A Constraint-based Method For Finding Backbone Motif In Cell-cycle Network

With the research of life genomics and proteomics, the theories for biologicalmacromolecule steps into the level of systems biology, which means that the focus ofwork is transferring from gene sequencing and isolated research to looking insightinto interrelated biological systems about life behaviors, treating all the biologicalcomponents in association. One of the main challenges is to extract effectiveinformation from biological macromolecular networks, like systematic structureorganization principles, systematic function structure units, and specific networkproperties and so on.An algorithm of finding backbone motif was presented by Guanyu Wang et al. toreveal the dynamic properties of gene regulatory networks (GRN). This algorithmfirstly adopted boolean network model as the dynamic model; then, with dynamic dataof cell-cycle networks, groups of dynamical equations were established and solved byalgebra analytic method for all the feasible solutions; lastly, finding the backbonemotif undertaking main biological functions, which was the minimal subnetwork ofthe original network. But a problem exists that it is too time-consuming whensearching the backbone motif from large space of all feasible solutions.This paper improved the algorithm by adding constraint conditions to reduce thespace of feasible networks, thus the backbone motif can be found quickly andprecisely. Experiments were implemented on two cell-cycle networks, budding yeastand fission yeast, and the results demonstrated that this constraint-based methodinherited the advantages of the previous algorithm, and had better performance.