model and prevalence among flowering plants is lacking. Here, we put forward a simple discrete model that postulates logical rules that formally summarize published ABC and non-ABC gene interaction data for Arabidopsis floral organ cell fate determination and integrates this data into a dynamic network model. This model shows that all possible initial conditions converge to few steady gene activity states that match gene expression profiles observed experimentally in primordial floral organ cells of wild-type and mutant plants. Therefore, the network proposed here provides a dynamical explanation for the ABC model and shows that precise signaling pathways are not required to restrain cell types to those found in Arabidopsis, but these are rather determined by the overall gene network dynamics. Furthermore, we performed robustness analyses that clearly show that the cell types recovered depend on the network architecture rather than on specific values of the model's gene interaction parameters. These results support the hypothesis that such a network constitutes a developmental module, and hence provide a possible explanation for the overall conservation of the ABC model and overall floral plan among angiosperms. In addition, we have been able to predict the effects of differences in network architecture between Arabidopsis and Petunia hybrida. PMID:15486106

  11. Algorithm of definition of parameters of Katkon—Element of optimization of electrical networks modes

    NASA Astrophysics Data System (ADS)

    Butyrin, P. A.; Gusev, G. G.; Mikheev, D. V.; Shakirzianov, F. N.

    2015-12-01

    The authors consider the algorithm of defining of basic parameters of a filter compensative device based on new element of electrical circuits—katkon. Katkon is a two-terminal network consisting of two parallel coiled and electrically unconnected coils having both inductive and capacitive properties. Filter compensative device based on katkon realizes reactive power compensation on the industrial frequency and filtering of high harmonics: in other words, it realizes functions of energy quality management in an electrical network. The design of such a filter compensative device and its equivalent circuit and algorithm of defining of its parameters that allows choosing its layout and design are described in the article. The results of physical experimental studies with katkon that confirm the adequacy of the proposed algorithm are demonstrated.

  12. An Arabidopsis gene regulatory network for secondary cell wall synthesis

    DOE PAGES

    Taylor-Teeples, M.; Lin, L.; de Lucas, M.; ...

    2014-12-24

    The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptional regulation of synthesis for each polymer is complex and vital to cell function. A regulatory hierarchy of developmental switches has been proposed, although the full complement of regulators remains unknown. In this paper, we present a protein–DNA network between Arabidopsis thaliana transcription factors and secondary cell wall metabolic genes with gene expression regulated bymore » a series of feed-forward loops. This model allowed us to develop and validate new hypotheses about secondary wall gene regulation under abiotic stress. Distinct stresses are able to perturb targeted genes to potentially promote functional adaptation. Finally, these interactions will serve as a foundation for understanding the regulation of a complex, integral plant component.« less

  13. Identification of a gene regulatory network associated with prion replication

    PubMed Central

    Marbiah, Masue M; Harvey, Anna; West, Billy T; Louzolo, Anais; Banerjee, Priya; Alden, Jack; Grigoriadis, Anita; Hummerich, Holger; Kan, Ho-Man; Cai, Ying; Bloom, George S; Jat, Parmjit; Collinge, John; Klöhn, Peter-Christian

    2014-01-01

    Prions consist of aggregates of abnormal conformers of the cellular prion protein (PrPC). They propagate by recruiting host-encoded PrPC although the critical interacting proteins and the reasons for the differences in susceptibility of distinct cell lines and populations are unknown. We derived a lineage of cell lines with markedly differing susceptibilities, unexplained by PrPC expression differences, to identify such factors. Transcriptome analysis of prion-resistant revertants, isolated from highly susceptible cells, revealed a gene expression signature associated with susceptibility and modulated by differentiation. Several of these genes encode proteins with a role in extracellular matrix (ECM) remodelling, a compartment in which disease-related PrP is deposited. Silencing nine of these genes significantly increased susceptibility. Silencing of Papss2 led to undersulphated heparan sulphate and increased PrPC deposition at the ECM, concomitantly with increased prion propagation. Moreover, inhibition of fibronectin 1 binding to integrin α8 by RGD peptide inhibited metalloproteinases (MMP)-2/9 whilst increasing prion propagation. In summary, we have identified a gene regulatory network associated with prion propagation at the ECM and governed by the cellular differentiation state. PMID:24843046

  14. Electrotransfer parameters as a tool for controlled and targeted gene expression in skin

    PubMed Central

    Kos, Spela; Blagus, Tanja; Cemazar, Maja; Lampreht Tratar, Ursa; Stimac, Monika; Prosen, Lara; Dolinsek, Tanja; Kamensek, Urska; Kranjc, Simona; Steinstraesser, Lars; Vandermeulen, Gaëlle; Préat, Véronique; Sersa, Gregor

    2016-01-01

    Skin is an attractive target for gene electrotransfer. It consists of different cell types that can be transfected, leading to various responses to gene electrotransfer. We demonstrate that these responses could be controlled by selecting the appropriate electrotransfer parameters. Specifically, the application of low or high electric pulses, applied by multi-electrode array, provided the possibility to control the depth of the transfection in the skin, the duration and the level of gene expression, as well as the local or systemic distribution of the transgene. The influence of electric pulse type was first studied using a plasmid encoding a reporter gene (DsRed). Then, plasmids encoding therapeutic genes (IL-12, shRNA against endoglin, shRNA against melanoma cell adhesion molecule) were used, and their effects on wound healing and cutaneous B16F10 melanoma tumors were investigated. The high-voltage pulses resulted in gene expression that was restricted to superficial skin layers and induced a local response. In contrast, the low-voltage electric pulses promoted transfection into the deeper skin layers, resulting in prolonged gene expression and higher transgene production, possibly with systemic distribution. Therefore, in the translation into the clinics, it will be of the utmost importance to adjust the electrotransfer parameters for different therapeutic approaches and specific mode of action of the therapeutic gene. PMID:27574782

  15. Infinitely robust order and local order-parameter tulips in Apollonian networks with quenched disorder

    NASA Astrophysics Data System (ADS)

    Kaplan, C. Nadir; Hinczewski, Michael; Berker, A. Nihat

    2009-06-01

    For a variety of quenched random spin systems on an Apollonian network, including ferromagnetic and antiferromagnetic bond percolation and the Ising spin glass, we find the persistence of ordered phases up to infinite temperature over the entire range of disorder. We develop a renormalization-group technique that yields highly detailed information, including the exact distributions of local magnetizations and local spin-glass order parameters, which turn out to exhibit, as function of temperature, complex and distinctive tulip patterns.

  16. Basic parameter estimation of binary neutron star systems by the advanced LIGO/Vigro network

    SciTech Connect

    Rodriguez, Carl L.; Farr, Benjamin; Raymond, Vivien; Farr, Will M.; Littenberg, Tyson B.; Fazi, Diego; Kalogera, Vicky

    2014-04-01

    Within the next five years, it is expected that the Advanced LIGO/Virgo network will have reached a sensitivity sufficient to enable the routine detection of gravitational waves. Beyond the initial detection, the scientific promise of these instruments relies on the effectiveness of our physical parameter estimation capabilities. A major part of this effort has been toward the detection and characterization of gravitational waves from compact binary coalescence, e.g., the coalescence of binary neutron stars. While several previous studies have investigated the accuracy of parameter estimation with advanced detectors, the majority have relied on approximation techniques such as the Fisher Matrix which are insensitive to the non-Gaussian nature of the gravitational wave posterior distribution function. Here we report average statistical uncertainties that will be achievable for strong detection candidates (S/N = 20) over a comprehensive sample of source parameters. We use the Markov Chain Monte Carlo based parameter estimation software developed by the LIGO/Virgo Collaboration with the goal of updating the previously quoted Fisher Matrix bounds. We find the recovery of the individual masses to be fractionally within 9% (15%) at the 68% (95%) credible intervals for equal-mass systems, and within 1.9% (3.7%) for unequal-mass systems. We also find that the Advanced LIGO/Virgo network will constrain the locations of binary neutron star mergers to a median uncertainty of 5.1 deg{sup 2} (13.5 deg{sup 2}) on the sky. This region is improved to 2.3 deg{sup 2} (6 deg{sup 2}) with the addition of the proposed LIGO India detector to the network. We also report the average uncertainties on the luminosity distances and orbital inclinations of strong detections that can be achieved by different network configurations.

  17. Infinitely robust order and local order-parameter tulips in Apollonian networks with quenched disorder.

    PubMed

    Kaplan, C Nadir; Hinczewski, Michael; Berker, A Nihat

    2009-06-01

    For a variety of quenched random spin systems on an Apollonian network, including ferromagnetic and antiferromagnetic bond percolation and the Ising spin glass, we find the persistence of ordered phases up to infinite temperature over the entire range of disorder. We develop a renormalization-group technique that yields highly detailed information, including the exact distributions of local magnetizations and local spin-glass order parameters, which turn out to exhibit, as function of temperature, complex and distinctive tulip patterns.

  18. Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

    PubMed Central

    2011-01-01

    Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our

  19. Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras.

    PubMed

    Wang, Tim; Yu, Haiyan; Hughes, Nicholas W; Liu, Bingxu; Kendirli, Arek; Klein, Klara; Chen, Walter W; Lander, Eric S; Sabatini, David M

    2017-02-23

    The genetic dependencies of human cancers widely vary. Here, we catalog this heterogeneity and use it to identify functional gene interactions and genotype-dependent liabilities in cancer. By using genome-wide CRISPR-based screens, we generate a gene essentiality dataset across 14 human acute myeloid leukemia (AML) cell lines. Sets of genes with correlated patterns of essentiality across the lines reveal new gene relationships, the essential substrates of enzymes, and the molecular functions of uncharacterized proteins. Comparisons of differentially essential genes between Ras-dependent and -independent lines uncover synthetic lethal partners of oncogenic Ras. Screens in both human AML and engineered mouse pro-B cells converge on a surprisingly small number of genes in the Ras processing and MAPK pathways and pinpoint PREX1 as an AML-specific activator of MAPK signaling. Our findings suggest general strategies for defining mammalian gene networks and synthetic lethal interactions by exploiting the natural genetic and epigenetic diversity of human cancer cells.

