論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available
論文名稱 Title |
結合知識與數據導向建模方法推導基因調控網路 Combining Knowledge-Driven and Data-Driven Modeling Approaches in Gene Regulatory Networks Inference |
||
系所名稱 Department |
|||
畢業學年期 Year, semester |
語文別 Language |
||
學位類別 Degree |
頁數 Number of pages |
150 |
|
研究生 Author |
|||
指導教授 Advisor |
|||
召集委員 Convenor |
|||
口試委員 Advisory Committee |
|||
口試日期 Date of Exam |
2013-11-08 |
繳交日期 Date of Submission |
2013-11-26 |
關鍵字 Keywords |
先備知識、S 系統、演化式演算法、基因調控網路、參數敏感度分析 Gene Regulatory Networks, Evolutionary Algorithms, S-system, Prior Knowledge, Parameter Sensitivity Analysis |
||
統計 Statistics |
本論文已被瀏覽 6011 次,被下載 117 次 The thesis/dissertation has been browsed 6011 times, has been downloaded 117 times. |
中文摘要 |
系統生物學是研究一個生物系統中「基因、RNA、蛋白質、與分子元件」的構成,以及不同元件間交互影響的科學。後基因體時代,首要任務是如何從大量的基因資訊中整理出其對生物體組織結構發展的影響,因而系統生物學科已引起學者高度重視。此學科中,以逆向工程方式從表現資料建構並描繪基因表現的基因調控網路的研究被視為能了解不同基因元件的互動過程;其中,基因表現資料是基因轉錄成mRNA時所量測的資料,理解這些數據資料將有助於瞭解基因網路複雜的結構與調控流程。本研究以逆向工程演算法為基礎探討推導調控網路的研究中,如何有效結合生物知識與演化式演算法建構基因調控網路之模型,並以結構性方式賦予計算模型最適當的生物意涵,而後能使產生出的結果回饋予生物學家進一步的研究發想。基於研究目的,本論文主要分為三大部分完成:(1)從現有文獻中,改良一套更有效率的演化式演算法推導基因調控網路;(2)探討若真實生物網路的調控關係尚未明確時,是否能以計算模型先行判斷調控網路中重要的調控參數,從而輔助生物學者解讀生物系統。此階段以參數敏感度分析、蒙特卡羅模擬法,結合演化式演算法進行實驗;實驗結果已與真實網路系統「SOS DNA repair system」驗證,證明計算模型與基因調控網路的參數對應關係;以及(3)從已知生物知識建立可數學模型化的調控關係,並進一步探討如何從已知的生物知識(Prior knowledge)獲得基因之間的調控規則,並將調控規則帶入計算模型中,限定演化式演算法的解空間搜尋,以求得同時符合計算與原調控模型之參數值。實驗結果證明本論文提出的研究架構與演算法能有效結合基因表現資料與生物知識並用以賦予計算模型適當的生物意涵,以及將其推導結果提供生物學家後續進行研究的參考資訊。 |
Abstract |
In the emergence of post-genomic research, one of the most important themes is to uncover the complex biological mechanisms involved in genetic regulation. The regulatory interactions controlled by cis-regulatory DNA modules provide clues about the development of biological processes. These regulatory links can be represented as network-like architectures, i.e. gene regulatory networks (GRNs), which indicate the causal gene expression relationships between instructional inputs and functional outputs of genes. Modeling GRNs, therefore, is essential for conceptualizing how genes express themselves as well as influence others. Thanks to modern measurement techniques for gene expression, researchers can investigate phenotypic behavior of a living being by reconstructing GRNs from expression data. Typically a reverse engineering approach is employed; it is an effective strategy to reproduce possible fitting models of GRNs. Under this strategy, however, two daunting tasks must be undertaken. One is to optimize the accuracy of inferred network behaviors; the other is to designate valid biological topologies for target networks. Though existing studies have addressed the two tasks for years, few are able to satisfy both requirements simultaneously. To cope with the difficulties, this thesis proposes an integrative modeling framework which consists of three aspects. First, a novel reverse engineering algorithm is developed to tackle the issue of efficiently optimizing network behaviors for GRNs. Second, a proposed sensitivity analysis approach coupling with the optimization algorithm is designed to identify critical regulatory interactions under the situation where biological knowledge is unavailable. Finally, an integrated modeling approach combining knowledge-based and data-driven input sources is constructed to conduct biological topologies with corresponding network behaviors. For each aspect, a series of experiments are performed. The results reveal that the proposed framework can successfully infer solutions that are satisfactory for both requirements of network behaviors and biological structures, and thus the outcomes are exploitable for future in vivo experimental design. |
