Characterizing the Topology of Probabilistic Biological Networks

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2013-07, Vol.10 (4), p.970-983
• Main Authors: Todor, Andrei, Dobra, Alin, Kahveci, Tamer
• Format: Article
• Language: English
• Subjects: Animals; Computational Biology - methods; degree distribution; Gene Regulatory Networks; Joints; Mathematical model; Maximum likelihood estimation; Metabolic Networks and Pathways; Models, Biological; Models, Statistical; Network topology; Probabilistic biological networks; Probabilistic logic; Protein Interaction Maps; random graphs; Random variables; Signal Transduction
• ISSN: 1545-5963; 1557-9964
• DOI: 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 joint-degree 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 protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal 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 node-degree 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/.