  20. An empiric comparison of linkage disequilibrium parameters in disease gene localizations; the myotonic dystrophy experience

    SciTech Connect

    Podolsky, L.; Baird, S.; Korneluk, R.G.

    1994-09-01

    Analyses of linkage disequilibrium (LD) between markers of known location and disease phenotypes often provide valuable information in efforts to clone the causative genes. However, there exist a number of factors which may attenuate a consistent inverse relationship between physical distance and LD for a given pairing of a genetic marker and a human disease gene. Chief among these is the effect of the general population frequency of an allele which demonstrates LD with a disease gene. Possibly as a result of this, a number of methods of calculating LD has been proposed. We have calculated seven such LD parameters for twelve physically mapped RFLP`s from a 1.3 Mb DM gene containing region of 19q13.3 using 107 DM and 213 non-DM chromosomes. Correlation of the DM-marker physical distance with LD for the 12 loci reveals the Yule coefficient and Dij{prime} parameter to give the most consistent relationship. The D{prime} parameter shown to have a relative allele frequency independence gave only a weak correlation. A similar analysis is being carried out on published cystic fibrosis genetic and physical mapping data. The parameters identified in this study may be the most appropriate for future LD based localizations of disease genes.

  1. Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

    PubMed Central

    Oros Klein, Kathleen; Oualkacha, Karim; Lafond, Marie-Hélène; Bhatnagar, Sahir; Tonin, Patricia N.; Greenwood, Celia M. T.

    2016-01-01

    In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology. PMID:27536319

  2. Coated or doped carbon nanotube network sensors as affected by environmental parameters

    NASA Technical Reports Server (NTRS)

    Li, Jing (Inventor)

    2011-01-01

    Methods for using modified single wall carbon nanotubes ("SWCNTs") to detect presence and/or concentration of a gas component, such as a halogen (e.g., Cl.sub.2), hydrogen halides (e.g., HCl), a hydrocarbon (e.g., C.sub.nH.sub.2n+2), an alcohol, an aldehyde or a ketone, to which an unmodified SWCNT is substantially non-reactive. In a first embodiment, a connected network of SWCNTs is coated with a selected polymer, such as chlorosulfonated polyethylene, hydroxypropyl cellulose, polystyrene and/or polyvinylalcohol, and change in an electrical parameter or response value (e.g., conductance, current, voltage difference or resistance) of the coated versus uncoated SWCNT networks is analyzed. In a second embodiment, the network is doped with a transition element, such as Pd, Pt, Rh, Ir, Ru, Os and/or Au, and change in an electrical parameter value is again analyzed. The parameter change value depends monotonically, not necessarily linearly, upon concentration of the gas component. Two general algorithms are presented for estimating concentration value(s), or upper or lower concentration bounds on such values, from measured differences of response values.

  3. Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks

    PubMed Central

    Rathinam, Muruhan; Sheppard, Patrick W.; Khammash, Mustafa

    2010-01-01

    Parametric sensitivity of biochemical networks is an indispensable tool for studying system robustness properties, estimating network parameters, and identifying targets for drug therapy. For discrete stochastic representations of biochemical networks where Monte Carlo methods are commonly used, sensitivity analysis can be particularly challenging, as accurate finite difference computations of sensitivity require a large number of simulations for both nominal and perturbed values of the parameters. In this paper we introduce the common random number (CRN) method in conjunction with Gillespie’s stochastic simulation algorithm, which exploits positive correlations obtained by using CRNs for nominal and perturbed parameters. We also propose a new method called the common reaction path (CRP) method, which uses CRNs together with the random time change representation of discrete state Markov processes due to Kurtz to estimate the sensitivity via a finite difference approximation applied to coupled reaction paths that emerge naturally in this representation. While both methods reduce the variance of the estimator significantly compared to independent random number finite difference implementations, numerical evidence suggests that the CRP method achieves a greater variance reduction. We also provide some theoretical basis for the superior performance of CRP. The improved accuracy of these methods allows for much more efficient sensitivity estimation. In two example systems reported in this work, speedup factors greater than 300 and 10 000 are demonstrated. PMID:20095724

  4. Parameter estimation for metabolic networks with two stage Bregman regularization homotopy inversion algorithm.

    PubMed

    Wang, Hong; Wang, Xi-cheng

    2014-02-21

    Metabolism is a very important cellular process and its malfunction contributes to human disease. Therefore, building dynamic models for metabolic networks with experimental data in order to analyze biological process rationally has attracted a lot of attention. Owing to the technical limitations, some unknown parameters contained in models need to be estimated effectively by means of the computational method. Generally, problems of parameter estimation of nonlinear biological network are known to be ill condition and multimodal. In particular, with the increasing amount and enlarging the scope of parameters, many optimization algorithms often fail to find a global solution. In this paper, two-stage variable factor Bregman regularization homotopy method is proposed. Discrete homotopy is used to identify the possible extreme region and continuous homotopy is executed for the purpose of stability of path tracing in the special region. Meanwhile, Latin hypercube sampling is introduced to get the good initial guess value and a perturbation strategy is developed to jump out of the local optimum. Three metabolic network inverse problems are investigated to demonstrate the effectiveness of the proposed method.

  5. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    PubMed Central

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-01-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

  6. Investigating the Combinatory Effects of Biological Networks on Gene Co-expression

    PubMed Central

    Zhang, Cheng; Lee, Sunjae; Mardinoglu, Adil; Hua, Qiang

    2016-01-01

    Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharomyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related. PMID:27445830

  7. Dose response relationship in anti-stress gene regulatory networks.

    PubMed

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

  8. Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish.

    PubMed

    Malki, Karim; Du Rietz, Ebba; Crusio, Wim E; Pain, Oliver; Paya-Cano, Jose; Karadaghi, Rezhaw L; Sluyter, Frans; de Boer, Sietse F; Sandnabba, Kenneth; Schalkwyk, Leonard C; Asherson, Philip; Tosto, Maria Grazia

    2016-09-01

    Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP < 0.05) in aggressive compared to non-aggressive mice. Seventy genes were differentially expressed in zebrafish exposed to a fight encounter compared to isolated zebrafish. Seven genes (Fos, Dusp1, Hdac4, Ier2, Bdnf, Btg2, and Nr4a1) were differentially expressed across both species 5 of which belonging to a gene-network centred on the c-Fos gene hub. Network analysis revealed an association with the MAPK signaling cascade. In human studies HDAC4 haploinsufficiency is a key genetic mechanism associated with brachydactyly mental retardation syndrome (BDMR), which is associated with aggressive behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc.

  9. Topological effects of data incompleteness of gene regulatory networks

    PubMed Central

    2012-01-01

    Background The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly. Results In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different levels. Conclusions In doing so, we identify the most relevant factors affecting the validity of previous findings, highlighting important caveats to future prokaryotic TRNs topological analysis. PMID:22920968

  10. Data identification for improving gene network inference using computational algebra.

    PubMed

    Dimitrova, Elena; Stigler, Brandilyn

    2014-11-01

    Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

  11. Data- and knowledge-based modeling of gene regulatory networks: an update

    PubMed Central

    Linde, Jörg; Schulze, Sylvie; Henkel, Sebastian G.; Guthke, Reinhard

    2015-01-01

    Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions. PMID:27047314

  12. Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

    PubMed Central

    Das, Samarendra; Meher, Prabina Kumar; Bhar, Lal Mohan; Mandal, Baidya Nath

    2017-01-01

    Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean. PMID:28056073

  13. Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes

    PubMed Central

    Coulombe-Huntington, Jasmin; Xia, Yu

    2017-01-01

    Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker’s yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved “core” genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates. PMID:28046110

  14. Discovery of Core Biotic Stress Responsive Genes in Arabidopsis by Weighted Gene Co-Expression Network Analysis

    PubMed Central

    Amrine, Katherine C. H.; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  15. Smaller, Scale-Free Gene Networks Increase Quantitative Trait Heritability and Result in Faster Population Recovery

    PubMed Central

    Malcom, Jacob W.

    2011-01-01

    One of the goals of biology is to bridge levels of organization. Recent technological advances are enabling us to span from genetic sequence to traits, and then from traits to ecological dynamics. The quantitative genetics parameter heritability describes how quickly a trait can evolve, and in turn describes how quickly a population can recover from an environmental change. Here I propose that we can link the details of the genetic architecture of a quantitative trait—i.e., the number of underlying genes and their relationships in a network—to population recovery rates by way of heritability. I test this hypothesis using a set of agent-based models in which individuals possess one of two network topologies or a linear genotype-phenotype map, 16–256 genes underlying the trait, and a variety of mutation and recombination rates and degrees of environmental change. I find that the network architectures introduce extensive directional epistasis that systematically hides and reveals additive genetic variance and affects heritability: network size, topology, and recombination explain 81% of the variance in average heritability in a stable environment. Network size and topology, the width of the fitness function, pre-change additive variance, and certain interactions account for ∼75% of the variance in population recovery times after a sudden environmental change. These results suggest that not only the amount of additive variance, but importantly the number of loci across which it is distributed, is important in regulating the rate at which a trait can evolve and populations can recover. Taken in conjunction with previous research focused on differences in degree of network connectivity, these results provide a set of theoretical expectations and testable hypotheses for biologists working to span levels of organization from the genotype to the phenotype, and from the phenotype to the environment. PMID:21347400

  16. Network and pathway analysis of microRNAs, transcription factors, target genes and host genes in human glioma

    PubMed Central

    ZHANG, YING; ZHAO, SHISHUN; XU, ZHIWEN

    2016-01-01

    To date, there has been rapid development with regard to gene and microRNA (miR/miRNA) research in gliomas. However, the regulatory mechanisms of the associated genes and miRNAs remain unclear. In the present study, the genes, miRNAs and transcription factors (TFs) were considered as elements in the regulatory network, and focus was placed on the associations between TFs and miRNAs, miRNAs and target genes, and miRNAs and host genes. In order to show the regulatory correlation clearly, all the elements were investigated and three regulatory networks, namely the differentially-expressed, related and global networks, were constructed. Certain important pathways were highlighted, with analysis of the similarities and differences among the networks. Next, the upstream and downstream elements of differentially-expressed genes, miRNAs and predicted TFs were listed. The most notable aspect of the present study was the three levels of network, particularly the differentially-expressed network, since the differentially-expressed associations that these networks provide appear at the initial stages of cancers such as glioma. If the states of the differentially-expressed associations can be adjusted to the normal state via alterations in regulatory associations, which were also recorded in the study networks and tables, it is likely that cancer can be regulated or even avoided. In the present study, the differentially-expressed network illuminated the pathogenesis of glioma; for example, a TF can regulate one or more miRNAs, and a target gene can be targeted by one or more miRNAs. Therefore, the host genes and target genes, the host genes and TFs, and the target genes and TFs indirectly affect each other through miRNAs. The association also exists between TFs and TFs, target genes and target genes, and host genes and host genes. The present study also demonstrated self-adaption associations and circle-regulations. The related network further described the regulatory mechanism

  17. Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach.

