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Journal articles on the topic 'Indirect interactions'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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1

Mahan, G. D., and Mark Mostoller. "Indirect Ionic Interactions." Physical Review Letters 57, no. 3 (July 21, 1986): 357–59. http://dx.doi.org/10.1103/physrevlett.57.357.

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2

LEE, HYUNYONG, EDUARDO R. MUCCIOLO, GEORGES BOUZERAR, and STEFAN KETTEMANN. "INDIRECT EXCHANGE INTERACTIONS IN GRAPHENE." International Journal of Modern Physics: Conference Series 11 (January 2012): 177–82. http://dx.doi.org/10.1142/s2010194512006095.

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We study the Ruderman-Kittel-Katsuya-Yoshida (RKKY) interactions in graphene controlling the gate voltage and applying nonmagnetic disorder. It is found that oscillations of the RKKY interactions in undoped graphene are characterized by the interference of two neighbor Dirac nodes K and K′ in the first Brillouin zone and decays with R-3 distance dependence. In the slightly doped graphene, a beating pattern, which consists of two characteristic wavevectors (K - K and kF), starts to appear. The distance dependence in this regime shows a crossover from the R-3 to R-2. We present the effect of weak disorder on the RKKY interactions in diffusive regime. The arithmetic averaged interaction over disorder configurations decreases exponentially at distances exceeding the elastic mean free path, while the geometrical average(typical) value has the same power-law as the clean limit.
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3

Xiao, Sa, and Richard Michalet. "Do indirect interactions always contribute to net indirect facilitation?" Ecological Modelling 268 (October 2013): 1–8. http://dx.doi.org/10.1016/j.ecolmodel.2013.07.029.

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4

Ghang, Whan, and Martin A. Nowak. "Indirect reciprocity with optional interactions." Journal of Theoretical Biology 365 (January 2015): 1–11. http://dx.doi.org/10.1016/j.jtbi.2014.09.036.

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5

Heil, Martin. "Indirect defence via tritrophic interactions." New Phytologist 178, no. 1 (April 2008): 41–61. http://dx.doi.org/10.1111/j.1469-8137.2007.02330.x.

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6

Rawat, Saurabh, Anushree Sah, and Ankur Dumka. "Direct-Indirect Link Matrix." International Journal of Information Technology Project Management 11, no. 4 (October 2020): 56–69. http://dx.doi.org/10.4018/ijitpm.2020100105.

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Testing of software remains a fundamentally significant way to check that software behaves as required. Component-based software testing (CBST) is a crucial activity of component-based software development (CBSD) and is based on two crucial proportions: components testing by developers with the source code (e.g., system testing, integration testing, unit testing, etc.) and components testing by end users without source code (black box testing). This work proposes a black box testing technique that calculates the total number of interactions made by component-based software. This technique is helpful to identify the number of test cases for those components where availability of source code is questionable. On the basis of interaction among components, the authors draw a component-link graph and a direct-indirect-link matrix, which helps to calculate the number of interactions in component-based software.
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7

CHUA, HON NIAN, KANG NING, WING-KIN SUNG, HON WAI LEONG, and LIMSOON WONG. "USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.

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Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.
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8

Weikl, T. R. "Indirect interactions of membrane-adsorbed cylinders." European Physical Journal E 12, no. 2 (October 2003): 265–73. http://dx.doi.org/10.1140/epje/i2003-10058-x.

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9

Okuyama, Toshinori, and Benjamin M. Bolker. "On quantitative measures of indirect interactions." Ecology Letters 10, no. 4 (April 2007): 264–71. http://dx.doi.org/10.1111/j.1461-0248.2007.01019.x.

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10

Roslin, Tomas, Helena Wirta, Tapani Hopkins, Bess Hardwick, and Gergely Várkonyi. "Indirect Interactions in the High Arctic." PLoS ONE 8, no. 6 (June 24, 2013): e67367. http://dx.doi.org/10.1371/journal.pone.0067367.

