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Sample records for genetic interaction networks

  1. Exploring drug combinations in genetic interaction network

    PubMed Central

    2012-01-01

    Background Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. Results In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. Conclusions This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations. PMID:22595004

  2. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed. PMID:25958956

  3. Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

    PubMed Central

    Qi, Yan; Suhail, Yasir; Lin, Yu-yi; Boeke, Jef D.; Bader, Joel S.

    2008-01-01

    The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to “friends” and genetic interactions analogous to “enemies” from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data. PMID:18832443

  4. Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions.

    PubMed

    Qi, Yan; Suhail, Yasir; Lin, Yu-yi; Boeke, Jef D; Bader, Joel S

    2008-12-01

    The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to "friends" and genetic interactions analogous to "enemies" from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data. PMID:18832443

  5. Prediction of Genetic Interactions Using Machine Learning and Network Properties

    PubMed Central

    Madhukar, Neel S.; Elemento, Olivier; Pandey, Gaurav

    2015-01-01

    A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases. PMID:26579514

  6. Prediction of Genetic Interactions Using Machine Learning and Network Properties.

    PubMed

    Madhukar, Neel S; Elemento, Olivier; Pandey, Gaurav

    2015-01-01

    A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases. PMID:26579514

  7. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

    PubMed Central

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A.; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J.; Vacic, Vladimir; Calderwood, Michael A.; Roth, Frederick P.; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E.; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M.

    2014-01-01

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. PMID:24722188

  8. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

    PubMed Central

    Davis, N A; Crowe, J E; Pajewski, N M; McKinney, B A

    2010-01-01

    The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene–gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene–gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor α (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response. PMID:20613780

  9. Genetic interaction network of the Saccharomyces cerevisiae type 1 phosphatase Glc7

    PubMed Central

    Logan, Michael R; Nguyen, Thao; Szapiel, Nicolas; Knockleby, James; Por, Hanting; Zadworny, Megan; Neszt, Michael; Harrison, Paul; Bussey, Howard; Mandato, Craig A; Vogel, Jackie; Lesage, Guillaume

    2008-01-01

    Background Protein kinases and phosphatases regulate protein phosphorylation, a critical means of modulating protein function, stability and localization. The identification of functional networks for protein phosphatases has been slow due to their redundant nature and the lack of large-scale analyses. We hypothesized that a genome-scale analysis of genetic interactions using the Synthetic Genetic Array could reveal protein phosphatase functional networks. We apply this approach to the conserved type 1 protein phosphatase Glc7, which regulates numerous cellular processes in budding yeast. Results We created a novel glc7 catalytic mutant (glc7-E101Q). Phenotypic analysis indicates that this novel allele exhibits slow growth and defects in glucose metabolism but normal cell cycle progression and chromosome segregation. This suggests that glc7-E101Q is a hypomorphic glc7 mutant. Synthetic Genetic Array analysis of glc7-E101Q revealed a broad network of 245 synthetic sick/lethal interactions reflecting that many processes are required when Glc7 function is compromised such as histone modification, chromosome segregation and cytokinesis, nutrient sensing and DNA damage. In addition, mitochondrial activity and inheritance and lipid metabolism were identified as new processes involved in buffering Glc7 function. An interaction network among 95 genes genetically interacting with GLC7 was constructed by integration of genetic and physical interaction data. The obtained network has a modular architecture, and the interconnection among the modules reflects the cooperation of the processes buffering Glc7 function. Conclusion We found 245 genes required for the normal growth of the glc7-E101Q mutant. Functional grouping of these genes and analysis of their physical and genetic interaction patterns bring new information on Glc7-regulated processes. PMID:18627629

  10. Encore: Genetic Association Interaction Network Centrality Pipeline and Application to SLE Exome Data

    PubMed Central

    Davis, Nicholas A.; Lareau, Caleb A.; White, Bill C.; Pandey, Ahwan; Wiley, Graham; Montgomery, Courtney G.; Gaffney, Patrick M.; McKinney, B.A.

    2014-01-01

    Open source tools are needed to facilitate the construction, analysis, and visualization of gene-gene interaction networks for sequencing data. To address this need, we present Encore, an open source network analysis pipeline for GWAS and rare variant data. Encore constructs Genetic Association Interaction Networks or Epistasis Networks using two optional approaches: our previous information-theory method or a generalized linear model approach. Additionally, Encore includes multiple data filtering options, including Random Forest/Random Jungle for main effect enrichment and Evaporative Cooling and Relief-F filters for enrichment of interaction effects. Encore implements SNPrank network centrality for identifying susceptibility hubs (nodes containing a large amount of disease susceptibility information through the combination of multivariate main effects and multiple gene-gene interactions in the network), and it provides appropriate files for interactive visualization of a network using tools from our online Galaxy instance. We implemented these algorithms in C++ using OpenMP for shared-memory parallel analysis on a server or desktop. To demonstrate Encore’s utility in analysis of genetic sequencing data, we present an analysis of exome resequencing data from healthy individuals and those with Systemic Lupus Erythematous (SLE). Our results verify the importance of the previously associated SLE genes HLA-DRB and NCF2, and these two genes had the highest gene-gene interaction degrees among the susceptibility hubs. An additional 14 genes previously associated with SLE emerged in our epistasis network model of the exome data, and three novel candidate genes, ST8SIA4, CMTM4, and C2CD4B, were implicated in the model. In summary, we present a comprehensive tool for epistasis network analysis and the first such analysis of exome data from a genetic study of SLE. Software Availability: http://insilico.utulsa.edu/encore.php. PMID:23740754

  11. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

    PubMed Central

    Lezon, Timothy R.; Banavar, Jayanth R.; Cieplak, Marek; Maritan, Amos; Fedoroff, Nina V.

    2006-01-01

    We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. PMID:17138668

  12. Discovering Main Genetic Interactions with LABNet LAsso-Based Network Inference

    PubMed Central

    Gadaleta, Francesco; Van Steen, Kristel

    2014-01-01

    Genome-wide association studies can potentially unravel the mechanisms behind complex traits and common genetic diseases. Despite the valuable results produced thus far, many questions remain unanswered. For instance, which specific genetic compounds are linked to the risk of the disease under investigation; what biological mechanism do they act through; or how do they interact with environmental and other external factors? The driving force of computational biology is the constantly growing amount of big data generated by high-throughput technologies. A practical framework that can deal with this abundance of information and that consent to discovering genetic associations and interactions is provided by means of networks. Unfortunately, high dimensionality, the presence of noise and the geometry of data can make the aforementioned problem extremely challenging. We propose a penalised linear regression approach that can deal with the aforementioned issues that affect genetic data. We analyse the gene expression profiles of individuals with a common trait to infer the network structure of interactions among genes. The permutation-based approach leads to more stable and reliable networks inferred from synthetic microarray data. We show that a higher number of permutations determines the number of predicted edges, improves the overall sensitivity and controls the number of false positives. PMID:25369052

  13. Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection

    PubMed Central

    Milias-Argeitis, Andreas; Oliveira, Ana Paula; Gerosa, Luca; Falter, Laura; Sauer, Uwe; Lygeros, John

    2016-01-01

    Understanding the structure and function of complex gene regulatory networks using classical genetic assays is an error-prone procedure that frequently generates ambiguous outcomes. Even some of the best-characterized gene networks contain interactions whose validity is not conclusively proven. Founded on dynamic experimental data, mechanistic mathematical models are able to offer detailed insights that would otherwise require prohibitively large numbers of genetic experiments. Here we attempt mechanistic modeling of the transcriptional network formed by the four GATA-factor proteins, a well-studied system of central importance for nitrogen-source regulation of transcription in the yeast Saccharomyces cerevisiae. To resolve ambiguities in the network organization, we encoded a set of five interactions hypothesized in the literature into a set of 32 mathematical models, and employed Bayesian model selection to identify the most plausible set of interactions based on dynamic gene expression data. The top-ranking model was validated on newly generated GFP reporter dynamic data and was subsequently used to gain a better understanding of how yeast cells organize their transcriptional response to dynamic changes of nitrogen sources. Our work constitutes a necessary and important step towards obtaining a holistic view of the yeast nitrogen regulation mechanisms; on the computational side, it provides a demonstration of how powerful Monte Carlo techniques can be creatively combined and used to address the great challenges of large-scale dynamical system inference. PMID:26967983

  14. Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

    PubMed Central

    Boucher, Benjamin; Lee, Anna Y.; Hallett, Michael; Jenna, Sarah

    2016-01-01

    A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution. PMID:26871911

  15. Identification of genetic networks.

    PubMed Central

    Xiong, Momiao; Li, Jun; Fang, Xiangzhong

    2004-01-01

    In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets. PMID:15020486

  16. Genetic programming-based approach to elucidate biochemical interaction networks from data.

    PubMed

    Kandpal, Manoj; Kalyan, Chakravarthy Mynampati; Samavedham, Lakshminarayanan

    2013-02-01

    Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods. PMID:23848052

  17. Interactive Genetics Tutorial Project.

    ERIC Educational Resources Information Center

    Wisconsin Univ., Madison. Dept. of Curriculum and Instruction.

    The Interactive Genetics Tutorial (IGT) project and the Intelligent Tutoring System for the IGT project named MENDEL supplement genetics instruction in biology courses by providing students with experience in designing, conducting, and evaluating genetics experiments. The MENDEL software is designed to: (1) simulate genetics experiments that…

  18. Frontiers of torenia research: innovative ornamental traits and study of ecological interaction networks through genetic engineering

    PubMed Central

    2013-01-01

    Advances in research in the past few years on the ornamental plant torenia (Torenia spps.) have made it notable as a model plant on the frontier of genetic engineering aimed at studying ornamental characteristics and pest control in horticultural ecosystems. The remarkable advantage of torenia over other ornamental plant species is the availability of an easy and high-efficiency transformation system for it. Unfortunately, most of the current torenia research is still not very widespread, because this species has not become prominent as an alternative to other successful model plants such as Arabidopsis, snapdragon and petunia. However, nowadays, a more global view using not only a few selected models but also several additional species are required for creating innovative ornamental traits and studying horticultural ecosystems. We therefore introduce and discuss recent research on torenia, the family Scrophulariaceae, for secondary metabolite bioengineering, in which global insights into horticulture, agriculture and ecology have been advanced. Floral traits, in torenia particularly floral color, have been extensively studied by manipulating the flavonoid biosynthetic pathways in flower organs. Plant aroma, including volatile terpenoids, has also been genetically modulated in order to understand the complicated nature of multi-trophic interactions that affect the behavior of predators and pollinators in the ecosystem. Torenia would accordingly be of great use for investigating both the variation in ornamental plants and the infochemical-mediated interactions with arthropods. PMID:23803155

  19. Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

    PubMed Central

    Demongeot, Jacques; Ben Amor, Hedi; Elena, Adrien; Gillois, Pierre; Noual, Mathilde; Sené, Sylvain

    2009-01-01

    Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control. PMID:20057955

  20. Understanding genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Kauffman, Stuart

    2003-04-01

    Random Boolean networks (RBM) were introduced about 35 years ago as first crude models of genetic regulatory networks. RBNs are comprised of N on-off genes, connected by a randomly assigned regulatory wiring diagram where each gene has K inputs, and each gene is controlled by a randomly assigned Boolean function. This procedure samples at random from the ensemble of all possible NK Boolean networks. The central ideas are to study the typical, or generic properties of this ensemble, and see 1) whether characteristic differences appear as K and biases in Boolean functions are introducted, and 2) whether a subclass of this ensemble has properties matching real cells. Such networks behave in an ordered or a chaotic regime, with a phase transition, "the edge of chaos" between the two regimes. Networks with continuous variables exhibit the same two regimes. Substantial evidence suggests that real cells are in the ordered regime. A key concept is that of an attractor. This is a reentrant trajectory of states of the network, called a state cycle. The central biological interpretation is that cell types are attractors. A number of properties differentiate the ordered and chaotic regimes. These include the size and number of attractors, the existence in the ordered regime of a percolating "sea" of genes frozen in the on or off state, with a remainder of isolated twinkling islands of genes, a power law distribution of avalanches of gene activity changes following perturbation to a single gene in the ordered regime versus a similar power law distribution plus a spike of enormous avalanches of gene changes in the chaotic regime, and the existence of branching pathway of "differentiation" between attractors induced by perturbations in the ordered regime. Noise is serious issue, since noise disrupts attractors. But numerical evidence suggests that attractors can be made very stable to noise, and meanwhile, metaplasias may be a biological manifestation of noise. As we learn more

  1. Genetic Networks in Osseointegration

    PubMed Central

    Nishimura, I.

    2013-01-01

    Osseointegration-based dental implants have become a well-accepted treatment modality for complete and partial edentulism. The success of this treatment largely depends on the stable integration and maintenance of implant fixtures in alveolar bone; however, the molecular and cellular mechanisms regulating this unique tissue reaction have not yet been fully uncovered. Radiographic and histologic observations suggest the sustained retention of peri-implant bone without an apparent susceptibility to catabolic bone remodeling; therefore, implant-induced bone formation continues to be intensively investigated. Increasing numbers of whole-genome transcriptome studies suggest complex molecular pathways that may play putative roles in osseointegration. This review highlights genetic networks related to bone quality, the transient chondrogenic phase, the vitamin D axis, and the peripheral circadian rhythm to elute the regulatory mechanisms underlying the establishment and maintenance of osseointegration. PMID:24158334

  2. Identifying Gene Interaction Networks

    PubMed Central

    Bebek, Gurkan

    2016-01-01

    In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network’s topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources. PMID:22307715

  3. Genome-Wide Gene-Sodium Interaction Analyses on Blood Pressure: The Genetic Epidemiology Network of Salt-Sensitivity Study.

    PubMed

    Li, Changwei; He, Jiang; Chen, Jing; Zhao, Jinying; Gu, Dongfeng; Hixson, James E; Rao, Dabeeru C; Jaquish, Cashell E; Gu, Charles C; Chen, Jichun; Huang, Jianfeng; Chen, Shufeng; Kelly, Tanika N

    2016-08-01

    We performed genome-wide analyses to identify genomic loci that interact with sodium to influence blood pressure (BP) using single-marker-based (1 and 2 df joint tests) and gene-based tests among 1876 Chinese participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study. Among GenSalt participants, the average of 3 urine samples was used to estimate sodium excretion. Nine BP measurements were taken using a random zero sphygmomanometer. A total of 2.05 million single-nucleotide polymorphisms were imputed using Affymetrix 6.0 genotype data and the Chinese Han of Beijing and Japanese of Tokyo HapMap reference panel. Promising findings (P<1.00×10(-4)) from GenSalt were evaluated for replication among 775 Chinese participants of the Multi-Ethnic Study of Atherosclerosis (MESA). Single-nucleotide polymorphism and gene-based results were meta-analyzed across the GenSalt and MESA studies to determine genome-wide significance. The 1 df tests identified interactions for UST rs13211840 on diastolic BP (P=3.13×10(-9)). The 2 df tests additionally identified associations for CLGN rs2567241 (P=3.90×10(-12)) and LOC105369882 rs11104632 (P=4.51×10(-8)) with systolic BP. The CLGN variant rs2567241 was also associated with diastolic BP (P=3.11×10(-22)) and mean arterial pressure (P=2.86×10(-15)). Genome-wide gene-based analysis identified MKNK1 (P=6.70×10(-7)), C2orf80 (P<1.00×10(-12)), EPHA6 (P=2.88×10(-7)), SCOC-AS1 (P=4.35×10(-14)), SCOC (P=6.46×10(-11)), CLGN (P=3.68×10(-13)), MGAT4D (P=4.73×10(-11)), ARHGAP42 (P≤1.00×10(-12)), CASP4 (P=1.31×10(-8)), and LINC01478 (P=6.75×10(-10)) that were associated with at least 1 BP phenotype. In summary, we identified 8 novel and 1 previously reported BP loci through the examination of single-nucleotide polymorphism and gene-based interactions with sodium. PMID:27271309

  4. MOEPGA: A novel method to detect protein complexes in yeast protein-protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm.

    PubMed

    Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan

    2015-10-01

    The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions. PMID:26298638

  5. Inferring genetic networks from microarray data.

    SciTech Connect

    May, Elebeoba Eni; Davidson, George S.; Martin, Shawn Bryan; Werner-Washburne, Margaret C.; Faulon, Jean-Loup Michel

    2004-06-01

    In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

  6. An integrated genetic, genomic and systems approach defines gene networks regulated by the interaction of light and carbon signaling pathways in Arabidopsis

    PubMed Central

    Thum, Karen E; Shin, Michael J; Gutiérrez, Rodrigo A; Mukherjee, Indrani; Katari, Manpreet S; Nero, Damion; Shasha, Dennis; Coruzzi, Gloria M

    2008-01-01

    Background Light and carbon are two important interacting signals affecting plant growth and development. The mechanism(s) and/or genes involved in sensing and/or mediating the signaling pathways involving these interactions are unknown. This study integrates genetic, genomic and systems approaches to identify a genetically perturbed gene network that is regulated by the interaction of carbon and light signaling in Arabidopsis. Results Carbon and light insensitive (cli) mutants were isolated. Microarray data from cli186 is analyzed to identify the genes, biological processes and gene networks affected by the integration of light and carbon pathways. Analysis of this data reveals 966 genes regulated by light and/or carbon signaling in wild-type. In cli186, 216 of these light/carbon regulated genes are misregulated in response to light and/or carbon treatments where 78% are misregulated in response to light and carbon interactions. Analysis of the gene lists show that genes in the biological processes "energy" and "metabolism" are over-represented among the 966 genes regulated by carbon and/or light in wild-type, and the 216 misregulated genes in cli186. To understand connections among carbon and/or light regulated genes in wild-type and the misregulated genes in cli186, the microarray data is interpreted in the context of metabolic and regulatory networks. The network created from the 966 light/carbon regulated genes in wild-type, reveals that cli186 is affected in the light and/or carbon regulation of a network of 60 connected genes, including six transcription factors. One transcription factor, HAT22 appears to be a regulatory "hub" in the cli186 network as it shows regulatory connections linking a metabolic network of genes involved in "amino acid metabolism", "C-compound/carbohydrate metabolism" and "glycolysis/gluconeogenesis". Conclusion The global misregulation of gene networks controlled by light and carbon signaling in cli186 indicates that it represents

  7. The Mosaic Ancestry of the Drosophila Genetic Reference Panel and the D. melanogaster Reference Genome Reveals a Network of Epistatic Fitness Interactions

    PubMed Central

    Pool, John E.

