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Li M, Zheng R, Li Y, Wu FX, Wang J. MGT-SM: A Method for Constructing Cellular Signal Transduction Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:417-424. [PMID: 28541220 DOI: 10.1109/tcbb.2017.2705143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A cellular signal transduction network is an important means to describe biological responses to environmental stimuli and exchange of biological signals. Constructing the cellular signal transduction network provides an important basis for the study of the biological activities, the mechanism of the diseases, drug targets and so on. The statistical approaches to network inference are popular in literature. Granger test has been used as an effective method for causality inference. Compared with bivariate granger tests, multivariate granger tests reduce the indirect causality and were used widely for the construction of cellular signal transduction networks. A multivariate Granger test requires that the number of time points in the time-series data is more than the number of nodes involved in the network. However, there are many real datasets with a few time points which are much less than the number of nodes in the network. In this study, we propose a new multivariate Granger test-based framework to construct cellular signal transduction network, called MGT-SM. Our MGT-SM uses SVD to compute the coefficient matrix from gene expression data and adopts Monte Carlo simulation to estimate the significance of directed edges in the constructed networks. We apply the proposed MGT-SM to Yeast Synthetic Network and MDA-MB-468, and evaluate its performance in terms of the recall and the AUC. The results show that MGT-SM achieves better results, compared with other popular methods (CGC2SPR, PGC, and DBN).
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Bansal M, He J, Peyton M, Kustagi M, Iyer A, Comb M, White M, Minna JD, Califano A. Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis. PLoS One 2019; 14:e0208646. [PMID: 30615629 PMCID: PMC6322741 DOI: 10.1371/journal.pone.0208646] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/20/2018] [Indexed: 12/26/2022] Open
Abstract
To understand drug combination effect, it is necessary to decipher the interactions between drug targets-many of which are signaling molecules. Previously, such signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including "novel' substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.
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Affiliation(s)
- Mukesh Bansal
- Psychogenics Inc., Paramus, New Jersey, United States of America
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Jing He
- Department of Systems Biology, Columbia University, New York, NY, United States of America
- Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, United States of America
- Department of Biomedical Informatics (DBMI), Columbia University, New York, NY, United States of America
| | - Michael Peyton
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - Manjunath Kustagi
- Department of Systems Biology, Columbia University, New York, NY, United States of America
| | - Archana Iyer
- Department of Systems Biology, Columbia University, New York, NY, United States of America
| | - Michael Comb
- Cell Signaling Technology, 3 Trask Lane, Danvers, MA, United States of America
| | - Michael White
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America
| | - John D. Minna
- Hamon Center for Therapeutic Oncology Research, Simmons Comprehensive Cancer Center, Departments of Pharmacology, and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Andrea Califano
- Department of Systems Biology, Columbia University, New York, NY, United States of America
- Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY, United States of America
- Department of Biomedical Informatics (DBMI), Columbia University, New York, NY, United States of America
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, United States of America
- Institute for Cancer Genetics, Columbia University, New York, NY, United States of America
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, United States of America
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Alim MA, Ay A, Hasan MM, Thai MT, Kahveci T. Construction of Signaling Pathways with RNAi Data and Multiple Reference Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1079-1091. [PMID: 30102599 DOI: 10.1109/tcbb.2017.2710129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Signaling networks are involved in almost all major diseases such as cancer. As a result of this, understanding how signaling networks function is vital for finding new treatments for many diseases. Using gene knockdown assays such as RNA interference (RNAi) technology, many genes involved in these networks can be identified. However, determining the interactions between these genes in the signaling networks using only experimental techniques is very challenging, as performing extensive experiments is very expensive and sometimes, even impractical. Construction of signaling networks from RNAi data using computational techniques have been proposed as an alternative way to solve this challenging problem. However, the earlier approaches are either not scalable to large scale networks, or their accuracy levels are not satisfactory. In this study, we integrate RNAi data given on a target network with multiple reference signaling networks and phylogenetic trees to construct the topology of the target signaling network. In our work, the network construction is considered as finding the minimum number of edit operations on given multiple reference networks, in which their contributions are weighted by their phylogenetic distances to the target network. The edit operations on the reference networks lead to a target network that satisfies the RNAi knockdown observations. Here, we propose two new reference-based signaling network construction methods that provide optimal results and scale well to large-scale signaling networks of hundreds of components. We compare the performance of these approaches to the state-of-the-art reference-based network construction method SiNeC on synthetic, semi-synthetic, and real datasets. Our analyses show that the proposed methods outperform SiNeC method in terms of accuracy. Furthermore, we show that our methods function well even if evolutionarily distant reference networks are used. Application of our methods to the Apoptosis and Wnt signaling pathways recovers the known protein-protein interactions and suggests additional relevant interactions that can be tested experimentally.
