regulatory and signaling networks

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Ground state robustness as an evolutionary design principle in signaling networks.

Ground state robustness as an evolutionary design principle in signaling networks.

With reference to the robustness principle, our approach is somewhat complementary to that of Shinar et al. [43]. There, the signal-on case is considered in which the output should accurately match the input despite variations in components. However, through our systematic approach we could analyze a particular set of models and highlight topological features which to our knowledge have not been considered before in the context of signaling networks. The generality of our approach required us to make some drastically simplifying assumptions, such as equal rate constants for deactivation processes. Clearly, for many biological systems such simplifications are unrealistic. To overcome the simplifications made on the rate constants, a future prospect could be to combine our notion of robustness with that introduced in [13] to also quantify the parameter ranges of stable behavior for cases for which the analytical results presented here do not hold. Another limitation of our theoretical framework is that we did only analyze positive regulatory structures and it would be an interesting and obvious extension of our approach to also include the stabilizing or destabilizing effects of negative feedback loops.
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RMOD: a tool for regulatory motif detection in signaling network.

RMOD: a tool for regulatory motif detection in signaling network.

Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.
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Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.

Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.

In Fig. 4, a network consisting of distinct structures involving elements of the growth factor signaling network is observed. This behavior is further quantified in Fig. S4 which considers the node distribution at different thresholds. We grouped the intercon- nected phosphorylation sites into four categories. One set (blue circle) consists largely of receptor and membrane proximal signaling and comprises the LDL receptor (an EGF binding domain-containing protein that plays a role in lipid transport in epithelial cells), an Epidermal growth factor receptor (EGFR) phosphorylation site, Epithelial Cell Receptor A2 (EPHA2), a receptor tyrosine kinase that also activates canonical downstream effector pathways), among others. Another group (black circle) consists of many phosphorylation sites known to be involved in lipid kinase secondary messenger signaling such as the PI3K pathway[19]. These phosphorylation sites include sites on PI3BP, the 59 inositol phosphatase SHIP2, and PIK3R, the p85 regulatory subunit of PI3K. Another set (red circle) contains phosphorylation sites involved in processes immediately down- stream of receptor activation such as endocytosis, integrin, and Jak/Stat signaling. These phosphorylation sites involve proteins having a number of functions in Endocytosis (e.g. STAM2), a phospholipase Annexin A2, and Caveolin. The other set of nodes (orange circle) contains many phosphorylation sites associated with cytoskeletal dynamics such as paxillin, filaminB, as well as SFRS9, a putative alternative splicing factor. The choice of these groupings was made to facilitate the biological interpretation of the network.
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Functional alignment of regulatory networks: a study of temperate phages.

Functional alignment of regulatory networks: a study of temperate phages.

alignment and the contribution of each pair to the signaling difference. The two alignments show good matches for late lytic genes as well as for the regulators CI, CII, and B from 186 aligned with CI, CII, and Q in k. Thus, in general, functions of proteins in one network teach us about protein properties in the other network. The lack of a good match between Apl (in 186) and Cro (in k) is due to the weak links from Cro and reflects a different functional role of Cro and Apl in the late lytic development of phages. Insisting on alignment of Cro Figure 1. The Genetic Regulatory Networks for Phage 186, Phage k, and Phage P22, All of Which Are Temperate and Infect E. coli
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Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

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.
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GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