    2015-01-01

    North American populations of Drosophila melanogaster derive from both European and African source populations, but despite their importance for genetic research, patterns of ancestry along their genomes are largely undocumented. Here, I infer geographic ancestry along genomes of the Drosophila Genetic Reference Panel (DGRP) and the D. melanogaster reference genome, which may have implications for reference alignment, association mapping, and population genomic studies in Drosophila. Overall, the proportion of African ancestry was estimated to be 20% for the DGRP and 9% for the reference genome. Combining my estimate of admixture timing with historical records, I provide the first estimate of natural generation time for this species (approximately 15 generations per year). Ancestry levels were found to vary strikingly across the genome, with less African introgression on the X chromosome, in regions of high recombination, and at genes involved in specific processes (e.g., circadian rhythm). An important role for natural selection during the admixture process was further supported by evidence that many unlinked pairs of loci showed a deficiency of Africa–Europe allele combinations between them. Numerous epistatic fitness interactions may therefore exist between African and European genotypes, leading to ongoing selection against incompatible variants. By focusing on hubs in this network of fitness interactions, I identified a set of interacting loci that include genes with roles in sensation and neuropeptide/hormone reception. These findings suggest that admixed D. melanogaster samples could become an important study system for the genetics of early-stage isolation between populations. PMID:26354524

  8. Stability analysis of genetic regulatory networks with multiple time delays.

    PubMed

    Wu, Fang-Xiang

    2007-01-01

    A genetic regulatory network is a dynamic system to describe interactions among genes (mRNA) and its products (proteins). From the statistic thermodynamics and biochemical reaction principle, a genetic regulatory network can be described by a group of nonlinear differential equations with time delays. Stability is one of interesting properties for genetic regulatory network. Previous studies have investigated stability of genetic regulatory networks with a single time delay. In this paper, we investigate properties of genetic regulatory networks with multiple time delays in the notion of delay-independent stability. We present necessary and sufficient condition for the local delay-independent stability of genetic regulatory network with multiple time delays which are independent or commensurate. PMID:18002223

  9. Congenital Diaphragmatic Hernia Interval on Chromosome 8p23.1 Characterized by Genetics and Protein Interaction Networks

    PubMed Central

    Longoni, Mauro; Lage, Kasper; Russell, Meaghan K.; Loscertales, Maria; Abdul-Rahman, Omar A.; Baynam, Gareth; Bleyl, Steven B.; Brady, Paul D.; Breckpot, Jeroen; Chen, Chih P.; Devriendt, Koenraad; Gillessen-Kaesbach, Gabriele; Grix, Arthur W.; Rope, Alan F.; Shimokawa, Osamu; Strauss, Bernarda; Wieczorek, Dagmar; Zackai, Elaine H.; Coletti, Caroline M.; Maalouf, Faouzi I.; Noonan, Kristin M.; Park, Ji H.; Tracy, Adam A.; Lee, Charles; Donahoe, Patricia K.; Pober, Barbara R.

    2013-01-01

    Chromosome 8p23.1 is a common hotspot associated with major congenital malformations, including congenital diaphragmatic hernia (CDH) and cardiac defects. We present findings from high-resolution arrays in patients who carry a loss (n =18) or a gain (n =1) of sub-band 8p23.1. We confirm a region involved in both diaphragmatic and heart malformations. Results from a novel CNVConnect algorithm, prioritizing protein–protein interactions between products of genes in the 8p23.1 hotspot and products of previously known CDH causing genes, implicated GATA4, NEIL2, and SOX7 in diaphragmatic defects. Sequence analysis of these genes in 226 chromosomally normal CDH patients, as well as in a small number of deletion 8p23.1 patients, showed rare unreported variants in the coding region; these may be contributing to the diaphragmatic phenotype. We also demonstrated that two of these three genes were expressed in the E11.5–12.5 primordial mouse diaphragm, the developmental stage at which CDH is thought to occur. This combination of bioinformatics and expression studies can be applied to other chromosomal hotspots, as well as private microdeletions or microduplications, to identify causative genes and their interaction networks. PMID:23165946

  10. From gene expressions to genetic networks

    NASA Astrophysics Data System (ADS)

    Cieplak, Marek

    2009-03-01

    A method based on the principle of entropy maximization is used to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles [1]. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher order correlations. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabollic oscillations identifies a gene interaction network that reflects the intracellular communication pathways. These pathways adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. The time-dependent behavior of the genetic network is found to involve only a few fundamental modes [2,3]. [4pt] REFERENCES:[0pt] [1] T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and N. Fedoroff, Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns, Proc. Natl. Acad. Sci. (USA) 103, 19033-19038 (2006) [0pt] [2] N. S. Holter, M. Mitra, A. Maritan, M. Cieplak, J. R. Banavar, and N. V. Fedoroff, Fundamental patterns underlying gene expression profiles: simplicity from complexity, Proc. Natl. Acad. Sci. USA 97, 8409-8414 (2000) [0pt] [3] N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff, and J. R. Banavar, Dynamic modeling of gene expression data, Proc. Natl. Acad. Sci. USA 98, 1693-1698 (2001)

  11. Interacting neural networks.

    PubMed

    Metzler, R; Kinzel, W; Kanter, I

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random. PMID:11088736

  12. Species interactions differ in their genetic robustness

    DOE PAGESBeta

    Chubiz, Lon M.; Granger, Brian R.; Segre, Daniel; Harcombe, William R.

    2015-04-14

    Conflict and cooperation between bacterial species drive the composition and function of microbial communities. Stability of these emergent properties will be influenced by the degree to which species' interactions are robust to genetic perturbations. We use genome-scale metabolic modeling to computationally analyze the impact of genetic changes when Escherichia coli and Salmonella enterica compete, or cooperate. We systematically knocked out in silico each reaction in the metabolic network of E. coli to construct all 2583 mutant stoichiometric models. Then, using a recently developed multi-scale computational framework, we simulated the growth of each mutant E. coli in the presence of S.more » enterica. The type of interaction between species was set by modulating the initial metabolites present in the environment. We found that the community was most robust to genetic perturbations when the organisms were cooperating. Species ratios were more stable in the cooperative community, and community biomass had equal variance in the two contexts. Additionally, the number of mutations that have a substantial effect is lower when the species cooperate than when they are competing. In contrast, when mutations were added to the S. enterica network the system was more robust when the bacteria were competing. These results highlight the utility of connecting metabolic mechanisms and studies of ecological stability. Cooperation and conflict alter the connection between genetic changes and properties that emerge at higher levels of biological organization.« less

  13. Synchronization of electronic genetic networks.

    PubMed

    Wagemakers, Alexandre; Buldú, Javier M; García-Ojalvo, Jordi; Sanjuán, Miguel A F

    2006-03-01

    We describe a simple analog electronic circuit that mimics the behavior of a well-known synthetic gene oscillator, the repressilator, which represents a set of three genes repressing one another. Synchronization of a population of such units is thoroughly studied, with the aim to compare the role of global coupling with that of global forcing on the population. Our results show that coupling is much more efficient than forcing in leading the gene population to synchronized oscillations. Furthermore, a modification of the proposed analog circuit leads to a simple electronic version of a genetic toggle switch, which is a simple network of two mutual repressor genes, where control by external forcing is also analyzed. PMID:16599758

  14. Variable Size Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Katagiri, Hironobu; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

    Genetic Network Programming (GNP) is a kind of volutionary methods, which evolves arbitrary directed graph programs. Previously, the program size of GNP was fixed. In the paper, a new method is proposed, where the program size is adaptively changed depending on the frequency of the use of nodes. To control and to decide a program size are important and difficult problems in Evolutionary Computation, especially, a well-known crossover operator tends to cause bloat. We introduce two additional operators, add operator and delete operator, that can change the number of each kind of nodes based on whether a node function is important in the environment or not. Simulation results shows that the proposed method brings about extremely better results compared with ordinary fixed size GNP.

  15. Species interactions differ in their genetic robustness

    SciTech Connect

    Chubiz, Lon M.; Granger, Brian R.; Segre, Daniel; Harcombe, William R.

    2015-04-14

    Conflict and cooperation between bacterial species drive the composition and function of microbial communities. Stability of these emergent properties will be influenced by the degree to which species' interactions are robust to genetic perturbations. We use genome-scale metabolic modeling to computationally analyze the impact of genetic changes when Escherichia coli and Salmonella enterica compete, or cooperate. We systematically knocked out in silico each reaction in the metabolic network of E. coli to construct all 2583 mutant stoichiometric models. Then, using a recently developed multi-scale computational framework, we simulated the growth of each mutant E. coli in the presence of S. enterica. The type of interaction between species was set by modulating the initial metabolites present in the environment. We found that the community was most robust to genetic perturbations when the organisms were cooperating. Species ratios were more stable in the cooperative community, and community biomass had equal variance in the two contexts. Additionally, the number of mutations that have a substantial effect is lower when the species cooperate than when they are competing. In contrast, when mutations were added to the S. enterica network the system was more robust when the bacteria were competing. These results highlight the utility of connecting metabolic mechanisms and studies of ecological stability. Cooperation and conflict alter the connection between genetic changes and properties that emerge at higher levels of biological organization.

  16. Propagation of genetic variation in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Plahte, Erik; Gjuvsland, Arne B.; Omholt, Stig W.

    2013-08-01

    A future quantitative genetics theory should link genetic variation to phenotypic variation in a causally cohesive way based on how genes actually work and interact. We provide a theoretical framework for predicting and understanding the manifestation of genetic variation in haploid and diploid regulatory networks with arbitrary feedback structures and intra-locus and inter-locus functional dependencies. Using results from network and graph theory, we define propagation functions describing how genetic variation in a locus is propagated through the network, and show how their derivatives are related to the network’s feedback structure. Similarly, feedback functions describe the effect of genotypic variation of a locus on itself, either directly or mediated by the network. A simple sign rule relates the sign of the derivative of the feedback function of any locus to the feedback loops involving that particular locus. We show that the sign of the phenotypically manifested interaction between alleles at a diploid locus is equal to the sign of the dominant feedback loop involving that particular locus, in accordance with recent results for a single locus system. Our results provide tools by which one can use observable equilibrium concentrations of gene products to disclose structural properties of the network architecture. Our work is a step towards a theory capable of explaining the pleiotropy and epistasis features of genetic variation in complex regulatory networks as functions of regulatory anatomy and functional location of the genetic variation.

  17. Genetic Network Inference Using Hierarchical Structure.

    PubMed

    Kimura, Shuhei; Tokuhisa, Masato; Okada-Hatakeyama, Mariko

    2016-01-01

    Many methods for inferring genetic networks have been proposed, but the regulations they infer often include false-positives. Several researchers have attempted to reduce these erroneous regulations by proposing the use of a priori knowledge about the properties of genetic networks such as their sparseness, scale-free structure, and so on. This study focuses on another piece of a priori knowledge, namely, that biochemical networks exhibit hierarchical structures. Based on this idea, we propose an inference approach that uses the hierarchical structure in a target genetic network. To obtain a reasonable hierarchical structure, the first step of the proposed approach is to infer multiple genetic networks from the observed gene expression data. We take this step using an existing method that combines a genetic network inference method with a bootstrap method. The next step is to extract a hierarchical structure from the inferred networks that is consistent with most of the networks. Third, we use the hierarchical structure obtained to assign confidence values to all candidate regulations. Numerical experiments are also performed to demonstrate the effectiveness of using the hierarchical structure in the genetic network inference. The improvement accomplished by the use of the hierarchical structure is small. However, the hierarchical structure could be used to improve the performances of many existing inference methods. PMID:26941653

  18. Topology of molecular interaction networks.

    PubMed

    Winterbach, Wynand; Van Mieghem, Piet; Reinders, Marcel; Wang, Huijuan; de Ridder, Dick

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

  19. Topology of molecular interaction networks

    PubMed Central

    2013-01-01

    Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further. PMID:24041013

  20. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.

    PubMed

    Sherif, Fayroz F; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. PMID:26366461

  1. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks

    PubMed Central

    Sherif, Fayroz F.; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. PMID:26366461

  2. Characterization of Genetic Networks Associated with Alzheimer's Disease.

    PubMed

    Zhang, Bin; Tran, Linh; Emilsson, Valur; Zhu, Jun

    2016-01-01

    At the molecular level, the genetics of complex disease such as Alzheimer's disease (AD) manifests itself as series of alterations in the molecular interactions in pathways and networks that define biological processes underlying the pathophysiological states of disease. While large-scale genome-wide association (GWA) studies of late-onset alzheimer's disease (LOAD) have uncovered prominent genomic regions linked to the disease, the cause for the vast majority of LOAD cases still remains unknown. Increasingly available large-scale genomic and genetic data related to LOAD has made it possible to comprehensively uncover the mechanisms causally lined to LOAD in a completely data-driven manner. Here we review the various aspects of systems/network biology approaches and methodology in constructing genetic networks associated with AD from large sampling of postmortem brain tissues. We describe in detail a multiscale network modeling approach (MNMA) that integrates interaction and causal gene networks to analyze large-scale DNA, gene expression and pathophysiological data from multiple post-mortem brain regions of LOAD patients as well non-demented normal controls. MNMA first employs weighted gene co-expression network analysis (WGCNA) to construct multi-tissue networks that simultaneously capture intra-tissue and inter-tissue gene-gene interactions and then quantifies the change in connectivity among highly co-expressed genes in LOAD with respect to the normal state. Co-expressed gene modules are then rank ordered by relevance to pathophysiological traits and enrichment of genes differentially expressed in LOAD. Causal regulatory relationships among the genes in each module are then determined by a Bayesian network inference framework that is used to formally integrate genetic and gene expression information. MNMA has uncovered a massive remodeling of network structures in LOAD and identified novel subnetworks and key regulators that are causally linked to LOAD. In the

  3. Interacting epidemics on overlay networks

    NASA Astrophysics Data System (ADS)

    Funk, Sebastian; Jansen, Vincent A. A.

    2010-03-01

    The interaction between multiple pathogens spreading on networks connecting a given set of nodes presents an ongoing theoretical challenge. Here, we aim to understand such interactions by studying bond percolation of two different processes on overlay networks of arbitrary joint degree distribution. We find that an outbreak of a first pathogen providing immunity to another one spreading subsequently on a second network connecting the same set of nodes does so most effectively if the degrees on the two networks are positively correlated. In that case, the protection is stronger the more heterogeneous the degree distributions of the two networks are. If, on the other hand, the degrees are uncorrelated or negatively correlated, increasing heterogeneity reduces the potential of the first process to prevent the second one from reaching epidemic proportions. We generalize these results to cases where the edges of the two networks overlap to arbitrary amount, or where the immunity granted is only partial. If both processes grant immunity to each other, we find a wide range of possible situations of coexistence or mutual exclusion, depending on the joint degree distribution of the underlying networks and the amount of immunity granted mutually. These results generalize the concept of a coexistence threshold and illustrate the impact of large-scale network structure on the interaction between multiple spreading agents.

  4. MicroRNA-dependent Genetic Networks During Neural Development

    PubMed Central

    Abernathy, Daniel G.; Yoo, Andrew S.

    2014-01-01

    The development of the structurally and functionally diverse mammalian nervous system requires the integration of numerous levels of gene regulation. Accumulating evidence suggests that microRNAs are key mediators of genetic networks during neural development. Importantly, microRNAs are found to regulate both feedback and feedforward loops during neural development leading to large changes in gene expression. These repressive interactions provide an additional mechanism that facilitates the establishment of complexity within the nervous system. Here, we review studies that have enabled the identification of brain-enriched microRNAs and discuss how genetic networks in neural development depend on microRNAs. PMID:24865244

  5. Dynamic and interacting complex networks

    NASA Astrophysics Data System (ADS)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  6. Networks of spatial genetic variation across species

    PubMed Central

    Fortuna, Miguel A.; Albaladejo, Rafael G.; Fernández, Laura; Aparicio, Abelardo; Bascompte, Jordi

    2009-01-01

    Spatial patterns of genetic variation provide information central to many ecological, evolutionary, and conservation questions. This spatial variability has traditionally been analyzed through summary statistics between pairs of populations, therefore missing the simultaneous influence of all populations. More recently, a network approach has been advocated to overcome these limitations. This network approach has been applied to a few cases limited to a single species at a time. The question remains whether similar patterns of spatial genetic variation and similar functional roles for specific patches are obtained for different species. Here we study the networks of genetic variation of four Mediterranean woody plant species inhabiting the same habitat patches in a highly fragmented forest mosaic in Southern Spain. Three of the four species show a similar pattern of genetic variation with well-defined modules or groups of patches holding genetically similar populations. These modules can be thought of as the long-sought-after, evolutionarily significant units or management units. The importance of each patch for the cohesion of the entire network, though, is quite different across species. This variation creates a tremendous challenge for the prioritization of patches to conserve the genetic variation of multispecies assemblages. PMID:19861546

  7. Splitting strategy for simulating genetic regulatory networks.

    PubMed

    You, Xiong; Liu, Xueping; Musa, Ibrahim Hussein

    2014-01-01

    The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. PMID:24624223

  8. Dynamic interactions in neural networks

    SciTech Connect

    Arbib, M.A. ); Amari, S. )

    1989-01-01

    The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

  9. Genetic specificity of a plant-insect food web: Implications for linking genetic variation to network complexity.

    PubMed

    Barbour, Matthew A; Fortuna, Miguel A; Bascompte, Jordi; Nicholson, Joshua R; Julkunen-Tiitto, Riitta; Jules, Erik S; Crutsinger, Gregory M

    2016-02-23

    Theory predicts that intraspecific genetic variation can increase the complexity of an ecological network. To date, however, we are lacking empirical knowledge of the extent to which genetic variation determines the assembly of ecological networks, as well as how the gain or loss of genetic variation will affect network structure. To address this knowledge gap, we used a common garden experiment to quantify the extent to which heritable trait variation in a host plant determines the assembly of its associated insect food web (network of trophic interactions). We then used a resampling procedure to simulate the additive effects of genetic variation on overall food-web complexity. We found that trait variation among host-plant genotypes was associated with resistance to insect herbivores, which indirectly affected interactions between herbivores and their insect parasitoids. Direct and indirect genetic effects resulted in distinct compositions of trophic interactions associated with each host-plant genotype. Moreover, our simulations suggest that food-web complexity would increase by 20% over the range of genetic variation in the experimental population of host plants. Taken together, our results indicate that intraspecific genetic variation can play a key role in structuring ecological networks, which may in turn affect network persistence. PMID:26858398

  10. Statistical Mechanics of Temporal and Interacting Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun

    In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide

  11. Bridging genetic networks and queueing theory

    NASA Astrophysics Data System (ADS)

    Arazi, Arnon; Ben-Jacob, Eshel; Yechiali, Uri

    2004-02-01

    One of the main challenges facing biology today is the understanding of the joint action of genes, proteins and RNA molecules, interwoven in intricate interdependencies commonly known as genetic networks. To this end, several mathematical approaches have been introduced to date. In addition to developing the analytical tools required for this task anew, one can utilize knowledge found in existing disciplines, specializing in the representation and analysis of systems featuring similar aspects. We suggest queueing theory as a possible source of such knowledge. This discipline, which focuses on the study of workloads forming in a variety of scenarios, offers an assortment of tools allowing for the derivation of the statistical properties of the inspected systems. We argue that a proper adaptation of modeling techniques and analytical methods used in queueing theory can contribute to the study of genetic regulatory networks. This is demonstrated by presenting a queueing-inspired model of a genetic network of arbitrary size and structure, for which the probability distribution function is derived. This model is further applied to the description of the lac operon regulation mechanism. In addition, we discuss the possible benefits stemming for queueing theory from the interdisciplinary dialogue with molecular biology-in particular, the incorporation of various dynamical behaviours into queueing networks.

  12. Adaptation by Plasticity of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Brenner, Naama

    2007-03-01

    Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.