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11

Mahan, G. D., and Mark Mostoller. "Indirect short-range interactions in insulators." Physical Review B 34, no. 8 (October 15, 1986): 5726–35. http://dx.doi.org/10.1103/physrevb.34.5726.

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12

Eberhardt, Rowena L., and David G. Ward. "Indirect adaptive flight control system interactions." International Journal of Robust and Nonlinear Control 9, no. 14 (December 15, 1999): 1013–31. http://dx.doi.org/10.1002/(sici)1099-1239(19991215)9:14<1013::aid-rnc450>3.0.co;2-4.

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13

Mahan, G. D., and M. Mostoller. "Lattice dynamics with indirect ionic interactions." Physical Review B 41, no. 15 (May 15, 1990): 10824–29. http://dx.doi.org/10.1103/physrevb.41.10824.

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14

Müller, C. B., and H. Charles J. Godfray. "Indirect interactions in aphid–parasitoid communities." Researches on Population Ecology 41, no. 1 (April 20, 1999): 93–106. http://dx.doi.org/10.1007/pl00011986.

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15

Ohgushi, Takayuki, and Peter A. Hambäck. "Toward a spatial perspective of plant-based indirect interaction webs: Scaling up trait-mediated indirect interactions." Perspectives in Plant Ecology, Evolution and Systematics 17, no. 6 (December 2015): 500–509. http://dx.doi.org/10.1016/j.ppees.2015.09.006.

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16

Tao, Leiling, Camden D. Gowler, Aamina Ahmad, Mark D. Hunter, and Jacobus C. de Roode. "Disease ecology across soil boundaries: effects of below-ground fungi on above-ground host–parasite interactions." Proceedings of the Royal Society B: Biological Sciences 282, no. 1817 (October 22, 2015): 20151993. http://dx.doi.org/10.1098/rspb.2015.1993.

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Host–parasite interactions are subject to strong trait-mediated indirect effects from other species. However, it remains unexplored whether such indirect effects may occur across soil boundaries and connect spatially isolated organisms. Here, we demonstrate that, by changing plant (milkweed Asclepias sp.) traits, arbuscular mycorrhizal fungi (AMF) significantly affect interactions between a herbivore (the monarch butterfly Danaus plexippus ) and its protozoan parasite ( Ophryocystis elektroscirrha ), which represents an interaction across four biological kingdoms. In our experiment, AMF affected parasite virulence, host resistance and host tolerance to the parasite. These effects were dependent on both the density of AMF and the identity of milkweed species: AMF indirectly increased disease in monarchs reared on some species, while alleviating disease in monarchs reared on other species. The species-specificity was driven largely by the effects of AMF on both plant primary (phosphorus) and secondary (cardenolides; toxins in milkweeds) traits. Our study demonstrates that trait-mediated indirect effects in disease ecology are extensive, such that below-ground interactions between AMF and plant roots can alter host–parasite interactions above ground. In general, soil biota may play an underappreciated role in the ecology of many terrestrial host–parasite systems.
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17

Miyashita, T. "Indirect Interactions between Deer and Spiders Disentangle Interaction Chains in Ecosystems." Journal of the Japanese Forest Society 90, no. 5 (2008): 321–26. http://dx.doi.org/10.4005/jjfs.90.321.

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18

Fazleyev, N. G. "Theoretical Study of the Indirect Quadrupole-Quadrupole Interactions in Metals and Alloys." Zeitschrift für Naturforschung A 45, no. 3-4 (April 1, 1990): 380–84. http://dx.doi.org/10.1515/zna-1990-3-428.