    PubMed

    Paskiabi, Farnoush Asghari; Mirzaei, Esmaeil; Amani, Amir; Shokrgozar, Mohammad Ali; Saber, Reza; Faridi-Majidi, Reza

    2015-11-01

    This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers.

  18. Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

    PubMed Central

    Golightly, Andrew; Wilkinson, Darren J.

    2011-01-01

    Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

  19. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.

  20. Model Reduction Using Multiple Time Scales in Stochastic Gene Regulatory Networks

    DTIC Science & Technology

    2006-08-28

    illustrate the efficiency of our method in two gene network examples, which describe two substantially different biological processes -- cellular heat shock response and expression of the pap gene in Escherichia coli bacteria .

  1. A Predictive Based Regression Algorithm for Gene Network Selection

    PubMed Central

    Guerrier, Stéphane; Mili, Nabil; Molinari, Roberto; Orso, Samuel; Avella-Medina, Marco; Ma, Yanyuan

    2016-01-01

    Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. We propose a new prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. Based on cross-validation techniques and the idea of importance sampling, our proposal scans low-dimensional models under the assumption of sparsity and, for each of them, estimates their objective function to assess their predictive power in order to select. Two applications on cancer data sets and a simulation study show that the proposal compares favorably with competing alternatives such as, for example, Elastic Net and Support Vector Machine. Indeed, the proposed method not only selects smaller models for better, or at least comparable, classification errors but also provides a set of selected models instead of a single one, allowing to construct a network of possible models for a target prediction accuracy level. PMID:27379155

  2. Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

    PubMed

    Wang, Gang; Briskot, Till; Hahn, Tobias; Baumann, Pascal; Hubbuch, Jürgen

    2017-03-03

    Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture.

  3. The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network.

    PubMed

    Garfield, David A; Runcie, Daniel E; Babbitt, Courtney C; Haygood, Ralph; Nielsen, William J; Wray, Gregory A

    2013-10-01

    Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear), allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

  4. MINER: exploratory analysis of gene interaction networks by machine learning from expression data

    PubMed Central

    2009-01-01

    Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing. PMID:19958480

  5. An efficient data assimilation schema for restoration and extension of gene regulatory networks using time-course observation data.

    PubMed

    Hasegawa, Takanori; Mori, Tomoya; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru; Akutsu, Tatsuya

    2014-11-01

    Gene regulatory networks (GRNs) play a central role in sustaining complex biological systems in cells. Although we can construct GRNs by integrating biological interactions that have been recorded in literature, they can include suspicious data and a lack of information. Therefore, there has been an urgent need for an approach by which the validity of constructed networks can be evaluated; simulation-based methods have been applied in which biological observational data are assimilated. However, these methods apply nonlinear models that require high computational power to evaluate even one network consisting of only several genes. Therefore, to explore candidate networks whose simulation models can better predict the data by modifying and extending literature-based GRNs, an efficient and versatile method is urgently required. We applied a combinatorial transcription model, which can represent combinatorial regulatory effects of genes, as a biological simulation model, to reproduce the dynamic behavior of gene expressions within a state space model. Under the model, we applied the unscented Kalman filter to obtain the approximate posterior probability distribution of the hidden state to efficiently estimate parameter values maximizing prediction ability for observational data by the EM-algorithm. Utilizing the method, we propose a novel algorithm to modify GRNs reported in the literature so that their simulation models become consistent with observed data. The effectiveness of our approach was validated through comparison analysis to the previous methods using synthetic networks. Finally, as an application example, a Kyoto Encyclopedia of Genes and Genomes (KEGG)-based yeast cell cycle network was extended with additional candidate genes to better predict the real mRNA expressions data using the proposed method.

  6. Gene interaction network analysis suggests differences between high and low doses of acetaminophen

    SciTech Connect

    Toyoshiba, Hiroyoshi . E-mail: toyoshiba.hiroyoshi@nies.go.jp; Sone, Hideko; Yamanaka, Takeharu; Parham, Frederick M.; Irwin, Richard D.; Boorman, Gary A.; Portier, Christopher J.

    2006-09-15

    Bayesian networks for quantifying linkages between genes were applied to detect differences in gene expression interaction networks between multiple doses of acetaminophen at multiple time points. Seventeen (17) genes were selected from the gene expression profiles from livers of rats orally exposed to 50, 150 and 1500 mg/kg acetaminophen (APAP) at 6, 24 and 48 h after exposure using a variety of statistical and bioinformatics approaches. The selected genes are related to three biological categories: apoptosis, oxidative stress and other. Gene interaction networks between all 17 genes were identified for the nine dose-time observation points by the TAO-Gen algorithm. Using k-means clustering analysis, the estimated nine networks could be clustered into two consensus networks, the first consisting of the low and middle dose groups, and the second consisting of the high dose. The analysis suggests that the networks could be segregated by doses and were consistent in structure over time of observation within grouped doses. The consensus networks were quantified to calculate the probability distribution for the strength of the linkage between genes connected in the networks. The quantifying analysis showed that, at lower doses, the genes related to the oxidative stress signaling pathway did not interact with the apoptosis-related genes. In contrast, the high-dose network demonstrated significant interactions between the oxidative stress genes and the apoptosis genes and also demonstrated a different network between genes in the oxidative stress pathway. The approaches shown here could provide predictive information to understand high- versus low-dose mechanisms of toxicity.

  7. Integration of neural networks with fuzzy reasoning for measuring operational parameters in a nuclear reactor

    SciTech Connect

    Ikonomopoulos, A.; Tsoukalas, L.H. . Dept. of Nuclear Engineering); Uhrig, R.E. )

    1993-10-01

    A novel approach is described for measuring variables with operational significance in a complex system such as a nuclear reactor. The methodology is based on the integration of artificial neural networks with fuzzy reasoning. Neural networks are used to map dynamic time series to a set of user-defined linguistic labels called fuzzy values. The process takes place in a manner analogous to that of measurement. Hence, the entire procedure is referred to as virtual measurement and its software implementation as a virtual measuring device. An optimization algorithm based on information criteria and fuzzy algebra augments the process and assists in the identification of different states of the monitored parameter. The proposed technique is applied for monitoring parameters such as performance, valve position, transient type, and reactivity. The results obtained from the application of the neural network-fuzzy reasoning integration in a high power research reactor clearly demonstrate the excellent tolerance of the virtual measuring device to faulty signals as well as its ability to accommodate noisy inputs.

  8. The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth

    PubMed Central

    Pechenick, Dov A.; Moore, Jason H.; Payne, Joshua L.

    2013-01-01

    Gene regulatory networks (GRNs) represent the interactions between genes and gene products, which drive the gene expression patterns that produce cellular phenotypes. GRNs display a number of characteristics that are beneficial for the development and evolution of organisms. For example, they are often robust to genetic perturbation, such as mutations in regulatory regions or loss of gene function. Simultaneously, GRNs are often evolvable as these genetic perturbations are occasionally exploited to innovate novel regulatory programs. Several topological properties, such as degree distribution, are known to influence the robustness and evolvability of GRNs. Assortativity, which measures the propensity of nodes of similar connectivity to connect to one another, is a separate topological property that has recently been shown to influence the robustness of GRNs to point mutations in cis-regulatory regions. However, it remains to be seen how assortativity may influence the robustness and evolvability of GRNs to other forms of genetic perturbation, such as gene birth via duplication or de novo origination. Here, we employ a computational model of genetic regulation to investigate whether the assortativity of a GRN influences its robustness and evolvability upon gene birth. We find that the robustness of a GRN generally increases with increasing assortativity, while its evolvability generally decreases. However, the rate of change in robustness outpaces that of evolvability, resulting in an increased proportion of assortative GRNs that are simultaneously robust and evolvable. By providing a mechanistic explanation for these observations, this work extends our understanding of how the assortativity of a GRN influences its robustness and evolvability upon gene birth. PMID:23542384

  9. Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

    PubMed

    Hu, Jin; Zeng, Chunna

    2017-02-01

    The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.

  10. Updating Parameters for Volcanic Hazard Assessment Using Multi-parameter Monitoring Data Streams And Bayesian Belief Networks

    NASA Astrophysics Data System (ADS)

    Odbert, Henry; Aspinall, Willy

    2014-05-01

    Evidence-based hazard assessment at volcanoes assimilates knowledge about the physical processes of hazardous phenomena and observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We discuss

  11. Construction of citrus gene coexpression networks from microarray data using random matrix theory.

    PubMed

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus.

  12. Construction of citrus gene coexpression networks from microarray data using random matrix theory

    PubMed Central

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G.

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus. PMID:26504573

  13. The Transcriptional and Gene Regulatory Network of Lactococcus lactis MG1363 during Growth in Milk

    PubMed Central

    de Jong, Anne; Hansen, Morten E.; Kuipers, Oscar P.; Kilstrup, Mogens; Kok, Jan

    2013-01-01

    In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs) are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the individual network building blocks. PMID:23349698

  14. An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks.

    PubMed

    He, Qinbin; Xia, Zhile; Lin, Bin

    2016-11-07

    Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

  15. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  16. Effects of bidirectional regulation on noises in gene networks.