  13. Genetic networks with canalyzing Boolean rules are always stable

    PubMed Central

    Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl

    2004-01-01

    We determine stability and attractor properties of random Boolean genetic network models with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in-degree distributions, we find that the networks are dynamically stable. Furthermore, for architectures with few inputs per node, the dynamics of the networks is close to critical. In addition, the fraction of genes that are active decreases with the number of inputs per node. These results are based upon investigating ensembles of networks using analytical methods. Also, for different in-degree distributions, the numbers of fixed points and cycles are calculated, with results intuitively consistent with stability analysis; fewer inputs per node implies more cycles, and vice versa. There are hints that genetic networks acquire broader degree distributions with evolution, and hence our results indicate that for single cells, the dynamics should become more stable with evolution. However, such an effect is very likely compensated for by multicellular dynamics, because one expects less stability when interactions among cells are included. We verify this by simulations of a simple model for interactions among cells. PMID:15572453

  14. Fluctuations and Slow Variables in Genetic Networks

    PubMed Central

    Bundschuh, R.; Hayot, F.; Jayaprakash, C.

    2003-01-01

    Computer simulations of large genetic networks are often extremely time consuming because, in addition to the biologically interesting translation and transcription reactions, many less interesting reactions like DNA binding and dimerizations have to be simulated. It is desirable to use the fact that the latter occur on much faster timescales than the former to eliminate the fast and uninteresting reactions and to obtain effective models of the slow reactions only. We use three examples of self-regulatory networks to show that the usual reduction methods where one obtains a system of equations of the Hill type fail to capture the fluctuations that these networks exhibit due to the small number of molecules; moreover, they may even miss describing the behavior of the average number of proteins. We identify the inclusion of fast-varying variables in the effective description as the cause for the failure of the traditional schemes. We suggest a different effective description, which entails the introduction of an additional species, not present in the original networks, that is slowly varying. We show that this description allows for a very efficient simulation of the reduced system while retaining the correct fluctuations and behavior of the full system. This approach ought to be applicable to a wide range of genetic networks. PMID:12609864

  15. A Review of Modeling Techniques for Genetic Regulatory Networks

    PubMed Central

    Yaghoobi, Hanif; Haghipour, Siyamak; Hamzeiy, Hossein; Asadi-Khiavi, Masoud

    2012-01-01

    Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time–series data. In this paper, we provide an extensive review of methods that have been used on time–series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods. PMID:23493097

  16. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  17. Effects of macromolecular crowding on genetic networks.

    PubMed

    Morelli, Marco J; Allen, Rosalind J; Wolde, Pieter Rein ten

    2011-12-21

    The intracellular environment is crowded with proteins, DNA, and other macromolecules. Under physiological conditions, macromolecular crowding can alter both molecular diffusion and the equilibria of bimolecular reactions and therefore is likely to have a significant effect on the function of biochemical networks. We propose a simple way to model the effects of macromolecular crowding on biochemical networks via an appropriate scaling of bimolecular association and dissociation rates. We use this approach, in combination with kinetic Monte Carlo simulations, to analyze the effects of crowding on a constitutively expressed gene, a repressed gene, and a model for the bacteriophage λ genetic switch, in the presence and absence of nonspecific binding of transcription factors to genomic DNA. Our results show that the effects of crowding are mainly caused by the shift of association-dissociation equilibria rather than the slowing down of protein diffusion, and that macromolecular crowding can have relevant and counterintuitive effects on biochemical network performance. PMID:22208186

  18. Effects of Macromolecular Crowding on Genetic Networks

    PubMed Central

    Morelli, Marco J.; Allen, Rosalind J.; Rein ten Wolde, Pieter

    2011-01-01

    The intracellular environment is crowded with proteins, DNA, and other macromolecules. Under physiological conditions, macromolecular crowding can alter both molecular diffusion and the equilibria of bimolecular reactions and therefore is likely to have a significant effect on the function of biochemical networks. We propose a simple way to model the effects of macromolecular crowding on biochemical networks via an appropriate scaling of bimolecular association and dissociation rates. We use this approach, in combination with kinetic Monte Carlo simulations, to analyze the effects of crowding on a constitutively expressed gene, a repressed gene, and a model for the bacteriophage λ genetic switch, in the presence and absence of nonspecific binding of transcription factors to genomic DNA. Our results show that the effects of crowding are mainly caused by the shift of association-dissociation equilibria rather than the slowing down of protein diffusion, and that macromolecular crowding can have relevant and counterintuitive effects on biochemical network performance. PMID:22208186

  19. Population Dynamics of Genetic Regulatory Networks

    NASA Astrophysics Data System (ADS)

    Braun, Erez

    2005-03-01

    Unlike common objects in physics, a biological cell processes information. The cell interprets its genome and transforms the genomic information content, through the action of genetic regulatory networks, into proteins which in turn dictate its metabolism, functionality and morphology. Understanding the dynamics of a population of biological cells presents a unique challenge. It requires to link the intracellular dynamics of gene regulation, through the mechanism of cell division, to the level of the population. We present experiments studying adaptive dynamics of populations of genetically homogeneous microorganisms (yeast), grown for long durations under steady conditions. We focus on population dynamics that do not involve random genetic mutations. Our experiments follow the long-term dynamics of the population distributions and allow to quantify the correlations among generations. We focus on three interconnected issues: adaptation of genetically homogeneous populations following environmental changes, selection processes on the population and population variability and expression distributions. We show that while the population exhibits specific short-term responses to environmental inputs, it eventually adapts to a robust steady-state, largely independent of external conditions. Cycles of medium-switch show that the adapted state is imprinted in the population and that this memory is maintained for many generations. To further study population adaptation, we utilize the process of gene recruitment whereby a gene naturally regulated by a specific promoter is placed under a different regulatory system. This naturally occurring process has been recognized as a major driving force in evolution. We have recruited an essential gene to a foreign regulatory network and followed the population long-term dynamics. Rewiring of the regulatory network allows us to expose their complex dynamics and phase space structure.

  20. Interactive Video Networks: Experiences, Issues and Challenges.

    ERIC Educational Resources Information Center

    Stahl, Bil

    1993-01-01

    Discusses multipoint interactive video networks and describes experiences with two networks in North Carolina, the MCNC CONCERT (COmmunications network of North Carolina for Education, Research, and Technology) and the Vision Carolina network. Digital video is explained, and issues concerning various components of the telecommunications industry…

  1. Character Recognition Using Genetically Trained Neural Networks

    SciTech Connect

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of

  2. Introduction to Focus Issue: Genetic Interactions

    NASA Astrophysics Data System (ADS)

    Segrè, Daniel; Marx, Christopher J.

    2010-06-01

    The perturbation of a gene in an organism's genome often causes changes in the organism's observable properties or phenotypes. It is not obvious a priori whether the simultaneous perturbation of two genes produces a phenotypic change that is easily predictable from the changes caused by individual perturbations. In fact, this is often not the case: the nonlinearity and interdependence between genetic variants in determining phenotypes, also known as epistasis, is a prevalent phenomenon in biological systems. This focus issue presents recent developments in the study of epistasis and genetic interactions, emphasizing the broad implications of this phenomenon in evolutionary biology, functional genomics, and human diseases.

  3. Genetic Regulatory Networks in Embryogenesis and Evolution

    NASA Technical Reports Server (NTRS)

    1998-01-01

    The article introduces a series of papers that were originally presented at a workshop titled Genetic Regulatory Network in Embryogenesis and Evaluation. Contents include the following: evolution of cleavage programs in relationship to axial specification and body plan evolution, changes in cell lineage specification elucidate evolutionary relations in spiralia, axial patterning in the leech: developmental mechanisms and evolutionary implications, hox genes in arthropod development and evolution, heterochronic genes in development and evolution, a common theme for LIM homeobox gene function across phylogeny, and mechanisms of specification in ascidian embryos.

  4. Functional Localization of Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

    According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.

  5. A random interacting network model for complex networks

    NASA Astrophysics Data System (ADS)

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-12-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.

  6. A random interacting network model for complex networks

    PubMed Central

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  7. A random interacting network model for complex networks.

    PubMed

    Goswami, Bedartha; Shekatkar, Snehal M; Rheinwalt, Aljoscha; Ambika, G; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  8. Protein-protein interactions and genetic diseases: The Interactome

    PubMed Central

    Lage, Kasper

    2014-01-01

    Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks have informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. PMID:24892209

  9. Fashion sketch design by interactive genetic algorithms

    NASA Astrophysics Data System (ADS)

    Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

    2012-11-01

    Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

  10. Network Physiology: How Organ Systems Dynamically Interact

    PubMed Central

    Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  11. Network Physiology: How Organ Systems Dynamically Interact.

    PubMed

    Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  12. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  13. A Network Synthesis Model for Generating Protein Interaction Network Families

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2012-01-01

    In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/. PMID:22912671

  14. Actor-network theory: a tool to support ethical analysis of commercial genetic testing.

    PubMed

    Williams-Jones, Bryn; Graham, Janice E

    2003-12-01

    Social, ethical and policy analysis of the issues arising from gene patenting and commercial genetic testing is enhanced by the application of science and technology studies, and Actor-Network Theory (ANT) in particular. We suggest the potential for transferring ANT's flexible nature to an applied heuristic methodology for gathering empirical information and for analysing the complex networks involved in the development of genetic technologies. Three concepts are explored in this paper--actor-networks, translation, and drift--and applied to the case of Myriad Genetics and their commercial BRACAnalysis genetic susceptibility test for hereditary breast cancer. Treating this test as an active participant in socio-technical networks clarifies the extent to which it interacts with, shapes and is shaped by people, other technologies, and institutions. Such an understanding enables more sophisticated and nuanced technology assessment, academic analysis, as well as public debate about the social, ethical and policy implications of the commercialization of new genetic technologies. PMID:15115034

  15. Markov Logic Networks in the Analysis of Genetic Data

    PubMed Central

    Sakhanenko, Nikita A.

    2010-01-01

    Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

  16. Dissection of DNA Damage Responses Using Multiconditional Genetic Interaction Maps

    PubMed Central

    Guénolé, Aude; Srivas, Rohith; Vreeken, Kees; Wang, Ze Zhong; Wang, Shuyi; Krogan, Nevan J.; Ideker, Trey; van Attikum, Haico

    2013-01-01

    SUMMARY To protect the genome, cells have evolved a diverse set of pathways designed to sense, signal, and repair multiple types of DNA damage. To assess the degree of coordination and crosstalk among these pathways, we systematically mapped changes in the cell's genetic network across a panel of different DNA-damaging agents, resulting in ~1,800,000 differential measurements. Each agent was associated with a distinct interaction pattern, which, unlike single-mutant phenotypes or gene expression data, has high statistical power to pinpoint the specific repair mechanisms at work. The agent-specific networks revealed roles for the histone acetyltranferase Rtt109 in the mutagenic bypass of DNA lesions and the neddylation machinery in cell-cycle regulation and genome stability, while the network induced by multiple agents implicates Irc21, an uncharacterized protein, in checkpoint control and DNA repair. Our multiconditional genetic interaction map provides a unique resource that identifies agent-specific and general DNA damage response pathways. PMID:23273983

  17. Use of the BioGRID Database for Analysis of Yeast Protein and Genetic Interactions.

    PubMed

    Oughtred, Rose; Chatr-aryamontri, Andrew; Breitkreutz, Bobby-Joe; Chang, Christie S; Rust, Jennifer M; Theesfeld, Chandra L; Heinicke, Sven; Breitkreutz, Ashton; Chen, Daici; Hirschman, Jodi; Kolas, Nadine; Livstone, Michael S; Nixon, Julie; O'Donnell, Lara; Ramage, Lindsay; Winter, Andrew; Reguly, Teresa; Sellam, Adnane; Stark, Chris; Boucher, Lorrie; Dolinski, Kara; Tyers, Mike

    2016-01-01

    The BioGRID database is an extensive repository of curated genetic and protein interactions for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe, and the yeast Candida albicans SC5314, as well as for several other model organisms and humans. This protocol describes how to use the BioGRID website to query genetic or protein interactions for any gene of interest, how to visualize the associated interactions using an embedded interactive network viewer, and how to download data files for either selected interactions or the entire BioGRID interaction data set. PMID:26729909

  18. Quantitative high-throughput analysis of synthetic genetic interactions in Caenorhabditis elegans by RNA interference

    PubMed Central

    Fortunato, Angelo

    2009-01-01

    Biological processes are highly dynamic but the current representation of molecular networks is static and largely qualitative. To investigate the dynamic property of genetic networks, a novel quantitative high-throughput method based on RNA interference and capable of calculating the relevance of each interaction, was developed. With this approach, it will be possible to identify not only the components of a network, but also to investigate quantitatively how network and biological processes react to perturbations. As a first application of this method, the genetic interactions of a weak loss-of-function mutation in the gene efl-1/E2F with all the genes of chromosome III were investigated during embryonic development of Caenorhabditis elegans. Fifteen synthetic genetic interactions of efl-1/E2F with the genes of chromosome III were detected, measured and ranked by statistical relevance. PMID:19059334

  19. Measuring specialization in species interaction networks

    PubMed Central

    Blüthgen, Nico; Menzel, Florian; Blüthgen, Nils

    2006-01-01

    Background Network analyses of plant-animal interactions hold valuable biological information. They are often used to quantify the degree of specialization between partners, but usually based on qualitative indices such as 'connectance' or number of links. These measures ignore interaction frequencies or sampling intensity, and strongly depend on network size. Results Here we introduce two quantitative indices using interaction frequencies to describe the degree of specialization, based on information theory. The first measure (d') describes the degree of interaction specialization at the species level, while the second measure (H2') characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and derived from Shannon entropy. The species-level index d' can be used to analyze variation within networks, while H2' as a network-level index is useful for comparisons across different interaction webs. Analyses of two published pollinator networks identified differences and features that have not been detected with previous approaches. For instance, plants and pollinators within a network differed in their average degree of specialization (weighted mean d'), and the correlation between specialization of pollinators and their relative abundance also differed between the webs. Rarefied sampling effort in both networks and null model simulations suggest that H2' is not affected by network size or sampling intensity. Conclusion Quantitative analyses reflect properties of interaction networks more appropriately than previous qualitative attempts, and are robust against variation in sampling intensity, network size and symmetry. These measures will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions. PMID:16907983

  20. Topology and static response of interaction networks in molecular biology.

    PubMed

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2006-02-22

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace-Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason-Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  1. Topology and static response of interaction networks in molecular biology

    PubMed Central

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2005-01-01

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace–Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason–Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  2. Dynamic network analysis of protein interactions

    NASA Astrophysics Data System (ADS)

    Almaas, Eivind; Deri, Joya

    2007-03-01

    Network approaches have recently become a popular tool to study complex systems such as cellular metabolism and protein interactions. A substantial number of analyses of the protein interaction network (PIN) of the yeast Saccharomyces cerevisiae have considered this network as a static entity, not taking the network's dynamic nature into account. Here, we examine the time-variation of gene regulation superimposed on the PIN by defining mRNA expression profiles throughout the cell cycle as node weights. To characterize these network dynamics, we have both developed a set of novel network measures as well as studied previously published measures for weighted networks. We expect that our approach will provide a deeper understanding of protein regulation during the cell cycle.

  3. A candidate multimodal functional genetic network for thermal adaptation.

    PubMed

    Wollenberg Valero, Katharina C; Pathak, Rachana; Prajapati, Indira; Bankston, Shannon; Thompson, Aprylle; Usher, Jaytriece; Isokpehi, Raphael D

    2014-01-01

    Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1), affect genes with different cellular functions, namely (2) lipoprotein metabolism, (3) membrane channels, (4) stress response, (5) response to oxidative stress, (6) muscle contraction and relaxation, and (7) vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and other

  4. A candidate multimodal functional genetic network for thermal adaptation

    PubMed Central

    Pathak, Rachana; Prajapati, Indira; Bankston, Shannon; Thompson, Aprylle; Usher, Jaytriece; Isokpehi, Raphael D.

    2014-01-01

    Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1), affect genes with different cellular functions, namely (2) lipoprotein metabolism, (3) membrane channels, (4) stress response, (5) response to oxidative stress, (6) muscle contraction and relaxation, and (7) vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and other

  5. Estimation of intermolecular interactions in polymer networks

    SciTech Connect

    Subrananian, P.R.; Galiatsatos, V.

    1993-12-31

    Strain-birefringence measurements have been used to estimate intermolecular interactions in polymer networks. The intensity of the interaction has been quantified through a theoretical scheme recently proposed by Erman. The results show that these interactions diminish with decreasing molecular weight between cross-links and decreasing cross-link functionality.

  6. Experimental evolution of protein–protein interaction networks

    PubMed Central

    Kaçar, Betül; Gaucher, Eric A.

    2013-01-01

    The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

  7. Integration of Genomic and Proteomic Data to Predict Synthetic Genetic Interactions Using Semi-supervised Learning

    NASA Astrophysics Data System (ADS)

    You, Zhuhong; Zhang, Shanwen; Li, Liping

    Genetic interaction, in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. However, little is known about how genes genetic interact to produce phenotypes and the comprehensive identification of genetic interaction in genome-scale by experiment is a laborious and time-consuming work. In this paper, we present a computational method of system biology to analyze synthetic genetic interactions. We firstly constructed a high-quality functional gene network by integrating protein interaction, protein complex and microarray gene expression data together. Then we extracted the network properties such as network centrality degree, clustering coefficient, etc., which reflect the local connectivity and global position of a gene and are supposed to correlate with its functional properties. Finally we find relationships between synthetic genetic interactions and function network properties using the graph-based semi-supervised learning which incorporates labeled and unlabeled data together. Experimental results showed that Semi-supervised method outperformed standard supervised learning algorithms and reached 97.1% accuracy at a maximum. Especially, the semi-supervised method largely outperformed when the number of training samples is very small.

  8. Saccharomyces cerevisiae Genetics Predicts Candidate Therapeutic Genetic Interactions at the Mammalian Replication Fork

    PubMed Central

    van Pel, Derek M.; Stirling, Peter C.; Minaker, Sean W.; Sipahimalani, Payal; Hieter, Philip

    2013-01-01

    The concept of synthetic lethality has gained popularity as a rational guide for predicting chemotherapeutic targets based on negative genetic interactions between tumor-specific somatic mutations and a second-site target gene. One hallmark of most cancers that can be exploited by chemotherapies is chromosome instability (CIN). Because chromosome replication, maintenance, and segregation represent conserved and cell-essential processes, they can be modeled effectively in simpler eukaryotes such as Saccharomyces cerevisiae. Here we analyze and extend genetic networks of CIN cancer gene orthologs in yeast, focusing on essential genes. This identifies hub genes and processes that are candidate targets for synthetic lethal killing of cancer cells with defined somatic mutations. One hub process in these networks is DNA replication. A nonessential, fork-associated scaffold, CTF4, is among the most highly connected genes. As Ctf4 lacks enzymatic activity, potentially limiting its development as a therapeutic target, we exploited its function as a physical interaction hub to rationally predict synthetic lethal interactions between essential Ctf4-binding proteins and CIN cancer gene orthologs. We then validated a subset of predicted genetic interactions in a human colorectal cancer cell line, showing that siRNA-mediated knockdown of MRE11A sensitizes cells to depletion of various replication fork-associated proteins. Overall, this work describes methods to identify, predict, and validate in cancer cells candidate therapeutic targets for tumors with known somatic mutations in CIN genes using data from yeast. We affirm not only replication stress but also the targeting of DNA replication fork proteins themselves as potential targets for anticancer therapeutic development. PMID:23390603

  9. Interactivity vs. fairness in networked linux systems

    SciTech Connect

    Wu, Wenji; Crawford, Matt; /Fermilab

    2007-01-01

    In general, the Linux 2.6 scheduler can ensure fairness and provide excellent interactive performance at the same time. However, our experiments and mathematical analysis have shown that the current Linux interactivity mechanism tends to incorrectly categorize non-interactive network applications as interactive, which can lead to serious fairness or starvation issues. In the extreme, a single process can unjustifiably obtain up to 95% of the CPU! The root cause is due to the facts that: (1) network packets arrive at the receiver independently and discretely, and the 'relatively fast' non-interactive network process might frequently sleep to wait for packet arrival. Though each sleep lasts for a very short period of time, the wait-for-packet sleeps occur so frequently that they lead to interactive status for the process. (2) The current Linux interactivity mechanism provides the possibility that a non-interactive network process could receive a high CPU share, and at the same time be incorrectly categorized as 'interactive.' In this paper, we propose and test a possible solution to address the interactivity vs. fairness problems. Experiment results have proved the effectiveness of the proposed solution.

