競爭性內源核糖核酸(ceRNA) 假設最近已成為生物訊息學研究中最熱門的話題之一。Cell 是在生物科學界上經常被引用的學術期刊，早前亦有一班學者在Cell 2011年同一期成功發佈四篇關於ceRNA 假設的學術文章。跟據有關ceRNA 假設的學術文章，大部份學者均以不同的個別例子成功驗證假定，可是，欠缺一個大規模的及全面性的分析。 / 在我兩年碩士的研究中，我引入了一個新的概念微核糖核酸及其目標對向聚類(MTB) 運用了ceRNA 的假設，還提出算法，成功從微核糖核酸與信使核糖核酸的相互數據中找出一系列的MTB' 還利用GENCODE 項目上大量的微核糖核酸及信使核糖核酸的表達數據去驗証MTB 的概念。一方面，我從大量的表達數據中成功推斷出微核糖核酸與信使核糖核酸之間的相反關連、信使核糖核酸之間的正面關運和微核糖核酸之間的正面關連;另一方面，這些關連進一步肯定ceRNA 假設的真實性。此外，我提出一個從大量基因組中找出基因功能分析的方法，並在大量的MTB 的基因組中找出重要的基因註解。最後，我提出另一個MTB 概念的應用一新算法來預測微核糖核酸與信使核糖核酸的相互影響。總括而吉， MTB 概念從複雜且混亂的微核糖核酸與信使核糖核酸網絡中定義簡單且穩固的模姐，提供一個系統生物學分析微核糖核酸調節能力的方法。 / The competing Endogenous RNA (ceRNA) hypothesis has become one of the hottest topics in bioinformatics research recently. Four papers related to the ceRNA hypothesis were published simultaneously in Cell in 2011, a top journal in life sciences. For most papers related to the ceRNA hypothesis, the corresponding studies have successfully validated the hypothesis with different individual examples, without a large-scale and comprehensive analysis. / In my Master of Philosophy study, a novel concept, called mi-RNA Target Bicluster (MTB), is introduced to model the ceRNA hypothesis. The MTBs are identified computationally from validated and/or predicted miRNA-mRNA interaction pairs. The MTB models were tested with the mRNAs and miRNAs expression data from the GENCODE Project. Statistically significant miRNA-mRNA anti-correlation, mRNA-mRNA correlation and miRNA-miRNA correlation in expression data are found, verifying the correlation relations among mRNAs and miRNAs stated in the ceRNA hypothesis with large-scale data support. Moreover, a novel large-scale functional enrichment analysis is performed, and the mRNAs selected by the MTBs are found to be biologically relevant. Besides, some new target prediction algorithms are suggested, as another application of the MTBs, are suggested. Overall, the concept of MTB defines simple and robust modules from the complex and noisy miRNA-mRNA network, suggesting ways for system biology analyses in miRNA-mediated regulations. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Yip, Kit Sang Danny. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves [117]-126). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Contributions --- p.1 / Chapter 1.2 --- Thesis Outline --- p.2 / Chapter 2 --- Background --- p.3 / Chapter 2.1 --- Bioinformatics --- p.3 / Chapter 2.2 --- Biological Background --- p.7 / Chapter 2.2.1 --- The Central Dogma of Molecular Biology . --- p.7 / Chapter 2.2.2 --- RNAs --- p.8 / Chapter 2.2.3 --- Competing Endogenous RNA (ceRNA) hypothesis --- p.9 / Chapter 2.2.4 --- Biological Considerations in Functional Enrichment Analysis --- p.11 / Chapter 2.3 --- Computational Background --- p.12 / Chapter 2.3.1 --- miRNA Genomic Annotation Prediction --- p.13 / Chapter 2.3.2 --- miRNA Target Interaction Prediction --- p.14 / Chapter 2.3.3 --- Applying Computational Algorithms on Related Problems --- p.16 / Chapter 2.3.4 --- Algorithms in Functional Enrichment Analysis --- p.16 / Chapter 2.4 --- Experiments and Data --- p.17 / Chapter 2.4.1 --- miRNA Target Interactions --- p.17 / Chapter 2.4.2 --- Expression Data --- p.18 / Chapter 2.4.3 --- Annotation Datasets --- p.19 / Chapter 2.5 --- Research Motivations --- p.20 / Chapter 3 --- Definitions of miRNA Target Biclusters (MTB) --- p.22 / Chapter 3.1 --- Representations --- p.22 / Chapter 3.1.1 --- Binary Association Matrix Representation --- p.23 / Chapter 3.1.2 --- Bipartite Graph Representation --- p.23 / Chapter 3.1.3 --- Mathematical Representation --- p.24 / Chapter 3.2 --- Concept of MTB --- p.24 / Chapter 3.2.1 --- MTB Restrictive Type (Type R) --- p.27 / Chapter 3.2.2 --- MTB Restrictive Type on miRNA (Type Rmi) --- p.31 / Chapter 3.2.3 --- MTB Restrictive Type on