Having established the broad technical requirements to which we were seeking a solution, the first task was to determine if software already existed that could fulfill our needs. A review by Pavlopoulos et al. (2015), describes the types, trends, and usage of visualization tools available for genomics and systems biology. Their list of 47 tools for network analysis is representative of what was available to us at our project inception in January 2014 (given the caveat that the list itself is a moving target with some tools dropping out, new ones being added, and others evolving in their functions). With such a large number of tools available, it would be reasonable to expect that one already existed that could fulfill our needs. However, our use case was narrow, and the tools we investigated out of this diverse set each had properties that limited their use for us. With regard to our first requirement, out of the 47 tools, 29 are stand-alone applications, requiring installation, versus 18 web applications. With respect to our second requirement, the more complex software packages out of the set have a steep learning curve. Our third and fourth requirements specify data types. Some packages were hardcoded for a different type of network than a GRN (e.g., metabolic or signaling pathways, protein-protein interaction networks) or retrieved data exclusively from a backend database, not allowing user-supplied data. None at the time would readily accept an adjacency matrix with the GRNmap specifications as input without some manipulation of the data format. Finally, with respect to the last requirement, the core functionality, some packages were designed for visualization and analysis of much larger networks than the ones in which we were interested or did not have the ability to display directed, weighted graphs.
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Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma.

Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma.

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.
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MicroRNAs distinguish cytogenetic subgroups in pediatric AML and contribute to complex regulatory networks in AML-relevant pathways.

MicroRNAs distinguish cytogenetic subgroups in pediatric AML and contribute to complex regulatory networks in AML-relevant pathways.

. Mapping the associated mRNAs with miRNA binding sites on KEGG pathways illustrated a concerted action of all four Ago proteins in regulation of about half of KEGG-annotated pathways. This is consistent with the observed overall functional redundancy of Ago proteins in Ago1-4 deficient mouse embryonic stem cells in rescuing the apoptosis phenotype by reintroduction of any Ago protein [68]. Among the top enriched and highly expressed Argonaute protein associated pathways are MAPK [70], mTOR, Phosphatidylinositol [71] and Wnt [72] pathways (Figure 4; Figure S4), that have been described as deregulated and important for the survival of AML cells. In KASUMI-1 cell line it is possible that the classical MAP-kinase signaling pathway is activated due to the repression of DUSP phosphatase by four different miRNA sequence groups (seqgrp-miR-29a, 17, 98 and 125a) we identified in association with all four Argonaute proteins. This could lead to stimulation of proliferative genes by activation of the mitogen- activated protein kinase ERK and to uncontrolled cell growth and survival of leukemic cells. Indeed, these relations have to be proved in further investigations.
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Training in regulatory strategy and regulatory affairs of health products

Training in regulatory strategy and regulatory affairs of health products

This review consisted in checking the translation from English into Portuguese, the evaluation of the conformity with the SPC content, and compliance with the Annex IIB of the Product Information – “Conditions of the Marketing Authorization”. It states that “prior to launch of the product in each MS, the MAH shall agree the content and format of the educational material with the NCA. The MAH should ensure that, when placing the Mp in the market, all healthcare professionals who are expected to prescribe product X, are provided with an Educational pack.” This chapter of the Product Information also indicates what must be the content of the educational pack, and the key elements for both prescriber and patient materials. Also important when reviewing the educational materials, is the critical analysis of the content so that I could exclude the presence of advertising information in the materials. This need for distinguish educational from advertising materials is a matter I will approach further in the discussion chapter of this report.
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Majority rules with random tie-breaking in Boolean gene regulatory networks

Majority rules with random tie-breaking in Boolean gene regulatory networks

Here, focussing on threshold Boolean networks, we propose that the majority rule is particularly suitable to combine deterministic and probabilistic updates. Indeed, the combined contribution of the regulators at a given time is not always conclusive to enable an unambiguous choice of the gene evolution. Hence, we propose a stochastic tie-breaking that associates a probability to the update value when positive effects countervail negative effects. Further- more, various majority rule settings can be devised that are specified and discussed in this paper. We extensively study a class of two gene networks, considering different majority rule settings. We show that this simple motif gives rise to a wide variety of behaviours and that the regulatory structure plays a role in the degree of stochasticity exhibited by the dynamics. We further revisit the Li et al.’s deterministic Boolean threshold model of the budding yeast cell cycle [8]. Interestingly, several studies have considered stochastic versions of this model, with intent to explore the model robustness (e.g. [18,24,25]). Here, we illustrate the interest of our approach to tackle this question. In particular, we demonstrate that steady state analysis can be rigorously performed and lead to effective predictions; these relate to the identification of interactions whose addition would ensure that a specific state is an absorbing state.
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Expression of mRNA Encoding Mcu and Other Mitochondrial Calcium Regulatory Genes Depends on Cell Type, Neuronal Subtype, and Ca2+ Signaling.