  10. Mutually-antagonistic interactions in baseball networks

    NASA Astrophysics Data System (ADS)

    Saavedra, Serguei; Powers, Scott; McCotter, Trent; Porter, Mason A.; Mucha, Peter J.

    2010-03-01

    We formulate the head-to-head matchups between Major League Baseball pitchers and batters from 1954 to 2008 as a bipartite network of mutually-antagonistic interactions. We consider both the full network and single-season networks, which exhibit structural changes over time. We find interesting structure in the networks and examine their sensitivity to baseball’s rule changes. We then study a biased random walk on the matchup networks as a simple and transparent way to (1) compare the performance of players who competed under different conditions and (2) include information about which particular players a given player has faced. We find that a player’s position in the network does not correlate with his placement in the random walker ranking. However, network position does have a substantial effect on the robustness of ranking placement to changes in head-to-head matchups.

  11. Genetic-algorithm-based tri-state neural networks

    NASA Astrophysics Data System (ADS)

    Uang, Chii-Maw; Chen, Wen-Gong; Horng, Ji-Bin

    2002-09-01

    A new method, using genetic algorithms, for constructing a tri-state neural network is presented. The global searching features of the genetic algorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) genetic algorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed genetic algorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.

  12. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance.

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

    Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M. PMID:24219635

  13. Scaling up: human genetics as a Cold War network.

    PubMed

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. PMID:24954362

  14. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  15. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    Energy Science and Technology Software Center (ESTSC)

    1994-09-28

    This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less

  16. The dissimilarity of species interaction networks.

    PubMed

    Poisot, Timothée; Canard, Elsa; Mouillot, David; Mouquet, Nicolas; Gravel, Dominique

    2012-12-01

    In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β-diversity. We emphasise that scaling up β-diversity of community composition to the β-diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions. PMID:22994257

  17. Quantitative Genetic Interaction Mapping Using the E-MAP Approach

    PubMed Central

    Collins, Sean R.; Roguev, Assen; Krogan, Nevan J.

    2010-01-01

    Genetic interactions represent the degree to which the presence of one mutation modulates the phenotype of a second mutation. In recent years, approaches for measuring genetic interactions systematically and quantitatively have proven to be effective tools for unbiased characterization of gene function and have provided valuable data for analyses of evolution. Here, we present protocols for systematic measurement of genetic interactions with respect to organismal growth rate for two yeast species. PMID:20946812

  18. Biotic interactions govern genetic adaptation to toxicants.

    PubMed

    Becker, Jeremias Martin; Liess, Matthias

    2015-05-01

    The genetic recovery of resistant populations released from pesticide exposure is accelerated by the presence of environmental stressors. By contrast, the relevance of environmental stressors for the spread of resistance during pesticide exposure has not been studied. Moreover, the consequences of interactions between different stressors have not been considered. Here we show that stress through intraspecific competition accelerates microevolution, because it enhances fitness differences between adapted and non-adapted individuals. By contrast, stress through interspecific competition or predation reduces intraspecific competition and thereby delays microevolution. This was demonstrated in mosquito populations (Culex quinquefasciatus) that were exposed to the pesticide chlorpyrifos. Non-selective predation through harvesting and interspecific competition with Daphnia magna delayed the selection for individuals carrying the ace-1(R) resistance allele. Under non-toxic conditions, susceptible individuals without ace-1(R) prevailed. Likewise, predation delayed the reverse adaptation of the populations to a non-toxic environment, while the effect of interspecific competition was not significant. Applying a simulation model, we further identified how microevolution is generally determined by the type and degree of competition and predation. We infer that interactions with other species-especially strong in ecosystems with high biodiversity-can delay the development of pesticide resistance. PMID:25833856

  19. Biotic interactions govern genetic adaptation to toxicants

    PubMed Central

    Becker, Jeremias Martin; Liess, Matthias

    2015-01-01

    The genetic recovery of resistant populations released from pesticide exposure is accelerated by the presence of environmental stressors. By contrast, the relevance of environmental stressors for the spread of resistance during pesticide exposure has not been studied. Moreover, the consequences of interactions between different stressors have not been considered. Here we show that stress through intraspecific competition accelerates microevolution, because it enhances fitness differences between adapted and non-adapted individuals. By contrast, stress through interspecific competition or predation reduces intraspecific competition and thereby delays microevolution. This was demonstrated in mosquito populations (Culex quinquefasciatus) that were exposed to the pesticide chlorpyrifos. Non-selective predation through harvesting and interspecific competition with Daphnia magna delayed the selection for individuals carrying the ace-1R resistance allele. Under non-toxic conditions, susceptible individuals without ace-1R prevailed. Likewise, predation delayed the reverse adaptation of the populations to a non-toxic environment, while the effect of interspecific competition was not significant. Applying a simulation model, we further identified how microevolution is generally determined by the type and degree of competition and predation. We infer that interactions with other species—especially strong in ecosystems with high biodiversity—can delay the development of pesticide resistance. PMID:25833856

  20. Linkage: from particulate to interactive genetics.

    PubMed

    Falk, Raphael

    2003-01-01

    Genetics was established on a strict particulate conception of heredity. Genetic linkage, the deviation from independent segregation of Mendelian factors, was conceived as a function of the material allocation of the factors to the chromosomes, rather than to the multiple effects (pleiotropy) of discrete factors. Although linkage maps were abstractions they provided strong support for the chromosomal theory of inheritance. Direct Cytogenetic evidence was scarce until X-ray induced major chromosomal rearrangements allowed direct correlation of genetic and cytological rearrangements. Only with the discovery of the polytenic giant chromosomes in Drosophila larvae in the 1930s were the virtual maps backed up by physical maps of the genetic loci. Genetic linkage became a pivotal experimental tool for the examination of the integration of genetic functions in development and in evolution. Genetic mapping has remained a hallmark of genetic analysis. The location of genes in DNA is a modern extension of the notion of genetic linkage. PMID:12778899

  1. A simple model for studying interacting networks

    NASA Astrophysics Data System (ADS)

    Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.

    2011-03-01

    Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.

  2. Genetic variation, predator–prey interactions and food web structure

    PubMed Central

    Moya-Laraño, Jordi

    2011-01-01

    Food webs are networks of species that feed on each other. The role that within-population phenotypic and genetic variation plays in food web structure is largely unknown. Here, I show via simulation how variation in two key traits, growth rates and phenology, by influencing the variability of body sizes present through time, can potentially affect several structural parameters in the direction of enhancing food web persistence: increased connectance, decreased interaction strengths, increased variation among interaction strengths and increased degree of omnivory. I discuss other relevant traits whose variation could affect the structure of food webs, such as morphological and additional life-history traits, as well as animal personalities. Furthermore, trait variation could also contribute to the stability of food web modules through metacommunity dynamics. I propose future research to help establish a link between within-population variation and food web structure. If appropriately established, such a link could have important consequences for biological conservation, as it would imply that preserving (functional) genetic variation within populations could ensure the preservation of entire communities. PMID:21444316

  3. Systematic computational prediction of protein interaction networks.

    PubMed

    Lees, J G; Heriche, J K; Morilla, I; Ranea, J A; Orengo, C A

    2011-06-01

    Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use. PMID:21572181

  4. Kin in space: social viscosity in a spatially and genetically substructured network

    PubMed Central

    Wolf, Jochen B.W; Trillmich, Fritz

    2008-01-01

    Population substructuring is a fundamental aspect of animal societies. A growing number of theoretical studies recognize that who-meets-whom is not random, but rather determined by spatial relationships or illustrated by social networks. Structural properties of large highly dynamic social systems are notoriously difficult to unravel. Network approaches provide powerful ways to analyse the intricate relationships between social behaviour, dispersal strategies and genetic structure. Applying network analytical tools to a colony of the highly gregarious Galápagos sea lion (Zalophus wollebaeki), we find several genetic clusters that correspond to spatially determined ‘network communities’. Overall relatedness was low, and genetic structure in the network can be interpreted as an emergent property of philopatry and seems not to be primarily driven by targeted interactions among highly related individuals in family groups. Nevertheless, social relationships between directly adjacent individuals in the network were stronger among genetically more similar individuals. Taken together, these results suggest that even small differences in the degree of relatedness can influence behavioural decisions. This raises the fascinating prospect that kin selection may also apply to low levels of relatedness within densely packed animal groups where less obvious co-operative interactions such as increased tolerance and stress reduction are important. PMID:18522913

  5. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Quantitative or complex traits are determined by the combined effects of many loci, and are affected by gene-gene interactions, genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional gene...

  6. Description of interatomic interactions with neural networks

    NASA Astrophysics Data System (ADS)

    Hajinazar, Samad; Shao, Junping; Kolmogorov, Aleksey N.

    Neural networks are a promising alternative to traditional classical potentials for describing interatomic interactions. Recent research in the field has demonstrated how arbitrary atomic environments can be represented with sets of general functions which serve as an input for the machine learning tool. We have implemented a neural network formalism in the MAISE package and developed a protocol for automated generation of accurate models for multi-component systems. Our tests illustrate the performance of neural networks and known classical potentials for a range of chemical compositions and atomic configurations. Supported by NSF Grant DMR-1410514.

  7. Broadband networks for interactive telemedical applications

    NASA Astrophysics Data System (ADS)

    Graschew, Georgi; Roelofs, Theo A.; Rakowsky, Stefan; Schlag, Peter M.

    2002-08-01

    Using off-the-shelf hardware components and a specially developed high-end software communication system (WinVicos) satellite networks for interactive telemedicine have been designed and developed. These networks allow for various telemedical applications, like intraoperative teleconsultation, second opinioning, teleteaching, telementoring, etc.. Based on the successful GALENOS network, several projects are currently being realized: MEDASHIP (Medical Assistance for Ships); DELTASS (Disaster Emergency Logistic Telemedicine Advanced Satellites Systems) and EMISPHER (Euro-Mediterranean Internet-Satellite Platform for Health, medical Education and Research).

  8. Stabilization of perturbed Boolean network attractors through compensatory interactions

    PubMed Central

    2014-01-01

    Background Understanding and ameliorating the effects of network damage are of significant interest, due in part to the variety of applications in which network damage is relevant. For example, the effects of genetic mutations can cascade through within-cell signaling and regulatory networks and alter the behavior of cells, possibly leading to a wide variety of diseases. The typical approach to mitigating network perturbations is to consider the compensatory activation or deactivation of system components. Here, we propose a complementary approach wherein interactions are instead modified to alter key regulatory functions and prevent the network damage from triggering a deregulatory cascade. Results We implement this approach in a Boolean dynamic framework, which has been shown to effectively model the behavior of biological regulatory and signaling networks. We show that the method can stabilize any single state (e.g., fixed point attractors or time-averaged representations of multi-state attractors) to be an attractor of the repaired network. We show that the approach is minimalistic in that few modifications are required to provide stability to a chosen attractor and specific in that interventions do not have undesired effects on the attractor. We apply the approach to random Boolean networks, and further show that the method can in some cases successfully repair synchronous limit cycles. We also apply the methodology to case studies from drought-induced signaling in plants and T-LGL leukemia and find that it is successful in both stabilizing desired behavior and in eliminating undesired outcomes. Code is made freely available through the software package BooleanNet. Conclusions The methodology introduced in this report offers a complementary way to manipulating node expression levels. A comprehensive approach to evaluating network manipulation should take an "all of the above" perspective; we anticipate that theoretical studies of interaction modification

  9. Genetic "code": representations and dynamical models of genetic components and networks.

    PubMed

    Gilman, Alex; Arkin, Adam P

    2002-01-01

    Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed. PMID:12142360

  10. A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

    PubMed Central

    Vizeacoumar, Franco J; Arnold, Roland; Vizeacoumar, Frederick S; Chandrashekhar, Megha; Buzina, Alla; Young, Jordan T F; Kwan, Julian H M; Sayad, Azin; Mero, Patricia; Lawo, Steffen; Tanaka, Hiromasa; Brown, Kevin R; Baryshnikova, Anastasia; Mak, Anthony B; Fedyshyn, Yaroslav; Wang, Yadong; Brito, Glauber C; Kasimer, Dahlia; Makhnevych, Taras; Ketela, Troy; Datti, Alessandro; Babu, Mohan; Emili, Andrew; Pelletier, Laurence; Wrana, Jeff; Wainberg, Zev; Kim, Philip M; Rottapel, Robert; O'Brien, Catherine A; Andrews, Brenda; Boone, Charles; Moffat, Jason

    2013-01-01

    Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN−/− DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model. PMID:24104479

  11. Optimizing the controllability of arbitrary networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xin-Feng; Lu, Zhe-Ming

    2016-04-01

    Recently, as the controllability of complex networks attracts much attention, how to optimize networks' controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov-Belevitch-Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations.

  12. BisoGenet: a new tool for gene network building, visualization and analysis

    PubMed Central

    2010-01-01

    Background The increasing availability and diversity of omics data in the post-genomic era offers new perspectives in most areas of biomedical research. Graph-based biological networks models capture the topology of the functional relationships between molecular entities such as gene, protein and small compounds and provide a suitable framework for integrating and analyzing omics-data. The development of software tools capable of integrating data from different sources and to provide flexible methods to reconstruct, represent and analyze topological networks is an active field of research in bioinformatics. Results BisoGenet is a multi-tier application for visualization and analysis of biomolecular relationships. The system consists of three tiers. In the data tier, an in-house database stores genomics information, protein-protein interactions, protein-DNA interactions, gene ontology and metabolic pathways. In the middle tier, a global network is created at server startup, representing the whole data on bioentities and their relationships retrieved from the database. The client tier is a Cytoscape plugin, which manages user input, communication with the Web Service, visualization and analysis of the resulting network. Conclusion BisoGenet is able to build and visualize biological networks in a fast and user-friendly manner. A feature of Bisogenet is the possibility to include coding relations to distinguish between genes and their products. This feature could be instrumental to achieve a finer grain representation of the bioentities and their relationships. The client application includes network analysis tools and interactive network expansion capabilities. In addition, an option is provided to allow other networks to be converted to BisoGenet. This feature facilitates the integration of our software with other tools available in the Cytoscape platform. BisoGenet is available at http://bio.cigb.edu.cu/bisogenet-cytoscape/. PMID:20163717

  13. The Networking of Interactive Bibliographic Retrieval Systems.

    ERIC Educational Resources Information Center

    Marcus, Richard S.; Reintjes, J. Francis

    Research in networking of heterogeneous interactive bibliographic retrieval systems is being conducted which centers on the concept of a virtual retrieval system. Such a virtual system would be created through a translating computer interface that would provide access to the different retrieval systems and data bases in a uniform and convenient…

  14. Environmental and genetic perturbations reveal different networks of metabolic regulation

    PubMed Central

    Greenberg, Anthony J; Hackett, Sean R; Harshman, Lawrence G; Clark, Andrew G

    2011-01-01

    Progress in systems biology depends on accurate descriptions of biological networks. Connections in a regulatory network are identified as correlations of gene expression across a set of environmental or genetic perturbations. To use this information to predict system behavior, we must test how the nature of perturbations affects topologies of networks they reveal. To probe this question, we focused on metabolism of Drosophila melanogaster. Our source of perturbations is a set of crosses among 92 wild-derived lines from five populations, replicated in a manner permitting separate assessment of the effects of genetic variation and environmental fluctuation. We directly assayed activities of enzymes and levels of metabolites. Using a multivariate Bayesian model, we estimated covariance among metabolic parameters and built fine-grained probabilistic models of network topology. The environmental and genetic co-regulation networks are substantially the same among five populations. However, genetic and environmental perturbations reveal qualitative differences in metabolic regulation, suggesting that environmental shifts, such as diet modifications, produce different systemic effects than genetic changes, even if the primary targets are the same. PMID:22186737

  15. Functional module identification in protein interaction networks by interaction patterns

    PubMed Central

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP2 by random walk. A spectral approximate algorithm SLCP2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP2 to a new algorithm (greedy algorithm for LCP2) GLCP2 to identify overlapping functional modules. We compare SLCP2 and GLCP2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/∼xqian/fmi/slcp2hop

  16. Molecular and genetic inflammation networks in major human diseases.

    PubMed

    Zhao, Yongzhong; Forst, Christian V; Sayegh, Camil E; Wang, I-Ming; Yang, Xia; Zhang, Bin

    2016-07-19

    It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic

  17. Genetic variants in Alzheimer disease - molecular and brain network approaches.

    PubMed

    Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher J; De Jager, Philip L; Bennett, David A

    2016-07-01

    Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

  18. Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases.

    PubMed

    Wu, Xiaodan; Chen, Luonan; Wang, Xiangdong

    2014-01-01

    Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers. PMID:24995123

  19. Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses

    PubMed Central

    Kogelman, Lisette J. A.; Pant, Sameer D.; Fredholm, Merete; Kadarmideen, Haja N.

    2014-01-01

    Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie

  20. Evolutionarily Conserved Herpesviral Protein Interaction Networks

    PubMed Central

    Fossum, Even; Friedel, Caroline C.; Rajagopala, Seesandra V.; Titz, Björn; Baiker, Armin; Schmidt, Tina; Kraus, Theo; Stellberger, Thorsten; Rutenberg, Christiane; Suthram, Silpa; Bandyopadhyay, Sourav; Rose, Dietlind; von Brunn, Albrecht; Uhlmann, Mareike; Zeretzke, Christine; Dong, Yu-An; Boulet, Hélène; Koegl, Manfred; Bailer, Susanne M.; Koszinowski, Ulrich; Ideker, Trey; Uetz, Peter; Zimmer, Ralf; Haas, Jürgen

    2009-01-01

    Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species. PMID:19730696

  1. Worming forward: amyotrophic lateral sclerosis toxicity mechanisms and genetic interactions in Caenorhabditis elegans

    PubMed Central

    Therrien, Martine; Parker, J. Alex

    2014-01-01

    Neurodegenerative diseases share pathogenic mechanisms at the cellular level including protein misfolding, excitotoxicity and altered RNA homeostasis among others. Recent advances have shown that the genetic causes underlying these pathologies overlap, hinting at the existence of a genetic network for neurodegeneration. This is perhaps best illustrated by the recent discoveries of causative mutations for amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). Once thought to be distinct entities, it is now recognized that these diseases exist along a genetic spectrum. With this wealth of discoveries comes the need to develop new genetic models of ALS and FTD to investigate not only pathogenic mechanisms linked to causative mutations, but to uncover potential genetic interactions that may point to new therapeutic targets. Given the conservation of many disease genes across evolution, Caenorhabditis elegans is an ideal system to investigate genetic interactions amongst these genes. Here we review the use of C. elegans to model ALS and investigate a putative genetic network for ALS/FTD that may extend to other neurological disorders. PMID:24860590

  2. Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

    PubMed Central

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-01-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. PMID:25631319

  3. Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

    PubMed

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-04-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. PMID:25631319

  4. Cooperative Tertiary Interaction Network Guides RNA Folding

    SciTech Connect

    Behrouzi, Reza; Roh, Joon Ho; Kilburn, Duncan; Briber, R.M.; Woodson, Sarah A.