目次 Table of Contents |
CHAPTER 1 INTRODUCTION 1 CHAPTER 2 BACKGROUND AND RELATED WORK 6 2.1 GENE REGULATORY NETWORKS 6 2.2 MICROARRAY GENE EXPRESSION DATA 7 2.3 COMPUTATIONAL MODELING METHODS FOR GENE REGULATORY NETWORKS 8 CHAPTER 3 INFERRING GENE REGULATORY NETWORKS BY A PROPOSED EVOLUTIONARY ALGORITHM – A HYBRID GA-PSO APPROACH 10 3.1 INTRODUCTION 10 3.2 COMPUTATIONAL MODEL 13 3.2.1 Evaluation function for the computational model15 3.3 AN ADAPTIVE GA-PSO HYBRID APPROACH 17 3.4 EXPERIMENT RESULTS AND DISCUSSION 23 3.4.1 Performance evaluation for inferring networks 24 3.4.2 Robustness test 33 3.4.3 Comparison with recent evolutionary algorithms 36 3.5 CHAPTER DISCUSSION AND SUMMARY 41 CHAPTER 4 A DATA-DRIVEN MODELING APPROACH BY A PROPOSED SENSITIVITY-BASED INCREMENTAL EVOLUTION METHOD 42 4.1 INTRODUCTION 42 4.2 NETWORK ROBUSTNESS FOR GRNS MODELING 43 4.3 SENSITIVITY ANALYSIS 45 4.4 PROPOSED PARAMETER SENSITIVITY ANALYSIS 46 4.4.1 Sensitivity analysis algorithm 48 4.4.1.1 A walkthrough example of m-MPSA 50 4.5 PROPOSED SENSITIVITY-BASED INCREMENTAL EVOLUTION ALGORITHM FOR GRNS INFERENCE 54 4.6 EXPERIMENT RESULTS AND DISCUSSION 58 4.6.1 Performance evaluation of sensitivity analysis in network modeling 59 4.6.2 Evaluation of network robustness 62 4.6.2.1 Evaluation of network robustness – a walkthrough example 63 4.6.3 Evaluation of the proposed approach on the SOS repair system in Ecoli67 4.6.3.1 Analysis of critical parameters of the SOS repair system 72 4.6.3.2 Patterns derived from inferred models 74 4.7 CHAPTER DISCUSSION AND SUMMARY 79 CHAPTER 5 A KNOWLEDGE-DRIVEN MODELING APPROACH FOR BIOLOGICAL NETWORK INFERENCE 80 5.1 INTRODUCTION 80 5.2 PRELIMINARY RESEARCH – ANALYSIS OF CONSTRAINT-BASED INFERENCE84 5.2.1 Evolution of knowledge-based constraints 86 5.3 THE PROPOSED KNOWLEDGE-DRIVEN MODELING FRAMEWORK 92 5.3.1 The source of PKDs selection and construction 92 5.3.2 Data pre-processing and integration 93 5.3.3 Computational model 95 5.3.4 Mapping prior knowledge onto S-system 95 5.3.5 PKDs modeling algorithm using the reverse-engineering approach 97 5.4 EXPERIMENT RESULTS AND DISCUSSION 101 5.4.1 The performance of proposed framework on the artificial dataset 102 5.4.2 Evaluation of the PKDs modeling algorithm on yeast Scerevisiae 107 5.5 CHAPTER DISCUSSION AND SUMMARY 113 CHAPTER 6 CONCLUSION AND FUTURE WORK 114 6.1 CONCLUSION 114 6.2 FUTURE WORK 116 APPENDIX 118 REFERENCE 125 |
參考文獻 References |
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, k., et al. (2008) Molecular biology of the cell. Garland Science, New York, USA. Alterovitz, G. and Ramoni, M.F. (2010) Knowledge-based bioinformatics: from analysis to interpretation. Wiley, Chichester, UK. Baeza-Yates, R. and Ribeiro-Neto, B. (1999) Modern information retrieval. Addison-Wesley, New York, USA. Bansal, M., Gatta, G.D. and di Bernardo, D. (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics: 22, 815-822. Barkai, N. and Shilo, B.