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Abstract The indirect multipole interactions of nuclei and impurity paramagnetic ions in metals and alloys via conduction electrons are investigated by means of the dielectric function method. The Hamiltonian of the indirect quadrupole-quadrupole interaction of impurity paramagnetic ions and nuclei is constructed selfconsistently, taking into account the exchange interactions and correlations of the conduction electrons as well as the antishielding effects. It is shown that the energy of these indirect quadrupole interactions of the nuclei and the paramagnetic ions decreases with the distance as R ~ 3 , oscillating with a period which is determined by the wave vector on the Fermi surface and the distance R. The influence of these indirect quadrupole-quadrupole interactions on the width and shape of the NMR lines is studied.
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19

Ohgushi, Takayuki. "Herbivore‐induced indirect interaction webs on terrestrial plants: the importance of non‐trophic, indirect, and facilitative interactions." Entomologia Experimentalis et Applicata 128, no. 1 (May 15, 2008): 217–29. http://dx.doi.org/10.1111/j.1570-7458.2008.00705.x.

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20

Shahzamal, Md, Raja Jurdak, Bernard Mans, and Frank de Hoog. "Indirect interactions influence contact network structure and diffusion dynamics." Royal Society Open Science 6, no. 8 (August 2019): 190845. http://dx.doi.org/10.1098/rsos.190845.

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Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT)-based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than same place same time transmission (SPST)-based models for the study of various airborne disease outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.
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21

Strong, Donald R. "Quick indirect interactions in intertidal food webs." Trends in Ecology & Evolution 12, no. 5 (May 1997): 173–74. http://dx.doi.org/10.1016/s0169-5347(97)01007-0.

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22

Kisdi, Éva. "Sensitivity analysis, indirect interactions and inconsistency problems." Trends in Ecology & Evolution 15, no. 8 (August 2000): 329. http://dx.doi.org/10.1016/s0169-5347(00)01886-3.

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23

Aikio, Sami. "Sensitivity analysis, indirect interactions and inconsistency problems." Trends in Ecology & Evolution 15, no. 8 (August 2000): 328–29. http://dx.doi.org/10.1016/s0169-5347(00)01887-5.

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24

Schranz, D. W., and S. G. Davison. "Indirect adatom interactions via sp-hybrid semiconductors." Journal of Molecular Structure: THEOCHEM 501-502 (April 2000): 465–77. http://dx.doi.org/10.1016/s0166-1280(99)00461-3.

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25

Roberts, Alan, and Lewi Stone. "Advantageous indirect interactions in systems of competition." Journal of Theoretical Biology 228, no. 3 (June 2004): 367–75. http://dx.doi.org/10.1016/j.jtbi.2004.01.013.

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26

Tripathi, Gouri S. "Many-body theory of indirect nuclear interactions." Physical Review B 31, no. 8 (April 15, 1985): 5143–54. http://dx.doi.org/10.1103/physrevb.31.5143.

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27

Gilman, Sarah E. "Predicting Indirect Effects of Predator–Prey Interactions." Integrative and Comparative Biology 57, no. 1 (June 23, 2017): 148–58. http://dx.doi.org/10.1093/icb/icx031.

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28

Poelman, Erik H., Rieta Gols, Tjeerd A. L. Snoeren, David Muru, Hans M. Smid, and Marcel Dicke. "Indirect plant-mediated interactions among parasitoid larvae." Ecology Letters 14, no. 7 (May 19, 2011): 670–76. http://dx.doi.org/10.1111/j.1461-0248.2011.01629.x.

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29

Petrov, D., and B. Angelov. "Indirect Exchange Interactions in Orthorhombic Lanthanide Aluminates." Acta Physica Polonica A 122, no. 4 (October 2012): 737–40. http://dx.doi.org/10.12693/aphyspola.122.737.

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30

Patnaik, R. C., R. L. Hota, and G. S. Tripathi. "Indirect nuclear spin-spin interactions in PbTe." Physical Review B 58, no. 7 (August 15, 1998): 3924–31. http://dx.doi.org/10.1103/physrevb.58.3924.

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31

Zhang, Kuan, David Lo, Ee-Peng Lim, and Philips Kokoh Prasetyo. "Mining indirect antagonistic communities from social interactions." Knowledge and Information Systems 35, no. 3 (June 27, 2012): 553–83. http://dx.doi.org/10.1007/s10115-012-0519-4.

