Theoretical knock-outs on biological networks

TL;DR

This paper introduces a formal method called theoretical knock-out (KO) to quantify the importance of biological agents within complex networks, using algebraic and algorithmic approaches based on flux vectors derived from random walks.

Contribution

The paper presents a novel formal procedure for assessing biological agent importance in complex networks through flux vectors and theoretical knock-outs, applicable to various biological phenomena.

Findings

The method effectively quantifies agent importance in biological networks.

It can be applied to any biological system with known agent roles and interactions.

Provides a tool for experimental biologists to predict agent significance.

Abstract

In this work we formalize a method to compute the degree of importance of biological agents that participates on the dynamics of a biological phenomenon build upon a complex network. We call this new procedure by theoretical knock-out (KO). 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.