Analysis And Control Of Boolean Networks
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Analysis And Control Of Boolean Networks
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Author : Daizhan Cheng
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-23
Analysis And Control Of Boolean Networks written by Daizhan Cheng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-23 with Science categories.
Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix product called the semi-tensor product (STP). Using the STP, a logical function can be expressed as a conventional discrete-time linear system. In the light of this linear expression, certain major issues concerning Boolean network topology – fixed points, cycles, transient times and basins of attractors – can be easily revealed by a set of formulae. This framework renders the state-space approach to dynamic control systems applicable to Boolean control networks. The bilinear-systemic representation of a Boolean control network makes it possible to investigate basic control problems including controllability, observability, stabilization, disturbance decoupling etc.
Algorithms For Analysis Inference And Control Of Boolean Networks
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Author : Akutsu Tatsuya
language : en
Publisher: World Scientific
Release Date : 2018-02-13
Algorithms For Analysis Inference And Control Of Boolean Networks written by Akutsu Tatsuya and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-13 with Computers categories.
The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many studies have been undertaken from a computational viewpoint. This book presents rigorous algorithmic results on important computational problems on BNs, which include inference of a BN, detection of singleton and periodic attractors in a BN, and control of a BN. This book also presents algorithmic results on fundamental computational problems on probabilistic Boolean networks and a Boolean model of metabolic networks. Although most contents of the book are based on the work by the author and collaborators, other important computational results and techniques are also reviewed or explained. Contents: Preliminaries Boolean Networks Detection of Attractors Detection of Singleton Attractors Detection of Periodic Attractors Identification of Boolean Networks Control of Boolean Networks Predecessor and Observability Problems Semi-Tensor Product Approach Analysis of Metabolic Networks Probabilistic Boolean Networks Identification of Probabilistic Boolean Networks Control of Probabilistic Boolean Networks Readership: Graduate students and researchers working on string theory and related topics. Keywords: Boolean Networks;Bioinformatics;Systems Biology;Combinatorial Algorithms;AttractorsReview: Key Features: Unique book focusing on computational aspects of Boolean networks Provide computational foundations on Boolean networks Contain recent and up-to-date results on algorithms for Boolean networks
Observer Design For Control And Fault Diagnosis Of Boolean Networks
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Author : Zhihua Zhang
language : en
Publisher: Springer Nature
Release Date : 2021-12-11
Observer Design For Control And Fault Diagnosis Of Boolean Networks written by Zhihua Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-11 with Technology & Engineering categories.
Boolean control networks (BCNs) are a kind of parameter-free model, which can be used to approximate the qualitative behavior of biological systems. After converting into a model similar to the standard discrete-time state-space model, control-theoretic problems of BCNs can be studied. In control theory, state observers can provide state estimation for any other applications. Reconstructibility condition is necessary for the existence of state observers. In this thesis explicit and recursive methods have been developed for reconstructibility analysis. Then, an approach to design Luenberger-like observer has been proposed, which works in a two-step process (i.e. predict and update). If a BCN is reconstructible, then an accurate state estimate can be provided by the observer no later than the minimal reconstructibility index. For a wide range of applications the approach has been extended to enable design of unknown input observer, distributed observers and reduced-order observer. The performance of the observers has been evaluated thoroughly. Furthermore, methods for output tracking control and fault diagnosis of BCNs have been developed. Finally, the developed schemes are tested with numerical examples.
Analysis And Control Of Finite Valued Systems
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Author : Haitao Li
language : en
Publisher: CRC Press
Release Date : 2018-05-11
Analysis And Control Of Finite Valued Systems written by Haitao Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-11 with Mathematics categories.
A comprehensive work in finite-value systems that covers the latest achievements using the semi-tensor product method, on various kinds of finite-value systems. These results occupy the highest position in the analysis and control of this field. It not only covers all aspects of research in finite-value systems, but also presents the mathematical derivation for each conclusion in depth. The book contains examples to provide a better understanding of the practical applications of finite-value systems. It will serve as a textbook for graduate students of Cybernetics, Mathematical, and Biology, and a reference for readers interested in the theory of finite-value systems.
Probabilistic Boolean Networks
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Author : Ilya Shmulevich
language : en
Publisher: SIAM
Release Date : 2010-01-01
Probabilistic Boolean Networks written by Ilya Shmulevich and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-01 with Mathematics categories.
