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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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2
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Esmaeilzadeh AA, Kashian M, Salman HM, Alsaffar MF, Jaber MM, Soltani S, Amiri Manjili D, Ilhan A, Bahrami A, Kastelic JW. Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing. BIOLOGY 2022; 11:1851. [PMID: 36552360 PMCID: PMC9776135 DOI: 10.3390/biology11121851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.
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Affiliation(s)
| | - Mahdis Kashian
- Department of Obstetrics and Gynecology, Medical College of Iran University, Tehran 14535, Iran;
| | - Hayder Mahmood Salman
- Department of Computer Science, Al-Turath University College Al Mansour, Baghdad 10011, Iraq;
| | - Marwa Fadhil Alsaffar
- Medical Laboratory Techniques Department, AL-Mustaqbal University College, Hillah 51001, Iraq;
| | - Mustafa Musa Jaber
- Computer Techniques Engineering Department, Dijlah University College, Baghdad 00964, Iraq;
- Computer Techniques Engineering Department, Al-Farahidi University, Baghdad 10011, Iraq
| | - Siamak Soltani
- Department of Forensic Medicine, School of Medicine, Iran University of Medical Sciences, Tehran 14535, Iran;
| | - Danial Amiri Manjili
- Department of Infectious Disease, School of Medicine, Babol University of Medical Sciences, Babol 47414, Iran
| | - Ahmet Ilhan
- Department of Medical Biochemistry, Faculty of Medicine, Cukurova University, Adana 01330, Turkey
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 1417643184, Iran;
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, 80333 Munich, Germany
| | - John W. Kastelic
- Department of Health, University of Calgary, Calgary, AB T2N 1N4, Canada;
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3
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Yang J, Li Z, Wu WKK, Yu S, Xu Z, Chu Q, Zhang Q. Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network. Brief Bioinform 2022; 23:6809964. [PMID: 36347526 DOI: 10.1093/bib/bbac469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/07/2022] [Accepted: 09/29/2022] [Indexed: 11/11/2022] Open
Abstract
The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.
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Affiliation(s)
- Jiannan Yang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - William Ka Kei Wu
- Department of Anaesthesia and Intensive Care, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shi Yu
- The USC Norris Center for Cancer Drug Development, University of Southern California, Los Angeles, CA, USA.,Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhongzhi Xu
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Chu
- Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
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4
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Yan J, Hu Z, Li ZW, Sun S, Guo WF. Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer. Front Oncol 2022; 12:891676. [PMID: 35712516 PMCID: PMC9195174 DOI: 10.3389/fonc.2022.891676] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022] Open
Abstract
Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.
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Affiliation(s)
- Jipeng Yan
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
| | - Zhuo Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Zong-Wei Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi’an, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
| | - Wei-Feng Guo
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Wei-Feng Guo, ; Shiren Sun,
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5
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Chen Z, Chen S, Qiang X. Identification of Biomarker in Brain-specific Gene Regulatory Network Using Structural Controllability Analysis. FRONTIERS IN BIOINFORMATICS 2022; 2:812314. [PMID: 36304271 PMCID: PMC9580899 DOI: 10.3389/fbinf.2022.812314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/05/2022] [Indexed: 12/09/2022] Open
Abstract
Brain tumor research has been stapled for human health while brain network research is crucial for us to understand brain activity. Here the structural controllability theory is applied to study three human brain-specific gene regulatory networks, including forebrain gene regulatory network, hindbrain gene regulatory network and neuron associated cells cancer related gene regulatory network, whose nodes are neural genes and the edges represent the gene expression regulation among the genes. The nodes are classified into two classes: critical nodes and ordinary nodes, based on the change of the number of driver nodes upon its removal. Eight topological properties (out-degree DO, in-degree DI, degree D, betweenness B, closeness CA, in-closeness CI, out-closeness CO and clustering coefficient CC) are calculated in this paper and the results prove that the critical genes have higher score of topological properties than the ordinary genes. Then two bioinformatic analysis are used to explore the biologic significance of the critical genes. On the one hand, the enrichment scores in several kinds of gene databases are calculated and reveal that the critical nodes are richer in essential genes, cancer genes and the neuron related disease genes than the ordinary nodes, which indicates that the critical nodes may be the biomarker in brain-specific gene regulatory network. On the other hand, GO analysis and KEGG pathway analysis are applied on them and the results show that the critical genes mainly take part in 14 KEGG pathways that are transcriptional misregulation in cancer, pathways in cancer and so on, which indicates that the critical genes are related to the brain tumor. Finally, by deleting the edges or routines in the network, the robustness analysis of node classification is realized, and the robustness of node classification is proved. The comparison of neuron associated cells cancer related GRN (Gene Regulatory Network) and normal brain-specific GRNs (including forebrain and hindbrain GRN) shows that the neuron-related cell cancer-related gene regulatory network is more robust than other types.