    PubMed

    Zheng, Xiudeng; Tao, Yi

    2010-03-14

    To investigate the effects of bidirectional regulation on the noise in protein concentration, a theoretical and simple three-gene network model is considered. The basic idea behind this model is from Paulsson's proposition (J. Paulsson, Phys. Life Rev. 2005, 2, 157-175), where the synthesis and degradation of a mRNA species corresponding to a target protein are regulated directly and indirectly by a certain sigma-factor, and a random increase in the concentration of the sigma-factor should increase both the synthesis and degradation rates of the mRNA species (bidirectional regulation). Using the standard Omega-expansion technique (linear noise approximation) and Monte Carlo simulation, our main results show clearly that for the steady-state statistics the effects of the noise of the sigma-factor on the stochastic fluctuation of the target protein could partially cancel out.

  17. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation.

    PubMed

    Zhang, Ke K; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-03-15

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5(+/) (-), Osr1(+/-), Osr1(-/-) and Tbx5(+/-)/Osr1(+/-) mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene-gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny.

  18. Gene regulation is governed by a core network in hepatocellular carcinoma

    PubMed Central

    2012-01-01

    Background Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. Results In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Conclusions Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to

  19. Predicting future conflict between team-members with parameter-free models of social networks.

    PubMed

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-01-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  20. Predicting future conflict between team-members with parameter-free models of social networks

    NASA Astrophysics Data System (ADS)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-06-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  1. Ground Motion Simulations for Bursa Region (Turkey) Using Input Parameters derived from the Regional Seismic Network

    NASA Astrophysics Data System (ADS)

    Unal, B.; Askan, A.

    2014-12-01

    Earthquakes are among the most destructive natural disasters in Turkey and it is important to assess seismicity in different regions with the use of seismic networks. Bursa is located in Marmara Region, Northwestern Turkey and to the south of the very active North Anatolian Fault Zone. With around three million inhabitants and key industrial facilities of the country, Bursa is the fourth largest city in Turkey. Since most of the focus is on North Anatolian Fault zone, despite its significant seismicity, Bursa area has not been investigated extensively until recently. For reliable seismic hazard estimations and seismic design of structures, assessment of potential ground motions in this region is essential using both recorded and simulated data. In this study, we employ stochastic finite-fault simulation with dynamic corner frequency approach to model previous events as well to assess potential earthquakes in Bursa. To ensure simulations with reliable synthetic ground motion outputs, the input parameters must be carefully derived from regional data. In this study, using strong motion data collected at 33 stations in the region, site-specific parameters such as near-surface high frequency attenuation parameter and amplifications are obtained. Similarly, source and path parameters are adopted from previous studies that as well employ regional data. Initially, major previous events in the region are verified by comparing the records with the corresponding synthetics. Then simulations of scenario events in the region are performed. We present the results in terms of spatial distribution of peak ground motion parameters and time histories at selected locations.

  2. Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms

    SciTech Connect

    Kurt Derr; Milos Manic

    2013-11-01

    A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.

  3. Quality assurance of the gene ontology using abstraction networks.

    PubMed

    Ochs, Christopher; Perl, Yehoshua; Halper, Michael; Geller, James; Lomax, Jane

    2016-06-01

    The gene ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks (AbNs) are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of various ontologies. Two kinds of AbNs, called the area taxonomy and the partial-area taxonomy, are developed for GO hierarchies and derived specifically for the biological process (BP) hierarchy. Within this framework, several QA heuristics, based on the identification of groups of anomalous terms which exhibit certain taxonomy-defined characteristics, are introduced. Such groups are expected to have higher error rates when compared to other terms. Thus, by focusing QA efforts on anomalous terms one would expect to find relatively more erroneous content. By automatically identifying these potential problem areas within an ontology, time and effort will be saved during manual reviews of GO's content. BP is used as a testbed, with samples of three kinds of anomalous BP terms chosen for a taxonomy-based QA review. Additional heuristics for QA are demonstrated. From the results of this QA effort, it is observed that different kinds of inconsistencies in the modeling of GO can be exposed with the use of the proposed heuristics. For comparison, the results of QA work on a sample of terms chosen from GO's general population are presented.

  4. Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

    PubMed Central

    2012-01-01

    Background Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks. Results We developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses. Conclusions The bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean

  5. Network analysis of microRNAs, transcription factors, target genes and host genes in human anaplastic astrocytoma

    PubMed Central

    XUE, LUCHEN; XU, ZHIWEN; WANG, KUNHAO; WANG, NING; ZHANG, XIAOXU; WANG, SHANG

    2016-01-01

    Numerous studies have investigated the roles played by various genes and microRNAs (miRNAs) in neoplasms, including anaplastic astrocytoma (AA). However, the specific regulatory mechanisms involving these genes and miRNAs remain unclear. In the present study, associated biological factors (miRNAs, transcription factors, target genes and host genes) from existing studies of human AA were combined methodically through the interactions between genes and miRNAs, as opposed to studying one or several. Three regulatory networks, including abnormally expressed, related and global networks were constructed with the aim of identifying significant gene and miRNA pathways. Each network is composed of three associations between miRNAs targeted at genes, transcription factors (TFs) regulating miRNAs and miRNAs located on their host genes. Among these, the abnormally expressed network, which involves the pathways of previously identified abnormally expressed genes and miRNAs, partially indicated the regulatory mechanism underlying AA. The network contains numerous abnormal regulation associations when AA emerges. By modifying the abnormally expressed network factors to a normal expression pattern, the faulty regulation may be corrected and tumorigenesis of AA may be prevented. Certain specific pathways are highlighted in AA, for example PTEN which is targeted by miR-21 and miR-106b, regulates miR-25 which in turn targets TP53. PTEN and miR-21 have been observed to form feedback loops. Furthermore, by comparing and analyzing the pathway predecessors and successors of abnormally expressed genes and miRNAs in three networks, similarities and differences of regulatory pathways may be identified and proposed. In summary, the present study aids in elucidating the occurrence, mechanism, prevention and treatment of AA. These results may aid further investigation into therapeutic approaches for this disease. PMID:27347075

  6. Modeling transmission parameters of polymer microstructured fibers for applications in FTTH networks

    NASA Astrophysics Data System (ADS)

    Gdula, P.; Welikow, K.; Szczepański, P.; Buczyński, R.; Piramidowicz, R.

    2011-10-01

    This paper is focused on selected aspects of designing and modeling of transmission parameters of plastic optical fibers (POFs), considered in the context of their potential applications in optical access networks and, specifically, in Fiber-To- The-Home (FTTH) systems. The survey of state-of-the-art solutions is presented and possibility of improving transmission properties of POFs by microstructurization is discussed on the basis of the first results of numerical modeling. In particular, the microstructured POF was designed supporting propagation of limited number of modes while keeping relatively large mode area and, simultaneously, significantly lowered bending losses.

  7. Infinitely Robust Order and Local Order-Parameter Tulips in Apollonian Networks with Quenched Disorder

    NASA Astrophysics Data System (ADS)

    Nadir Kaplan, C.; Hinczewski, Michael; Berker, A. Nihat

    2009-03-01

    For a variety of quenched random spin systems on an Apollonian network, including ferromagnetic and antiferromagnetic bond percolation and the Ising spin glass, we find the persistence of ordered phases up to infinite temperature over the entire range of disorder.[1] We develop a renormalization-group technique that yields highly detailed information, including the exact distributions of local magnetizations and local spin-glass order parameters, which turn out to exhibit, as function of temperature, complex and distinctive tulip patterns. [1] C.N. Kaplan, M. Hinczewski, and A.N. Berker, arXiv:0811.3437v1 [cond-mat.dis-nn] (2008).

  8. Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors.

    PubMed

    Emmert-Streib, Frank

    2013-01-01

    The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

  9. Programmable cells: Interfacing natural and engineered gene networks

    NASA Astrophysics Data System (ADS)

    Kobayashi, Hideki; Kærn, Mads; Araki, Michihiro; Chung, Kristy; Gardner, Timothy S.; Cantor, Charles R.; Collins, James J.

    2004-06-01

    Novel cellular behaviors and characteristics can be obtained by coupling engineered gene networks to the cell's natural regulatory circuitry through appropriately designed input and output interfaces. Here, we demonstrate how an engineered genetic circuit can be used to construct cells that respond to biological signals in a predetermined and programmable fashion. We employ a modular design strategy to create Escherichia coli strains where a genetic toggle switch is interfaced with: (i) the SOS signaling pathway responding to DNA damage, and (ii) a transgenic quorum sensing signaling pathway from Vibrio fischeri. The genetic toggle switch endows these strains with binary response dynamics and an epigenetic inheritance that supports a persistent phenotypic alteration in response to transient signals. These features are exploited to engineer cells that form biofilms in response to DNA-damaging agents and cells that activate protein synthesis when the cell population reaches a critical density. Our work represents a step toward the development of "plug-and-play" genetic circuitry that can be used to create cells with programmable behaviors. heterologous gene expression | synthetic biology | Escherichia coli

  10. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation

    PubMed Central

    Zhang, Ke K.; Xiang, Menglan; Zhou, Lun; Liu, Jielin; Curry, Nathan; Heine Suñer, Damian; Garcia-Pavia, Pablo; Zhang, Xiaohua; Wang, Qin; Xie, Linglin

    2016-01-01

    Atrial septal defects (ASDs) are a common human congenital heart disease (CHD) that can be induced by genetic abnormalities. Our previous studies have demonstrated a genetic interaction between Tbx5 and Osr1 in the second heart field (SHF) for atrial septation. We hypothesized that Osr1 and Tbx5 share a common signaling networking and downstream targets for atrial septation. To identify this molecular networks, we acquired the RNA-Seq transcriptome data from the posterior SHF of wild-type, Tbx5+/−, Osr1+/−, Osr1−/− and Tbx5+/−/Osr1+/− mutant embryos. Gene set analysis was used to identify the Kyoto Encyclopedia of Genes and Genomes pathways that were affected by the doses of Tbx5 and Osr1. A gene network module involving Tbx5 and Osr1 was identified using a non-parametric distance metric, distance correlation. A subset of 10 core genes and gene–gene interactions in the network module were validated by gene expression alterations in posterior second heart field (pSHF) of Tbx5 and Osr1 transgenic mouse embryos, a time-course gene expression change during P19CL6 cell differentiation. Pcsk6 was one of the network module genes that were linked to Tbx5. We validated the direct regulation of Tbx5 on Pcsk6 using immunohistochemical staining of pSHF, ChIP-quantitative polymerase chain reaction and luciferase reporter assay. Importantly, we identified Pcsk6 as a novel gene associated with ASD via a human genotyping study of an ASD family. In summary, our study implicated a gene network involving Tbx5, Osr1 and Pcsk6 interaction in SHF for atrial septation, providing a molecular framework for understanding the role of Tbx5 in CHD ontogeny. PMID:26744331

  11. Linking chemical parameters to sensory panel results through neural networks to distinguish olive oil quality.

    PubMed

    Cancilla, John C; Wang, Selina C; Díaz-Rodríguez, Pablo; Matute, Gemma; Cancilla, John D; Flynn, Dan; Torrecilla, José S

    2014-11-05

    A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.