mRNA (Type Rm) --- p.34 / Chapter 3.2.4 --- MTB Restrictive and General Type (Type Rgen) --- p.37 / Chapter 3.2.5 --- MTB Loose Type (Type L) --- p.44 / Chapter 3.2.6 --- MTB Loose Type but restricts on miRNA (Type Lmi) --- p.47 / Chapter 3.2.7 --- MTB Loose Type but restricts on mRNA (Type Lm) --- p.50 / Chapter 3.2.8 --- MTB Loose and General Type (Type Lgen) --- p.53 / Chapter 3.2.9 --- A General Definition on all Eight Types --- p.58 / Chapter 3.2.10 --- Discussions --- p.60 / Chapter 4 --- MTB Workflow in Checking Correlation Relations --- p.61 / Chapter 4.1 --- MTB Workflow in Checking Correlation Relations --- p.61 / Chapter 4.1.1 --- MTB Identification --- p.62 / Chapter 4.1.2 --- Correlation Coefficients --- p.63 / Chapter 4.1.3 --- Scoring Scheme --- p.64 / Chapter 4.1.4 --- Background Construction --- p.65 / Chapter 4.1.5 --- Wilcoxon Rank-sum Test --- p.66 / Chapter 4.1.6 --- Preliminary Studies --- p.67 / Chapter 4.2 --- miRNA-mRNA Anti-correlation in Expression Data --- p.68 / Chapter 4.2.1 --- Interaction Datasets --- p.69 / Chapter 4.2.2 --- Expression Datasets --- p.72 / Chapter 4.2.3 --- Independence of the Choices of Datasets --- p.73 / Chapter 4.2.4 --- Independence of the Types of MTBs --- p.76 / Chapter 4.2.5 --- Independence of the Choices of Correlation Coefficients --- p.78 / Chapter 4.2.6 --- Dependence on the Way to Score --- p.79 / Chapter 4.2.7 --- Independence of theWay to Construct Background --- p.81 / Chapter 4.2.8 --- Independence of Natural Bias in Datasets --- p.82 / Chapter 4.3 --- mRNA-mRNA Correlation in Expression Data --- p.84 / Chapter 4.3.1 --- Variations in the Analysis --- p.85 / Chapter 4.3.2 --- Discussions --- p.87 / Chapter 4.4 --- miRNA-miRNA Correlation in Expression Data --- p.88 / Chapter 4.4.1 --- Variations in the Analysis --- p.89 / Chapter 4.4.2 --- Discussions --- p.92 / Chapter 5 --- Target Prediction Aided by MTB --- p.94 / Chapter 5.1 --- Workflow in Target Prediction --- p.94 / Chapter 5.2 --- Contingency Table Approach --- p.96 / Chapter 5.2.1 --- One-tailed Hypothesis Testing --- p.97 / Chapter 5.3 --- Ranked List Approach --- p.98 / Chapter 5.3.1 --- Wilcoxon Signed Rank Test --- p.99 / Chapter 5.4 --- Results and Discussions --- p.99 / Chapter 6 --- Large-scale Functional Enrichment Analysis --- p.102 / Chapter 6.1 --- Principles in Functional Enrichment Analysis --- p.102 / Chapter 6.1.1 --- Annotation Files --- p.104 / Chapter 6.1.2 --- Functional Enrichment Analysis on a gene --- p.set105 / Chapter 6.1.3 --- Functional Enrichment Analysis on many gene sets --- p.106 / Chapter 6.2 --- Results and Discussions --- p.107 / Chapter 7 --- Future Perspectives and Conclusions --- p.112 / Chapter 7.1 --- Applying MTB definition on other problems --- p.112 / Chapter 7.2 --- Matrix Definitions and Optimization Problems --- p.113 / Chapter 7.3 --- Non-binary association matrix problem settings --- p.114 / Chapter 7.4 --- Limitations --- p.114 / Chapter 7.5 --- Conclusions --- p.116 / Bibliography --- p.117 / Chapter A --- Publications --- p.127 / Chapter A.1 --- Publications --- p.127

Tak-Chung Lau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 190-198). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background Information --- p.4 / Chapter 1.2 --- Importance of the Problem --- p.6 / Chapter 1.3 --- Problem Definition and Proposed Algorithm Outline --- p.7 / Chapter 1.4 --- Simple Illustration --- p.10 / Chapter 1.5 --- Outline of the Thesis --- p.12 / Chapter 2 --- Survey --- p.14 / Chapter 2.1 --- Introduction --- p.14 / Chapter 2.2 --- Dynamic Programming (DP) --- p.15 / Chapter 2.2.1 --- Introduction --- p.15 / Chapter 2.2.2 --- Algorithm --- p.15 / Chapter 2.2.3 --- Example --- p.16 / Chapter 2.2.4 --- Complexity Analysis --- p.20 / Chapter 2.2.5 --- Summary --- p.21 / Chapter 2.3 --- General Alignment Tools --- p.21 / Chapter 2.4 --- K-Nearest Neighbor (KNN) --- p.22 / Chapter 2.4.1 --- Value of K --- p.22 / Chapter 2.4.2 --- Example --- p.23 / Chapter 2.4.3 --- Variations in KNN --- p.24 / Chapter 2.4.4 --- Summary --- p.24 / Chapter 2.5 --- Decision Tree --- p.25 / Chapter 2.5.1 --- General Information of Decision Tree --- p.25 / Chapter 2.5.2 --- Classification in Decision Tree --- p.26 / Chapter 2.5.3 --- Disadvantages in Decision Tree --- p.27 / Chapter 2.5.4 --- Comparison on Different Types of Trees --- p.28 / Chapter 2.5.5 --- Conclusion --- p.29 / Chapter 2.6 --- Hidden Markov Model (HMM) --- p.29 / Chapter 2.6.1 --- Markov Process --- p.29 / Chapter 2.6.2 --- Hidden Markov Model --- p.31 / Chapter 2.6.3 --- General Framework in HMM --- p.32 / Chapter 2.6.4 --- Example --- p.34 / Chapter 2.6.5 --- Drawbacks in HMM --- p.35 / Chapter 2.7 --- Chapter Summary --- p.36 / Chapter 3 --- Related Work --- p.37 / Chapter 3.1 --- Resonant Recognition Model (RRM) --- p.37 / Chapter 3.1.1 --- Introduction --- p.37 / Chapter 3.1.2 --- Encoding Stage --- p.39 / Chapter 3.1.3 --- Transformation Stage --- p.41 / Chapter 3.1.4 --- Evaluation Stage --- p.43 / Chapter 3.1.5 --- Important Conclusion in RRM --- p.47 / Chapter 3.1.6 --- Summary --- p.48 / Chapter 3.2 --- Motivation --- p.49 / Chapter 3.2.1 --- Example --- p.51 / Chapter 3.3 --- Chapter Summary --- p.53 / Chapter 4 --- Group Classification --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Design --- p.55 / Chapter 4.2.1 --- Data Preprocessing --- p.55 / Chapter 4.2.2 --- Encoding Stage --- p.58 / Chapter 4.2.3 --- Transformation Stage --- p.63 / Chapter 4.2.4 --- Evaluation Stage --- p.64 / Chapter 4.2.5 --- Classification --- p.72 / Chapter 4.2.6 --- Summary --- p.75 / Chapter 4.3 --- Experimental Settings --- p.75 / Chapter 4.3.1 --- "Statistics from Database of Secondary Structure in Pro- teins (DSSP) [27], [54]" --- p.76 / Chapter 4.3.2 --- Parameters Used --- p.77 / Chapter 4.3.3 --- Experimental Procedure --- p.79 / Chapter 4.4 --- Experimental Results --- p.79 / Chapter 4.4.1 --- Reference Group - Neurotoxin --- p.80 / Chapter 4.4.2 --- Reference Group - Biotin --- p.82 / Chapter 4.4.3 --- Average Results of all the Groups --- p.84 / Chapter 4.4.4 --- Conclusion in Experimental Results --- p.88 / Chapter 4.5 --- Discussion --- p.89 / Chapter 4.5.1 --- Discussion on the Experimental Results --- p.89 / Chapter 4.5.2 --- Complexity Analysis --- p.94 / Chapter 4.5.3 --- Other Discussion --- p.99 / Chapter 4.6 --- Chapter Summary --- p.102 / Chapter 5 --- Individual Classification --- p.103 / Chapter 5.1 --- Design --- p.103 / Chapter 5.1.1 --- Group Profile Generation --- p.104 / Chapter 5.1.2 --- Preparation of Each Testing Examples --- p.104 / Chapter 5.2 --- Design with Clustering --- p.104 / Chapter 5.2.1 --- Motivation --- p.105 / Chapter 5.2.2 --- Data Exception --- p.105 / Chapter 5.2.3 --- Clustering Technique --- p.110 / Chapter 5.2.4 --- Classification --- p.116 / Chapter 5.3 --- Hybridization of Our Approach and Sequence Alignment --- p.116 / Chapter 5.3.1 --- AlignRemove and AlignChange --- p.117 / Chapter 5.3.2 --- Classification --- p.119 / Chapter 5.4 --- Experimental Settings --- p.120 / Chapter 5.4.1 --- Parameters Used --- p.120 / Chapter 5.4.2 --- Choosing of Protein Functional Groups --- p.121 / Chapter 5.5 --- Experimental Results --- p.122 / Chapter 5.5.1 --- Experimental Results Setup --- p.122 / Chapter 5.5.2 --- Receiver Operating Characteristics (ROC) Curves --- p.123 / Chapter 5.5.3 --- Interpretation of Comparison Results --- p.125 / Chapter 5.5.4 --- Area under the Curve --- p.138 / Chapter 5.5.5 --- Classification with KNN --- p.141 / Chapter 5.5.6 --- Three Types of KNN --- p.142 / Chapter 5.5.7 --- Results in Three Types of KNN --- p.143 / Chapter 5.6 --- Complexity Analysis --- p.144 / Chapter 5.6.1 --- Complexity in Individual Classification --- p.144 / Chapter 5.6.2 --- Complexity in Individual Clustering Classification --- p.146 / Chapter 5.6.3 --- Complexity of Individual Classification in DP --- p.148 / Chapter 5.6.4 --- Conclusion --- p.148 / Chapter 5.7 --- Discussion --- p.149 / Chapter 5.7.1 --- Domain Expert Opinions --- p.149 / Chapter 5.7.2 --- Choosing the Threshold --- p.149 / Chapter 5.7.3 --- Statistical Support in an Individual Protein --- p.150 / Chapter 5.7.4 --- Discussion on Clustering --- p.151 / Chapter 5.7.5 --- Poor Performance in Hybridization --- p.154 / Chapter 5.8 --- Chapter Summary --- p.155 / Chapter 6 --- Application --- p.157 / Chapter 6.1 --- Introduction --- p.157 / Chapter 6.1.1 --- Construct the Correlation Graph --- p.157 / Chapter 6.1.2 --- Minimum Spanning Tree (MST) --- p.161 / Chapter 6.2 --- Application in Group Classification --- p.164 / Chapter 6.2.1 --- Groups with Weak Relationship --- p.164 / Chapter 6.2.2 --- Groups with Strong Relationship --- p.166 / Chapter 6.3 --- Application in Individual Classification --- p.168 / Chapter 6.4 --- Chapter Summary --- p.171 / Chapter 7 --- Discussion on Other Analysis --- p.172 / Chapter 7.1 --- Distanced MLN Encoding Scheme --- p.172 / Chapter 7.2 --- Unique Encoding Method --- p.174 / Chapter 7.3 --- Protein with Multiple Functions? --- p.175 / Chapter 7.4 --- Discussion on Sequence Similarity --- p.176 / Chapter 7.5 --- Functional Blocks in Proteins --- p.177 / Chapter 7.6 --- Issues in DSSP --- p.178 / Chapter 7.7 --- Flexible Encoding --- p.179 / Chapter 7.8 --- Advantages over Dynamic Programming --- p.179 / Chapter 7.9 --- Novel Research Direction --- p.180 / Chapter 8 --- Future Works --- p.182 / Chapter 8.1 --- Improvement in Encoding Scheme --- p.182 / Chapter 8.2 --- Analysis on Primary Protein Sequences --- p.183 / Chapter 8.3 --- In Between Spectrum Scaling --- p.184 / Chapter 8.4 --- Improvement in Hybridization --- p.185 / Chapter 8.5 --- Fuzzy Threshold Boundaries --- p.185 / Chapter 8.6 --- Optimal Parameters Setting --- p.186 / Chapter 8.7 --- Generalization Tool --- p.187 / Chapter 9 --- Conclusion --- p.188 / Bibliography --- p.190 / Chapter A --- Fourier Transform --- p.199 / Chapter A.1 --- Introduction --- p.199 / Chapter A.2 --- Example --- p.201 / Chapter A.3 --- Physical Meaning of Fourier Transform --- p.201

Approximate pattern matching problem is, given a reference sequence T, a pattern (query) Q, and a maximum allowed error e, to find all the substrings in the reference, such that the edit distance between the substrings and the pattern is smaller than or equal to the maximum allowed error. Though it is a well-studied problem in Computer Science, it gains a resurrection in Bioinformatics in recent years, largely due to the emergence of the next-generation high-throughput sequencing technologies. This thesis contributes in a novel generalized pattern matching framework, and applies it to solve pattern matching problems in general and alternative splicing detection (AS) in particular. AS is to map a large amount of next-generation sequencing short reads data to a reference human genome, which is the first and an important step in analyzing the sequenced data for further Biological analysis. The four parts of my research are as follows. / In the first part of my research work, we propose a novel deterministic pattern matching algorithm which applies Agrep, a well-known bit-parallel matching algorithm, to a truncated suffix array. Due to the linear cost of Agrep, the cost of our approach is linear to the number of characters processed in the truncated suffix array. We analyze the