Integrative systems control approach for reactivating Kaposi's sarcoma-associated herpesvirus (KSHV) with combinatory drugs

PubMed Central

Sun, Chien-Pin; Usui, Takane; Yu, Fuqu; Al-Shyoukh, Ibrahim; Shamma, Jeff; Sun, Ren; Ho, Chih-Ming

2009-01-01

Cells serve as basic units of life and represent intricate biological molecular systems. The vast number of cellular molecules with their signaling and regulatory circuitries forms an intertwined network. In this network, each pathway interacts non-linearly with others through different intermediates. Thus, the challenge of manipulating cellular functions for desired outcomes, such as cancer eradication and controlling viral infection lies within the integrative system of regulatory circuitries. By using a closed-loop system control scheme, we can efficiently analyze biological signaling networks and manipulate their behavior through multiple stimulations on a collection of pathways. Specifically, we aimed to maximize the reactivation of Kaposi's Sarcoma-associated Herpesvirus (KSHV) in a Primary Effusion Lymphoma cell line. The advantage of this approach is that it is well-suited to study complex integrated systems; it circumvents the need for detailed information of individual signaling components; and it investigates the network as a whole by utilizing key systemic outputs as indicators. PMID:19851479

Integrative systems control approach for reactivating Kaposi's sarcoma-associated herpesvirus (KSHV) with combinatory drugs.

PubMed

Sun, Chien-Pin; Usui, Takane; Yu, Fuqu; Al-Shyoukh, Ibrahim; Shamma, Jeff; Sun, Ren; Ho, Chih-Ming

2009-01-01

Cells serve as basic units of life and represent intricate biological molecular systems. The vast number of cellular molecules with their signaling and regulatory circuitries forms an intertwined network. In this network, each pathway interacts non-linearly with others through different intermediates. Thus, the challenge of manipulating cellular functions for desired outcomes, such as cancer eradication and controlling viral infection lies within the integrative system of regulatory circuitries. By using a closed-loop system control scheme, we can efficiently analyze biological signaling networks and manipulate their behavior through multiple stimulations on a collection of pathways. Specifically, we aimed to maximize the reactivation of Kaposi's Sarcoma-associated Herpesvirus (KSHV) in a Primary Effusion Lymphoma cell line. The advantage of this approach is that it is well-suited to study complex integrated systems; it circumvents the need for detailed information of individual signaling components; and it investigates the network as a whole by utilizing key systemic outputs as indicators.

Transcriptional Networks Controlled by NKX2-1 in the Development of Forebrain GABAergic Neurons

DOE PAGES

Sandberg, Magnus; Flandin, Pierre; Silberberg, Shanni; ...

2016-09-21

The embryonic basal ganglia generates multiple projection neurons and interneuron subtypes from distinct progenitor domains. Combinatorial interactions of transcription factors and chromatin are thought to regulate gene expression. In the medial ganglionic eminence, the NKX2-1 transcription factor controls regional identity and, with LHX6, is necessary to specify pallidal projection neurons and forebrain interneurons. Here, we dissected the molecular functions of NKX2-1 by defining its chromosomal binding, regulation of gene expression, and epigenetic state. NKX2-1 binding at distal regulatory elements led to a repressed epigenetic state and transcriptional repression in the ventricular zone. Conversely, NKX2-1 is required to establish a permissivemore » chromatin state and transcriptional activation in the sub-ventricular and mantle zones. Moreover, combinatorial binding of NKX2-1 and LHX6 promotes transcriptionally permissive chromatin and activates genes expressed in cortical migrating interneurons. Our integrated approach gives a foundation for elucidating transcriptional networks guiding the development of the MGE and its descendants.« less

GeNeDA: An Open-Source Workflow for Design Automation of Gene Regulatory Networks Inspired from Microelectronics.

PubMed

Madec, Morgan; Pecheux, François; Gendrault, Yves; Rosati, Elise; Lallement, Christophe; Haiech, Jacques

2016-10-01

The topic of this article is the development of an open-source automated design framework for synthetic biology, specifically for the design of artificial gene regulatory networks based on a digital approach. In opposition to other tools, GeNeDA is an open-source online software based on existing tools used in microelectronics that have proven their efficiency over the last 30 years. The complete framework is composed of a computation core directly adapted from an Electronic Design Automation tool, input and output interfaces, a library of elementary parts that can be achieved with gene regulatory networks, and an interface with an electrical circuit simulator. Each of these modules is an extension of microelectronics tools and concepts: ODIN II, ABC, the Verilog language, SPICE simulator, and SystemC-AMS. GeNeDA is first validated on a benchmark of several combinatorial circuits. The results highlight the importance of the part library. Then, this framework is used for the design of a sequential circuit including a biological state machine.

Intrinsic limits to gene regulation by global crosstalk

PubMed Central

Friedlander, Tamar; Prizak, Roshan; Guet, Călin C.; Barton, Nicholas H.; Tkačik, Gašper

2016-01-01

Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements. PMID:27489144

TFmiR: a web server for constructing and analyzing disease-specific transcription factor and miRNA co-regulatory networks.

PubMed

Hamed, Mohamed; Spaniol, Christian; Nazarieh, Maryam; Helms, Volkhard

2015-07-01

TFmiR is a freely available web server for deep and integrative analysis of combinatorial regulatory interactions between transcription factors, microRNAs and target genes that are involved in disease pathogenesis. Since the inner workings of cells rely on the correct functioning of an enormously complex system of activating and repressing interactions that can be perturbed in many ways, TFmiR helps to better elucidate cellular mechanisms at the molecular level from a network perspective. The provided topological and functional analyses promote TFmiR as a reliable systems biology tool for researchers across the life science communities. TFmiR web server is accessible through the following URL: http://service.bioinformatik.uni-saarland.de/tfmir. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Quaking and PTB control overlapping splicing regulatory networks during muscle cell differentiation

PubMed Central

Hall, Megan P.; Nagel, Roland J.; Fagg, W. Samuel; Shiue, Lily; Cline, Melissa S.; Perriman, Rhonda J.; Donohue, John Paul; Ares, Manuel

2013-01-01

Alternative splicing contributes to muscle development, but a complete set of muscle-splicing factors and their combinatorial interactions are unknown. Previous work identified ACUAA (“STAR” motif) as an enriched intron sequence near muscle-specific alternative exons such as Capzb exon 9. Mass spectrometry of myoblast proteins selected by the Capzb exon 9 intron via RNA affinity chromatography identifies Quaking (QK), a protein known to regulate mRNA function through ACUAA motifs in 3′ UTRs. We find that QK promotes inclusion of Capzb exon 9 in opposition to repression by polypyrimidine tract-binding protein (PTB). QK depletion alters inclusion of 406 cassette exons whose adjacent intron sequences are also enriched in ACUAA motifs. During differentiation of myoblasts to myotubes, QK levels increase two- to threefold, suggesting a mechanism for QK-responsive exon regulation. Combined analysis of the PTB- and QK-splicing regulatory networks during myogenesis suggests that 39% of regulated exons are under the control of one or both of these splicing factors. This work provides the first evidence that QK is a global regulator of splicing during muscle development in vertebrates and shows how overlapping splicing regulatory networks contribute to gene expression programs during differentiation. PMID:23525800

Cell type-selective disease-association of genes under high regulatory load

PubMed Central

Galhardo, Mafalda; Berninger, Philipp; Nguyen, Thanh-Phuong; Sauter, Thomas; Sinkkonen, Lasse

2015-01-01

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3′ UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner. PMID:26338775

Dynamic combinatorial libraries: new opportunities in systems chemistry.

PubMed

Hunt, Rosemary A R; Otto, Sijbren

2011-01-21

Combinatorial chemistry is a tool for selecting molecules with special properties. Dynamic combinatorial chemistry started off aiming to be just that. However, unlike ordinary combinatorial chemistry, the interconnectedness of dynamic libraries gives them an extra dimension. An understanding of these molecular networks at systems level is essential for their use as a selection tool and creates exciting new opportunities in systems chemistry. In this feature article we discuss selected examples and considerations related to the advanced exploitation of dynamic combinatorial libraries for their originally conceived purpose of identifying strong binding interactions. Also reviewed are examples illustrating a trend towards increasing complexity in terms of network behaviour and reversible chemistry. Finally, new applications of dynamic combinatorial chemistry in self-assembly, transport and self-replication are discussed.

A platform for rapid prototyping of synthetic gene networks in mammalian cells

PubMed Central

Duportet, Xavier; Wroblewska, Liliana; Guye, Patrick; Li, Yinqing; Eyquem, Justin; Rieders, Julianne; Rimchala, Tharathorn; Batt, Gregory; Weiss, Ron

2014-01-01

Mammalian synthetic biology may provide novel therapeutic strategies, help decipher new paths for drug discovery and facilitate synthesis of valuable molecules. Yet, our capacity to genetically program cells is currently hampered by the lack of efficient approaches to streamline the design, construction and screening of synthetic gene networks. To address this problem, here we present a framework for modular and combinatorial assembly of functional (multi)gene expression vectors and their efficient and specific targeted integration into a well-defined chromosomal context in mammalian cells. We demonstrate the potential of this framework by assembling and integrating different functional mammalian regulatory networks including the largest gene circuit built and chromosomally integrated to date (6 transcription units, 27kb) encoding an inducible memory device. Using a library of 18 different circuits as a proof of concept, we also demonstrate that our method enables one-pot/single-flask chromosomal integration and screening of circuit libraries. This rapid and powerful prototyping platform is well suited for comparative studies of genetic regulatory elements, genes and multi-gene circuits as well as facile development of libraries of isogenic engineered cell lines. PMID:25378321

A global view of regulatory networks in lung cancer: An approach to understand homogeneity and heterogeneity.

PubMed

Lu, Jiapei; Wang, William; Xu, Menglin; Li, Yuping; Chen, Chengshui; Wang, Xiangdong

2017-02-01

A number of new biotechnologies are used to identify potential biomarkers for the early detection of lung cancer, enabling a personalized therapy to be developed in response. The combinatorial cross-regulation of hundreds of biological function-specific transcription factors (TFs) is defined as the understanding of regulatory networks of molecules within the cell. Here we integrated global databases with 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without cancer with the understanding of TF functions. The present review aims at the homogeneity or heterogeneity of gene expression profiles among subtypes of lung cancer. About 5, 136, 52, or 16 up-regulated or 19, 24, 122, or 97down-regulated type-special TF genes were identified in ADC, SCC, LCC or SCLC, respectively. DNA-binding and transcription regulator activity associated genes play a dominant role in the differentiation of subtypes in lung cancer. Subtype-specific TF gene regulatory networks with elements should be an alternative for diagnostic and therapeutic targets for early identification of lung cancer and can provide insightful clues to etiology and pathogenesis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combinatorial complexity of pathway analysis in metabolic networks.

PubMed

Klamt, Steffen; Stelling, Jörg

2002-01-01

Elementary flux mode analysis is a promising approach for a pathway-oriented perspective of metabolic networks. However, in larger networks it is hampered by the combinatorial explosion of possible routes. In this work we give some estimations on the combinatorial complexity including theoretical upper bounds for the number of elementary flux modes in a network of a given size. In a case study, we computed the elementary modes in the central metabolism of Escherichia coli while utilizing four different substrates. Interestingly, although the number of modes occurring in this complex network can exceed half a million, it is still far below the upper bound. Hence, to a certain extent, pathway analysis of central catabolism is feasible to assess network properties such as flexibility and functionality.

Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.

PubMed

Müller-Molina, Arnoldo J; Schöler, Hans R; Araúzo-Bravo, Marcos J

2012-01-01

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.

Comprehensive Human Transcription Factor Binding Site Map for Combinatory Binding Motifs Discovery

PubMed Central

Müller-Molina, Arnoldo J.; Schöler, Hans R.; Araúzo-Bravo, Marcos J.

2012-01-01

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%–20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory “DNA words.” From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%—far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of “DNA words,” newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters. PMID:23209563

Cell type-selective disease-association of genes under high regulatory load.

PubMed

Galhardo, Mafalda; Berninger, Philipp; Nguyen, Thanh-Phuong; Sauter, Thomas; Sinkkonen, Lasse

2015-10-15

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3' UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Regulatory states in the developmental control of gene expression.

PubMed

Peter, Isabelle S

2017-09-01

A growing body of evidence shows that gene expression in multicellular organisms is controlled by the combinatorial function of multiple transcription factors. This indicates that not the individual transcription factors or signaling molecules, but the combination of expressed regulatory molecules, the regulatory state, should be viewed as the functional unit in gene regulation. Here, I discuss the concept of the regulatory state and its proposed role in the genome-wide control of gene expression. Recent analyses of regulatory gene expression in sea urchin embryos have been instrumental for solving the genomic control of cell fate specification in this system. Some of the approaches that were used to determine the expression of regulatory states during sea urchin embryogenesis are reviewed. Significant developmental changes in regulatory state expression leading to the distinct specification of cell fates are regulated by gene regulatory network circuits. How these regulatory state transitions are encoded in the genome is illuminated using the sea urchin endoderm-mesoderms cell fate decision circuit as an example. These observations highlight the importance of considering developmental gene regulation, and the function of individual transcription factors, in the context of regulatory states. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Decoding the genome with an integrative analysis tool: combinatorial CRM Decoder.

PubMed

Kang, Keunsoo; Kim, Joomyeong; Chung, Jae Hoon; Lee, Daeyoup

2011-09-01

The identification of genome-wide cis-regulatory modules (CRMs) and characterization of their associated epigenetic features are fundamental steps toward the understanding of gene regulatory networks. Although integrative analysis of available genome-wide information can provide new biological insights, the lack of novel methodologies has become a major bottleneck. Here, we present a comprehensive analysis tool called combinatorial CRM decoder (CCD), which utilizes the publicly available information to identify and characterize genome-wide CRMs in a species of interest. CCD first defines a set of the epigenetic features which is significantly associated with a set of known CRMs as a code called 'trace code', and subsequently uses the trace code to pinpoint putative CRMs throughout the genome. Using 61 genome-wide data sets obtained from 17 independent mouse studies, CCD successfully catalogued ∼12 600 CRMs (five distinct classes) including polycomb repressive complex 2 target sites as well as imprinting control regions. Interestingly, we discovered that ∼4% of the identified CRMs belong to at least two different classes named 'multi-functional CRM', suggesting their functional importance for regulating spatiotemporal gene expression. From these examples, we show that CCD can be applied to any potential genome-wide datasets and therefore will shed light on unveiling genome-wide CRMs in various species.

Combinatorial study of degree assortativity in networks.

PubMed

Estrada, Ernesto

2011-10-01

Why are some networks degree-degree correlated (assortative), while most of the real-world ones are anticorrelated (disassortative)? Here, we prove, by combinatorial methods, that the assortativity of a network depends only on three structural factors: transitivity (clustering coefficient), intermodular connectivity, and branching. Then, a network is assortative if the contributions of the first two factors are larger than that of the third. Highly branched networks are likely to be disassortative.

Leveraging Modeling Approaches: Reaction Networks and Rules

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

Leveraging modeling approaches: reaction networks and rules.

PubMed

Blinov, Michael L; Moraru, Ion I

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

PubMed Central

Lohmann, Ingrid

2012-01-01

In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression. PMID:23272209

A Functionally Conserved Gene Regulatory Network Module Governing Olfactory Neuron Diversity.

PubMed

Li, Qingyun; Barish, Scott; Okuwa, Sumie; Maciejewski, Abigail; Brandt, Alicia T; Reinhold, Dominik; Jones, Corbin D; Volkan, Pelin Cayirlioglu

2016-01-01

Sensory neuron diversity is required for organisms to decipher complex environmental cues. In Drosophila, the olfactory environment is detected by 50 different olfactory receptor neuron (ORN) classes that are clustered in combinations within distinct sensilla subtypes. Each sensilla subtype houses stereotypically clustered 1-4 ORN identities that arise through asymmetric divisions from a single multipotent sensory organ precursor (SOP). How each class of SOPs acquires a unique differentiation potential that accounts for ORN diversity is unknown. Previously, we reported a critical component of SOP diversification program, Rotund (Rn), increases ORN diversity by generating novel developmental trajectories from existing precursors within each independent sensilla type lineages. Here, we show that Rn, along with BarH1/H2 (Bar), Bric-à-brac (Bab), Apterous (Ap) and Dachshund (Dac), constitutes a transcription factor (TF) network that patterns the developing olfactory tissue. This network was previously shown to pattern the segmentation of the leg, which suggests that this network is functionally conserved. In antennal imaginal discs, precursors with diverse ORN differentiation potentials are selected from concentric rings defined by unique combinations of these TFs along the proximodistal axis of the developing antennal disc. The combinatorial code that demarcates each precursor field is set up by cross-regulatory interactions among different factors within the network. Modifications of this network lead to predictable changes in the diversity of sensilla subtypes and ORN pools. In light of our data, we propose a molecular map that defines each unique SOP fate. Our results highlight the importance of the early prepatterning gene regulatory network as a modulator of SOP and terminally differentiated ORN diversity. Finally, our model illustrates how conserved developmental strategies are used to generate neuronal diversity.

Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.

PubMed

Li, Yang Eric; Xiao, Mu; Shi, Binbin; Yang, Yu-Cheng T; Wang, Dong; Wang, Fei; Marcia, Marco; Lu, Zhi John

2017-09-08

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs

PubMed Central

Sharifpoor, Sara; van Dyk, Dewald; Costanzo, Michael; Baryshnikova, Anastasia; Friesen, Helena; Douglas, Alison C.; Youn, Ji-Young; VanderSluis, Benjamin; Myers, Chad L.; Papp, Balázs; Boone, Charles; Andrews, Brenda J.

2012-01-01

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase–substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks. PMID:22282571

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

NASA Technical Reports Server (NTRS)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks.

PubMed

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-05-12

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks

PubMed Central

Yuan, Qi; Ma, Chunguang; Yu, Haitao; Bian, Xuefen

2018-01-01

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity. PMID:29757244

Functional integrative levels in the human interactome recapitulate organ organization.

PubMed

Souiai, Ouissem; Becker, Emmanuelle; Prieto, Carlos; Benkahla, Alia; De las Rivas, Javier; Brun, Christine

2011-01-01

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This 'Largest Common Interactome Network' represents a 'functional interactome core'. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.

Generation of Diverse Biological Forms through Combinatorial Interactions between Tissue Polarity and Growth

PubMed Central

Kennaway, Richard; Coen, Enrico; Green, Amelia; Bangham, Andrew

2011-01-01

A major problem in biology is to understand how complex tissue shapes may arise through growth. In many cases this process involves preferential growth along particular orientations raising the question of how these orientations are specified. One view is that orientations are specified through stresses in the tissue (axiality-based system). Another possibility is that orientations can be specified independently of stresses through molecular signalling (polarity-based system). The axiality-based system has recently been explored through computational modelling. Here we develop and apply a polarity-based system which we call the Growing Polarised Tissue (GPT) framework. Tissue is treated as a continuous material within which regionally expressed factors under genetic control may interact and propagate. Polarity is established by signals that propagate through the tissue and is anchored in regions termed tissue polarity organisers that are also under genetic control. Rates of growth parallel or perpendicular to the local polarity may then be specified through a regulatory network. The resulting growth depends on how specified growth patterns interact within the constraints of mechanically connected tissue. This constraint leads to the emergence of features such as curvature that were not directly specified by the regulatory networks. Resultant growth feeds back to influence spatial arrangements and local orientations of tissue, allowing complex shapes to emerge from simple rules. Moreover, asymmetries may emerge through interactions between polarity fields. We illustrate the value of the GPT-framework for understanding morphogenesis by applying it to a growing Snapdragon flower and indicate how the underlying hypotheses may be tested by computational simulation. We propose that combinatorial intractions between orientations and rates of growth, which are a key feature of polarity-based systems, have been exploited during evolution to generate a range of observed biological shapes. PMID:21698124

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio.

PubMed

Weidmann, Chase A; Qiu, Chen; Arvola, René M; Lou, Tzu-Fang; Killingsworth, Jordan; Campbell, Zachary T; Tanaka Hall, Traci M; Goldstrohm, Aaron C

2016-08-02

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAs that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo. Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weidmann, Chase A.; Qiu, Chen; Arvola, René M.

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation byDrosophilaPumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAs that aremore » not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulatedin vivo. Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.« less

Neural Meta-Memes Framework for Combinatorial Optimization

NASA Astrophysics Data System (ADS)

Song, Li Qin; Lim, Meng Hiot; Ong, Yew Soon

In this paper, we present a Neural Meta-Memes Framework (NMMF) for combinatorial optimization. NMMF is a framework which models basic optimization algorithms as memes and manages them dynamically when solving combinatorial problems. NMMF encompasses neural networks which serve as the overall planner/coordinator to balance the workload between memes. We show the efficacy of the proposed NMMF through empirical study on a class of combinatorial problem, the quadratic assignment problem (QAP).

Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks

PubMed Central

Grocott, Timothy; Thomas, Paul; Münsterberg, Andrea E.

2016-01-01

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states. PMID:26864723

Atlas Toolkit: Fast registration of 3D morphological datasets in the absence of landmarks.

PubMed

Grocott, Timothy; Thomas, Paul; Münsterberg, Andrea E

2016-02-11

Image registration is a gateway technology for Developmental Systems Biology, enabling computational analysis of related datasets within a shared coordinate system. Many registration tools rely on landmarks to ensure that datasets are correctly aligned; yet suitable landmarks are not present in many datasets. Atlas Toolkit is a Fiji/ImageJ plugin collection offering elastic group-wise registration of 3D morphological datasets, guided by segmentation of the interesting morphology. We demonstrate the method by combinatorial mapping of cell signalling events in the developing eyes of chick embryos, and use the integrated datasets to predictively enumerate Gene Regulatory Network states.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors.

PubMed

Cebola, Inês; Rodríguez-Seguí, Santiago A; Cho, Candy H-H; Bessa, José; Rovira, Meritxell; Luengo, Mario; Chhatriwala, Mariya; Berry, Andrew; Ponsa-Cobas, Joan; Maestro, Miguel Angel; Jennings, Rachel E; Pasquali, Lorenzo; Morán, Ignasi; Castro, Natalia; Hanley, Neil A; Gomez-Skarmeta, Jose Luis; Vallier, Ludovic; Ferrer, Jorge

2015-05-01

The genomic regulatory programmes that underlie human organogenesis are poorly understood. Pancreas development, in particular, has pivotal implications for pancreatic regeneration, cancer and diabetes. We have now characterized the regulatory landscape of embryonic multipotent progenitor cells that give rise to all pancreatic epithelial lineages. Using human embryonic pancreas and embryonic-stem-cell-derived progenitors we identify stage-specific transcripts and associated enhancers, many of which are co-occupied by transcription factors that are essential for pancreas development. We further show that TEAD1, a Hippo signalling effector, is an integral component of the transcription factor combinatorial code of pancreatic progenitor enhancers. TEAD and its coactivator YAP activate key pancreatic signalling mediators and transcription factors, and regulate the expansion of pancreatic progenitors. This work therefore uncovers a central role for TEAD and YAP as signal-responsive regulators of multipotent pancreatic progenitors, and provides a resource for the study of embryonic development of the human pancreas.

Emergent latent symbol systems in recurrent neural networks

NASA Astrophysics Data System (ADS)

Monner, Derek; Reggia, James A.

2012-12-01

Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

Mathematical model of a gene regulatory network reconciles effects of genetic perturbations on hematopoietic stem cell emergence.

PubMed

Narula, Jatin; Williams, C J; Tiwari, Abhinav; Marks-Bluth, Jonathon; Pimanda, John E; Igoshin, Oleg A

2013-07-15

Interlinked gene regulatory networks (GRNs) are vital for the spatial and temporal control of gene expression during development. The hematopoietic transcription factors (TFs) Scl, Gata2 and Fli1 form one such densely connected GRN which acts as a master regulator of embryonic hematopoiesis. This triad has been shown to direct the specification of the hemogenic endothelium and emergence of hematopoietic stem cells (HSCs) in response to Notch1 and Bmp4-Smad signaling. Here we employ previously published data to construct a mathematical model of this GRN network and use this model to systematically investigate the network dynamical properties. Our model uses a statistical-thermodynamic framework to describe the combinatorial regulation of gene expression and reconciles, mechanistically, several previously published but unexplained results from different genetic perturbation experiments. In particular, our results demonstrate how the interactions of Runx1, an essential hematopoietic TF, with components of the Bmp4 signaling pathway allow it to affect triad activation and acts as a key regulator of HSC emergence. We also explain why heterozygous deletion of this essential TF, Runx1, speeds up the network dynamics leading to accelerated HSC emergence. Taken together our results demonstrate that the triad, a master-level controller of definitive hematopoiesis, is an irreversible bistable switch whose dynamical properties are modulated by Runx1 and components of the Bmp4 signaling pathway. Copyright © 2013 Elsevier Inc. All rights reserved.

CRX ChIP-seq reveals the cis-regulatory architecture of mouse photoreceptors

PubMed Central

Corbo, Joseph C.; Lawrence, Karen A.; Karlstetter, Marcus; Myers, Connie A.; Abdelaziz, Musa; Dirkes, William; Weigelt, Karin; Seifert, Martin; Benes, Vladimir; Fritsche, Lars G.; Weber, Bernhard H.F.; Langmann, Thomas

2010-01-01

Approximately 98% of mammalian DNA is noncoding, yet we understand relatively little about the function of this enigmatic portion of the genome. The cis-regulatory elements that control gene expression reside in noncoding regions and can be identified by mapping the binding sites of tissue-specific transcription factors. Cone-rod homeobox (CRX) is a key transcription factor in photoreceptor differentiation and survival, but its in vivo targets are largely unknown. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) on CRX to identify thousands of cis-regulatory regions around photoreceptor genes in adult mouse retina. CRX directly regulates downstream photoreceptor transcription factors and their target genes via a network of spatially distributed regulatory elements around each locus. CRX-bound regions act in a synergistic fashion to activate transcription and contain multiple CRX binding sites which interact in a spacing- and orientation-dependent manner to fine-tune transcript levels. CRX ChIP-seq was also performed on Nrl−/− retinas, which represent an enriched source of cone photoreceptors. Comparison with the wild-type ChIP-seq data set identified numerous rod- and cone-specific CRX-bound regions as well as many shared elements. Thus, CRX combinatorially orchestrates the transcriptional networks of both rods and cones by coordinating the expression of photoreceptor genes including most retinal disease genes. In addition, this study pinpoints thousands of noncoding regions of relevance to both Mendelian and complex retinal disease. PMID:20693478

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks

PubMed Central

Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

PubMed Central

Weidmann, Chase A; Qiu, Chen; Arvola, René M; Lou, Tzu-Fang; Killingsworth, Jordan; Campbell, Zachary T; Tanaka Hall, Traci M; Goldstrohm, Aaron C

2016-01-01

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAs that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo. Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics. DOI: http://dx.doi.org/10.7554/eLife.17096.001 PMID:27482653

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

DOE PAGES

Weidmann, Chase A.; Qiu, Chen; Arvola, René M.; ...

2016-08-02

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAsmore » that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo. Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.« less

Drosophila Nanos acts as a molecular clamp that modulates the RNA-binding and repression activities of Pumilio

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weidmann, Chase A.; Qiu, Chen; Arvola, René M.

Collaboration among the multitude of RNA-binding proteins (RBPs) is ubiquitous, yet our understanding of these key regulatory complexes has been limited to single RBPs. We investigated combinatorial translational regulation by Drosophila Pumilio (Pum) and Nanos (Nos), which control development, fertility, and neuronal functions. Our results show how the specificity of one RBP (Pum) is modulated by cooperative RNA recognition with a second RBP (Nos) to synergistically repress mRNAs. Crystal structures of Nos-Pum-RNA complexes reveal that Nos embraces Pum and RNA, contributes sequence-specific contacts, and increases Pum RNA-binding affinity. Nos shifts the recognition sequence and promotes repression complex formation on mRNAsmore » that are not stably bound by Pum alone, explaining the preponderance of sub-optimal Pum sites regulated in vivo. Our results illuminate the molecular mechanism of a regulatory switch controlling crucial gene expression programs, and provide a framework for understanding how the partnering of RBPs evokes changes in binding specificity that underlie regulatory network dynamics.« less

Exploiting combinatorial cultivation conditions to infer transcriptional regulation

PubMed Central

Knijnenburg, Theo A; de Winde, Johannes H; Daran, Jean-Marc; Daran-Lapujade, Pascale; Pronk, Jack T; Reinders, Marcel JT; Wessels, Lodewyk FA

2007-01-01

Background Regulatory networks often employ the model that attributes changes in gene expression levels, as observed across different cellular conditions, to changes in the activity of transcription factors (TFs). Although the actual conditions that trigger a change in TF activity should form an integral part of the generated regulatory network, they are usually lacking. This is due to the fact that the large heterogeneity in the employed conditions and the continuous changes in environmental parameters in the often used shake-flask cultures, prevent the unambiguous modeling of the cultivation conditions within the computational framework. Results We designed an experimental setup that allows us to explicitly model the cultivation conditions and use these to infer the activity of TFs. The yeast Saccharomyces cerevisiae was cultivated under four different nutrient limitations in both aerobic and anaerobic chemostat cultures. In the chemostats, environmental and growth parameters are accurately controlled. Consequently, the measured transcriptional response can be directly correlated with changes in the limited nutrient or oxygen concentration. We devised a tailor-made computational approach that exploits the systematic setup of the cultivation conditions in order to identify the individual and combined effects of nutrient limitations and oxygen availability on expression behavior and TF activity. Conclusion Incorporating the actual growth conditions when inferring regulatory relationships provides detailed insight in the functionality of the TFs that are triggered by changes in the employed cultivation conditions. For example, our results confirm the established role of TF Hap4 in both aerobic regulation and glucose derepression. Among the numerous inferred condition-specific regulatory associations between gene sets and TFs, also many novel putative regulatory mechanisms, such as the possible role of Tye7 in sulfur metabolism, were identified. PMID:17241460

Exploiting combinatorial cultivation conditions to infer transcriptional regulation.

PubMed

Knijnenburg, Theo A; de Winde, Johannes H; Daran, Jean-Marc; Daran-Lapujade, Pascale; Pronk, Jack T; Reinders, Marcel J T; Wessels, Lodewyk F A

2007-01-22

Regulatory networks often employ the model that attributes changes in gene expression levels, as observed across different cellular conditions, to changes in the activity of transcription factors (TFs). Although the actual conditions that trigger a change in TF activity should form an integral part of the generated regulatory network, they are usually lacking. This is due to the fact that the large heterogeneity in the employed conditions and the continuous changes in environmental parameters in the often used shake-flask cultures, prevent the unambiguous modeling of the cultivation conditions within the computational framework. We designed an experimental setup that allows us to explicitly model the cultivation conditions and use these to infer the activity of TFs. The yeast Saccharomyces cerevisiae was cultivated under four different nutrient limitations in both aerobic and anaerobic chemostat cultures. In the chemostats, environmental and growth parameters are accurately controlled. Consequently, the measured transcriptional response can be directly correlated with changes in the limited nutrient or oxygen concentration. We devised a tailor-made computational approach that exploits the systematic setup of the cultivation conditions in order to identify the individual and combined effects of nutrient limitations and oxygen availability on expression behavior and TF activity. Incorporating the actual growth conditions when inferring regulatory relationships provides detailed insight in the functionality of the TFs that are triggered by changes in the employed cultivation conditions. For example, our results confirm the established role of TF Hap4 in both aerobic regulation and glucose derepression. Among the numerous inferred condition-specific regulatory associations between gene sets and TFs, also many novel putative regulatory mechanisms, such as the possible role of Tye7 in sulfur metabolism, were identified.

Functional Integrative Levels in the Human Interactome Recapitulate Organ Organization

PubMed Central

Prieto, Carlos; Benkahla, Alia; De Las Rivas, Javier; Brun, Christine

2011-01-01

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization. PMID:21799769

Neuronal cell fate specification by the molecular convergence of different spatio-temporal cues on a common initiator terminal selector gene

PubMed Central

Stratmann, Johannes

2017-01-01

The extensive genetic regulatory flows underlying specification of different neuronal subtypes are not well understood at the molecular level. The Nplp1 neuropeptide neurons in the developing Drosophila nerve cord belong to two sub-classes; Tv1 and dAp neurons, generated by two distinct progenitors. Nplp1 neurons are specified by spatial cues; the Hox homeotic network and GATA factor grn, and temporal cues; the hb -> Kr -> Pdm -> cas -> grh temporal cascade. These spatio-temporal cues combine into two distinct codes; one for Tv1 and one for dAp neurons that activate a common terminal selector feedforward cascade of col -> ap/eya -> dimm -> Nplp1. Here, we molecularly decode the specification of Nplp1 neurons, and find that the cis-regulatory organization of col functions as an integratory node for the different spatio-temporal combinatorial codes. These findings may provide a logical framework for addressing spatio-temporal control of neuronal sub-type specification in other systems. PMID:28414802

Building synthetic gene circuits from combinatorial libraries: screening and selection strategies.

PubMed

Schaerli, Yolanda; Isalan, Mark

2013-07-01

The promise of wide-ranging biotechnology applications inspires synthetic biologists to design novel genetic circuits. However, building such circuits rationally is still not straightforward and often involves painstaking trial-and-error. Mimicking the process of natural selection can help us to bridge the gap between our incomplete understanding of nature's design rules and our desire to build functional networks. By adopting the powerful method of directed evolution, which is usually applied to protein engineering, functional networks can be obtained through screening or selecting from randomised combinatorial libraries. This review first highlights the practical options to introduce combinatorial diversity into gene circuits and then examines strategies for identifying the potentially rare library members with desired functions, either by screening or selection.

Two projects in theoretical neuroscience: A convolution-based metric for neural membrane potentials and a combinatorial connectionist semantic network method

NASA Astrophysics Data System (ADS)

Evans, Garrett Nolan

In this work, I present two projects that both contribute to the aim of discovering how intelligence manifests in the brain. The first project is a method for analyzing recorded neural signals, which takes the form of a convolution-based metric on neural membrane potential recordings. Relying only on integral and algebraic operations, the metric compares the timing and number of spikes within recordings as well as the recordings' subthreshold features: summarizing differences in these with a single "distance" between the recordings. Like van Rossum's (2001) metric for spike trains, the metric is based on a convolution operation that it performs on the input data. The kernel used for the convolution is carefully chosen such that it produces a desirable frequency space response and, unlike van Rossum's kernel, causes the metric to be first order both in differences between nearby spike times and in differences between same-time membrane potential values: an important trait. The second project is a combinatorial syntax method for connectionist semantic network encoding. Combinatorial syntax has been a point on which those who support a symbol-processing view of intelligent processing and those who favor a connectionist view have had difficulty seeing eye-to-eye. Symbol-processing theorists have persuasively argued that combinatorial syntax is necessary for certain intelligent mental operations, such as reasoning by analogy. Connectionists have focused on the versatility and adaptability offered by self-organizing networks of simple processing units. With this project, I show that there is a way to reconcile the two perspectives and to ascribe a combinatorial syntax to a connectionist network. The critical principle is to interpret nodes, or units, in the connectionist network as bound integrations of the interpretations for nodes that they share links with. Nodes need not correspond exactly to neurons and may correspond instead to distributed sets, or assemblies, of neurons.

Rapid evolution of regulatory element libraries for tunable transcriptional and translational control of gene expression.

PubMed

Jin, Erqing; Wong, Lynn; Jiao, Yun; Engel, Jake; Holdridge, Benjamin; Xu, Peng

2017-12-01

Engineering cell factories for producing biofuels and pharmaceuticals has spurred great interests to develop rapid and efficient synthetic biology tools customized for modular pathway engineering. Along the way, combinatorial gene expression control through modification of regulatory element offered tremendous opportunity for fine-tuning gene expression and generating digital-like genetic circuits. In this report, we present an efficient evolutionary approach to build a range of regulatory control elements. The reported method allows for rapid construction of promoter, 5'UTR, terminator and trans -activating RNA libraries. Synthetic overlapping oligos with high portion of degenerate nucleotides flanking the regulatory element could be efficiently assembled to a vector expressing fluorescence reporter. This approach combines high mutation rate of the synthetic DNA with the high assembly efficiency of Gibson Mix. Our constructed library demonstrates broad range of transcriptional or translational gene expression dynamics. Specifically, both the promoter library and 5'UTR library exhibits gene expression dynamics spanning across three order of magnitude. The terminator library and trans -activating RNA library displays relatively narrowed gene expression pattern. The reported study provides a versatile toolbox for rapidly constructing a large family of prokaryotic regulatory elements. These libraries also facilitate the implementation of combinatorial pathway engineering principles and the engineering of more efficient microbial cell factory for various biomanufacturing applications.

Joining the dots - protein-RNA interactions mediating local mRNA translation in neurons.

PubMed

Gallagher, Christopher; Ramos, Andres

2018-06-01

Establishing and maintaining the complex network of connections required for neuronal communication requires the transport and in situ translation of large groups of mRNAs to create local proteomes. In this Review, we discuss the regulation of local mRNA translation in neurons and the RNA-binding proteins that recognise RNA zipcode elements and connect the mRNAs to the cellular transport networks, as well as regulate their translation control. However, mRNA recognition by the regulatory proteins is mediated by the combinatorial action of multiple RNA-binding domains. This increases the specificity and affinity of the interaction, while allowing the protein to recognise a diverse set of targets and mediate a range of mechanisms for translational regulation. The structural and molecular understanding of the interactions can be used together with novel microscopy and transcriptome-wide data to build a mechanistic framework for the regulation of local mRNA translation. © 2018 Federation of European Biochemical Societies.

Combinatorial control of messenger RNAs by Pumilio, Nanos and Brain Tumor Proteins

PubMed Central

Arvola, René M.