-Z. (2007) Variability and robustness in biomolecular systems. Molecular Cell: 28, 755-760. Bentele, M., Lavrik, I., Ulrich, M., Stosser, S., Heermann, D., et al. (2004) Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. The Journal of Cell Biology: 166, 839. Bishop, C.M. (1994) Training with noise is equivalent to Tikhonov regularization. Neural Computation: 7, 108-116. Bolouri, H. (2008) Computional modeling of gene regulatory networks - a primer. Imperial College Press, London, UK. Bornholdt, S. (2005) Systems biology: less is more in modeling large genetic networks. Science Signaling: 310, 449. Branzei, D. and Foiani, M. (2008) Regulation of DNA repair throughout the cell cycle. Nature Reviews Molecular Cell Biology: 9, 297-308. Butte, A. (2002) The use and analysis of microarray data. Nature Reviews Drug Discovery: 1, 951-960. Cao, H., Kang, L., Chen, Y. and Yu, J. (2000) Evolutionary modeling of systems of ordinary differential equations with genetic programming. Genetic Programming and Evolvable Machines: 1, 309-337. Chen, L., Wang, R.-S. and Zhang, X.-S. (2009) Biomolecular networks: methods and applications in systems biology. John Wiley & Sons, Hoboken, New Jersey. Cherry, J.M., Adler, C., Ball, C., Chervitz, S.A., Dwight, S.S., et al. (1998) SGD: Saccharomyces genome database. Nucleic Acids Research: 26, 73-79. Cho, D.-Y., Cho, K.-H. and Zhang, B.-T. (2006) Identification of biochemical networks by S-tree based genetic programming. Bioinformatics: 22, 1631-1640. Cho, K., Choo, S., Jung, S., Kim, J., Choi, H., et al. (2007) Reverse engineering of gene regulatory networks. IET Systems Biology: 1, 149-163. Cho, K., Shin, S., Kolch, W. and Wolkenhauer, O. (2003) Experimental design in systems biology, based on parameter sensitivity analysis using a monte carlo method: a case study for the TNFα-Mediated NF-kB signal transduction pathway. Simulation: 79, 726-729. Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell: 2, 65-73. Chou, I.C. and Voit, E.O. (2009) Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences: 219, 57. Ciliberti, S., Martin, O.C. and Wagner, A. (2007) Innovation and robustness in complex regulatory gene networks. Proceedings of the National Academy of Sciences of the United States of America: 104, 13591-13596. Clerc, M. and Kennedy, J. (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation: 6, 58-73. Costanzo, M., Baryshnikova, A., Myers, C.L., Andrews, B. and Boone, C. (2011) Charting the genetic interaction map of a cell. Current Opinion in Biotechnology: 22, 66-74. Crick., F. (1958) On protein synthesis. Symposium of the Society of Experimental Biology: 12, 138–167. Csete, M.E. and Doyle, J.C. (2002) Reverse engineering of biological complexity. Science: 295, 1664-1669. Das, S., Mukhopadhyay, A., Roy, A., Abraham, A. and Panigrahi, B.K. (2011) Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics: 41, 89-106. Davidson, E. and Levin, M. (2005) Gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America: 102, 4935-4935. De Jong, H. (2002) Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology: 9, 67-103. Degenring, D., Froemel, C., Dikta, G. and Takors, R. (2004) Sensitivity analysis for the reduction of complex metabolism models. Journal of Process Control: 14, 729-745. Doerfler, L., Harris, L., Viebranz, E. and Schmidt, K.H. (2011) Differential genetic interactions between Sgs1, DNA-damage checkpoint components and DNA repair factors in the maintenance of chromosome stability. Genome Integrity: 2, 8. Eberhart, R.C. and Shi, Y. (2000) Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of IEEE the Congress on Evolutionary Computation. pp. 84-88. Eduati, F., De Las Rivas, J., Di Camillo, B., Toffolo, G. and Saez-Rodriguez, J. (2012) Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics: 28, 2311-2317. Elhossini, A., Areibi, S. and Dony, R. (2010) Strength pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evolutionary