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- Observability of Boolean Control Networks with Time-Variant Delays in States
- Modeling and Simulation of Genetic Regulatory Systems: A Literature Review
- Identification of control targets in Boolean molecular network models via computational algebra
- Controllability Analysis and Control Design of Biological Systems Modeled by Boolean Networks

*Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment.*

This paper gives an equivalent condition for the observability of Boolean control networks BCNs with time-variant delays in states under a mild assumption by using the graph-theoretic method under the framework of the semi-tensor product of matrices. First, the BCN under consideration is split into a finite number of subsystems with no time delays. Second, the observability of the BCN is verified by testing the observability of the so-called observability constructed path a special subsystem without time delays based on graph theory.

These results extend the recent related results on the observability of BCNs. Examples are shown to illustrate the effectiveness of the results. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve.

Genomics Hum. Kauffman S A, Metabolic stability and epigenesis in randomly constructed genetic nets, J. Theoretical Biology , , 22 : — Google Scholar. Akutsu T, Miyano S, and Kuhara S, Inferring qualitative relations in genetic networks and metabolic pathways, Bioinformatics , , 16 : — Theoretical Biology , , : — Control , , 58 6 : — Control , , 61 9 : — Control , , 56 1 : 2— Zou Y and Zhu J.

Kalman decomposition for Boolean control networks, Automatica , , 54 : 65— Chen H and Sun J, Output controllability and optimal output control of state-dependent switched Boolean control network, Automatica , , 50 : — China Ser. F , , 57 : — Li F and Sun J, Controllability of Boolean control networks with time delays in states, Automatica , , 47 : — Neural Networks , 22 6 : — Neural Networks and Learning Systems , , 24 : — Download references.

Correspondence to Dapeng Jiang. Reprints and Permissions. Jiang, D. J Syst Sci Complex 31, — Download citation. Received : 06 July Revised : 16 February Published : 08 December Issue Date : April Search SpringerLink Search. Abstract This paper gives an equivalent condition for the observability of Boolean control networks BCNs with time-variant delays in states under a mild assumption by using the graph-theoretic method under the framework of the semi-tensor product of matrices.

View author publications. Rights and permissions Reprints and Permissions. About this article. Cite this article Jiang, D.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. This document was translated from BibT E X by bibtex2html. All publications sorted by year. Metastasis can occur after malignant cells transition from the epithelial phenotype to the mesenchymal phenotype. This transformation allows cells to migrate via the circulatory system and subsequently settle in distant organs after undergoing the reverse transition. In this work, we formulate a mathematical model and analyze its long-term behavior.

Metrics details. Random Boolean Networks RBNs are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system LCS — an eXtended Classifier System XCS can evolve a set of condition-action rules that direct an RBN from any state to a target state attractor. However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We then compare the performance of this optimal rule calculator algorithm ORC and the XCS variant of learning classifier systems.

If the address matches an existing account you will receive an email with instructions to reset your password. If the address matches an existing account you will receive an email with instructions to retrieve your username. In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms.

Springer proceedings of the conference will be available for download to all conference participants for 4 weeks upon publishing. Due to delays induced by postponing deadlines, the proceedings may not be ready before the conference starts. During the conference 23rd Sept. Here, we present a model revision tool, capable of repairing inconsistent Boolean biological models. Moreover, the tool is able to confront the models, both with steady state observations, as well as time-series data, considering both synchronous and asynchronous update schemes. The tool was tested with a well-known biological model that was corrupted with different random changes.

This paper gives an equivalent condition for the observability of Boolean control networks BCNs with time-variant delays in states under a mild assumption by using the graph-theoretic method under the framework of the semi-tensor product of matrices. First, the BCN under consideration is split into a finite number of subsystems with no time delays. Second, the observability of the BCN is verified by testing the observability of the so-called observability constructed path a special subsystem without time delays based on graph theory.

In this paper, we present a systematic transition scheme for a large class of ordinary differential equations ODEs into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required.

Metrics details. Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network. In this study, we applied semi-tensor product to represent boolean functions in a network and explored controllability of a boolean network based on the transition matrix and time transition diagram.

*This paper addresses the problems of robust-output-controllability and robust optimal output control for incomplete Boolean control networks with disturbance inputs.*

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