2017-01-01

ABSTRACT Eukaryotes possess a vast array of RNA-binding proteins (RBPs) that affect mRNAs in diverse ways to control protein expression. Combinatorial regulation of mRNAs by RBPs is emerging as the rule. No example illustrates this as vividly as the partnership of 3 Drosophila RBPs, Pumilio, Nanos and Brain Tumor, which have overlapping functions in development, stem cell maintenance and differentiation, fertility and neurologic processes. Here we synthesize 30 y of research with new insights into their molecular functions and mechanisms of action. First, we provide an overview of the key properties of each RBP. Next, we present a detailed analysis of their collaborative regulatory mechanism using a classic example of the developmental morphogen, hunchback, which is spatially and temporally regulated by the trio during embryogenesis. New biochemical, structural and functional analyses provide insights into RNA recognition, cooperativity, and regulatory mechanisms. We integrate these data into a model of combinatorial RNA binding and regulation of translation and mRNA decay. We then use this information, transcriptome wide analyses and bioinformatics predictions to assess the global impact of Pumilio, Nanos and Brain Tumor on gene regulation. Together, the results support pervasive, dynamic post-transcriptional control. PMID:28318367

Combinatorial control of messenger RNAs by Pumilio, Nanos and Brain Tumor Proteins.

PubMed

Arvola, René M; Weidmann, Chase A; Tanaka Hall, Traci M; Goldstrohm, Aaron C

2017-11-02

Eukaryotes possess a vast array of RNA-binding proteins (RBPs) that affect mRNAs in diverse ways to control protein expression. Combinatorial regulation of mRNAs by RBPs is emerging as the rule. No example illustrates this as vividly as the partnership of 3 Drosophila RBPs, Pumilio, Nanos and Brain Tumor, which have overlapping functions in development, stem cell maintenance and differentiation, fertility and neurologic processes. Here we synthesize 30 y of research with new insights into their molecular functions and mechanisms of action. First, we provide an overview of the key properties of each RBP. Next, we present a detailed analysis of their collaborative regulatory mechanism using a classic example of the developmental morphogen, hunchback, which is spatially and temporally regulated by the trio during embryogenesis. New biochemical, structural and functional analyses provide insights into RNA recognition, cooperativity, and regulatory mechanisms. We integrate these data into a model of combinatorial RNA binding and regulation of translation and mRNA decay. We then use this information, transcriptome wide analyses and bioinformatics predictions to assess the global impact of Pumilio, Nanos and Brain Tumor on gene regulation. Together, the results support pervasive, dynamic post-transcriptional control.

Dynamic combinatorial libraries: from exploring molecular recognition to systems chemistry.

PubMed

Li, Jianwei; Nowak, Piotr; Otto, Sijbren

2013-06-26

Dynamic combinatorial chemistry (DCC) is a subset of combinatorial chemistry where the library members interconvert continuously by exchanging building blocks with each other. Dynamic combinatorial libraries (DCLs) are powerful tools for discovering the unexpected and have given rise to many fascinating molecules, ranging from interlocked structures to self-replicators. Furthermore, dynamic combinatorial molecular networks can produce emergent properties at systems level, which provide exciting new opportunities in systems chemistry. In this perspective we will highlight some new methodologies in this field and analyze selected examples of DCLs that are under thermodynamic control, leading to synthetic receptors, catalytic systems, and complex self-assembled supramolecular architectures. Also reviewed are extensions of the principles of DCC to systems that are not at equilibrium and may therefore harbor richer functional behavior. Examples include self-replication and molecular machines.

Induction of the neural crest state: Control of stem cell attributes by gene regulatory, post-transcriptional and epigenetic interactions

PubMed Central

Prasad, Maneeshi S.; Sauka-Spengler, Tatjana; LaBonne, Carole

2012-01-01

Neural crest cells are a population of multipotent stem cell-like progenitors that arise at the neural plate border in vertebrates, migrate extensively, and give rise to diverse derivatives such as melanocytes, craniofacial cartilage and bone, smooth muscle, peripheral and enteric neurons and glia. The neural crest gene regulatory network (NC-GRN) includes a number of key factors that are used reiteratively to control multiple steps in the development of neural crest cells, including the acquisition of stem cell attributes. It is therefore essential to understand the mechanisms that control the distinct functions of such reiteratively used factors in different cellular contexts. The context-dependent control of neural crest specification is achieved through combinatorial interaction with other factors, post-transcriptional and post-translational modifications, and the epigenetic status and chromatin state of target genes. Here we review the current understanding of the NC-GRN, including the role of the neural crest specifiers, their links to the control of “stemness,” and their dynamic context-dependent regulation during the formation of neural crest progenitors. PMID:22583479

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

PubMed Central

Gerstein, Mark

2016-01-01

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems. PMID:27760135

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.

PubMed

Wang, Daifeng; He, Fei; Maslov, Sergei; Gerstein, Mark

2016-10-01

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the "state" and "control" in the model refer to its own (internal) and another subsystem's (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.

A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

PubMed

Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju

2004-10-01

Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.

PubMed

Fogelmark, Karl; Peterson, Carsten; Troein, Carl

2016-01-01

Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.

Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data.

PubMed

Badr, Eman; ElHefnawi, Mahmoud; Heath, Lenwood S

2016-01-01

Alternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously reported algorithms. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, we identified 28,100 differentially used exons across the four tissues. We identified tissue-specific exonic splicing enhancers that overlap with various previously published experimental and computational databases. A complicated exonic enhancer regulatory network was revealed, where multiple exonic enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the exonic enhancers are found to be co-occurring with multiple exonic silencers and vice versa, which demonstrates a complicated relationship between tissue-specific exonic enhancers and silencers.

Programming gene expression with combinatorial promoters

PubMed Central

Cox, Robert Sidney; Surette, Michael G; Elowitz, Michael B

2007-01-01

Promoters control the expression of genes in response to one or more transcription factors (TFs). The architecture of a promoter is the arrangement and type of binding sites within it. To understand natural genetic circuits and to design promoters for synthetic biology, it is essential to understand the relationship between promoter function and architecture. We constructed a combinatorial library of random promoter architectures. We characterized 288 promoters in Escherichia coli, each containing up to three inputs from four different TFs. The library design allowed for multiple −10 and −35 boxes, and we observed varied promoter strength over five decades. To further analyze the functional repertoire, we defined a representation of promoter function in terms of regulatory range, logic type, and symmetry. Using these results, we identified heuristic rules for programming gene expression with combinatorial promoters. PMID:18004278

Dynamical analysis of continuous higher-order hopfield networks for combinatorial optimization.

PubMed

Atencia, Miguel; Joya, Gonzalo; Sandoval, Francisco

2005-08-01

In this letter, the ability of higher-order Hopfield networks to solve combinatorial optimization problems is assessed by means of a rigorous analysis of their properties. The stability of the continuous network is almost completely clarified: (1) hyperbolic interior equilibria, which are unfeasible, are unstable; (2) the state cannot escape from the unitary hypercube; and (3) a Lyapunov function exists. Numerical methods used to implement the continuous equation on a computer should be designed with the aim of preserving these favorable properties. The case of nonhyperbolic fixed points, which occur when the Hessian of the target function is the null matrix, requires further study. We prove that these nonhyperbolic interior fixed points are unstable in networks with three neurons and order two. The conjecture that interior equilibria are unstable in the general case is left open.

Molecular codes for neuronal individuality and cell assembly in the brain

PubMed Central

Yagi, Takeshi

2012-01-01

The brain contains an enormous, but finite, number of neurons. The ability of this limited number of neurons to produce nearly limitless neural information over a lifetime is typically explained by combinatorial explosion; that is, by the exponential amplification of each neuron's contribution through its incorporation into “cell assemblies” and neural networks. In development, each neuron expresses diverse cellular recognition molecules that permit the formation of the appropriate neural cell assemblies to elicit various brain functions. The mechanism for generating neuronal assemblies and networks must involve molecular codes that give neurons individuality and allow them to recognize one another and join appropriate networks. The extensive molecular diversity of cell-surface proteins on neurons is likely to contribute to their individual identities. The clustered protocadherins (Pcdh) is a large subfamily within the diverse cadherin superfamily. The clustered Pcdh genes are encoded in tandem by three gene clusters, and are present in all known vertebrate genomes. The set of clustered Pcdh genes is expressed in a random and combinatorial manner in each neuron. In addition, cis-tetramers composed of heteromultimeric clustered Pcdh isoforms represent selective binding units for cell-cell interactions. Here I present the mathematical probabilities for neuronal individuality based on the random and combinatorial expression of clustered Pcdh isoforms and their formation of cis-tetramers in each neuron. Notably, clustered Pcdh gene products are known to play crucial roles in correct axonal projections, synaptic formation, and neuronal survival. Their molecular and biological features induce a hypothesis that the diverse clustered Pcdh molecules provide the molecular code by which neuronal individuality and cell assembly permit the combinatorial explosion of networks that supports enormous processing capability and plasticity of the brain. PMID:22518100

Evolution, learning, and cognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Y.C.

1988-01-01

The book comprises more than fifteen articles in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.

Core and region-enriched networks of behaviorally regulated genes and the singing genome

PubMed Central

Whitney, Osceola; Pfenning, Andreas R.; Howard, Jason T.; Blatti, Charles A; Liu, Fang; Ward, James M.; Wang, Rui; Audet, Jean-Nicolas; Kellis, Manolis; Mukherjee, Sayan; Sinha, Saurabh; Hartemink, Alexander J.; West, Anne E.; Jarvis, Erich D.

2015-01-01

Songbirds represent an important model organism for elucidating molecular mechanisms that link genes with complex behaviors, in part because they have discrete vocal learning circuits that have parallels with those that mediate human speech. We found that ~10% of the genes in the avian genome were regulated by singing, and we found a striking regional diversity of both basal and singing-induced programs in the four key song nuclei of the zebra finch, a vocal learning songbird. The region-enriched patterns were a result of distinct combinations of region-enriched transcription factors (TFs), their binding motifs, and presinging acetylation of histone 3 at lysine 27 (H3K27ac) enhancer activity in the regulatory regions of the associated genes. RNA interference manipulations validated the role of the calcium-response transcription factor (CaRF) in regulating genes preferentially expressed in specific song nuclei in response to singing. Thus, differential combinatorial binding of a small group of activity-regulated TFs and predefined epigenetic enhancer activity influences the anatomical diversity of behaviorally regulated gene networks. PMID:25504732

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

PubMed Central

Kozlov, Konstantin N.; Kulakovskiy, Ivan V.; Zubair, Asif; Marjoram, Paul; Lawrie, David S.; Nuzhdin, Sergey V.; Samsonova, Maria G.

2017-01-01

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects. PMID:28898266

Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks

PubMed Central

Fogelmark, Karl; Peterson, Carsten; Troein, Carl

2016-01-01

Background Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. Methodology To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. Principal Findings We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks. PMID:26927540

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

PubMed Central

Sun, Hong; Guns, Tias; Fierro, Ana Carolina; Thorrez, Lieven; Nijssen, Siegfried; Marchal, Kathleen

2012-01-01

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method ‘CPModule’. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC. PMID:22422841

A genome-wide SNP scan accelerates trait-regulatory genomic loci identification in chickpea

PubMed Central

Kujur, Alice; Bajaj, Deepak; Upadhyaya, Hari D.; Das, Shouvik; Ranjan, Rajeev; Shree, Tanima; Saxena, Maneesha S.; Badoni, Saurabh; Kumar, Vinod; Tripathi, Shailesh; Gowda, C.L.L.; Sharma, Shivali; Singh, Sube; Tyagi, Akhilesh K.; Parida, Swarup K.

2015-01-01

We identified 44844 high-quality SNPs by sequencing 92 diverse chickpea accessions belonging to a seed and pod trait-specific association panel using reference genome- and de novo-based GBS (genotyping-by-sequencing) assays. A GWAS (genome-wide association study) in an association panel of 211, including the 92 sequenced accessions, identified 22 major genomic loci showing significant association (explaining 23–47% phenotypic variation) with pod and seed number/plant and 100-seed weight. Eighteen trait-regulatory major genomic loci underlying 13 robust QTLs were validated and mapped on an intra-specific genetic linkage map by QTL mapping. A combinatorial approach of GWAS, QTL mapping and gene haplotype-specific LD mapping and transcript profiling uncovered one superior haplotype and favourable natural allelic variants in the upstream regulatory region of a CesA-type cellulose synthase (Ca_Kabuli_CesA3) gene regulating high pod and seed number/plant (explaining 47% phenotypic variation) in chickpea. The up-regulation of this superior gene haplotype correlated with increased transcript expression of Ca_Kabuli_CesA3 gene in the pollen and pod of high pod/seed number accession, resulting in higher cellulose accumulation for normal pollen and pollen tube growth. A rapid combinatorial genome-wide SNP genotyping-based approach has potential to dissect complex quantitative agronomic traits and delineate trait-regulatory genomic loci (candidate genes) for genetic enhancement in crop plants, including chickpea. PMID:26058368

Regulation of alternative splicing in Drosophila by 56 RNA binding proteins

DOE PAGES

Brooks, Angela N.; Duff, Michael O.; May, Gemma; ...

2015-08-20

Alternative splicing is regulated by RNA binding proteins (RBPs) that recognize pre-mRNA sequence elements and activate or repress adjacent exons. Here, we used RNA interference and RNA-seq to identify splicing events regulated by 56 Drosophila proteins, some previously unknown to regulate splicing. Nearly all proteins affected alternative first exons, suggesting that RBPs play important roles in first exon choice. Half of the splicing events were regulated by multiple proteins, demonstrating extensive combinatorial regulation. We observed that SR and hnRNP proteins tend to act coordinately with each other, not antagonistically. We also identified a cross-regulatory network where splicing regulators affected themore » splicing of pre-mRNAs encoding other splicing regulators. In conclusion, this large-scale study substantially enhances our understanding of recent models of splicing regulation and provides a resource of thousands of exons that are regulated by 56 diverse RBPs.« less

Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

PubMed

Francis, Andrew; Moulton, Vincent

2018-06-07

Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Biological Signal Processing with a Genetic Toggle Switch

PubMed Central

Hillenbrand, Patrick; Fritz, Georg; Gerland, Ulrich

2013-01-01

Complex gene regulation requires responses that depend not only on the current levels of input signals but also on signals received in the past. In digital electronics, logic circuits with this property are referred to as sequential logic, in contrast to the simpler combinatorial logic without such internal memory. In molecular biology, memory is implemented in various forms such as biochemical modification of proteins or multistable gene circuits, but the design of the regulatory interface, which processes the input signals and the memory content, is often not well understood. Here, we explore design constraints for such regulatory interfaces using coarse-grained nonlinear models and stochastic simulations of detailed biochemical reaction networks. We test different designs for biological analogs of the most versatile memory element in digital electronics, the JK-latch. Our analysis shows that simple protein-protein interactions and protein-DNA binding are sufficient, in principle, to implement genetic circuits with the capabilities of a JK-latch. However, it also exposes fundamental limitations to its reliability, due to the fact that biological signal processing is asynchronous, in contrast to most digital electronics systems that feature a central clock to orchestrate the timing of all operations. We describe a seemingly natural way to improve the reliability by invoking the master-slave concept from digital electronics design. This concept could be useful to interpret the design of natural regulatory circuits, and for the design of synthetic biological systems. PMID:23874595

A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen

PubMed Central

Yoon, Sung Ho; Turkarslan, Serdar; Reiss, David J.; Pan, Min; Burn, June A.; Costa, Kyle C.; Lie, Thomas J.; Slagel, Joseph; Moritz, Robert L.; Hackett, Murray; Leigh, John A.; Baliga, Nitin S.

2013-01-01

Methanogens catalyze the critical methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter. Here, we present the first predictive model of global gene regulation of methanogenesis in a hydrogenotrophic methanogen, Methanococcus maripaludis. We generated a comprehensive list of genes (protein-coding and noncoding) for M. maripaludis through integrated analysis of the transcriptome structure and a newly constructed Peptide Atlas. The environment and gene-regulatory influence network (EGRIN) model of the strain was constructed from a compendium of transcriptome data that was collected over 58 different steady-state and time-course experiments that were performed in chemostats or batch cultures under a spectrum of environmental perturbations that modulated methanogenesis. Analyses of the EGRIN model have revealed novel components of methanogenesis that included at least three additional protein-coding genes of previously unknown function as well as one noncoding RNA. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to intercoordinate key steps of methanogenesis with different processes such as motility, ATP biosynthesis, and carbon assimilation. Through a combination of genetic and environmental perturbation experiments we have validated the EGRIN-predicted role of two novel transcription factors in the regulation of phosphate-dependent repression of formate dehydrogenase—a key enzyme in the methanogenesis pathway. The EGRIN model demonstrates regulatory affiliations within methanogenesis as well as between methanogenesis and other cellular functions. PMID:24089473

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

DOE PAGES

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting; ...

2018-03-28

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

Thermal analysis of combinatorial solid geometry models using SINDA

NASA Technical Reports Server (NTRS)

Gerencser, Diane; Radke, George; Introne, Rob; Klosterman, John; Miklosovic, Dave

1993-01-01

Algorithms have been developed using Monte Carlo techniques to determine the thermal network parameters necessary to perform a finite difference analysis on Combinatorial Solid Geometry (CSG) models. Orbital and laser fluxes as well as internal heat generation are modeled to facilitate satellite modeling. The results of the thermal calculations are used to model the infrared (IR) images of targets and assess target vulnerability. Sample analyses and validation are presented which demonstrate code products.

Genome-wide analysis of ABA-responsive elements ABRE and CE3 reveals divergent patterns in Arabidopsis and rice

PubMed Central

Gómez-Porras, Judith L; Riaño-Pachón, Diego Mauricio; Dreyer, Ingo; Mayer, Jorge E; Mueller-Roeber, Bernd

2007-01-01

Background In plants, complex regulatory mechanisms are at the core of physiological and developmental processes. The phytohormone abscisic acid (ABA) is involved in the regulation of various such processes, including stomatal closure, seed and bud dormancy, and physiological responses to cold, drought and salinity stress. The underlying tissue or plant-wide control circuits often include combinatorial gene regulatory mechanisms and networks that we are only beginning to unravel with the help of new molecular tools. The increasing availability of genomic sequences and gene expression data enables us to dissect ABA regulatory mechanisms at the individual gene expression level. In this paper we used an in-silico-based approach directed towards genome-wide prediction and identification of specific features of ABA-responsive elements. In particular we analysed the genome-wide occurrence and positional arrangements of two well-described ABA-responsive cis-regulatory elements (CREs), ABRE and CE3, in thale cress (Arabidopsis thaliana) and rice (Oryza sativa). Results Our results show that Arabidopsis and rice use the ABA-responsive elements ABRE and CE3 distinctively. Earlier reports for various monocots have identified CE3 as a coupling element (CE) associated with ABRE. Surprisingly, we found that while ABRE is equally abundant in both species, CE3 is practically absent in Arabidopsis. ABRE-ABRE pairs are common in both genomes, suggesting that these can form functional ABA-responsive complexes (ABRCs) in Arabidopsis and rice. Furthermore, we detected distinct combinations, orientation patterns and DNA strand preferences of ABRE and CE3 motifs in rice gene promoters. Conclusion Our computational analyses revealed distinct recruitment patterns of ABA-responsive CREs in upstream sequences of Arabidopsis and rice. The apparent absence of CE3s in Arabidopsis suggests that another CE pairs with ABRE to establish a functional ABRC capable of interacting with transcription factors. Further studies will be needed to test whether the observed differences are extrapolatable to monocots and dicots in general, and to understand how they contribute to the fine-tuning of the hormonal response. The outcome of our investigation can now be used to direct future experimentation designed to further dissect the ABA-dependent regulatory networks. PMID:17672917

Genome-wide analysis of ABA-responsive elements ABRE and CE3 reveals divergent patterns in Arabidopsis and rice.

PubMed

Gómez-Porras, Judith L; Riaño-Pachón, Diego Mauricio; Dreyer, Ingo; Mayer, Jorge E; Mueller-Roeber, Bernd

2007-08-01

In plants, complex regulatory mechanisms are at the core of physiological and developmental processes. The phytohormone abscisic acid (ABA) is involved in the regulation of various such processes, including stomatal closure, seed and bud dormancy, and physiological responses to cold, drought and salinity stress. The underlying tissue or plant-wide control circuits often include combinatorial gene regulatory mechanisms and networks that we are only beginning to unravel with the help of new molecular tools. The increasing availability of genomic sequences and gene expression data enables us to dissect ABA regulatory mechanisms at the individual gene expression level. In this paper we used an in-silico-based approach directed towards genome-wide prediction and identification of specific features of ABA-responsive elements. In particular we analysed the genome-wide occurrence and positional arrangements of two well-described ABA-responsive cis-regulatory elements (CREs), ABRE and CE3, in thale cress (Arabidopsis thaliana) and rice (Oryza sativa). Our results show that Arabidopsis and rice use the ABA-responsive elements ABRE and CE3 distinctively. Earlier reports for various monocots have identified CE3 as a coupling element (CE) associated with ABRE. Surprisingly, we found that while ABRE is equally abundant in both species, CE3 is practically absent in Arabidopsis. ABRE-ABRE pairs are common in both genomes, suggesting that these can form functional ABA-responsive complexes (ABRCs) in Arabidopsis and rice. Furthermore, we detected distinct combinations, orientation patterns and DNA strand preferences of ABRE and CE3 motifs in rice gene promoters. Our computational analyses revealed distinct recruitment patterns of ABA-responsive CREs in upstream sequences of Arabidopsis and rice. The apparent absence of CE3s in Arabidopsis suggests that another CE pairs with ABRE to establish a functional ABRC capable of interacting with transcription factors. Further studies will be needed to test whether the observed differences are extrapolatable to monocots and dicots in general, and to understand how they contribute to the fine-tuning of the hormonal response. The outcome of our investigation can now be used to direct future experimentation designed to further dissect the ABA-dependent regulatory networks.

Achieving Remission in Gulf War Illness: A Simulation-Based Approach to Treatment Design.

PubMed

Craddock, Travis J A; Del Rosario, Ryan R; Rice, Mark; Zysman, Joel P; Fletcher, Mary Ann; Klimas, Nancy G; Broderick, Gordon

2015-01-01

Gulf War Illness (GWI) is a chronic multi-symptom disorder affecting up to one-third of the 700,000 returning veterans of the 1991 Persian Gulf War and for which there is no known cure. GWI symptoms span several of the body's principal regulatory systems and include debilitating fatigue, severe musculoskeletal pain, cognitive and neurological problems. Using computational models, our group reported previously that GWI might be perpetuated at least in part by natural homeostatic regulation of the neuroendocrine-immune network. In this work, we attempt to harness these regulatory dynamics to identify treatment courses that might produce lasting remission. Towards this we apply a combinatorial optimization scheme to the Monte Carlo simulation of a discrete ternary logic model that represents combined hypothalamic-pituitary-adrenal (HPA), gonadal (HPG), and immune system regulation in males. In this work we found that no single intervention target allowed a robust return to normal homeostatic control. All combined interventions leading to a predicted remission involved an initial inhibition of Th1 inflammatory cytokines (Th1Cyt) followed by a subsequent inhibition of glucocorticoid receptor function (GR). These first two intervention events alone ended in stable and lasting return to the normal regulatory control in 40% of the simulated cases. Applying a second cycle of this combined treatment improved this predicted remission rate to 2 out of 3 simulated subjects (63%). These results suggest that in a complex illness such as GWI, a multi-tiered intervention strategy that formally accounts for regulatory dynamics may be required to reset neuroendocrine-immune homeostasis and support extended remission.

Exact model reduction of combinatorial reaction networks

PubMed Central

Conzelmann, Holger; Fey, Dirk; Gilles, Ernst D

2008-01-01

Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs) to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks. PMID:18755034

Combinatorial delivery of small interfering RNAs reduces RNAi efficacy by selective incorporation into RISC

PubMed Central

Castanotto, Daniela; Sakurai, Kumi; Lingeman, Robert; Li, Haitang; Shively, Louise; Aagaard, Lars; Soifer, Harris; Gatignol, Anne; Riggs, Arthur; Rossi, John J.

2007-01-01

Despite the great potential of RNAi, ectopic expression of shRNA or siRNAs holds the inherent risk of competition for critical RNAi components, thus altering the regulatory functions of some cellular microRNAs. In addition, specific siRNA sequences can potentially hinder incorporation of other siRNAs when used in a combinatorial approach. We show that both synthetic siRNAs and expressed shRNAs compete against each other and with the endogenous microRNAs for transport and for incorporation into the RNA induced silencing complex (RISC). The same siRNA sequences do not display competition when expressed from a microRNA backbone. We also show that TAR RNA binding protein (TRBP) is one of the sensors for selection and incorporation of the guide sequence of interfering RNAs. These findings reveal that combinatorial siRNA approaches can be problematic and have important implications for the methodology of expression and use of therapeutic interfering RNAs. PMID:17660190

Projective Ponzano-Regge spin networks and their symmetries

NASA Astrophysics Data System (ADS)

Aquilanti, Vincenzo; Marzuoli, Annalisa

2018-02-01

We present a novel hierarchical construction of projective spin networks of the Ponzano-Regge type from an assembling of five quadrangles up to the combinatorial 4-simplex compatible with a geometrical realization in Euclidean 4-space. The key ingredients are the projective Desargues configuration and the incidence structure given by its space-dual, on the one hand, and the Biedenharn-Elliott identity for the 6j symbol of SU(2), on the other. The interplay between projective-combinatorial and algebraic features relies on the recoupling theory of angular momenta, an approach to discrete quantum gravity models carried out successfully over the last few decades. The role of Regge symmetry-an intriguing discrete symmetry of the 6j which goes beyond the standard tetrahedral symmetry of this symbol-will be also discussed in brief to highlight its role in providing a natural regularization of projective spin networks that somehow mimics the standard regularization through a q-deformation of SU(2).

Cross-Domain Analogies as Relating Derived Relations among Two Separate Relational Networks

PubMed Central

Ruiz, Francisco J; Luciano, Carmen

2011-01-01

Contemporary behavior analytic research is making headway in analyzing analogy as the establishment of a relation of coordination among common types of trained or derived relations. Previous studies have been focused on within-domain analogy. The current study expands previous research by analyzing cross-domain analogy as relating relations among separate relational networks and by correlating participants' performance with a standard measure of analogical reasoning. In two experiments, adult participants first completed general intelligence and analogical reasoning tests. Subsequently, they were exposed to a computerized conditional discrimination training procedure designed to create two relational networks, each consisting of two 3-member equivalence classes. The critical test was a two-part analogical test in which participants had to relate combinatorial relations of coordination and distinction between the two relational networks. In Experiment 1, combinatorial relations for each network were individually tested prior to analogical testing, but in Experiment 2 they were not. Across both experiments, 65% of participants passed the analogical test on the first attempt. Moreover, results from the training procedure were strongly correlated with the standard measure of analogical reasoning. PMID:21547072

Deciphering the combinatorial architecture of a Drosophila homeotic gene enhancer

PubMed Central

Drewell, Robert A.; Nevarez, Michael J.; Kurata, Jessica S.; Winkler, Lauren N.; Li, Lily; Dresch, Jacqueline M.

2013-01-01

Summary In Drosophila, the 330 kb bithorax complex regulates cellular differentiation along the anterio-posterior axis during development in the thorax and abdomen and is comprised of three homeotic genes: Ultrabithorax, abdominal-A, and Abdominal-B. The expression of each of these genes is in turn controlled through interactions between transcription factors and a number of cis-regulatory modules in the neighboring intergenic regions. In this study, we examine how the sequence architecture of transcription factor binding sites mediates the functional activity of one of these cis-regulatory modules. Using computational, mathematical modeling and experimental molecular genetic approaches we investigate the IAB7b enhancer, which regulates Abdominal-B expression specifically in the presumptive seventh and ninth abdominal segments of the early embryo. A cross-species comparison of the IAB7b enhancer reveals an evolutionarily conserved signature motif containing two FUSHI-TARAZU activator transcription factor binding sites. We find that the transcriptional repressors KNIRPS, KRUPPEL and GIANT are able to restrict reporter gene expression to the posterior abdominal segments, using different molecular mechanisms including short-range repression and competitive binding. Additionally, we show the functional importance of the spacing between the two FUSHI-TARAZU binding sites and discuss the potential importance of cooperativity for transcriptional activation. Our results demonstrate that the transcriptional output of the IAB7b cis-regulatory module relies on a complex set of combinatorial inputs mediated by specific transcription factor binding and that the sequence architecture at this enhancer is critical to maintain robust regulatory function. PMID:24514265

Towards Breaking the Histone Code – Bayesian Graphical Models for Histone Modifications

PubMed Central

Mitra, Riten; Müller, Peter; Liang, Shoudan; Xu, Yanxun; Ji, Yuan

2013-01-01

Background Histones are proteins that wrap DNA around in small spherical structures called nucleosomes. Histone modifications (HMs) refer to the post-translational modifications to the histone tails. At a particular genomic locus, each of these HMs can either be present or absent, and the combinatory patterns of the presence or absence of multiple HMs, or the ‘histone codes,’ are believed to co-regulate important biological processes. We aim to use raw data on HM markers at different genomic loci to (1) decode the complex biological network of HMs in a single region and (2) demonstrate how the HM networks differ in different regulatory regions. We suggest that these differences in network attributes form a significant link between histones and genomic functions. Methods and Results We develop a powerful graphical model under Bayesian paradigm. Posterior inference is fully probabilistic, allowing us to compute the probabilities of distinct dependence patterns of the HMs using graphs. Furthermore, our model-based framework allows for easy but important extensions for inference on differential networks under various conditions, such as the different annotations of the genomic locations (e.g., promoters versus insulators). We applied these models to ChIP-Seq data based on CD4+ T lymphocytes. The results confirmed many existing findings and provided a unified tool to generate various promising hypotheses. Differential network analyses revealed new insights on co-regulation of HMs of transcriptional activities in different genomic regions. Conclusions The use of Bayesian graphical models and borrowing strength across different conditions provide high power to infer histone networks and their differences. PMID:23748248

Abductive networks applied to electronic combat

NASA Astrophysics Data System (ADS)

Montgomery, Gerard J.; Hess, Paul; Hwang, Jong S.

1990-08-01

A practical approach to dealing with combinatorial decision problems and uncertainties associated with electronic combat through the use of networks of high-level functional elements called abductive networks is presented. It describes the application of the Abductory Induction Mechanism (AIMTM) a supervised inductive learning tool for synthesizing polynomial abductive networks to the electronic combat problem domain. From databases of historical expert-generated or simulated combat engagements AIM can often induce compact and robust network models for making effective real-time electronic combat decisions despite significant uncertainties or a combinatorial explosion of possible situations. The feasibility of applying abductive networks to realize advanced combat decision aiding capabilities was demonstrated by applying AIM to a set of electronic combat simulations. The networks synthesized by AIM generated accurate assessments of the intent lethality and overall risk associated with a variety of simulated threats and produced reasonable estimates of the expected effectiveness of a group of electronic countermeasures for a large number of simulated combat scenarios. This paper presents the application of abductive networks to electronic combat summarizes the results of experiments performed using AIM discusses the benefits and limitations of applying abductive networks to electronic combat and indicates why abductive networks can often result in capabilities not attainable using alternative approaches. 1. ELECTRONIC COMBAT. UNCERTAINTY. AND MACHINE LEARNING Electronic combat has become an essential part of the ability to make war and has become increasingly complex since

An Integrative Platform of TCM Network Pharmacology and Its Application on a Herbal Formula, Qing-Luo-Yin

PubMed Central

Zhang, Bo; Wang, Xu; Li, Shao

2013-01-01

The scientific understanding of traditional Chinese medicine (TCM) has been hindered by the lack of methods that can explore the complex nature and combinatorial rules of herbal formulae. On the assumption that herbal ingredients mainly target a molecular network to adjust the imbalance of human body, here we present a-self-developed TCM network pharmacology platform for discovering herbal formulae in a systematic manner. This platform integrates a set of network-based methods that we established previously to catch the network regulation mechanism and to identify active ingredients as well as synergistic combinations for a given herbal formula. We then provided a case study on an antirheumatoid arthritis (RA) formula, Qing-Luo-Yin (QLY), to demonstrate the usability of the platform. We revealed the target network of QLY against RA-related key processes including angiogenesis, inflammatory response, and immune response, based on which we not only predicted active and synergistic ingredients from QLY but also interpreted the combinatorial rule of this formula. These findings are either verified by the literature evidence or have the potential to guide further experiments. Therefore, such a network pharmacology strategy and platform is expected to make the systematical study of herbal formulae achievable and to make the TCM drug discovery predictable. PMID:23653662

Technical Advance: Transcription factor, promoter, and enhancer utilization in human myeloid cells.

PubMed

Joshi, Anagha; Pooley, Christopher; Freeman, Tom C; Lennartsson, Andreas; Babina, Magda; Schmidl, Christian; Geijtenbeek, Teunis; Michoel, Tom; Severin, Jessica; Itoh, Masayoshi; Lassmann, Timo; Kawaji, Hideya; Hayashizaki, Yoshihide; Carninci, Piero; Forrest, Alistair R R; Rehli, Michael; Hume, David A

2015-05-01

The generation of myeloid cells from their progenitors is regulated at the level of transcription by combinatorial control of key transcription factors influencing cell-fate choice. To unravel the global dynamics of this process at the transcript level, we generated transcription profiles for 91 human cell types of myeloid origin by use of CAGE profiling. The CAGE sequencing of these samples has allowed us to investigate diverse aspects of transcription control during myelopoiesis, such as identification of novel transcription factors, miRNAs, and noncoding RNAs specific to the myeloid lineage. We further reconstructed a transcription regulatory network by clustering coexpressed transcripts and associating them with enriched cis-regulatory motifs. With the use of the bidirectional expression as a proxy for enhancers, we predicted over 2000 novel enhancers, including an enhancer 38 kb downstream of IRF8 and an intronic enhancer in the KIT gene locus. Finally, we highlighted relevance of these data to dissect transcription dynamics during progressive maturation of granulocyte precursors. A multifaceted analysis of the myeloid transcriptome is made available (www.myeloidome.roslin.ed.ac.uk). This high-quality dataset provides a powerful resource to study transcriptional regulation during myelopoiesis and to infer the likely functions of unannotated genes in human innate immunity. © The Author(s).

Synergistic Effect of Combinatorial Treatment with Curcumin and Mitomycin C on the Induction of Apoptosis of Breast Cancer Cells: A cDNA Microarray Analysis

PubMed Central

Zhou, Qian-Mei; Chen, Qi-Long; Du, Jia; Wang, Xiu-Feng; Lu, Yi-Yu; Zhang, Hui; Su, Shi-Bing

2014-01-01

In order to explore the synergistic mechanisms of combinatorial treatment using curcumin and mitomycin C (MMC) for breast cancer, MCF-7 breast cancer xenografts were conducted to observe the synergistic effect of combinatorial treatment using curcumin and MMC at various dosages. The synergistic mechanisms of combinatorial treatment using curcumin and MMC on the inhibition of tumor growth were explored by differential gene expression profile, gene ontology (GO), ingenuity pathway analysis (IPA) and Signal–Net network analysis. The expression levels of selected genes identified by cDNA microarray expression profiling were validated by quantitative RT-PCR (qRT-PCR) and Western blot analysis. Effect of combinatorial treatment on the inhibition of cell growth was observed by MTT assay. Apoptosis was detected by flow cytometric analysis and Hoechst 33258 staining. The combinatorial treatment of 100 mg/kg curcumin and 1.5 mg/kg MMC revealed synergistic inhibition on tumor growth. Among 1501 differentially expressed genes, the expression of 25 genes exhibited an obvious change and a significant difference in 27 signal pathways was observed (p < 0.05). In addition, Mapk1 (ERK) and Mapk14 (MAPK p38) had more cross-interactions with other genes and revealed an increase in expression by 8.14- and 11.84-fold, respectively during the combinatorial treatment by curcumin and MMC when compared with the control. Moreover, curcumin can synergistically improve tumoricidal effect of MMC in another human breast cancer MDA-MB-231 cells. Apoptosis was significantly induced by the combinatorial treatment (p < 0.05) and significantly inhibited by ERK inhibitor (PD98059) in MCF-7 cells (p < 0.05). The synergistic effect of combinatorial treatment by curcumin and MMC on the induction of apoptosis in breast cancer cells may be via the ERK pathway. PMID:25226537

Drosophila growth cones: a genetically tractable platform for the analysis of axonal growth dynamics.

PubMed

Sánchez-Soriano, Natalia; Gonçalves-Pimentel, Catarina; Beaven, Robin; Haessler, Ulrike; Ofner-Ziegenfuss, Lisa; Ballestrem, Christoph; Prokop, Andreas

2010-01-01

The formation of neuronal networks, during development and regeneration, requires outgrowth of axons along reproducible paths toward their appropriate postsynaptic target cells. Axonal extension occurs at growth cones (GCs) at the tips of axons. GC advance and navigation requires the activity of their cytoskeletal networks, comprising filamentous actin (F-actin) in lamellipodia and filopodia as well as dynamic microtubules (MTs) emanating from bundles of the axonal core. The molecular mechanisms governing these two cytoskeletal networks, their cross-talk, and their response to extracellular signaling cues are only partially understood, hindering our conceptual understanding of how regulated changes in GC behavior are controlled. Here, we introduce Drosophila GCs as a suitable model to address these mechanisms. Morphological and cytoskeletal readouts of Drosophila GCs are similar to those of other models, including mammals, as demonstrated here for MT and F-actin dynamics, axonal growth rates, filopodial structure and motility, organizational principles of MT networks, and subcellular marker localization. Therefore, we expect fundamental insights gained in Drosophila to be translatable into vertebrate biology. The advantage of the Drosophila model over others is its enormous amenability to combinatorial genetics as a powerful strategy to address the complexity of regulatory networks governing axonal growth. Thus, using pharmacological and genetic manipulations, we demonstrate a role of the actin cytoskeleton in a specific form of MT organization (loop formation), known to regulate GC pausing behavior. We demonstrate these events to be mediated by the actin-MT linking factor Short stop, thus identifying an essential molecular player in this context.