    2013-04-08

    Noncoding RNAs form unique 3D structures, which perform many regulatory functions. To understand how RNAs fold uniquely despite a small number of tertiary interaction motifs, we mutated the major tertiary interactions in a group I ribozyme by single-base substitutions. The resulting perturbations to the folding energy landscape were measured using SAXS, ribozyme activity, hydroxyl radical footprinting, and native PAGE. Double- and triple-mutant cycles show that most tertiary interactions have a small effect on the stability of the native state. Instead, the formation of core and peripheral structural motifs is cooperatively linked in near-native folding intermediates, and this cooperativity depends on the native helix orientation. The emergence of a cooperative interaction network at an early stage of folding suppresses nonnative structures and guides the search for the native state. We suggest that cooperativity in noncoding RNAs arose from natural selection of architectures conducive to forming a unique, stable fold.

  5. Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.

    PubMed

    Wildenhain, Jan; Spitzer, Michaela; Dolma, Sonam; Jarvik, Nick; White, Rachel; Roy, Marcia; Griffiths, Emma; Bellows, David S; Wright, Gerard D; Tyers, Mike

    2015-12-23

    The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species. PMID:27136353

  6. Genetics of gene expression responses to temperature stress in a sea urchin gene network.

    PubMed

    Runcie, Daniel E; Garfield, David A; Babbitt, Courtney C; Wygoda, Jennifer A; Mukherjee, Sayan; Wray, Gregory A

    2012-09-01

    Stress responses play an important role in shaping species distributions and robustness to climate change. We investigated how stress responses alter the contribution of additive genetic variation to gene expression during development of the purple sea urchin, Strongylocentrotus purpuratus, under increased temperatures that model realistic climate change scenarios. We first measured gene expression responses in the embryos by RNA-seq to characterize molecular signatures of mild, chronic temperature stress in an unbiased manner. We found that an increase from 12 to 18 °C caused widespread alterations in gene expression including in genes involved in protein folding, RNA processing and development. To understand the quantitative genetic architecture of this response, we then focused on a well-characterized gene network involved in endomesoderm and ectoderm specification. Using a breeding design with wild-caught individuals, we measured genetic and gene-environment interaction effects on 72 genes within this network. We found genetic or maternal effects in 33 of these genes and that the genetic effects were correlated in the network. Fourteen network genes also responded to higher temperatures, but we found no significant genotype-environment interactions in any of the genes. This absence may be owing to an effective buffering of the temperature perturbations within the network. In support of this hypothesis, perturbations to regulatory genes did not affect the expression of the genes that they regulate. Together, these results provide novel insights into the relationship between environmental change and developmental evolution and suggest that climate change may not expose large amounts of cryptic genetic variation to selection in this species. PMID:22856327

  7. The activation of interactive attentional networks.

    PubMed

    Xuan, Bin; Mackie, Melissa-Ann; Spagna, Alfredo; Wu, Tingting; Tian, Yanghua; Hof, Patrick R; Fan, Jin

    2016-04-01

    Attention can be conceptualized as comprising the functions of alerting, orienting, and executive control. Although the independence of these functions has been demonstrated, the neural mechanisms underlying their interactions remain unclear. Using the revised attention network test and functional magnetic resonance imaging, we examined cortical and subcortical activity related to these attentional functions and their interactions. Results showed that areas in the extended frontoparietal network (FPN), including dorsolateral prefrontal cortex, frontal eye fields (FEF), areas near and along the intraparietal sulcus, anterior cingulate and anterior insular cortices, basal ganglia, and thalamus were activated across multiple attentional functions. Specifically, the alerting function was associated with activation in the locus coeruleus (LC) in addition to regions in the FPN. The orienting functions were associated with activation in the superior colliculus (SC) and the FEF. The executive control function was mainly associated with activation of the FPN and cerebellum. The interaction effect of alerting by executive control was also associated with activation of the FPN, while the interaction effect of orienting validity by executive control was mainly associated with the activation in the pulvinar. The current findings demonstrate that cortical and specific subcortical areas play a pivotal role in the implementation of attentional functions and underlie their dynamic interactions. PMID:26794640

  8. Genetic Network Programming with Intron-Like Nodes

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Chen, Yan; Eto, Shinji; Shimada, Kaoru; Hirasawa, Kotaro

    Recently, Genetic Network Programming (GNP) has been proposed, which is an extension of Genetic Algorithm(GA) and Genetic Programming(GP). GNP can make compact programs and can memorize the past history in it implicitly, because it expresses the solution by directed graphs and therefore, it can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that the intron-like nodes are useful for improving the training speed and generalization ability.

  9. Stochasticity, Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks

    PubMed Central

    Sorek, Matan; Balaban, Nathalie Q.; Loewenstein, Yonatan

    2013-01-01

    It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population. PMID:23990765

  10. Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms

    NASA Astrophysics Data System (ADS)

    Hernández-Lemus, Enrique; Velázquez-Fernández, David; Estrada-Gil, Jesús K.; Silva-Zolezzi, Irma; Herrera-Hernández, Miguel F.; Jiménez-Sánchez, Gerardo

    2009-12-01

    Most common pathologies in humans are not caused by the mutation of a single gene, rather they are complex diseases that arise due to the dynamic interaction of many genes and environmental factors. This plethora of interacting genes generates a complexity landscape that masks the real effects associated with the disease. To construct dynamic maps of gene interactions (also called genetic regulatory networks) we need to understand the interplay between thousands of genes. Several issues arise in the analysis of experimental data related to gene function: on the one hand, the nature of measurement processes generates highly noisy signals; on the other hand, there are far more variables involved (number of genes and interactions among them) than experimental samples. Another source of complexity is the highly nonlinear character of the underlying biochemical dynamics. To overcome some of these limitations, we generated an optimized method based on the implementation of a Maximum Entropy Formalism (MaxEnt) to deconvolute a genetic regulatory network based on the most probable meta-distribution of gene-gene interactions. We tested the methodology using experimental data for Papillary Thyroid Cancer (PTC) and Thyroid Goiter tissue samples. The optimal MaxEnt regulatory network was obtained from a pool of 25,593,993 different probability distributions. The group of observed interactions was validated by several (mostly in silico) means and sources. For the associated Papillary Thyroid Cancer Gene Regulatory Network (PTC-GRN) the majority of the nodes (genes) have very few links (interactions) whereas a small number of nodes are highly connected. PTC-GRN is also characterized by high clustering coefficients and network heterogeneity. These properties have been recognized as characteristic of topological robustness, and they have been largely described in relation to biological networks. A number of biological validity outcomes are discussed with regard to both the

  11. Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

    PubMed Central

    Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.; Gulukota, Kamalakar; Yang, Shengjie; Pesce, Lorenzo L.; Mitra, Riten; Müller, Peter; Sengupta, Subhajit; Guo, Wentian; Silverstein, Jonathan C.; Foster, Ian; Parsad, Nigel; White, Kevin P.

    2015-01-01

    Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer. PMID:25956356

  12. Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

    SciTech Connect

    Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.; Gulukota, Kamalakar; Yang, Shengjie; Pesce, Lorenzo L.; Mitra, Riten; Muller, Peter; Sengupta, Subhajit; Guo, Wentian; Foster, Ian; Bullock, JaQuel A.

    2015-08-01

    Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

  13. Dynamics of interacting information waves in networks.

    PubMed

    Mirshahvalad, A; Esquivel, A V; Lizana, L; Rosvall, M

    2014-01-01

    To better understand the inner workings of information spreading, network researchers often use simple models to capture the spreading dynamics. But most models only highlight the effect of local interactions on the global spreading of a single information wave, and ignore the effects of interactions between multiple waves. Here we take into account the effect of multiple interacting waves by using an agent-based model in which the interaction between information waves is based on their novelty. We analyzed the global effects of such interactions and found that information that actually reaches nodes reaches them faster. This effect is caused by selection between information waves: lagging waves die out and only leading waves survive. As a result, and in contrast to models with noninteracting information dynamics, the access to information decays with the distance from the source. Moreover, when we analyzed the model on various synthetic and real spatial road networks, we found that the decay rate also depends on the path redundancy and the effective dimension of the system. In general, the decay of the information wave frequency as a function of distance from the source follows a power-law distribution with an exponent between -0.2 for a two-dimensional system with high path redundancy and -0.5 for a tree-like system with no path redundancy. We found that the real spatial networks provide an infrastructure for information spreading that lies in between these two extremes. Finally, to better understand the mechanics behind the scaling results, we provide analytical calculations of the scaling for a one-dimensional system. PMID:24580283

  14. Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

    NASA Technical Reports Server (NTRS)

    Szallasi, Zoltan; Liang, Shoudan

    2000-01-01

    In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

  15. Genetic interactions underlying tree branch orientation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Expanding our understanding of the molecular and genetic mechanisms behind branch orientation in trees both addresses a fundamental developmental phenomenon and can lead to significant impacts on tree crop agriculture and forestry. Using the p-nome (pooled genome) sequencing-based mapping approac...

  16. Interaction prediction using conserved network motifs in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2005-03-01

    High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. This talk will describe the recent developments in analyzing and understanding protein interaction networks, then present a method that uses the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a ``leave-one-ou approach we find average success rates between 20-50% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org

  17. Genetic architecture and regulatory networks in oilseed development

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic analysis of global gene expression level variation provides evidence for transcriptional regulators and gene network relationships. Plant seeds are an important source of oil and protein, and a genome-wide assessment of transcriptional regulation during seed development offers insight into t...

  18. AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES

    PubMed Central

    DARABOS, CHRISTIAN; QIU, JINGYA; MOORE, JASON H.

    2015-01-01

    Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single “tripartite” network. Despite extensive research, the mechanisms by which chemical agents disrupt biological pathways are still poorly understood. In this study, we use our integrated network model to identify specific biological pathway candidates possibly disrupted by environmental agents. We conjecture that a higher number of co-occurrences between an environmental substance and biological pathway pair can be associated with a higher likelihood that the substance is involved in disrupting that pathway. We validate our model by demonstrating its ability to detect known arsenic and signal transduction pathway interactions and speculate on candidate cell-cell junction organization pathways disrupted by cadmium. The validation was supported by distinct publications of cell biology and genetic studies that associated environmental exposure to pathway disruption. The integrated network approach is a novel method for detecting the biological effects of environmental exposures. A better understanding of the molecular processes associated with specific environmental exposures will help in developing targeted molecular therapies for patients who have been exposed to the toxicity of environmental chemicals. PMID:26776169

  19. Structure and Interactions in Neurofilament Networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna; Ojeda-Lopez, Miguel; Safinya, Cyrus

    2004-03-01

    Neurofilaments (NFs) are a major constituent of myelinated axons of nerve cells, which assemble from three subunit proteins of low, medium, and high molecular weight to form a 10 nm diameter rod with sidearms radiating from the center. The sidearm interactions impart structural stability and result in an oriented network of NFs running parallel to the axon. Over or under expression of NF subunits is related to abnormal NF-networks, which are known hallmarks of motor neuron diseases (ALS). Here, we reassemble NFs from subunit proteins purified from bovine spinal cord. We demonstrate the formation of the NF network in vitro where synchrotron x-ray scattering (SSRL) reveals a well-defined interfilament spacing while the defect structure in polarized optical microcopy shows the liquid crystalline nature. The spacing varies depending on subunit molar ratios and salt conditions and we relate this change to the mechanical stability of the lattice. This change in lattice spacing yields insight into the stabilizing interactions between the NF sidearms. Supported by NSF DMR- 0203755, CTS-0103516, and NIH GM-59288.

  20. Using genetic algorithms to construct a network for financial prediction

    NASA Astrophysics Data System (ADS)

    Patel, Devesh

    1996-03-01

    Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.

  1. Genetic algorithm application in optimization of wireless sensor networks.

    PubMed

    Norouzi, Ali; Zaim, A Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

  2. Genetic Algorithm Application in Optimization of Wireless Sensor Networks

    PubMed Central

    Norouzi, Ali; Zaim, A. Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

  3. Information theory and the ethylene genetic network.

    PubMed

    González-García, José S; Díaz, José

    2011-10-01

    The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon's theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of

  4. A Novel Approach to Detect Cumulative Genetic Effects and Genetic Interactions in Crohn's Disease

    PubMed Central

    Wang, Ming-Hsi; Fiocchi, Claudio; Duerr, Richard H.; Zhu, Xiaofeng; Ripke, Stephan; Achkar, Jean-Paul

    2013-01-01

    Background Genome-wide association studies (GWAS) have identified at least 71 Crohn's disease (CD) genetic risk loci but the role of gene-gene interactions is unclear. The value of genetic variants in clinical practice is not defined due to limited explained heritability. Materials and Methods We examined model predictability of combining the 71 CD risk alleles and genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case-Control Consortium (WTCCC) IBD GWAS was used as a replicate cohort. We used logic regression (LR), an adaptive regression methodology, to search for high order binary predictors (e.g. single nucleotide polymorphism [SNP]) interactions. Results The combined 71 CD SNPs had good CD risk predictability (area under the curve, AUCs of 0.75 and 0.73 in the 2 cohorts). Higher cumulative allele score predicted higher CD risk, but a relatively small difference in cumulative allele scores was observed between CD and controls (49 vs. 47, P<0.001). Through LR, we identified high order genetic interactions and significantly improved the model predictability (AUC, 0.75 to 0.77, P<0.0001). A genetic interaction model including NOD2, ATG16L1, IL10/IL19, C13orf31 and chr21q loci was discovered and successfully replicated in the independent WTCCC cohort. The explained heritability of the 71 CD SNPs alone was 24% and increased to 27% after adding the genetic interactions. Conclusions A novel approach allowed the identification and replication of genetic interactions among NOD2, ATG16L1, IL10/IL19, C13orf31 and chr21q loci. CD risk can be predicted by a model of 71 CD loci and improved by adding genetic interactions. PMID:23598818

  5. Functional and genetic analysis of the colon cancer network

    PubMed Central

    2014-01-01

    Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network. PMID:25079297

  6. Functional and genetic analysis of the colon cancer network.

    PubMed

    Emmert-Streib, Frank; de Matos Simoes, Ricardo; Glazko, Galina; McDade, Simon; Haibe-Kains, Benjamin; Holzinger, Andreas; Dehmer, Matthias; Campbell, Frederick

    2014-01-01

    Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network. PMID:25079297

  7. Node-based measures of connectivity in genetic networks.

    PubMed

    Koen, Erin L; Bowman, Jeff; Wilson, Paul J

    2016-01-01

    At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal. PMID:25917123

  8. Enhanced energy transport in genetically engineered excitonic networks.

    PubMed

    Park, Heechul; Heldman, Nimrod; Rebentrost, Patrick; Abbondanza, Luigi; Iagatti, Alessandro; Alessi, Andrea; Patrizi, Barbara; Salvalaggio, Mario; Bussotti, Laura; Mohseni, Masoud; Caruso, Filippo; Johnsen, Hannah C; Fusco, Roberto; Foggi, Paolo; Scudo, Petra F; Lloyd, Seth; Belcher, Angela M

    2016-02-01

    One of the challenges for achieving efficient exciton transport in solar energy conversion systems is precise structural control of the light-harvesting building blocks. Here, we create a tunable material consisting of a connected chromophore network on an ordered biological virus template. Using genetic engineering, we establish a link between the inter-chromophoric distances and emerging transport properties. The combination of spectroscopy measurements and dynamic modelling enables us to elucidate quantum coherent and classical incoherent energy transport at room temperature. Through genetic modifications, we obtain a significant enhancement of exciton diffusion length of about 68% in an intermediate quantum-classical regime. PMID:26461447

  9. Enhanced energy transport in genetically engineered excitonic networks

    NASA Astrophysics Data System (ADS)

    Park, Heechul; Heldman, Nimrod; Rebentrost, Patrick; Abbondanza, Luigi; Iagatti, Alessandro; Alessi, Andrea; Patrizi, Barbara; Salvalaggio, Mario; Bussotti, Laura; Mohseni, Masoud; Caruso, Filippo; Johnsen, Hannah C.; Fusco, Roberto; Foggi, Paolo; Scudo, Petra F.; Lloyd, Seth; Belcher, Angela M.

    2016-02-01

    One of the challenges for achieving efficient exciton transport in solar energy conversion systems is precise structural control of the light-harvesting building blocks. Here, we create a tunable material consisting of a connected chromophore network on an ordered biological virus template. Using genetic engineering, we establish a link between the inter-chromophoric distances and emerging transport properties. The combination of spectroscopy measurements and dynamic modelling enables us to elucidate quantum coherent and classical incoherent energy transport at room temperature. Through genetic modifications, we obtain a significant enhancement of exciton diffusion length of about 68% in an intermediate quantum-classical regime.

  10. Immune allied genetic algorithm for Bayesian network structure learning

    NASA Astrophysics Data System (ADS)

    Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

  11. Multivariate analysis of noise in genetic regulatory networks.

    PubMed

    Tomioka, Ryota; Kimura, Hidenori; J Kobayashi, Tetsuya; Aihara, Kazuyuki

    2004-08-21

    Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations. PMID:15246787

  12. Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests.

    PubMed

    Frånberg, Mattias; Gertow, Karl; Hamsten, Anders; Lagergren, Jens; Sennblad, Bengt

    2015-09-01

    Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex

  13. Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests

    PubMed Central

    Frånberg, Mattias; Gertow, Karl; Hamsten, Anders; Lagergren, Jens; Sennblad, Bengt

    2015-01-01

    Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex

  14. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  15. Intricate environment-modulated genetic networks control isoflavone accumulation in soybean seeds

    PubMed Central

    2010-01-01

    Background Soybean (Glycine max [L] Merr.) seed isoflavones have long been considered a desirable trait to target in selection programs for their contribution to human health and plant defense systems. However, attempts to modify seed isoflavone contents have not always produced the expected results because their genetic basis is polygenic and complex. Undoubtedly, the extreme variability that seed isoflavones display over environments has obscured our understanding of the genetics involved. Results In this study, a mapping population of RILs with three replicates was analyzed in four different environments (two locations over two years). We found a total of thirty-five main-effect genomic regions and many epistatic interactions controlling genistein, daidzein, glycitein and total isoflavone accumulation in seeds. The use of distinct environments permitted detection of a great number of environment-modulated and minor-effect QTL. Our findings suggest that isoflavone seed concentration is controlled by a complex network of multiple minor-effect loci interconnected by a dense epistatic map of interactions. The magnitude and significance of the effects of many of the nodes and connections in the network varied depending on the environmental conditions. In an attempt to unravel the genetic architecture underlying the traits studied, we searched on a genome-wide scale for genomic regions homologous to the most important identified isoflavone biosynthetic genes. We identified putative candidate genes for several of the main-effect and epistatic QTL and for QTL reported by other groups. Conclusions To better understand the underlying genetics of isoflavone accumulation, we performed a large scale analysis to identify genomic regions associated with isoflavone concentrations. We not only identified a number of such regions, but also found that they can interact with one another and with the environment to form a complex adaptable network controlling seed isoflavone levels

  16. How social and genetic factors predict friendship networks

    PubMed Central

    Boardman, Jason D.; Domingue, Benjamin W.; Fletcher, Jason M.

