SINEC : large scale signaling network topology reconstruction using protein - protein interactions and RNAi data

Download

index.pdf

Date

2012

Author

Hashemikbahir, Seyedsasan

Metadata

Show full item record

Item Usage Stats

143
views

69
downloads

Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this thesis, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfy the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose an approach that provides near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed method on synthetic and real datasets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.

Subject Keywords

Protein-protein interactions, RNA-protein interactions, Cellular signal transduction., Information theory in biology.

URI

http://etd.lib.metu.edu.tr/upload/12614586/index.pdf
https://hdl.handle.net/11511/21734

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Optimal deployment in randomly deployed heterogeneous WSNs: A connected coverage approach SEVGİ, CÜNEYT; Koçyiğit, Altan (2014-11-01) Wireless sensor networks (WSNs) are resource-scarce networks and the optimization of the resources is challenging. As far as random deployment is concerned, the optimization of these resources becomes even more difficult In this paper, a novel framework is proposed for solving optimal deployment problems for randomly deployed and clustered WSNs. In several existing approaches to solve these problems, either only partial-coverage is considered or only connectivity is analyzed when full-coverage is assured. T...
Reinforcement learning in random neural networks for cascaded decisions Halıcı, Uğur (1997-01-01) The Random Neural Network (RNN) model, in which signals travel as voltage spikes rather than as fixed signal levels, represents more closely the manner in which signals are transmitted in biophysical neural networks. In this paper a reinforcement learning strategy is proposed to make a sequence of cascaded decisions to achieve a goal while aiming to optimize the total cost of the cascaded decisions. For this purpose, RANs are used to model the system and a weight update rule together with a reinforcement fu...
Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks Qu, K.; Haidar, A. Abi; Fan, J.; Ensman, L.; Tuncay, Kağan; Jolly, M.; Ortoleva, P. (Elsevier BV, 2007-05-21) It is hypothesized that the many human cell types corresponding to multiple states is supported by an underlying nonlinear dynamical system (NDS) of transcriptional regulatory network (TRN) processes. This hypothesis is validated for epithelial cells whose TRN is found to support an extremely complex array of states that we term a "bifurcation nexus", for which we introduce a quantitative measure of complexity. The TRN used is constructed and analyzed by integrating a database of TRN information, cDNA micro...
Large-Scale Signaling Network Reconstruction Hashemikhabir, Seyedsasan; Ayaz, Eyup Serdar; Kavurucu, Yusuf; Can, Tolga; Kahveci, Tamer (Institute of Electrical and Electronics Engineers (IEEE), 2012-11) Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this paper, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of ed...
Stochastic geometry analysis of IEEE 802.15.6 UWB WBAN performance with game theoretical power management Balevi, Eren; Gitlin, Richard D. (2018-05-23) © 2018 IEEE.Inter-network interference in ultra-wideband (UWB) wireless body area networks (WBANs) is analyzed using stochastic geometry with the objective of quantifying the inherent interference tolerance of UWB WBANs in terms of the bit error probability. Such networks are expected to be common in the IoT segment of 5G networks and our methodology may be extended to other network configurations. Our results show that the amount of interference that can be tolerated depends on the node density of a Poisso...

Citation Formats

S. Hashemikbahir, “SINEC : large scale signaling network topology reconstruction using protein - protein interactions and RNAi data,” M.S. - Master of Science, Middle East Technical University, 2012.