Disentangling the many layers of eukaryotic transcriptional regulation.

PubMed

Lelli, Katherine M; Slattery, Matthew; Mann, Richard S

2012-01-01

Regulation of gene expression in eukaryotes is an extremely complex process. In this review, we break down several critical steps, emphasizing new data and techniques that have expanded current gene regulatory models. We begin at the level of DNA sequence where cis-regulatory modules (CRMs) provide important regulatory information in the form of transcription factor (TF) binding sites. In this respect, CRMs function as instructional platforms for the assembly of gene regulatory complexes. We discuss multiple mechanisms controlling complex assembly, including cooperative DNA binding, combinatorial codes, and CRM architecture. The second section of this review places CRM assembly in the context of nucleosomes and condensed chromatin. We discuss how DNA accessibility and histone modifications contribute to TF function. Lastly, new advances in chromosomal mapping techniques have provided increased understanding of intra- and interchromosomal interactions. We discuss how these topological maps influence gene regulatory models.

Interrogating the topological robustness of gene regulatory circuits by randomization

PubMed Central

Levine, Herbert; Onuchic, Jose N.

2017-01-01

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression. PMID:28362798

Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems.

PubMed

Nishimoto, Ryu; Tani, Jun

2004-09-01

This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the sequences of mimicking FSMs are encoded utilizing the transient regions rather than the invariant sets of the evolved dynamical systems of the RNNs.

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

PubMed

García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

2006-05-01

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Modeling discrete combinatorial systems as alphabetic bipartite networks: theory and applications.

PubMed

Choudhury, Monojit; Ganguly, Niloy; Maiti, Abyayananda; Mukherjee, Animesh; Brusch, Lutz; Deutsch, Andreas; Peruani, Fernando

2010-03-01

Genes and human languages are discrete combinatorial systems (DCSs), in which the basic building blocks are finite sets of elementary units: nucleotides or codons in a DNA sequence, and letters or words in a language. Different combinations of these finite units give rise to potentially infinite numbers of genes or sentences. This type of DCSs can be represented as an alphabetic bipartite network (ABN) where there are two kinds of nodes, one type represents the elementary units while the other type represents their combinations. Here, we extend and generalize recent analytical findings for ABNs derived in [Peruani, Europhys. Lett. 79, 28001 (2007)] and empirically investigate two real world systems in terms of ABNs, the codon gene and the phoneme-language network. The one-mode projections onto the elementary basic units are also studied theoretically as well as in real world ABNs. We propose the use of ABNs as a means for inferring the mechanisms underlying the growth of real world DCSs.

Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework.

PubMed

Kwok, T; Smith, K A

2000-09-01

The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.

The Development of a High-Throughput/Combinatorial Workflow for the Study of Porous Polymer Networks

DTIC Science & Technology

2012-04-05

poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared. Changes in opacity of the blends as they cured...allowed for the identification of compositional variables and process variables that enabled the production of porous networks. Keywords: high...in polymer network cross-link density,poragen composition , poragen level, and cure temperature. A total of 216 unique compositions were prepared

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

XML Encoding of Features Describing Rule-Based Modeling of Reaction Networks with Multi-Component Molecular Complexes

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2011-01-01

Multi-state molecules and multi-component complexes are commonly involved in cellular signaling. Accounting for molecules that have multiple potential states, such as a protein that may be phosphorylated on multiple residues, and molecules that combine to form heterogeneous complexes located among multiple compartments, generates an effect of combinatorial complexity. Models involving relatively few signaling molecules can include thousands of distinct chemical species. Several software tools (StochSim, BioNetGen) are already available to deal with combinatorial complexity. Such tools need information standards if models are to be shared, jointly evaluated and developed. Here we discuss XML conventions that can be adopted for modeling biochemical reaction networks described by user-specified reaction rules. These could form a basis for possible future extensions of the Systems Biology Markup Language (SBML). PMID:21464833

Regulatory RNA-assisted genome engineering in microorganisms.

PubMed

Si, Tong; HamediRad, Mohammad; Zhao, Huimin

2015-12-01

Regulatory RNAs are increasingly recognized and utilized as key modulators of gene expression in diverse organisms. Thanks to their modular and programmable nature, trans-acting regulatory RNAs are especially attractive in genome-scale applications. Here we discuss the recent examples in microbial genome engineering implementing various trans-acting RNA platforms, including sRNA, RNAi, asRNA and CRISRP-Cas. In particular, we focus on how the scalable and multiplex nature of trans-acting RNAs has been used to tackle the challenges in creating genome-wide and combinatorial diversity for functional genomics and metabolic engineering applications. Advances in computational design and context-dependent regulation are also discussed for their contribution in improving fine-tuning capabilities of trans-acting RNAs. Copyright © 2015 Elsevier Ltd. All rights reserved.

DMirNet: Inferring direct microRNA-mRNA association networks.

PubMed

Lee, Minsu; Lee, HyungJune

2016-12-05

MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed. In this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets. Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network. We believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.

Synergistic and Dose-Controlled Regulation of Cellulase Gene Expression in Penicillium oxalicum.

PubMed

Li, Zhonghai; Yao, Guangshan; Wu, Ruimei; Gao, Liwei; Kan, Qinbiao; Liu, Meng; Yang, Piao; Liu, Guodong; Qin, Yuqi; Song, Xin; Zhong, Yaohua; Fang, Xu; Qu, Yinbo

2015-09-01

Filamentous fungus Penicillium oxalicum produces diverse lignocellulolytic enzymes, which are regulated by the combinations of many transcription factors. Here, a single-gene disruptant library for 470 transcription factors was constructed and systematically screened for cellulase production. Twenty transcription factors (including ClrB, CreA, XlnR, Ace1, AmyR, and 15 unknown proteins) were identified to play putative roles in the activation or repression of cellulase synthesis. Most of these regulators have not been characterized in any fungi before. We identified the ClrB, CreA, XlnR, and AmyR transcription factors as critical dose-dependent regulators of cellulase expression, the core regulons of which were identified by analyzing several transcriptomes and/or secretomes. Synergistic and additive modes of combinatorial control of each cellulase gene by these regulatory factors were achieved, and cellulase expression was fine-tuned in a proper and controlled manner. With one of these targets, the expression of the major intracellular β-glucosidase Bgl2 was found to be dependent on ClrB. The Bgl2-deficient background resulted in a substantial gene activation by ClrB and proved to be closely correlated with the relief of repression mediated by CreA and AmyR during cellulase induction. Our results also signify that probing the synergistic and dose-controlled regulation mechanisms of cellulolytic regulators and using it for reconstruction of expression regulation network (RERN) may be a promising strategy for cellulolytic fungi to develop enzyme hyper-producers. Based on our data, ClrB was identified as focal point for the synergistic activation regulation of cellulase expression by integrating cellulolytic regulators and their target genes, which refined our understanding of transcriptional-regulatory network as a "seesaw model" in which the coordinated regulation of cellulolytic genes is established by counteracting activators and repressors.

Synergistic and Dose-Controlled Regulation of Cellulase Gene Expression in Penicillium oxalicum

PubMed Central

Li, Zhonghai; Yao, Guangshan; Wu, Ruimei; Gao, Liwei; Kan, Qinbiao; Liu, Meng; Yang, Piao; Liu, Guodong; Qin, Yuqi; Song, Xin; Zhong, Yaohua; Fang, Xu; Qu, Yinbo

2015-01-01

Filamentous fungus Penicillium oxalicum produces diverse lignocellulolytic enzymes, which are regulated by the combinations of many transcription factors. Here, a single-gene disruptant library for 470 transcription factors was constructed and systematically screened for cellulase production. Twenty transcription factors (including ClrB, CreA, XlnR, Ace1, AmyR, and 15 unknown proteins) were identified to play putative roles in the activation or repression of cellulase synthesis. Most of these regulators have not been characterized in any fungi before. We identified the ClrB, CreA, XlnR, and AmyR transcription factors as critical dose-dependent regulators of cellulase expression, the core regulons of which were identified by analyzing several transcriptomes and/or secretomes. Synergistic and additive modes of combinatorial control of each cellulase gene by these regulatory factors were achieved, and cellulase expression was fine-tuned in a proper and controlled manner. With one of these targets, the expression of the major intracellular β-glucosidase Bgl2 was found to be dependent on ClrB. The Bgl2-deficient background resulted in a substantial gene activation by ClrB and proved to be closely correlated with the relief of repression mediated by CreA and AmyR during cellulase induction. Our results also signify that probing the synergistic and dose-controlled regulation mechanisms of cellulolytic regulators and using it for reconstruction of expression regulation network (RERN) may be a promising strategy for cellulolytic fungi to develop enzyme hyper-producers. Based on our data, ClrB was identified as focal point for the synergistic activation regulation of cellulase expression by integrating cellulolytic regulators and their target genes, which refined our understanding of transcriptional-regulatory network as a “seesaw model” in which the coordinated regulation of cellulolytic genes is established by counteracting activators and repressors. PMID:26360497

Gateway Vectors for Efficient Artificial Gene Assembly In Vitro and Expression in Yeast Saccharomyces cerevisiae

PubMed Central

Giuraniuc, Claudiu V.; MacPherson, Murray; Saka, Yasushi

2013-01-01

Construction of synthetic genetic networks requires the assembly of DNA fragments encoding functional biological parts in a defined order. Yet this may become a time-consuming procedure. To address this technical bottleneck, we have created a series of Gateway shuttle vectors and an integration vector, which facilitate the assembly of artificial genes and their expression in the budding yeast Saccharomyces cerevisiae. Our method enables the rapid construction of an artificial gene from a promoter and an open reading frame (ORF) cassette by one-step recombination reaction in vitro. Furthermore, the plasmid thus created can readily be introduced into yeast cells to test the assembled gene’s functionality. As flexible regulatory components of a synthetic genetic network, we also created new versions of the tetracycline-regulated transactivators tTA and rtTA by fusing them to the auxin-inducible degron (AID). Using our gene assembly approach, we made yeast expression vectors of these engineered transactivators, AIDtTA and AIDrtTA and then tested their functions in yeast. We showed that these factors can be regulated by doxycycline and degraded rapidly after addition of auxin to the medium. Taken together, the method for combinatorial gene assembly described here is versatile and would be a valuable tool for yeast synthetic biology. PMID:23675537

Rule-based modeling and simulations of the inner kinetochore structure.

PubMed

Tschernyschkow, Sergej; Herda, Sabine; Gruenert, Gerd; Döring, Volker; Görlich, Dennis; Hofmeister, Antje; Hoischen, Christian; Dittrich, Peter; Diekmann, Stephan; Ibrahim, Bashar

2013-09-01

Combinatorial complexity is a central problem when modeling biochemical reaction networks, since the association of a few components can give rise to a large variation of protein complexes. Available classical modeling approaches are often insufficient for the analysis of very large and complex networks in detail. Recently, we developed a new rule-based modeling approach that facilitates the analysis of spatial and combinatorially complex problems. Here, we explore for the first time how this approach can be applied to a specific biological system, the human kinetochore, which is a multi-protein complex involving over 100 proteins. Applying our freely available SRSim software to a large data set on kinetochore proteins in human cells, we construct a spatial rule-based simulation model of the human inner kinetochore. The model generates an estimation of the probability distribution of the inner kinetochore 3D architecture and we show how to analyze this distribution using information theory. In our model, the formation of a bridge between CenpA and an H3 containing nucleosome only occurs efficiently for higher protein concentration realized during S-phase but may be not in G1. Above a certain nucleosome distance the protein bridge barely formed pointing towards the importance of chromatin structure for kinetochore complex formation. We define a metric for the distance between structures that allow us to identify structural clusters. Using this modeling technique, we explore different hypothetical chromatin layouts. Applying a rule-based network analysis to the spatial kinetochore complex geometry allowed us to integrate experimental data on kinetochore proteins, suggesting a 3D model of the human inner kinetochore architecture that is governed by a combinatorial algebraic reaction network. This reaction network can serve as bridge between multiple scales of modeling. Our approach can be applied to other systems beyond kinetochores. Copyright © 2013 Elsevier Ltd. All rights reserved.

Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.

PubMed

Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen

2016-07-01

This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.

Causal gene identification using combinatorial V-structure search.

PubMed

Cai, Ruichu; Zhang, Zhenjie; Hao, Zhifeng

2013-07-01

With the advances of biomedical techniques in the last decade, the costs of human genomic sequencing and genomic activity monitoring are coming down rapidly. To support the huge genome-based business in the near future, researchers are eager to find killer applications based on human genome information. Causal gene identification is one of the most promising applications, which may help the potential patients to estimate the risk of certain genetic diseases and locate the target gene for further genetic therapy. Unfortunately, existing pattern recognition techniques, such as Bayesian networks, cannot be directly applied to find the accurate causal relationship between genes and diseases. This is mainly due to the insufficient number of samples and the extremely high dimensionality of the gene space. In this paper, we present the first practical solution to causal gene identification, utilizing a new combinatorial formulation over V-Structures commonly used in conventional Bayesian networks, by exploring the combinations of significant V-Structures. We prove the NP-hardness of the combinatorial search problem under a general settings on the significance measure on the V-Structures, and present a greedy algorithm to find sub-optimal results. Extensive experiments show that our proposal is both scalable and effective, particularly with interesting findings on the causal genes over real human genome data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Synthetic plant defense elicitors

PubMed Central

Bektas, Yasemin; Eulgem, Thomas

2015-01-01

To defend themselves against invading pathogens plants utilize a complex regulatory network that coordinates extensive transcriptional and metabolic reprogramming. Although many of the key players of this immunity-associated network are known, the details of its topology and dynamics are still poorly understood. As an alternative to forward and reverse genetic studies, chemical genetics-related approaches based on bioactive small molecules have gained substantial popularity in the analysis of biological pathways and networks. Use of such molecular probes can allow researchers to access biological space that was previously inaccessible to genetic analyses due to gene redundancy or lethality of mutations. Synthetic elicitors are small drug-like molecules that induce plant defense responses, but are distinct from known natural elicitors of plant immunity. While the discovery of some synthetic elicitors had already been reported in the 1970s, recent breakthroughs in combinatorial chemical synthesis now allow for inexpensive high-throughput screens for bioactive plant defense-inducing compounds. Along with powerful reverse genetics tools and resources available for model plants and crop systems, comprehensive collections of new synthetic elicitors will likely allow plant scientists to study the intricacies of plant defense signaling pathways and networks in an unparalleled fashion. As synthetic elicitors can protect crops from diseases, without the need to be directly toxic for pathogenic organisms, they may also serve as promising alternatives to conventional biocidal pesticides, which often are harmful for the environment, farmers and consumers. Here we are discussing various types of synthetic elicitors that have been used for studies on the plant immune system, their modes-of-action as well as their application in crop protection. PMID:25674095

Combinatorial optimization games

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, X.; Ibaraki, Toshihide; Nagamochi, Hiroshi

1997-06-01

We introduce a general integer programming formulation for a class of combinatorial optimization games, which immediately allows us to improve the algorithmic result for finding amputations in the core (an important solution concept in cooperative game theory) of the network flow game on simple networks by Kalai and Zemel. An interesting result is a general theorem that the core for this class of games is nonempty if and only if a related linear program has an integer optimal solution. We study the properties for this mathematical condition to hold for several interesting problems, and apply them to resolve algorithmic andmore » complexity issues for their cores along the line as put forward in: decide whether the core is empty; if the core is empty, find an imputation in the core; given an imputation x, test whether x is in the core. We also explore the properties of totally balanced games in this succinct formulation of cooperative games.« less

Experimental Design for Combinatorial and High Throughput Materials Development

NASA Astrophysics Data System (ADS)

Cawse, James N.

2002-12-01

In the past decade, combinatorial and high throughput experimental methods have revolutionized the pharmaceutical industry, allowing researchers to conduct more experiments in a week than was previously possible in a year. Now high throughput experimentation is rapidly spreading from its origins in the pharmaceutical world to larger industrial research establishments such as GE and DuPont, and even to smaller companies and universities. Consequently, researchers need to know the kinds of problems, desired outcomes, and appropriate patterns for these new strategies. Editor James Cawse's far-reaching study identifies and applies, with specific examples, these important new principles and techniques. Experimental Design for Combinatorial and High Throughput Materials Development progresses from methods that are now standard, such as gradient arrays, to mathematical developments that are breaking new ground. The former will be particularly useful to researchers entering the field, while the latter should inspire and challenge advanced practitioners. The book's contents are contributed by leading researchers in their respective fields. Chapters include: -High Throughput Synthetic Approaches for the Investigation of Inorganic Phase Space -Combinatorial Mapping of Polymer Blends Phase Behavior -Split-Plot Designs -Artificial Neural Networks in Catalyst Development -The Monte Carlo Approach to Library Design and Redesign This book also contains over 200 useful charts and drawings. Industrial chemists, chemical engineers, materials scientists, and physicists working in combinatorial and high throughput chemistry will find James Cawse's study to be an invaluable resource.

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Global Analysis of Photosynthesis Transcriptional Regulatory Networks

PubMed Central

Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

2014-01-01

Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis. PMID:25503406

Global analysis of photosynthesis transcriptional regulatory networks.

PubMed

Imam, Saheed; Noguera, Daniel R; Donohue, Timothy J

2014-12-01

Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in Rhodobacter sphaeroides. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis.

Enhancing gene regulatory network inference through data integration with markov random fields

DOE PAGES

Banf, Michael; Rhee, Seung Y.

2017-02-01

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

Enhancing gene regulatory network inference through data integration with markov random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banf, Michael; Rhee, Seung Y.

Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less

Performance evaluation of coherent Ising machines against classical neural networks

NASA Astrophysics Data System (ADS)

Haribara, Yoshitaka; Ishikawa, Hitoshi; Utsunomiya, Shoko; Aihara, Kazuyuki; Yamamoto, Yoshihisa

2017-12-01

The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

PubMed Central

2011-01-01

Background Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. Results This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network. Conclusions The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions. PMID:21668997

English and Chinese languages as weighted complex networks

NASA Astrophysics Data System (ADS)

Sheng, Long; Li, Chunguang

2009-06-01

In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

Combinatorial expression of Lef1, Lhx2, Lhx5, Lhx9, Lmo3, Lmo4, and Prox1 helps to identify comparable subdivisions in the developing hippocampal formation of mouse and chicken

PubMed Central

Abellán, Antonio; Desfilis, Ester; Medina, Loreta

2014-01-01

We carried out a study of the expression patterns of seven developmental regulatory genes (Lef1, Lhx2, Lhx9, Lhx5, Lmo3, Lmo4, and Prox1), in combination with topological position, to identify the medial pallial derivatives, define its major subdivisions, and compare them between mouse and chicken. In both species, the medial pallium is defined as a pallial sector adjacent to the cortical hem and roof plate/choroid tela, showing moderate to strong ventricular zone expression of Lef1, Lhx2, and Lhx9, but not Lhx5. Based on this, the hippocampal formation (indusium griseum, dentate gyrus, Ammon's horn fields, and subiculum), the medial entorhinal cortex, and part of the amygdalo-hippocampal transition area of mouse appeared to derive from the medial pallium. In the chicken, based on the same position and gene expression profile, we propose that the hippocampus (including the V-shaped area), the parahippocampal area (including its caudolateral part), the entorhinal cortex, and the amygdalo-hippocampal transition area are medial pallial derivatives. Moreover, the combinatorial expression of Lef1, Prox1, Lmo4, and Lmo3 allowed the identification of dentate gyrus/CA3-like, CA1/subicular-like, and medial entorhinal-like comparable sectors in mouse and chicken, and point to the existence of mostly conserved molecular networks involved in hippocampal complex development. Notably, while the mouse medial entorhinal cortex derives from the medial pallium (similarly to the hippocampal formation, both being involved in spatial navigation and spatial memory), the lateral entorhinal cortex (involved in processing non-spatial, contextual information) appears to derive from a distinct dorsolateral caudal pallial sector. PMID:25071464

Combination Effect of Regulatory T-Cell Depletion and Ionizing Radiation in Mouse Models of Lung and Colon Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Son, Cheol-Hun; Department of Biochemistry, Pusan National University School of Medicine, Yangsan; Bae, Jae-Ho

2015-06-01

Purpose: To investigate the potential of low-dose cyclophosphamide (LD-CTX) and anti-CD25 antibody to prevent activation of regulatory T cells (Tregs) during radiation therapy. Methods and Materials: We used LD-CTX and anti-CD25 monoclonal antibody as a means to inhibit Tregs and improve the therapeutic effect of radiation in a mouse model of lung and colon cancer. Mice were irradiated on the tumor mass of the right leg and treated with LD-CTX and anti-CD25 antibody once per week for 3 weeks. Results: Combined treatment of LD-CTX or anti-CD25 antibody with radiation significantly decreased Tregs in the spleen and tumor compared with control andmore » irradiation only in both lung and colon cancer. Combinatorial treatments resulted in a significant increase in the effector T cells, longer survival rate, and suppressed irradiated and distal nonirradiated tumor growth. Specifically, the combinatorial treatment of LD-CTX with radiation resulted in outstanding regression of local and distant tumors in colon cancer, and almost all mice in this group survived until the end of the study. Conclusions: Our results suggest that Treg depletion strategies may enhance radiation-mediated antitumor immunity and further improve outcomes after radiation therapy.« less

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets.

PubMed

Naegle, Kristen M; Welsch, Roy E; Yaffe, Michael B; White, Forest M; Lauffenburger, Douglas A

2011-07-01

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems. © 2011 Naegle et al.

A prior-based integrative framework for functional transcriptional regulatory network inference

PubMed Central

Siahpirani, Alireza F.

2017-01-01

Abstract Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization. PMID:27794550

Dissection of combinatorial control by the Met4 transcriptional complex.

PubMed

Lee, Traci A; Jorgensen, Paul; Bognar, Andrew L; Peyraud, Caroline; Thomas, Dominique; Tyers, Mike

2010-02-01

Met4 is the transcriptional activator of the sulfur metabolic network in Saccharomyces cerevisiae. Lacking DNA-binding ability, Met4 must interact with proteins called Met4 cofactors to target promoters for transcription. Two types of DNA-binding cofactors (Cbf1 and Met31/Met32) recruit Met4 to promoters and one cofactor (Met28) stabilizes the DNA-bound Met4 complexes. To dissect this combinatorial system, we systematically deleted each category of cofactor(s) and analyzed Met4-activated transcription on a genome-wide scale. We defined a core regulon for Met4, consisting of 45 target genes. Deletion of both Met31 and Met32 eliminated activation of the core regulon, whereas loss of Met28 or Cbf1 interfered with only a subset of targets that map to distinct sectors of the sulfur metabolic network. These transcriptional dependencies roughly correlated with the presence of Cbf1 promoter motifs. Quantitative analysis of in vivo promoter binding properties indicated varying levels of cooperativity and interdependency exists between members of this combinatorial system. Cbf1 was the only cofactor to remain fully bound to target promoters under all conditions, whereas other factors exhibited different degrees of regulated binding in a promoter-specific fashion. Taken together, Met4 cofactors use a variety of mechanisms to allow differential transcription of target genes in response to various cues.

Deciphering principles of transcription regulation in eukaryotic genomes

PubMed Central

Nguyen, Dat H; D'haeseleer, Patrik

2006-01-01

Transcription regulation has been responsible for organismal complexity and diversity in the course of biological evolution and adaptation, and it is determined largely by the context-dependent behavior of cis-regulatory elements (CREs). Therefore, understanding principles underlying CRE behavior in regulating transcription constitutes a fundamental objective of quantitative biology, yet these remain poorly understood. Here we present a deterministic mathematical strategy, the motif expression decomposition (MED) method, for deriving principles of transcription regulation at the single-gene resolution level. MED operates on all genes in a genome without requiring any a priori knowledge of gene cluster membership, or manual tuning of parameters. Applying MED to Saccharomyces cerevisiae transcriptional networks, we identified four functions describing four different ways that CREs can quantitatively affect gene expression levels. These functions, three of which have extrema in different positions in the gene promoter (short-, mid-, and long-range) whereas the other depends on the motif orientation, are validated by expression data. We illustrate how nature could use these principles as an additional dimension to amplify the combinatorial power of a small set of CREs in regulating transcription. PMID:16738557

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

PubMed Central

Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

2015-01-01

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org PMID:26424082

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

Nucleophilic catalysis of acylhydrazone equilibration for protein-directed dynamic covalent chemistry

PubMed Central

Bhat, Venugopal T.; Caniard, Anne M.; Luksch, Torsten; Brenk, Ruth; Campopiano, Dominic J.; Greaney, Michael F.

2010-01-01

Dynamic covalent chemistry uses reversible chemical reactions to set up an equilibrating network of molecules at thermodynamic equilibrium, which can adjust its composition in response to any agent capable of altering the free energy of the system. When the target is a biological macromolecule, such as a protein, the process corresponds to the protein directing the synthesis of its own best ligand. Here, we demonstrate that reversible acylhydrazone formation is an effective chemistry for biological dynamic combinatorial library formation. In the presence of aniline as a nucleophilic catalyst, dynamic combinatorial libraries equilibrate rapidly at pH 6.2, are fully reversible, and may be switched on or off by means of a change in pH. We have interfaced these hydrazone dynamic combinatorial libraries with two isozymes from the glutathione S-transferase class of enzyme, and observed divergent amplification effects, where each protein selects the best-fitting hydrazone for the hydrophobic region of its active site. PMID:20489719

Regulatory network rewiring for secondary metabolism in Arabidopsis thaliana under various conditions

PubMed Central

2014-01-01

Background Plant secondary metabolites are critical to various biological processes. However, the regulations of these metabolites are complex because of regulatory rewiring or crosstalk. To unveil how regulatory behaviors on secondary metabolism reshape biological processes, we constructed and analyzed a dynamic regulatory network of secondary metabolic pathways in Arabidopsis. Results The dynamic regulatory network was constructed through integrating co-expressed gene pairs and regulatory interactions. Regulatory interactions were either predicted by conserved transcription factor binding sites (TFBSs) or proved by experiments. We found that integrating two data (co-expression and predicted regulatory interactions) enhanced the number of highly confident regulatory interactions by over 10% compared with using single data. The dynamic changes of regulatory network systematically manifested regulatory rewiring to explain the mechanism of regulation, such as in terpenoids metabolism, the regulatory crosstalk of RAV1 (AT1G13260) and ATHB1 (AT3G01470) on HMG1 (hydroxymethylglutaryl-CoA reductase, AT1G76490); and regulation of RAV1 on epoxysqualene biosynthesis and sterol biosynthesis. Besides, we investigated regulatory rewiring with expression, network topology and upstream signaling pathways. Regulatory rewiring was revealed by the variability of genes’ expression: pathway genes and transcription factors (TFs) were significantly differentially expressed under different conditions (such as terpenoids biosynthetic genes in tissue experiments and E2F/DP family members in genotype experiments). Both network topology and signaling pathways supported regulatory rewiring. For example, we discovered correlation among the numbers of pathway genes, TFs and network topology: one-gene pathways (such as δ-carotene biosynthesis) were regulated by a fewer TFs, and were not critical to metabolic network because of their low degrees in topology. Upstream signaling pathways of 50 TFs were identified to comprehend the underlying mechanism of TFs’ regulatory rewiring. Conclusion Overall, this dynamic regulatory network largely improves the understanding of perplexed regulatory rewiring in secondary metabolism in Arabidopsis. PMID:24993737

Control of Multilayer Networks

PubMed Central

Menichetti, Giulia; Dall’Asta, Luca; Bianconi, Ginestra

2016-01-01

The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable. PMID:26869210

High-throughput flow injection analysis mass spectroscopy with networked delivery of color-rendered results. 2. Three-dimensional spectral mapping of 96-well combinatorial chemistry racks.

PubMed

Görlach, E; Richmond, R; Lewis, I

1998-08-01

For the last two years, the mass spectroscopy section of the Novartis Pharma Research Core Technology group has analyzed tens of thousands of multiple parallel synthesis samples from the Novartis Pharma Combinatorial Chemistry program, using an in-house developed automated high-throughput flow injection analysis electrospray ionization mass spectroscopy system. The electrospray spectra of these samples reflect the many structures present after the cleavage step from the solid support. The overall success of the sequential synthesis is mirrored in the purity of the expected end product, but the partial success of individual synthesis steps is evident in the impurities in the mass spectrum. However this latter reaction information, which is of considerable utility to the combinatorial chemist, is effectively hidden from view by the very large number of analyzed samples. This information is now revealed at the workbench of the combinatorial chemist by a novel three-dimensional display of each rack's complete mass spectral ion current using the in-house RackViewer Visual Basic application. Colorization of "forbidden loss" and "forbidden gas-adduct" zones, normalization to expected monoisotopic molecular weight, colorization of ionization intensity, and sorting by row or column were used in combination to highlight systematic patterns in the mass spectroscopy data.

A transcription factor network coordinates attraction, repulsion, and adhesion combinatorially to control motor axon pathway selection.

PubMed

Zarin, Aref Arzan; Asadzadeh, Jamshid; Hokamp, Karsten; McCartney, Daniel; Yang, Long; Bashaw, Greg J; Labrador, Juan-Pablo

2014-03-19

Combinations of transcription factors (TFs) instruct precise wiring patterns in the developing nervous system; however, how these factors impinge on surface molecules that control guidance decisions is poorly understood. Using mRNA profiling, we identified the complement of membrane molecules regulated by the homeobox TF Even-skipped (Eve), the major determinant of dorsal motor neuron (dMN) identity in Drosophila. Combinatorial loss- and gain-of-function genetic analyses of Eve target genes indicate that the integrated actions of attractive, repulsive, and adhesive molecules direct eve-dependent dMN axon guidance. Furthermore, combined misexpression of Eve target genes is sufficient to partially restore CNS exit and can convert the guidance behavior of interneurons to that of dMNs. Finally, we show that a network of TFs, comprised of eve, zfh1, and grain, induces the expression of the Unc5 and Beaten-path guidance receptors and the Fasciclin 2 and Neuroglian adhesion molecules to guide individual dMN axons. Copyright © 2014 Elsevier Inc. All rights reserved.

A Transcription Factor Network Coordinates Attraction, Repulsion, and Adhesion Combinatorially to Control Motor Axon Pathway Selection

PubMed Central

Zarin, Aref Arzan; Asadzadeh, Jamshid; Hokamp, Karsten; McCartney, Daniel; Yang, Long; Bashaw, Greg J.; Labrador, Juan-Pablo

2014-01-01

SUMMARY Combinations of transcription factors (TFs) instruct precise wiring patterns in the developing nervous system; however, how these factors impinge on surface molecules that control guidance decisions is poorly understood. Using mRNA profiling, we identified the complement of membrane molecules regulated by the homeobox TF Even-skipped (Eve), the major determinant of dorsal motor neuron (dMN) identity in Drosophila. Combinatorial loss- and gain-of-function genetic analyses of Eve target genes indicate that the integrated actions of attractive, repulsive, and adhesive molecules direct eve-dependent dMN axon guidance. Furthermore, combined misexpression of Eve target genes is sufficient to partially restore CNS exit and can convert the guidance behavior of interneurons to that of dMNs. Finally, we show that a network of TFs, comprised of eve, zfh1, and grain, induces the expression of the Unc5 and Beaten-path guidance receptors and the Fasciclin 2 and Neuroglian adhesion molecules to guide individual dMN axons. PMID:24560702

Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring.

PubMed

Gong, Xinqi; Wang, Panwen; Yang, Feng; Chang, Shan; Liu, Bin; He, Hongqiu; Cao, Libin; Xu, Xianjin; Li, Chunhua; Chen, Weizu; Wang, Cunxin

2010-11-15

Protein-protein docking has made much progress in recent years, but challenges still exist. Here we present the application of our docking approach HoDock in CAPRI. In this approach, a binding site prediction is implemented to reduce docking sampling space and filter out unreasonable docked structures, and a network-based enhanced combinatorial scoring function HPNCscore is used to evaluate the decoys. The experimental information was combined with the predicted binding site to pick out the most likely key binding site residues. We applied the HoDock method in the recent rounds of the CAPRI experiments, and got good results as predictors on targets 39, 40, and 41. We also got good results as scorers on targets 35, 37, 40, and 41. This indicates that our docking approach can contribute to the progress of protein-protein docking methods and to the understanding of the mechanism of protein-protein interactions. © 2010 Wiley-Liss, Inc.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.

PubMed

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-08-10

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)-cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK-cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

Parallel computation with molecular-motor-propelled agents in nanofabricated networks.

PubMed

Nicolau, Dan V; Lard, Mercy; Korten, Till; van Delft, Falco C M J M; Persson, Malin; Bengtsson, Elina; Månsson, Alf; Diez, Stefan; Linke, Heiner; Nicolau, Dan V

2016-03-08

The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

ChIP-less analysis of chromatin states.

PubMed

Su, Zhangli; Boersma, Melissa D; Lee, Jin-Hee; Oliver, Samuel S; Liu, Shichong; Garcia, Benjamin A; Denu, John M

2014-01-01

Histone post-translational modifications (PTMs) are key epigenetic regulators in chromatin-based processes. Increasing evidence suggests that vast combinations of PTMs exist within chromatin histones. These complex patterns, rather than individual PTMs, are thought to define functional chromatin states. However, the ability to interrogate combinatorial histone PTM patterns at the nucleosome level has been limited by the lack of direct molecular tools. Here we demonstrate an efficient, quantitative, antibody-free, chromatin immunoprecipitation-less (ChIP-less) method for interrogating diverse epigenetic states. At the heart of the workflow are recombinant chromatin reader domains, which target distinct chromatin states with combinatorial PTM patterns. Utilizing a newly designed combinatorial histone peptide microarray, we showed that three reader domains (ATRX-ADD, ING2-PHD and AIRE-PHD) displayed greater specificity towards combinatorial PTM patterns than corresponding commercial histone antibodies. Such specific recognitions were employed to develop a chromatin reader-based affinity enrichment platform (matrix-assisted reader chromatin capture, or MARCC). We successfully applied the reader-based platform to capture unique chromatin states, which were quantitatively profiled by mass spectrometry to reveal interconnections between nucleosomal histone PTMs. Specifically, a highly enriched signature that harbored H3K4me0, H3K9me2/3, H3K79me0 and H4K20me2/3 within the same nucleosome was identified from chromatin enriched by ATRX-ADD. This newly reported PTM combination was enriched in heterochromatin, as revealed by the associated DNA. Our results suggest the broad utility of recombinant reader domains as an enrichment tool specific to combinatorial PTM patterns, which are difficult to probe directly by antibody-based approaches. The reader affinity platform is compatible with several downstream analyses to investigate the physical coexistence of nucleosomal PTM states associated with specific genomic loci. Collectively, the reader-based workflow will greatly facilitate our understanding of how distinct chromatin states and reader domains function in gene regulatory mechanisms.

Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

PubMed Central

Coronnello, Claudia; Hartmaier, Ryan; Arora, Arshi; Huleihel, Luai; Pandit, Kusum V.; Bais, Abha S.; Butterworth, Michael; Kaminski, Naftali; Stormo, Gary D.; Oesterreich, Steffi; Benos, Panayiotis V.

2012-01-01

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies. PMID:23284279

Landscape of histone modifications in a sponge reveals the origin of animal cis-regulatory complexity

PubMed Central

Gaiti, Federico; Jindrich, Katia; Fernandez-Valverde, Selene L; Roper, Kathrein E; Degnan, Bernard M; Tanurdžić, Miloš

2017-01-01

Combinatorial patterns of histone modifications regulate developmental and cell type-specific gene expression and underpin animal complexity, but it is unclear when this regulatory system evolved. By analysing histone modifications in a morphologically-simple, early branching animal, the sponge Amphimedonqueenslandica, we show that the regulatory landscape used by complex bilaterians was already in place at the dawn of animal multicellularity. This includes distal enhancers, repressive chromatin and transcriptional units marked by H3K4me3 that vary with levels of developmental regulation. Strikingly, Amphimedon enhancers are enriched in metazoan-specific microsyntenic units, suggesting that their genomic location is extremely ancient and likely to place constraints on the evolution of surrounding genes. These results suggest that the regulatory foundation for spatiotemporal gene expression evolved prior to the divergence of sponges and eumetazoans, and was necessary for the evolution of animal multicellularity. DOI: http://dx.doi.org/10.7554/eLife.22194.001 PMID:28395144

Signatures of combinatorial regulation in intrinsic biological noise

PubMed Central

Warmflash, Aryeh; Dinner, Aaron R.

2008-01-01

Gene expression is controlled by the action of transcription factors that bind to DNA and influence the rate at which a gene is transcribed. The quantitative mapping between the regulator concentrations and the output of the gene is known as the cis-regulatory input function (CRIF). Here, we show how the CRIF shapes the form of the joint probability distribution of molecular copy numbers of the regulators and the product of a gene. Namely, we derive a class of fluctuation-based relations that relate the moments of the distribution to the derivatives of the CRIF. These relations are useful because they enable statistics of naturally arising cell-to-cell variations in molecular copy numbers to substitute for traditional manipulations for probing regulatory mechanisms. We demonstrate that these relations can distinguish super- and subadditive gene regulatory scenarios (molecular analogs of AND and OR logic operations) in simulations that faithfully represent bacterial gene expression. Applications and extensions to other regulatory scenarios are discussed. PMID:18981421

Attractor Signaling Models for Discovery of Combinatorial Therapies

DTIC Science & Technology

2014-11-01

acquired!drug!resistance!still!makes!the!5-year!survival!rate!for!this! disease ! less!than!15%.!Over!the!years,!many!specific!mechanisms!associated!with!drug...Moreover, it has been suggested that a biological system in a chronic or therapy- resistant disease state can be seen as a network that has become...therapeutic methods for complex diseases such as cancer. Even if our knowledge of biological networks is incomplete, rapid progress is currently being

Analysis of the SWI/SNF chromatin-remodeling complex during early heart development and BAF250a repression cardiac gene transcription during P19 cell differentiation

PubMed Central

Singh, Ajeet Pratap; Archer, Trevor K.

