Algorithms for Inference, Analysis and Control of Boolean Networks

Akutsu, Tatsuya; Hayashida, Morihiro; Tamura, Takeyuki

doi:10.1007/978-3-540-85101-1_1

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…Example 1. (Boolean Networks with Perturbation): Boolean networks (BNs), first proposed by Kauffman [23], have been extensively studied in many disciplines, including system biology [2], physics [4], and control theory [12]. BNs, where nodes have two states, representing active/inactive or ON/OFF, can be used to describe genetic regulatory networks, neural networks, disordered systems in statistical mechanics, and so on.…”

Section: A Motivating Examplesmentioning

confidence: 99%

“…BNs, where nodes have two states, representing active/inactive or ON/OFF, can be used to describe genetic regulatory networks, neural networks, disordered systems in statistical mechanics, and so on. To capture the intrinsic stochastic properties of these dynamics, researchers proposed various random versions of BNs [2]. Among these models, a particular one for network dynamics analyzed in [4], [21], can be mathematically described as follows.…”

Section: A Motivating Examplesmentioning

confidence: 99%

“…System (1) is an example of a BN with perturbation (BNp), where η measures the intensity of the perturbation. Note that the function f (·) in (1) is a special case of Boolean threshold functions, which can be used to represent many Boolean functions [2]. Note also that (1) is related to another class of BNs called restricted BNs [36].…”

Section: A Motivating Examplesmentioning

confidence: 99%

“…For example, by estimating traffic volume between all pairs of nodes in a network from traffic flow, known as network tomography, traffic engineers can design new links to avoid network congestion [41]. Theoretical modeling and empirical verification of gene regulatory networks can enhance our understanding of diseases and development [2]. Lastly, inferring social structures such as friendship and influence can help to analyze and predict collective behaviors in complex social networks [10].…”

Section: Introductionmentioning

confidence: 99%

“…More attention has been paid, however, on batch algorithms for network estimation, e.g., [31], [35]. Quantized data are ubiquitous across domains, for example, active/inactive states of a gene [2], ordinal rating of an individual [16], and failure conditions of an infrastructure [5]. Network estimation problems based on quantized timeseries data need to be investigated in more depth with rigorous performance analysis [2], [31].…”

Section: Introductionmentioning

confidence: 99%

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Network Weight Estimation for Binary-Valued Observation Models

Fang

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. The observation sequence is generated from nonlinear networked dynamics in which agents exchange and display binary outputs. Sufficient conditions are given to ensure stability of the observation sequence and identifiability of the system parameters. It is shown that stability and identifiability can be guaranteed under the assumption of independent standard Gaussian disturbances. Via a maximum likelihood approach, the estimation problem is transformed into an optimization problem, and it is verified that its solution is the true parameter vector under the independent standard Gaussian assumption. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Finally, numerical simulations are conducted to illustrate the results and to show that the proposed algorithm is insensitive to small unmodeled factors.

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Section: A Motivating Examplesmentioning

confidence: 99%

Section: A Motivating Examplesmentioning

confidence: 99%

Section: A Motivating Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network Weight Estimation for Binary-Valued Observation Models

Fang

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Analyzing Large Network Dynamics with Process Hitting

Paulevé¹,

Chancellor²,

Folschette³

et al. 2014

Logical Modeling of Biological Systems

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The modeling challenge Regulation is a key aspect of biological systems, all the way from the molecular scale to the ecological one. Gaining a precise understanding of regulation is one of the main goals of systems biology. This discipline has emerged from the synergy between cell biology and cybernetics [WIE 48], from the collaboration of biologists, physicists and computer scientists [IDE 01]. The modeling approach presented in this chapter derives from this heritage by studying the interactions of components of a biological system and analyzing how these interactions impact the function and behavior of the system as a whole. Here, we will focus on applications to genetics, but the potential field of the approach is broader: the Process Hitting framework is relevant to any interactive system, whether it is a biological regulatory network, a logistic scheme or an embedded system. The recent progress in molecular biology has made it possible to obtain a comprehensive map of the genomes of many living organisms. Simultaneously, the development of DNA micro array technology has given access to time series data of the expression of several thousands of genes. One of the main challenges now is to Chapter written by Loïc PAULEVÉ and Courtney CHANCELLOR and Maxime FOLSCHETTE and Morgan MAGNIN and Olivier ROUX.

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Efficient Local Search for Nonlinear Real Arithmetic

Wang,

Zhan,

et al. 2023

Lecture Notes in Computer Science

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Algorithms for Inference, Analysis and Control of Boolean Networks

Cited by 24 publications

References 33 publications

Network Weight Estimation for Binary-Valued Observation Models

Network Weight Estimation for Binary-Valued Observation Models

Analyzing Large Network Dynamics with Process Hitting

Efficient Local Search for Nonlinear Real Arithmetic

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