This is the first comprehensive treatment of probabilistic Boolean networks (PBNs), an important model class for studying genetic regulatory networks. This book covers basic model properties, including the relationships between network structure and dynamics, steady-state analysis, and relationships to other model classes." "Researchers in mathematics, computer science, and engineering are exposed to important applications in systems biology and presented with ample opportunities for developing new approaches and methods. The book is also appropriate for advanced undergraduates, graduate students, and scientists working in the fields of computational biology, genomic signal processing, control and systems theory, and computer science.
Logic Synthesis For Genetic Diseases
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Author : Pey-Chang Kent Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-31
Logic Synthesis For Genetic Diseases written by Pey-Chang Kent Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-31 with Technology & Engineering categories.
This book brings to bear a body of logic synthesis techniques, in order to contribute to the analysis and control of Boolean Networks (BN) for modeling genetic diseases such as cancer. The authors provide several VLSI logic techniques to model the genetic disease behavior as a BN, with powerful implicit enumeration techniques. Coverage also includes techniques from VLSI testing to control a faulty BN, transforming its behavior to a healthy BN, potentially aiding in efforts to find the best candidates for treatment of genetic diseases.
Analysis And Identification Of Boolean Networks Using Harmonic Analysis
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Author : Steffen Schober
language : en
Publisher:
Release Date : 2011
Analysis And Identification Of Boolean Networks Using Harmonic Analysis written by Steffen Schober and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Use Boolean Network To Model And Control Within And Between Person Dynamics
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Author : Xiao Yang
language : en
Publisher:
Release Date : 2020
Use Boolean Network To Model And Control Within And Between Person Dynamics written by Xiao Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This body of work introduces and forwards a Boolean network-based method for studying psychological dynamics, both within-person and between-persons. I outline the Boolean network method, provide a guide for implementation, and illustrate how the method is applied in two empirical settings -- study of children's self-regulation, and study of group-therapy processes. The work highlights the utility of the method for obtaining intuitive descriptions of individual or group processes and deriving strategies for directing the individual or group towards desired outcomes. Developmental science is making use of dynamical system methods to explain the mechanisms of change driving human development and to predict how and when individuals or groups will change. A natural next step is to understand how to intervene when problematic patterns or change arise. Although psychological researchers have proposed and explored use of network methods to design interventions, applications are sparse. My aim is to enrich the repertoire of methods researchers can use to learn about and direct individuals' and groups' psychological functioning, and in doing so to prompt further use of network methods for modeling behavior change. In Chapter 1, I outline the motivation for introducing a Boolean network method that can be used to describe psychological systems and design interventions that may optimize how those systems function. Although a number of researchers have outlined the possibility of using dynamical system methods to guide psychological processes to desired levels, methods for deriving control strategies have remained theoretical. In this chapter, I identify a gap in the research on methods for analysis of developmental and psychological change processes -- specifically, the sparsity of empirical applications of control system design despite its theoretical importance -- and introduce how a Boolean network control method (Kauffman, 1969; 1993) can address this gap. Second, I briefly explain why network control is useful for guiding developmental processes, and how methods at the overlap between dynamical systems methods and network analysis can be used to develop that guidance. Third, I clarify how within- and between-person dynamics are conceptualized in this project, and how the definitions used here are analogous to other terms used in psychology. Fourth, I explain why the same dynamical system method can be used to describe both within- and between-person dynamics. I then briefly outline two empirical studies where I demonstrate how the Boolean network method can be applied to study and control of both within- and between-person dynamics. In Chapter 2, I revisit how dynamical system methods are used to model the nonlinear dynamics of multivariate systems. Despite the interest and advancement of control theory to direct psychological dynamics toward desired goals, control has been less studied and rarely applied in nonlinear psychological systems. We introduce the Boolean network method to address this gap. This method is useful because it can be used to model the nonlinear dynamics in multivariate systems and to develop network control strategies that might be used to manage the system toward a desired