  12. A receding horizon scheme for discrete-time polytopic linear parameter varying systems in networked architectures

    NASA Astrophysics Data System (ADS)

    Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

    2014-12-01

    This paper proposes a Model Predictive Control (MPC) strategy to address regulation problems for constrained polytopic Linear Parameter Varying (LPV) systems subject to input and state constraints in which both plant measurements and command signals in the loop are sent through communication channels subject to time-varying delays (Networked Control System (NCS)). The results here proposed represent a significant extension to the LPV framework of a recent Receding Horizon Control (RHC) scheme developed for the so-called robust case. By exploiting the parameter availability, the pre-computed sequences of one- step controllable sets inner approximations are less conservative than the robust counterpart. The resulting framework guarantees asymptotic stability and constraints fulfilment regardless of plant uncertainties and time-delay occurrences. Finally, experimental results on a laboratory two-tank test-bed show the effectiveness of the proposed approach.

  13. Calculation of PID controller parameters by using a fuzzy neural network.

    PubMed

    Lee, Ching-Hung; Teng, Ching-Cheng

    2003-07-01

    In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (Ms). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and Ms. To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.

  14. Implications of Developmental Gene Regulatory Networks Inside and Outside Developmental Biology.

    PubMed

    Peter, Isabelle S; Davidson, Eric H

    2016-01-01

    The insight that the genomic control of developmental process is encoded in the form of gene regulatory networks has profound impacts on many areas of modern bioscience. Most importantly, it affects developmental biology itself, as it means that a causal understanding of development requires knowledge of the architecture of regulatory network interactions. Furthermore, it follows that functional changes in developmental gene regulatory networks have to be considered as a primary mechanism for evolutionary process. We here discuss some of the recent advances in gene regulatory network biology and how they have affected our current understanding of development, evolution, and regulatory genomics.

  15. Genetic control of root growth: from genes to networks

    PubMed Central

    Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B.; Ristova, Daniela; Busch, Wolfgang

    2016-01-01

    Background Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. Scope This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. Conclusions While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. PMID

  16. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    PubMed

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus.

  17. CCor: A whole genome network-based similarity measure between two genes.

    PubMed

    Hu, Yiming; Zhao, Hongyu

    2016-12-01

    Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network-based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coefficient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules.

  18. CCor: a whole genome network-based similarity measure between two genes

    PubMed Central

    Hu, Yiming; Zhao, Hongyu

    2016-01-01

    Summary Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network-based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coe cient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules. PMID:26953524

  19. A network-based gene-weighting approach for pathway analysis.

    PubMed

    Fang, Zhaoyuan; Tian, Weidong; Ji, Hongbin

    2012-03-01

    Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.

  20. Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics

    PubMed Central

    Remondini, D.; O'Connell, B.; Intrator, N.; Sedivy, J. M.; Neretti, N.; Castellani, G. C.; Cooper, L. N.

    2005-01-01

    This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes. PMID:15867157

  1. Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

    PubMed Central

    2014-01-01

    This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

  2. Relationship between DNA mismatch repair genes expression, Ku-genes expression and ploidy-related parameters in the progression of pigmented lesions of the skin.

    PubMed

    Korabiowska, Monika; Tscherny, Michael; Stachura, Jerzy; Ruschenburg, Ilka; Cordon-Cardo, Carlos; Brinck, Ulrich

    2002-01-01

    Defects of DNA repair systems in cutaneous tumours are related to DNA mismatch repair genes (MLH1, MSH2, PMS1, PMS2) and Ku70/80 genes involved in double- strand repair. In this study we investigated the statistical relationship between these systems and DNA-ploidy-related parameters in 19 naevus cell naevi, 23 lentigos maligna, 76 primary melanomas and 31 melanoma metastases, applying the correlation coefficient according to Spearman. In naevi significant correlations were found between Ku70/80 gene expression and some ploidy-related parameters. In lentigos, additionally, some significant correlations between the expression of DNA mismatch repair genes were found. Similar results were demonstrated for primary melanomas. In metastases no one significant correlation between DNA mismatch repair genes and Ku-genes was present. We postulate that DNA mismatch repair genes and Ku70/80 genes are functionally independent and that some of them are able to influence ploidy-related parameters.

  3. The network of microRNAs, transcription factors, target genes and host genes in human renal cell carcinoma

    PubMed Central

    SONG, CHENGLU; XU, ZHIWEN; JIN, YUE; ZHU, MINGHUI; WANG, KUNHAO; WANG, NING

    2015-01-01

    At present, scientists have performed numerous studies investigating the morbidity of renal cell carcinoma (RCC) in the genetic and microRNA (miRNA) fields, obtaining a substantial amount of knowledge. However, the experimentally validated data of genes, miRNA and transcription factors (TFs) cannot be found in a unified form, which makes it challenging to decipher the regulatory mechanisms. In the present study, the genes, miRNAs and TFs involved in RCC are regarded as elements in the regulatory network, and the present study therefore focuses on the association between each entity. Three regulatory networks were constructed hierarchically to indicate the regulatory association between the genes, miRNAs and TFs clearly, including the differentially expressed, associated and global networks. All the elements were macroscopically investigated in these networks, instead of only investigating one or several of them. The present study not only compared and analyzed the similarities and the differences between the three networks, but also systematically expounded the pathogenesis of RCC and supplied theoretical foundations for future gene therapy investigations. Following the construction of the three networks, certain important pathways were highlighted. The upstream and downstream element table of differentially expressed genes and miRNAs was listed, in which self-adaption associations and circle-regulations were identified. In future studies, the identified genes and miRNAs should be granted more attention. PMID:25436016

  4. Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

    PubMed Central

    Huang, Lei; Liao, Li; Wu, Cathy H.

    2016-01-01

    Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

  5. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

    PubMed Central

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

  6. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe.

    PubMed

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

  7. Functional networks of nucleocytoplasmic transport-related genes differentiate ischemic and dilated cardiomyopathies. A new therapeutic opportunity.

    PubMed

    Molina-Navarro, María Micaela; Triviño, Juan Carlos; Martínez-Dolz, Luis; Lago, Francisca; González-Juanatey, Jose Ramón; Portolés, Manuel; Rivera, Miguel

    2014-01-01

    Heart failure provokes alterations in the expression of nucleocytoplasmic transport-related genes. To elucidate the nucleocytoplasmic transport-linked functional network underlying the two major causes of heart failure, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM), we examined global transcriptome profiles of left ventricular myocardium tissue samples from 31 patients (ICM, n = 10; DCM, n = 13) undergoing heart transplantation and control donors (CNT, n = 8) using RNA-Sequencing and GeneMANIA. Comparative profiling of ICM versus control and DCM versus control showed 1081 and 2440 differentially expressed genes, respectively (>1.29-fold; P<0.05). GeneMANIA revealed differentially regulated functional networks specific to ICM and DCM. In comparison with CNT, differential expression was seen in 9 and 12 nucleocytoplasmic transport-related genes in ICM and DCM groups, respectively. DDX3X, KPNA2, and PTK2B were related to ICM, while SMURF2, NUP153, IPO5, RANBP3, NOXA1, and RHOJ were involved in DCM pathogenesis. Furthermore, the two pathologies shared 6 altered genes: XPO1, ARL4, NFKB2, FHL3, RANBP2, and RHOU showing an identical trend in expression in both ICM and DCM. Notably, the core of the derived functional networks composed of nucleocytoplasmic transport-related genes (XPO1, RANBP2, NUP153, IPO5, KPNA2, and RANBP3) branched into several pathways with downregulated genes. Moreover, we identified genes whose expression levels correlated with left ventricular mass index and left ventricular function parameters in HF patients. Collectively, our study provides a clear distinction between the two pathologies at the transcriptome level and opens up new possibilities to search for appropriate therapeutic targets for ICM and DCM.

  8. Integrated feature and parameter optimization for an evolving spiking neural network: exploring heterogeneous probabilistic models.

    PubMed

    Schliebs, Stefan; Defoin-Platel, Michaël; Worner, Sue; Kasabov, Nikola

    2009-01-01

    This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

  9. Gene classification using parameter-free semi-supervised manifold learning.

    PubMed

    Huang, Hong; Feng, Hailiang

    2012-01-01

    A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.

  10. Deciphering the onychophoran 'segmentation gene cascade': Gene expression reveals limited involvement of pair rule gene orthologs in segmentation, but a highly conserved segment polarity gene network.

