랜덤 불리언 네트워크 모델을 이용한 되먹임 루프가 네트워크 강건성에 미치는 영향

권영근(Yung-Keun Kwon) 한국정보과학회 2010 정보과학회논문지 : 시스템 및 이론 Vol.37 No.3

생체네트워크는 여러 종류의 환경 변화에 매우 강건하다고 알려져 있지만 그 메커니즘은 아직 밝혀지지 않고 있다. 본 논문에서는 랜덤 네트워크에 비해 생체네트워크에 되먹임 루프가 매우 많이 존재한다는 구조적 특징을 발견하고 그것이 네트워크의 강건성에 어떤 영향을 미치는지를 살펴보았다. 이를 위해 불리언 네트워크 모델을 이용하여 네트워크 강건성을 적절하게 측정하는 방법을 정의하고 많은 불리언 네트워크에 대해서 시뮬레이션하였다. 그 결과, 불리언 네트워크에서 되먹임 루프의 개수가 증가하면 고정점 끌개의 개수는 거의 변화가 없지만 유한순환 끌개의 개수는 크게 줄어든다는 사실을 밝혔다. 또한, 되먹임 루프의 개수가 증가함에 따라 고정점 끌개로 수렴하는 거대한 끌개 영역이 생성됨을 보였다. 이러한 사실들은 매우 많은 수의 되먹임 루프가 네트워크의 강건성을 높이는 데 중요한 요인임을 설명한다. It is well known that many biological networks are very robust against various types of perturbations, but we still do not know the mechanism of robustness. In this paper, we find that there exist a number of feedback loops in a real biological network compared to randomly generated networks. Moreover, we investigate how the topological property affects network robustness. To this end, we properly define the notion of robustness based on a Boolean network model. Through extensive simulations, we show that the Boolean networks create a nearly constant number of fixed-point attractors, while they create a smaller number of limit-cycle attractors as they contain a larger number of feedback loops. In addition, we elucidate that a considerably large basin of a fixed-point attractor is generated in the networks with a large number of feedback loops. All these results imply that the existence of a large number of feedback loops in biological networks can be a critical factor for their robust behaviors.

An Effective Approach of Attractor Calculation for Boolean Control Networks

Qinbin He,Siyue He 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.6

Boolean networks have rich dynamics properties widely used in modeling and investigating circuit design, artificial intelligence, biological research. Generally, a gene regulation network is not independent. It interacts between external networks and external regulation signals. Therefore, the Boolean network with external control signals, that is, Boolean control network, has become a potential effective tool for studying gene regulatory networks. Attractor calculation is the main research content of Boolean control networks. In this study, an approach of attractor calculation was proposed for Boolean control networks. Examples and simulations also confirmed that the proposed approach is very effective and can get all attractors of large-scale Boolean control networks.

불리언 네트워크 계산 모델을 이용한 무척도 네트워크와 생체 네트워크 강건성의 관계 분석

권영근(Yung-Keun Kwon) 한국정보과학회 2011 정보과학회논문지 : 소프트웨어 및 응용 Vol.38 No.6

생체 네트워크의 동역학은 네트워크의 구조적 특성과 관련이 있으며 그 관계의 규명은 생체 네트워크의 설계 원리를 이해하는 데 중요하다. 본 논문에서는 생체 네트워크에서 정점의 차수가 멱급수 분포를 따르는 무척도 특성이 있음을 주목하고 이것이 네트워크 강건성에 어떤 영향을 미치는지를 살펴보았다. 비록 무척도 특성과 네트워크 강건성의 관계를 보인 기존의 연구들이 있었지만, 시뮬레이션을 하는데 있어 단순히 상태 궤적의 차이를 비교한 점, 네트워크의 연결성을 고려하지 않은 점, 모든 가능한 네트워크 상태를 고려하지 않은 점 등에서 아직 그 관계에 대한 이해가 부족하다. 이러한 점에서 본 논문에서는 연결된 네트워크 구조만을 생성하는 불리언 네트워크 모델을 제안하여 모든 가능한 네트워크 상태에 대해 수렴하는 끌개가 얼마나 잘 유지되는지에 관한 강건성을 조사하였다. 대량의 시뮬레이션을 통해 무척도 네트워크가 랜덤 네트워크에 비해 환경 변화와 같은 상태 변이에 더 강건하다는 사실을 밝혔다. 또한, 무척도 네트워크에서는 차수가 매우 큰 허브 정점이 생성되지만 이들의 강건성은 상대적으로 매우 약함을 보였다. 실제로 신경세포의 신호전달네트워크를 분석해 본 결과 차수가 큰 정점에 해당하는 유전자들 집합에서 치사유전자의 비율이 높다는 것을 관찰할 수 있었는데 이는 허브 정점이 강건하지 못하다는 사실을 잘 뒷받침해 준다. 이러한 결과들은 생체 네트워크가 보이는 무척도 특성이 랜덤 네트워크와는 매우 다른 동역학적 특성을 유도하는 중요한 설계 원리중의 하나임을 설명한다. Dynamical behaviors of biological networks are related to their structural characteristics and thus investigations on such relationships are important in understanding a design principle of biological networks. In this paper, we note that biological networks have a scale-free property where degree distribution of nodes follows a power law and try to elucidate the effects of the scale-free property on the network robustness. Although there have been previous studies on the relationship between the scale-free property and robustness, our understanding is still unclear since they simply focused on the difference of state trajectories, did not consider the network connectivity, and did not examine dynamics over all possible network states. In this regard, we propose a Boolean network model which generates only connected networks and investigate the robustness in terms of the converging attractors over all possible states. Through extensive simulations, we show that scale-free networks are more robust against perturbations than random networks. In addition, it is shown that the scale-free networks generate hub nodes with a considerably large degree but their robustness is very small. This is supported by the observation that the proportion of lethal genes in the set of hub genes is relatively large in a signal transduction network. All these results imply that the scale-free property is an important design principle of biological networks to induce different dynamics from that of random networks.