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32

Huang, Li-Wei, Horng-Tay Jeng, Wei-Bin Su, and Chia-Seng Chang. "Indirect interactions of metal nanoparticles through graphene." Carbon 174 (April 2021): 132–37. http://dx.doi.org/10.1016/j.carbon.2020.10.071.

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33

Utsumi, Shunsuke, Osamu Kishida, and Takayuki Ohgushi. "Trait-mediated indirect interactions in ecological communities." Population Ecology 52, no. 4 (August 21, 2010): 457–59. http://dx.doi.org/10.1007/s10144-010-0236-3.

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34

Belokon, Valery I., and Olga I. Dyachenko. "Phase Transitions in Magnets with Competing Exchange Interactions." Solid State Phenomena 215 (April 2014): 119–22. http://dx.doi.org/10.4028/www.scientific.net/ssp.215.119.

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In this investigations the systems of the nanoparticles with competing exchange interactions are considered. The critical concentrations and possible types of magnetic states of particles in the case of direct exchange and RKKY interaction in the framework of the random interaction field method are determined. It is observed that in magnetic materials with the competition of the direct and indirect exchanges changing the type of ordering is possible at the change in the intensity of the indirect exchange under the influence of external factors.
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35

Zhong, Zhiwei, Xiaofei Li, Dean Pearson, Deli Wang, Dirk Sanders, Yu Zhu, and Ling Wang. "Ecosystem engineering strengthens bottom-up and weakens top-down effects via trait-mediated indirect interactions." Proceedings of the Royal Society B: Biological Sciences 284, no. 1863 (September 20, 2017): 20170894. http://dx.doi.org/10.1098/rspb.2017.0894.

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Trophic interactions and ecosystem engineering are ubiquitous and powerful forces structuring ecosystems, yet how these processes interact to shape natural systems is poorly understood. Moreover, trophic effects can be driven by both density- and trait-mediated interactions. Microcosm studies demonstrate that trait-mediated interactions may be as strong as density-mediated interactions, but the relative importance of these pathways at natural spatial and temporal scales is underexplored. Here, we integrate large-scale field experiments and microcosms to examine the effects of ecosystem engineering on trophic interactions while also exploring how ecological scale influences density- and trait-mediated interaction pathways. We demonstrate that (i) ecosystem engineering can shift the balance between top-down and bottom-up interactions, (ii) such effects can be driven by cryptic trait-mediated interactions, and (iii) the relative importance of density- versus trait-mediated interaction pathways can be scale dependent. Our findings reveal the complex interplay between ecosystem engineering, trophic interactions, and ecological scale in structuring natural systems.
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36

O'Keeffe, Kayleigh R., Anita Simha, and Charles E. Mitchell. "Indirect interactions among co-infecting parasites and a microbial mutualist impact disease progression." Proceedings of the Royal Society B: Biological Sciences 288, no. 1956 (August 11, 2021): 20211313. http://dx.doi.org/10.1098/rspb.2021.1313.

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Interactions among parasites and other microbes within hosts can impact disease progression, yet study of such interactions has been mostly limited to pairwise combinations of microbes. Given the diversity of microbes within hosts, indirect interactions among more than two microbial species may also impact disease. To test this hypothesis, we performed inoculation experiments that investigated interactions among two fungal parasites, Rhizoctonia solani and Colletotrichum cereale, and a systemic fungal endophyte, Epichloë coenophiala, within the grass, tall fescue ( Lolium arundinaceum ). Both direct and indirect interactions impacted disease progression. While the endophyte did not directly influence R. solani disease progression or C. cereale symptom development, the endophyte modified the interaction between the two parasites . The magnitude of the facilitative effect of C. cereale on the growth of R. solani tended to be greater when the endophyte was present. Moreover, this interaction modification strongly affected leaf mortality. For plants lacking the endophyte, parasite co-inoculation did not increase leaf mortality compared to single-parasite inoculations. By contrast, for endophyte-infected plants, parasite co-inoculation increased leaf mortality compared to inoculation with R. solani or C. cereale alone by 1.9 or 4.9 times, respectively. Together, these results show that disease progression can be strongly impacted by indirect interactions among microbial symbionts.
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37

Wong, Lydia, Tess Nahanni Grainger, Denon Start, and Benjamin Gilbert. "An invasive herbivore structures plant competitive dynamics." Biology Letters 13, no. 11 (November 2017): 20170374. http://dx.doi.org/10.1098/rsbl.2017.0374.

