Theoretical knock-outs on biological networks. (21st August 2016)

• Record Type: Journal Article
• Authors: Miranda, Pedro J.; de S. Pinto, Sandro E.; Baptista, Murilo S.; La Guardia, Giuliano G.
• Is Part Of: Journal of theoretical biology. Volume 403(2016)
• Page Start: 38
• Page End: 44
• Publication Date: 2016-08-21
• Subjects: Relational biology -- (M, R)-system -- Complex networks -- Random walks -- Theoretical KOs; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2016.05.021
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: In this work we redefine the concept of biological importance and how to compute it, based on a model of complex networks and random walk. We call this new procedure, theoretical knock-out (KO). The proposed method generalizes the procedure presented in a recent study about Oral Tolerance. To devise this method, we make two approaches: algebraically and algorithmically. In both cases we compute a vector on an asymptotic state, called flux vector. The flux is given by a random walk on a directed graph that represents a biological phenomenon. This vector gives us the information about the relative flux of walkers on a vertex which represents a biological agent. With two vector of this kind, we can calculate the relative mean error between them by averaging over its coefficients. This quantity allows us to assess the degree of importance of each vertex of a complex network that evolves in time and has experimental background. We find out that this procedure can be applied in any sort of biological phenomena in which we can know the role and interrelationships of its agents. These results also provide experimental biologists to predict the order of importance of biological agents on a mounted complex network. Highlights: A generic model applicable to biological phenomena described by directed graphs. A way to generate the order of importance of biological agents. A random walk model for a directed graph based on biological phenomena. Two methods to compute the flux of walkers in a directed graph. A diffusion model of "stimuli" in a biological network.