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Liu X, Hong Z, Liu J, Lin Y, Rodríguez-Patón A, Zou Q, Zeng X. Computational methods for identifying the critical nodes in biological networks. Brief Bioinform 2019; 21:486-497. [DOI: 10.1093/bib/bbz011] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/03/2018] [Accepted: 01/11/2019] [Indexed: 12/28/2022] Open
Abstract
Abstract
A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.
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Affiliation(s)
- Xiangrong Liu
- Department of Computer Science, Xiamen University, China
| | - Zengyan Hong
- Department of Computer Science, Xiamen University, China
| | - Juan Liu
- Department of Computer Science, Xiamen University, China
| | - Yuan Lin
- ITOP Section, DNB Bank ASA, Solheimsgaten, Bergen, Norway
| | - Alfonso Rodríguez-Patón
- Universidad Politécnica de Madrid (UPM) Campus Montegancedo s/n, Boadilla del Monte, Madrid, Spain
| | - Quan Zou
- Department of Computer Science, Xiamen University, China
- Insitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China
- School of Computer Science and Technology, Tianjin University, Tianjin, China
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Perumal TM, Gunawan R. pathPSA: A Dynamical Pathway-Based Parametric Sensitivity Analysis. Ind Eng Chem Res 2014. [DOI: 10.1021/ie403277d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Thanneer Malai Perumal
- Luxembourg
Centre for Systems Biomedicine, University of Luxembourg, Esch/Alzette 4362, Luxembourg
| | - Rudiyanto Gunawan
- Institute
for Chemical and Bioengineering, ETH Zurich, Zurich 8093, Switzerland
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Nassiri I, Masoudi-Nejad A, Jalili M, Moeini A. Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches. Genomics 2013; 102:195-201. [PMID: 23912059 DOI: 10.1016/j.ygeno.2013.07.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Revised: 07/22/2013] [Accepted: 07/26/2013] [Indexed: 11/25/2022]
Abstract
A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.
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Affiliation(s)
- Isar Nassiri
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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ElBakry O, Ahmad MO, Swamy MNS. Inference of gene regulatory networks with variable time delay from time-series microarray data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:671-687. [PMID: 24091400 DOI: 10.1109/tcbb.2013.73] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Regulatory interactions among genes and gene products are dynamic processes and hence modeling these processes is of great interest. Since genes work in a cascade of networks, reconstruction of gene regulatory network (GRN) is a crucial process for a thorough understanding of the underlying biological interactions. We present here an approach based on pairwise correlations and lasso to infer the GRN, taking into account the variable time delays between various genes. The proposed method is applied to both synthetic and real data sets, and the results on synthetic data show that the proposed approach outperforms the current methods. Further, the results using real data are more consistent with the existing knowledge concerning the possible gene interactions.
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MMPREDOX/NO Interplay in Periodontitis and Its Inhibition withSatureja hortensisL. Essential Oil. Chem Biodivers 2013; 10:507-23. [DOI: 10.1002/cbdv.201200375] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Indexed: 11/07/2022]
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Zeidán-Chuliá F, Rybarczyk-Filho JL, Salmina AB, de Oliveira BHN, Noda M, Moreira JCF. Exploring the Multifactorial Nature of Autism Through Computational Systems Biology: Calcium and the Rho GTPase RAC1 Under the Spotlight. Neuromolecular Med 2013; 15:364-83. [DOI: 10.1007/s12017-013-8224-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2012] [Accepted: 02/16/2013] [Indexed: 01/08/2023]
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Wang RS, Saadatpour A, Albert R. Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol 2012; 9:055001. [DOI: 10.1088/1478-3975/9/5/055001] [Citation(s) in RCA: 293] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Tang H, Zhong F, Xie H. A quick guide to biomolecular network studies: construction, analysis, applications, and resources. Biochem Biophys Res Commun 2012; 424:7-11. [PMID: 22732414 DOI: 10.1016/j.bbrc.2012.06.085] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2012] [Accepted: 06/18/2012] [Indexed: 10/28/2022]
Abstract
Over the past decade, a rapid increase in network data including signaling, transcription regulation, metabolic reaction, protein-protein interaction and genetic interaction has been observed. Many biology issues have been investigated by analyzing these diverse networks, providing new insights into biology. Networks also play an important role in disease studies including disease gene screening and clinical diagnosis. Large amounts of databases and software have been developed to facilitate the storage, exchange, integration, and analysis of network data and network analysis is becoming a routine procedure for biologists to infer biological information. In this review, several main aspects of network studies are discussed, including network construction, analysis, application, and resources.