    PubMed

    Janssen, Ralf; Budd, Graham E

    2013-10-01

    The hallmark of the arthropods is their segmented body, although origin of segmentation, however, is unresolved. In order to shed light on the origin of segmentation we investigated orthologs of pair rule genes (PRGs) and segment polarity genes (SPGs) in a member of the closest related sister-group to the arthropods, the onychophorans. Our gene expression data analysis suggests that most of the onychophoran PRGs do not play a role in segmentation. One possible exception is the even-skipped (eve) gene that is expressed in the posterior end of the onychophoran where new segments are likely patterned, and is also expressed in segmentation-gene typical transverse stripes in at least a number of newly formed segments. Other onychophoran PRGs such as runt (run), hairy/Hes (h/Hes) and odd-skipped (odd) do not appear to have a function in segmentation at all. Onychophoran PRGs that act low in the segmentation gene cascade in insects, however, are potentially involved in segment-patterning. Most obvious is that from the expression of the pairberry (pby) gene ortholog that is expressed in a typical SPG-pattern. Since this result suggested possible conservation of the SPG-network we further investigated SPGs (and associated factors) such as Notum in the onychophoran. We find that the expression patterns of SPGs in arthropods and the onychophoran are highly conserved, suggesting a conserved SPG-network in these two clades, and indeed also in an annelid. This may suggest that the common ancestor of lophotrochozoans and ecdysozoans was already segmented utilising the same SPG-network, or that the SPG-network was recruited independently in annelids and onychophorans/arthropods.

  11. Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes

    PubMed Central

    Gong, Bin-Sheng; Zhang, Qing-Pu; Zhang, Guang-Mei; Zhang, Shao-Jun; Zhang, Wei; Lv, Hong-Chao; Zhang, Fan; Lv, Sa-Li; Li, Chuan-Xing; Rao, Shao-Qi; Li, Xia

    2007-01-01

    Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges. PMID:18466544

  12. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    PubMed

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene.

  13. Enhancing gene regulatory network inference through data integration with markov random fields.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-02-01

    A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.

  14. Enhancing gene regulatory network inference through data integration with markov random fields

    PubMed Central

    Banf, Michael; Rhee, Seung Y.

    2017-01-01

    A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation. PMID:28145456

  15. Enhancing gene regulatory network inference through data integration with markov random fields

    DOE PAGES

    Banf, Michael; Rhee, Seung Y.

    2017-02-01

    Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

  16. Integrated module and gene-specific regulatory inference implicates upstream signaling networks.

    PubMed

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A; Stewart, Ron; Gasch, Audrey P

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.

  17. Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

    PubMed Central

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

  18. Functional models for large-scale gene regulation networks: realism and fiction.

    PubMed

    Lagomarsino, Marco Cosentino; Bassetti, Bruno; Castellani, Gastone; Remondini, Daniel

    2009-04-01

    High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

  19. NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

    PubMed

    Fang, Minghong; Hu, Xiaohua; Wang, Yan; Zhao, Junmin; Shen, Xianjun; He, Tingting

    2015-07-01

    Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.

  20. Earth Rotation Parameter Solutions using BDS and GPS Data from MEGX Network

    NASA Astrophysics Data System (ADS)

    Xu, Tianhe; Yu, Sumei; Li, Jiajing; He, Kaifei

    2014-05-01

    Earth rotation parameters (ERPs) are necessary parameters to achieve mutual transformation of the celestial reference frame and earth-fix reference frame. They are very important for satellite precise orbit determination (POD), high-precision space navigation and positioning. In this paper, the determination of ERPs including polar motion (PM), polar motion rate (PMR) and length of day (LOD) are presented using BDS and GPS data of June 2013 from MEGX network based on least square (LS) estimation with constraint condition. BDS and GPS data of 16 co-location stations from MEGX network are the first time used to estimate the ERPs. The results show that the RMSs of x and y component errors of PM and PM rate are about 0.9 mas, 1.0 mas, 0.2 mas/d and 0.3 mas/d respectively using BDS data. The RMS of LOD is about 0.03 ms/d using BDS data. The RMSs of x and y component errors of PM and PM rate are about 0.2 mas, 0.2 mas/d respectively using GPS data. The RMS of LOD is about 0.02 ms/d using GPS data. The optimal relative weight is determined by using variance component estimation when combining BDS and GPS data. The accuracy improvements of adding BDS data is between 8% to 20% for PM and PM rate. There is no obvious improvement in LOD when BDS data is involved. System biases between BDS and GPS are also resolved per station. They are very stable from day to day with the average accuracy of about 20 cm. Keywords: Earth rotation parameter; International GNSS Service; polar motion; length of day; least square with constraint condition Acknowledgments: This work was supported by Natural Science Foundation of China (41174008) and the Foundation for the Author of National Excellent Doctoral Dissertation of China (2007B51) .

  1. Regulatory network analysis of genes and microRNAs in human hepatoblastoma

    PubMed Central

    He, Jimin; Guo, Xiaoxin; Sun, Linlin; Wang, Ning; Bao, Jiwei

    2016-01-01

    Hepatoblastoma (HB) is a common type of primary tumor in children. Previous studies have examined the expression of genes, including transcription factors (TFs), target genes, host genes and microRNAs (miRNAs or miRs) associated with HB. However, the regulatory pathways of miRNAs and genes remain unclear. In the present study, a novel perspective is proposed, which focuses on HB and the associated regulatory pathways, to construct three networks at various levels, including a differentially expressed network, an associated network and a global network. Genes and miRNAs are considered as key factors in the network. In the three networks, the associations between each pair of factors, including TFs that regulate miRNAs, miRNAs that interact with target genes and miRNAs that are located at host genes, were analyzed. The differentially expressed network is considered to be the most crucial of the three networks. All factors in the differentially expressed network were mutated or differentially expressed, which indicated that the majority of the factors were cancerogenic factors that may lead to HB. In addition, the network contained numerous abnormal linkages that may trigger HB. If the expression of each factor was corrected to a normal level, HB may be successfully treated. The associated network included more HB-associated genes and miRNAs, and was useful for analyzing the pathogenesis of HB. By analyzing these close associations, the first and the last factor of the regulatory pathways were revealed to have important roles in HB. For example, v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) was observed to regulate Homo sapiens (hsa)-miR-221, hsa-miR-18a and hsa-miR-17-5p, but no miRNAs targeted MYCN. In conclusion, the pathways and mechanisms underlying HB were expounded in the present study, which proposed a fundamental hypothesis for additional studies. PMID:27895778

  2. Gene network inference via structural equation modeling in genetical genomics experiments.

    PubMed

    Liu, Bing; de la Fuente, Alberto; Hoeschele, Ina

    2008-03-01

    Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis-trans-, and trans-regulation. After using local structural models to identify regulator-target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator-target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis-trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occam's window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.

  3. Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

    PubMed Central

    McDowell, Ian C.; Zhao, Shiwen; Brown, Christopher D.; Engelhardt, Barbara E.

    2016-01-01

    Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues. PMID:27467526

  4. Inversion of snow parameters from passive microwave remote sensing measurements by a neural network trained with a multiple scattering model

    NASA Technical Reports Server (NTRS)

    Tsang, Leung; Chen, Zhengxiao; Oh, Seho; Marks, Robert J., II; Chang, A. T. C.

    1992-01-01

    Simultaneous inversion of the three parameters was performed which included mean-grain size of ice particles in snow, snow density, and snow temperatures from five brightness temperatures. Good results for the inversion of parameters were obtained using the neural network based on the simulated data computed from the dense media radiative transfer equation that takes into account the effects of multiple scattering.

  5. GeneSense: a new approach for human gene annotation integrated with protein-protein interaction networks.

    PubMed

    Chen, Zhongzhong; Zhang, Tianhong; Lin, Jun; Yan, Zidan; Wang, Yongren; Zheng, Weiqiang; Weng, Kevin C

    2014-03-26

    Virtually all cellular functions involve protein-protein interactions (PPIs). As an increasing number of PPIs are identified and vast amount of information accumulated, researchers are finding different ways to interrogate the data and understand the interactions in context. However, it is widely recognized that a significant portion of the data is scattered, redundant, not considered high quality, and not readily accessible to researchers in a systematic fashion. In addition, it is challenging to identify the optimal protein targets in the current PPI networks. The GeneSense server was developed to integrate gene annotation and PPI networks in an expandable architecture that incorporates selected databases with the aim to assemble, analyze, evaluate and disseminate protein-protein association information in a comprehensive and user-friendly manner. Three network models including nodenet, leafnet and loopnet are used to identify the optimal protein targets in the complex networks. GeneSense is freely available at www.biomedsense.org/genesense.php.

  6. Integrative neural networks model for prediction of sediment rating curve parameters for ungauged basins

    NASA Astrophysics Data System (ADS)

    Atieh, M.; Mehltretter, S. L.; Gharabaghi, B.; Rudra, R.

    2015-12-01

    One of the most uncertain modeling tasks in hydrology is the prediction of ungauged stream sediment load and concentration statistics. This study presents integrated artificial neural networks (ANN) models for prediction of sediment rating curve parameters (rating curve coefficient α and rating curve exponent β) for ungauged basins. The ANN models integrate a comprehensive list of input parameters to improve the accuracy achieved; the input parameters used include: soil, land use, topographic, climatic, and hydrometric data sets. The ANN models were trained on the randomly selected 2/3 of the dataset of 94 gauged streams in Ontario, Canada and validated on the remaining 1/3. The developed models have high correlation coefficients of 0.92 and 0.86 for α and β, respectively. The ANN model for the rating coefficient α is directly proportional to rainfall erosivity factor, soil erodibility factor, and apportionment entropy disorder index, whereas it is inversely proportional to vegetation cover and mean annual snowfall. The ANN model for the rating exponent β is directly proportional to mean annual precipitation, the apportionment entropy disorder index, main channel slope, standard deviation of daily discharge, and inversely proportional to the fraction of basin area covered by wetlands and swamps. Sediment rating curves are essential tools for the calculation of sediment load, concentration-duration curve (CDC), and concentration-duration-frequency (CDF) analysis for more accurate assessment of water quality for ungauged basins.