state. The Boolean network method is a discrete-time dynamical system method, and we introduce this method in three steps: (1) inference of the temporal relations between multiple binary variables as Boolean functions and construction of Boolean networks in which the binary variables are nodes and the Boolean functions are edges, (2) extraction of attractors based on the inferred dynamics and assignment of desirability for each attractor, and (3) design of network control to direct a psychological system toward a desired attractor by identifying how the Boolean network needs to be updated. To demonstrate how the Boolean network can describe and prescribe control for emotion regulation dynamics, we applied this method to an observational dataset of children's regulation of anger using bidding and/or distraction behavior (N = 120, T = 480 seconds). Network control strategies were designed to move the child into attractors where anger is OFF. The sample shows heterogeneous emotion regulation dynamics across children in 22 distinct Boolean networks, and heterogeneous control strategies regarding which behavior to perturb and how to perturb it. The presentation and illustration forward the Boolean network method as a novel method to describe nonlinear dynamics in multivariate psychological systems and a control method to guide nonlinear psychological systems toward desired goals. In Chapter 3, I revisit theories suggesting group processes can induce desired or undesired behavior change in individuals in a group because they are under social influence. Empirical modeling of group processes often assumes the social influence is assimilative only, and network-based interventions that aim to manage group processes and promote desired behavior change does not apply when the social network is fully connected. We introduce the Boolean network method to address these two gaps because it allows both assimilative and repulsive social influence to be modeled simultaneously, and prescribes network control strategies by changing a few group members' behavior regardless of network topology. The Boolean network method is a dynamical system method that models the group-specific temporal relations between group members' behavior as a Boolean network, and also allows for control theory to design group management strategies and direct the groups toward desired behavior. The Boolean network method is applied to empirical data of individuals' self-disclosure behavior in multi-week therapy groups (N = 155, 18 groups, T = 10~16 weeks), to model and manage group-specific processes of self-disclosure. Results show the method can estimate each group member's self-disclosure with error rate of 0.14 (SD = 0.10). Both assimilative and repulsive social influence are found in 14 out of 18 groups. Group-specific network control strategies were designed to elicit the majority of the group self-disclose by encouraging a few group members' self-disclose behavior. This example illustrates the Boolean network as a flexible method that allows for modeling of assimilative and repulsive social influences that simultaneously operate in a group process and design of strategies that can be used to direct the group process to desired states (without manipulating the social ties). This dissertation introduces and forwards the Boolean network method as a method that can be used to describe and control a system's trajectory. The final chapter, Chapter 4, summarizes the contribution of this dissertation in terms of method innovation, theory, data, and potential applications, and begins to elaborate how the method might be extended further. To our knowledge, this is the first application of the Boolean network method in describing and controlling nonlinear psychological processes. The Boolean network method follows the long-standing tradition of using dynamical system methods to explain, model, and predict how complex psychological systems operate and change over time. This dissertation adds to that literature by providing the methodological steps and empirical examples that will enable control system design for nonlinear within- and between-person dynamics. Our demonstration emphasizes the appeal of this method for both theory and practice -- providing simple descriptions and explanations of system dynamics and system control strategies. Altogether, this dissertation forwards and provides access to a useful tool that can help researchers discover, understand, and shape many different kinds of psychological dynamics.
Algebraic Biology
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Author : Katsuhisa Horimoto
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-07-18
Algebraic Biology written by Katsuhisa Horimoto and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-18 with Computers categories.
This book constitutes the refereed proceedings of the Third International Conference on Algebraic Biology, AB 2008, held at the Castle of Hagenberg, Austria in July 2008 as part of the RISC Summer 2008, organized by the Research Institute for Symbolic Computation. The 14 revised full papers presented together with 3 tutorial lectures were carefully reviewed and selected from 27 submissions. The conference is the interdisciplinary forum for the presentation of research on all aspects of applications of symbolic computation (computer algebra, computational logic, and related methods) to various issues in biology and life sciences as well as other problems in biology being approached with symbolic methods.