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Affiliation(s)
- Hailin Tang
- College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha 410073, China
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Wang RS, Albert R. Elementary signaling modes predict the essentiality of signal transduction network components. BMC SYSTEMS BIOLOGY 2011; 5:44. [PMID: 21426566 PMCID: PMC3070649 DOI: 10.1186/1752-0509-5-44] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2010] [Accepted: 03/22/2011] [Indexed: 12/22/2022]
Abstract
Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM.
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Affiliation(s)
- Rui-Sheng Wang
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
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Pflieger D, Gonnet F, de la Fuente van Bentem S, Hirt H, de la Fuente A. Linking the proteins--elucidation of proteome-scale networks using mass spectrometry. MASS SPECTROMETRY REVIEWS 2011; 30:268-297. [PMID: 21337599 DOI: 10.1002/mas.20278] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2009] [Revised: 10/05/2009] [Accepted: 10/05/2009] [Indexed: 05/30/2023]
Abstract
Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.
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Affiliation(s)
- Delphine Pflieger
- Laboratoire Analyse et Modélisation pour la Biologie et l'Environnement, Université d'Evry Val d'Essonne, CNRS UMR 8587, Evry, France
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Integrating Omics data for signaling pathways, interactome reconstruction, and functional analysis. Methods Mol Biol 2011; 719:415-33. [PMID: 21370095 DOI: 10.1007/978-1-61779-027-0_19] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Omics data and computational approaches are today providing a key to disentangle the complex architecture of living systems. The integration and analysis of data of different nature allows to extract meaningful representations of signaling pathways and protein interactions networks, helpful in achieving an increased understanding of such intricate biochemical processes. We here describe a general workflow and relative hurdles in integrating online Omics data and analyzing reconstructed representations by using the available computational platforms.
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Gao X, Zhang X, Zheng J, He F. Proteomics in China: Ready for prime time. SCIENCE CHINA-LIFE SCIENCES 2010; 53:22-33. [DOI: 10.1007/s11427-010-0027-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2009] [Accepted: 12/28/2009] [Indexed: 12/27/2022]
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Bioinformatics analyses for signal transduction networks. ACTA ACUST UNITED AC 2008; 51:994-1002. [PMID: 18989642 DOI: 10.1007/s11427-008-0134-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2008] [Accepted: 07/24/2008] [Indexed: 10/21/2022]
Abstract
Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioinformatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.
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Kwon YK, Cho KH. Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. ACTA ACUST UNITED AC 2008; 24:987-94. [PMID: 18285369 DOI: 10.1093/bioinformatics/btn060] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now. RESULTS Through extensive computational experiments, we found that networks with a larger number of positive feedback loops and a smaller number of negative feedback loops are likely to be more robust against perturbations. Moreover, we found that the nodes of a robust network subject to perturbations are mostly involved with a smaller number of feedback loops compared with the other nodes not usually subject to perturbations. This topological characteristic eventually makes the robust network fragile against unexpected mutations at the nodes not previously exposed to perturbations.
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Affiliation(s)
- Yung-Keun Kwon
- Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea
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Roberts S, Mazurie A, Buck G. Integrating Genome-Scale Data for Gene Essentiality Prediction. Chem Biodivers 2007; 4:2618-30. [DOI: 10.1002/cbdv.200790214] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Kwon YK, Choi SS, Cho KH. Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling. BMC Bioinformatics 2007; 8:384. [PMID: 17935633 PMCID: PMC2100072 DOI: 10.1186/1471-2105-8-384] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2007] [Accepted: 10/15/2007] [Indexed: 11/10/2022] Open
Abstract
Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks. Results We discovered that the number of feedback loops (NuFBL) is a crucial measure for evaluating the importance of a network node and verified this through a signal transduction network in the hippocampal CA1 neuron of mice as well as through generalized biological network models represented by Boolean networks. In particular, we observed that the proteins with a larger NuFBL are more likely to be essential and to evolve slowly in the hippocampal CA1 neuronal signal transduction network. Then, from extensive simulations based on the Boolean network models, we proved that a network node with the larger NuFBL is likely to be more important as the mutations of the initial state or the update rule of such a node made the network converge to a different attractor. These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics. Conclusion The presented analysis on topological characteristics of biological networks showed that the number of feedback loops is positively correlated with the functional importance of network nodes. This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.
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Affiliation(s)
- Yung-Keun Kwon
- Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea.
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