    2012-01-01

    Recent research suggests that the genotype of one individual in a friendship pair is predictive of the genotype of his/her friend. These results provide tentative support for the genetic homophily perspective, which has important implications for social and genetic epidemiology because it substantiates a particular form of gene–environment correlation. This process may also have important implications for social scientists who study the social factors related to health and health-related behaviors. We extend this work by considering the ways in which school context shapes genetically similar friendships. Using the network, school, and genetic information from the National Longitudinal Study of Adolescent Health, we show that genetic homophily for the TaqI A polymorphism within the DRD2 gene is stronger in schools with greater levels of inequality. Our results suggest that individuals with similar genotypes may not actively select into friendships; rather, they may be placed into these contexts by institutional mechanisms outside of their control. Our work highlights the fundamental role played by broad social structures in the extent to which genetic factors explain complex behaviors, such as friendships. PMID:23045663

  17. Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Gen, Mitsuo; Lin, Lin; Cheng, Runwei

    Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

  18. Genetic Interactions Between Transcription Factors Cause Natural Variation in Yeast

    PubMed Central

    Gerke, Justin; Lorenz, Kim; Cohen, Barak

    2016-01-01

    Our understanding of the genetic basis of phenotypic diversity is limited by the paucity of examples in which multiple, interacting loci have been identified. We show that natural variation in the efficiency of sporulation, the program in yeast that initiates the sexual phase of the life cycle, between oak tree and vineyard strains is due to allelic variation between four nucleotide changes in three transcription factors: IME1, RME1, and RSF1. Furthermore, we identified that selection has shaped quantitative variation in yeast sporulation between strains. These results illustrate how genetic interactions between transcription factors are a major source of phenotypic diversity within species. PMID:19164747

  19. Optimal Design of Geodetic Network Using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Vajedian, Sanaz; Bagheri, Hosein

    2010-05-01

    A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied

  20. Community structure of non-coding RNA interaction network.

    PubMed

    Nacher, Jose C

    2013-01-01

    Rapid technological advances have shown that the ratio of non-protein coding genes rises to 98.5% in humans, suggesting that current knowledge on genetic information processing might be largely incomplete. It implies that protein-coding sequences only represent a small fraction of cellular transcriptional information. Here, we examine the community structure of the network defined by functional interactions between non-coding RNAs (ncRNAs) and proteins related bio-macromolecules (PRMs) using a two-fold approach: modularity in bipartite network and k-clique community detection. First, the high modularity scores as well as the distribution of community sizes showing a scaling-law revealed manifestly non-random features. Second, the k-clique sub-graphs and overlaps show that the identified communities of the ncRNA molecules of H. sapiens can potentially be associated with certain functions. These findings highlight the complex modular structure of ncRNA interactions and its possible regulatory roles in the cell. PMID:23545211

  1. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

    PubMed Central

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  2. MAC protocol for ad hoc networks using a genetic algorithm.

    PubMed

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  3. Transcriptional Derepression Uncovers Cryptic Higher-Order Genetic Interactions

    PubMed Central

    Taylor, Matthew B.; Ehrenreich, Ian M.

    2015-01-01

    Disruption of certain genes can reveal cryptic genetic variants that do not typically show phenotypic effects. Because this phenomenon, which is referred to as ‘phenotypic capacitance’, is a potential source of trait variation and disease risk, it is important to understand how it arises at the genetic and molecular levels. Here, we use a cryptic colony morphology trait that segregates in a yeast cross to explore the mechanisms underlying phenotypic capacitance. We find that the colony trait is expressed when a mutation in IRA2, a negative regulator of the Ras pathway, co-occurs with specific combinations of cryptic variants in six genes. Four of these genes encode transcription factors that act downstream of the Ras pathway, indicating that the phenotype involves genetically complex changes in the transcriptional regulation of Ras targets. We provide evidence that the IRA2 mutation reveals the phenotypic effects of the cryptic variants by disrupting the transcriptional silencing of one or more genes that contribute to the trait. Supporting this role for the IRA2 mutation, deletion of SFL1, a repressor that acts downstream of the Ras pathway, also reveals the phenotype, largely due to the same cryptic variants that were detected in the IRA2 mutant cross. Our results illustrate how higher-order genetic interactions among mutations and cryptic variants can result in phenotypic capacitance in specific genetic backgrounds, and suggests these interactions might reflect genetically complex changes in gene expression that are usually suppressed by negative regulation. PMID:26484664

  4. Towards a predictive theory for genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Tkacik, Gasper

    When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

  5. The mystery of missing heritability: Genetic interactions create phantom heritability

    PubMed Central

    Zuk, Or; Hechter, Eliana; Sunyaev, Shamil R.; Lander, Eric S.

    2012-01-01

    Human genetics has been haunted by the mystery of “missing heritability” of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator—that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating “phantom heritability.” Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions. PMID:22223662

  6. Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks

    PubMed Central

    Davis, Darcy A.; Chawla, Nitesh V.

    2011-01-01

    The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine. PMID:21829475

  7. Genetics of Addiction: Future Focus on Gene × Environment Interaction?

    PubMed

    Vink, Jacqueline M

    2016-09-01

    The heritability of substance use is moderate to high. Successful efforts to find genetic variants associated with substance use (smoking, alcohol, cannabis) have been undertaken by large consortia. However, the proportion of phenotypic variance explained by the identified genetic variants is small. Interestingly, there is overlap between the genetic variants that influence different substances. Moreover, there are sets of "substance-specific" genes and sets of genes contributing to a "vulnerability for addictive behavior" in general. It is important to recognize that genes alone do not determine addiction phenotypes: Environmental factors such as parental monitoring, peer pressure, or socioeconomic status also play an important role. Despite a rich epidemiologic literature focused on the social determinants of substance use, few studies have examined the moderation of genetic influences like gene-environment (G × E) interactions. Understanding this balance may hold the key to understanding the individual differences in substance use, abuse, and addictive behavior. Recommendations for future research are described in this commentary and include increasing the power of G × E studies by using state-of-the-art methods such as polygenic risk scores instead of single genetic variants and taking genetic overlap between substances into account. Future genetic studies should also investigate environmental risk factors for addictive behavior more extensively to unravel the interaction between nature and nurture. Focusing on G × E interactions not only will give insight into the underlying biological mechanism but will also characterize subgroups (based on environmental factors) at high risk for addictive behaviors. With this information, we could bridge the gap between fundamental research and applications for society. PMID:27588524

  8. A Simple Interactive Introduction to Teaching Genetic Engineering

    ERIC Educational Resources Information Center

    Child, Paula

    2013-01-01

    In the UK, at key stage 4, students aged 14-15 studying GCSE Core Science or Unit 1 of the GCSE Biology course are required to be able to describe the process of genetic engineering to produce bacteria that can produce insulin. The simple interactive introduction described in this article allows students to consider the problem, devise a model and…

  9. A UV–Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring

    PubMed Central

    Srivas, Rohith; Costelloe, Thomas; Carvunis, Anne-Ruxandra; Sarkar, Sovan; Malta, Erik; Sun, Su Ming; Pool, Marijke; Licon, Katherine; van Welsem, Tibor; van Leeuwen, Fred; McHugh, Peter J.; van Attikum, Haico; Ideker, Trey

    2014-01-01

    SUMMARY Efficient repair of UV-induced DNA damage requires the precise coordination of nucleotide excision repair (NER) with numerous other biological processes. To map this crosstalk, we generated a differential genetic interaction map centered on quantitative growth measurements of >45,000 double mutants before and after different doses of UV radiation. Integration of genetic data with physical interaction networks identified a global map of 89 UV-induced functional interactions amongst 62 protein complexes, including a number of links between the RSC complex and several NER factors. We show that RSC is recruited to both silenced and transcribed loci following UV damage where it facilitates efficient repair by promoting nucleosome remodeling. Finally, a comparison of the response to high versus low levels of UV shows that the degree of genetic rewiring correlates with dose of UV and reveals a network of dose-specific interactions. This study makes available a large resource of UV-induced interactions, and it illustrates a methodology for identifying dose-dependent interactions based on quantitative shifts in genetic networks. PMID:24360959

  10. Genetic network properties of the human cortex based on regional thickness and surface area measures

    PubMed Central

    Docherty, Anna R.; Sawyers, Chelsea K.; Panizzon, Matthew S.; Neale, Michael C.; Eyler, Lisa T.; Fennema-Notestine, Christine; Franz, Carol E.; Chen, Chi-Hua; McEvoy, Linda K.; Verhulst, Brad; Tsuang, Ming T.; Kremen, William S.

    2015-01-01

    We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function. PMID:26347632

  11. Dynamic interactions of proteins in complex networks

    SciTech Connect

    Appella, E.; Anderson, C.

    2009-10-01

    Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact with the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

  12. Multiple tipping points and optimal repairing in interacting networks.

    PubMed

    Majdandzic, Antonio; Braunstein, Lidia A; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Stanley, H Eugene; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks-such as the human body with its biological networks or the global economic network consisting of regional clusters-often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two 'forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  13. Multiple tipping points and optimal repairing in interacting networks

    NASA Astrophysics Data System (ADS)

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-03-01

    Systems composed of many interacting dynamical networks--such as the human body with its biological networks or the global economic network consisting of regional clusters--often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two `forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.

  14. Reconstructing direct and indirect interactions in networked public goods game

    PubMed Central

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  15. Reconstructing direct and indirect interactions in networked public goods game

    NASA Astrophysics Data System (ADS)

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-07-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

  16. Reconstructing direct and indirect interactions in networked public goods game.

    PubMed

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  17. Non-coding RNAs and complex distributed genetic networks

    NASA Astrophysics Data System (ADS)

    Zhdanov, Vladimir P.

    2011-08-01

    In eukaryotic cells, the mRNA-protein interplay can be dramatically influenced by non-coding RNAs (ncRNAs). Although this new paradigm is now widely accepted, an understanding of the effect of ncRNAs on complex genetic networks is lacking. To clarify what may happen in this case, we propose a mean-field kinetic model describing the influence of ncRNA on a complex genetic network with a distributed architecture including mutual protein-mediated regulation of many genes transcribed into mRNAs. ncRNA is considered to associate with mRNAs and inhibit their translation and/or facilitate degradation. Our results are indicative of the richness of the kinetics under consideration. The main complex features are found to be bistability and oscillations. One could expect to find kinetic chaos as well. The latter feature has however not been observed in our calculations. In addition, we illustrate the difference in the regulation of distributed networks by mRNA and ncRNA.

  18. Indirect Genetic Effects and the Dynamics of Social Interactions

    PubMed Central

    Trubenová, Barbora; Novak, Sebastian; Hager, Reinmar

    2015-01-01

    Background Indirect genetic effects (IGEs) occur when genes expressed in one individual alter the expression of traits in social partners. Previous studies focused on the evolutionary consequences and evolutionary dynamics of IGEs, using equilibrium solutions to predict phenotypes in subsequent generations. However, whether or not such steady states may be reached may depend on the dynamics of interactions themselves. Results In our study, we focus on the dynamics of social interactions and indirect genetic effects and investigate how they modify phenotypes over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics; rather we consider within-individual phenotypic changes, also referred to as phenotypic plasticity. We analyse iterative interactions, when individuals interact in a series of discontinuous events, and investigate the stability of steady state solutions and the dependence on model parameters, such as population size, strength, and the nature of interactions. We show that for interactions where a feedback loop occurs, the possible parameter space of interaction strength is fairly limited, affecting the evolutionary consequences of IGEs. We discuss the implications of our results for current IGE model predictions and their limitations. PMID:25993124

  19. Genetic Interaction Maps in Escherichia coli Reveal Functional Crosstalk among Cell Envelope Biogenesis Pathways

    PubMed Central

    Vlasblom, James; Gagarinova, Alla; Phanse, Sadhna; Graham, Chris; Yousif, Fouad; Ding, Huiming; Xiong, Xuejian; Nazarians-Armavil, Anaies; Alamgir, Md; Ali, Mehrab; Pogoutse, Oxana; Pe'er, Asaf; Arnold, Roland; Michaut, Magali; Parkinson, John; Golshani, Ashkan; Whitfield, Chris; Wodak, Shoshana J.; Moreno-Hagelsieb, Gabriel; Greenblatt, Jack F.; Emili, Andrew

    2011-01-01

    As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target. PMID:22125496

  20. Environmentally-Modulated Changes in Fluorescence Distribution in Cells with Oscillatory Genetic Network Dynamics

    PubMed Central

    Portle, Stephanie; Iadevaia, Sergio; San, Ka-Yiu; Bennett, George N.; Mantzaris, Nikos

    2009-01-01

    We investigated the distribution of green fluorescent protein (GFP) expression levels in a population of E. coli cells expressing an artificial genetic regulatory network, known as the “repressilator” (Elowitz and Leibler, 2000), which exhibits oscillations at the single-cell level. A series of shake flask experiments were performed and analyzed using flow cytometry to test how cell populations carrying this system could be controlled extracellularly using the inducers anhydrotetracycline (aTc) and isopropyl-β-D-thiogalactopyranoside (IPTG). With variation of [aTc], it exhibits bi-threshold behavior, such that the entire culture reaches one of three steady states at a quasi-time invariant “reference state.” Also, there is significant hysteresis. Transiently, the middle state shows damping oscillations, while the low and high states show a stable steady state. The addition of IPTG serves to fine-tune the characteristics of the aTc-only expression, lowering the average and CV of the distributions, and possibly perturbing the network to a different state. However, in modeling this system, the multiplicity and bi-threshold behavior are not theoretically possible according to the designed interactions. In order to explain this discrepancy, we hypothesize that one or more of the repressors has a significant nonspecific interaction with a promoter that does not contain its operator site. The new modeling results incorporating these extra interactions qualitatively match our experimental findings. After constructing plasmids to test these hypotheses, we discover that at least four of these interactions exist, which can create the low and high states and multiplicity seen experimentally. This genetic architecture has flexibility in its behavior that has not been demonstrated before, and the combination of experiment and modeling enlightened our understanding of the molecular interactions driving the network's behavior, leading us to discover the significance of

  1. Superiority of artificial neural networks for a genetic classification procedure.

    PubMed

    Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

    2015-01-01

    The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions. PMID:26345924

  2. Modifier Genes and the Plasticity of Genetic Networks in Mice

    PubMed Central

    Hamilton, Bruce A.; Yu, Benjamin D.

    2012-01-01

    Modifier genes are an integral part of the genetic landscape in both humans and experimental organisms, but have been less well explored in mammals than other systems. A growing number of modifier genes in mouse models of disease nonetheless illustrate the potential for novel findings, while new technical advances promise many more to come. Modifier genes in mouse models include induced mutations and spontaneous or wild-derived variations captured in inbred strains. Identification of modifiers among wild-derived variants in particular should detect disease modifiers that have been shaped by selection and might therefore be compatible with high fitness and function. Here we review selected examples and argue that modifier genes derived from natural variation may provide a bias for nodes in genetic networks that have greater intrinsic plasticity and whose therapeutic manipulation may therefore be more resilient to side effects than conventional targets. PMID:22511884

  3. Protein-protein interaction networks (PPI) and complex diseases

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

    2014-01-01

    The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

  4. Combining neural networks and genetic algorithms for hydrological flow forecasting

    NASA Astrophysics Data System (ADS)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  5. Applications of genetic algorithms and neural networks to interatomic potentials

    NASA Astrophysics Data System (ADS)

    Hobday, Steven; Smith, Roger; BelBruno, Joe

    1999-06-01

    Applications of two modern artificial intelligence (AI) techniques, genetic algorithms (GA) and neural networks (NN) to computer simulations are reported. It is shown that the GA are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions and generally outperform other optimisation methods for this task. A number of applications are given including covalent, and close packed structures of single or multi-component atomic species. It is also shown that (many body) interatomic potential functions for multi-component systems can be derived by training a specially constructed NN on a variety of structural data.

  6. Genetic networks specifying the functional architecture of orientation domains in V1

    NASA Astrophysics Data System (ADS)

    Liedtke, Joscha; Wolf, Fred

    2015-03-01

    Although genetic information is critically important for brain development and structure, it is widely believed that neocortical functional architecture is largely shaped by activity dependent mechanisms. Here we show theoretically that mathematical models of genetic networks of principal neurons interacting by long range axonal morphogen transport can generate morphogen patterns that exactly prescribe the functional architecture of the primary visual cortex (V1) as experimentally observed. We analyze in detail an example genetic network that encodes the functional architecture of V1 by a dynamically generated morphogen pattern. We use analytical methods from weakly non-linear analysis [Cross & Hohenberg 1993] complemented by numerical simulations to obtain solutions of the model. In particular we find that the pinwheel statistics are in quantitative agreement with high precision experimental measurements [Kaschube et al. 2010]. This theory opens a novel perspective on the experimentally observed robustness of V1's architecture against radically abnormal developmental conditions such a dark rearing [White et al. 2001]. Furthermore, it provides for the first time a scheme how the pattern of a complex cortical architecture can be specified using only a small genetic bandwidth.

  7. NACE: A web-based tool for prediction of intercompartmental efficiency of human molecular genetic networks.

    PubMed

    Popik, Olga V; Ivanisenko, Timofey V; Saik, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-06-15

    Molecular genetic processes generally involve proteins from distinct intracellular localisations. Reactions that follow the same process are distributed among various compartments within the cell. In this regard, the reaction rate and the efficiency of biological processes can depend on the subcellular localisation of proteins. Previously, the authors proposed a method of evaluating the efficiency of biological processes based on the analysis of the distribution of protein subcellular localisation (Popik et al., 2014). Here, NACE is presented, which is an open access web-oriented program that implements this method and allows the user to evaluate the intercompartmental efficiency of human molecular genetic networks. The method has been extended by a new feature that provides the evaluation of the tissue-specific efficiency of networks for more than 2800 anatomical structures. Such assessments are important in cases when molecular genetic pathways in different tissues proceed with the participation of various proteins with a number of intracellular localisations. For example, an analysis of KEGG pathways, conducted using the developed program, showed that the efficiencies of many KEGG pathways are tissue-specific. Analysis of efficiencies of regulatory pathways in the liver, linking proteins of the hepatitis C virus with human proteins involved in the KEGG apoptosis pathway, showed that intercompartmental efficiency might play an important role in host-pathogen interactions. Thus, the developed tool can be useful in the study of the effectiveness of functioning of various molecular genetic networks, including metabolic, regulatory, host-pathogen interactions and others taking into account tissue-specific gene expression. The tool is available via the following link: http://www-bionet.sscc.ru/nace/. PMID:27109913

  8. Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

    PubMed Central

    2015-01-01

    Background Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Results Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. Conclusion The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers

  9. Multiple tipping points and optimal repairing in interacting networks

    PubMed Central

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  10. A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

    PubMed

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease. PMID:24228248

  11. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

    PubMed Central

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease. PMID:24228248

  12. Applying the logic of genetic interaction to discover small molecules that functionally interact with human disease alleles

    PubMed Central

    Brettman, Ari D.; Tan, Pauline H.; Tran, Khoa; Shaw, Stanley Y.