Computation: 18, 127-156. Emmert-Streib, F., Glazko, G.V., Altay, G. and de Matos Simoes, R. (2012) Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in Genetics: 3. Engelbrecht, A.P. (2005) Fundamentals of computational swarm intelligence. Wiley. Feng, X., Hooshangi, S., Chen, D., Li, G., Weiss, R., et al. (2004) Optimizing genetic circuits by global sensitivity analysis. Biophysical Journal: 87, 2195-2202. Fomekong-Nanfack, Y., Postma, M. and Kaandorp, J. (2009) Inferring Drosophila gap gene regulatory network: a parameter sensitivity and perturbation analysis. BMC Systems Biology: 3, 94. Fu, Y., Pastushok, L. and Xiao, W. (2008) DNA damage-induced gene expression in Saccharomyces cerevisiae. FEMS microbiology reviews: 32, 908-926. Greenfield, A., Hafemeister, C. and Bonneau, R. (2013) Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics: 29, 1060-1067. Griffiths, P.E. and Stotz, K. (2006) Genes in the postgenomic era. Theoretical Medicine and Bioethics: 27, 499-521. Grimaccia, F., Mussetta, M. and Zich, R.E. (2007) Genetical swarm optimization: self-adaptive hybrid evolutionary algorithm for electromagnetics. IEEE Transactions on Antennas and Propagation: 55, 781-785. Guan, S.U. and Zhang, S. (2003) Incremental evolution of cellular automata for random number generation. International Journal of Modern Physics C: 14, 881-896. Gunawardena, J. (2010) Models in systems biology: the parameter problem and the meanings of robustness. Elements of Computational Systems Biology: 1, 21-47. Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. and Guthke, R. (2009) Gene regulatory network inference: data integration in dynamic models — a review. Biosystems: 96, 86-103. Hlavacek, W.S. and Savageau, M.A. (1996) Rules for coupled expression of regulator and effector genes in inducible circuits. Journal of Molecular Biology: 255, 121-139. Ho, S.-Y., Hsieh, C.-H., Yu, F.-C. and Huang, H.-L. (2007) An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics: 4, 648-704. Hovatta, I., Kimppa, K., Lehmussola, A., Pasanen, T., Saarela, J., et al. (2005) DNA microarray data analysis. CSC, Scientific Computing Ltd, Helsinki. Hsiao, Y.-T. and Lee, W.-P. (2012) Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method. BMC Bioinformatics: 13(Suppl 7):S8. Huang, H.-L., Chen, K.-W., Ho, S.-J. and Ho, S.-Y. (2008) Inferring S-system models of genetic networks from a time-series real data set of gene expression profiles. Proceedings of the IEEE Congress on Evolutionary Computation. pp. 2788-2793. Huber, W., Heydebreck, A. and Vingron, M. (2003) Analysis of microarray gene expression data. Handbook of Statistical Genetics. Wiley, New York. Jaqaman, K. and Danuser, G. (2006) Linking data to models: data regression. Nature Reviews Molecular Cell Biology: 7, 813-819. Kabir, M., Noman, N. and Iba, H. (2010) Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinformatics: 11, S56. Karlebach, G. and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nature Reviews Molecular Cell Biology: 9, 770-780. Keedwell, E. and Narayanan, A. (2005) Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Transactions on Computational Biology and Bioinformatics: 2, 231-242. Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K. and Tomita, M. (2003) Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics: 19, 643-650. Kimura, S., Hatakeyama, M. and Konagaya, A. (2003) Inference of S-system models of genetic networks using a genetic local search. The 2003 Congress on Evolutionary Computation. IEEE, pp. 631-638. Kimura, S., Ide, K., Kashihara, A., Kano, M., Hatakeyama, M., et al. (2005) Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics: 21, 1154-1163. Kimura, S., Nakayama, S. and Hatakeyama, M. (2009) Genetic network inference as a series of discrimination tasks. Bioinformatics: 25, 918-925. Kimura, S., Sonoda, K., Yamane, S., Maeda, H., Matsumura, K., et al. (2008) Function approximation approach to the inference of reduced ngnet models of genetic networks. BMC Bioinformatics: 9, 23. King, H.C. and Sinha, A.A. (2001) Gene expression profile analysis by DNA microarrays. The journal of the American Medical Association: 286, 2280-2288. Kitano, H. (2004) Biological robustness. Nature Reviews Genetics: 5, 826-837. Klipp, E., Liebermeister, W., Wierling, C., Kowald, A., Lehrach, H., et al. (2009) Systems biology: a textbook. Wiley-VCH, Weinheim, Germany. Klose, J. (1999) Genotypes and phenotypes. Electrophoresis: 20, 643-652. Lee, I., Li, Z. and Marcotte, E.M. (2007) An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae. PloS one: 2, e988. Lee, W.-P. and Hsiao, Y.-T. (2011) An adaptive GA-PSO approach with gene clustering to infer S-system models of gene regulatory network. The Computer Journal: 54, 1449-1464. Lee, W.-P. and Hsiao, Y.-T. (2012) Inferring gene regulatory networks using a hybrid GA-PSO approach with numerical constraints and network decomposition. Information Sciences: 188, 80-99. Lee, W.-P. and Tzou, W.-S. (2009) Computational methods for discovering gene networks from expression data. Briefings in Bioinformatics: 10, 408-423. Lee, W.-P. and Yang, K.-C. (2008) A clustering-based approach for inferring recurrent neural networks as gene regulatory networks. Neurocomputing: 71, 600-610. Lee, Y., Chen, P.-W. and Voit, E.O. (2011) Analysis of operating principles with S-system models. Mathematical Biosciences: 231, 49-60. Leloup, J. and Goldbeter, A. (2004) Modeling the mammalian circadian clock: sensitivity analysis and multiplicity of oscillatory mechanisms. Journal of Theoretical Biology: 230, 541-562. Liu, P.-K. and Wang, F.-S. (2008) Inference of biochemical network models in S-system using multiobjective optimization approach. Bioinformatics: 24, 1085-1092. Lodhi, H.M. and Muggleton, S.H. (2010) Elements of computational systems biology. Wiley, Hoboken, US. Müssel, C., Hopfensitz, M. and Kestler, H.A. (2010) BoolNet—an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics: 26, 1378-1380. MacNeil, L.T. and Walhout, A.J.M. (2011) Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Research: 21, 645-657. Mager, W.H. and Winderickx, J. (2005) Yeast as a model for medical and medicinal research. Trends in Pharmacological Sciences: 26, 265-273. Mahdavi, A., Davey, R., Bhola, P., Yin, T. and Zandstra, P. (2007) Sensitivity analysis of intracellular signaling pathway kinetics predicts targets for stem cell fate control. PLoS Computational Biology: 3, e130. Maki, Y., Ueda, T., Okamoto, M., Uematsu, N., Inamura, K., et al. (2002) Inference of genetic network using the expression profile time course data of mouse P19 cells. Genome Informatics, 382-383. Mallipeddi, R., Mallipeddi, S. and Suganthan, P. (2010) Ensemble strategies with adaptive evolutionary programming. Information Sciences: 180, 1571-1581. Marino, S., Hogue, I.B., Ray, C.J. and Kirschner, D.E. (2008) A methodology for performing global uncertainty and sensitivity analysis in systems biology. Journal of Theoretical Biology: 254, 178-196. Martin, S., Zhang, Z., Martino, A. and Faulon, J.L. (2007) Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics: 23, 866-874. McLachlan, G., Do, K.