matching cost theoretically, and .obtain empirical costs from experiments. We carry out experiments using both synthetic and real DNA sequence data (queries) and search them in Chromosome-X of a reference human genome. The experimental results show that our approach achieves a speed-up of several magnitudes over standard Agrep algorithm. / In the fourth part, we focus on the seeding strategies for alternative splicing detection. We review the history of seeding-and-extending (SAE), and assess both theoretically and empirically the seeding strategies adopted in existing splicing detection tools, including Bowtie's heuristic and ABMapper's exact seedings, against the novel complementary quad-seeding strategy we proposed and the corresponding novel splice detection tool called CS4splice, which can handle inexact seeding (with errors) and all 3 types of errors including mismatch (substitution), insertion, and deletion. We carry out experiments using short reads (queries) of length 105bp comprised of several data sets consisting of various levels of errors, and align them back to a reference human genome (hg18). On average, CS4splice can align 88. 44% (recall rate) of 427,786 short reads perfectly back to the reference; while the other existing tools achieve much smaller recall rates: SpliceMap 48.72%, MapSplice 58.41%, and ABMapper 51.39%. The accuracies of CS4splice are also the highest or very close to the highest in all the experiments carried out. But due to the complementary quad-seeding that CS4splice use, it takes more computational resources, about twice (or more) of the other alternative splicing detection tools, which we think is practicable and worthy. / In the second part, we define a novel generalized pattern (query) and a framework of generalized pattern matching, for which we propose a heuristic matching algorithm. Simply speaking, a generalized pattern is Q 1G1Q2 ... Qc--1Gc--1 Qc, which consists of several substrings Q i and gaps Gi occurring in-between two substrings. The prototypes of the generalized pattern come from several real Biological problems that can all be modeled as generalized pattern matching problems. Based on a well-known seeding-and-extending heuristic, we propose a dual-seeding strategy, with which we solve the matching problem effectively and efficiently. We also develop a specialized matching tool called Gpattern-match. We carry out experiments using 10,000 generalized patterns and search them in a reference human genome (hg18). Over 98.74% of them can be recovered from the reference. It takes 1--2 seconds on average to recover a pattern, and memory peak goes to a little bit more than 1G. / In the third part, a natural extension of the second part, we model a real biological problem, alternative splicing detection, into a generalized pattern matching problem, and solve it using a proposed bi-directional seeding-and-extending algorithm. Different from all the other tools which depend on third-party tools, our mapping tool, ABMapper, is not only stand-alone but performs unbiased alignments. We carry out experiments using 427,786 real next-generation sequencing short reads data (queries) and align them back to a reference human genome (hg18). ABMapper achieves 98.92% accuracy and 98.17% recall rate, and is much better than the other state-of-the-art tools: SpliceMap achieves 94.28% accuracy and 78.13% recall rate;while TopHat 88.99% accuracy and 76.33% recall rate. When the seed length is set to 12 in ABMapper, the whole searching and alignment process takes about 20 minutes, and memory peak goes to a little bit more than 2G. / Ni, Bing. / Adviser: Kwong-Sak Leung. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical referencesTexture mapping (leaves 151-161). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Indiana University-Purdue University Indianapolis (IUPUI) / Carbon nanomaterials are widely produced and used in industry, medicine and scientific research. To examine the impact of exposure to nanoparticles on human health, the human airway epithelial cell line, Calu-3, was used to evaluate changes in the cellular proteome that could account for alterations in cellular function of airway epithelia after 24 h exposure to 10 μg/mL and 100 ng/mL of two common carbon nanoparticles, singleand multi-wall carbon nanotubes (SWCNT, MWCNT). After exposure to the nanoparticles, label-free quantitative mass spectrometry (LFQMS) was used to study differential protein expression. Ingenuity Pathway Analysis (IPA) was used to conduct a bioinformatics analysis of proteins identified by LFQMS. Interestingly, after exposure to a high concentration (10 μg/mL; 0.4 μg/cm2) of MWCNT or SWCNT, only 8 and 13 proteins, respectively, exhibited changes in abundance. In contrast, the abundance of hundreds of proteins was altered in response to a low concentration (100 ng/mL; 4 ng/cm2) of either CNT. Of the 281 and 282 proteins that were significantly altered in response to MWCNT or SWCNT, respectively, 231 proteins were the same. Bioinformatic analyses found that the proteins common to both kinds of nanotubes are associated with the cellular functions of cell death and survival, cell-to-cell signaling and interaction, cellular assembly and organization, cellular growth and proliferation, infectious disease, molecular transport and protein synthesis. The decrease in expression of the majority proteins suggests a general stress response to protect cells. The STRING database was used to analyze the various functional protein networks. Interestingly, some proteins like cadherin 1 (CDH1), signal transducer and activator of transcription 1 (STAT1), junction plakoglobin (JUP), and apoptosis-associated speck-like protein containing a CARD (PYCARD), appear in several functional categories and tend to be in the center of the networks. This central positioning suggests they may play important roles in multiple cellular functions and activities that are altered in response to carbon nanotube exposure. To examine the effect of nanotubes on the plasma membrane, we investigated the interaction of short purified MWCNT with model lipid membranes using a planar bilayer workstation. Bilayer lipid membranes were synthesized using neutral 1, 2-diphytanoylsn-glycero-3-phosphocholine (DPhPC) in 1 M KCl. The ion channel model protein, Gramicidin A (gA), was incorporated into the bilayers and used to measure the effect of MWCNT on ion transport. The opening and closing of ion channels, amplitude of current, and open probability and lifetime of ion channels were measured and analyzed by Clampfit. The presence of an intermediate concentration of MWCNT (2 μg/ml) could be related to a statistically significant decrease of the open probability and lifetime of gA channels. The proteomic studies revealed changes in response to CNT exposure. An analysis of the changes using multiple databases revealed alterations in pathways, which were consistent with the physiological changes that were observed in cultured cells exposed to very low concentrations of CNT. The physiological changes included the break down of the barrier function and the inhibition of the mucocillary clearance, both of which could increase the risk of CNT’s toxicity to human health. The biophysical studies indicate MWCNTs have an effect on single channel kinetics of Gramicidin A model cation channel. These changes are consistent with the inhibitory effect of nanoparticles on hormone stimulated transepithelial ion flux, but additional experiments will be necessary to substantiate this correlation.