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Ren Y, Wang Q, Hasan MM, Ay A, Kahveci T. Identifying the topology of signaling networks from partial RNAi data. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 2:53. [PMID: 27490106 PMCID: PMC4977480 DOI: 10.1186/s12918-016-0301-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background Methods for inferring signaling networks using single gene knockdown RNAi experiments and reference networks have been proposed in recent years. These methods assume that RNAi information is available for all the genes in the signal transduction pathway, i.e., complete. This assumption does not always hold up since RNAi experiments are often incomplete and information for some genes is missing. Results In this article, we develop two methods to construct signaling networks from incomplete RNAi data with the help of a reference network. These methods infer the RNAi constraints for the missing genes such that the inferred network is closest to the reference network. We perform extensive experiments with both real and synthetic networks and demonstrate that these methods produce accurate results efficiently. Conclusions Application of our methods to Wnt signal transduction pathway has shown that our methods can be used to construct highly accurate signaling networks from experimental data in less than 100 ms. The two methods that produce accurate results efficiently show great promise of constructing real signaling networks.
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Affiliation(s)
- Yuanfang Ren
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA.
| | - Qiyao Wang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA
| | - Md Mahmudul Hasan
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA
| | - Ahmet Ay
- Department of Biology & Mathematics, Colgate University, Hamilton, 13346, NY, USA
| | - Tamer Kahveci
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, 32611, FL, USA
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Budak G, Eren Ozsoy O, Aydin Son Y, Can T, Tuncbag N. Reconstruction of the temporal signaling network in Salmonella-infected human cells. Front Microbiol 2015; 6:730. [PMID: 26257716 PMCID: PMC4507143 DOI: 10.3389/fmicb.2015.00730] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 07/03/2015] [Indexed: 12/02/2022] Open
Abstract
Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.
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Affiliation(s)
- Gungor Budak
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey
| | - Oyku Eren Ozsoy
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey
| | - Yesim Aydin Son
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey
| | - Tolga Can
- Department of Computer Engineering, College of Engineering, Middle East Technical University Ankara, Turkey
| | - Nurcan Tuncbag
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey
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Rodriguez A, Crespo I, Fournier A, del Sol A. Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET. PLoS One 2015; 10:e0127216. [PMID: 26058016 PMCID: PMC4461287 DOI: 10.1371/journal.pone.0127216] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 04/13/2015] [Indexed: 01/09/2023] Open
Abstract
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.
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Affiliation(s)
- Ana Rodriguez
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Isaac Crespo
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Anna Fournier
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
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Controllability of time-delayed Boolean multiplex control networks under asynchronous stochastic update. Sci Rep 2014; 4:7522. [PMID: 25516009 PMCID: PMC4268650 DOI: 10.1038/srep07522] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 11/27/2014] [Indexed: 12/28/2022] Open
Abstract
In this article, the controllability of asynchronous Boolean multiplex control networks (ABMCNs) with time delay is studied. Firstly, dynamical model of Boolean multiplex control networks is constructed, which is assumed to be under Harvey' asynchronous update and time delay is introduced both in states and controls. By using of semi-tensor product (STP) approach, the logical dynamics is converted into an equivalent algebraic form by obtaining the control-depending network transition matrices of delayed system. Secondly, a necessary and sufficient condition is proved that only control-depending fixed points of the studied dynamics can be controlled with probability one. Thirdly, respectively for two types of controls, the controllability of dynamical control system is investigated. When initial states and time delay are given, formulae are obtained to show a) the reachable set at time s under specified controls; b) the reachable set at time s under arbitrary controls; c) the reachable probabilities to different destination states. Furthermore, an approach is discussed to find a precise control sequence which can steer dynamical system into a specified target with the maximum reachable probability. Examples are shown to illustrate the feasibility of the proposed scheme.
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Knapp B, Kaderali L. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming. PLoS One 2013; 8:e69220. [PMID: 23935958 PMCID: PMC3728289 DOI: 10.1371/journal.pone.0069220] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Accepted: 06/06/2013] [Indexed: 12/23/2022] Open
Abstract
Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
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Affiliation(s)
- Bettina Knapp
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- ViroQuant Research Group Modeling, BioQuant, Heidelberg University, Heidelberg, Germany
| | - Lars Kaderali
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- ViroQuant Research Group Modeling, BioQuant, Heidelberg University, Heidelberg, Germany
- * E-mail:
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Ozsoy OE, Can T. A divide and conquer approach for construction of large-scale signaling networks from PPI and RNAi data using linear programming. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:869-883. [PMID: 24334382 DOI: 10.1109/tcbb.2013.80] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.
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Affiliation(s)
| | - Tolga Can
- Middle East Technical University, Ankara
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Luo C, Wang X. Algebraic representation of asynchronous multiple-valued networks and its dynamics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:927-938. [PMID: 24334387 DOI: 10.1109/tcbb.2013.112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, dynamics of asynchronous multiple-valued networks (AMVNs) are investigated based on linear representation. By semitensor product of matrices, we convert AMVNs into the discrete-time linear representation. A general formula to calculate all of network transition matrices of a specific AMVN is achieved. A necessary and sufficient algebraic criterion to determine whether a given state belongs to loose attractors of length s is proposed. Formulas for the numbers of attractors in AMVNs are provided. Finally, algorithms are presented to detect all of the attractors and basins. Examples are shown to demonstrate the feasibility of the proposed scheme.
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Affiliation(s)
- Chao Luo
- Dalian University of Technology, Dalian
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