2014-01-01

The regulatory networks of differentiation programs and the molecular mechanisms of lineage-specific gene regulation in mammalian embryos remain only partially defined. We document differential expression and temporal switching of BRG1-associated factor (BAF) subunits, core pluripotency factors and cardiac-specific genes during post-implantation development and subsequent early organogenesis. Using affinity purification of BRG1 ATPase coupled to mass spectrometry, we characterized the cardiac-enriched remodeling complexes present in E8.5 mouse embryos. The relative abundance and combinatorial assembly of the BAF subunits provides functional specificity to Switch/Sucrose NonFermentable (SWI/SNF) complexes resulting in a unique gene expression profile in the developing heart. Remarkably, the specific depletion of the BAF250a subunit demonstrated differential effects on cardiac-specific gene expression and resulted in arrhythmic contracting cardiomyocytes in vitro. Indeed, the BAF250a physically interacts and functionally cooperates with Nucleosome Remodeling and Histone Deacetylase (NURD) complex subunits to repressively regulate chromatin structure of the cardiac genes by switching open and poised chromatin marks associated with active and repressed gene expression. Finally, BAF250a expression modulates BRG1 occupancy at the loci of cardiac genes regulatory regions in P19 cell differentiation. These findings reveal specialized and novel cardiac-enriched SWI/SNF chromatin-remodeling complexes, which are required for heart formation and critical for cardiac gene expression regulation at the early stages of heart development. PMID:24335282

Epigenetic Principles and Mechanisms Underlying Nervous System Functions in Health and Disease

PubMed Central

Mehler, Mark F.

2009-01-01

Epigenetics and epigenomic medicine encompass a new science of brain and behavior that are already providing unique insights into the mechanisms underlying brain development, evolution, neuronal and network plasticity and homeostasis, senescence, the etiology of diverse neurological diseases and neural regenerative processes. Epigenetic mechanisms include DNA methylation, histone modifications, nucleosome repositioning, higher-order chromatin remodeling, non-coding RNAs, and RNA and DNA editing. RNA is centrally involved in directing these processes, implying that the transcriptional state of the cell is the primary determinant of epigenetic memory. This transcriptional state can be modified by internal and external cues affecting gene expression and post-transcriptional processing, but also by RNA and DNA editing through activity-dependent intracellular transport and modulation of RNAs and RNA regulatory supercomplexes, and through trans-neuronal and systemic trafficking of functional RNA subclasses. These integrated processes promote dynamic reorganization of nuclear architecture and the genomic landscape to modulate functional gene and neural networks with complex temporal and spatial trajectories. Epigenetics represents the long sought after molecular interface mediating gene-environmental interactions during critical periods throughout the lifecycle. The discipline of environmental epigenomics has begun to identify combinatorial profiles of environmental stressors modulating the latency, initiation and progression of specific neurological disorders, and more selective disease biomarkers and graded molecular responses to emerging therapeutic interventions. Pharmacoepigenomic therapies will promote accelerated recovery of impaired and seemingly irrevocably lost cognitive, behavioral, sensorimotor functions through epigenetic reprogramming of endogenous regional neural stem cell fate decisions, targeted tissue remodeling and restoration of neural network integrity, plasticity and connectivity. PMID:18940229

Regulatory gene networks and the properties of the developmental process

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

PubMed

Zhou, Yikang; Li, Gang; Dong, Junkai; Xing, Xin-Hui; Dai, Junbiao; Zhang, Chong

2018-05-01

Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (MiYA) for combinatorial optimizing the large biosynthetic genotypic space of heterologous metabolic pathways in Saccharomyces cerevisiae. Using β-carotene biosynthetic pathway as example, we first demonstrated that MiYA has the power to search only a small fraction (2-5%) of combinatorial space to precisely tune the expression level of each gene with a machine-learning algorithm of an artificial neural network (ANN) ensemble to avoid over-fitting problem when dealing with a small number of training samples. We then applied MiYA to improve the biosynthesis of violacein. Feed with initial data from a colorimetric plate-based, pre-screened pool of 24 strains producing violacein, MiYA successfully predicted, and verified experimentally, the existence of a strain that showed a 2.42-fold titer improvement in violacein production among 3125 possible designs. Furthermore, MiYA was able to largely avoid the branch pathway of violacein biosynthesis that makes deoxyviolacein, and produces very pure violacein. Together, MiYA combines the advantages of standardized building blocks and machine learning to accelerate the Design-Build-Test-Learn (DBTL) cycle for combinatorial optimization of metabolic pathways, which could significantly accelerate the development of microbial cell factories. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

PubMed Central

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

2013-01-01

Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

Combinatorial Reliability and Repair

DTIC Science & Technology

1992-07-01

Press, Oxford, 1987. [2] G. Gordon and L. Traldi, Generalized activities and the Tutte polynomial, Discrete Math . 85 (1990), 167-176. [3] A. B. Huseby, A...Chromatic polynomials and network reliability, Discrete Math . 67 (1987), 57-79. [7] A. Satayanarayana and R. K. Wood, A linear-time algorithm for comput- ing...K-terminal reliability in series-parallel networks, SIAM J. Comput. 14 (1985), 818-832. [8] L. Traldi, Generalized activities and K-terminal reliability, Discrete Math . 96 (1991), 131-149. 4

Tabu Search enhances network robustness under targeted attacks

NASA Astrophysics Data System (ADS)

Sun, Shi-wen; Ma, Yi-lin; Li, Rui-qi; Wang, Li; Xia, Cheng-yi

2016-03-01

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree-degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.

Combinatorial explosion in model gene networks

NASA Astrophysics Data System (ADS)

Edwards, R.; Glass, L.

2000-09-01

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

Combinatorial explosion in model gene networks.

PubMed

Edwards, R.; Glass, L.

2000-09-01

The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such networks are extremely rich and they offer novel ways to think about how mutations can alter dynamics. (c) 2000 American Institute of Physics.

The transmission process: A combinatorial stochastic process for the evolution of transmission trees over networks.

PubMed

Sainudiin, Raazesh; Welch, David

2016-12-07

We derive a combinatorial stochastic process for the evolution of the transmission tree over the infected vertices of a host contact network in a susceptible-infected (SI) model of an epidemic. Models of transmission trees are crucial to understanding the evolution of pathogen populations. We provide an explicit description of the transmission process on the product state space of (rooted planar ranked labelled) binary transmission trees and labelled host contact networks with SI-tags as a discrete-state continuous-time Markov chain. We give the exact probability of any transmission tree when the host contact network is a complete, star or path network - three illustrative examples. We then develop a biparametric Beta-splitting model that directly generates transmission trees with exact probabilities as a function of the model parameters, but without explicitly modelling the underlying contact network, and show that for specific values of the parameters we can recover the exact probabilities for our three example networks through the Markov chain construction that explicitly models the underlying contact network. We use the maximum likelihood estimator (MLE) to consistently infer the two parameters driving the transmission process based on observations of the transmission trees and use the exact MLE to characterize equivalence classes over the space of contact networks with a single initial infection. An exploratory simulation study of the MLEs from transmission trees sampled from three other deterministic and four random families of classical contact networks is conducted to shed light on the relation between the MLEs of these families with some implications for statistical inference along with pointers to further extensions of our models. The insights developed here are also applicable to the simplest models of "meme" evolution in online social media networks through transmission events that can be distilled from observable actions such as "likes", "mentions", "retweets" and "+1s" along with any concomitant comments. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The regulatory network analysis of long noncoding RNAs in human colorectal cancer.

PubMed

Zhang, Yuwei; Tao, Yang; Li, Yang; Zhao, Jinshun; Zhang, Lina; Zhang, Xiaohong; Dong, Changzheng; Xie, Yangyang; Dai, Xiaoyu; Zhang, Xinjun; Liao, Qi

2018-05-01

Colorectal cancer (CRC) is among one of the most prevalent and lethiferous diseases worldwide. Long noncoding RNAs (lncRNAs) are commonly accepted to function as a key regulatory factor in human cancer, but the potential regulatory mechanisms of CRC-associated lncRNA are largely obscure. Here, we integrated several expression profiles to obtain 55 differentially expressed (DE) lncRNAs. We first detected lncRNA interactions with transcription factors, microRNAs, mRNAs, and RNA-binding proteins to construct a regulatory network and then create functional enrichment analyses for them using bioinformatics approaches. We found the upregulated genes in the regulatory network are enriched in cell cycle and DNA damage response, while the downregulated genes are enriched in cell differentiation, cellular response, and cell signaling. We then employed module-based methods to mine several intriguing modules from the overall network, which helps to classify the functions of genes more specifically. Next, we confirmed the validity of our network by comparisons with a randomized network using computational method. Finally, we attempted to annotate lncRNA functions based on the regulatory network, which indicated its potential application. Our study of the lncRNA regulatory network provided significant clues to unveil lncRNAs potential regulatory mechanisms in CRC and laid a foundation for further experimental investigation.

Signal Correlations in Ecological Niches Can Shape the Organization and Evolution of Bacterial Gene Regulatory Networks

PubMed Central

Dufour, Yann S.; Donohue, Timothy J.

2015-01-01

Transcriptional regulation plays a significant role in the biological response of bacteria to changing environmental conditions. Therefore, mapping transcriptional regulatory networks is an important step not only in understanding how bacteria sense and interpret their environment but also to identify the functions involved in biological responses to specific conditions. Recent experimental and computational developments have facilitated the characterization of regulatory networks on a genome-wide scale in model organisms. In addition, the multiplication of complete genome sequences has encouraged comparative analyses to detect conserved regulatory elements and infer regulatory networks in other less well-studied organisms. However, transcription regulation appears to evolve rapidly, thus, creating challenges for the transfer of knowledge to nonmodel organisms. Nevertheless, the mechanisms and constraints driving the evolution of regulatory networks have been the subjects of numerous analyses, and several models have been proposed. Overall, the contributions of mutations, recombination, and horizontal gene transfer are complex. Finally, the rapid evolution of regulatory networks plays a significant role in the remarkable capacity of bacteria to adapt to new or changing environments. Conversely, the characteristics of environmental niches determine the selective pressures and can shape the structure of regulatory network accordingly. PMID:23046950

A genomic regulatory network for development

NASA Technical Reports Server (NTRS)

Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar;

Network Modeling Reveals Prevalent Negative Regulatory Relationships between Signaling Sectors in Arabidopsis Immune Signaling

PubMed Central

Sato, Masanao; Tsuda, Kenichi; Wang, Lin; Coller, John; Watanabe, Yuichiro; Glazebrook, Jane; Katagiri, Fumiaki

2010-01-01

Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a “sector-switching” network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. PMID:20661428

Genes under weaker stabilizing selection increase network evolvability and rapid regulatory adaptation to an environmental shift.

PubMed

Laarits, T; Bordalo, P; Lemos, B

2016-08-01

Regulatory networks play a central role in the modulation of gene expression, the control of cellular differentiation, and the emergence of complex phenotypes. Regulatory networks could constrain or facilitate evolutionary adaptation in gene expression levels. Here, we model the adaptation of regulatory networks and gene expression levels to a shift in the environment that alters the optimal expression level of a single gene. Our analyses show signatures of natural selection on regulatory networks that both constrain and facilitate rapid evolution of gene expression level towards new optima. The analyses are interpreted from the standpoint of neutral expectations and illustrate the challenge to making inferences about network adaptation. Furthermore, we examine the consequence of variable stabilizing selection across genes on the strength and direction of interactions in regulatory networks and in their subsequent adaptation. We observe that directional selection on a highly constrained gene previously under strong stabilizing selection was more efficient when the gene was embedded within a network of partners under relaxed stabilizing selection pressure. The observation leads to the expectation that evolutionarily resilient regulatory networks will contain optimal ratios of genes whose expression is under weak and strong stabilizing selection. Altogether, our results suggest that the variable strengths of stabilizing selection across genes within regulatory networks might itself contribute to the long-term adaptation of complex phenotypes. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Integration of a splicing regulatory network within the meiotic gene expression program of Saccharomyces cerevisiae

PubMed Central

Munding, Elizabeth M.; Igel, A. Haller; Shiue, Lily; Dorighi, Kristel M.; Treviño, Lisa R.; Ares, Manuel

2010-01-01

Splicing regulatory networks are essential components of eukaryotic gene expression programs, yet little is known about how they are integrated with transcriptional regulatory networks into coherent gene expression programs. Here we define the MER1 splicing regulatory network and examine its role in the gene expression program during meiosis in budding yeast. Mer1p splicing factor promotes splicing of just four pre-mRNAs. All four Mer1p-responsive genes also require Nam8p for splicing activation by Mer1p; however, other genes require Nam8p but not Mer1p, exposing an overlapping meiotic splicing network controlled by Nam8p. MER1 mRNA and three of the four Mer1p substrate pre-mRNAs are induced by the transcriptional regulator Ume6p. This unusual arrangement delays expression of Mer1p-responsive genes relative to other genes under Ume6p control. Products of Mer1p-responsive genes are required for initiating and completing recombination and for activation of Ndt80p, the activator of the transcriptional network required for subsequent steps in the program. Thus, the MER1 splicing regulatory network mediates the dependent relationship between the UME6 and NDT80 transcriptional regulatory networks in the meiotic gene expression program. This study reveals how splicing regulatory networks can be interlaced with transcriptional regulatory networks in eukaryotic gene expression programs. PMID:21123654

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

PubMed Central

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/ PMID:26363020

Mechanism of IRSp53 inhibition and combinatorial activation by Cdc42 and downstream effectors.

PubMed

Kast, David J; Yang, Changsong; Disanza, Andrea; Boczkowska, Malgorzata; Madasu, Yadaiah; Scita, Giorgio; Svitkina, Tatyana; Dominguez, Roberto

2014-04-01

The Rho family GTPase effector IRSp53 has essential roles in filopodia formation and neuronal development, but its regulatory mechanism is poorly understood. IRSp53 contains a membrane-binding BAR domain followed by an unconventional CRIB motif that overlaps with a proline-rich region (CRIB-PR) and an SH3 domain that recruits actin cytoskeleton effectors. Using a fluorescence reporter assay, we show that human IRSp53 adopts a closed inactive conformation that opens synergistically with the binding of human Cdc42 to the CRIB-PR and effector proteins, such as the tumor-promoting factor Eps8, to the SH3 domain. The crystal structure of Cdc42 bound to the CRIB-PR reveals a new mode of effector binding to Rho family GTPases. Structure-inspired mutations disrupt autoinhibition and Cdc42 binding in vitro and decouple Cdc42- and IRSp53-dependent filopodia formation in cells. The data support a combinatorial mechanism of IRSp53 activation.

Solid-phase synthesis and screening of N-acylated polyamine (NAPA) combinatorial libraries for protein binding.

PubMed

Iera, Jaclyn A; Jenkins, Lisa M Miller; Kajiyama, Hiroshi; Kopp, Jeffrey B; Appella, Daniel H

2010-11-15

Inhibitors for protein-protein interactions are challenging to design, in part due to the unique and complex architectures of each protein's interaction domain. Most approaches to develop inhibitors for these interactions rely on rational design, which requires prior structural knowledge of the target and its ligands. In the absence of structural information, a combinatorial approach may be the best alternative to finding inhibitors of a protein-protein interaction. Current chemical libraries, however, consist mostly of molecules designed to inhibit enzymes. In this manuscript, we report the synthesis and screening of a library based on an N-acylated polyamine (NAPA) scaffold that we designed to have specific molecular features necessary to inhibit protein-protein interactions. Screens of the library identified a member with favorable binding properties to the HIV viral protein R (Vpr), a regulatory protein from HIV, that is involved in numerous interactions with other proteins critical for viral replication. Published by Elsevier Ltd.

Computational architecture of the yeast regulatory network

NASA Astrophysics Data System (ADS)

Maslov, Sergei; Sneppen, Kim

2005-12-01

The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.

Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks

PubMed Central

Yamanaka, Ryota; Kitano, Hiroaki

2013-01-01

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks. PMID:24278007

Gene regulatory networks and the underlying biology of developmental toxicity

EPA Science Inventory

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

The relationship between gene transcription and combinations of histone modifications

NASA Astrophysics Data System (ADS)

Cui, Xiangjun; Li, Hong; Luo, Liaofu

2012-09-01

Histone modification is an important subject of epigenetics which plays an intrinsic role in transcriptional regulation. It is known that multiple histone modifications act in a combinatorial fashion. In this study, we demonstrated that the pathways within constructed Bayesian networks can give an indication for the combinations among 12 histone modifications which have been studied in the TSS+1kb region in S. cerevisiae. After Bayesian networks for the genes with high transcript levels (H-network) and low transcript levels (L-network) were constructed, the combinations of modifications within the two networks were analyzed from the view of transcript level. The results showed that different combinations played dissimilar roles in the regulation of gene transcription when there exist differences for gene expression at transcription level.

Floral homeotic proteins modulate the genetic program for leaf development to suppress trichome formation in flowers.

PubMed

Ó'Maoiléidigh, Diarmuid S; Stewart, Darragh; Zheng, Beibei; Coupland, George; Wellmer, Frank

2018-02-13

As originally proposed by Goethe in 1790, floral organs are derived from leaf-like structures. The conversion of leaves into different types of floral organ is mediated by floral homeotic proteins, which, as described by the ABCE model of flower development, act in a combinatorial manner. However, how these transcription factors bring about this transformation process is not well understood. We have previously shown that floral homeotic proteins are involved in suppressing the formation of branched trichomes, a hallmark of leaf development, on reproductive floral organs of Arabidopsis Here, we present evidence that the activities of the C function gene AGAMOUS ( AG ) and the related SHATTERPROOF1 / 2 genes are superimposed onto the regulatory network that controls the distribution of trichome formation in an age-dependent manner. We show that AG regulates cytokinin responses and genetically interacts with the organ polarity gene KANADI1 to suppress trichome initiation on gynoecia. Thus, our results show that parts of the genetic program for leaf development remain active during flower formation but have been partially rewired through the activities of the floral homeotic proteins. © 2018. Published by The Company of Biologists Ltd.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.

PubMed

Guo, Liyuan; Wang, Jing

2018-01-04

Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element-target gene pairs (E-G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks

PubMed Central

2018-01-01

Abstract Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element–target gene pairs (E–G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. PMID:29140525

Regulatory networks and connected components of the neutral space. A look at functional islands

NASA Astrophysics Data System (ADS)

Boldhaus, G.; Klemm, K.

2010-09-01

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

Breeding novel solutions in the brain: a model of Darwinian neurodynamics.

PubMed

Szilágyi, András; Zachar, István; Fedor, Anna; de Vladar, Harold P; Szathmáry, Eörs

2016-01-01

Background : The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Methods : We combine known components of the brain - recurrent neural networks (acting as attractors), the action selection loop and implicit working memory - to provide the appropriate Darwinian architecture. We employ a population of attractor networks with palimpsest memory. The action selection loop is employed with winners-share-all dynamics to select for candidate solutions that are transiently stored in implicit working memory. Results : We document two processes: selection of stored solutions and evolutionary search for novel solutions. During the replication of candidate solutions attractor networks occasionally produce recombinant patterns, increasing variation on which selection can act. Combinatorial search acts on multiplying units (activity patterns) with hereditary variation and novel variants appear due to (i) noisy recall of patterns from the attractor networks, (ii) noise during transmission of candidate solutions as messages between networks, and, (iii) spontaneously generated, untrained patterns in spurious attractors. Conclusions : Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana

PubMed Central

Van Leene, Jelle; Hollunder, Jens; Eeckhout, Dominique; Persiau, Geert; Van De Slijke, Eveline; Stals, Hilde; Van Isterdael, Gert; Verkest, Aurine; Neirynck, Sandy; Buffel, Yelle; De Bodt, Stefanie; Maere, Steven; Laukens, Kris; Pharazyn, Anne; Ferreira, Paulo C G; Eloy, Nubia; Renne, Charlotte; Meyer, Christian; Faure, Jean-Denis; Steinbrenner, Jens; Beynon, Jim; Larkin, John C; Van de Peer, Yves; Hilson, Pierre; Kuiper, Martin; De Veylder, Lieven; Van Onckelen, Harry; Inzé, Dirk; Witters, Erwin; De Jaeger, Geert

2010-01-01

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants. PMID:20706207

A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

NASA Technical Reports Server (NTRS)

Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar;

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Cut set-based risk and reliability analysis for arbitrarily interconnected networks

DOEpatents

Wyss, Gregory D.

2000-01-01

Method for computing all-terminal reliability for arbitrarily interconnected networks such as the United States public switched telephone network. The method includes an efficient search algorithm to generate minimal cut sets for nonhierarchical networks directly from the network connectivity diagram. Efficiency of the search algorithm stems in part from its basis on only link failures. The method also includes a novel quantification scheme that likewise reduces computational effort associated with assessing network reliability based on traditional risk importance measures. Vast reductions in computational effort are realized since combinatorial expansion and subsequent Boolean reduction steps are eliminated through analysis of network segmentations using a technique of assuming node failures to occur on only one side of a break in the network, and repeating the technique for all minimal cut sets generated with the search algorithm. The method functions equally well for planar and non-planar networks.

Regulation of N-formyl peptide receptor signaling and trafficking by individual carboxyl-terminal serine and threonine residues.

PubMed

Potter, Ross M; Maestas, Diane C; Cimino, Daniel F; Prossnitz, Eric R

2006-05-01

Adaptation, defined as the diminution of receptor signaling in the presence of continued or repeated stimulation, is critical to cellular function. G protein-coupled receptors (GPCRs) undergo multiple adaptive processes, including desensitization and internalization, through phosphorylation of cytoplasmic serine and threonine residues. However, the relative importance of individual and combined serine and threonine residues to these processes is not well understood. We examined this mechanism in the context of the N-formyl peptide receptor (FPR), a well-characterized member of the chemoattractant/chemokine family of GPCRs critical to neutrophil function. To evaluate the contributions of individual and combinatorial serine and threonine residues to internalization, desensitization, and arrestin2 binding, 30 mutant forms of the FPR, expressed in the human promyelocytic U937 cell line, were characterized. We found that residues Ser(328), Ser(332), and Ser(338) are individually critical, and indeed sufficient, for internalization, desensitization, and arrestin2 binding, but that the presence of neighboring threonine residues can inhibit these processes. Additionally, we observed no absolute correlation between arrestin binding and either internalization or desensitization, suggesting the existence of arrestin-independent mechanisms for these processes. Our results suggest C-terminal serine and threonine residues of the FPR represent a combinatorial code, capable of both positively and negatively regulating signaling and trafficking. This study is among the first detailed analyses of a complex regulatory site in a GPCR, and provides insight into GPCR regulatory mechanisms.

Discovery of DNA repair inhibitors by combinatorial library profiling

PubMed Central

Moeller, Benjamin J.; Sidman, Richard L.; Pasqualini, Renata; Arap, Wadih

2011-01-01

Small molecule inhibitors of DNA repair are emerging as potent and selective anti-cancer therapies, but the sheer magnitude of the protein networks involved in DNA repair processes poses obstacles to discovery of effective candidate drugs. To address this challenge, we used a subtractive combinatorial selection approach to identify a panel of peptide ligands that bind DNA repair complexes. Supporting the concept that these ligands have therapeutic potential, we show that one selected peptide specifically binds and non-competitively inactivates DNA-PKcs, a protein kinase critical in double-strand DNA break repair. In doing so, this ligand sensitizes BRCA-deficient tumor cells to genotoxic therapy. Our findings establish a platform for large-scale parallel screening for ligand-directed DNA repair inhibitors, with immediate applicability to cancer therapy. PMID:21343400

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

PubMed

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

2015-01-01

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

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

PubMed Central

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P.; Gerstein, Mark

2010-01-01

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems. PMID:20439753

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks.

PubMed

Yan, Koon-Kiu; Fang, Gang; Bhardwaj, Nitin; Alexander, Roger P; Gerstein, Mark

2010-05-18

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers' continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

PubMed Central

Baumbach, Jan

2007-01-01

Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at . PMID:17986320

Reconstructing directed gene regulatory network by only gene expression data.

PubMed

Zhang, Lu; Feng, Xi Kang; Ng, Yen Kaow; Li, Shuai Cheng

2016-08-18

Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors. By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.

Inference of cancer-specific gene regulatory networks using soft computing rules.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2010-03-24

Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer) using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

ReNE: A Cytoscape Plugin for Regulatory Network Enhancement

PubMed Central

Politano, Gianfranco; Benso, Alfredo; Savino, Alessandro; Di Carlo, Stefano

2014-01-01

One of the biggest challenges in the study of biological regulatory mechanisms is the integration, americanmodeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (http://apps.cytoscape.org/apps/rene) and the full source code is freely available for download through a SVN repository accessible at http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers. PMID:25541727

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Applications of self-organizing neural networks in virtual screening and diversity selection.

PubMed

Selzer, Paul; Ertl, Peter

2006-01-01

Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

NASA Astrophysics Data System (ADS)

Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

On Singularities and Black Holes in Combination-Driven Models of Technological Innovation Networks

PubMed Central

Solé, Ricard; Amor, Daniel R.; Valverde, Sergi

2016-01-01

It has been suggested that innovations occur mainly by combination: the more inventions accumulate, the higher the probability that new inventions are obtained from previous designs. Additionally, it has been conjectured that the combinatorial nature of innovations naturally leads to a singularity: at some finite time, the number of innovations should diverge. Although these ideas are certainly appealing, no general models have been yet developed to test the conditions under which combinatorial technology should become explosive. Here we present a generalised model of technological evolution that takes into account two major properties: the number of previous technologies needed to create a novel one and how rapidly technology ages. Two different models of combinatorial growth are considered, involving different forms of ageing. When long-range memory is used and thus old inventions are available for novel innovations, singularities can emerge under some conditions with two phases separated by a critical boundary. If the ageing has a characteristic time scale, it is shown that no singularities will be observed. Instead, a “black hole” of old innovations appears and expands in time, making the rate of invention creation slow down into a linear regime. PMID:26821277

On Singularities and Black Holes in Combination-Driven Models of Technological Innovation Networks.

PubMed

Solé, Ricard; Amor, Daniel R; Valverde, Sergi

2016-01-01

It has been suggested that innovations occur mainly by combination: the more inventions accumulate, the higher the probability that new inventions are obtained from previous designs. Additionally, it has been conjectured that the combinatorial nature of innovations naturally leads to a singularity: at some finite time, the number of innovations should diverge. Although these ideas are certainly appealing, no general models have been yet developed to test the conditions under which combinatorial technology should become explosive. Here we present a generalised model of technological evolution that takes into account two major properties: the number of previous technologies needed to create a novel one and how rapidly technology ages. Two different models of combinatorial growth are considered, involving different forms of ageing. When long-range memory is used and thus old inventions are available for novel innovations, singularities can emerge under some conditions with two phases separated by a critical boundary. If the ageing has a characteristic time scale, it is shown that no singularities will be observed. Instead, a "black hole" of old innovations appears and expands in time, making the rate of invention creation slow down into a linear regime.

The tailless Ortholog nhr-67 Regulates Patterning of Gene Expression and Morphogenesis in the C. elegans Vulva

PubMed Central

Fernandes, Jolene S; Sternberg, Paul W

2007-01-01

Regulation of spatio-temporal gene expression in diverse cell and tissue types is a critical aspect of development. Progression through Caenorhabditis elegans vulval development leads to the generation of seven distinct vulval cell types (vulA, vulB1, vulB2, vulC, vulD, vulE, and vulF), each with its own unique gene expression profile. The mechanisms that establish the precise spatial patterning of these mature cell types are largely unknown. Dissection of the gene regulatory networks involved in vulval patterning and differentiation would help us understand how cells generate a spatially defined pattern of cell fates during organogenesis. We disrupted the activity of 508 transcription factors via RNAi and assayed the expression of ceh-2, a marker for vulB fate during the L4 stage. From this screen, we identified the tailless ortholog nhr-67 as a novel regulator of gene expression in multiple vulval cell types. We find that one way in which nhr-67 maintains cell identity is by restricting inappropriate cell fusion events in specific vulval cells, namely vulE and vulF. nhr-67 exhibits a dynamic expression pattern in the vulval cells and interacts with three other transcriptional regulators cog-1 (Nkx6.1/6.2), lin-11 (LIM), and egl-38 (Pax2/5/8) to generate the composite expression patterns of their downstream targets. We provide evidence that egl-38 regulates gene expression in vulB1, vulC, vulD, vulE, as well as vulF cells. We demonstrate that the pairwise interactions between these regulatory genes are complex and vary among the seven cell types. We also discovered a striking regulatory circuit that affects a subset of the vulval lineages: cog-1 and nhr-67 inhibit both one another and themselves. We postulate that the differential levels and combinatorial patterns of lin-11, cog-1, and nhr-67 expression are a part of a regulatory code for the mature vulval cell types. PMID:17465684

Gene regulatory network inference using fused LASSO on multiple data sets

PubMed Central

Omranian, Nooshin; Eloundou-Mbebi, Jeanne M. O.; Mueller-Roeber, Bernd; Nikoloski, Zoran

2016-01-01

Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions. PMID:26864687

Dynamic analysis of the combinatorial regulation involving transcription factors and microRNAs in cell fate decisions.

PubMed

Yan, Fang; Liu, Haihong; Liu, Zengrong

2014-01-01

P53 and E2F1 are critical transcription factors involved in the choices between different cell fates including cell differentiation, cell cycle arrest or apoptosis. Recent experiments have shown that two families of microRNAs (miRNAs), p53-responsive miR34 (miRNA-34 a, b and c) and E2F1-inducible miR449 (miRNA-449 a, b and c) are potent inducers of these different fates and might have an important role in sensitizing cancer cells to drug treatment and tumor suppression. Identifying the mechanisms responsible for the combinatorial regulatory roles of these two transcription factors and two miRNAs is an important and challenging problem. Here, based in part on the model proposed in Tongli Zhang et al. (2007), we developed a mathematical model of the decision process and explored the combinatorial regulation between these two transcription factors and two miRNAs in response to DNA damage. By analyzing nonlinear dynamic behaviors of the model, we found that p53 exhibits pulsatile behavior. Moreover, a comparison is given to reveal the subtle differences of the cell fate decision process between regulation and deregulation of miR34 on E2F1. It predicts that miR34 plays a critical role in promoting cell cycle arrest. In addition, a computer simulation result also predicts that the miR449 is necessary for apoptosis in response to sustained DNA damage. In agreement with experimental observations, our model can account for the intricate regulatory relationship between these two transcription factors and two miRNAs in the cell fate decision process after DNA damage. These theoretical results indicate that miR34 and miR449 are effective tumor suppressors and play critical roles in cell fate decisions. The work provides a dynamic mechanism that shows how cell fate decisions are coordinated by two transcription factors and two miRNAs. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology and Clinical Implications. Guest Editor: Yudong Cai. Crown Copyright © 2013. All rights reserved.

CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

2014-12-01

Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

CoryneRegNet: an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks.

PubMed

Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

2006-02-14

The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Uncovering transcription factor and microRNA risk regulatory pathways associated with osteoarthritis by network analysis.

PubMed

Song, Zhenhua; Zhang, Chi; He, Lingxiao; Sui, Yanfang; Lin, Xiafei; Pan, Jingjing

2018-06-12

Osteoarthritis (OA) is the most common form of joint disease. The development of inflammation have been considered to play a key role during the progression of OA. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, deciphering these risk regulatory pathways is critical for elucidating the mechanisms underlying OA. We constructed an OA-specific regulatory network by integrating comprehensive curated transcription and post-transcriptional resource involving transcription factor (TF) and microRNA (miRNA). To deepen our understanding of underlying molecular mechanisms of OA, we developed an integrated systems approach to identify OA-specific risk regulatory pathways. In this study, we identified 89 significantly differentially expressed genes between normal and inflamed areas of OA patients. We found the OA-specific regulatory network was a standard scale-free network with small-world properties. It significant enriched many immune response-related functions including leukocyte differentiation, myeloid differentiation and T cell activation. Finally, 141 risk regulatory pathways were identified based on OA-specific regulatory network, which contains some known regulator of OA. The risk regulatory pathways may provide clues for the etiology of OA and be a potential resource for the discovery of novel OA-associated disease genes. Copyright © 2018 Elsevier Inc. All rights reserved.

Using Synchronous Boolean Networks to Model Several Phenomena of Collective Behavior

PubMed Central

Kochemazov, Stepan; Semenov, Alexander

2014-01-01

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where the majority of simple agents are inactive to a state with the majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with the majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained. PMID:25526612

Rule-based spatial modeling with diffusing, geometrically constrained molecules.

PubMed

Gruenert, Gerd; Ibrahim, Bashar; Lenser, Thorsten; Lohel, Maiko; Hinze, Thomas; Dittrich, Peter

2010-06-07

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Rule-based spatial modeling with diffusing, geometrically constrained molecules

PubMed Central

2010-01-01

Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Results Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. Conclusions We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly. PMID:20529264

Context-dependent control of alternative splicing by RNA-binding proteins

PubMed Central

Fu, Xiang-Dong; Ares, Manuel

2015-01-01

Sequence-specific RNA-binding proteins (RBPs) bind to pre-mRNA to control alternative splicing, but it is not yet possible to read the ‘splicing code’ that dictates splicing regulation on the basis of genome sequence. Each alternative splicing event is controlled by multiple RBPs, the combined action of which creates a distribution of alternatively spliced products in a given cell type. As each cell type expresses a distinct array of RBPs, the interpretation of regulatory information on a given RNA target is exceedingly dependent on the cell type. RBPs also control each other’s functions at many levels, including by mutual modulation of their binding activities on specific regulatory RNA elements. In this Review, we describe some of the emerging rules that govern the highly context-dependent and combinatorial nature of alternative splicing regulation. PMID:25112293

Solving a combinatorial problem via self-organizing process: an application of the Kohonen algorithm to the traveling salesman problem.

PubMed

Fort, J C

1988-01-01

We present an application of the Kohonen algorithm to the traveling salesman problem: Using only this algorithm, without energy function nor any parameter chosen "ad hoc", we found good suboptimal tours. We give a neural model version of this algorithm, closer to classical neural networks. This is illustrated with various numerical examples.

Interference Aware Routing Using Spatial Reuse in Wireless Sensor Networks

DTIC Science & Technology

2013-12-01

practice there is no optimal STDMA algorithm due to the computational complexity of the STDMA implementation; therefore, the common approach is to...Applications, Springer Berlin Heidelberg, pp. 653–657, 2001. [26] B. Korte and J. Vygen, “Shortest Paths,” Combinatorial Optimization Theory and...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited INTERFERENCE

Insights into Hox protein function from a large scale combinatorial analysis of protein domains.

PubMed

Merabet, Samir; Litim-Mecheri, Isma; Karlsson, Daniel; Dixit, Richa; Saadaoui, Mehdi; Monier, Bruno; Brun, Christine; Thor, Stefan; Vijayraghavan, K; Perrin, Laurent; Pradel, Jacques; Graba, Yacine

2011-10-01

Protein function is encoded within protein sequence and protein domains. However, how protein domains cooperate within a protein to modulate overall activity and how this impacts functional diversification at the molecular and organism levels remains largely unaddressed. Focusing on three domains of the central class Drosophila Hox transcription factor AbdominalA (AbdA), we used combinatorial domain mutations and most known AbdA developmental functions as biological readouts to investigate how protein domains collectively shape protein activity. The results uncover redundancy, interactivity, and multifunctionality of protein domains as salient features underlying overall AbdA protein activity, providing means to apprehend functional diversity and accounting for the robustness of Hox-controlled developmental programs. Importantly, the results highlight context-dependency in protein domain usage and interaction, allowing major modifications in domains to be tolerated without general functional loss. The non-pleoitropic effect of domain mutation suggests that protein modification may contribute more broadly to molecular changes underlying morphological diversification during evolution, so far thought to rely largely on modification in gene cis-regulatory sequences.

Insights into Hox Protein Function from a Large Scale Combinatorial Analysis of Protein Domains

PubMed Central

Karlsson, Daniel; Dixit, Richa; Saadaoui, Mehdi; Monier, Bruno; Brun, Christine; Thor, Stefan; Vijayraghavan, K.; Perrin, Laurent; Pradel, Jacques; Graba, Yacine

2011-01-01

Protein function is encoded within protein sequence and protein domains. However, how protein domains cooperate within a protein to modulate overall activity and how this impacts functional diversification at the molecular and organism levels remains largely unaddressed. Focusing on three domains of the central class Drosophila Hox transcription factor AbdominalA (AbdA), we used combinatorial domain mutations and most known AbdA developmental functions as biological readouts to investigate how protein domains collectively shape protein activity. The results uncover redundancy, interactivity, and multifunctionality of protein domains as salient features underlying overall AbdA protein activity, providing means to apprehend functional diversity and accounting for the robustness of Hox-controlled developmental programs. Importantly, the results highlight context-dependency in protein domain usage and interaction, allowing major modifications in domains to be tolerated without general functional loss. The non-pleoitropic effect of domain mutation suggests that protein modification may contribute more broadly to molecular changes underlying morphological diversification during evolution, so far thought to rely largely on modification in gene cis-regulatory sequences. PMID:22046139

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

PubMed Central

Hurley, Daniel; Araki, Hiromitsu; Tamada, Yoshinori; Dunmore, Ben; Sanders, Deborah; Humphreys, Sally; Affara, Muna; Imoto, Seiya; Yasuda, Kaori; Tomiyasu, Yuki; Tashiro, Kosuke; Savoie, Christopher; Cho, Vicky; Smith, Stephen; Kuhara, Satoru; Miyano, Satoru; Charnock-Jones, D. Stephen; Crampin, Edmund J.; Print, Cristin G.

2012-01-01

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions. PMID:22121215

Recurrent rewiring and emergence of RNA regulatory networks.

PubMed

Wilinski, Daniel; Buter, Natascha; Klocko, Andrew D; Lapointe, Christopher P; Selker, Eric U; Gasch, Audrey P; Wickens, Marvin

2017-04-04

Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data.

PubMed

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/. © The Author(s) 2015. Published by Oxford University Press.

CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks

PubMed Central

Baumbach, Jan; Brinkrolf, Karina; Czaja, Lisa F; Rahmann, Sven; Tauch, Andreas

2006-01-01

Background The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions. Description CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria. Conclusion CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation. PMID:16478536

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

DOE PAGES

Martin, Shawn; Zhang, Zhaoduo; Martino, Anthony; ...

2007-01-31

Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this paper we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our methodmore » first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. In conclusion, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics.« less

Variable neighborhood search for reverse engineering of gene regulatory networks.

PubMed

Nicholson, Charles; Goodwin, Leslie; Clark, Corey

2017-01-01

A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time. Copyright © 2016 Elsevier Inc. All rights reserved.

Dynamics and function of distal regulatory elements during neurogenesis and neuroplasticity

PubMed Central

Thakurela, Sudhir; Sahu, Sanjeeb Kumar; Garding, Angela; Tiwari, Vijay K.

2015-01-01

Gene regulation in mammals involves a complex interplay between promoters and distal regulatory elements that function in concert to drive precise spatiotemporal gene expression programs. However, the dynamics of the distal gene regulatory landscape and its function in the transcriptional reprogramming that underlies neurogenesis and neuronal activity remain largely unknown. Here, we performed a combinatorial analysis of genome-wide data sets for chromatin accessibility (FAIRE-seq) and the enhancer mark H3K27ac, revealing the highly dynamic nature of distal gene regulation during neurogenesis, which gets progressively restricted to distinct genomic regions as neurons acquire a post-mitotic, terminally differentiated state. We further find that the distal accessible and active regions serve as target sites for distinct transcription factors that function in a stage-specific manner to contribute to the transcriptional program underlying neuronal commitment and maturation. Mature neurons respond to a sustained activity of NMDA receptors by epigenetic reprogramming at a large number of distal regulatory regions as well as dramatic reorganization of super-enhancers. Such massive remodeling of the distal regulatory landscape in turn results in a transcriptome that confers a transient loss of neuronal identity and gain of cellular plasticity. Furthermore, NMDA receptor activity also induces many novel prosurvival genes that function in neuroprotective pathways. Taken together, these findings reveal the dynamics of the distal regulatory landscape during neurogenesis and uncover novel regulatory elements that function in concert with epigenetic mechanisms and transcription factors to generate the transcriptome underlying neuronal development and activity. PMID:26170447

Regulative recovery in the sea urchin embryo and the stabilizing role of fail-safe gene network wiring

PubMed Central

Smith, Joel; Davidson, Eric H.

2009-01-01

Design features that ensure reproducible and invariant embryonic processes are major characteristics of current gene regulatory network models. New cis-regulatory studies on a gene regulatory network subcircuit activated early in the development of the sea urchin embryo reveal a sequence of encoded “fail-safe” regulatory devices. These ensure the maintenance of fate separation between skeletogenic and nonskeletogenic mesoderm lineages. An unexpected consequence of the network design revealed in the course of these experiments is that it enables the embryo to “recover” from regulatory interference that has catastrophic effects if this feature is disarmed. A reengineered regulatory system inserted into the embryo was used to prove how this system operates in vivo. Genomically encoded backup control circuitry thus provides the mechanism underlying a specific example of the regulative development for which the sea urchin embryo has long been famous. PMID:19822764

Comparative analysis of gene regulatory networks: from network reconstruction to evolution.

PubMed

Thompson, Dawn; Regev, Aviv; Roy, Sushmita

2015-01-01

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Modelling and analysis of gene regulatory network using feedback control theory

NASA Astrophysics Data System (ADS)

El-Samad, H.; Khammash, M.

2010-01-01

Molecular pathways are a part of a remarkable hierarchy of regulatory networks that operate at all levels of organisation. These regulatory networks are responsible for much of the biological complexity within the cell. The dynamic character of these pathways and the prevalence of feedback regulation strategies in their operation make them amenable to systematic mathematical analysis using the same tools that have been used with success in analysing and designing engineering control systems. In this article, we aim at establishing this strong connection through various examples where the behaviour exhibited by gene networks is explained in terms of their underlying control strategies. We complement our analysis by a survey of mathematical techniques commonly used to model gene regulatory networks and analyse their dynamic behaviour.

Hopfield networks for solving Tower of Hanoi problems

NASA Astrophysics Data System (ADS)

Kaplan, G. B.; Güzeliş, Cüneyt

2001-08-01

In this paper, Hopfield neural networks have been considered in solving the Tower of Hanoi test which is used in the determining of deficit of planning capability of the human prefrontal cortex. The main difference between this paper and the ones in the literature which use neural networks is that the Tower of Hanoi problem has been formulated here as a special shortest-path problem. In the literature, some Hopfield networks are developed for solving the shortest path problem which is a combinatorial optimization problem having a diverse field of application. The approach given in this paper gives the possibility of solving the Tower of Hanoi problem using these Hopfield networks. Also, the paper proposes new Hopfield network models for the shortest path and hence the Tower of Hanoi problems and compares them to the available ones in terms of the memory and time (number of steps) needed in the simulations.

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments[OPEN

PubMed Central

Hafemeister, Christoph; Nicotra, Adrienne B.; Jagadish, S.V. Krishna; Bonneau, Richard; Purugganan, Michael

2016-01-01

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference. PMID:27655842

Effect of the Implicit Combinatorial Model on Combinatorial Reasoning in Secondary School Pupils.

ERIC Educational Resources Information Center

Batanero, Carmen; And Others

1997-01-01

Elementary combinatorial problems may be classified into three different combinatorial models: (1) selection; (2) partition; and (3) distribution. The main goal of this research was to determine the effect of the implicit combinatorial model on pupils' combinatorial reasoning before and after instruction. Gives an analysis of variance of the…

Conserved Non-Coding Regulatory Signatures in Arabidopsis Co-Expressed Gene Modules

PubMed Central

Spangler, Jacob B.; Ficklin, Stephen P.; Luo, Feng; Freeling, Michael; Feltus, F. Alex

2012-01-01

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome. PMID:23024789

Conserved non-coding regulatory signatures in Arabidopsis co-expressed gene modules.

PubMed

Spangler, Jacob B; Ficklin, Stephen P; Luo, Feng; Freeling, Michael; Feltus, F Alex

2012-01-01

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.

Challenges for modeling global gene regulatory networks during development: insights from Drosophila.

PubMed

Wilczynski, Bartek; Furlong, Eileen E M

2010-04-15

Development is regulated by dynamic patterns of gene expression, which are orchestrated through the action of complex gene regulatory networks (GRNs). Substantial progress has been made in modeling transcriptional regulation in recent years, including qualitative "coarse-grain" models operating at the gene level to very "fine-grain" quantitative models operating at the biophysical "transcription factor-DNA level". Recent advances in genome-wide studies have revealed an enormous increase in the size and complexity or GRNs. Even relatively simple developmental processes can involve hundreds of regulatory molecules, with extensive interconnectivity and cooperative regulation. This leads to an explosion in the number of regulatory functions, effectively impeding Boolean-based qualitative modeling approaches. At the same time, the lack of information on the biophysical properties for the majority of transcription factors within a global network restricts quantitative approaches. In this review, we explore the current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks. We suggest to integrate coarse- and find-grain approaches to model gene regulatory networks in cis. We focus on two very well-studied examples from Drosophila, which likely represent typical developmental regulatory modules across metazoans. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE PAGES

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.; ...

2016-03-18

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

PubMed Central

Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. Database URL: http://abasy.ccg.unam.mx PMID:27242034

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO 2 Levels

DOE PAGES

Levering, Jennifer; Dupont, Christopher L.; Allen, Andrew E.; ...

2017-02-14

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and sharedmore » metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.« less

Integrated Regulatory and Metabolic Networks of the Marine Diatom Phaeodactylum tricornutum Predict the Response to Rising CO 2 Levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levering, Jennifer; Dupont, Christopher L.; Allen, Andrew E.

Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and sharedmore » metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.« less

Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation.

PubMed

Weber, Michael; Sotoca, Ana M; Kupfer, Peter; Guthke, Reinhard; van Zoelen, Everardus J

2013-11-12

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network. Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1. Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.

Modeling gene regulatory network motifs using statecharts

PubMed Central

2012-01-01

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

A data-driven network model of primary myelofibrosis: transcriptional and post-transcriptional alterations in CD34+ cells.

PubMed

Calura, E; Pizzini, S; Bisognin, A; Coppe, A; Sales, G; Gaffo, E; Fanelli, T; Mannarelli, C; Zini, R; Norfo, R; Pennucci, V; Manfredini, R; Romualdi, C; Guglielmelli, P; Vannucchi, A M; Bortoluzzi, S

2016-06-24

microRNAs (miRNAs) are relevant in the pathogenesis of primary myelofibrosis (PMF) but our understanding is limited to specific target genes and the overall systemic scenario islacking. By both knowledge-based and ab initio approaches for comparative analysis of CD34+ cells of PMF patients and healthy controls, we identified the deregulated pathways involving miRNAs and genes and new transcriptional and post-transcriptional regulatory circuits in PMF cells. These converge in a unique and integrated cellular process, in which the role of specific miRNAs is to wire, co-regulate and allow a fine crosstalk between the involved processes. The PMF pathway includes Akt signaling, linked to Rho GTPases, CDC42, PLD2, PTEN crosstalk with the hypoxia response and Calcium-linked cellular processes connected to cyclic AMP signaling. Nested on the depicted transcriptional scenario, predicted circuits are reported, opening new hypotheses. Links between miRNAs (miR-106a-5p, miR-20b-5p, miR-20a-5p, miR-17-5p, miR-19b-3p and let-7d-5p) and key transcription factors (MYCN, ATF, CEBPA, REL, IRF and FOXJ2) and their common target genes tantalizingly suggest new path to approach the disease. The study provides a global overview of transcriptional and post-transcriptional deregulations in PMF, and, unifying consolidated and predicted data, could be helpful to identify new combinatorial therapeutic strategy. Interactive PMF network model: http://compgen.bio.unipd.it/pmf-net/.

Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

PubMed

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2014-01-01

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

Transversal homoclinic orbits in a transiently chaotic neural network.

PubMed

Chen, Shyan-Shiou; Shih, Chih-Wen

2002-09-01

We study the existence of snap-back repellers, hence the existence of transversal homoclinic orbits in a discrete-time neural network. Chaotic behaviors for the network system in the sense of Li and Yorke or Marotto can then be concluded. The result is established by analyzing the structures of the system and allocating suitable parameters in constructing the fixed points and their pre-images for the system. The investigation provides a theoretical confirmation on the scenario of transient chaos for the system. All the parameter conditions for the theory can be examined numerically. The numerical ranges for the parameters which yield chaotic dynamics and convergent dynamics provide significant information in the annealing process in solving combinatorial optimization problems using this transiently chaotic neural network. (c) 2002 American Institute of Physics.

An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury

PubMed Central

2013-01-01

Background The regenerative response of Schwann cells after peripheral nerve injury is a critical process directly related to the pathophysiology of a number of neurodegenerative diseases. This SC injury response is dependent on an intricate gene regulatory program coordinated by a number of transcription factors and microRNAs, but the interactions among them remain largely unknown. Uncovering the transcriptional and post-transcriptional regulatory networks governing the Schwann cell injury response is a key step towards a better understanding of Schwann cell biology and may help develop novel therapies for related diseases. Performing such comprehensive network analysis requires systematic bioinformatics methods to integrate multiple genomic datasets. Results In this study we present a computational pipeline to infer transcription factor and microRNA regulatory networks. Our approach combined mRNA and microRNA expression profiling data, ChIP-Seq data of transcription factors, and computational transcription factor and microRNA target prediction. Using mRNA and microRNA expression data collected in a Schwann cell injury model, we constructed a regulatory network and studied regulatory pathways involved in Schwann cell response to injury. Furthermore, we analyzed network motifs and obtained insights on cooperative regulation of transcription factors and microRNAs in Schwann cell injury recovery. Conclusions This work demonstrates a systematic method for gene regulatory network inference that may be used to gain new information on gene regulation by transcription factors and microRNAs. PMID:23387820

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

PubMed Central

2013-01-01

Background In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. Results We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Conclusions Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes. PMID:23663484

Intersection of FOXO- and RUNX1-mediated gene expression programs in single breast epithelial cells during morphogenesis and tumor progression.

PubMed

Wang, Lixin; Brugge, Joan S; Janes, Kevin A

2011-10-04

Gene expression networks are complicated by the assortment of regulatory factors that bind DNA and modulate transcription combinatorially. Single-cell measurements can reveal biological mechanisms hidden by population averages, but their value has not been fully explored in the context of mRNA regulation. Here, we adapted a single-cell expression profiling technique to examine the gene expression program downstream of Forkhead box O (FOXO) transcription factors during 3D breast epithelial acinar morphogenesis. By analyzing patterns of mRNA fluctuations among individual matrix-attached epithelial cells, we found that a subset of FOXO target genes was jointly regulated by the transcription factor Runt-related transcription factor 1 (RUNX1). Knockdown of RUNX1 causes hyperproliferation and abnormal morphogenesis, both of which require normal FOXO function. Down-regulating RUNX1 and FOXOs simultaneously causes widespread oxidative stress, which arrests proliferation and restores normal acinar morphology. In hormone-negative breast cancers lacking human epidermal growth factor receptor 2 (HER2) amplification, we find that RUNX1 down-regulation is strongly associated with up-regulation of FOXO1, which may be required to support growth of RUNX1-negative tumors. The coordinate function of these two tumor suppressors may provide a failsafe mechanism that inhibits cancer progression.

Nothing in Evolution Makes Sense Except in the Light of Genomics: Read-Write Genome Evolution as an Active Biological Process.

PubMed

Shapiro, James A

2016-06-08

The 21st century genomics-based analysis of evolutionary variation reveals a number of novel features impossible to predict when Dobzhansky and other evolutionary biologists formulated the neo-Darwinian Modern Synthesis in the middle of the last century. These include three distinct realms of cell evolution; symbiogenetic fusions forming eukaryotic cells with multiple genome compartments; horizontal organelle, virus and DNA transfers; functional organization of proteins as systems of interacting domains subject to rapid evolution by exon shuffling and exonization; distributed genome networks integrated by mobile repetitive regulatory signals; and regulation of multicellular development by non-coding lncRNAs containing repetitive sequence components. Rather than single gene traits, all phenotypes involve coordinated activity by multiple interacting cell molecules. Genomes contain abundant and functional repetitive components in addition to the unique coding sequences envisaged in the early days of molecular biology. Combinatorial coding, plus the biochemical abilities cells possess to rearrange DNA molecules, constitute a powerful toolbox for adaptive genome rewriting. That is, cells possess "Read-Write Genomes" they alter by numerous biochemical processes capable of rapidly restructuring cellular DNA molecules. Rather than viewing genome evolution as a series of accidental modifications, we can now study it as a complex biological process of active self-modification.

Nothing in Evolution Makes Sense Except in the Light of Genomics: Read–Write Genome Evolution as an Active Biological Process

PubMed Central

Shapiro, James A.

2016-01-01

The 21st century genomics-based analysis of evolutionary variation reveals a number of novel features impossible to predict when Dobzhansky and other evolutionary biologists formulated the neo-Darwinian Modern Synthesis in the middle of the last century. These include three distinct realms of cell evolution; symbiogenetic fusions forming eukaryotic cells with multiple genome compartments; horizontal organelle, virus and DNA transfers; functional organization of proteins as systems of interacting domains subject to rapid evolution by exon shuffling and exonization; distributed genome networks integrated by mobile repetitive regulatory signals; and regulation of multicellular development by non-coding lncRNAs containing repetitive sequence components. Rather than single gene traits, all phenotypes involve coordinated activity by multiple interacting cell molecules. Genomes contain abundant and functional repetitive components in addition to the unique coding sequences envisaged in the early days of molecular biology. Combinatorial coding, plus the biochemical abilities cells possess to rearrange DNA molecules, constitute a powerful toolbox for adaptive genome rewriting. That is, cells possess “Read–Write Genomes” they alter by numerous biochemical processes capable of rapidly restructuring cellular DNA molecules. Rather than viewing genome evolution as a series of accidental modifications, we can now study it as a complex biological process of active self-modification. PMID:27338490

Modularity and design principles in the sea urchin embryo gene regulatory network

PubMed Central

Peter, Isabelle S.; Davidson, Eric H.

2010-01-01

The gene regulatory network (GRN) established experimentally for the pre-gastrular sea urchin embryo provides causal explanations of the biological functions required for spatial specification of embryonic regulatory states. Here we focus on the structure of the GRN which controls the progressive increase in complexity of territorial regulatory states during embryogenesis; and on the types of modular subcircuits of which the GRN is composed. Each of these subcircuit topologies executes a particular operation of spatial information processing. The GRN architecture reflects the particular mode of embryogenesis represented by sea urchin development. Network structure not only specifies the linkages constituting the genomic regulatory code for development, but also indicates the various regulatory requirements of regional developmental processes. PMID:19932099

Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma

PubMed Central

Sun, Jingchun; Gong, Xue; Purow, Benjamin; Zhao, Zhongming

2012-01-01

Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers. PMID:22829753

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

PubMed Central

Liu, Zhi-Ping

2015-01-01

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

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

Optofluidic wavelength division multiplexing for single-virus detection

PubMed Central

Ozcelik, Damla; Parks, Joshua W.; Wall, Thomas A.; Stott, Matthew A.; Cai, Hong; Parks, Joseph W.; Hawkins, Aaron R.; Schmidt, Holger

2015-01-01

Optical waveguides simultaneously transport light at different colors, forming the basis of fiber-optic telecommunication networks that shuttle data in dozens of spectrally separated channels. Here, we reimagine this wavelength division multiplexing (WDM) paradigm in a novel context––the differentiated detection and identification of single influenza viruses on a chip. We use a single multimode interference (MMI) waveguide to create wavelength-dependent spot patterns across the entire visible spectrum and enable multiplexed single biomolecule detection on an optofluidic chip. Each target is identified by its time-dependent fluorescence signal without the need for spectral demultiplexing upon detection. We demonstrate detection of individual fluorescently labeled virus particles of three influenza A subtypes in two implementations: labeling of each virus using three different colors and two-color combinatorial labeling. By extending combinatorial multiplexing to three or more colors, MMI-based WDM provides the multiplexing power required for differentiated clinical tests and the growing field of personalized medicine. PMID:26438840

Partial differential equations constrained combinatorial optimization on an adiabatic quantum computer

NASA Astrophysics Data System (ADS)

Chandra, Rishabh

Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient.

PubMed

Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd

2015-12-01

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. Copyright © 2015 Elsevier Ltd. All rights reserved.

MASM: a market architecture for sensor management in distributed sensor networks

NASA Astrophysics Data System (ADS)

Viswanath, Avasarala; Mullen, Tracy; Hall, David; Garga, Amulya

2005-03-01

Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.

Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation.

PubMed

Preissl, Sebastian; Fang, Rongxin; Huang, Hui; Zhao, Yuan; Raviram, Ramya; Gorkin, David U; Zhang, Yanxiao; Sos, Brandon C; Afzal, Veena; Dickel, Diane E; Kuan, Samantha; Visel, Axel; Pennacchio, Len A; Zhang, Kun; Ren, Bing

2018-03-01

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

PubMed Central

Liu, Wei; Zhu, Wen; Liao, Bo; Chen, Xiangtao

2016-01-01

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method. PMID:27829000

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

PubMed

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Transcriptional network control of normal and leukaemic haematopoiesis

PubMed Central

Sive, Jonathan I.; Göttgens, Berthold

2014-01-01

Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies. PMID:25014893

Transcriptional network control of normal and leukaemic haematopoiesis.

PubMed

Sive, Jonathan I; Göttgens, Berthold

2014-12-10

Transcription factors (TFs) play a key role in determining the gene expression profiles of stem/progenitor cells, and defining their potential to differentiate into mature cell lineages. TF interactions within gene-regulatory networks are vital to these processes, and dysregulation of these networks by TF overexpression, deletion or abnormal gene fusions have been shown to cause malignancy. While investigation of these processes remains a challenge, advances in genome-wide technologies and growing interactions between laboratory and computational science are starting to produce increasingly accurate network models. The haematopoietic system provides an attractive experimental system to elucidate gene regulatory mechanisms, and allows experimental investigation of both normal and dysregulated networks. In this review we examine the principles of TF-controlled gene regulatory networks and the key experimental techniques used to investigate them. We look in detail at examples of how these approaches can be used to dissect out the regulatory mechanisms controlling normal haematopoiesis, as well as the dysregulated networks associated with haematological malignancies. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Dendritic Cytoskeletal Architecture Is Modulated by Combinatorial Transcriptional Regulation in Drosophila melanogaster.

PubMed

Das, Ravi; Bhattacharjee, Shatabdi; Patel, Atit A; Harris, Jenna M; Bhattacharya, Surajit; Letcher, Jamin M; Clark, Sarah G; Nanda, Sumit; Iyer, Eswar Prasad R; Ascoli, Giorgio A; Cox, Daniel N

2017-12-01

Transcription factors (TFs) have emerged as essential cell autonomous mediators of subtype specific dendritogenesis; however, the downstream effectors of these TFs remain largely unknown, as are the cellular events that TFs control to direct morphological change. As dendritic morphology is largely dictated by the organization of the actin and microtubule (MT) cytoskeletons, elucidating TF-mediated cytoskeletal regulatory programs is key to understanding molecular control of diverse dendritic morphologies. Previous studies in Drosophila melanogaster have demonstrated that the conserved TFs Cut and Knot exert combinatorial control over aspects of dendritic cytoskeleton development, promoting actin and MT-based arbor morphology, respectively. To investigate transcriptional targets of Cut and/or Knot regulation, we conducted systematic neurogenomic studies, coupled with in vivo genetic screens utilizing multi-fluor cytoskeletal and membrane marker reporters. These analyses identified a host of putative Cut and/or Knot effector molecules, and a subset of these putative TF targets converge on modulating dendritic cytoskeletal architecture, which are grouped into three major phenotypic categories, based upon neuromorphometric analyses: complexity enhancer, complexity shifter, and complexity suppressor. Complexity enhancer genes normally function to promote higher order dendritic growth and branching with variable effects on MT stabilization and F-actin organization, whereas complexity shifter and complexity suppressor genes normally function in regulating proximal-distal branching distribution or in restricting higher order branching complexity, respectively, with spatially restricted impacts on the dendritic cytoskeleton. Collectively, we implicate novel genes and cellular programs by which TFs distinctly and combinatorially govern dendritogenesis via cytoskeletal modulation. Copyright © 2017 by the Genetics Society of America.

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target.

PubMed

Tian, Tongde; Chen, Chuanliang; Yang, Feng; Tang, Jingwen; Pei, Junwen; Shi, Bian; Zhang, Ning; Zhang, Jianhua

2017-03-01

The paper aimed to screen out genetic markers applicable to early diagnosis for colorectal cancer and establish apoptotic regulatory network model for colorectal cancer, and to analyze the current situation of traditional Chinese medicine (TCM) target, thereby providing theoretical evidence for early diagnosis and targeted therapy of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched and performed comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. KEGG analysis was employed to establish apoptotic regulatory network model based on screened genetic markers, and optimization was conducted on TCM targets. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, among which DCC has the highest diagnostic efficiency. Apoptotic regulatory network was built by KEGG analysis. Currently, it was reported that TCM has regulatory function on gene locus in apoptotic regulatory network. The apoptotic regulatory model of colorectal cancer established in this study provides theoretical evidence for early diagnosis and TCM targeted therapy of colorectal cancer in clinic.

State Space Model with hidden variables for reconstruction of gene regulatory networks.

PubMed

Wu, Xi; Li, Peng; Wang, Nan; Gong, Ping; Perkins, Edward J; Deng, Youping; Zhang, Chaoyang

2011-01-01

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN. True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks. Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN. This study provides useful information in handling the hidden variables and improving the inference precision.

Using food-web theory to conserve ecosystems

PubMed Central

McDonald-Madden, E.; Sabbadin, R.; Game, E. T.; Baxter, P. W. J.; Chadès, I.; Possingham, H. P.

2016-01-01

Food-web theory can be a powerful guide to the management of complex ecosystems. However, we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs. This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. While no single network theory index provides an appropriate guide to management for all food webs, a modified version of the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes. Our analysis shows that by prioritizing ecosystem management based on the network-wide impact of species protection rather than species loss, we can substantially improve conservation outcomes. PMID:26776253

Biologically inspired intelligent decision making

PubMed Central

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2014-01-01

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

PubMed Central

2014-01-01

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism. PMID:24517200

Evolutionary rewiring of bacterial regulatory networks

PubMed Central

Taylor, Tiffany B.; Mulley, Geraldine; McGuffin, Liam J.; Johnson, Louise J.; Brockhurst, Michael A.; Arseneault, Tanya; Silby, Mark W.; Jackson, Robert W.

2015-01-01

Bacteria have evolved complex regulatory networks that enable integration of multiple intracellular and extracellular signals to coordinate responses to environmental changes. However, our knowledge of how regulatory systems function and evolve is still relatively limited. There is often extensive homology between components of different networks, due to past cycles of gene duplication, divergence, and horizontal gene transfer, raising the possibility of cross-talk or redundancy. Consequently, evolutionary resilience is built into gene networks - homology between regulators can potentially allow rapid rescue of lost regulatory function across distant regions of the genome. In our recent study [Taylor, et al. Science (2015), 347(6225)] we find that mutations that facilitate cross-talk between pathways can contribute to gene network evolution, but that such mutations come with severe pleiotropic costs. Arising from this work are a number of questions surrounding how this phenomenon occurs. PMID:28357301

The disadvantage of combinatorial communication.

PubMed Central

Lachmann, Michael; Bergstrom, Carl T.

2004-01-01

Combinatorial communication allows rapid and efficient transfer of detailed information, yet combinatorial communication is used by few, if any, non-human species. To complement recent studies illustrating the advantages of combinatorial communication, we highlight a critical disadvantage. We use the concept of information value to show that deception poses a greater and qualitatively different threat to combinatorial signalling than to non-combinatorial systems. This additional potential for deception may represent a strategic barrier that has prevented widespread evolution of combinatorial communication. Our approach has the additional benefit of drawing clear distinctions among several types of deception that can occur in communication systems. PMID:15556886

The disadvantage of combinatorial communication.

PubMed

Lachmann, Michael; Bergstrom, Carl T

2004-11-22

Combinatorial communication allows rapid and efficient transfer of detailed information, yet combinatorial communication is used by few, if any, non-human species. To complement recent studies illustrating the advantages of combinatorial communication, we highlight a critical disadvantage. We use the concept of information value to show that deception poses a greater and qualitatively different threat to combinatorial signalling than to non-combinatorial systems. This additional potential for deception may represent a strategic barrier that has prevented widespread evolution of combinatorial communication. Our approach has the additional benefit of drawing clear distinctions among several types of deception that can occur in communication systems.

CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

PubMed

Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

2016-12-23

A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties of their dynamical state space.

PubMed

Ahnert, S E; Fink, T M A

2016-07-01

Network motifs have been studied extensively over the past decade, and certain motifs, such as the feed-forward loop, play an important role in regulatory networks. Recent studies have used Boolean network motifs to explore the link between form and function in gene regulatory networks and have found that the structure of a motif does not strongly determine its function, if this is defined in terms of the gene expression patterns the motif can produce. Here, we offer a different, higher-level definition of the 'function' of a motif, in terms of two fundamental properties of its dynamical state space as a Boolean network. One is the basin entropy, which is a complexity measure of the dynamics of Boolean networks. The other is the diversity of cyclic attractor lengths that a given motif can produce. Using these two measures, we examine all 104 topologically distinct three-node motifs and show that the structural properties of a motif, such as the presence of feedback loops and feed-forward loops, predict fundamental characteristics of its dynamical state space, which in turn determine aspects of its functional versatility. We also show that these higher-level properties have a direct bearing on real regulatory networks, as both basin entropy and cycle length diversity show a close correspondence with the prevalence, in neural and genetic regulatory networks, of the 13 connected motifs without self-interactions that have been studied extensively in the literature. © 2016 The Authors.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

PubMed

Ibarra-Arellano, Miguel A; Campos-González, Adrián I; Treviño-Quintanilla, Luis G; Tauch, Andreas; Freyre-González, Julio A

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx. © The Author(s) 2016. Published by Oxford University Press.

Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell

PubMed Central

Lim, Wendell A.; Lee, Connie M.; Tang, Chao

2013-01-01

A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241

Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach.

PubMed

Freyre-González, Julio A; Alonso-Pavón, José A; Treviño-Quintanilla, Luis G; Collado-Vides, Julio

2008-10-27

Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes that integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture composed of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.

Efficient experimental design for uncertainty reduction in gene regulatory networks.

PubMed

Dehghannasiri, Roozbeh; Yoon, Byung-Jun; Dougherty, Edward R

2015-01-01

An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Efficient experimental design for uncertainty reduction in gene regulatory networks

PubMed Central

2015-01-01

Background An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Conclusions Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/. PMID:26423515

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

Passing messages between biological networks to refine predicted interactions.

PubMed

Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

2013-01-01

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

An electronic regulatory document management system for a clinical trial network.

PubMed

Zhao, Wenle; Durkalski, Valerie; Pauls, Keith; Dillon, Catherine; Kim, Jaemyung; Kolk, Deneil; Silbergleit, Robert; Stevenson, Valerie; Palesch, Yuko

2010-01-01

A computerized regulatory document management system has been developed as a module in a comprehensive Clinical Trial Management System (CTMS) designed for an NIH-funded clinical trial network in order to more efficiently manage and track regulatory compliance. Within the network, several institutions and investigators are involved in multiple trials, and each trial has regulatory document requirements. Some of these documents are trial specific while others apply across multiple trials. The latter causes a possible redundancy in document collection and management. To address these and other related challenges, a central regulatory document management system was designed. This manuscript shares the design of the system as well as examples of it use in current studies. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Anticipated Ethics and Regulatory Challenges in PCORnet: The National Patient-Centered Clinical Research Network.

PubMed

Ali, Joseph; Califf, Robert; Sugarman, Jeremy

2016-01-01

PCORnet, the National Patient-Centered Clinical Research Network, seeks to establish a robust national health data network for patient-centered comparative effectiveness research. This article reports the results of a PCORnet survey designed to identify the ethics and regulatory challenges anticipated in network implementation. A 12-item online survey was developed by leadership of the PCORnet Ethics and Regulatory Task Force; responses were collected from the 29 PCORnet networks. The most pressing ethics issues identified related to informed consent, patient engagement, privacy and confidentiality, and data sharing. High priority regulatory issues included IRB coordination, privacy and confidentiality, informed consent, and data sharing. Over 150 IRBs and five different approaches to managing multisite IRB review were identified within PCORnet. Further empirical and scholarly work, as well as practical and policy guidance, is essential if important initiatives that rely on comparative effectiveness research are to move forward.

Why is combinatorial communication rare in the natural world, and why is language an exception to this trend?

PubMed

Scott-Phillips, Thomas C; Blythe, Richard A

2013-11-06

In a combinatorial communication system, some signals consist of the combinations of other signals. Such systems are more efficient than equivalent, non-combinatorial systems, yet despite this they are rare in nature. Why? Previous explanations have focused on the adaptive limits of combinatorial communication, or on its purported cognitive difficulties, but neither of these explains the full distribution of combinatorial communication in the natural world. Here, we present a nonlinear dynamical model of the emergence of combinatorial communication that, unlike previous models, considers how initially non-communicative behaviour evolves to take on a communicative function. We derive three basic principles about the emergence of combinatorial communication. We hence show that the interdependence of signals and responses places significant constraints on the historical pathways by which combinatorial signals might emerge, to the extent that anything other than the most simple form of combinatorial communication is extremely unlikely. We also argue that these constraints can be bypassed if individuals have the socio-cognitive capacity to engage in ostensive communication. Humans, but probably no other species, have this ability. This may explain why language, which is massively combinatorial, is such an extreme exception to nature's general trend for non-combinatorial communication.

Gene regulation is governed by a core network in hepatocellular carcinoma.

PubMed

Gu, Zuguang; Zhang, Chenyu; Wang, Jin

2012-05-01

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to HCC and we believe further experimental validation is worthwhile.

Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella.

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Yoon, Hyunjin; Nakayasu, Ernesto S.

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated a large amount of datamore » and driven development of computational approaches required for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird’s eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.« less

Examining the intersection between splicing, nuclear export and small RNA pathways.

PubMed

Nabih, Amena; Sobotka, Julia A; Wu, Monica Z; Wedeles, Christopher J; Claycomb, Julie M

2017-11-01

Nuclear Argonaute/small RNA pathways in a variety of eukaryotic species are generally known to regulate gene expression via chromatin modulation and transcription attenuation in a process known as transcriptional gene silencing (TGS). However, recent data, including genetic screens, phylogenetic profiling, and molecular mechanistic studies, also point to a novel and emerging intersection between the splicing and nuclear export machinery with nuclear Argonaute/small RNA pathways in many organisms. In this review, we summarize the field's current understanding regarding the relationship between splicing, export and small RNA pathways, and consider the biological implications for coordinated regulation of transcripts by these pathways. We also address the importance and available approaches for understanding the RNA regulatory logic generated by the intersection of these particular pathways in the context of synthetic biology. The interactions between various eukaryotic RNA regulatory pathways, particularly splicing, nuclear export and small RNA pathways provide a type of combinatorial code that informs the identity ("self" versus "non-self") and dictates the fate of each transcript in a cell. Although the molecular mechanisms for how splicing and nuclear export impact small RNA pathways are not entirely clear at this early stage, the links between these pathways are widespread across eukaryotic phyla. The link between splicing, nuclear export, and small RNA pathways is emerging and establishes a new frontier for understanding the combinatorial logic of gene regulation across species that could someday be harnessed for therapeutic, biotechnology and agricultural applications. This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue. Copyright © 2017 Elsevier B.V. All rights reserved.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

PubMed

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Genome-wide network of regulatory genes for construction of a chordate embryo.

PubMed

Shoguchi, Eiichi; Hamaguchi, Makoto; Satoh, Nori

2008-04-15

Animal development is controlled by gene regulation networks that are composed of sequence-specific transcription factors (TF) and cell signaling molecules (ST). Although housekeeping genes have been reported to show clustering in the animal genomes, whether the genes comprising a given regulatory network are physically clustered on a chromosome is uncertain. We examined this question in the present study. Ascidians are the closest living relatives of vertebrates, and their tadpole-type larva represents the basic body plan of chordates. The Ciona intestinalis genome contains 390 core TF genes and 119 major ST genes. Previous gene disruption assays led to the formulation of a basic chordate embryonic blueprint, based on over 3000 genetic interactions among 79 zygotic regulatory genes. Here, we mapped the regulatory genes, including all 79 regulatory genes, on the 14 pairs of Ciona chromosomes by fluorescent in situ hybridization (FISH). Chromosomal localization of upstream and downstream regulatory genes demonstrates that the components of coherent developmental gene networks are evenly distributed over the 14 chromosomes. Thus, this study provides the first comprehensive evidence that the physical clustering of regulatory genes, or their target genes, is not relevant for the genome-wide control of gene expression during development.

Automatic inference of multicellular regulatory networks using informative priors.

PubMed

Sun, Xiaoyun; Hong, Pengyu

2009-01-01

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

Stochastic methods for analysis of power flow in electric networks

NASA Astrophysics Data System (ADS)

1982-09-01

The modeling and effects of probabilistic behavior on steady state power system operation were analyzed. A solution to the steady state network flow equations which adhere both to Kirchoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques was obtained. The development of sound techniques for producing meaningful data to serve as input is examined. Electric demand modeling, equipment failure analysis, and algorithm development are investigated. Two major development areas are described: a decomposition of stochastic processes which gives stationarity, ergodicity, and even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

Reverse engineering biological networks :applications in immune responses to bio-toxins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Dynamics of Bacterial Gene Regulatory Networks.

PubMed

Shis, David L; Bennett, Matthew R; Igoshin, Oleg A

2018-05-20

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

Use of Network Inference to Elucidate Common and Chemical-specific Effects on Steoidogenesis

EPA Science Inventory

Microarray data is a key source for modeling gene regulatory interactions. Regulatory network models based on multiple datasets are potentially more robust and can provide greater confidence. In this study, we used network modeling on microarray data generated by exposing the fat...

Evolution of regulatory networks towards adaptability and stability in a changing environment

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2014-11-01

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

A Hox regulatory network establishes motor neuron pool identity and target-muscle connectivity.

PubMed

Dasen, Jeremy S; Tice, Bonnie C; Brenner-Morton, Susan; Jessell, Thomas M

2005-11-04

Spinal motor neurons acquire specialized "pool" identities that determine their ability to form selective connections with target muscles in the limb, but the molecular basis of this striking example of neuronal specificity has remained unclear. We show here that a Hox transcriptional regulatory network specifies motor neuron pool identity and connectivity. Two interdependent sets of Hox regulatory interactions operate within motor neurons, one assigning rostrocaudal motor pool position and a second directing motor pool diversity at a single segmental level. This Hox regulatory network directs the downstream transcriptional identity of motor neuron pools and defines the pattern of target-muscle connectivity.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

PubMed

Abduallah, Yasser; Turki, Turki; Byron, Kevin; Du, Zongxuan; Cervantes-Cervantes, Miguel; Wang, Jason T L

2017-01-01

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Regulatory Aspects of Smart Water Networks in the U.S.

EPA Science Inventory

The presentation addresses regulatory aspects of smart water networks in the U.S. It will be presented at the Smart Water Networks Forum (SWAN) annual conference in London, England from April 29-30, 2015. The conference will bring together key voices in the smart water space f...

Amlodipine and atorvastatin improve ventricular hypertrophy and diastolic function via inhibiting TNF-α, IL-1β and NF-κB inflammatory cytokine networks in elderly spontaneously hypertensive rats.