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Species interactions are central to our understanding of ecological communities, but may change rapidly with the introduction of invasive species. Invasive species can alter species interactions and community dynamics directly by having larger detrimental effects on some species than others, or indirectly by changing the ways in which native species compete among themselves. We tested the direct and indirect effects of an invasive aphid herbivore on a native aphid species and two host milkweed species. The invasive aphid caused a 10-fold decrease in native aphid populations, and a 30% increase in plant mortality (direct effects). The invasive aphid also increased the strength of interspecific competition between the two native plant hosts (indirect effects). By investigating the role that indirect effects play in shaping species interactions in native communities, our study highlights an understudied component of species invasions.
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38

TSONG, TIEN T., and CHONG-LIN CHEN. "IMPURITY ADSORPTION INDUCED SURFACE CHARGE-DENSITY OSCILLATION AND INDIRECT ATOMIC INTERACTIONS." Modern Physics Letters B 04, no. 12 (July 10, 1990): 775–82. http://dx.doi.org/10.1142/s0217984990000957.

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Neon field ion image spots of Ta impurities, deposited on the Ir (100) surface, show a halo-ring structure. This image structure is most probably produced by the impurity adsorption induced oscillatory electronic charge-density distribution at the surface. The oscillatory electronic charge-density modulation is the cause of electronic indirect interactions of atoms in alloys and on metal surfaces. Manifestations of these interactions can be found in the compositional variation in the near surface layers of alloys in surface segregation, and in the pair-interaction of adsorbed atoms.
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39

Olejarz, Jason, Whan Ghang, and Martin Nowak. "Indirect Reciprocity with Optional Interactions and Private Information." Games 6, no. 4 (September 30, 2015): 438–57. http://dx.doi.org/10.3390/g6040438.

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40

Viana Lopes, João, and Yuri G. Pogorelov. "Indirect interactions between magnetic nanoparticles in granular alloys." Journal of Magnetism and Magnetic Materials 272-276 (May 2004): E1249—E1250. http://dx.doi.org/10.1016/j.jmmm.2003.12.303.

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41

Dickman, C. R. "Indirect interactions and conservation in human-modified environments." Animal Conservation 11, no. 1 (February 2008): 11–12. http://dx.doi.org/10.1111/j.1469-1795.2008.00159.x.

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42

Zhang, Y., Z. Xi, R. S. Hegde, Z. Shakked, and D. M. Crothers. "Predicting indirect readout effects in protein-DNA interactions." Proceedings of the National Academy of Sciences 101, no. 22 (May 17, 2004): 8337–41. http://dx.doi.org/10.1073/pnas.0402319101.

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43

Tiersten, S. C., T. L. Reinecke, and S. C. Ying. "Phonon-mediated indirect interactions between adatoms on surfaces." Physical Review B 39, no. 17 (June 15, 1989): 12575–84. http://dx.doi.org/10.1103/physrevb.39.12575.

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44

Gordan, R., A. J. Hartemink, and M. L. Bulyk. "Distinguishing direct versus indirect transcription factor-DNA interactions." Genome Research 19, no. 11 (August 3, 2009): 2090–100. http://dx.doi.org/10.1101/gr.094144.109.