-A. and Ambroise, C. (2004) Analyzing microarray gene expression data. John Wiley & Sons, New Jersey. Mehra, S., Hu, W.-S. and Karypis, G. (2004) A Boolean algorithm for reconstructing the structure of regulatory networks. Metabolic engineering: 6, 326-339. Michalewicz, Z. (1999) Genetic algorithms + data structures. Springer. Noman, N. and Iba, H. (2005) Inference of gene regulatory networks using S-system and differential evolution. Proceedings of the 2005 Conference on Genetic and Evolutionary Computation. Citeseer, Washington, DC, pp. 439-446. Noman, N. and Iba, H. (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Transactions on Computational Biology and Bioinformatics: 4, 634-647. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., et al. (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research: 27, 29-34. Pan, X., Ye, P., Yuan, D.S., Wang, X., Bader, J.S., et al. (2006) A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell: 124, 1069-1081. Pechenick, D.A., Moore, J.H. and Payne, J.L. (2013) The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth. Journal of Theoretical Biology. Pico, A.R., Kelder, T., van Iersel, M.P., Hanspers, K., Conklin, B.R., et al. (2008) WikiPathways: pathway editing for the people. PLoS Biology: 6, e184. Pratt, B., Howbert, J.J., Tasman, N.I. and Nilsson, E.J. (2012) MR-Tandem: parallel X! tandem using hadoop mapReduce on amazon web services. Bioinformatics: 28, 136-137. Przytycka, T.M. and Kim, Y.-A. (2010) Network integration meets network dynamics. BMC Biology: 8, 48. Radhakrishnan, K., Edwards, J.S., Lidke, D.S., Jovin, T.M., Wilson, B.S., et al. (2009) Sensitivity analysis predicts that the ERK-pMEK interaction regulates ERK nuclear translocation. IET Systems Biology: 3, 329-341. Ronen, M., Rosenberg, R., Shraiman, B.I. and Alon, U. (2002) Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proceedings of the National Academy of Sciences of the United States of America: 99, 10555-10560. Rué, P. and Garcia-Ojalvo, J. (2013) Modeling gene expression in time and space. Annual Review of Biophysics: 42, 605-627. Sîrbu, A., Ruskin, H. and Crane, M. (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics: 11, 59. Saithong, T., Bumee, S., Liamwirat, C. and Meechai, A. (2012) Analysis and practical guideline of constraint-based Boolean method in genetic network inference. PloS one: 7, e30232. Savageau, M.A. and Rosen, R. (1976) Biochemical systems analysis: a study of function and design in molecular biology. Addison-Wesley, Reading, Mass, USA. Schlitt, T. and Brazma, A. (2007) Current approaches to gene regulatory network modelling. BMC Bioinformatics: 8, S9. Schwefel, H.-P. (1981) Numerical optimization of computer models. John Wiley & Sons, Inc., New York, NY. Settles, M., Nathan, P. and Soule, T. (2005) Breeding swarms: a new approach to recurrent neural network training. Proceedings of the 2005 conference on Genetic and evolutionary computation. pp. 185-192. Shi, Y. and Eberhart, R. (1999) Empirical study of particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation. Washington, DC , USA. Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell: 9, 3273-3297. Spieth, C., Streichert, F., Speer, N. and Zell, A. (2004) Optimizing topology and parameters of gene regulatory network models from time-series experiments. Genetic and Evolutionary Computation–GECCO 2004. Springer, pp. 461-470. Stanley, K.O. and Miikkulainen, R. (2002) Evolving neural networks through augmenting topologies. Evolutionary Computation: 10, 99-127. Sugawara, N., Goldfarb, T., Studamire, B., Alani, E. and Haber, J.E. (2004) Heteroduplex rejection during single-strand annealing requires Sgs1 helicase and mismatch repair proteins Msh2 and Msh6 but not Pms1. Proceedings of the National Academy of Sciences of the United States of America: 101, 9315-9320. Sugimoto, N. and Iba, H. (2004) Inference of gene regulatory networks by means of dynamic differential bayesian networks and nonparametric regression. Genome Informatics: 15, 121. Terfve, C., Cokelaer, T., Henriques, D., MacNamara, A., Goncalves, E., et al. (2012) CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology: 6, 133. Tominaga, D., Okamoto, M., Maki, Y., Watanabe, S. and Eguchi, Y. (1999) Nonlinear numerical optimization technique based on a genetic algorithm for inverse problems: towards the inference of genetic networks. Proceedings of German Conference on Bioinformatics. pp. 127-140. Tong, A.H.Y., Lesage, G., Bader, G.D., Ding, H., Xu, H., et al. (2004) Global mapping of the yeast genetic interaction network. Science Signaling: 303, 808. Van Someren, E.P., Wessels, L., Reinders, M. and Backer, E. (2001) Robust genetic network modeling by adding noisy data. Proceedings of IEEE Workshop on Nonlinear Signal and Image Processing. pp. 234-246. Vilela, M., Chou, I.C., Vinga, S., Vasconcelos, A., Voit, E.O., et al. (2008) Parameter optimization in S-system models. BMC Systems Biology: 2, 35. Voit, E.O. (2000) Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists. Cambridge University Press, Cambridge, UK. Voit, E.O. (2013) Biochemical systems theory: a review. ISRN Biomathematics: 2013, 1-53. Voit, E.O. and Almeida, J. (2004) Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics: 20, 1670-1681. Vu, T.T. and Vohradsky, J. (2002) Genexp—a genetic network simulation environment. Bioinformatics: 18, 1400-1401. Wang, F.S., Su, T.L. and Jang, H.J. (2001) Hybrid differential evolution for problems of kinetic parameter estimation and dynamic optimization of an ethanol fermentation process. Chemical Engineering Science: 40, 2876-2885. Wen, X., Fuhrman, S., Michaels, G.S., Carr, D.B., Smith, S., et al. (1998) Large-scale temporal gene expression mapping of central nervous system development. Proceedings of the National Academy of Sciences of the United States of America: 95, 334-339. Xu, R., Wunsch II, D.C. and Frank, R. (2007) Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics: 4, 681-692. Xuan, N., Chetty, M., Coppel, R. and Wangikar, P.P. (2012) Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network. BMC Bioinformatics: 13, 131. Zeisel, A., Amir, A., Köstler, W.J. and Domany, E. (2010) Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes. BMC Bioinformatics: 11, 400. Zhang, H.-X., William P. Dempsey, J. and Goutsias, J. (2009) Probabilistic sensitivity analysis of biochemical reaction systems. The Journal of Chemical Physics: 131, 094101-094119. Zheng, Y. and Rundell, A. (2006) Comparative study of parameter sensitivity analyses of the TCR-Activated Erk-MAPK signalling pathway. IEE Proceedings - Systems Biology: 153, 201-211. |
電子全文 Fulltext |
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。 論文使用權限 Thesis access permission:自定論文開放時間 user define 開放時間 Available: 校內 Campus: 已公開 available 校外 Off-campus: 已公開 available |
紙本論文 Printed copies |
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。 開放時間 available 已公開 available |
QR Code |