PubMed

Lu, Jingchao; Liu, Fan; Chen, Fei; Jin, Yaqiong; Chen, Huiqiang; Liu, Demin; Cui, Wei

2016-10-01

This study aimed to examine the effects of amlodipine and atorvastatin alone or in combination on the regulation of inflammatory cytokines and the underlying mechanisms in elderly spontaneously hypertensive (SH) rats. The level of serum hs-CRP was detected with ELISA. The serum TNF-α and IL-1β levels were assessed by radioimmunity assay (RIA). Cardiac inflammatory cell infiltration was observed by HE staining. The protein levels of TNF-α, IL-1β, of NF-κB P65 and IκBα were detected by immunoblotting. The intracellular localization of NF-κB p65 was observed using immunohistochemistry. Amlodipine or atorvastatin obviously ameliorated the myocardial inflammatory cell infiltration in SH rats, which was further improved by combinatorial treatment with amlodipine and atorvastatin. Either amlodipine or atorvastatin decreased plasma IL-1β content in SH rats, but there was no significant difference when compared with untreated SH rats. However, the combination of amlodipine and atorvastatin significantly decreased plasma IL-1β level in SH rats. Moreover, amlodipine or atorvastatin intervention significantly reduced myocardial TNF-α and IL-1β protein levels in SH rats, which was further suppressed by the combination of amlodipine and atorvastatin. In addition, amlodipine or atorvastatin inhibited the activity of NF-κB signaling in SH rats, which was further suppressed by combinatorial treatment. Furthermore, amlodipine or atorvastatin restored the activity of IκB-α in SH rats, which was enhanced by combinatorial treatment. Our results demonstrated amlodipine and atorvastatin improved ventricular hypertrophy and diastolic function possibly through the intervention of TNF-α, IL-1β, NF-κB/IκB inflammatory cytokine network. Our study suggests that amlodipine combined with atorvastatin may have additive effect on inhibiting inflammatory response. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice.

PubMed

Smita, Shuchi; Katiyar, Amit; Chinnusamy, Viswanathan; Pandey, Dev M; Bansal, Kailash C

2015-01-01

MYB transcription factor (TF) is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by "top-down" and "guide-gene" approaches. More than 50% of OsMYBs were strongly correlated under 50 experimental conditions with 51 hub genes via "top-down" approach. Further, clusters were identified using Markov Clustering (MCL). To maximize the clustering performance, parameter evaluation of the MCL inflation score (I) was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by "guide-gene" approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought response in rice. Thus, the co-regulatory network analysis facilitated the identification of complex OsMYB regulatory networks, and candidate target regulon genes of selected guide MYB genes. The results contribute to the candidate gene screening, and experimentally testable hypotheses for potential regulatory MYB TFs, and their targets under stress conditions.

Unbiased Combinatorial Genomic Approaches to Identify Alternative Therapeutic Targets within the TSC Signaling Network

DTIC Science & Technology

2014-06-01

Specifically, we combined the CRISPR genome editing system with a novel approach allowing efficient single cell cloning of Drosophila cells with the aim of...and culture these to produce cultures completely lacking wildtype sequence at the target locus. No robust methods existed to clone single Drosophila ...targeting all kinases and phosphatases (563 genes) in the Drosophila genome . 65 samples that displayed synthetic lethality (15 genes) or synthetic

Combinatorial Statistics on Trees and Networks

DTIC Science & Technology

2010-09-29

interaction graph is drawn from the Erdos- Renyi , G(n,p), where each edge is present independently with probability p. For this model we establish a double...special interest is the behavior of Gibbs sampling on the Erdos- Renyi random graph G{n, d/n), where each edge is chosen independently with...which have no counterparts in the coloring setting. Our proof presented here exploits in novel ways the local treelike structure of Erdos- Renyi

Attractor Signaling Models for Discovery of Combinatorial Therapies

DTIC Science & Technology

2013-09-01

year!survival!rate!for!this! disease ! less!than!15%.!Over!the!years,!many!specific!mechanisms!associated!with!drug!resistance!in!lung!cancer! have!been...reprogramming of pluripotent stem cells [4]. More- over, it has been suggested that a biological system in a chronic or therapy-resistant disease state can...designing new therapeutic methods for complex diseases such as can- cer. Even if our knowledge of biological networks is in- complete, fast progress

Supercomputing '91; Proceedings of the 4th Annual Conference on High Performance Computing, Albuquerque, NM, Nov. 18-22, 1991

NASA Technical Reports Server (NTRS)

1991-01-01

Various papers on supercomputing are presented. The general topics addressed include: program analysis/data dependence, memory access, distributed memory code generation, numerical algorithms, supercomputer benchmarks, latency tolerance, parallel programming, applications, processor design, networks, performance tools, mapping and scheduling, characterization affecting performance, parallelism packaging, computing climate change, combinatorial algorithms, hardware and software performance issues, system issues. (No individual items are abstracted in this volume)

Synthesis of Chemiluminescent Esters: A Combinatorial Synthesis Experiment for Organic Chemistry Students

ERIC Educational Resources Information Center

Duarte, Robert; Nielson, Janne T.; Dragojlovic, Veljko

2004-01-01

A group of techniques aimed at synthesizing a large number of structurally diverse compounds is called combinatorial synthesis. Synthesis of chemiluminescence esters using parallel combinatorial synthesis and mix-and-split combinatorial synthesis is experimented.

Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

PubMed Central

Michailidis, George

2014-01-01

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network. PMID:24586224

Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

PubMed Central

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

2014-01-01

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

Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

PubMed Central

Wang, Yi Kan; Hurley, Daniel G.; Schnell, Santiago; Print, Cristin G.; Crampin, Edmund J.

2013-01-01

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data. PMID:23967277

Network of time-multiplexed optical parametric oscillators as a coherent Ising machine

NASA Astrophysics Data System (ADS)

Marandi, Alireza; Wang, Zhe; Takata, Kenta; Byer, Robert L.; Yamamoto, Yoshihisa

2014-12-01

Finding the ground states of the Ising Hamiltonian maps to various combinatorial optimization problems in biology, medicine, wireless communications, artificial intelligence and social network. So far, no efficient classical and quantum algorithm is known for these problems and intensive research is focused on creating physical systems—Ising machines—capable of finding the absolute or approximate ground states of the Ising Hamiltonian. Here, we report an Ising machine using a network of degenerate optical parametric oscillators (OPOs). Spins are represented with above-threshold binary phases of the OPOs and the Ising couplings are realized by mutual injections. The network is implemented in a single OPO ring cavity with multiple trains of femtosecond pulses and configurable mutual couplings, and operates at room temperature. We programmed a small non-deterministic polynomial time-hard problem on a 4-OPO Ising machine and in 1,000 runs no computational error was detected.

Biophysical constraints on the computational capacity of biochemical signaling networks

NASA Astrophysics Data System (ADS)

Wang, Ching-Hao; Mehta, Pankaj

Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).

Foraging Behaviors and Potential Computational Ability of Problem-Solving in an Amoeba

NASA Astrophysics Data System (ADS)

Nakagaki, Toshiyuki

We study cell behaviors in the complex situations: multiple locations of food were simultaneously given. An amoeba-like organism of true slime mold gathered at the multiple food locations while body shape made of tubular network was totally changed. Then only a few tubes connected all of food locations through a network shape. By taking the network shape of body, the plasmodium could meet its own physiological requirements: as fast absorption of nutrient as possible and sufficient circulation of chemical signals and nutrients through a whole body. Optimality of network shape was evaluated in relation to a combinatorial optimization problem. Here we reviewed the potential computational ability of problem-solving in the amoeba, which was much higher than we'd though. The main message of this article is that we had better to change our stupid opinion that an amoeba is stupid.

Nucleoside antibiotics: biosynthesis, regulation, and biotechnology.

PubMed

Niu, Guoqing; Tan, Huarong

2015-02-01

The alarming rise in antibiotic-resistant pathogens has coincided with a decline in the supply of new antibiotics. It is therefore of great importance to find and create new antibiotics. Nucleoside antibiotics are a large family of natural products with diverse biological functions. Their biosynthesis is a complex process through multistep enzymatic reactions and is subject to hierarchical regulation. Genetic and biochemical studies of the biosynthetic machinery have provided the basis for pathway engineering and combinatorial biosynthesis to create new or hybrid nucleoside antibiotics. Dissection of regulatory mechanisms is leading to strategies to increase the titer of bioactive nucleoside antibiotics. Copyright © 2014. Published by Elsevier Ltd.

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012

Passing Messages between Biological Networks to Refine Predicted Interactions

PubMed Central

Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

2013-01-01

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net. PMID:23741402

Exploring information transmission in gene networks using stochastic simulation and machine learning

NASA Astrophysics Data System (ADS)

Park, Kyemyung; Prüstel, Thorsten; Lu, Yong; Narayanan, Manikandan; Martins, Andrew; Tsang, John

How gene regulatory networks operate robustly despite environmental fluctuations and biochemical noise is a fundamental question in biology. Mathematically the stochastic dynamics of a gene regulatory network can be modeled using chemical master equation (CME), but nonlinearity and other challenges render analytical solutions of CMEs difficult to attain. While approaches of approximation and stochastic simulation have been devised for simple models, obtaining a more global picture of a system's behaviors in high-dimensional parameter space without simplifying the system substantially remains a major challenge. Here we present a new framework for understanding and predicting the behaviors of gene regulatory networks in the context of information transmission among genes. Our approach uses stochastic simulation of the network followed by machine learning of the mapping between model parameters and network phenotypes such as information transmission behavior. We also devised ways to visualize high-dimensional phase spaces in intuitive and informative manners. We applied our approach to several gene regulatory circuit motifs, including both feedback and feedforward loops, to reveal underexplored aspects of their operational behaviors. This work is supported by the Intramural Program of NIAID/NIH.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

PubMed

Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

2017-05-01

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

The vertebrate Hox gene regulatory network for hindbrain segmentation: Evolution and diversification: Coupling of a Hox gene regulatory network to hindbrain segmentation is an ancient trait originating at the base of vertebrates.

PubMed

Parker, Hugo J; Bronner, Marianne E; Krumlauf, Robb

2016-06-01

Hindbrain development is orchestrated by a vertebrate gene regulatory network that generates segmental patterning along the anterior-posterior axis via Hox genes. Here, we review analyses of vertebrate and invertebrate chordate models that inform upon the evolutionary origin and diversification of this network. Evidence from the sea lamprey reveals that the hindbrain regulatory network generates rhombomeric compartments with segmental Hox expression and an underlying Hox code. We infer that this basal feature was present in ancestral vertebrates and, as an evolutionarily constrained developmental state, is fundamentally important for patterning of the vertebrate hindbrain across diverse lineages. Despite the common ground plan, vertebrates exhibit neuroanatomical diversity in lineage-specific patterns, with different vertebrates revealing variations of Hox expression in the hindbrain that could underlie this diversification. Invertebrate chordates lack hindbrain segmentation but exhibit some conserved aspects of this network, with retinoic acid signaling playing a role in establishing nested domains of Hox expression. © 2016 WILEY Periodicals, Inc.

Criticality Is an Emergent Property of Genetic Networks that Exhibit Evolvability

PubMed Central

Torres-Sosa, Christian; Huang, Sui; Aldana, Maximino

2012-01-01

Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype) while allowing for switching between multiple phenotypes (network states) as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i) preserve all the already acquired phenotypes (dynamical attractor states) and (ii) generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation) while conserving the existing phenotypes (conservation) suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators) similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape. PMID:22969419

Asessing for Structural Understanding in Childrens' Combinatorial Problem Solving.

ERIC Educational Resources Information Center

English, Lyn

1999-01-01

Assesses children's structural understanding of combinatorial problems when presented in a variety of task situations. Provides an explanatory model of students' combinatorial understandings that informs teaching and assessment. Addresses several components of children's structural understanding of elementary combinatorial problems. (Contains 50…

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

PubMed Central

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

2012-01-01

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms. PMID:22807664

Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

NASA Astrophysics Data System (ADS)

Tkačik, Gašper

2016-07-01

The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to ;randomized; ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

SATRAT: Staphylococcus aureus transcript regulatory network analysis tool.

PubMed

Gopal, Tamilselvi; Nagarajan, Vijayaraj; Elasri, Mohamed O

2015-01-01

Staphylococcus aureus is a commensal organism that primarily colonizes the nose of healthy individuals. S. aureus causes a spectrum of infections that range from skin and soft-tissue infections to fatal invasive diseases. S. aureus uses a large number of virulence factors that are regulated in a coordinated fashion. The complex regulatory mechanisms have been investigated in numerous high-throughput experiments. Access to this data is critical to studying this pathogen. Previously, we developed a compilation of microarray experimental data to enable researchers to search, browse, compare, and contrast transcript profiles. We have substantially updated this database and have built a novel exploratory tool-SATRAT-the S. aureus transcript regulatory network analysis tool, based on the updated database. This tool is capable of performing deep searches using a query and generating an interactive regulatory network based on associations among the regulators of any query gene. We believe this integrated regulatory network analysis tool would help researchers explore the missing links and identify novel pathways that regulate virulence in S. aureus. Also, the data model and the network generation code used to build this resource is open sourced, enabling researchers to build similar resources for other bacterial systems.

Following the Footsteps of Chlamydial Gene Regulation

PubMed Central

Domman, D.; Horn, M.

2015-01-01

Regulation of gene expression ensures an organism responds to stimuli and undergoes proper development. Although the regulatory networks in bacteria have been investigated in model microorganisms, nearly nothing is known about the evolution and plasticity of these networks in obligate, intracellular bacteria. The phylum Chlamydiae contains a vast array of host-associated microbes, including several human pathogens. The Chlamydiae are unique among obligate, intracellular bacteria as they undergo a complex biphasic developmental cycle in which large swaths of genes are temporally regulated. Coupled with the low number of transcription factors, these organisms offer a model to study the evolution of regulatory networks in intracellular organisms. We provide the first comprehensive analysis exploring the diversity and evolution of regulatory networks across the phylum. We utilized a comparative genomics approach to construct predicted coregulatory networks, which unveiled genus- and family-specific regulatory motifs and architectures, most notably those of virulence-associated genes. Surprisingly, our analysis suggests that few regulatory components are conserved across the phylum, and those that are conserved are involved in the exploitation of the intracellular niche. Our study thus lends insight into a component of chlamydial evolution that has otherwise remained largely unexplored. PMID:26424812

"Inject-mix-react-separate-and-quantitate" (IMReSQ) method for screening enzyme inhibitors.

PubMed

Wong, Edmund; Okhonin, Victor; Berezovski, Maxim V; Nozaki, Tomoyoshi; Waldmann, Herbert; Alexandrov, Kirill; Krylov, Sergey N

2008-09-10

Many regulatory enzymes are considered attractive therapeutic targets, and their inhibitors are potential drug candidates. Screening combinatorial libraries for enzyme inhibitors is pivotal to identifying hit compounds for the development of drugs targeting regulatory enzymes. Here, we introduce the first inhibitor screening method that consumes only nanoliters of the reactant solutions and is applicable to regulatory enzymes. The method is termed inject-mix-react-separate-and-quantitate (IMReSQ) and includes five steps. First, nanoliter volumes of substrate, candidate inhibitor, and enzyme solutions are injected by pressure into a capillary as separate plugs. Second, the plugs are mixed inside this capillary microreactor by transverse diffusion of laminar flow profiles. Third, the reaction mixture is incubated to form the enzymatic product. Fourth, the product is separated from the substrate inside the capillary by electrophoresis. Fifth, the amounts of the product and substrate are quantitated. In this proof-of-principle work, we applied IMReSQ to study inhibition of recently cloned protein farnesyltransferase from parasite Entamoeba histolytica. This enzyme is a potential therapeutic target for antiparasitic drugs. We identified three previously unknown inhibitors of this enzyme and proved that IMReSQ could be used for quantitatively ranking the potencies of inhibitors.

Functional autonomy of distant-acting human enhancers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Visel, Axel; Akiyama, Jennifer A.; Shoukry, Malak

2009-02-19

Many human genes are associated with dispersed arrays of transcriptional enhancers that regulate their expression in time and space. Studies in invertebrate model systems have suggested that these elements function as discrete and independent regulatory units, but the in vivo combinatorial properties of vertebrate enhancers remain poorly understood. To explore the modularity and regulatory autonomy of human developmental enhancers, we experimentally concatenated up to four enhancers from different genes and used a transgenic mouse assay to compare the in vivo activity of these compound elements with that of the single modules. In all of the six different combinations of elementsmore » tested, the reporter gene activity patterns were additive without signs of interference between the individual modules, indicating that regulatory specificity was maintained despite the presence of closely-positioned heterologous enhancers. Even in cases where two elements drove expression in close anatomical proximity, such as within neighboring subregions of the developing limb bud, the compound patterns did not show signs of cross-inhibition between individual elements or novel expression sites. These data indicate that human developmental enhancers are highly modular and functionally autonomous and suggest that genomic enhancer shuffling may have contributed to the evolution of complex gene expression patterns in vertebrates« less

Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication

ERIC Educational Resources Information Center

Wolf, Michael Maclean

2009-01-01

Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific…

Combinatorial compatibility as habit-controlling factor in lysozyme crystallization I. Monomeric and tetrameric F faces derived graph-theoretically

NASA Astrophysics Data System (ADS)

Strom, C. S.; Bennema, P.

1997-03-01

A series of two articles discusses possible morphological evidence for oligomerization of growth units in the crystallization of tetragonal lysozyme, based on a rigorous graph-theoretic derivation of the F faces. In the first study (Part I), the growth layers are derived as valid networks satisfying the conditions of F slices in the context of the PBC theory using the graph-theoretic method implemented in program FFACE [C.S. Strom, Z. Krist. 172 (1985) 11]. The analysis is performed in monomeric and alternative tetrameric and octameric formulations of the unit cell, assuming tetramer formation according to the strongest bonds. F (flat) slices with thickness Rdhkl ( {1}/{2} < R ≤ 1 ) are predicted theoretically in the forms 1 1 0, 0 1 1, 1 1 1. The relevant energies are established in the broken bond model. The relation between possible oligomeric specifications of the unit cell and combinatorially feasible F slice compositions in these orientations is explored.

Proteomic analysis of sweet algerian apricot kernels (Prunus armeniaca L.) by combinatorial peptide ligand libraries and LC-MS/MS.

PubMed

Ghorab, Hamida; Lammi, Carmen; Arnoldi, Anna; Kabouche, Zahia; Aiello, Gilda

2018-01-15

An investigation on the proteome of the sweet kernel of apricot, based on equalisation with combinatorial peptide ligand libraries (CPLLs), SDS-PAGE, nLC-ESI-MS/MS, and database search, permitted identifying 175 proteins. Gene ontology analysis indicated that their main molecular functions are in nucleotide binding (20.9%), hydrolase activities (10.6%), kinase activities (7%), and catalytic activity (5.6%). A protein-protein association network analysis using STRING software permitted to build an interactomic map of all detected proteins, characterised by 34 interactions. In order to forecast the potential health benefits deriving from the consumption of these proteins, the two most abundant, i.e. Prunin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively. Searching their sequences in the database BIOPEP, it was possible to suggest a variety of bioactivities, including dipeptidyl peptidase-IV (DPP-IV) and angiotensin converting enzyme I (ACE) inhibition, glucose uptake stimulation and antioxidant properties. Copyright © 2017 Elsevier Ltd. All rights reserved.

Portrait of Candida Species Biofilm Regulatory Network Genes.

PubMed

Araújo, Daniela; Henriques, Mariana; Silva, Sónia

2017-01-01

Most cases of candidiasis have been attributed to Candida albicans, but Candida glabrata, Candida parapsilosis and Candida tropicalis, designated as non-C. albicans Candida (NCAC), have been identified as frequent human pathogens. Moreover, Candida biofilms are an escalating clinical problem associated with significant rates of mortality. Biofilms have distinct developmental phases, including adhesion/colonisation, maturation and dispersal, controlled by complex regulatory networks. This review discusses recent advances regarding Candida species biofilm regulatory network genes, which are key components for candidiasis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Homophyly/Kinship Model: Naturally Evolving Networks

NASA Astrophysics Data System (ADS)

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-10-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

Homophyly/Kinship Model: Naturally Evolving Networks

PubMed Central

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-01-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network. PMID:26478264

Combinatorial structures to modeling simple games and applications

NASA Astrophysics Data System (ADS)

Molinero, Xavier

2017-09-01

We connect three different topics: combinatorial structures, game theory and chemistry. In particular, we establish the bases to represent some simple games, defined as influence games, and molecules, defined from atoms, by using combinatorial structures. First, we characterize simple games as influence games using influence graphs. It let us to modeling simple games as combinatorial structures (from the viewpoint of structures or graphs). Second, we formally define molecules as combinations of atoms. It let us to modeling molecules as combinatorial structures (from the viewpoint of combinations). It is open to generate such combinatorial structures using some specific techniques as genetic algorithms, (meta-)heuristics algorithms and parallel programming, among others.

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Coordinated Platoon Routing in a Metropolitan Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larson, Jeffrey; Munson, Todd; Sokolov, Vadim

2016-10-10

Platooning vehicles—connected and automated vehicles traveling with small intervehicle distances—use less fuel because of reduced aerodynamic drag. Given a network de- fined by vertex and edge sets and a set of vehicles with origin/destination nodes/times, we model and solve the combinatorial optimization problem of coordinated routing of vehicles in a manner that routes them to their destination on time while using the least amount of fuel. Common approaches decompose the platoon coordination and vehicle routing into separate problems. Our model addresses both problems simultaneously to obtain the best solution. We use modern modeling techniques and constraints implied from analyzing themore » platoon routing problem to address larger numbers of vehicles and larger networks than previously considered. While the numerical method used is unable to certify optimality for candidate solutions to all networks and parameters considered, we obtain excellent solutions in approximately one minute for much larger networks and vehicle sets than previously considered in the literature.« less

Investigation of the multifunctional gene AOP3 expands the regulatory network fine-tuning glucosinolate production in Arabidopsis

PubMed Central

Jensen, Lea M.; Kliebenstein, Daniel J.; Burow, Meike

2015-01-01

Quantitative trait loci (QTL) mapping studies enable identification of loci that are part of regulatory networks controlling various phenotypes. Detailed investigations of genes within these loci are required to ultimately understand the function of individual genes and how they interact with other players in the network. In this study, we use transgenic plants in combination with natural variation to investigate the regulatory role of the AOP3 gene found in GS-AOP locus previously suggested to contribute to the regulation of glucosinolate defense compounds. Phenotypic analysis and QTL mapping in F2 populations with different AOP3 transgenes support that the enzymatic function and the AOP3 RNA both play a significant role in controlling glucosinolate accumulation. Furthermore, we find different loci interacting with either the enzymatic activity or the RNA of AOP3 and thereby extend the regulatory network controlling glucosinolate accumulation. PMID:26442075

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

PubMed

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

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

PubMed

Zhou, Xionghui; Liu, Juan

2014-01-01

Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for phenotypic change.

Seeking Global Minima

NASA Astrophysics Data System (ADS)

Tajuddin, Wan Ahmad

1994-02-01

Ease in finding the configuration at the global energy minimum in a symmetric neural network is important for combinatorial optimization problems. We carry out a comprehensive survey of available strategies for seeking global minima by comparing their performances in the binary representation problem. We recall our previous comparison of steepest descent with analog dynamics, genetic hill-climbing, simulated diffusion, simulated annealing, threshold accepting and simulated tunneling. To this, we add comparisons to other strategies including taboo search and one with field-ordered updating.

Unbiased Combinatorial Genomic Approaches to Identify Alternative Therapeutic Targets within the TSC Signaling Network

DTIC Science & Technology

2015-09-01

assessed the specificity of mutation in Drosophila S2R+ cells. We generated a quantitative mutation reporter vector in which an sgRNA target sequence ...phosphatases (563 genes) in the Drosophila genome (Figure 4). 65 samples that displayed synthetic lethality (15 genes) or synthetic increases in viability...targeting all kinases and phosphatases (563 genes) in the Drosophila genome . . Identified three hits (mRNA-Cap, Pitslre and CycT) that scored as

Seamless Integration of Detection and Therapy for Breast Cancer using Targeted Engineered Nanoparticles

DTIC Science & Technology

2006-06-01

Rangel, R.; Sun, Y.; Zaoui, K.; Schmidt, M.; Kalle, C. v.; Weitzman, M. D.; Gelovani, J. G.; Pasqualini , R.; Arap, W., A Hybrid Vector for Ligand...A.; Holbeck, S. L.; Sausville, E. A.; Arap, 7 W.; Pasqualini , R., Ligand-Directed Surface Profiling of Human Cancer Cells with Combinatorial...Ozawa, M. G.; Snyder, E. Y.; Sidman, R. L.; Miller, J. H.; Arap, W.; Pasqualini , R., Networks of gold nanoparticles and bacteriophage as biological

Combinatorial semantics strengthens angular-anterior temporal coupling.

PubMed

Molinaro, Nicola; Paz-Alonso, Pedro M; Duñabeitia, Jon Andoni; Carreiras, Manuel

2015-04-01

The human semantic combinatorial system allows us to create a wide number of new meanings from a finite number of existing representations. The present study investigates the neural dynamics underlying the semantic processing of different conceptual constructions based on predictions from previous neuroanatomical models of the semantic processing network. In two experiments, participants read sentences for comprehension containing noun-adjective pairs in three different conditions: prototypical (Redundant), nonsense (Anomalous) and low-typical but composable (Contrastive). In Experiment 1 we examined the processing costs associated to reading these sentences and found a processing dissociation between Anomalous and Contrastive word pairs, compared to prototypical (Redundant) stimuli. In Experiment 2, functional connectivity results showed strong co-activation across conditions between inferior frontal gyrus (IFG) and posterior middle temporal gyrus (MTG), as well as between these two regions and middle frontal gyrus (MFG), anterior temporal cortex (ATC) and fusiform gyrus (FG), consistent with previous neuroanatomical models. Importantly, processing of low-typical (but composable) meanings relative to prototypical and anomalous constructions was associated with a stronger positive coupling between ATC and angular gyrus (AG). Our results underscore the critical role of IFG-MTG co-activation during semantic processing and how other relevant nodes within the semantic processing network come into play to handle visual-orthographic information, to maintain multiple lexical-semantic representations in working memory and to combine existing representations while creatively constructing meaning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Receptors rather than signals change in expression in four physiological regulatory networks during evolutionary divergence in threespine stickleback.

PubMed

Di Poi, Carole; Bélanger, Dominic; Amyot, Marc; Rogers, Sean; Aubin-Horth, Nadia

2016-07-01

The molecular mechanisms underlying behavioural evolution following colonization of novel environments are largely unknown. Molecules that interact to control equilibrium within an organism form physiological regulatory networks. It is essential to determine whether particular components of physiological regulatory networks evolve or if the network as a whole is affected in populations diverging in behavioural responses, as this may affect the nature, amplitude and number of impacted traits. We studied the regulation of four physiological regulatory networks in freshwater and marine populations of threespine stickleback raised in a common environment, which were previously characterized as showing evolutionary divergence in behaviour and stress reactivity. We measured nineteen components of these networks (ligands and receptors) using mRNA and monoamine levels in the brain, pituitary and interrenal gland, as well as hormone levels. Freshwater fish showed higher expression in the brain of adrenergic (adrb2a), serotonergic (htr2a) and dopaminergic (DRD2) receptors, but lower expression of the htr2b receptor. Freshwater fish also showed higher expression of the mc2r receptor of the glucocorticoid axis in the interrenals. Collectively, our results suggest that the inheritance of the regulation of these networks may be implicated in the evolution of behaviour and stress reactivity in association with population divergence. Our results also suggest that evolutionary change in freshwater threespine stickleback may be more associated with the expression of specific receptors rather than with global changes of all the measured constituents of the physiological regulatory networks. © 2016 John Wiley & Sons Ltd.

Identification of critical regulatory genes in cancer signaling network using controllability analysis

NASA Astrophysics Data System (ADS)

Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

2017-05-01

Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

Noncoding somatic and inherited single-nucleotide variants converge to promote ESR1 expression in breast cancer.

PubMed

Bailey, Swneke D; Desai, Kinjal; Kron, Ken J; Mazrooei, Parisa; Sinnott-Armstrong, Nicholas A; Treloar, Aislinn E; Dowar, Mark; Thu, Kelsie L; Cescon, David W; Silvester, Jennifer; Yang, S Y Cindy; Wu, Xue; Pezo, Rossanna C; Haibe-Kains, Benjamin; Mak, Tak W; Bedard, Philippe L; Pugh, Trevor J; Sallari, Richard C; Lupien, Mathieu

2016-10-01

Sustained expression of the estrogen receptor-α (ESR1) drives two-thirds of breast cancer and defines the ESR1-positive subtype. ESR1 engages enhancers upon estrogen stimulation to establish an oncogenic expression program. Somatic copy number alterations involving the ESR1 gene occur in approximately 1% of ESR1-positive breast cancers, suggesting that other mechanisms underlie the persistent expression of ESR1. We report significant enrichment of somatic mutations within the set of regulatory elements (SRE) regulating ESR1 in 7% of ESR1-positive breast cancers. These mutations regulate ESR1 expression by modulating transcription factor binding to the DNA. The SRE includes a recurrently mutated enhancer whose activity is also affected by rs9383590, a functional inherited single-nucleotide variant (SNV) that accounts for several breast cancer risk-associated loci. Our work highlights the importance of considering the combinatorial activity of regulatory elements as a single unit to delineate the impact of noncoding genetic alterations on single genes in cancer.

The Global Regulatory Architecture of Transcription during the Caulobacter Cell Cycle

PubMed Central

Zhou, Bo; Schrader, Jared M.; Kalogeraki, Virginia S.; Abeliuk, Eduardo; Dinh, Cong B.; Pham, James Q.; Cui, Zhongying Z.; Dill, David L.; McAdams, Harley H.; Shapiro, Lucy

2015-01-01

Each Caulobacter cell cycle involves differentiation and an asymmetric cell division driven by a cyclical regulatory circuit comprised of four transcription factors (TFs) and a DNA methyltransferase. Using a modified global 5′ RACE protocol, we globally mapped transcription start sites (TSSs) at base-pair resolution, measured their transcription levels at multiple times in the cell cycle, and identified their transcription factor binding sites. Out of 2726 TSSs, 586 were shown to be cell cycle-regulated and we identified 529 binding sites for the cell cycle master regulators. Twenty-three percent of the cell cycle-regulated promoters were found to be under the combinatorial control of two or more of the global regulators. Previously unknown features of the core cell cycle circuit were identified, including 107 antisense TSSs which exhibit cell cycle-control, and 241 genes with multiple TSSs whose transcription levels often exhibited different cell cycle timing. Cumulatively, this study uncovered novel new layers of transcriptional regulation mediating the bacterial cell cycle. PMID:25569173

Distal Limb Patterning Requires Modulation of cis-Regulatory Activities by HOX13

DOE PAGES

Sheth, Rushikesh; Barozzi, Iros; Langlais, David; ...

2016-12-13

The combinatorial expression of Hox genes along the body axes is a major determinant of cell fate and plays a pivotal role in generating the animal body plan. Loss of HOXA13 and HOXD13 transcription factors (HOX13) leads to digit agenesis in mice, but how HOX13 proteins regulate transcriptional outcomes and confer identity to the distal-most limb cells has remained elusive. Here, we report on the genome-wide profiling of HOXA13 and HOXD13 in vivo binding and changes of the transcriptome and chromatin state in the transition from the early to the late-distal limb developmental program, as well as in Hoxa13–/–; Hoxd13–/– limbs. Ourmore » results show that proper termination of the early limb transcriptional program and activation of the late-distal limb program are coordinated by the dual action of HOX13 on cis-regulatory modules.« less

Functional Characterization of Schizophrenia-Associated Variation in CACNA1C

PubMed Central

Eckart, Nicole; Song, Qifeng; Yang, Rebecca; Wang, Ruihua; Zhu, Heng; McCallion, Andrew S.; Avramopoulos, Dimitrios

2016-01-01

Calcium channel subunits, including CACNA1C, have been associated with multiple psychiatric disorders. Specifically, genome wide association studies (GWAS) have repeatedly identified the single nucleotide polymorphism (SNP) rs1006737 in intron 3 of CACNA1C to be strongly associated with schizophrenia and bipolar disorder. Here, we show that rs1006737 marks a quantitative trait locus for CACNA1C transcript levels. We test 16 SNPs in high linkage disequilibrium with rs1007637 and find one, rs4765905, consistently showing allele-dependent regulatory function in reporter assays. We find allele-specific protein binding for 13 SNPs including rs4765905. Using protein microarrays, we identify several proteins binding ≥3 SNPs, but not control sequences, suggesting possible functional interactions and combinatorial haplotype effects. Finally, using circular chromatin conformation capture, we show interaction of the disease-associated region including the 16 SNPs with the CACNA1C promoter and other potential regulatory regions. Our results elucidate the pathogenic relevance of one of the best-supported risk loci for schizophrenia and bipolar disorder. PMID:27276213

The global regulatory architecture of transcription during the Caulobacter cell cycle.

PubMed

Zhou, Bo; Schrader, Jared M; Kalogeraki, Virginia S; Abeliuk, Eduardo; Dinh, Cong B; Pham, James Q; Cui, Zhongying Z; Dill, David L; McAdams, Harley H; Shapiro, Lucy

2015-01-01

Each Caulobacter cell cycle involves differentiation and an asymmetric cell division driven by a cyclical regulatory circuit comprised of four transcription factors (TFs) and a DNA methyltransferase. Using a modified global 5' RACE protocol, we globally mapped transcription start sites (TSSs) at base-pair resolution, measured their transcription levels at multiple times in the cell cycle, and identified their transcription factor binding sites. Out of 2726 TSSs, 586 were shown to be cell cycle-regulated and we identified 529 binding sites for the cell cycle master regulators. Twenty-three percent of the cell cycle-regulated promoters were found to be under the combinatorial control of two or more of the global regulators. Previously unknown features of the core cell cycle circuit were identified, including 107 antisense TSSs which exhibit cell cycle-control, and 241 genes with multiple TSSs whose transcription levels often exhibited different cell cycle timing. Cumulatively, this study uncovered novel new layers of transcriptional regulation mediating the bacterial cell cycle.

The G-Box Transcriptional Regulatory Code in Arabidopsis1[OPEN

PubMed Central

Shepherd, Samuel J.K.; Brestovitsky, Anna; Dickinson, Patrick; Biswas, Surojit

2017-01-01

Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations. PMID:28864470

Empirical Bayes conditional independence graphs for regulatory network recovery.

PubMed

Mahdi, Rami; Madduri, Abishek S; Wang, Guoqing; Strulovici-Barel, Yael; Salit, Jacqueline; Hackett, Neil R; Crystal, Ronald G; Mezey, Jason G

2012-08-01

Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. ramimahdi@yahoo.com or jgm45@cornell.edu Supplementary data are available at Bioinformatics online.

Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems

DOEpatents

Van Benthem, Mark H.; Keenan, Michael R.

2008-11-11

A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.

Genomic analysis of the hierarchical structure of regulatory networks

PubMed Central

Yu, Haiyuan; Gerstein, Mark

2006-01-01

A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors.

PubMed

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

2013-06-01

A common approach to the modeling of gene regulatory networks is to represent activating or repressing interactions using ordinary differential equations for target gene concentrations that include Hill function dependences on regulator gene concentrations. An alternative formulation represents the same interactions using Boolean logic with time delays associated with each network link. We consider the attractors that emerge from the two types of models in the case of a simple but nontrivial network: a figure-8 network with one positive and one negative feedback loop. We show that the different modeling approaches give rise to the same qualitative set of attractors with the exception of a possible fixed point in the ordinary differential equation model in which concentrations sit at intermediate values. The properties of the attractors are most easily understood from the Boolean perspective, suggesting that time-delay Boolean modeling is a useful tool for understanding the logic of regulatory networks.

A transcriptional dynamic network during Arabidopsis thaliana pollen development.

PubMed

Wang, Jigang; Qiu, Xiaojie; Li, Yuhua; Deng, Youping; Shi, Tieliu

2011-01-01

To understand transcriptional regulatory networks (TRNs), especially the coordinated dynamic regulation between transcription factors (TFs) and their corresponding target genes during development, computational approaches would represent significant advances in the genome-wide expression analysis. The major challenges for the experiments include monitoring the time-specific TFs' activities and identifying the dynamic regulatory relationships between TFs and their target genes, both of which are currently not yet available at the large scale. However, various methods have been proposed to computationally estimate those activities and regulations. During the past decade, significant progresses have been made towards understanding pollen development at each development stage under the molecular level, yet the regulatory mechanisms that control the dynamic pollen development processes remain largely unknown. Here, we adopt Networks Component Analysis (NCA) to identify TF activities over time course, and infer their regulatory relationships based on the coexpression of TFs and their target genes during pollen development. We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube). We constructed a regulatory network, including 19 TFs, 101 target genes and 319 regulatory interactions. The computationally estimated TF activities were well correlated to their coordinated genes' expressions during the development process. We clustered the expression of their target genes in the context of regulatory influences, and inferred new regulatory relationships between those TFs and their target genes, such as transcription factor WRKY34, which was identified that specifically expressed in pollen, and regulated several new target genes. Our finding facilitates the interpretation of the expression patterns with more biological relevancy, since the clusters corresponding to the activity of specific TF or the combination of TFs suggest the coordinated regulation of TFs to their target genes. Through integrating different resources, we constructed a dynamic regulatory network of Arabidopsis thaliana during pollen development with gene coexpression and NCA. The network illustrated the relationships between the TFs' activities and their target genes' expression, as well as the interactions between TFs, which provide new insight into the molecular mechanisms that control the pollen development.

