Pdf Controllability And Observability Of Boolean Networks Arising From Biology
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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.
- Observability of Boolean Control Networks with Time-Variant Delays in States
- Controllability Analysis and Control Design of Biological Systems Modeled by Boolean Networks
- Learning versus optimal intervention in random Boolean networks
- Accepted Contributions
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.
Observability of Boolean Control Networks with Time-Variant Delays in States
This paper addresses the problems of robust-output-controllability and robust optimal output control for incomplete Boolean control networks with disturbance inputs. First, by resorting to the semi-tensor product technique, the system is expressed as an algebraic form, based on which several necessary and sufficient conditions for the robust output controllability are presented. Second, the Mayer-type robust optimal output control issue is studied and an algorithm is established to find a control scheme which can minimize the cost functional regardless of the effect of disturbance inputs. Finally, a numerical example is given to demonstrate the effectiveness of the obtained new results. Since then, it has been used to analyze and simulate cellular networks. A typical BN consists of nodes, and each can take one of the following two values: 1 or 0, representing the gene is expressed or not, respectively. Furthermore, the state evolution of each node can be determined by Boolean functions.
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.
Controllability Analysis and Control Design of Biological Systems Modeled by Boolean Networks
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.
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. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers.
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.
Learning versus optimal intervention in random Boolean networks
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Cell signaling networks are often modeled using ordinary differential equations ODEs , which represent network components with continuous variables.
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.
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. We determined the necessary and sufficient condition for a controllable Boolean network and mapped this requirement in transition matrix to real boolean functions and structure property of a network. An efficient tool is offered to assess controllability of an arbitrary Boolean network and to determine all reachable and non-reachable states.
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A salient problem in systems biology is to develop a con- trol theory for complex and nonlinear biological systems. A number of mathematical models have been.
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