This article studies the optimal control of probabilistic Boolean control networks (PBCNs) with the infinite horizon average cost criterion. By resorting to the semitensor product (STP) of matrices, a nested optimality equation for the optimal control problem of PBCNs is proposed. The Laurent series expression technique and the Jordan decomposition method derive a novel policy iteration-type algorithm, where finite iteration steps can provide the optimal state feedback law, which is presented. Finally, the intervention problem of the probabilistic Ara operon in E. coil, as a biological application, is solved to demonstrate the effectiveness and feasibility of the proposed theoretical approach and algorithms. Index Terms-Boolean networks (BNs), infinite horizon problem, logical networks, optimal control, probabilistic BNs (PBNs), semitensor product (STP) of matrix.
I. INTRODUCTIONB OOLEAN networks (BNs), as a special kind of discrete (logical) dynamical models with Boolean-valued variables, were first proposed by a theoretical biologist Kauffman [1] in 1969 to model and analyze the complex biological behavior in biological systems, including gene regularity networks [2]- [4]. Since BNs modeling may be the simplest representation of the relevant biological and physical concepts for some finite-state systems, BNs have been also used in various theoretical and practical applications, such as fault detection in logic circuits [5], [6], game theory [7], [8], combustion engines [9], and many other areas.After introducing the semitensor product (STP) of matrix [10] to BNs, as an effective approach, some fundamental Manuscript
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