MARCC (Matrix-Assisted Reader Chromatin Capture): an antibody-free method to enrich and analyze combinatorial nucleosome modifications

PubMed Central

Su, Zhangli

2016-01-01

Combinatorial patterns of histone modifications are key indicators of different chromatin states. Most of the current approaches rely on the usage of antibodies to analyze combinatorial histone modifications. Here we detail an antibody-free method named MARCC (Matrix-Assisted Reader Chromatin Capture) to enrich combinatorial histone modifications. The combinatorial patterns are enriched on native nucleosomes extracted from cultured mammalian cells and prepared by micrococcal nuclease digestion. Such enrichment is achieved by recombinant chromatin-interacting protein modules, or so-called reader domains, which can bind in a combinatorial modification-dependent manner. The enriched chromatin can be quantified by western blotting or mass spectrometry for the co-existence of histone modifications, while the associated DNA content can be analyzed by qPCR or next-generation sequencing. Altogether, MARCC provides a reproducible, efficient and customizable solution to enrich and analyze combinatorial histone modifications. PMID:26131849

A New Approach for Proving or Generating Combinatorial Identities

ERIC Educational Resources Information Center

Gonzalez, Luis

2010-01-01

A new method for proving, in an immediate way, many combinatorial identities is presented. The method is based on a simple recursive combinatorial formula involving n + 1 arbitrary real parameters. Moreover, this formula enables one not only to prove, but also generate many different combinatorial identities (not being required to know them "a…

A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

PubMed Central

Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

2016-01-01

Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Combinatorial techniques to efficiently investigate and optimize organic thin film processing and properties.

PubMed

Wieberger, Florian; Kolb, Tristan; Neuber, Christian; Ober, Christopher K; Schmidt, Hans-Werner

2013-04-08

In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

PubMed

Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

2017-12-28

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

PubMed Central

2011-01-01

Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis. PMID:21375730

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

PubMed Central

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

2013-01-01

Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis. Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/ Contact: wangx@ee.columbia.edu or mssamoilov@lbl.gov Supplementary information: Supplementary data are available at Bioinformatics online PMID:23271269

Phenotypic stability and plasticity in GMP-derived cells as determined by their underlying regulatory network.

PubMed

Ramírez, Carlos; Mendoza, Luis

2018-04-01

Blood cell formation has been recognized as a suitable system to study celular differentiation mainly because of its experimental accessibility, and because it shows characteristics such as hierarchical and gradual bifurcated patterns of commitment, which are present in several developmental processes. Although hematopoiesis has been extensively studied and there is a wealth of molecular and cellular data about it, it is not clear how the underlying molecular regulatory networks define or restrict cellular differentiation processes. Here, we infer the molecular regulatory network that controls the differentiation of a blood cell subpopulation derived from the granulocyte-monocyte precursor (GMP), comprising monocytes, neutrophils, eosinophils, basophils and mast cells. We integrate published qualitative experimental data into a model to describe temporal expression patterns observed in GMP-derived cells. The model is implemented as a Boolean network, and its dynamical behavior is studied. Steady states of the network can be clearly identified with the expression profiles of monocytes, mast cells, neutrophils, basophils, and eosinophils, under wild-type and mutant backgrounds. All scripts are publicly available at https://github.com/caramirezal/RegulatoryNetworkGMPModel. lmendoza@biomedicas.unam.mx. Supplementary data are available at Bioinformatics online.

A statistical method for measuring activation of gene regulatory networks.

PubMed

Esteves, Gustavo H; Reis, Luiz F L

2018-06-13

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks. We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material. This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.

The Reconstruction and Analysis of Gene Regulatory Networks.

PubMed

Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Optimal Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2012-02-01

A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.

A Systems' Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

PubMed Central

Kunz, Manfred; Vera, Julio; Wolkenhauer, Olaf

2013-01-01

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts. PMID:24350286

Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii

DOE PAGES

Gargouri, Mahmoud; Park, Jeong -Jin; Holguin, F. Omar; ...

2015-05-28

Microalgae-based biofuels are promising sources of alternative energy, but improvements throughout the production process are required to establish them as economically feasible. One of the most influential improvements would be a significant increase in lipid yields, which could be achieved by altering the regulation of lipid biosynthesis and accumulation. Chlamydomonas reinhardtii accumulates oil (triacylglycerols, TAG) in response to nitrogen (N) deprivation. Although a few important regulatory genes have been identified that are involved in controlling this process, a global understanding of the larger regulatory network has not been developed. In order to uncover this network in this species, a combinedmore » omics (transcriptomic, proteomic and metabolomic) analysis was applied to cells grown in a time course experiment after a shift from N-replete to N-depleted conditions. Changes in transcript and protein levels of 414 predicted transcription factors (TFs) and transcriptional regulators (TRs) were monitored relative to other genes. The TF and TR genes were thus classified by two separate measures: up-regulated versus down-regulated and early response versus late response relative to two phases of polar lipid synthesis (before and after TAG biosynthesis initiation). Lipidomic and primary metabolite profiling generated compound accumulation levels that were integrated with the transcript dataset and TF profiling to produce a transcriptional regulatory network. In conclusion, evaluation of this proposed regulatory network led to the identification of several regulatory hubs that control many aspects of cellular metabolism, from N assimilation and metabolism, to central metabolism, photosynthesis and lipid metabolism.« less

Heterodimers of Retinoic Acid Receptors and Thyroid Hormone Receptors Display Unique Combinatorial Regulatory Properties

PubMed Central

Lee, Sangho; Privalsky, Martin L.

2009-01-01

Nuclear receptors are ligand-regulated transcription factors that regulate key aspects of metazoan development, differentiation, and homeostasis. Nuclear receptors recognize target genes by binding to specific DNA recognition sequences, denoted hormone response elements (HREs). Many nuclear receptors can recognize HREs as either homodimers or heterodimers. Retinoid X receptors (RXRs), in particular, serve as important heterodimer partners for many other nuclear receptors, including thyroid hormone receptors (TRs), and RXR/TR heterodimers have been proposed to be the primary mediators of target gene regulation by T3 hormone. Here, we report that the retinoic acid receptors (RARs), a distinct class of nuclear receptors, are also efficient heterodimer partners for TRs. These RAR/TR heterodimers form with similar affinities as RXR/TR heterodimers on an assortment of consensus and natural HREs, and preferentially assemble with the RAR partner 5′ of the TR moiety. The corepressor and coactivator recruitment properties of these RAR/TR heterodimers and their transcriptional activities in vivo are distinct from those observed with the corresponding RXR heterodimers. Our studies indicate that RXRs are not unique in their ability to partner with TRs, and that RARs can also serve as robust heterodimer partners and combinatorial regulators of T3-modulated gene expression. PMID:15650024

Genome-wide inference of regulatory networks in Streptomyces coelicolor.

PubMed

Castro-Melchor, Marlene; Charaniya, Salim; Karypis, George; Takano, Eriko; Hu, Wei-Shou

2010-10-18

The onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses. In this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements. Despite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.

Unbiased Combinatorial Genomic Approaches to Identify Alternative Therapeutic Targets within the TSC Signaling Network

DTIC Science & Technology

2013-06-01

number of ways to generate either random mutations or specific alterations to the genome sequence . Unlike previous approaches however, both TALENs and...made to the donor construct will be incorporated into the endogenous genomic sequence (examples in Liu et al., 2012; Zu et al., 2013). One challenge... Drosophila with the CRISPR RNA-guided Cas9 nuclease. Genetics. 2013. Hwang WY, Fu Y, Reyon D, Maeder ML, Tsai SQ, Sander JD, et al. Efficient genome

On k-ary n-cubes: Theory and applications

NASA Technical Reports Server (NTRS)

Mao, Weizhen; Nicol, David M.

1994-01-01

Many parallel processing networks can be viewed as graphs called k-ary n-cubes, whose special cases include rings, hypercubes and toruses. In this paper, combinatorial properties of k-ary n-cubes are explored. In particular, the problem of characterizing the subgraph of a given number of nodes with the maximum edge count is studied. These theoretical results are then used to compute a lower bounding function in branch-and-bound partitioning algorithms and to establish the optimality of some irregular partitions.

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso; Elena, Santiago F

2009-01-01

Background Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes. Results We show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures. Conclusions These data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote. PMID:19754933

Intrinsic noise and deviations from criticality in Boolean gene-regulatory networks

NASA Astrophysics Data System (ADS)

Villegas, Pablo; Ruiz-Franco, José; Hidalgo, Jorge; Muñoz, Miguel A.

2016-10-01

Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent tradeoff between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way -even for asynchronous updating rules- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.

Hodge Decomposition of Information Flow on Small-World Networks.

PubMed

Haruna, Taichi; Fujiki, Yuuya

2016-01-01

We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.

A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.

PubMed

Gregoretti, Francesco; Belcastro, Vincenzo; di Bernardo, Diego; Oliva, Gennaro

2010-04-21

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

Sequence-based model of gap gene regulatory network.

PubMed

Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

2014-01-01

The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays.

cDREM: inferring dynamic combinatorial gene regulation.

PubMed

Wise, Aaron; Bar-Joseph, Ziv

2015-04-01

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.

Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Acquaah-Mensah, George K.; Taylor, Ronald C.

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means. The Allen Brain Atlas in situ hybridization (ISH) data sets (Jones et al., 2009) represent a potentially valuable alternative source of high-throughput brain region-specific gene expression data for such purposes. In this study, Allen BrainAtlasmouse ISH data in the hippocampal fields were extracted, focusing on 508 genesmore » relevant to neurodegeneration. Transcriptional regulatory networkswere learned using three high-performing network inference algorithms. Only 17% of regulatory edges from a network reverse-engineered based on brain region-specific ISH data were also found in a network constructed upon gene expression correlations inmousewhole brain microarrays, thus showing the specificity of gene expression within brain sub-regions. Furthermore, the ISH data-based networks were used to identify instructive transcriptional regulatory relationships. Ncor2, Sp3 and Usf2 form a unique three-party regulatory motif, potentially affecting memory formation pathways. Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, andSyn2). Further, Nfe2l1, Egr1 and Usf2 are sensitive to dietary factors and could be among links between dietary influences and genes in the AD etiology. Thus, this approach of harnessing brain region-specific ISH data represents a rare opportunity for gleaning unique etiological insights for diseases such as AD.« less

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

PubMed

Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

2013-09-22

High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

Biosynthesis and Heterologous Production of Epothilones

NASA Astrophysics Data System (ADS)

Müller, Rolf

Although a variety of chemical syntheses for the epothilones and various derivatives have been described, modifying the backbone of those natural products remains a major challenge. One alternative to chemical alteration is the elucidation and subsequent manipulation of the biosynthetic pathway via genetic engineering in the producing organism. This type of approach is known as “combinatorial biosynthesis” and holds great promise, especially in conjunction with semi-synthesis methods to alter the structure of the natural product. In parallel, production can be optimized in the natural producer if the regulatory mechanisms governing the biosynthesis are understood. Alternatively, the entire gene cluster can be transferred into a heterologous host, more amenable both to genetic alteration and overexpression.

A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer

PubMed Central

Debily, Marie-Anne; Marhomy, Sandrine El; Boulanger, Virginie; Eveno, Eric; Mariage-Samson, Régine; Camarca, Alessandra; Auffray, Charles; Piatier-Tonneau, Dominique; Imbeaud, Sandrine

2009-01-01

Background The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. Principal Findings Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP−] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. Conclusions Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator. PMID:19262752

Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries.

PubMed

Lee, M L; Schneider, G

2001-01-01

Natural products were analyzed to determine whether they contain appealing novel scaffold architectures for potential use in combinatorial chemistry. Ring systems were extracted and clustered on the basis of structural similarity. Several such potential scaffolds for combinatorial chemistry were identified that are not present in current trade drugs. For one of these scaffolds a virtual combinatorial library was generated. Pharmacophoric properties of natural products, trade drugs, and the virtual combinatorial library were assessed using a self-organizing map. Obviously, current trade drugs and natural products have several topological pharmacophore patterns in common. These features can be systematically explored with selected combinatorial libraries based on a combination of natural product-derived and synthetic molecular building blocks.

Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

USDA-ARS?s Scientific Manuscript database

Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

MicroRNA-mediated regulatory circuits: outlook and perspectives

NASA Astrophysics Data System (ADS)

Cora', Davide; Re, Angela; Caselle, Michele; Bussolino, Federico

2017-08-01

MicroRNAs have been found to be necessary for regulating genes implicated in almost all signaling pathways, and consequently their dysfunction influences many diseases, including cancer. Understanding of the complexity of the microRNA-mediated regulatory network has grown in terms of size, connectivity and dynamics with the development of computational and, more recently, experimental high-throughput approaches for microRNA target identification. Newly developed studies on recurrent microRNA-mediated circuits in regulatory networks, also known as network motifs, have substantially contributed to addressing this complexity, and therefore to helping understand the ways by which microRNAs achieve their regulatory role. This review provides a summarizing view of the state-of-the-art, and perspectives of research efforts on microRNA-mediated regulatory motifs. In this review, we discuss the topological properties characterizing different types of circuits, and the regulatory features theoretically enabled by such properties, with a special emphasis on examples of circuits typifying their biological significance in experimentally validated contexts. Finally, we will consider possible future developments, in particular regarding microRNA-mediated circuits involving long non-coding RNAs and epigenetic regulators.

Use of combinatorial chemistry to speed drug discovery.

PubMed

Rádl, S

1998-10-01

IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.

Integration of heterogeneous molecular networks to unravel gene-regulation in Mycobacterium tuberculosis.

PubMed

van Dam, Jesse C J; Schaap, Peter J; Martins dos Santos, Vitor A P; Suárez-Diez, María

2014-09-26

Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers. Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different networks. By simultaneously exploring these networks and metadata, we gained insights into regulatory mechanisms in M. tuberculosis that could not be obtained through the separate analysis of each data type.

RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria.

PubMed

Novichkov, Pavel S; Kazakov, Alexey E; Ravcheev, Dmitry A; Leyn, Semen A; Kovaleva, Galina Y; Sutormin, Roman A; Kazanov, Marat D; Riehl, William; Arkin, Adam P; Dubchak, Inna; Rodionov, Dmitry A

2013-11-01

Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise (http://regprecise.lbl.gov) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in bacterial genomes. Analytical capabilities include exploration of: regulon content, structure and function; TF binding site motifs; conservation and variations in genome-wide regulatory networks across all taxonomic groups of Bacteria. RegPrecise 3.0 was selected as a core resource on transcriptional regulation of the Department of Energy Systems Biology Knowledgebase, an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses, and model interactions in microbes, plants, and their communities.

A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles

PubMed Central

Stojnic, Robert; Fu, Audrey Qiuyan; Adryan, Boris

2012-01-01

Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations. Overlaps in TF binding profiles may also result from measurements taken at closely related time intervals. It is thus of great interest to distinguish TFs that directly regulate gene expression from those that are indirectly associated with gene expression. Graphical models, in particular Bayesian networks, provide a powerful mathematical framework to infer different types of dependencies. However, existing methods do not perform well when the features (here: TF binding profiles) are highly correlated, when their association with the biological outcome is weak, and when the sample size is small. Here, we develop a novel computational method, the Neighbourhood Consistent PC (NCPC) algorithms, which deal with these scenarios much more effectively than existing methods do. We further present a novel graphical representation, the Direct Dependence Graph (DDGraph), to better display the complex interactions among variables. NCPC and DDGraph can also be applied to other problems involving highly correlated biological features. Both methods are implemented in the R package ddgraph, available as part of Bioconductor (http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html). Applied to real data, our method identified TFs that specify different classes of cis-regulatory modules (CRMs) in Drosophila mesoderm differentiation. Our analysis also found depletion of the early transcription factor Twist binding at the CRMs regulating expression in visceral and somatic muscle cells at later stages, which suggests a CRM-specific repression mechanism that so far has not been characterised for this class of mesodermal CRMs. PMID:23144600

Origin of hyperbolicity in brain-to-brain coordination networks

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka; Andjelković, Miroslav; Šuvakov, Milovan

2018-02-01

Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are delta-hyperbolic with delta_max=1 and the graph diameter D=3 in each brain. While the emergent hyperbolicity in the two-brain networks satisfies delta_max/D/2 < 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neuronal correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener’s self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

Forecasting PM10 in metropolitan areas: Efficacy of neural networks.

PubMed

Fernando, H J S; Mammarella, M C; Grandoni, G; Fedele, P; Di Marco, R; Dimitrova, R; Hyde, P

2012-04-01

Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis

PubMed Central

Chauhan, Rinki; Ravi, Janani; Datta, Pratik; Chen, Tianlong; Schnappinger, Dirk; Bassler, Kevin E.; Balázsi, Gábor; Gennaro, Maria Laura

2016-01-01

Accessory sigma factors, which reprogram RNA polymerase to transcribe specific gene sets, activate bacterial adaptive responses to noxious environments. Here we reconstruct the complete sigma factor regulatory network of the human pathogen Mycobacterium tuberculosis by an integrated approach. The approach combines identification of direct regulatory interactions between M. tuberculosis sigma factors in an E. coli model system, validation of selected links in M. tuberculosis, and extensive literature review. The resulting network comprises 41 direct interactions among all 13 sigma factors. Analysis of network topology reveals (i) a three-tiered hierarchy initiating at master regulators, (ii) high connectivity and (iii) distinct communities containing multiple sigma factors. These topological features are likely associated with multi-layer signal processing and specialized stress responses involving multiple sigma factors. Moreover, the identification of overrepresented network motifs, such as autoregulation and coregulation of sigma and anti-sigma factor pairs, provides structural information that is relevant for studies of network dynamics. PMID:27029515

Applying gene regulatory network logic to the evolution of social behavior.

PubMed

Baran, Nicole M; McGrath, Patrick T; Streelman, J Todd

2017-06-06

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.

Transcriptional Regulatory Networks in Saccharomyces cerevisiae

NASA Astrophysics Data System (ADS)

Lee, Tong Ihn; Rinaldi, Nicola J.; Robert, François; Odom, Duncan T.; Bar-Joseph, Ziv; Gerber, Georg K.; Hannett, Nancy M.; Harbison, Christopher T.; Thompson, Craig M.; Simon, Itamar; Zeitlinger, Julia; Jennings, Ezra G.; Murray, Heather L.; Gordon, D. Benjamin; Ren, Bing; Wyrick, John J.; Tagne, Jean-Bosco; Volkert, Thomas L.; Fraenkel, Ernest; Gifford, David K.; Young, Richard A.

2002-10-01

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.

Programmable synaptic devices for electronic neural nets

NASA Technical Reports Server (NTRS)

Moopenn, A.; Thakoor, A. P.

1990-01-01

The architecture, design, and operational characteristics of custom VLSI and thin film synaptic devices are described. The devices include CMOS-based synaptic chips containing 1024 reprogrammable synapses with a 6-bit dynamic range, and nonvolatile, write-once, binary synaptic arrays based on memory switching in hydrogenated amorphous silicon films. Their suitability for embodiment of fully parallel and analog neural hardware is discussed. Specifically, a neural network solution to an assignment problem of combinatorial global optimization, implemented in fully parallel hardware using the synaptic chips, is described. The network's ability to provide optimal and near optimal solutions over a time scale of few neuron time constants has been demonstrated and suggests a speedup improvement of several orders of magnitude over conventional search methods.

An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability

PubMed Central

Schütte, Judith; Wang, Huange; Antoniou, Stella; Jarratt, Andrew; Wilson, Nicola K; Riepsaame, Joey; Calero-Nieto, Fernando J; Moignard, Victoria; Basilico, Silvia; Kinston, Sarah J; Hannah, Rebecca L; Chan, Mun Chiang; Nürnberg, Sylvia T; Ouwehand, Willem H; Bonzanni, Nicola; de Bruijn, Marella FTR; Göttgens, Berthold

2016-01-01

Transcription factor (TF) networks determine cell-type identity by establishing and maintaining lineage-specific expression profiles, yet reconstruction of mammalian regulatory network models has been hampered by a lack of comprehensive functional validation of regulatory interactions. Here, we report comprehensive ChIP-Seq, transgenic and reporter gene experimental data that have allowed us to construct an experimentally validated regulatory network model for haematopoietic stem/progenitor cells (HSPCs). Model simulation coupled with subsequent experimental validation using single cell expression profiling revealed potential mechanisms for cell state stabilisation, and also how a leukaemogenic TF fusion protein perturbs key HSPC regulators. The approach presented here should help to improve our understanding of both normal physiological and disease processes. DOI: http://dx.doi.org/10.7554/eLife.11469.001 PMID:26901438

Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations

PubMed Central

Logsdon, Benjamin A.; Mezey, Jason

2010-01-01

Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data. PMID:21152011

Coding and non-coding gene regulatory networks underlie the immune response in liver cirrhosis.

PubMed

Gao, Bo; Zhang, Xueming; Huang, Yongming; Yang, Zhengpeng; Zhang, Yuguo; Zhang, Weihui; Gao, Zu-Hua; Xue, Dongbo

2017-01-01

Liver cirrhosis is recognized as being the consequence of immune-mediated hepatocyte damage and repair processes. However, the regulation of these immune responses underlying liver cirrhosis has not been elucidated. In this study, we used GEO datasets and bioinformatics methods to established coding and non-coding gene regulatory networks including transcription factor-/lncRNA-microRNA-mRNA, and competing endogenous RNA interaction networks. Our results identified 2224 mRNAs, 70 lncRNAs and 46 microRNAs were differentially expressed in liver cirrhosis. The transcription factor -/lncRNA- microRNA-mRNA network we uncovered that results in immune-mediated liver cirrhosis is comprised of 5 core microRNAs (e.g., miR-203; miR-219-5p), 3 transcription factors (i.e., FOXP3, ETS1 and FOS) and 7 lncRNAs (e.g., ENTS00000671336, ENST00000575137). The competing endogenous RNA interaction network we identified includes a complex immune response regulatory subnetwork that controls the entire liver cirrhosis network. Additionally, we found 10 overlapping GO terms shared by both liver cirrhosis and hepatocellular carcinoma including "immune response" as well. Interestingly, the overlapping differentially expressed genes in liver cirrhosis and hepatocellular carcinoma were enriched in immune response-related functional terms. In summary, a complex gene regulatory network underlying immune response processes may play an important role in the development and progression of liver cirrhosis, and its development into hepatocellular carcinoma.

Broad Integration of Expression Maps and Co-Expression Networks Compassing Novel Gene Functions in the Brain

PubMed Central

Okamura-Oho, Yuko; Shimokawa, Kazuro; Nishimura, Masaomi; Takemoto, Satoko; Sato, Akira; Furuichi, Teiichi; Yokota, Hideo

2014-01-01

Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas. PMID:25382412

Consistency of biological networks inferred from microarray and sequencing data.

PubMed

Vinciotti, Veronica; Wit, Ernst C; Jansen, Rick; de Geus, Eco J C N; Penninx, Brenda W J H; Boomsma, Dorret I; 't Hoen, Peter A C

2016-06-24

Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges. Careful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data.

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

PubMed

Schmitt, Michael

2004-09-01

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Networking Omic Data to Envisage Systems Biological Regulation.

PubMed

Kalapanulak, Saowalak; Saithong, Treenut; Thammarongtham, Chinae

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network. In the drive to decipher such a code of lives, a spectrum of technologies has continuously been developed in the post-genomic era. With current advances, high-throughput sequencing technologies are tremendously powerful for facilitating genomics and systems biology studies in the attempt to understand system regulation inside the cells. The ability to explore relevant regulatory components which infer transcriptional and signaling regulation, driving core cellular processes, is thus enhanced. This chapter reviews high-throughput sequencing technologies, including second and third generation sequencing technologies, which support the investigation of genomics and transcriptomics data. Utilization of this high-throughput data to form the virtual network of systems regulation is explained, particularly transcriptional regulatory networks. Analysis of the resulting regulatory networks could lead to an understanding of cellular systems regulation at the mechanistic and dynamics levels. The great contribution of the biological networking approach to envisage systems regulation is finally demonstrated by a broad range of examples.

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development1[C][W][OA

PubMed Central

Mounet, Fabien; Moing, Annick; Garcia, Virginie; Petit, Johann; Maucourt, Michael; Deborde, Catherine; Bernillon, Stéphane; Le Gall, Gwénaëlle; Colquhoun, Ian; Defernez, Marianne; Giraudel, Jean-Luc; Rolin, Dominique; Rothan, Christophe; Lemaire-Chamley, Martine

2009-01-01

Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12–35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality. PMID:19144766

Expression quantitative trait loci and genetic regulatory network analysis reveals that Gabra2 is involved in stress responses in the mouse.

PubMed

Dai, Jiajuan; Wang, Xusheng; Chen, Ying; Wang, Xiaodong; Zhu, Jun; Lu, Lu

2009-11-01

Previous studies have revealed that the subunit alpha 2 (Gabra2) of the gamma-aminobutyric acid receptor plays a critical role in the stress response. However, little is known about the gentetic regulatory network for Gabra2 and the stress response. We combined gene expression microarray analysis and quantitative trait loci (QTL) mapping to characterize the genetic regulatory network for Gabra2 expression in the hippocampus of BXD recombinant inbred (RI) mice. Our analysis found that the expression level of Gabra2 exhibited much variation in the hippocampus across the BXD RI strains and between the parental strains, C57BL/6J, and DBA/2J. Expression QTL (eQTL) mapping showed three microarray probe sets of Gabra2 to have highly significant linkage likelihood ratio statistic (LRS) scores. Gene co-regulatory network analysis showed that 10 genes, including Gria3, Chka, Drd3, Homer1, Grik2, Odz4, Prkag2, Grm5, Gabrb1, and Nlgn1 are directly or indirectly associated with stress responses. Eleven genes were implicated as Gabra2 downstream genes through mapping joint modulation. The genetical genomics approach demonstrates the importance and the potential power of the eQTL studies in identifying genetic regulatory networks that contribute to complex traits, such as stress responses.

Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

PubMed

Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

2017-10-01

The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

Microbatteries for Combinatorial Studies of Conventional Lithium-Ion Batteries

NASA Technical Reports Server (NTRS)

West, William; Whitacre, Jay; Bugga, Ratnakumar

2003-01-01

Integrated arrays of microscopic solid-state batteries have been demonstrated in a continuing effort to develop microscopic sources of power and of voltage reference circuits to be incorporated into low-power integrated circuits. Perhaps even more importantly, arrays of microscopic batteries can be fabricated and tested in combinatorial experiments directed toward optimization and discovery of battery materials. The value of the combinatorial approach to optimization and discovery has been proven in the optoelectronic, pharmaceutical, and bioengineering industries. Depending on the specific application, the combinatorial approach can involve the investigation of hundreds or even thousands of different combinations; hence, it is time-consuming and expensive to attempt to implement the combinatorial approach by building and testing full-size, discrete cells and batteries. The conception of microbattery arrays makes it practical to bring the advantages of the combinatorial approach to the development of batteries.

Improved mine blast algorithm for optimal cost design of water distribution systems

NASA Astrophysics Data System (ADS)

Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon

2015-12-01

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network.

PubMed

Goto, Hayato

2016-02-22

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network

NASA Astrophysics Data System (ADS)

Goto, Hayato

2016-02-01

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.

Constant Communities in Complex Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy; Srinivasan, Sriram; Ganguly, Niloy; Bhowmick, Sanjukta; Mukherjee, Animesh

2013-05-01

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing

NASA Astrophysics Data System (ADS)

Jansen, Peter A.; Watter, Scott

2012-03-01

Connectionist language modelling typically has difficulty with syntactic systematicity, or the ability to generalise language learning to untrained sentences. This work develops an unsupervised connectionist model of infant grammar learning. Following the semantic boostrapping hypothesis, the network distils word category using a developmentally plausible infant-scale database of grounded sensorimotor conceptual representations, as well as a biologically plausible semantic co-occurrence activation function. The network then uses this knowledge to acquire an early benchmark clausal grammar using correlational learning, and further acquires separate conceptual and grammatical category representations. The network displays strongly systematic behaviour indicative of the general acquisition of the combinatorial systematicity present in the grounded infant-scale language stream, outperforms previous contemporary models that contain primarily noun and verb word categories, and successfully generalises broadly to novel untrained sensorimotor grounded sentences composed of unfamiliar nouns and verbs. Limitations as well as implications to later grammar learning are discussed.

Comprehensive analyses of tissue-specific networks with implications to psychiatric diseases

PubMed Central

Lin, Guan Ning; Corominas, Roser; Nam, Hyun-Jun; Urresti, Jorge; Iakoucheva, Lilia M.

2017-01-01

Recent advances in genome sequencing and “omics” technologies are opening new opportunities for improving diagnosis and treatment of human diseases. The precision medicine initiative in particular aims at developing individualized treatment options that take into account individual variability in genes and environment of each person. Systems biology approaches that group genes, transcripts and proteins into functionally meaningful networks will play crucial role in the future of personalized medicine. They will allow comparison of healthy and disease-affected tissues and organs from the same individual, as well as between healthy and disease-afflicted individuals. However, the field faces a multitude of challenges ranging from data integration to statistical and combinatorial issues in data analyses. This chapter describes computational approaches developed by us and the others to tackle challenges in tissue-specific network analyses, with the main focus on psychiatric diseases. PMID:28849569

MINER: exploratory analysis of gene interaction networks by machine learning from expression data.

PubMed

Kadupitige, Sidath Randeni; Leung, Kin Chun; Sellmeier, Julia; Sivieng, Jane; Catchpoole, Daniel R; Bain, Michael E; Gaëta, Bruno A

2009-12-03

The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

A gene regulatory network armature for T-lymphocyte specification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fung, Elizabeth-sharon

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

MIFT: GIFT Combinatorial Geometry Input to VCS Code

DTIC Science & Technology

1977-03-01

r-w w-^ H ^ß0318is CQ BRL °RCUMr REPORT NO. 1967 —-S: ... MIFT: GIFT COMBINATORIAL GEOMETRY INPUT TO VCS CODE Albert E...TITLE (and Subtitle) MIFT: GIFT Combinatorial Geometry Input to VCS Code S. TYPE OF REPORT & PERIOD COVERED FINAL 6. PERFORMING ORG. REPORT NUMBER...Vehicle Code System (VCS) called MORSE was modified to accept the GIFT combinatorial geometry package. GIFT , as opposed to the geometry package

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

PubMed Central

Haubrock, Martin; Hartmann, Fabian; Wingender, Edgar

2016-01-01

ChIP-seq experiments detect the chromatin occupancy of known transcription factors in a genome-wide fashion. The comparisons of several species-specific ChIP-seq libraries done for different transcription factors have revealed a complex combinatorial and context-specific co-localization behavior for the identified binding regions. In this study we have investigated human derived ChIP-seq data to identify common cis-regulatory principles for the human transcription factor c-Fos. We found that in four different cell lines, c-Fos targeted proximal and distal genomic intervals show prevalences for either AP-1 motifs or CCAAT boxes as known binding motifs for the transcription factor NF-Y, and thereby act in a mutually exclusive manner. For proximal regions of co-localized c-Fos and NF-YB binding, we gathered evidence that a characteristic configuration of repeating CCAAT motifs may be responsible for attracting c-Fos, probably provided by a nearby AP-1 bound enhancer. Our results suggest a novel regulatory function of NF-Y in gene-proximal regions. Specific CCAAT dimer repeats bound by the transcription factor NF-Y define this novel cis-regulatory module. Based on this behavior we propose a new enhancer promoter interaction model based on AP-1 motif defined enhancers which interact with CCAAT-box characterized promoter regions. PMID:27517874

Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle

USDA-ARS?s Scientific Manuscript database

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e., hypothal...

Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum

USDA-ARS?s Scientific Manuscript database

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

Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

PubMed Central

Wang, Jianxin; Chen, Bo; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran-Son-Tay, Roger; Berceli, Scott A.; Wu, Rongling

2013-01-01

The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments. PMID:23470995

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data.

PubMed

Modrák, Martin; Vohradský, Jiří

2018-04-13

Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

Pluripotency and lineages in the mammalian blastocyst: an evolutionary view.

PubMed

Cañon, Susana; Fernandez-Tresguerres, Beatriz; Manzanares, Miguel

2011-06-01

Early mammalian development is characterized by a highly specific stage, the blastocyst, by which embryonic and extraembryonic lineages have been determined, but pattern formation has not yet begun. The blastocyst is also of interest because cell precursors of the embryo proper retain for a certain time the capability to generate all the cell types of the adult animal. This embryonic pluripotency is established and maintained by a regulatory network under the control of a small set of transcription factors, comprising Oct4, Sox2 and Nanog. This network is largely conserved in eutherian mammals, but there is scarce information about how it arose in vertebrates. We have analysed the conservation of gene regulatory networks controlling blastocyst lineages and pluripotency in the mouse by comparison with the chick. We found that few of elements of the network are novel to mammals; rather, most of them were present before the separation of the mammalian lineage from other amniotes, but acquired novel expression domains during early mammalian development. Our results strongly support the hypothesis that mammalian blastocyst regulatory networks evolved through rewiring of pre-existing components, involving the co-option and duplication of existing genes and the establishment of new regulatory interactions among them.

Systems and methods for the combinatorial synthesis of novel materials

DOEpatents

Wu, Xin Di; Wang, Youqi; Goldwasser, Isy

2000-01-01

Methods and apparatus for the preparation of a substrate having an array of diverse materials in predefined regions thereon. A substrate having an array of diverse materials thereon is generally prepared by depositing components of target materials to predefined regions on the substrate, and, in some embodiments, simultaneously reacting the components to form at least two resulting materials. In particular, the present invention provides novel masking systems and methods for applying components of target materials onto a substrate in a combinatorial fashion, thus creating arrays of resulting materials that differ slightly in composition, stoichiometry, and/or thickness. Using the novel masking systems of the present invention, components can be delivered to each site in a uniform distribution, or in a gradient of stoichiometries, thicknesses, compositions, etc. Resulting materials which can be prepared using the methods and apparatus of the present invention include, for example, covalent network solids, ionic solids and molecular solids. Once prepared, these resulting materials can be screened sequentially, or in parallel, for useful properties including, for example, electrical, thermal, mechanical, morphological, optical, magnetic, chemical and other properties.

A quantum annealing approach for fault detection and diagnosis of graph-based systems

NASA Astrophysics Data System (ADS)

Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.

2015-02-01

Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

Conditions for duality between fluxes and concentrations in biochemical networks

PubMed Central

Fleming, Ronan M.T.; Vlassis, Nikos; Thiele, Ines; Saunders, Michael A.

2016-01-01

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality. The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes. PMID:27345817

Conditions for duality between fluxes and concentrations in biochemical networks

DOE PAGES

Fleming, Ronan M. T.; Vlassis, Nikos; Thiele, Ines; ...

2016-06-23

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We alsomore » provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes« less

Conditions for duality between fluxes and concentrations in biochemical networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fleming, Ronan M. T.; Vlassis, Nikos; Thiele, Ines

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We alsomore » provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality.The condition prescribes that, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes« less

Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.

On the role of sparseness in the evolution of modularity in gene regulatory networks

PubMed Central

2018-01-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases. PMID:29775459

On the role of sparseness in the evolution of modularity in gene regulatory networks.

PubMed

Espinosa-Soto, Carlos

2018-05-01

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Fifty years of the European medicines regulatory network: reflections for strengthening intra-regional cooperation in the Region of the Americas.

PubMed

Allchurch, Martin Harvey; Barbano, Dirceu Brás Aparecido; Pinheiro, Marie-Hélène; Lazdin-Helds, Janis

2016-05-01

This report considers how the experience of the European regulatory system might be applied to help strengthen the regulatory systems for medicines in the Region of the Americas. The work of the European Medicines Agencies (EMA) is carried out through its scientific committees, composed of members from European Economic Area countries. A robust legal framework allows EMA to coordinate resources from Member States' competent authorities, including, for example, assisting candidate countries as they prepare to join the European Union (EU). Capacity-building programs help countries adjust their regulatory systems ahead of full participation in the European medicines regulatory network. These programs facilitate adoption of common technical requirements, identify areas where action might be needed to ensure the smooth transposition of EU pharmaceutical law into national legislation, and prepare candidate countries for participation in EMA committees and the European regulatory network. The methodology of these programs could be of potential interest to the Pan American Health Organization (PAHO), the Regional Office of the World Health Organization for the Americas. Given resolutions adopted by the World Health Assembly and the PAHO Directing Council, there is a strong indication that the countries of the Region of the Americas wish to assemble a system that uses the existing regulatory capacity of some countries to strengthen local regulatory capacities in others.

FOREWORD: Focus on Combinatorial Materials Science Focus on Combinatorial Materials Science

NASA Astrophysics Data System (ADS)

Chikyo, Toyohiro

2011-10-01

About 15 years have passed since the introduction of modern combinatorial synthesis and high-throughput techniques for the development of novel inorganic materials; however, similar methods existed before. The most famous was reported in 1970 by Hanak who prepared composition-spread films of metal alloys by sputtering mixed-material targets. Although this method was innovative, it was rarely used because of the large amount of data to be processed. This problem is solved in the modern combinatorial material research, which is strongly related to computer data analysis and robotics. This field is still at the developing stage and may be enriched by new methods. Nevertheless, given the progress in measurement equipment and procedures, we believe the combinatorial approach will become a major and standard tool of materials screening and development. The first article of this journal, published in 2000, was titled 'Combinatorial solid state materials science and technology', and this focus issue aims to reintroduce this topic to the Science and Technology of Advanced Materials audience. It covers recent progress in combinatorial materials research describing new results in catalysis, phosphors, polymers and metal alloys for shape memory materials. Sophisticated high-throughput characterization schemes and innovative synthesis tools are also presented, such as spray deposition using nanoparticles or ion plating. On a technical note, data handling systems are introduced to familiarize researchers with the combinatorial methodology. We hope that through this focus issue a wide audience of materials scientists can learn about recent and future trends in combinatorial materials science and high-throughput experimentation.

Integrating non-coding RNAs in JAK-STAT regulatory networks

PubMed Central

Witte, Steven; Muljo, Stefan A

2014-01-01

Being a well-characterized pathway, JAK-STAT signaling serves as a valuable paradigm for studying the architecture of gene regulatory networks. The discovery of untranslated or non-coding RNAs, namely microRNAs and long non-coding RNAs, provides an opportunity to elucidate their roles in such networks. In principle, these regulatory RNAs can act as downstream effectors of the JAK-STAT pathway and/or affect signaling by regulating the expression of JAK-STAT components. Examples of interactions between signaling pathways and non-coding RNAs have already emerged in basic cell biology and human diseases such as cancer, and can potentially guide the identification of novel biomarkers or drug targets for medicine. PMID:24778925

A mixed incoherent feed-forward loop contributes to the regulation of bacterial photosynthesis genes.