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Affiliation(s)
- Zhihua Chen
- The Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Siyuan Chen
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoli Qiang
- The Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
- *Correspondence: Xiaoli Qiang,
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6
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Guo WF, Zhang SW, Zeng T, Akutsu T, Chen L. Network control principles for identifying personalized driver genes in cancer. Brief Bioinform 2021; 21:1641-1662. [PMID: 31711128 DOI: 10.1093/bib/bbz089] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 02/02/2023] Open
Abstract
To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.
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Affiliation(s)
- Wei-Feng Guo
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan
| | - Luonan Chen
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.,Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China.,School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China.,Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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7
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Liu X, Maiorino E, Halu A, Glass K, Prasad RB, Loscalzo J, Gao J, Sharma A. Robustness and lethality in multilayer biological molecular networks. Nat Commun 2020; 11:6043. [PMID: 33247151 PMCID: PMC7699651 DOI: 10.1038/s41467-020-19841-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 10/26/2020] [Indexed: 12/27/2022] Open
Abstract
Robustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein-protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system's robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.
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Affiliation(s)
- Xueming Liu
- Key Laboratory of Imaging Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Arda Halu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Rashmi B Prasad
- Genomics Diabetes and Endocrinology, Lund University Diabetes Centre, CRC, Malmö, SE, 20502, Sweden
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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8
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Leitold D, Vathy-Fogarassy Á, Abonyi J. Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox. F1000Res 2019; 8:646. [PMID: 31608146 PMCID: PMC6777013 DOI: 10.12688/f1000research.19029.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/11/2019] [Indexed: 11/20/2022] Open
Abstract
The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.
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Affiliation(s)
- Dániel Leitold
- Department of Computer Science and Systems Technology, University of Pannonia, Egyetem u. 10, Veszprém, 8200, Hungary.,MTA-PE Lendulet Complex Systems Monitoring Research Group, University of Pannonia, Egyetem u. 10, POB. 158, Veszprém, 8200, Hungary
| | - Ágnes Vathy-Fogarassy
- Department of Computer Science and Systems Technology, University of Pannonia, Egyetem u. 10, Veszprém, 8200, Hungary.,MTA-PE Lendulet Complex Systems Monitoring Research Group, University of Pannonia, Egyetem u. 10, POB. 158, Veszprém, 8200, Hungary
| | - János Abonyi
- MTA-PE Lendulet Complex Systems Monitoring Research Group, University of Pannonia, Egyetem u. 10, POB. 158, Veszprém, 8200, Hungary
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9
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Optimal control nodes in disease-perturbed networks as targets for combination therapy. Nat Commun 2019; 10:2180. [PMID: 31097707 PMCID: PMC6522545 DOI: 10.1038/s41467-019-10215-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 04/29/2019] [Indexed: 12/11/2022] Open
Abstract
Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition. Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.
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10
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Leitold D, Vathy-Fogarassy Á, Abonyi J. Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox. F1000Res 2019; 8:646. [DOI: 10.12688/f1000research.19029.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/02/2019] [Indexed: 01/13/2023] Open
Abstract
Network science has become increasingly important in life science over the last decade. The proposed Octave and MATLAB-compatible NOCAD toolbox provides a set of methods which enables the structural controllability and observability analysis of dynamical systems. In this paper, the functionality of the toolbox is presented, and the implemented functions demonstrated.
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11
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Liu X, Pan L, Stanley HE, Gao J. Multiple phase transitions in networks of directed networks. Phys Rev E 2019; 99:012312. [PMID: 30780251 DOI: 10.1103/physreve.99.012312] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Indexed: 11/07/2022]
Abstract
The robustness in real-world complex systems with dependency connectivities differs from that in isolated networks. Although most complex network research has focused on interdependent undirected systems, many real-world networks-such as gene regulatory networks and traffic networks-are directed. We thus develop an analytical framework for examining the robustness of networks made up of directed networks of differing topologies. We use it to predict the phase transitions that occur during node failures and to generate the phase diagrams of a number of different systems, including treelike and random regular (RR) networks of directed Erdős-Rényi (ER) networks and scale-free networks. We find that the the phase transition and phase diagram of networks of directed networks differ from those of networks of undirected networks. For example, the RR networks of directed ER networks show a hybrid phase transition that does not occur in networks of undirected ER networks. In addition, system robustness is affected by network topology in networks of directed networks. As coupling strength q increases, treelike networks of directed ER networks change from a second-order phase transition to a first-order phase transition, and RR networks of directed ER networks change from a second-order phase transition to a hybrid phase transition, then to a first-order phase transition, and finally to a region of collapse. We also find that heterogenous network systems are more robust than homogeneous network systems. We note that there are multiple phase transitions and triple points in the phase diagram of RR networks of directed networks and this helps us understand how to increase network robustness when designing interdependent infrastructure systems.
