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Characterizing the Topology of Probabilistic Biological Networks
JulyAug. 2013 (vol. 10 no. 4)
pp. 970983
ASCII Text  x  
Andrei Todor, Alin Dobra, Tamer Kahveci, "Characterizing the Topology of Probabilistic Biological Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 4, pp. 970983, JulyAug., 2013.  
BibTex  x  
@article{ 10.1109/TCBB.2013.108, author = {Andrei Todor and Alin Dobra and Tamer Kahveci}, title = {Characterizing the Topology of Probabilistic Biological Networks}, journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics}, volume = {10}, number = {4}, issn = {15455963}, year = {2013}, pages = {970983}, doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.108}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE/ACM Transactions on Computational Biology and Bioinformatics TI  Characterizing the Topology of Probabilistic Biological Networks IS  4 SN  15455963 SP970 EP983 EPD  970983 A1  Andrei Todor, A1  Alin Dobra, A1  Tamer Kahveci, PY  2013 KW  topology KW  biochemistry KW  molecular biophysics KW  polynomials KW  probability KW  proteins KW  nodedegree distribution computation KW  probabilistic biological network topology KW  biological interactions KW  alternative interaction topologies KW  jointdegree distributions KW  node pairs KW  mathematical model KW  polynomial time KW  proteinprotein interaction networks KW  PPI networks KW  biological networks KW  classical deterministic networks KW  flexible probabilistic networks KW  powerlaw models KW  lognormal models KW  deterministic networks KW  Probabilistic logic KW  Random variables KW  Maximum likelihood estimation KW  Joints KW  Network topology KW  Mathematical model KW  random graphs KW  Probabilistic biological networks KW  network topology KW  degree distribution VL  10 JA  IEEE/ACM Transactions on Computational Biology and Bioinformatics ER   
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.108
Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the jointdegree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire proteinprotein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of nodedegree and jointdegree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that powerlaw and lognormal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for nodedegree distribution computation than the deterministic ones. Availability: all the data sets used, the software implemented and the alignments found in this paper are available at >http://bioinformatics.cise.ufl.edu/projects/probNet/.
Index Terms:
topology,biochemistry,molecular biophysics,polynomials,probability,proteins,nodedegree distribution computation,probabilistic biological network topology,biological interactions,alternative interaction topologies,jointdegree distributions,node pairs,mathematical model,polynomial time,proteinprotein interaction networks,PPI networks,biological networks,classical deterministic networks,flexible probabilistic networks,powerlaw models,lognormal models,deterministic networks,Probabilistic logic,Random variables,Maximum likelihood estimation,Joints,Network topology,Mathematical model,random graphs,Probabilistic biological networks,network topology,degree distribution
Citation:
Andrei Todor, Alin Dobra, Tamer Kahveci, "Characterizing the Topology of Probabilistic Biological Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 4, pp. 970983, JulyAug. 2013, doi:10.1109/TCBB.2013.108
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