Expression of mRNA Encoding Mcu and Other Mitochondrial Calcium Regulatory Genes Depends on Cell Type, Neuronal Subtype, and Ca2+ Signaling.

RNA isolation from cultures was performed essentially as described [60]. Briefly, RNA was iso- lated using the Roche High Pure RNA Isolation Kit (including optional DNase treatment), according to manufacturer’s instructions (Roche, Hertfordshire, UK). For isolation of RNA from the adult hippocampus, the CA1 and CA3 regions of the hippocampus were dissected from brain slices of adult mice. RNA was isolated using the Roche High Pure RNA Tissue Kit (including optional DNase treatment), according to manufacturer’s instructions (Roche, Hert- fordshire, UK). The hippocampal regions in the Roche lysis buffer were homogenized using the QIAGEN QIAshreddar (QIAGEN Ltd., Manchester, UK), before being placed into the Roche High Pure RNA Tissue Kit columns. For qPCR, cDNA was synthesized from 1–3 μg RNA using the Roche Transcriptor First Strand cDNA Synthesis Kit, according to manufactur- er’s instructions, and as previously described [61]. cDNA was then stored at -20°C or immedi- ately diluted (equivalent of 6 ng of initial RNA per 15 μl qPCR reaction, per gene of interest) for real-time PCR in a Stratagene Mx3000P QPCR System (Agilent Technologies, Waldbronn, Germany), using the Roche FS universal SYBR Green MasterRox mix, according to manufac- turer’s instructions. The required amount of template was mixed with water, SYBR Green Mas- terRox mix and forward and reverse primers (200 nM each final concentration) to the required reaction volume. Technical replicates as well as no template and no RT negative controls were included and at least 3 biological replicates were studied per experiment. The qRT-PCR cycling programme was 10 min. at 95°C; 40 cycles of 30 sec. at 95°C, 40 sec. at 60°C with detection of fluorescence and 30 sec. at 72°C; 1 cycle (for dissociation curve) of 1 min. at 95°C and 30 sec. at 55°C with a ramp up to 30 sec. at 95°C (ramp rate: 0.2°C/sec) with continuous detection of fluorescence on the 55–95°C ramp. Data was analysed using the MxPro qPCR analysis software (Stratagene), with 18s or GAPDH expression utilized as an internal normaliser. Primer sequences are noted below in Table 1.
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Networks and Firm Location

Networks and Firm Location

This issue is important on two grounds. The first is the relationship between input specificity and the incentive to vertical integration, which was established by Williamson (1981) and Joskow (1987). Under input specificity, an incentive to a long-term bilateral relationship between buyer and seller is created, which can be best governed (in the sense of minimizing transaction costs) in the context of vertical integration rather than through the open market. Although this is an important strand in the literature, the focus on the importance of input specificity in this paper will lie elsewhere.
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A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

For human beings, the potential complexity of the resulting network is daunting. The number of functionally relevant interactions between the components of this network, repre- senting the links of the interaction, is expected to be much larger. To test the performance of these algorithms on larger examples, we use the BoolNet package [32] in the R environment [31] to generate the N-K random Boolean networks. The parameters of generateRandomNKNetwork function are set to K = 2 and K = 3, and topology = ‘‘scale_free’’ based on the literature [5,35]. We generate a series of GRNs with the nodes from 100 to 1000 and choose 100 instances with a special number of nodes and parameter K in which the attractors can be found in limited time by BNS solver. The
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Understanding the Gene Regulatory Networks Involved in Sensory Hair Cell Differentiation