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Affiliation(s)
- Xueming Liu
- Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.,Department of Physics, Center for Polymer Studies, Boston University, Boston, Massachusetts 02215, USA
| | - Linqiang Pan
- Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - H Eugene Stanley
- Department of Physics, Center for Polymer Studies, Boston University, Boston, Massachusetts 02215, USA
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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12
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Li M, Gao H, Wang J, Wu FX. Control principles for complex biological networks. Brief Bioinform 2018; 20:2253-2266. [DOI: 10.1093/bib/bby088] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 08/16/2018] [Accepted: 08/18/2018] [Indexed: 11/13/2022] Open
Abstract
Abstract
Networks have been widely used to model the structure of various biological systems. Currently, a series of approaches have been developed to construct reliable biological networks. However, the ultimate understanding of a biological system is to steer its states to the desired ones by imposing signals. The control process is dominated by the intrinsic structure and the dynamic propagation. To understand the underlying mechanisms behind the life process, the control theory can be applied to biological networks with specific target requirements. In this article, we first introduce the structural controllability of complex networks and discuss its advantages and disadvantages. Then, we review the effective control to meet the specific requirements for complex biological networks. Moreover, we summarize the existing methods for finding the unique minimum set of driver nodes via the optimal control for complex networks. Finally, we discuss the relationships between biological networks and structural controllability, effective control and optimal control. Moreover, potential applications of general control principles are pointed out.
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Affiliation(s)
- Min Li
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Hao Gao
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
| | - Fang-Xiang Wu
- School of Information Science and Engineering, Central South University, Changsha, Hunan, China
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada
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13
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Chu Y, Wang Z, Wang R, Zhang N, Li J, Hu Y, Teng M, Wang Y. WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network. J Bioinform Comput Biol 2017; 15:1750021. [DOI: 10.1142/s0219720017500214] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability. In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder. The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .
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Affiliation(s)
- Yanshuo Chu
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Zhenxing Wang
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Rongjie Wang
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Ningyi Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Mingxiang Teng
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, P. R. China
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14
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Wu L, Tang L, Li M, Wang J, Wu FX. Biomolecular Network Controllability With Drug Binding Information. IEEE Trans Nanobioscience 2017; 16:326-332. [DOI: 10.1109/tnb.2017.2705106] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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15
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Liu X, Pan L, Stanley HE, Gao J. Controllability of giant connected components in a directed network. Phys Rev E 2017; 95:042318. [PMID: 28505769 DOI: 10.1103/physreve.95.042318] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Indexed: 02/02/2023]
Abstract
When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=p_{m}. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by p_{m}〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.
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Affiliation(s)
- Xueming Liu
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.,Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA
| | - Linqiang Pan
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.,School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
| | - H Eugene Stanley
- Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA
| | - Jianxi Gao
- Center for Complex Network Research and Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
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16
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Zheng H, Wang C, Wang H. Analysis of Organization of the Interactome Using Dominating Sets: A Case Study on Cell Cycle Interaction Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:282-289. [PMID: 28368806 DOI: 10.1109/tcbb.2015.2459712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this study, a minimum dominating set based approach was developed and implemented as a Cytoscape plugin to identify critical and redundant proteins in a protein interaction network. We focused on the investigation of the properties associated with critical proteins in the context of the analysis of interaction networks specific to cell cycle in both yeast and human. A total of 132 yeast genes and 129 human proteins have been identified as critical nodes while 950 in yeast and 980 in human have been categorized as redundant nodes. A clear distinction between critical and redundant proteins was observed when examining their topological parameters including betweenness centrality, suggesting a central role of critical proteins in the control of a network. The significant differences in terms of gene coexpression and functional similarity were observed between the two sets of proteins in yeast. Critical proteins were found to be enriched with essential genes in both networks and have a more deleterious effect on the network integrity than their redundant counterparts. Furthermore, we obtained statistically significant enrichments of proteins that govern human diseases including cancer-related and virus-targeted genes in the corresponding set of critical proteins.
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17
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Sun X, Hu F, Wu S, Qiu X, Linel P, Wu H. Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human. Infect Dis Model 2016; 1:52-70. [PMID: 29928721 PMCID: PMC5963324 DOI: 10.1016/j.idm.2016.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 07/08/2016] [Indexed: 12/20/2022] Open
Abstract
Background Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. Results Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. Conclusions The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.
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Affiliation(s)
- Xiaodian Sun
- Biostatistics and Bioinformatics Core, Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA
| | - Fang Hu
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Shuang Wu
- Genus PLC, ABS Global, Deforest, WI, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Hulin Wu
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
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18
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Liu X, Pan L. Identifying Driver Nodes in the Human Signaling Network Using Structural Controllability Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:467-472. [PMID: 26357232 DOI: 10.1109/tcbb.2014.2360396] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.
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Wu L, Shen Y, Li M, Wu FX. Network output controllability-based method for drug target identification. IEEE Trans Nanobioscience 2015; 14:184-91. [PMID: 25643411 DOI: 10.1109/tnb.2015.2391175] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.
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