Structures of M-Invariant Dual Subspaces with Respect to a Boolean Network
Dongyao Bi, Lijun Zhang, Kuize Zhang, Shenggui Zhang
TL;DR
The paper tackles the problem of understanding how dual subspace structures in Boolean networks relate to observable behavior. By establishing a bijection between dual subspaces and state partitions and proving that an $M$-invariant dual subspace corresponds exactly to an equitable partition, it provides a complete graph-theoretic characterization of the smallest $M$-invariant dual subspaces generated by given outputs. It then shows that BN observability is equivalent to the triviality of the partition associated with the unobservable subspace, and it offers a method to construct output functions that render the BN observable. The results yield a practical framework for analyzing dual dynamics via quotient digraphs and for designing observable outputs in large-scale BN/BCN models, enabling compact representations and systematic observability design.
Abstract
This paper presents the following research findings on Boolean networks (BNs) and their dual subspaces.First, we establish a bijection between the dual subspaces of a BN and the partitions of its state set. Furthermore, we demonstrate that a dual subspace is $M$-invariant if and only if the associated partition is equitable (i.e., for every two cells of the partition, every two states in the former have the same number of out-neighbors in the latter) for the BN's state-transition graph (STG). Here $M$ represents the structure matrix of the BN.Based on the equitable graphic representation, we provide, for the first time, a complete structural characterization of the smallest $M$-invariant dual subspaces generated by a set of Boolean functions. Given a set of output functions, we prove that a BN is observable if and only if the partition corresponding to the smallest $M$-invariant dual subspace generated by this set of functions is trivial (i.e., all partition cells are singletons). Building upon our structural characterization, we also present a method for constructing output functions that render the BN observable.