    2015-01-01

    Summary Despite rapid advances in the genetics of complex human diseases, understanding the significance of human disease alleles remains a critical roadblock to clinical translation. Here, we present a chemical biology approach that uses perturbation with small molecules of known mechanism to reveal mechanistic and therapeutic consequences of human disease alleles. To maximize human applicability, we perform chemical screening on multiple cell lines isolated from individual patients, allowing the effects of disease alleles to be studied in their native genetic context. Chemical screen analysis combines the logic of traditional genetic interaction screens with analytic methods from high-dimensionality gene expression analyses. We rank compounds according to their ability to discriminate between cell lines that are mutant vs. wild-type at a disease gene (i.e., the compounds induce phenotypes that differ the most across the two classes). A technique called Compound Set Enrichment Analysis (CSEA), modeled after a widely used method to identify pathways from gene expression data, identifies sets of functionally or structurally related compounds that are statistically enriched among the most discriminating compounds. This chemical:genetic interaction approach was applied to patient-derived cells in a monogenic form of diabetes and identified several classes of compounds (including FDA-approved drugs) that show functional interactions with the causative disease gene, and also modulate insulin secretion, a critical disease phenotype. In summary, perturbation of patient-derived cells with small molecules of known mechanism, together with compound-set based pathway analysis, can identify small molecules and pathways that functionally interact with disease alleles, and that can modulate disease networks for therapeutic effect. PMID:25618333

  13. Socioeconomic networks with long-range interactions

    NASA Astrophysics Data System (ADS)

    Carvalho, Rui; Iori, Giulia

    2008-07-01

    We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communication of information and allocation of goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights, parametrized by the variable δ , decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing δ , the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterized by shorter average path length, lower degree assortativity, and higher central point dominance. In the second part of the paper we compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.

  14. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network

    PubMed Central

    Keith, Benjamin P.; Robertson, David L.; Hentges, Kathryn E.

    2014-01-01

    Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity. PMID:25538735

  15. A genetic interaction map of cell cycle regulators.

    PubMed

    Billmann, Maximilian; Horn, Thomas; Fischer, Bernd; Sandmann, Thomas; Huber, Wolfgang; Boutros, Michael

    2016-04-15

    Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis inDrosophilaS2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle-relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for theDrosophilaCCR4 mRNA processing complex componentl(2)NC136during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes. PMID:26912791

  16. A genetic interaction map of cell cycle regulators

    PubMed Central

    Billmann, Maximilian; Horn, Thomas; Fischer, Bernd; Sandmann, Thomas; Huber, Wolfgang; Boutros, Michael

    2016-01-01

    Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle–relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes. PMID:26912791

  17. Systems Analysis of Plant Functional, Transcriptional, Physical Interaction, and Metabolic Networks

    PubMed Central

    Bassel, George W.; Gaudinier, Allison; Brady, Siobhan M.; Hennig, Lars; Rhee, Seung Y.; De Smet, Ive

    2012-01-01

    Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach. PMID:23110892

  18. RNA-RNA interaction prediction using genetic algorithm

    PubMed Central

    2014-01-01

    Background RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. Results In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations. Conclusions This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches. PMID:25114714

  19. Neural-network-biased genetic algorithms for materials design

    NASA Astrophysics Data System (ADS)

    Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David

    Machine learning tools have been progressively adopted by the materials science community to accelerate design of materials with targeted properties. However, in the search for new materials exhibiting properties and performance beyond that previously achieved, machine learning approaches are frequently limited by two major shortcomings. First, they are intrinsically interpolative. They are therefore better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require the availability of large datasets, which in some fields are not available and would be prohibitively expensive to produce. Here we describe a new strategy for combining genetic algorithms, neural networks and other machine learning tools, and molecular simulation to discover materials with extremal properties in the absence of pre-existing data. Predictions from progressively constructed machine learning tools are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct molecular dynamics simulation. We survey several initial materials design problems we have addressed with this framework and compare its performance to that of standard genetic algorithm approaches. We acknowledge the W. M. Keck Foundation for support of this work.

  20. Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks

    PubMed Central

    Beckett, Stephen J.; Williams, Hywel T. P.

    2013-01-01

    Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage–bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such ‘nested-modular’ interaction networks can be produced by a simple model of host–phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins. PMID:24516719

  1. Coevolutionary diversification creates nested-modular structure in phage-bacteria interaction networks.

    PubMed

    Beckett, Stephen J; Williams, Hywel T P

    2013-12-01

    Phage and their bacterial hosts are the most diverse and abundant biological entities in the oceans, where their interactions have a major impact on marine ecology and ecosystem function. The structure of interaction networks for natural phage-bacteria communities offers insight into their coevolutionary origin. At small phylogenetic scales, observed communities typically show a nested structure, in which both hosts and phages can be ranked by their range of resistance and infectivity, respectively. A qualitatively different multi-scale structure is seen at larger phylogenetic scales; a natural assemblage sampled from the Atlantic Ocean displays large-scale modularity and local nestedness within each module. Here, we show that such 'nested-modular' interaction networks can be produced by a simple model of host-phage coevolution in which infection depends on genetic matching. Negative frequency-dependent selection causes diversification of hosts (to escape phages) and phages (to track their evolving hosts). This creates a diverse community of bacteria and phage, maintained by kill-the-winner ecological dynamics. When the resulting communities are visualized as bipartite networks of who infects whom, they show the nested-modular structure characteristic of the Atlantic sample. The statistical significance and strength of this observation varies depending on whether the interaction networks take into account the density of the interacting strains, with implications for interpretation of interaction networks constructed by different methods. Our results suggest that the apparently complex community structures associated with marine bacteria and phage may arise from relatively simple coevolutionary origins. PMID:24516719

  2. A Co-Association Network Analysis of the Genetic Determination of Pig Conformation, Growth and Fatness

    PubMed Central

    Puig-Oliveras, Anna; Ballester, Maria; Corominas, Jordi; Revilla, Manuel; Estellé, Jordi; Fernández, Ana I.; Ramayo-Caldas, Yuliaxis; Folch, Josep M.

    2014-01-01

    Background Several QTLs have been identified for major economically relevant traits in livestock, such as growth and meat quality, revealing the complex genetic architecture of these traits. The use of network approaches considering the interactions of multiple molecules and traits provides useful insights into the molecular underpinnings of complex traits. Here, a network based methodology, named Association Weight Matrix, was applied to study gene interactions and pathways affecting pig conformation, growth and fatness traits. Results The co-association network analysis underpinned three transcription factors, PPARγ, ELF1, and PRDM16 involved in mesoderm tissue differentiation. Fifty-four genes in the network belonged to growth-related ontologies and 46 of them were common with a similar study for growth in cattle supporting our results. The functional analysis uncovered the lipid metabolism and the corticotrophin and gonadotrophin release hormone pathways among the most important pathways influencing these traits. Our results suggest that the genes and pathways here identified are important determining either the total body weight of the animal and the fat content. For instance, a switch in the mesoderm tissue differentiation may determinate the age-related preferred pathways being in the puberty stage those related with the miogenic and osteogenic lineages; on the contrary, in the maturity stage cells may be more prone to the adipocyte fate. Hence, our results demonstrate that an integrative genomic co-association analysis is a powerful approach for identifying new connections and interactions among genes. Conclusions This work provides insights about pathways and key regulators which may be important determining the animal growth, conformation and body proportions and fatness traits. Molecular information concerning genes and pathways here described may be crucial for the improvement of genetic breeding programs applied to pork meat production. PMID:25503799

  3. Jimena: efficient computing and system state identification for genetic regulatory networks

    PubMed Central

    2013-01-01

    Background Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. Results (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Conclusions Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior. PMID:24118878

  4. Joint clustering of protein interaction networks through Markov random walk

    PubMed Central

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  5. Joint clustering of protein interaction networks through Markov random walk.

    PubMed

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  6. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  7. From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions.

    PubMed

    Ulitsky, Igor; Shlomi, Tomer; Kupiec, Martin; Shamir, Ron

    2008-01-01

    Recent technological breakthroughs allow the quantification of hundreds of thousands of genetic interactions (GIs) in Saccharomyces cerevisiae. The interpretation of these data is often difficult, but it can be improved by the joint analysis of GIs along with complementary data types. Here, we describe a novel methodology that integrates genetic and physical interaction data. We use our method to identify a collection of functional modules related to chromosomal biology and to investigate the relations among them. We show how the resulting map of modules provides clues for the elucidation of function both at the level of individual genes and at the level of functional modules. PMID:18628749

  8. Guaranteeing global synchronization in networks with stochastic interactions

    NASA Astrophysics Data System (ADS)

    Klinglmayr, Johannes; Kirst, Christoph; Bettstetter, Christian; Timme, Marc

    2012-07-01

    We design the interactions between oscillators communicating via variably delayed pulse coupling to guarantee their synchronization on arbitrary network topologies. We identify a class of response functions and prove convergence to network-wide synchrony from arbitrary initial conditions. Synchrony is achieved if the pulse emission is unreliable or intentionally probabilistic. These results support the design of scalable, reliable and energy-efficient communication protocols for fully distributed synchronization as needed, e.g., in mobile phone networks, embedded systems, sensor networks and autonomously interacting swarm robots.

  9. Domain-mediated protein interaction prediction: From genome to network.

    PubMed

    Reimand, Jüri; Hui, Shirley; Jain, Shobhit; Law, Brian; Bader, Gary D

    2012-08-14

    Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease. PMID:22561014

  10. Genome complexity, robustness and genetic interactions in digital organisms

    NASA Astrophysics Data System (ADS)

    Lenski, Richard E.; Ofria, Charles; Collier, Travis C.; Adami, Christoph

    1999-08-01

    Digital organisms are computer programs that self-replicate, mutate and adapt by natural selection. They offer an opportunity to test generalizations about living systems that may extend beyond the organic life that biologists usually study. Here we have generated two classes of digital organism: simple programs selected solely for rapid replication, and complex programs selected to perform mathematical operations that accelerate replication through a set of defined `metabolic' rewards. To examine the differences in their genetic architecture, we introduced millions of single and multiple mutations into each organism and measured the effects on the organism's fitness. The complex organisms are more robust than the simple ones with respect to the average effects of single mutations. Interactions among mutations are common and usually yield higher fitness than predicted from the component mutations assuming multiplicative effects; such interactions are especially important in the complex organisms. Frequent interactions among mutations have also been seen in bacteria, fungi and fruitflies. Our findings support the view that interactions are a general feature of genetic systems.

  11. Problem Solving Interactions on Electronic Networks.

    ERIC Educational Resources Information Center

    Waugh, Michael; And Others

    Arguing that electronic networking provides a medium which is qualitatively superior to the traditional classroom for conducting certain types of problem solving exercises, this paper details the Water Problem Solving Project, which was conducted on the InterCultural Learning Network in 1985 and 1986 with students from the United States, Mexico,…

  12. Genetic Epidemiology and Insights into Interactive Genetic and Environmental Effects in Autism Spectrum Disorders

    PubMed Central

    Kim, Young Shin; Leventhal, Bennett L.

    2014-01-01

    Understanding the pathogenesis of Neurodevelopmental Disorders (NDDs) has proven to be challenging. Using Autism Spectrum Disorder (ASD) as a paradigmatic NDD, this paper reviews the existing literature on the etiologic substrates of ASD and explores how genetic epidemiology approaches including gene-environment interactions (GxE) can play roles in identifying factors associated with ASD etiology. New genetic and bioinformatics strategies have yielded important clues to ASD genetic substrates. Next steps for understanding ASD pathogenesis require significant effort to focus on how genes and environment interact with one another in typical development and its perturbations. Along with larger sample sizes, future study designs should include sample ascertainment that is epidemiologic and population-based to capture the entire ASD spectrum with both categorical and dimensional phenotypic characterization, environmental measurement with accuracy, validity and biomarkers, statistical methods to address population stratification, multiple comparisons and GxE of rare variants, animal models to test hypotheses and, new methods to broaden the capacity to search for GxE, including genome-wide and environment-wide association studies, precise estimation of heritability using dense genetic markers and consideration of GxE both as the disease cause and a disease course modifier. While examination of GxE appears to be a daunting task, tremendous recent progress in gene discovery opens new horizons for advancing our understanding the role of GxE in the pathogenesis of, and ultimately identifying the causes, treatments and even prevention for ASD and other NDDs. PMID:25483344

  13. Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders

    PubMed Central

    Sahni, Nidhi; Yi, Song; Taipale, Mikko; Fuxman Bass, Juan I.; Coulombe-Huntington, Jasmin; Yang, Fan; Peng, Jian; Weile, Jochen; Karras, Georgios I.; Wang, Yang; Kovács, István A.; Kamburov, Atanas; Krykbaeva, Irina; Lam, Mandy H.; Tucker, George; Khurana, Vikram; Sharma, Amitabh; Liu, Yang-Yu; Yachie, Nozomu; Zhong, Quan; Shen, Yun; Palagi, Alexandre; San-Miguel, Adriana; Fan, Changyu; Balcha, Dawit; Dricot, Amelie; Jordan, Daniel M.; Walsh, Jennifer M.; Shah, Akash A.; Yang, Xinping; Stoyanova, Ani; Leighton, Alex; Calderwood, Michael A.; Jacob, Yves; Cusick, Michael E.; Salehi-Ashtiani, Kourosh; Whitesell, Luke J.; Sunyaev, Shamil; Berger, Bonnie; Barabási, Albert-László; Charloteaux, Benoit; Hill, David E.; Hao, Tong; Roth, Frederick P.; Xia, Yu; Walhout, Albertha J.M.; Lindquist, Susan; Vidal, Marc

    2015-01-01

    SUMMARY How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to “edgetic” alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread. PMID:25910212

  14. Does self-construal predict activity in the social brain network? A genetic moderation effect

    PubMed Central

    Ma, Yina; Han, Shihui

    2014-01-01

    Neural activity in the social brain network varies across individuals with different cultural traits and different genetic polymorphisms. It remains unknown whether a specific genetic polymorphism may influence the association between cultural traits and neural activity in the social brain network. We tested whether the serotonin transporter promoter polymorphism (5-HTTLPR) affects the association between self-construals and neural activity involved in reflection of personal attributes of oneself and a significant other (i.e., mother). Using functional MRI, we scanned Chinese adults with short/short (s/s) or long/long (l/l) variants of the 5-HTTLPR during reflection of personal attributes of oneself and one’s mother. We found that, while s/s and l/l genotype groups did not differ significantly in self-construals measured by the Self-Construal Scale, the relationship between self-construal scores and neural responses to reflection of oneself and mother was significantly different between the two genotype groups. Specifically, l/l but not s/s genotype group showed significant association between self-construal scores and activity in the medial prefrontal cortex, bilateral middle frontal cortex, temporoparietal junction, insula and hippocampus during reflection on mental attributes of oneself and mother. Our findings suggest that a specific genetic polymorphism may interact with a cultural trait to shape the neural substrates underlying social cognition. PMID:24009354

  15. Does self-construal predict activity in the social brain network? A genetic moderation effect.

    PubMed

    Ma, Yina; Wang, Chenbo; Li, Bingfeng; Zhang, Wenxia; Rao, Yi; Han, Shihui

    2014-09-01

    Neural activity in the social brain network varies across individuals with different cultural traits and different genetic polymorphisms. It remains unknown whether a specific genetic polymorphism may influence the association between cultural traits and neural activity in the social brain network. We tested whether the serotonin transporter promoter polymorphism (5-HTTLPR) affects the association between self-construals and neural activity involved in reflection of personal attributes of oneself and a significant other (i.e., mother). Using functional MRI, we scanned Chinese adults with short/short (s/s) or long/long (l/l) variants of the 5-HTTLPR during reflection of personal attributes of oneself and one's mother. We found that, while s/s and l/l genotype groups did not differ significantly in self-construals measured by the Self-Construal Scale, the relationship between self-construal scores and neural responses to reflection of oneself and mother was significantly different between the two genotype groups. Specifically, l/l but not s/s genotype group showed significant association between self-construal scores and activity in the medial prefrontal cortex, bilateral middle frontal cortex, temporoparietal junction, insula and hippocampus during reflection on mental attributes of oneself and mother. Our findings suggest that a specific genetic polymorphism may interact with a cultural trait to shape the neural substrates underlying social cognition. PMID:24009354

  16. Interaction Networks: Generating High Level Hints Based on Network Community Clustering

    ERIC Educational Resources Information Center

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

    We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

  17. Empirical evaluation of neutral interactions in host-parasite networks.

    PubMed

    Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

    2014-04-01

    While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization. PMID:24642492

  18. Genetic network identifies novel pathways contributing to atherosclerosis susceptibility in the innominate artery

    PubMed Central

    2014-01-01

    Background Atherosclerosis, the underlying cause of cardiovascular disease, results from both genetic and environmental factors. Methods In the current study we take a systems-based approach using weighted gene co-expression analysis to identify a candidate pathway of genes related to atherosclerosis. Bioinformatic analyses are performed to identify candidate genes and interactions and several novel genes are characterized using in-vitro studies. Results We identify 1 coexpression module associated with innominate artery atherosclerosis that is also enriched for inflammatory and macrophage gene signatures. Using a series of bioinformatics analysis, we further prioritize the genes in this pathway and identify Cd44 as a critical mediator of the atherosclerosis. We validate our predictions generated by the network analysis using Cd44 knockout mice. Conclusion These results indicate that alterations in Cd44 expression mediate inflammation through a complex transcriptional network involving a number of previously uncharacterized genes. PMID:25115202

  19. Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Xu, Liming

    The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

  20. Genetic exploration of interactive domains in RNA polymerase II subunits.

    PubMed Central

    Martin, C; Okamura, S; Young, R

    1990-01-01

    The two large subunits of RNA polymerase II, RPB1 and RPB2, contain regions of extensive homology to the two large subunits of Escherichia coli RNA polymerase. These homologous regions may represent separate protein domains with unique functions. We investigated whether suppressor genetics could provide evidence for interactions between specific segments of RPB1 and RPB2 in Saccharomyces cerevisiae. A plasmid shuffle method was used to screen thoroughly for mutations in RPB2 that suppress a temperature-sensitive mutation, rpb1-1, which is located in region H of RPB1. All six RPB2 mutations that suppress rpb1-1 were clustered in region I of RPB2. The location of these mutations and the observation that they were allele specific for suppression of rpb1-1 suggests an interaction between region H of RPB1 and region I of RPB2. A similar experiment was done to isolate and map mutations in RPB1 that suppress a temperature-sensitive mutation, rpb2-2, which occurs in region I of RPB2. These suppressor mutations were not clustered in a particular region. Thus, fine structure suppressor genetics can provide evidence for interactions between specific segments of two proteins, but the results of this type of analysis can depend on the conditional mutation to be suppressed. Images PMID:2183012