Understanding the Gene Regulatory Networks Involved in Sensory Hair Cell Differentiation

To the Parkside Terrace OG’s, together we shared all the experience of taking a master degree abroad. Matteo, I will miss all your life stories that could have been told in five minutes, and yet you could talk for an all evening. I will always cherish that. Telma and Livia, thank you for all the garlic bread, burger, brownies, pizza, fries, wine and cheese and bossy nights and Arthur’s seat singing evenings. To my Grace, you witnessed all my highs and lows and for that I cannot love you more. I am sure you will continue to succeed and buy us our house by the beach where we will sing ‘Da Weasel’ and maybe have our business. I am also grateful that you shared with me all your highs and lows. To my serotonin impersonated into an ukulele sister “such is the way of the world, you can never know just where to put all your faith and how will it grow. Gonna rise up, burning black holes in dark memories. Gonna rise up, turning mistakes into gold”. I will always be there for you!
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Signaling strategies for consumer oriented Grid over Optical Burst Switching networks

Signaling strategies for consumer oriented Grid over Optical Burst Switching networks

The Edge Router is responsible to collect data from upper layer users, such as Asynchronous Transfer Mode (ATM) switches, Internet Protocol (IP) routers, etc. The data is collected, sorted and assembled in super size packets called bursts (DB) based on the destination address and its QoS requirements. For each DB is created a Control Packet, which is transmitted to the destination address an offset time before the burst for the network resources configuration. The CP contains information about the burst length, destination address, offset time, etc. OBS use out-of-band signaling, using different wavelengths for the data and the signaling, because the Control Packets are significantly smaller than the bursts, a single control channel is enough to carry the Control Packets associated to multiple data channels. Figure 2.1 proposed by [11], present how the CP and the DB are transmitted using different channels. The CP is converted from the optical domain to the electrical domain and next for the optical domain again, while the burst remain in the optical domain. The offset time is used to guarantee that the network resources are already reserved when the data burst arrives to a node that belongs to the path. The offset is necessary because the Control Packet need to be electrically processed. The information carried by the CPs is used to configure the path nodes used to transmit the DB. OBS uses a one way reservation scheme; the Data Bursts are transmitted without receiving a positive acknowledgement from the network. This network configuration enables OBS DB to be transmitted optically, without the need of buffers (optical Random Access Memory (RAM) or Fiber Delay Line (FDL)). The all optical transmission without the need of buffers is only possible if the offset time is enough to guarantee that the DB is transmitted without waiting at any intermediate node for its resources configuration.
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Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

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.
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Exploring the regulatory networks of spermatogenesis: from candidate gene approaches to integrative functional genomics

Exploring the regulatory networks of spermatogenesis: from candidate gene approaches to integrative functional genomics

(Humphery-Smith 2004; Mueller et al. 2007). This is known to result from, and can only be explained by the complexity of multilayered mechanisms of gene expression regulation reported in mammalian cells. Actually, the introduction of elaborate gene regulatory circuits is thought to have been one of the driving forces of eukaryotic evolution, given the apparent lack of correlation between genome size and organismal complexity (Gregory 2001). Figure 2 depicts the main regulatory layers within eukaryotic cells. In this concern, germ cells appear to have evolved a very elaborate regulatory network. For instance, many genes are specifically or differentially expressed in the germ cells (Chalmel et al. 2007) with complex mechanisms of alternative splicing (Elliott and Grellscheid 2006). Also, long non-coding (lncRNAs) and small non-coding (sncRNAs) RNAs, such as microRNAs (miRNAs) and Piwi-interacting RNAs (piRNAs) are major players in posttranscriptional regulation and essential for normal spermatogenesis progression (For reviews see de Mateo and Sassone-Corsi (2014); Luk et al. (2014)). These non-coding RNAs, as well as the RNA-binding proteins (RBPs), are known to be involved in translational repression of mRNAs, which is a common mechanism of transcript regulation extensively used by germ cells (Iguchi et al. 2006; Bettegowda and Wilkinson 2010; Gan et al. 2013; de Mateo and Sassone-Corsi 2014). At the protein level, as an example, both proteolytic and non-proteolytic functions of the ubiquitin-protease system are crucial during spermatogenesis, being required for proper spermatogonial development, meiosis, meiotic sex chromosome inactivation and spermiogenesis (Reviewed in Bose et al. (2014)). Integrating knowledge of the mechanisms involved in all of these different regulatory layers has the potential to elucidate the molecular interactions driving spermatogenesis but also to identify candidate genes for infertility and for the development of diagnostic tools and contraceptives.
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Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