  7. Identification of thin elastic isotropic plate parameters applying Guided Wave Measurement and Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Pabisek, Ewa; Waszczyszyn, Zenon

    2015-12-01

    A new hybrid computational system for material identification (HCSMI) is presented, developed for the identification of homogeneous, elastic, isotropic plate parameters. Attention is focused on the construction of dispersion curves, related to Lamb waves. The main idea of the system HCSMI lies in separation of two essential basic computational stages, corresponding to direct or inverse analyses. In the frame of the first stage an experimental dispersion curve DCexp is constructed, applying Guided Wave Measurement (GWM) technique. Then, in the other stage, corresponding to the inverse analysis, an Artificial Neural Network (ANN) is trained 'off line'. The substitution of results of the first stage, treated as inputs of the ANN, gives the values of identified plate parameters. In such a way no iteration is needed, unlike to the classical approach. In such an approach, the "distance" between the approximate experimental curves DCexp and dispersion curves DCnum obtained in the direct analysis, is iteratively minimized. Two case studies are presented, corresponding either to measurements in laboratory tests or those related to pseudo-experimental noisy data of computer simulations. The obtained results prove high numerical efficiency of HCSMI, applied to the identification of aluminum plate parameters.

  8. An artificial neural network approach for ranking quenching parameters in central galaxies

    NASA Astrophysics Data System (ADS)

    Teimoorinia, Hossen; Bluck, Asa F. L.; Ellison, Sara L.

    2016-04-01

    We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400 000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilize a variety of physical galaxy properties for training the pattern recognition algorithm to recognize star-forming and passive systems, for a `training set' of ˜100 000 galaxies. We then apply the ANN model to a `verification set' of ˜100 000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and bulge-to-total stellar mass ratio are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disc stellar masses), or those linked to environment (halo masses or δ5), perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.

  9. Understanding gene expression in coronary artery disease through global profiling, network analysis and independent validation of key candidate genes.

    PubMed

    Arvind, Prathima; Jayashree, Shanker; Jambunathan, Srikarthika; Nair, Jiny; Kakkar, Vijay V

    2015-12-01

    Molecular mechanism underlying the patho-physiology of coronary artery disease (CAD) is complex. We used global expression profiling combined with analysis of biological network to dissect out potential genes and pathways associated with CAD in a representative case-control Asian Indian cohort. We initially performed blood transcriptomics profiling in 20 subjects, including 10 CAD patients and 10 healthy controls on the Agilent microarray platform. Data was analysed with Gene Spring Gx12.5, followed by network analysis using David v 6.7 and Reactome databases. The most significant differentially expressed genes from microarray were independently validated by real time PCR in 97 cases and 97 controls. A total of 190 gene transcripts showed significant differential expression (fold change>2,P<0.05) between the cases and the controls of which 142 genes were upregulated and 48 genes were downregulated. Genes associated with inflammation, immune response, cell regulation, proliferation and apoptotic pathways were enriched, while inflammatory and immune response genes were displayed as hubs in the network, having greater number of interactions with the neighbouring genes. Expression of EGR1/2/3, IL8, CXCL1, PTGS2, CD69, IFNG, FASLG, CCL4, CDC42, DDX58, NFKBID and NR4A2 genes were independently validated; EGR1/2/3 and IL8 showed >8-fold higher expression in cases relative to the controls implying their important role in CAD. In conclusion, global gene expression profiling combined with network analysis can help in identifying key genes and pathways for CAD.

  10. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

    PubMed Central

    2015-01-01

    Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. Results We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery. PMID:26099491

  11. Genome-wide network of regulatory genes for construction of a chordate embryo.

    PubMed

    Shoguchi, Eiichi; Hamaguchi, Makoto; Satoh, Nori

    2008-04-15

    Animal development is controlled by gene regulation networks that are composed of sequence-specific transcription factors (TF) and cell signaling molecules (ST). Although housekeeping genes have been reported to show clustering in the animal genomes, whether the genes comprising a given regulatory network are physically clustered on a chromosome is uncertain. We examined this question in the present study. Ascidians are the closest living relatives of vertebrates, and their tadpole-type larva represents the basic body plan of chordates. The Ciona intestinalis genome contains 390 core TF genes and 119 major ST genes. Previous gene disruption assays led to the formulation of a basic chordate embryonic blueprint, based on over 3000 genetic interactions among 79 zygotic regulatory genes. Here, we mapped the regulatory genes, including all 79 regulatory genes, on the 14 pairs of Ciona chromosomes by fluorescent in situ hybridization (FISH). Chromosomal localization of upstream and downstream regulatory genes demonstrates that the components of coherent developmental gene networks are evenly distributed over the 14 chromosomes. Thus, this study provides the first comprehensive evidence that the physical clustering of regulatory genes, or their target genes, is not relevant for the genome-wide control of gene expression during development.

  12. Gene Regulatory Networks from Multifactorial Perturbations Using Graphical Lasso: Application to the DREAM4 Challenge

    PubMed Central

    Menéndez, Patricia; Kourmpetis, Yiannis A. I.; ter Braak, Cajo J. F.; van Eeuwijk, Fred A.

    2010-01-01

    A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network. Due to the static character of the challenge data, we tackled the problem via a sparse Gaussian Markov Random Field, which relates network topology with the covariance inverse generated by the gene measurements. As for the computations, we used the Graphical Lasso algorithm which provided a large range of candidate network topologies. The main task was to select the optimal network topology and for that, different model selection criteria were explored. The selected networks were compared with the golden standards and the results ranked using the scoring metrics applied in the challenge, giving a better insight in our submission and the way to improve it. Our approach provides an easy statistical and computational framework to infer gene regulatory networks that is suitable for large networks, even if the number of the observations (perturbations) is greater than the number of variables (genes). PMID:21188141

  13. Kriging-Based Parameter Estimation Algorithm for Metabolic Networks Combined with Single-Dimensional Optimization and Dynamic Coordinate Perturbation.

    PubMed

    Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu

    2016-01-01

    The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.

  14. Reverse engineering and analysis of large genome-scale gene networks.

    PubMed

    Aluru, Maneesha; Zola, Jaroslaw; Nettleton, Dan; Aluru, Srinivas

    2013-01-07

    Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.

  15. Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

    PubMed Central

    Liu, Wei; Zhu, Wen; Liao, Bo; Chen, Xiangtao

    2016-01-01

    Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method. PMID:27829000

  16. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    PubMed Central

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  17. Control of sleep by a network of cell cycle genes.

    PubMed

    Afonso, Dinis J S; Machado, Daniel R; Koh, Kyunghee

    2015-01-01

    Sleep is essential for health and cognition, but the molecular and neural mechanisms of sleep regulation are not well understood. We recently reported the identification of TARANIS (TARA) as a sleep-promoting factor that acts in a previously unknown arousal center in Drosophila. tara mutants exhibit a dose-dependent reduction in sleep amount of up to ∼60%. TARA and its mammalian homologs, the Trip-Br (Transcriptional Regulators Interacting with PHD zinc fingers and/or Bromodomains) family of proteins, are primarily known as transcriptional coregulators involved in cell cycle progression, and contain a conserved Cyclin-A (CycA) binding homology domain. We found that tara and CycA synergistically promote sleep, and CycA levels are reduced in tara mutants. Additional data demonstrated that Cyclin-dependent kinase 1 (Cdk1) antagonizes tara and CycA to promote wakefulness. Moreover, we identified a subset of CycA expressing neurons in the pars lateralis, a brain region proposed to be analogous to the mammalian hypothalamus, as an arousal center. In this Extra View article, we report further characterization of tara mutants and provide an extended discussion of our findings and future directions within the framework of a working model, in which a network of cell cycle genes, tara, CycA, and Cdk1, interact in an arousal center to regulate sleep.

  18. Control of sleep by a network of cell cycle genes

    PubMed Central

    Afonso, Dinis J. S.; Machado, Daniel R.; Koh, Kyunghee

    2015-01-01

    ABSTRACT Sleep is essential for health and cognition, but the molecular and neural mechanisms of sleep regulation are not well understood. We recently reported the identification of TARANIS (TARA) as a sleep-promoting factor that acts in a previously unknown arousal center in Drosophila. tara mutants exhibit a dose-dependent reduction in sleep amount of up to ∼60%. TARA and its mammalian homologs, the Trip-Br (Transcriptional Regulators Interacting with PHD zinc fingers and/or Bromodomains) family of proteins, are primarily known as transcriptional coregulators involved in cell cycle progression, and contain a conserved Cyclin-A (CycA) binding homology domain. We found that tara and CycA synergistically promote sleep, and CycA levels are reduced in tara mutants. Additional data demonstrated that Cyclin-dependent kinase 1 (Cdk1) antagonizes tara and CycA to promote wakefulness. Moreover, we identified a subset of CycA expressing neurons in the pars lateralis, a brain region proposed to be analogous to the mammalian hypothalamus, as an arousal center. In this Extra View article, we report further characterization of tara mutants and provide an extended discussion of our findings and future directions within the framework of a working model, in which a network of cell cycle genes, tara, CycA, and Cdk1, interact in an arousal center to regulate sleep. PMID:26925838

  19. Network Candidate Genes in Breeding for Drought Tolerant Crops

    PubMed Central

    Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

    2015-01-01

    Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance. PMID:26193269

  20. Network Candidate Genes in Breeding for Drought Tolerant Crops.

    PubMed

    Krannich, Christoph Tim; Maletzki, Lisa; Kurowsky, Christina; Horn, Renate

    2015-07-17

    Climate change leading to increased periods of low water availability as well as increasing demands for food in the coming years makes breeding for drought tolerant crops a high priority. Plants have developed diverse strategies and mechanisms to survive drought stress. However, most of these represent drought escape or avoidance strategies like early flowering or low stomatal conductance that are not applicable in breeding for crops with high yields under drought conditions. Even though a great deal of research is ongoing, especially in cereals, in this regard, not all mechanisms involved in drought tolerance are yet understood. The identification of candidate genes for drought tolerance that have a high potential to be used for breeding drought tolerant crops represents a challenge. Breeding for drought tolerant crops has to focus on acceptable yields under water-limited conditions and not on survival. However, as more and more knowledge about the complex networks and the cross talk during drought is available, more options are revealed. In addition, it has to be considered that conditioning a crop for drought tolerance might require the production of metabolites and might cost the plants energy and resources that cannot be used in terms of yield. Recent research indicates that yield penalty exists and efficient breeding for drought tolerant crops with acceptable yields under well-watered and drought conditions might require uncoupling yield penalty from drought tolerance.