Managing Biological Networks by Using Text Mining and Computer-aided Curation

유석종,조용성,이민호,임종태,유재수 한국물리학회 2015 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.67 No.9

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

Observability Analysis of Boolean Networks with Biological Applications

Koichi Kobayashi,Jun-ichi Imura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

This paper discusses the observability analys is of Boolean networks. A Boolean network model is one of the typical models of biological networks such as gene regulatory networks and metabolic networks, and it is one of the significant to picsto consider the analysis and control problems using Boolean network models. In this paper, after the notion of the observability is defined, we derive a necessary and sufficient condition for the system to be observable. Furthermore, we show an example of a neurotransmitter signaling pathway.

Implementation of Boolean Control Network Based Intelligent System in Smart Home

M. Humayun Kabir,M. Robiul Hoque,Sung-Hyun Yang 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.3

Smart home is a nondeterministic complex environment. Sensors and actuators network are involved in smart home to collect environmental information and to control the devices used by the user for comfort of life. Generally smart home system is implemented by logical rules which described the relation between each element (sensors and actuators). Controlling using logical rules is difficult. In this paper, we have represented an intelligent system for smart home by using Boolean Control Network. For easy control, we have used matrix expression of logic. The system is controlled in several states and in each state different device is operated through actuator network. Matlab based simulation work is done to show the state changes of the system. The result shows that using matrix expression it is easy to control the state of this system.

Time-variant Feedback Stabilization of Constrained Delayed Boolean Networks Under Nonuniform Sampled-data Control

Xiangshan Kong,Haitao Li 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.5

This paper investigates the time-variant state feedback stabilization of constrained delayed Boolean control networks under nonuniform sampled-data control. Firstly, using the augmented algebraic form of constrained delayed Boolean control networks, the constrained nonuniform sampled-data controllability matrix is constructed. Secondly, based on the controllability matrix, a necessary and sufficient condition is proposed for the nonuniform sampled-data reachability of constrained delayed Boolean control networks. Thirdly, by virtue of the nonuniform sampled-data reachability, a new procedure is established to design time-variant state feedback sampled-data stabilizers via reachable set approach. The obtained results are applied to the cell survival regulation of apoptosis networks.

Statistical Approaches to Genome-wide Biological Networks

도진환,최동국,Satoru Miyano 한국바이오칩학회 2009 BioChip Journal Vol.3 No.3

The experiments based on high-throughput technology have been producing massive genomic data including protein-protein interactions, genome-wide mRNA expression and whole genome sequences, which allows the reconstruction of genome-wide biological networks representing relationships or interactions between genes or proteins. The network approach to biology is becoming the main framework to understand biological systems consisting of numerous dynamic networks of biochemical reactions and signaling interactions between cellular components. This is mainly due to efficient representation of a large amount of biological information. Many statistical models have been built and applied to construction of genome-wide biological networks from various type of high-throughput data. In this study, we survey statistical approaches to construction of four main biological networks with their pros and cons: gene regulatory networks, protein-protein interaction networks, metabolic networks and signal transduction networks. In addition, we also investigate the methods describing dynamic behavior of gene regulatory networks and signal transduction networks. The experiments based on high-throughput technology have been producing massive genomic data including protein-protein interactions, genome-wide mRNA expression and whole genome sequences, which allows the reconstruction of genome-wide biological networks representing relationships or interactions between genes or proteins. The network approach to biology is becoming the main framework to understand biological systems consisting of numerous dynamic networks of biochemical reactions and signaling interactions between cellular components. This is mainly due to efficient representation of a large amount of biological information. Many statistical models have been built and applied to construction of genome-wide biological networks from various type of high-throughput data. In this study, we survey statistical approaches to construction of four main biological networks with their pros and cons: gene regulatory networks, protein-protein interaction networks, metabolic networks and signal transduction networks. In addition, we also investigate the methods describing dynamic behavior of gene regulatory networks and signal transduction networks.

Hierarchical closeness efficiently predicts disease genes in a directed signaling network

Tran, T.D.,Kwon, Y.K. Pergamon 2014 Computational biology and chemistry Vol.53 No.2

Background: Many structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks. Results: In this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes. Conclusion: Taken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.

A Study on the Design of a Biologizing Control System

Park, Byung-Jae,Wang, Paul P. Korean Institute of Intelligent Systems 2004 한국지능시스템학회논문지 Vol.14 No.5

According to the progress of an information-oriented society, more human friendly systems are required. The systems can be implemented by a kind of intelligent algorithms. In this paper we propose the possibility of the implementation of an intelligent algorithm from gene, behavior of human beings, which has some properties such as self organization and self regulation. The regulation of gene behavior is widely analyzed by Boolean network. Also the SORE (Self Organizable and Regulating Engine) is one of those algorithms. This paper does not report detailed research results; rather, it studies the feasibility of gene behavior in biocontrol systems based upon computer simulations.