PubMed

Mank, Nils N; Berghoff, Bork A; Klug, Gabriele

2013-03-01

Living cells use a variety of regulatory network motifs for accurate gene expression in response to changes in their environment or during differentiation processes. In Rhodobacter sphaeroides, a complex regulatory network controls expression of photosynthesis genes to guarantee optimal energy supply on one hand and to avoid photooxidative stress on the other hand. Recently, we identified a mixed incoherent feed-forward loop comprising the transcription factor PrrA, the sRNA PcrZ and photosynthesis target genes as part of this regulatory network. This point-of-view provides a comparison to other described feed-forward loops and discusses the physiological relevance of PcrZ in more detail.

A mixed incoherent feed-forward loop contributes to the regulation of bacterial photosynthesis genes

PubMed Central

Mank, Nils N.; Berghoff, Bork A.; Klug, Gabriele

2013-01-01

Living cells use a variety of regulatory network motifs for accurate gene expression in response to changes in their environment or during differentiation processes. In Rhodobacter sphaeroides, a complex regulatory network controls expression of photosynthesis genes to guarantee optimal energy supply on one hand and to avoid photooxidative stress on the other hand. Recently, we identified a mixed incoherent feed-forward loop comprising the transcription factor PrrA, the sRNA PcrZ and photosynthesis target genes as part of this regulatory network. This point-of-view provides a comparison to other described feed-forward loops and discusses the physiological relevance of PcrZ in more detail. PMID:23392242

Edge usage, motifs, and regulatory logic for cell cycling genetic networks

NASA Astrophysics Data System (ADS)

Zagorski, M.; Krzywicki, A.; Martin, O. C.

2013-01-01

The cell cycle is a tightly controlled process, yet it shows marked differences across species. Which of its structural features follow solely from the ability to control gene expression? We tackle this question in silico by examining the ensemble of all regulatory networks which satisfy the constraint of producing a given sequence of gene expressions. We focus on three cell cycle profiles coming from baker's yeast, fission yeast, and mammals. First, we show that the networks in each of the ensembles use just a few interactions that are repeatedly reused as building blocks. Second, we find an enrichment in network motifs that is similar in the two yeast cell cycle systems investigated. These motifs do not have autonomous functions, yet they reveal a regulatory logic for cell cycling based on a feed-forward cascade of activating interactions.

A transcription factor hierarchy defines an environmental stress response network.

PubMed

Song, Liang; Huang, Shao-Shan Carol; Wise, Aaron; Castanon, Rosa; Nery, Joseph R; Chen, Huaming; Watanabe, Marina; Thomas, Jerushah; Bar-Joseph, Ziv; Ecker, Joseph R

2016-11-04

Environmental stresses are universally encountered by microbes, plants, and animals. Yet systematic studies of stress-responsive transcription factor (TF) networks in multicellular organisms have been limited. The phytohormone abscisic acid (ABA) influences the expression of thousands of genes, allowing us to characterize complex stress-responsive regulatory networks. Using chromatin immunoprecipitation sequencing, we identified genome-wide targets of 21 ABA-related TFs to construct a comprehensive regulatory network in Arabidopsis thaliana Determinants of dynamic TF binding and a hierarchy among TFs were defined, illuminating the relationship between differential gene expression patterns and ABA pathway feedback regulation. By extrapolating regulatory characteristics of observed canonical ABA pathway components, we identified a new family of transcriptional regulators modulating ABA and salt responsiveness and demonstrated their utility to modulate plant resilience to osmotic stress. Copyright © 2016, American Association for the Advancement of Science.

Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

PubMed

Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-03-01

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of the Optimum Composition of Low-Temperature Fuel Cell Electrocatalysts for Methanol Oxidation by Combinatorial Screening.

PubMed

Antolini, Ermete

2017-02-13

Combinatorial chemistry and high-throughput screening represent an innovative and rapid tool to prepare and evaluate a large number of new materials, saving time and expense for research and development. Considering that the activity and selectivity of catalysts depend on complex kinetic phenomena, making their development largely empirical in practice, they are prime candidates for combinatorial discovery and optimization. This review presents an overview of recent results of combinatorial screening of low-temperature fuel cell electrocatalysts for methanol oxidation. Optimum catalyst compositions obtained by combinatorial screening were compared with those of bulk catalysts, and the effect of the library geometry on the screening of catalyst composition is highlighted.

Combinatorial Nano-Bio Interfaces.

PubMed

Cai, Pingqiang; Zhang, Xiaoqian; Wang, Ming; Wu, Yun-Long; Chen, Xiaodong

2018-06-08

Nano-bio interfaces are emerging from the convergence of engineered nanomaterials and biological entities. Despite rapid growth, clinical translation of biomedical nanomaterials is heavily compromised by the lack of comprehensive understanding of biophysicochemical interactions at nano-bio interfaces. In the past decade, a few investigations have adopted a combinatorial approach toward decoding nano-bio interfaces. Combinatorial nano-bio interfaces comprise the design of nanocombinatorial libraries and high-throughput bioevaluation. In this Perspective, we address challenges in combinatorial nano-bio interfaces and call for multiparametric nanocombinatorics (composition, morphology, mechanics, surface chemistry), multiscale bioevaluation (biomolecules, organelles, cells, tissues/organs), and the recruitment of computational modeling and artificial intelligence. Leveraging combinatorial nano-bio interfaces will shed light on precision nanomedicine and its potential applications.

Combinatorial computational chemistry approach for materials design: applications in deNOx catalysis, Fischer-Tropsch synthesis, lanthanoid complex, and lithium ion secondary battery.

PubMed

Koyama, Michihisa; Tsuboi, Hideyuki; Endou, Akira; Takaba, Hiromitsu; Kubo, Momoji; Del Carpio, Carlos A; Miyamoto, Akira

2007-02-01

Computational chemistry can provide fundamental knowledge regarding various aspects of materials. While its impact in scientific research is greatly increasing, its contributions to industrially important issues are far from satisfactory. In order to realize industrial innovation by computational chemistry, a new concept "combinatorial computational chemistry" has been proposed by introducing the concept of combinatorial chemistry to computational chemistry. This combinatorial computational chemistry approach enables theoretical high-throughput screening for materials design. In this manuscript, we review the successful applications of combinatorial computational chemistry to deNO(x) catalysts, Fischer-Tropsch catalysts, lanthanoid complex catalysts, and cathodes of the lithium ion secondary battery.

Modeling gene regulatory networks: A network simplification algorithm

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

The Transcription Factors Islet and Lim3 Combinatorially Regulate Ion Channel Gene Expression

PubMed Central

Wolfram, Verena; Southall, Tony D.; Günay, Cengiz; Prinz, Astrid A.; Brand, Andrea H.

2014-01-01

Expression of appropriate ion channels is essential to allow developing neurons to form functional networks. Our previous studies have identified LIM-homeodomain (HD) transcription factors (TFs), expressed by developing neurons, that are specifically able to regulate ion channel gene expression. In this study, we use the technique of DNA adenine methyltransferase identification (DamID) to identify putative gene targets of four such TFs that are differentially expressed in Drosophila motoneurons. Analysis of targets for Islet (Isl), Lim3, Hb9, and Even-skipped (Eve) identifies both ion channel genes and genes predicted to regulate aspects of dendritic and axonal morphology. Significantly, some ion channel genes are bound by more than one TF, consistent with the possibility of combinatorial regulation. One such gene is Shaker (Sh), which encodes a voltage-dependent fast K+ channel (Kv1.1). DamID reveals that Sh is bound by both Isl and Lim3. We used body wall muscle as a test tissue because in conditions of low Ca2+, the fast K+ current is carried solely by Sh channels (unlike neurons in which a second fast K+ current, Shal, also contributes). Ectopic expression of isl, but not Lim3, is sufficient to reduce both Sh transcript and Sh current level. By contrast, coexpression of both TFs is additive, resulting in a significantly greater reduction in both Sh transcript and current compared with isl expression alone. These observations provide evidence for combinatorial activity of Isl and Lim3 in regulating ion channel gene expression. PMID:24523544

The role of the left anterior temporal lobe in semantic composition vs. semantic memory.

PubMed

Westerlund, Masha; Pylkkänen, Liina

2014-05-01

The left anterior temporal lobe (LATL) is robustly implicated in semantic processing by a growing body of literature. However, these results have emerged from two distinct bodies of work, addressing two different processing levels. On the one hand, the LATL has been characterized as a 'semantic hub׳ that binds features of concepts across a distributed network, based on results from semantic dementia and hemodynamic findings on the categorization of specific compared to basic exemplars. On the other, the LATL has been implicated in combinatorial operations in language, as shown by increased activity in this region associated with the processing of sentences and of basic phrases. The present work aimed to reconcile these two literatures by independently manipulating combination and concept specificity within a minimal MEG paradigm. Participants viewed simple nouns that denoted either low specificity (fish) or high specificity categories (trout) presented in either combinatorial (spotted fish/trout) or non-combinatorial contexts (xhsl fish/trout). By combining these paradigms from the two literatures, we directly compared the engagement of the LATL in semantic memory vs. semantic composition. Our results indicate that although noun specificity subtly modulates the LATL activity elicited by single nouns, it most robustly affects the size of the composition effect when these nouns are adjectivally modified, with low specificity nouns eliciting a much larger effect. We conclude that these findings are compatible with an account in which the specificity and composition effects arise from a shared mechanism of meaning specification. Copyright © 2014 Elsevier Ltd. All rights reserved.

Combinatorial Strategies for Improving Multiple-Stress Resistance in Industrially Relevant Escherichia coli Strains

PubMed Central

Herrgård, Markus J.

2014-01-01

High-cell-density fermentation for industrial production of chemicals can impose numerous stresses on cells due to high substrate, product, and by-product concentrations; high osmolarity; reactive oxygen species; and elevated temperatures. There is a need to develop platform strains of industrial microorganisms that are more tolerant toward these typical processing conditions. In this study, the growth of six industrially relevant strains of Escherichia coli was characterized under eight stress conditions representative of fed-batch fermentation, and strains W and BL21(DE3) were selected as platforms for transposon (Tn) mutagenesis due to favorable resistance characteristics. Selection experiments, followed by either targeted or genome-wide next-generation-sequencing-based Tn insertion site determination, were performed to identify mutants with improved growth properties under a subset of three stress conditions and two combinations of individual stresses. A subset of the identified loss-of-function mutants were selected for a combinatorial approach, where strains with combinations of two and three gene deletions were systematically constructed and tested for single and multistress resistance. These approaches allowed identification of (i) strain-background-specific stress resistance phenotypes, (ii) novel gene deletion mutants in E. coli that confer single and multistress resistance in a strain-background-dependent manner, and (iii) synergistic effects of multiple gene deletions that confer improved resistance over single deletions. The results of this study underscore the suboptimality and strain-specific variability of the genetic network regulating growth under stressful conditions and suggest that further exploration of the combinatorial gene deletion space in multiple strain backgrounds is needed for optimizing strains for microbial bioprocessing applications. PMID:25085490

Measuring the Evolutionary Rewiring of Biological Networks

PubMed Central

Shou, Chong; Bhardwaj, Nitin; Lam, Hugo Y. K.; Yan, Koon-Kiu; Kim, Philip M.; Snyder, Michael; Gerstein, Mark B.

2011-01-01

We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or “rewire”, at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of “commonplace” networks such as family trees, co-authorships and linux-kernel function dependencies. PMID:21253555

Networks in Cell Biology

NASA Astrophysics Data System (ADS)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes.

PubMed

Ball, Robyn L; Fujiwara, Yasuhiro; Sun, Fengyun; Hu, Jianjun; Hibbs, Matthew A; Handel, Mary Ann; Carter, Gregory W

2016-08-12

The continuous and non-synchronous nature of postnatal male germ-cell development has impeded stage-specific resolution of molecular events of mammalian meiotic prophase in the testis. Here the juvenile onset of spermatogenesis in mice is analyzed by combining cytological and transcriptomic data in a novel computational analysis that allows decomposition of the transcriptional programs of spermatogonia and meiotic prophase substages. Germ cells from testes of individual mice were obtained at two-day intervals from 8 to 18 days post-partum (dpp), prepared as surface-spread chromatin and immunolabeled for meiotic stage-specific protein markers (STRA8, SYCP3, phosphorylated H2AFX, and HISTH1T). Eight stages were discriminated cytologically by combinatorial antibody labeling, and RNA-seq was performed on the same samples. Independent principal component analyses of cytological and transcriptomic data yielded similar patterns for both data types, providing strong evidence for substage-specific gene expression signatures. A novel permutation-based maximum covariance analysis (PMCA) was developed to map co-expressed transcripts to one or more of the eight meiotic prophase substages, thereby linking distinct molecular programs to cytologically defined cell states. Expression of meiosis-specific genes is not substage-limited, suggesting regulation of substage transitions at other levels. This integrated analysis provides a general method for resolving complex cell populations. Here it revealed not only features of meiotic substage-specific gene expression, but also a network of substage-specific transcription factors and relationships to potential target genes.

Profiling the array of Ca(v)3.1 variants from the human T-type calcium channel gene CACNA1G: alternative structures, developmental expression, and biophysical variations.

PubMed

Emerick, Mark C; Stein, Rebecca; Kunze, Robin; McNulty, Megan M; Regan, Melissa R; Hanck, Dorothy A; Agnew, William S

2006-08-01

We describe the regulated transcriptome of CACNA1G, a human gene for T-type Ca(v)3.1 calcium channels that is subject to extensive alternative RNA splicing. Fifteen sites of transcript variation include 2 alternative 5'-UTR promoter sites, 2 alternative 3'-UTR polyadenylation sites, and 11 sites of alternative splicing within the open reading frame. A survey of 1580 fetal and adult human brain full-length complementary DNAs reveals a family of 30 distinct transcripts, including multiple functional forms that vary in expression with development. Statistical analyses of fetal and adult transcript populations reveal patterns of linkages among intramolecular splice site configurations that change dramatically with development. A shift from nearly independent, biased splicing in fetal transcripts to strongly concerted splicing in adult transcripts suggests progressive activation of multiple "programs" of splicing regulation that reorganize molecular structures in differentiating cells. Patch-clamp studies of nine selected variants help relate splicing regulation to permutations of the gating parameters most likely to modify T-channel physiology in expressing neurons. Gating behavior reflects combinatorial interactions between variable domains so that molecular phenotype depends on ensembles of coselected domains, consistent with the observed emergence of concerted splicing during development. We conclude that the structural gene and networks of splicing regulatory factors define an integrated system for the phenotypic variation of Ca(v)3.1 biophysics during nervous system development. Copyright 2006 Wiley-Liss, Inc.

Characterization of the regulatory network of BoMYB2 in controlling anthocyanin biosynthesis in purple cauliflower

USDA-ARS?s Scientific Manuscript database

Purple cauliflower (Brassica oleracea L. var. botrytis) Graffiti represents a unique mutant in conferring ectopic anthocyanin biosynthesis, which is caused by the tissue specific activation of BoMYB2, an ortholog of Arabidopsis PAP2 or MYB113. To gain a better understanding of the regulatory network...

Getting to the Root of Things: Spatiotemporal Regulatory Networks (JGI Seventh Annual User Meeting 2012: Genomics of Energy and Environment)

ScienceCinema

Brady, Siobhan

2018-02-12

Siobhan Brady from University of California, Davis, gives a talk titled "Getting to the Root of things: Spatiotemporal Regulatory Networks" at the JGI 7th Annual Users Meeting: Genomics of Energy & Environment Meeting on March 22, 2012 in Walnut Creek, California.

Getting to the Root of Things: Spatiotemporal Regulatory Networks (JGI Seventh Annual User Meeting 2012: Genomics of Energy and Environment)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brady, Siobhan

2012-03-22

Siobhan Brady from University of California, Davis, gives a talk titled "Getting to the Root of things: Spatiotemporal Regulatory Networks" at the JGI 7th Annual Users Meeting: Genomics of Energy & Environment Meeting on March 22, 2012 in Walnut Creek, California.

78 FR 74197 - Self-Regulatory Organizations; NYSE MKT LLC; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-10

... Fees for a Lower-Latency 10 Gigabit Liquidity Center Network Connection in the Exchange's Data Center...-regulatory organization. The Commission is publishing this notice to solicit comments on the proposed rule... Network (``LCN'') connection in the Exchange's data center. The Exchange proposes to implement the fee...

Gap Gene Regulatory Dynamics Evolve along a Genotype Network

PubMed Central

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

2016-01-01

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

Viruses Associated with Human Cancer

PubMed Central

McLaughlin-Drubin, Margaret E.; Munger, Karl

2008-01-01

It is estimated that viral infections contribute to 15–20% of all human cancers. As obligatory intracellular parasites, viruses encode proteins that reprogram host cellular signaling pathways that control proliferation, differentiation, cell death, genomic integrity, and recognition by the immune system. These cellular processes are governed by complex and redundant regulatory networks and are surveyed by sentinel mechanisms that ensure that aberrant cells are removed from the proliferative pool. Given that the genome size of a virus is highly restricted to ensure packaging within an infectious structure, viruses must target cellular regulatory nodes with limited redundancy and need to inactivate surveillance mechanisms that would normally recognize and extinguish such abnormal cells. In many cases, key proteins in these same regulatory networks are subject to mutation in non-virally associated diseases and cancers. Oncogenic viruses have thus served as important experimental models to identify and molecularly investigate such cellular networks. These include the discovery of oncogenes and tumor suppressors, identification of regulatory networks that are critical for maintenance of genomic integrity, and processes that govern immune surveillance. PMID:18201576

Coding and non-coding gene regulatory networks underlie the immune response in liver cirrhosis

PubMed Central

Zhang, Xueming; Huang, Yongming; Yang, Zhengpeng; Zhang, Yuguo; Zhang, Weihui; Gao, Zu-hua; Xue, Dongbo

2017-01-01

Liver cirrhosis is recognized as being the consequence of immune-mediated hepatocyte damage and repair processes. However, the regulation of these immune responses underlying liver cirrhosis has not been elucidated. In this study, we used GEO datasets and bioinformatics methods to established coding and non-coding gene regulatory networks including transcription factor-/lncRNA-microRNA-mRNA, and competing endogenous RNA interaction networks. Our results identified 2224 mRNAs, 70 lncRNAs and 46 microRNAs were differentially expressed in liver cirrhosis. The transcription factor -/lncRNA- microRNA-mRNA network we uncovered that results in immune-mediated liver cirrhosis is comprised of 5 core microRNAs (e.g., miR-203; miR-219-5p), 3 transcription factors (i.e., FOXP3, ETS1 and FOS) and 7 lncRNAs (e.g., ENTS00000671336, ENST00000575137). The competing endogenous RNA interaction network we identified includes a complex immune response regulatory subnetwork that controls the entire liver cirrhosis network. Additionally, we found 10 overlapping GO terms shared by both liver cirrhosis and hepatocellular carcinoma including “immune response” as well. Interestingly, the overlapping differentially expressed genes in liver cirrhosis and hepatocellular carcinoma were enriched in immune response-related functional terms. In summary, a complex gene regulatory network underlying immune response processes may play an important role in the development and progression of liver cirrhosis, and its development into hepatocellular carcinoma. PMID:28355233

Insights into the etiology-associated gene regulatory networks in hepatocellular carcinoma from The Cancer Genome Atlas.

PubMed

Seshachalam, Veerabrahma Pratap; Sekar, Karthik; Hui, Kam M

2018-04-19

Hepatitis B virus, hepatitis C virus, alcoholic consumption and non-alcoholic fatty liver are the major known risk factors for Hepatocellular carcinoma (HCC). There have been very few studies comparing the underlying biological mechanisms associated with the different etiologies of HCC. In this study, we hypothesized the existence of different regulatory networks associated with different liver disease etiologies involved in hepatocarcinogenesis. Using upstream regulatory analysis tool in ingenuity pathway analysis software, URs were predicted using differential expressed genes for HCC to facilitate the interrogation of global gene regulation. Analysis of regulatory networks for HBV HCC revealed E2F1 as activated UR, regulating genes involved in cell cycle and DNA replication and HNF4A and HNF1A as inhibited UR. In HCV HCC, IFNG, involved in cellular movement and signaling was activated while IL1RN, MAPK1 involved in IL-22 signaling and immune response was inhibited. In Alcoholic-consumption HCC, ERBB2 involved in inflammatory response and cellular movement was activated, whereas HNF4A, NUPR1 were inhibited. For HCC derived from Non-alcoholic fatty liver disease, miR-1249-5p was activated and NUPR1 involved in cell cycle and apoptosis was inhibited. The prognostic value of representative genes identified in the regulatory networks for HBV HCC can be further validated by an independent HBV HCC dataset established in our laboratory with survival data. Our study identified functionally distinct candidate URs for HCC developed from different etiologic risk factors. Further functional validation studies of these regulatory networks could facilitate the management of HCC towards personalized medicine. This article is protected by copyright. All rights reserved.

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

PubMed Central

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

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

PubMed

Chow, Chi-Nga; Zheng, Han-Qin; Wu, Nai-Yun; Chien, Chia-Hung; Huang, Hsien-Da; Lee, Tzong-Yi; Chiang-Hsieh, Yi-Fan; Hou, Ping-Fu; Yang, Tien-Yi; Chang, Wen-Chi

2016-01-04

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Inference of developmental gene regulatory networks beyond classical model systems: new approaches in the post-genomic era.

PubMed

Fernandez-Valverde, Selene L; Aguilera, Felipe; Ramos-Díaz, René Alexander

2018-06-18

The advent of high-throughput sequencing technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyse cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in high-throughput sequencing technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global of gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.

Noncoding somatic and inherited single-nucleotide variants converge to promote ESR1 expression in breast cancer

PubMed Central

Bailey, Swneke D.; Desai, Kinjal; Kron, Ken J.; Mazrooei, Parisa; Sinnott-Armstrong, Nicholas A.; Treloar, Aislinn E.; Dowar, Mark; Thu, Kelsie L.; Cescon, David W.; Silvester, Jennifer; Yang, S. Y. Cindy; Wu, Xue; Pezo, Rossanna C.; Haibe-Kains, Benjamin; Mak, Tak W.; Bedard, Philippe L.; Pugh, Trevor J.; Sallari, Richard C.; Lupien, Mathieu

2016-01-01

Sustained expression of the oestrogen receptor alpha (ESR1) drives two-thirds of breast cancer and defines the ESR1-positive subtype. ESR1 engages enhancers upon oestrogen stimulation to establish an oncogenic expression program1. Somatic copy number alterations involving the ESR1 gene occur in approximately 1% of ESR1-positive breast cancers2–5, implying that other mechanisms underlie the persistent expression of ESR1. We report the significant enrichment of somatic mutations within the set of regulatory elements (SRE) regulating ESR1 in 7% of ESR1-positive breast cancers. These mutations regulate ESR1 expression by modulating transcription factor binding to the DNA. The SRE includes a recurrently mutated enhancer whose activity is also affected by a functional inherited single nucleotide variant (SNV) rs9383590 that accounts for several breast cancer risk-loci. Our work highlights the importance of considering the combinatorial activity of regulatory elements as a single unit to delineate the impact of noncoding genetic alterations on single genes in cancer. PMID:27571262

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products and Molecular Libraries Small Molecule Repository

PubMed Central

Singh, Narender; Guha, Rajarshi; Giulianotti, Marc; Pinilla, Clemencia; Houghten, Richard; Medina-Franco, Jose L.

2009-01-01

A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR and natural products collections. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different to current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries are located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space. PMID:19301827

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

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

PubMed Central

2011-01-01

Background Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices. PMID:21771321

Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.

PubMed

Ocone, Andrea; Millar, Andrew J; Sanguinetti, Guido

2013-04-01

Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem. Here, we develop a general statistical inference framework for stochastic transcription-translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions. http://homepages.inf.ed.ac.uk/gsanguin/software.html. Supplementary data are available at Bioinformatics online.

Statistical identification of gene association by CID in application of constructing ER regulatory network

PubMed Central

Liu, Li-Yu D; Chen, Chien-Yu; Chen, Mei-Ju M; Tsai, Ming-Shian; Lee, Cho-Han S; Phang, Tzu L; Chang, Li-Yun; Kuo, Wen-Hung; Hwa, Hsiao-Lin; Lien, Huang-Chun; Jung, Shih-Ming; Lin, Yi-Shing; Chang, King-Jen; Hsieh, Fon-Jou

2009-01-01

Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor α (ERα) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. Availability the implementation of CID in R codes can be freely downloaded from . PMID:19292896

Identification of functional elements and regulatory circuits by Drosophila modENCODE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy, Sushmita; Ernst, Jason; Kharchenko, Peter V.

2010-12-22

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- andmore » tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation. Several years after the complete genetic sequencing of many species, it is still unclear how to translate genomic information into a functional map of cellular and developmental programs. The Encyclopedia of DNA Elements (ENCODE) (1) and model organism ENCODE (modENCODE) (2) projects use diverse genomic assays to comprehensively annotate the Homo sapiens (human), Drosophila melanogaster (fruit fly), and Caenorhabditis elegans (worm) genomes, through systematic generation and computational integration of functional genomic data sets. Previous genomic studies in flies have made seminal contributions to our understanding of basic biological mechanisms and genome functions, facilitated by genetic, experimental, computational, and manual annotation of the euchromatic and heterochromatic genome (3), small genome size, short life cycle, and a deep knowledge of development, gene function, and chromosome biology. The functions of {approx}40% of the protein and nonprotein-coding genes [FlyBase 5.12 (4)] have been determined from cDNA collections (5, 6), manual curation of gene models (7), gene mutations and comprehensive genome-wide RNA interference screens (8-10), and comparative genomic analyses (11, 12). The Drosophila modENCODE project has generated more than 700 data sets that profile transcripts, histone modifications and physical nucleosome properties, general and specific transcription factors (TFs), and replication programs in cell lines, isolated tissues, and whole organisms across several developmental stages (Fig. 1). Here, we computationally integrate these data sets and report (i) improved and additional genome annotations, including full-length proteincoding genes and peptides as short as 21 amino acids; (ii) noncoding transcripts, including 132 candidate structural RNAs and 1608 nonstructural transcripts; (iii) additional Argonaute (Ago)-associated small RNA genes and pathways, including new microRNAs (miRNAs) encoded within protein-coding exons and endogenous small interfering RNAs (siRNAs) from 3-inch untranslated regions; (iv) chromatin 'states' defined by combinatorial patterns of 18 chromatin marks that are associated with distinct functions and properties; (v) regions of high TF occupancy and replication activity with likely epigenetic regulation; (vi)mixed TF and miRNA regulatory networks with hierarchical structure and enriched feed-forward loops; (vii) coexpression- and co-regulation-based functional annotations for nearly 3000 genes; (viii) stage- and tissue-specific regulators; and (ix) predictive models of gene expression levels and regulator function.« less

Data-driven integration of genome-scale regulatory and metabolic network models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Data-driven integration of genome-scale regulatory and metabolic network models

DOE PAGES

Imam, Saheed; Schauble, Sascha; Brooks, Aaron N.; ...

2015-05-05

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or moremore » network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. Lastly, in this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.« less

Gene regulatory networks reused to build novel traits: co-option of an eye-related gene regulatory network in eye-like organs and red wing patches on insect wings is suggested by optix expression.

PubMed

Monteiro, Antónia

2012-03-01

Co-option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, are discussed regarding the type of empirical evidence and burden of proof that have been used to infer gene network co-option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co-option is proposed. Novel research directions to facilitate inference of network co-option are also highlighted, especially in cases where the pre-existent and novel traits do not resemble each other. Copyright © 2012 WILEY Periodicals, Inc.

A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks.

PubMed

Jambusaria, Ankit; Klomp, Jeff; Hong, Zhigang; Rafii, Shahin; Dai, Yang; Malik, Asrar B; Rehman, Jalees

2018-06-07

The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes.

From globally coupled maps to complex-systems biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaneko, Kunihiko, E-mail: kaneko@complex.c.u-tokyo.ac.jp

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

A compositional approach to building applications in a computational environment

NASA Astrophysics Data System (ADS)

Roslovtsev, V. V.; Shumsky, L. D.; Wolfengagen, V. E.

2014-04-01

The paper presents an approach to creating an applicative computational environment to feature computational processes and data decomposition, and a compositional approach to application building. The approach in question is based on the notion of combinator - both in systems with variable binding (such as λ-calculi) and those allowing programming without variables (combinatory logic style). We present a computation decomposition technique based on objects' structural decomposition, with the focus on computation decomposition. The computational environment's architecture is based on a network with nodes playing several roles simultaneously.

Experimental realization of a highly secure chaos communication under strong channel noise

NASA Astrophysics Data System (ADS)

Ye, Weiping; Dai, Qionglin; Wang, Shihong; Lu, Huaping; Kuang, Jinyu; Zhao, Zhenfeng; Zhu, Xiangqing; Tang, Guoning; Huang, Ronghuai; Hu, Gang

2004-09-01

A one-way coupled spatiotemporally chaotic map lattice is used to construct cryptosystem. With the combinatorial applications of both chaotic computations and conventional algebraic operations, our system has optimal cryptographic properties much better than the separative applications of known chaotic and conventional methods. We have realized experiments to practice duplex voice secure communications in realistic Wired Public Switched Telephone Network by applying our chaotic system and the system of Advanced Encryption Standard (AES), respectively, for cryptography. Our system can work stably against strong channel noise when AES fails to work.

Smooth Constrained Heuristic Optimization of a Combinatorial Chemical Space

DTIC Science & Technology

2015-05-01

ARL-TR-7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...7294•MAY 2015 US Army Research Laboratory Smooth ConstrainedHeuristic Optimization of a Combinatorial Chemical Space by Berend Christopher...

Preparation of cherry-picked combinatorial libraries by string synthesis.

PubMed

Furka, Arpád; Dibó, Gábor; Gombosuren, Naran

2005-03-01

String synthesis [1-3] is an efficient and cheap manual method for preparation of combinatorial libraries by using macroscopic solid support units. Sorting the units between two synthetic steps is an important operation of the procedure. The software developed to guide sorting can be used only when complete combinatorial libraries are prepared. Since very often only selected components of the full libraries are needed, new software was constructed that guides sorting in preparation of non-complete combinatorial libraries. Application of the software is described in details.

Directed network modules

NASA Astrophysics Data System (ADS)

Palla, Gergely; Farkas, Illés J.; Pollner, Péter; Derényi, Imre; Vicsek, Tamás

2007-06-01

A search technique locating network modules, i.e. internally densely connected groups of nodes in directed networks is introduced by extending the clique percolation method originally proposed for undirected networks. After giving a suitable definition for directed modules we investigate their percolation transition in the Erdos-Rényi graph both analytically and numerically. We also analyse four real-world directed networks, including Google's own web-pages, an email network, a word association graph and the transcriptional regulatory network of the yeast Saccharomyces cerevisiae. The obtained directed modules are validated by additional information available for the nodes. We find that directed modules of real-world graphs inherently overlap and the investigated networks can be classified into two major groups in terms of the overlaps between the modules. Accordingly, in the word-association network and Google's web-pages, overlaps are likely to contain in-hubs, whereas the modules in the email and transcriptional regulatory network tend to overlap via out-hubs.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space.

PubMed

Ma, Chihua; Luciani, Timothy; Terebus, Anna; Liang, Jie; Marai, G Elisabeta

2017-02-15

Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture.

PubMed

Jothi, Raja; Balaji, S; Wuster, Arthur; Grochow, Joshua A; Gsponer, Jörg; Przytycka, Teresa M; Aravind, L; Babu, M Madan

2009-01-01

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

Individual and combined effects of Aflatoxin B1, Deoxynivalenol and Zearalenone on HepG2 and RAW 264.7 cell lines.

PubMed

Zhou, Hongyuan; George, Saji; Hay, Crystal; Lee, Joel; Qian, He; Sun, Xiulan

2017-05-01

To understand the combinatorial toxicity of mycotoxins, we measured the effects of individual, binary and tertiary combinations of Aflatoxin B 1 (AFB 1 ), Deoxynivalenol (DON) and Zearalenone (ZEN) on the cell viability and cellular perturbations of HepG2 and RAW 264.7 cells. The nature of mycotoxins interactions was assessed using mathematical modeling (Chou-Talalay). Mechanisms of cytotoxicity were studied using high content screening (HCS) that probed cytotoxicity responses, such as changes in intracellular reactive oxygen species (ROS), mitochondrial membrane potential (MMP), intracellular calcium ([Ca 2+ ] i ) flux, and cell membrane damage. Our results showed that individual cytotoxicity of mycotoxins in a decreasing order was DON>AFB 1 >ZEN. Varying combinations of mycotoxins at differing concentrations showed different types of interactions. Most of the mixtures showed increasing toxic effects-synergism and/or addition while antagonistic effects were observed with combination of AFB 1 +ZEN. Generally, combination of mycotoxins showed significantly increased intracellular ROS production and [Ca 2+ ] i flux, and decreased MMP in both cell lines, showing that the synergistic and additive effects of mycotoxin combination originate from perturbations of multiple cellular functions. Additionally, this study demonstrated the applicability of HCS for gaining mechanistic understanding on the toxicity of individual as well as combinatorial mycotoxins, and also provided scientific bases for formulating regulatory policies. Copyright © 2017. Published by Elsevier Ltd.

Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage.

PubMed

Freyre-González, Julio A; Tauch, Andreas

2017-09-10

Corynebacterium glutamicum is a Gram-positive, anaerobic, rod-shaped soil bacterium able to grow on a diversity of carbon sources like sugars and organic acids. It is a biotechnological relevant organism because of its highly efficient ability to biosynthesize amino acids, such as l-glutamic acid and l-lysine. Here, we reconstructed the most complete C. glutamicum regulatory network to date and comprehensively analyzed its global organizational properties, systems-level features and functional architecture. Our analyses show the tremendous power of Abasy Atlas to study the functional organization of regulatory networks. We created two models of the C. glutamicum regulatory network: all-evidences (containing both weak and strong supported interactions, genomic coverage=73%) and strongly-supported (only accounting for strongly supported evidences, genomic coverage=71%). Using state-of-the-art methodologies, we prove that power-law behaviors truly govern the connectivity and clustering coefficient distributions. We found a non-previously reported circuit motif that we named complex feed-forward motif. We highlighted the importance of feedback loops for the functional architecture, beyond whether they are statistically over-represented or not in the network. We show that the previously reported top-down approach is inadequate to infer the hierarchy governing a regulatory network because feedback bridges different hierarchical layers, and the top-down approach disregards the presence of intermodular genes shaping the integration layer. Our findings all together further support a diamond-shaped, three-layered hierarchy exhibiting some feedback between processing and coordination layers, which is shaped by four classes of systems-level elements: global regulators, locally autonomous modules, basal machinery and intermodular genes. Copyright © 2016 Elsevier B.V. All rights reserved.

A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity

PubMed Central

Martinez-Sanchez, Mariana Esther; Mendoza, Luis; Villarreal, Carlos; Alvarez-Buylla, Elena R.

2015-01-01

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-β and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions. PMID:26090929

Validation of an Instrument and Testing Protocol for Measuring the Combinatorial Analysis Schema.

ERIC Educational Resources Information Center

Staver, John R.; Harty, Harold

1979-01-01

Designs a testing situation to examine the presence of combinatorial analysis, to establish construct validity in the use of an instrument, Combinatorial Analysis Behavior Observation Scheme (CABOS), and to investigate the presence of the schema in young adolescents. (Author/GA)

Extended evolution: A conceptual framework for integrating regulatory networks and niche construction

PubMed Central

Renn, Jürgen

2015-01-01

ABSTRACT This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path‐dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems. J. Exp. Zool. (Mol. Dev. Evol.) 324B: 565–577, 2015. © 2015 The Authors. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution published by Wiley Periodicals, Inc. PMID:26097188

Transcription Factor Networks Directing the Development, Function, and Evolution of Innate Lymphoid Effectors

PubMed Central

Kang, Joonsoo; Malhotra, Nidhi

2015-01-01

Mammalian lymphoid immunity is mediated by fast and slow responders to pathogens. Fast innate lymphocytes are active within hours after infections in mucosal tissues. Slow adaptive lymphocytes are conventional T and B cells with clonal antigen receptors that function days after pathogen exposure. A transcription factor (TF) regulatory network guiding early T cell development is at the core of effector function diversification in all innate lymphocytes, and the kinetics of immune responses is set by developmental programming. Operational units within the innate lymphoid system are not classified by the types of pathogen-sensing machineries but rather by discrete effector functions programmed by regulatory TF networks. Based on the evolutionary history of TFs of the regulatory networks, fast effectors likely arose earlier in the evolution of animals to fortify body barriers, and in mammals they often develop in fetal ontogeny prior to the establishment of fully competent adaptive immunity. PMID:25650177

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML.

PubMed

Vlaic, Sebastian; Hoffmann, Bianca; Kupfer, Peter; Weber, Michael; Dräger, Andreas

2013-09-01

GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage. GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490. General information on GRN2SBML, examples and tutorials are available at the tool's web page.

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

PubMed

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

2017-04-01

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

PyPanda: a Python package for gene regulatory network reconstruction

PubMed Central

van IJzendoorn, David G.P.; Glass, Kimberly; Quackenbush, John; Kuijjer, Marieke L.

2016-01-01

Summary: PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of ‘omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. Availability and implementation: The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda. Contact: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl PMID:27402905

PyPanda: a Python package for gene regulatory network reconstruction.

PubMed

van IJzendoorn, David G P; Glass, Kimberly; Quackenbush, John; Kuijjer, Marieke L

2016-11-01

PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of 'omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda CONTACT: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl. © The Author 2016. Published by Oxford University Press.