In summary, by assigning genetic regulatory network motifs to Proto operators, we can automatically transform a Proto dataflow computation into an abstract genetic regulatory network, and the resulting genetic regulatory network can then be optimized using adapted forms of standard code optimization techniques. As we have shown in [26], the Proto language can be used to express more sophisticated programming constructs than the ones Figure 11. Large-scale example of Proto motif-based compilation: (a) a two-bit adder program, interpreted into a Proto computation and (b) transformed into an optimized genetic regulatory network (GRN) which is approximately half the size of the original network. The image is color coded to distinguish crossing edges; small-molecule binding reactions are elided. Note that although in this case the initial gene network has a one-to-one mapping between Proto operations and regulatory proteins, the final implementation logic is largely but not entirely inverted.
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Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Genes in living organisms do not function in isolation, but may interact with each other and act together forming intricate networks [1]. Deciphering the structure of gene regulatory networks is crucial for understanding gene functions and cellular dynamics, as well as for system-level modeling of individual genes and cellular functions. Although physical interactions among individual genes can be experimentally deduced (e.g., by identifying transcription factors and their regulatory target genes or discovering protein-protein interactions), such experimental approach is time-consuming and labor intensive. Given the explosive number of combinations of genes involved in any possible gene interaction, such an approach may not be practically feasible to reconstruct or ‘‘reverse engineer’’ gene networks. On the other hand, technological advances allow for high-throughput measurement of gene expression levels to be carried out efficiently and in a cost-effective manner. These genome-wide expression data reflect the state of the underlying network in a specific condition and provide valuable information that can be fruitfully exploited to infer the network structure.
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Dll4-Notch signaling determines the formation of native arterial collateral networks and arterial function in mouse ischemia models

Dll4-Notch signaling determines the formation of native arterial collateral networks and arterial function in mouse ischemia models

markers in the aorta, consistent with impaired arterial specification (Duarte et al., 2004; Gale et al., 2004). Conditional overexpression of Dll4 results in an enlarged aorta, and reduced vascular branching during embryogenesis (Trindade et al., 2008). Dll4 acts as a repressor of endothelial tip cell formation and loss of Dll4 stimulates excessive tip cell formation resulting in hyperbranching of retinal vessels (Hellström et al., 2007; Suchting et al., 2007). The Dll4- Notch pathway regulates VEGF receptor expression in sprouting vessels and decreases VEGF responsiveness (Phng and Gerhardt, 2009). VEGF is a potent pro-angiogenic factor in ischemic conditions, and is thought to be a determinant of adult arteriogenesis in mice (Clayton et al., 2008). The cellular mechanism is believed to involve sprouting angiogenesis (Lucitti et al., 2012). Acute blockade of Notch signaling using an extracellular Dll4 decoy increases sprouting of ischemic capillaries following femoral artery ligation in mice (Al Haj Zen et al., 2010), indicating that Dll4-Notch signaling restricts post-ischemic angiogenesis. Likewise, Notch signaling blockade leads to hypersprouting of tumor vessels and decreases tumor growth because of failure to form a functional vascular network (Thurston et al., 2007).
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