  1. Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

    PubMed Central

    Bourdon, Jérémie; Eveillard, Damien; Siegel, Anne

    2011-01-01

    Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments. PMID:21935350

  2. Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code.

    PubMed

    Hartman, Jessica H; Cothren, Steven D; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A; Miller, Grover P

    2013-07-01

    Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

  3. GFD-Net: a novel semantic similarity methodology for the analysis of gene networks.

    PubMed

    Díaz-Montaña, Juan J; Díaz-Díaz, Norberto; Gómez-Vela, Francisco

    2017-03-05

    Since the popularization of biological network inference methods, it has become crucial to create methods to validate the resulting models. Here we present GFD-Net, the first methodology that applies the concept of semantic similarity to gene network analysis. GFD-Net combines the concept of semantic similarity with the use of gene network topology to analyze the functional dissimilarity of gene networks based on Gene Ontology (GO). The main innovation of GFD-Net lies in the way that semantic similarity is used to analyze gene networks taking into account the network topology. GFD-Net selects a functionality for each gene (specified by a GO term), weights each edge according to the dissimilarity between the nodes at its ends and calculates a quantitative measure of the network functional dissimilarity, i.e. a quantitative value of the degree of dissimilarity between the connected genes. The robustness of GFD-Net as a gene network validation tool was demonstrated by performing a ROC analysis on several network repositories. Furthermore, a well-known network was analyzed showing that GFD-Net can also be used to infer knowledge. The relevance of GFD-Net becomes more evident in subSection 3.3 where an example of how GFD-Net can be applied to the study of human diseases is presented. GFD-Net is available as an open-source Cytoscape app which offers a user-friendly interface to configure and execute the algorithm as well as the ability to visualize and interact with the results (http://apps.cytoscape.org/apps/gfdnet).

  4. Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

    PubMed

    Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

  5. Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

    PubMed Central

    Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

  6. Gene network coherence based on prior knowledge using direct and indirect relationships.

    PubMed

    Gómez-Vela, Francisco; Lagares, José Antonio; Díaz-Díaz, Norberto

    2015-06-01

    Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene-gene interactions for validation, leaving aside the weak (indirect) relationships. We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene-gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/. The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene-gene relationships, offers a new robust solution to the problem of GNs biological validation.

  7. Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization.

    PubMed

    Zhao, Zhi-Qin; Han, Guo-Sheng; Yu, Zu-Guo; Li, Jinyan

    2015-08-01

    Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

  8. Artificial Neural Network Reconstruction of The Fault Macro-parameters By Audiomagnetotelluric Data

    NASA Astrophysics Data System (ADS)

    Spichak, V.; Popova, I.

    A Neural Network Expert System MT-NET is used for estimation of three- dimensional fault parameters given audiomagnetotelluric (AMT) data. The reconstruction methodology is based on using ANN teached in advance by synthetic AMT data calculated for 3D conductivity model of the fault buried in a two-layered earth. A special noise treatment technique based on pre-processing of both teaching and testing data enables to diminish greatly the recognition errors for all unknown parameters (to less than 5% even if the level of noise in the data reaches 100%). Results of numerous experiments demonstrate that teached ANN possesses good interpolation and extrapolation abilities, which, in turn, enable to use it for estimation of 3D fault parameters in a few seconds even using micro-computers. Scalar controlled source AMT data collected in a northern part of the Minou fault area (Kyushu Island, Japan) are interpreted by means of MT-NET in terms of 3-D earth macro-parameters. The results of ANN reconstruction are compared with the resistivity distribution obtained for the same area using fast 3-D imaging based on synthesis of 1-D Bostick transforms of the apparent resistivities beneath each site as well as on 2-D TM mode inversion along four profiles. The best-fitting model reconstructed by ANN belongs to the guessed model class formed by "dykes buried in the two-layered earth", on the one hand, and to the equivalence class formed by all models giving rms misfit less than the noise level in the data, on the other hand.

  9. Damages of the Tallinn District Heating Networks and Indicative Parameters for an Estimation of the Networks General Condition

    NASA Astrophysics Data System (ADS)

    Hlebnikov, Aleksandr; Volkova, Anna; Dzuba, Olga; Poobus, Arvi; Kask, Ülo

    2010-01-01

    District heating networks in Estonia are mostly old and in bad condition. The state of the district heating networks of Tallinn is typical for the rest of Estonian DH networks. The paper includes an analysis of the Tallinn district heating networks. Valid data about damages in district heating systems received for the last 12 years were used for an analysis of the network damages. Different types of network damages are analysed: external corrosion, internal corrosion, defect of installation, factory defects, defect of construction and other reasons. The scale of damages for the different elements of networks is compared in the paper: armature, compensator, construction and pipes. The main factors which influence damages in district heating networks are the age of networks, the quality of construction works and the network operation conditions. The damage quantity dependence on the age of networks is also defined and analysed in the paper. The scale of damages can be diminished by reducing the average age of the networks. This is possible by replacing old pipelines and other network system elements. The pipes average age changes for a 20 year period are simulated according to different intensities of renovation works.

  10. Use of Artificial Neural Networks to Examine Parameters Affecting the Immobilization of Streptokinase in Chitosan

    PubMed Central

    Modaresi, Seyed Mohamad Sadegh; Faramarzi, Mohammad Ali; Soltani, Arash; Baharifar, Hadi; Amani, Amir

    2014-01-01

    Streptokinase is a potent fibrinolytic agent which is widely used in treatment of deep vein thrombosis (DVT), pulmonary embolism (PE) and acute myocardial infarction (MI). Major limitation of this enzyme is its short biological half-life in the blood stream. Our previous report showed that complexing streptokinase with chitosan could be a solution to overcome this limitation. The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme. Three variables, namely, chitosan concentration, buffer pH and enzyme concentration were considered as input values and the loading efficiency was used as output. Subsequently, the experimental data were modeled and the model was validated against a set of unseen data. The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency. PMID:25587327

  11. Use of artificial neural networks to examine parameters affecting the immobilization of streptokinase in chitosan.

    PubMed

    Modaresi, Seyed Mohamad Sadegh; Faramarzi, Mohammad Ali; Soltani, Arash; Baharifar, Hadi; Amani, Amir

    2014-01-01

    Streptokinase is a potent fibrinolytic agent which is widely used in treatment of deep vein thrombosis (DVT), pulmonary embolism (PE) and acute myocardial infarction (MI). Major limitation of this enzyme is its short biological half-life in the blood stream. Our previous report showed that complexing streptokinase with chitosan could be a solution to overcome this limitation. The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme. Three variables, namely, chitosan concentration, buffer pH and enzyme concentration were considered as input values and the loading efficiency was used as output. Subsequently, the experimental data were modeled and the model was validated against a set of unseen data. The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

  12. Improving control and estimation for distributed parameter systems utilizing mobile actuator-sensor network.

    PubMed

    Mu, Wenying; Cui, Baotong; Li, Wen; Jiang, Zhengxian

    2014-07-01

    This paper proposes a scheme for non-collocated moving actuating and sensing devices which is unitized for improving performance in distributed parameter systems. By Lyapunov stability theorem, each moving actuator/sensor agent velocity is obtained. To enhance state estimation of a spatially distributes process, two kinds of filters with consensus terms which penalize the disagreement of the estimates are considered. Both filters can result in the well-posedness of the collective dynamics of state errors and can converge to the plant state. Numerical simulations demonstrate that the effectiveness of such a moving actuator-sensor network in enhancing system performance and the consensus filters converge faster to the plant state when consensus terms are included.

  13. Digital Signal Processing and Control for the Study of Gene Networks

    PubMed Central

    Shin, Yong-Jun

    2016-01-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

  14. Digital Signal Processing and Control for the Study of Gene Networks.

    PubMed

    Shin, Yong-Jun

    2016-04-22

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  15. Digital Signal Processing and Control for the Study of Gene Networks

    NASA Astrophysics Data System (ADS)

    Shin, Yong-Jun

    2016-04-01

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  16. Modelling gene and protein regulatory networks with answer set programming.

    PubMed

    Fayruzov, Timur; Janssen, Jeroen; Vermeir, Dirk; Cornelis, Chris; De Cock, Martine

    2011-01-01

    Recently, many approaches to model regulatory networks have been proposed in the systems biology domain. However, the task is far from being solved. In this paper, we propose an Answer Set Programming (ASP)-based approach to model interaction networks. We build a general ASP framework that describes the network semantics and allows modelling specific networks with little effort. ASP provides a rich and flexible toolbox that allows expanding the framework with desired features. In this paper, we tune our framework to mimic Boolean network behaviour and apply it to model the Budding Yeast and Fission Yeast cell cycle networks. The obtained steady states of these networks correspond to those of the Boolean networks.

  17. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  18. Application of Neural Networks for Real Time Determination of High-β Disruption Boundary and Current Profile Parameters

    NASA Astrophysics Data System (ADS)

    Wroblewski, D.; Jahns, G. L.; Leuer, J. A.; Ferron, J. R.; Kellman, A. G.

    1996-11-01

    Neural networks are adept at reproducing multidemensional non-linear mappings and, due to the simplicity of computation of network outputs, are particularly suitable for real time applications. A neural network empirical model of the high-β disruption boundary was constructed and its real-time performance demonstrated on the DIII--D tokamak. Neural network using multiple diagnostic signals provides much better evaluation of the disruption boundary than the Troyon limit, and can predict the β-limit tens of milliseconds before the disruption occurs, which makes this approach applicable in a disruption avoidance scheme. In another study, a neural network was successfully used to provide a mapping from internal and external magnetic measurements to selected parameters of the safety factor profile. The neural network approach circumvents the speed limitations of the MHD equilibrium codes that are presently used to reconstruct the plasma current profile, and may be used in feedback control.

  19. Coexpression landscape in ATTED-II: usage of