  1. Genetic by environment interactions affect plant–soil linkages

    PubMed Central

    Pregitzer, Clara C; Bailey, Joseph K; Schweitzer, Jennifer A

    2013-01-01

    The role of plant intraspecific variation in plant–soil linkages is poorly understood, especially in the context of natural environmental variation, but has important implications in evolutionary ecology. We utilized three 18- to 21-year-old common gardens across an elevational gradient, planted with replicates of five Populus angustifolia genotypes each, to address the hypothesis that tree genotype (G), environment (E), and G × E interactions would affect soil carbon and nitrogen dynamics beneath individual trees. We found that soil nitrogen and carbon varied by over 50% and 62%, respectively, across all common garden environments. We found that plant leaf litter (but not root) traits vary by genotype and environment while soil nutrient pools demonstrated genotype, environment, and sometimes G × E interactions, while process rates (net N mineralization and net nitrification) demonstrated G × E interactions. Plasticity in tree growth and litter chemistry was significantly related to the variation in soil nutrient pools and processes across environments, reflecting tight plant–soil linkages. These data overall suggest that plant genetic variation can have differential affects on carbon storage and nitrogen cycling, with implications for understanding the role of genetic variation in plant–soil feedback as well as management plans for conservation and restoration of forest habitats with a changing climate. PMID:23919173

  2. Genetic associations with micronutrient levels identified in immune and gastrointestinal networks.

    PubMed

    Morine, Melissa J; Monteiro, Jacqueline Pontes; Wise, Carolyn; Teitel, Candee; Pence, Lisa; Williams, Anna; Ning, Baitang; McCabe-Sellers, Beverly; Champagne, Catherine; Turner, Jerome; Shelby, Beatrice; Bogle, Margaret; Beger, Richard D; Priami, Corrado; Kaput, Jim

    2014-07-01

    The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6-14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein-protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene-nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions. PMID:24879315

  3. Percolation on networks with antagonistic and dependent interactions

    NASA Astrophysics Data System (ADS)

    Kotnis, Bhushan; Kuri, Joy

    2015-03-01

    Drawing inspiration from real world interacting systems, we study a system consisting of two networks that exhibit antagonistic and dependent interactions. By antagonistic and dependent interactions we mean that a proportion of functional nodes in a network cause failure of nodes in the other, while failure of nodes in the other results in failure of links in the first. In contrast to interdependent networks, which can exhibit first-order phase transitions, we find that the phase transitions in such networks are continuous. Our analysis shows that, compared to an isolated network, the system is more robust against random attacks. Surprisingly, we observe a region in the parameter space where the giant connected components of both networks start oscillating. Furthermore, we find that for Erdős-Rényi and scale-free networks the system oscillates only when the dependence and antagonism between the two networks are very high. We believe that this study can further our understanding of real world interacting systems.

  4. Development of Attention Networks and Their Interactions in Childhood

    ERIC Educational Resources Information Center

    Pozuelos, Joan P.; Paz-Alonso, Pedro M.; Castillo, Alejandro; Fuentes, Luis J.; Rueda, M. Rosario

    2014-01-01

    In the present study, we investigated developmental trajectories of alerting, orienting, and executive attention networks and their interactions over childhood. Two cross-sectional experiments were conducted with different samples of 6-to 12-year-old children using modified versions of the attention network task (ANT). In Experiment 1 (N = 106),…

  5. Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.

    ERIC Educational Resources Information Center

    Odell, Kerry S.; And Others

    1994-01-01

    Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric Technology" (Berry). (JOW)

  6. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    SciTech Connect

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  7. Echoed time series predictions, neural networks and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Conway, A.

    This work aims to illustrate a potentially serious and previously unrecognised problem in using Neural Networks (NNs), and possibly other techniques, to predict Time Series (TS). It also demonstrates how a new training scheme using a genetic algorithm can alleviate this problem. Although it is already established that NNs can predict TS such as Sunspot Number (SSN) with reasonable success, the accuracy of these predictions is often judged solely by an RMS or related error. The use of this type of error overlooks the presence of what we have termed echoing, where the NN outputs its most recent input as its prediction. Therefore, a method of detecting echoed predictions is introduced, called time-shifting. Reasons for the presence of echo are discussed and then related to the choice of TS sampling. Finally, a new specially designed training scheme is described, which is a hybrid of a genetic algorithm search and back propagation. With this method we have successfully trained NNs to predict without any echo.

  8. Cellular and network mechanisms of genetically-determined absence seizures.

    PubMed

    Pinault, Didier; O'Brien, Terence J

    2005-01-01

    network mechanisms responsible for the switch from physiological, wake-related, natural oscillations into pathological spike-and-wave discharges? We speculate on possible answers to this, building particularly on recent findings from genetic models in rats. PMID:21909233

  9. Quantitative Genetic Interactions Reveal Layers of Biological Modularity

    PubMed Central

    Beltrao, Pedro; Cagney, Gerard; Krogan, Nevan J.

    2010-01-01

    In the past, biomedical research has embraced a reductionist approach, primarily focused on characterizing the individual components that comprise a system of interest. Recent technical developments have significantly increased the size and scope of data describing biological systems. At the same time, advances in the field of systems biology have evoked a broader view of how the underlying components are interconnected. In this essay, we discuss how quantitative genetic interaction mapping has enhanced our view of biological systems, allowing a deeper functional interrogation at different biological scales. PMID:20510918

  10. Modularity in the evolution of yeast protein interaction network

    PubMed Central

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

    Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution. PMID:25914446

  11. Exhaustive search for conservation networks of populations representing genetic diversity.

    PubMed

    Diniz-Filho, J A F; Diniz, J V B P L; Telles, M P C

    2016-01-01

    Conservation strategies routinely use optimization methods to identify the smallest number of units required to represent a set of features that need to be conserved, including biomes, species, and populations. In this study, we provide R scripts to facilitate exhaustive search for solutions that represent all of the alleles in networks with the smallest possible number of populations. The script also allows other variables to be added to describe the populations, thereby providing the basis for multi-objective optimization and the construction of Pareto curves by averaging the values in the solutions. We applied this algorithm to an empirical dataset that comprised 23 populations of Eugenia dysenterica, which is a tree species with a widespread distribution in the Cerrado biome. We observed that 15 populations would be necessary to represent all 249 alleles based on 11 microsatellite loci, and that the likelihood of representing all of the alleles with random networks is less than 0.0001. We selected the solution (from two with the smallest number of populations) obtained for the populations with a higher level of climatic stability as the best strategy for in situ conservation of genetic diversity of E. dysenterica. The scripts provided in this study are a simple and efficient alternative to more complex optimization methods, especially when the number of populations is relatively small (i.e., <25 populations). PMID:26909939

  12. Accurate measurements of dynamics and reproducibility in small genetic networks

    PubMed Central

    Dubuis, Julien O; Samanta, Reba; Gregor, Thomas

    2013-01-01

    Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes. PMID:23340845

  13. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

    PubMed Central

    Xia, Jianguo; Benner, Maia J.; Hancock, Robert E. W.

    2014-01-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  14. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  15. Genetic Networks Required to Coordinate Chromosome Replication by DNA Polymerases α, δ, and ε in Saccharomyces cerevisiae

    PubMed Central

    Dubarry, Marion; Lawless, Conor; Banks, A. Peter; Cockell, Simon; Lydall, David

    2015-01-01

    Three major DNA polymerases replicate the linear eukaryotic chromosomes. DNA polymerase α-primase (Pol α) and DNA polymerase δ (Pol δ) replicate the lagging-strand and Pol α and DNA polymerase ε (Pol ε) the leading-strand. To identify factors affecting coordination of DNA replication, we have performed genome-wide quantitative fitness analyses of budding yeast cells containing defective polymerases. We combined temperature-sensitive mutations affecting the three replicative polymerases, Pol α, Pol δ, and Pol ε with genome-wide collections of null and reduced function mutations. We identify large numbers of genetic interactions that inform about the roles that specific genes play to help Pol α, Pol δ, and Pol ε function. Surprisingly, the overlap between the genetic networks affecting the three DNA polymerases does not represent the majority of the genetic interactions identified. Instead our data support a model for division of labor between the different DNA polymerases during DNA replication. For example, our genetic interaction data are consistent with biochemical data showing that Pol ε is more important to the Pre-Loading complex than either Pol α or Pol δ. We also observed distinct patterns of genetic interactions between leading- and lagging-strand DNA polymerases, with particular genes being important for coupling proliferating cell nuclear antigen loading/unloading (Ctf18, Elg1) with nucleosome assembly (chromatin assembly factor 1, histone regulatory HIR complex). Overall our data reveal specialized genetic networks that affect different aspects of leading- and lagging-strand DNA replication. To help others to engage with these data we have generated two novel, interactive visualization tools, DIXY and Profilyzer. PMID:26297725

  16. Genetic Networks Required to Coordinate Chromosome Replication by DNA Polymerases α, δ, and ε in Saccharomyces cerevisiae.

    PubMed

    Dubarry, Marion; Lawless, Conor; Banks, A Peter; Cockell, Simon; Lydall, David

    2015-10-01

    Three major DNA polymerases replicate the linear eukaryotic chromosomes. DNA polymerase α-primase (Pol α) and DNA polymerase δ (Pol δ) replicate the lagging-strand and Pol α and DNA polymerase ε (Pol ε) the leading-strand. To identify factors affecting coordination of DNA replication, we have performed genome-wide quantitative fitness analyses of budding yeast cells containing defective polymerases. We combined temperature-sensitive mutations affecting the three replicative polymerases, Pol α, Pol δ, and Pol ε with genome-wide collections of null and reduced function mutations. We identify large numbers of genetic interactions that inform about the roles that specific genes play to help Pol α, Pol δ, and Pol ε function. Surprisingly, the overlap between the genetic networks affecting the three DNA polymerases does not represent the majority of the genetic interactions identified. Instead our data support a model for division of labor between the different DNA polymerases during DNA replication. For example, our genetic interaction data are consistent with biochemical data showing that Pol ε is more important to the Pre-Loading complex than either Pol α or Pol δ. We also observed distinct patterns of genetic interactions between leading- and lagging-strand DNA polymerases, with particular genes being important for coupling proliferating cell nuclear antigen loading/unloading (Ctf18, Elg1) with nucleosome assembly (chromatin assembly factor 1, histone regulatory HIR complex). Overall our data reveal specialized genetic networks that affect different aspects of leading- and lagging-strand DNA replication. To help others to engage with these data we have generated two novel, interactive visualization tools, DIXY and Profilyzer. PMID:26297725

  17. Model of mobile agents for sexual interactions networks

    NASA Astrophysics Data System (ADS)

    González, M. C.; Lind, P. G.; Herrmann, H. J.

    2006-02-01

    We present a novel model to simulate real social networks of complex interactions, based in a system of colliding particles (agents). The network is build by keeping track of the collisions and evolves in time with correlations which emerge due to the mobility of the agents. Therefore, statistical features are a consequence only of local collisions among its individual agents. Agent dynamics is realized by an event-driven algorithm of collisions where energy is gained as opposed to physical systems which have dissipation. The model reproduces empirical data from networks of sexual interactions, not previously obtained with other approaches.

  18. Properties of interaction networks underlying the minority game

    NASA Astrophysics Data System (ADS)

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N ) and the agent's capacity to process information (m ), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results.

  19. Properties of interaction networks underlying the minority game.

    PubMed

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N) and the agent's capacity to process information (m), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results. PMID:25493843

  20. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks.

    PubMed

    Teschendorff, Andrew E; Banerji, Christopher R S; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  1. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    PubMed Central

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  2. Optimization of an interactive distributive computer network

    NASA Technical Reports Server (NTRS)

    Frederick, V.

    1985-01-01

    The activities under a cooperative agreement for the development of a computer network are briefly summarized. Research activities covered are: computer operating systems optimization and integration; software development and implementation of the IRIS (Infrared Imaging of Shuttle) Experiment; and software design, development, and implementation of the APS (Aerosol Particle System) Experiment.

  3. Genetic Interactions That Regulate Inflorescence Development in Arabidopsis.

    PubMed Central

    Shannon, S; Meeks-Wagner, DR

    1993-01-01

    In Arabidopsis, floral meristems arise in continuous succession directly on the flanks of the inflorescence meristem. Thus, the pathways that regulate inflorescence and floral meristem identity must operate both simultaneously and in close spatial proximity. The TERMINAL FLOWER 1 (TFL1) gene of Arabidopsis is required for normal inflorescence meristem function, and the LEAFY (LFY), APETALA 1 (AP1), and APETALA 2 (AP2) genes are required for normal floral meristem function. We present evidence that inflorescence meristem identity is promoted by TFL1 and that floral meristem identity is promoted by parallel developmental pathways, one defined by LFY and the other defined by AP1/AP2. Our analysis suggests that the acquisition of meristem identity during inflorescence development is mediated by antagonistic interactions between TFL1 and LFY and between TFL1 and AP1/AP2. Based on this study, we propose a simple model for the genetic regulation of inflorescence development in Arabidopsis. This model is discussed in relation to the proposed interactions between the inflorescence and the floral meristem identity genes and in regard to other genes that are likely to be part of the genetic hierarchy regulating the establishment and maintenance of inflorescence and floral meristems. PMID:12271079

  4. Rapid identification of chemical genetic interactions in Saccharomyces cerevisiae.

    PubMed

    Dilworth, David; Nelson, Christopher J

    2015-01-01

    Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described. PMID:25867090

  5. Pattern Discovery in Breast Cancer Specific Protein Interaction Network

    PubMed Central

    Wu, Xiaogang; Harrison, Scott H.; Chen, Jake Yue

    2009-01-01

    The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein’s biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of “ant colonies” on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network. PMID:21347162

  6. Recurrent interactions in spiking networks with arbitrary topology.

    PubMed

    Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan

    2012-03-01

    The population activity of random networks of excitatory and inhibitory leaky integrate-and-fire neurons has been studied extensively. In particular, a state of asynchronous activity with low firing rates and low pairwise correlations emerges in sparsely connected networks. We apply linear response theory to evaluate the influence of detailed network structure on neuron dynamics. It turns out that pairwise correlations induced by direct and indirect network connections can be related to the matrix of direct linear interactions. Furthermore, we study the influence of the characteristics of the neuron model. Interpreting the reset as self-inhibition, we examine its influence, via the spectrum of single-neuron activity, on network autocorrelation functions and the overall correlation level. The neuron model also affects the form of interaction kernels and consequently the time-dependent correlation functions. We find that a linear instability of networks with Erdös-Rényi topology coincides with a global transition to a highly correlated network state. Our work shows that recurrent interactions have a profound impact on spike train statistics and provides tools to study the effects of specific network topologies. PMID:22587132

  7. Modeling the dynamical interaction between epidemics on overlay networks

    NASA Astrophysics Data System (ADS)

    Marceau, Vincent; Noël, Pierre-André; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2011-08-01

    Epidemics seldom occur as isolated phenomena. Typically, two or more viral agents spread within the same host population and may interact dynamically with each other. We present a general model where two viral agents interact via an immunity mechanism as they propagate simultaneously on two networks connecting the same set of nodes. By exploiting a correspondence between the propagation dynamics and a dynamical process performing progressive network generation, we develop an analytical approach that accurately captures the dynamical interaction between epidemics on overlay networks. The formalism allows for overlay networks with arbitrary joint degree distribution and overlap. To illustrate the versatility of our approach, we consider a hypothetical delayed intervention scenario in which an immunizing agent is disseminated in a host population to hinder the propagation of an undesirable agent (e.g., the spread of preventive information in the context of an emerging infectious disease).

  8. Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction

    PubMed Central

    Escalera, Sergio; Baró, Xavier; Vitrià, Jordi; Radeva, Petia; Raducanu, Bogdan

    2012-01-01

    Social interactions are a very important component in people’s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links’ weights are a measure of the “influence” a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. PMID:22438733

  9. Long-Lasting, Kin-Directed Female Interactions in a Spatially Structured Wild Boar Social Network

    PubMed Central

    Podgórski, Tomasz; Lusseau, David; Scandura, Massimo; Sönnichsen, Leif; Jędrzejewska, Bogumiła

    2014-01-01

    Individuals can increase inclusive fitness benefits through a complex network of social interactions directed towards kin. Preferential relationships with relatives lead to the emergence of kin structures in the social system. Cohesive social groups of related individuals and female philopatry of wild boar create conditions for cooperation through kin selection and make the species a good biological model for studying kin structures. Yet, the role of kinship in shaping the social structure of wild boar populations is still poorly understood. In the present study, we investigated spatio-temporal patterns of associations and the social network structure of the wild boar Sus scrofa population in Białowieża National Park, Poland, which offered a unique opportunity to understand wild boar social interactions away from anthropogenic factors. We used a combination of telemetry data and genetic information to examine the impact of kinship on network cohesion and the strength of social bonds. Relatedness and spatial proximity between individuals were positively related to the strength of social bond. Consequently, the social network was spatially and genetically structured with well-defined and cohesive social units. However, spatial proximity between individuals could not entirely explain the association patterns and network structure. Genuine, kin-targeted, and temporarily stable relationships of females extended beyond spatial proximity between individuals while males interactions were short-lived and not shaped by relatedness. The findings of this study confirm the matrilineal nature of wild boar social structure and show how social preferences of individuals translate into an emergent socio-genetic population structure. PMID:24919178

  10. Class II HLA interactions modulate genetic risk for multiple sclerosis

    PubMed Central

    Dilthey, Alexander T; Xifara, Dionysia K; Ban, Maria; Shah, Tejas S; Patsopoulos, Nikolaos A; Alfredsson, Lars; Anderson, Carl A; Attfield, Katherine E; Baranzini, Sergio E; Barrett, Jeffrey; Binder, Thomas M C; Booth, David; Buck, Dorothea; Celius, Elisabeth G; Cotsapas, Chris; D’Alfonso, Sandra; Dendrou, Calliope A; Donnelly, Peter; Dubois, Bénédicte; Fontaine, Bertrand; Fugger, Lars; Goris, An; Gourraud, Pierre-Antoine; Graetz, Christiane; Hemmer, Bernhard; Hillert, Jan; Kockum, Ingrid; Leslie, Stephen; Lill, Christina M; Martinelli-Boneschi, Filippo; Oksenberg, Jorge R; Olsson, Tomas; Oturai, Annette; Saarela, Janna; Søndergaard, Helle Bach; Spurkland, Anne; Taylor, Bruce; Winkelmann, Juliane; Zipp, Frauke; Haines, Jonathan L; Pericak-Vance, Margaret A; Spencer, Chris C A; Stewart, Graeme; Hafler, David A; Ivinson, Adrian J; Harbo, Hanne F; Hauser, Stephen L; De Jager, Philip L; Compston, Alastair; McCauley, Jacob L; Sawcer, Stephen; McVean, Gil

    2016-01-01

    Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles. PMID:26343388