Attractor Identification And Control In Boolean Models Of Plant Pollinator Networks
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Author : Fatemehsadat Fateminasrollahi
language : en
Publisher:
Release Date : 2023
Attractor Identification And Control In Boolean Models Of Plant Pollinator Networks written by Fatemehsadat Fateminasrollahi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Ecological and biological systems consist of numerous interlinked components that interact and exchange information; such interactions give rise to emergent, collective behaviors that are of interest for ecologists and life scientists. The study of the relationship between the interactions and dynamics of individual components and the emergent dynamics of the system is important because it can lead to the development of control methods to manipulate the collective dynamics. In turn, these control methods can be used for ecological community management or restoration, or for therapeutic medical applications. One promising method to gain a deeper insight into such complex systems is to model the interactions among elements using a network and couple it with a predictive dynamical model. The analysis of such dynamical models provides us with a platform to advance our knowledge of the intricate behaviors exhibited by ecological and biological networks, and it has wide-ranging implications across various domains, spanning conservation efforts, the development of community management strategies, and drug target identification in the context of drug design. The innate challenge that arises when analyzing these models is the large size of the system and the non-linearity of the dynamical processes. Recently, a new approach has been developed by Jorge Gómez Tejeda Zañudo and collaborators that focuses on the stable motifs in the network; stable motifs are minimal positive feedback loops that maintain a specific state regardless of the state of the rest of the components in the system. By characterizing the stable motifs and the conditions that lead to their lock-in, this method can identify the system's dynamic repertoire, predict the outcome of specific interventions and suggest management and control methods. In this dissertation, the main focus is on mutualistic plant-pollinator networks, and specifically on their description by a well-established predictive dynamical model developed by Colin Campbell and collaborators. The study of such systems is of ecological significance as pollinator species face considerable degradation across the world. The loss of pollinator species has a dramatic negative effect on crops as the majority of food crops require pollination to survive. The examination of the reliability and stability of these communities holds great significance for agricultural management and ecological preservation endeavors. There is a great need for measures and methods to predict the magnitude of any cascading effects of species extinction, and for prevention and restoration strategies to maintain the communities. I contribute to this field of study by making it possible for the first time to apply stable motif analysis to plant-pollinator communities. I transform the equations of the existing model by changing threshold functions into suitable logical functions of plant-pollinator networks so that stable motif analysis can be applied to it. I then extend the classical stable motif analysis and introduce a novel method based on stable motifs that determines the stable communities of large plant-pollinator systems efficiently. This method relies on a new concept called the network of functional relationships among stable motifs; I show that these relationships can be leveraged to identify stable communities and accelerate the process significantly. Put into the ecological context, stable motifs can be intuitively interpreted as small groups of species in which species can maintain a specific survival state. I show how such groups of species and the relationships of these groups determine the final community outcomes in plant-pollinator networks. Once the stable communities are characterized, I study their reaction to perturbation and analyze the behavior of the system in the case of species extinction. I extend Boolean modeling concepts, so far only defined for functions of a specific logical form, to the plant-pollinator Boolean threshold functions and introduce a new algorithm to measure the cascading effects caused by species extinction. I then use the information gained from stable motifs to first identify the species whose extinction leads to massive catastrophe in the community and next suggest restoration measures that can be incorporated in ecological sciences. In chapters 1 and 2, I introduce the mutualistic plant-pollinator networks, the Campbell et al. Boolean model of community formation, and the key concepts of Boolean modeling respectively. In chapter 3 I present my contributions to the methodological advancements in the field of Boolean modeling and computational ecology. The methods in this chapter are presented in the context of plant-pollinator networks, but are general and can be implemented in other types of Boolean networks. Chapter 4 describes the properties of the alternative stable states available to the same group of species. Chapter 5 describes the response of plant-pollinator communities to the extinction of a species; specifically, whether there will be cascading effects. This chapter also proposed multiple damage prevention and community restoration measures. The analysis results in these two chapters rely heavily on the concept of stable motifs and the methods introduced in chapter 3. I demonstrate that stable motifs successfully pinpoint the crucial species and this method outperforms the previous well-established measures. Finally, in chapter 6 I study network control in Boolean networks that have a modular structure. In general network control means that by externally fixing the state or the dynamics of a group of nodes, the system as a whole will converge into a desired state or attractor. In this analysis, I aim to identify methods that identify control targets, relying solely on the properties of the network. Taking advantage of the fact that many ecological and biological networks are composed of smaller densely connected modules, I propose a novel module-based method to localize the search for control targets - nodes that if externally controlled, the system will converge into a desired dynamical outcome (e.g., a rich and bio-diverse stable community) or move away from the unwanted dynamical outcome (i.e., full collapse of the community). In this analysis, I study a large ensemble of biologically inspired synthetic Boolean networks to capture the properties of these systems across different levels of modularity. I show that it is considerably more efficient and advantageous to localize the search for control targets in networks with clear modular structure. Chapter 7 presents conclusions and possible future research directions.