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45

Simmons, Benno I., Alyssa R. Cirtwill, Nick J. Baker, Hannah S. Wauchope, Lynn V. Dicks, Daniel B. Stouffer, and William J. Sutherland. "Motifs in bipartite ecological networks: uncovering indirect interactions." Oikos 128, no. 2 (October 9, 2018): 154–70. http://dx.doi.org/10.1111/oik.05670.

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46

Holbrook, Sally J., Russell J. Schmitt, and Andrew J. Brooks. "Indirect effects of species interactions on habitat provisioning." Oecologia 166, no. 3 (January 28, 2011): 739–49. http://dx.doi.org/10.1007/s00442-011-1912-5.

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47

Schranz, D. W., and S. G. Davison. "Indirect adatom interactions via III-V semiconductor substrates." International Journal of Quantum Chemistry 67, no. 6 (1998): 377–97. http://dx.doi.org/10.1002/(sici)1097-461x(1998)67:6<377::aid-qua4>3.0.co;2-s.

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48

Kotula, Hannah J., Guadalupe Peralta, Carol M. Frost, Jacqui H. Todd, and Jason M. Tylianakis. "Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches." PLOS ONE 16, no. 6 (June 1, 2021): e0252448. http://dx.doi.org/10.1371/journal.pone.0252448.

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Biological pest control (i.e. ‘biocontrol’) agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological networks offers a promising approach to understanding the community-wide impacts of biocontrol agents (via direct and indirect interactions). Independently, species traits and phylogenies have been shown to successfully predict species interactions and network structure (alleviating the need to collect quantitative interaction data), but whether these approaches can be combined to predict indirect impacts of natural enemies remains untested. Whether predictions of interactions (i.e. direct effects) can be made equally well for generalists vs. specialists, abundant vs. less abundant species, and across different habitat types is also untested for consumer-prey interactions. Here, we used two machine-learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality or abundance of the interacting partners, or on the source (habitat type) of the training data. Finally, we used these predicted networks to generate predictions of indirect effects via shared natural enemies (i.e. apparent competition), and tested these predictions against empirically observed indirect effects between hosts. We found that random-forest models predicted host-parasitoid pairwise interactions (which could be used to predict attack of non-target host species) more successfully than KNN. This predictive ability depended on the generality of the interacting partners for KNN models, and depended on species’ abundances for both random-forest and KNN models, but did not depend on the source (habitat type) of data used to train the models. Further, although our machine-learning informed methods could significantly predict indirect effects, the explanatory power of our machine-learning models for indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol introductions, and could be applied more generally to predicting species interactions such as impacts of invasive species.
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49

Jang, In Sock, Adam Margolin, and Andrea Califano. "hARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests." Interface Focus 3, no. 4 (August 6, 2013): 20130011. http://dx.doi.org/10.1098/rsfs.2013.0011.

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A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
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50

Astles, Philip A., Allen J. Moore, and Richard F. Preziosi. "Genetic variation in response to an indirect ecological effect." Proceedings of the Royal Society B: Biological Sciences 272, no. 1581 (October 4, 2005): 2577–81. http://dx.doi.org/10.1098/rspb.2005.3174.

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Indirect ecological effects (IEEs) are widespread and often as strong as the phenotypic effects arising from direct interactions in natural communities. Indirect effects can influence competitive interactions, and are thought to be important selective forces. However, the extent that selection arising from IEEs results in long-term evolutionary change depends on genetic variation underlying the phenotypic response—that is, a genotype-by-IEE interaction. We provide the first data on genetic variation in the response of traits to an IEE, and illustrate how such genetic variation might be detected and analysed. We used a model tri-trophic system to investigate the effect of host plants on two populations of predatory ladybirds through a clonal aphid herbivore. A split-family experimental design allowed us to estimate the effects of aphid host plant on ladybird traits (IEE) and the extent of genetic variation in ladybird predators for response to these effects (genotype-by-indirect environmental effect interaction). We found significant genetic variation in the response of ladybird phenotypes to the indirect effect of host plant of their aphid prey, demonstrating the potential for evolutionary responses to selection arising from the prey host.
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