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研究生： 林勁甫
Lin, Jin-Fu
論文名稱： 通過系統生物學和深度學習方法研究老化過程機制並設計多分子藥物以緩解人體皮膚老化
Aging Progression Mechanism Investigation and Potential Multiple-Molecule Drugs Design for Mitigating Human Skin Aging via Systems Biology and Deep Learning Methods
指導教授： 陳博現
Chen, Bor-Sen
口試委員: 黃宣誠
Huang, Suan-cheng
阮雪芬
Juan, Hsueh-Fen
汪宏達
Wang, Horng-Dar
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2019
畢業學年度： 108
語文別： 英文
論文頁數： 66
中文關鍵詞： 皮膚老化 、氧化應激 、老化進展機制 、全基因組基因與表關遺傳網路 、系統藥物設計 、藥物目標交互作用模型 、深度類神經網路學習 、多分子藥物
外文關鍵詞： skin aging, oxidative stress, aging progression mechanism, genome-wide genetic and epigenetic network (GWGEN), systems medicine design, drug-target interaction (DTI) model, deep neural network (DNN) learning, multiple-molecule drug
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    • 皮膚衰老受到許多生物信號傳導途徑、微環境因素和表關遺傳調控的影響。隨著用以防止或逆轉皮膚老化的化妝品和藥物的需求逐年增加，設計減緩皮膚老化的多分子藥物是必不可少的。在這項研究中，我們開發了基於系統生物學和深度學習方法的系統醫學設計策略。在系統生物學方法中，我們通過大數據挖掘以建構候選的全基因組基因和表關遺傳網路(GWGEN)，並通過系統識別和模型順序檢測方法以及相應的真實的微陣列數據，為每個衰老階段確定真實的GWGEN。從真實的GWGENs提取的核心信號通路闡明了衰老的進展機制。同時鑑定了造成細胞功能障礙的重要的生物標誌物AIFM1, AKT1, CAT, IGF1R 和LMNA在中期皮膚老化，和IL6, MMP9, YAP1和NGF在老年皮膚老化。基於預先訓練的藥物標靶相互作用(DTI)深度學習(DNN)模型，我們可以獲得選定的重要生物標記物的潛在候選藥物。通過連通性圖(CMap)的調節能力與預測的LD50，提出了兩種多分子藥物組合來改善皮膚衰老，也就是tamoxifen, raloxifene和colforsin用於治療中期皮膚，cimetidine, omeprazole和resveratrol用於治療晚期皮膚。運用系統生物學，真實時間概況微陣列數據，大數據挖掘，DTI模型和DNN方法，基於機制和少量副作用的系統藥物設計可以節省長期的人類皮膚臨床試驗。

    Skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increased requirement of cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology and deep learning methods. In systems biology method, we constructed candidate genome-wide genetic and epigenetic network (GWGEN) via big databases mining and identified real GWGEN for each aging stage through system identification and model order detection method with the real microarray data of different stages of skin aging. The core signaling pathways extracted from the real GWGENs shed the light on the aging progression mechanisms. The significant biomarkers AIFM1, AKT1, CAT, IGF1R and LMNA for middle-stage skin aging and IL6, MMP9, YAP1 and NGF for elder-stage skin aging resulting in cellular dysfunctions were identified. By the pre-trained drug target interaction (DTI) model based on deep neural network (DNN), we could obtain potential candidate molecular drugs for the selected significant biomarkers. Filtered by the regulation ability via connectivity map (Cmap) and toxicity threshold with LD50 value, two combination multiple-molecule drugs for ameliorating skin aging were proposed for middle and elder-stage aging skin, respectively, i.e., tamoxifen, raloxifene and colforsin for middle-stage skin aging and cimetidine, omeprazole and resveratrol for elder-stage skin aging. Applying systems biology, real time-prolife microarray data, big data mining, DTI model and DNN method, the proposed systems drug design based on mechanism and less side effect for mitigating skin aging could save long period human skin clinical experiments.

    Chapter 1. Introduction 1 Chapter 2. Results 6 2.1. Extracting core signaling pathways from identified GWGEN and core GWGENs in each aging stage of human skin 6 2.2. Investigating skin aging mechanism by differential core pathways from young-stage to middle-stage skin aging 17 2.3. Investigating skin aging progression mechanism of differential core pathways from middle-stage to elder-stage skin aging 18 2.4. Drug target selection and multiple-molecule drug design for young-stage to elder-stage skin aging. 21 Chapter 3. Discussion 24 3.1. Overview of molecular mechanism for skin aging progression in different stages 24 3.2. Investigating aging mechanisms by differential core pathways from young stage to middle stage of human skin aging 24 3.3. The aging progression mechanism from middle-stage to elder-stage human skin aging 27 3.4. The genetic and epigenetic progression mechanism from young-stage to elder-stage skin aging 29 3.5. Systems drug discovery and design for skin aging via deep neural network learning approach and data mining method 31 Chapter 4. Materials and Methods 36 4.1. Overview of the construction for core GWGENs of human skin from young-stage to elder-stage 36 4.2. Big data mining and data preprocessing of microarray data for human skin 37 4.3. Dynamic system models of the interspecies GWGENs for human skin 38 4.4. System identification approach of dynamic models of candidate GWGEN via microarray data 40 4.5. Pruning false-positives in candidate GWGENs to obtain real GWGENs by system order detection scheme 47 4.6. Extracting the core network structures from real GWGENs by the PNP method 49 4.7. Applying Drug-Target Interaction Model with DNN and Drug Toxicity Filter to Systems Drug Discovery and Design for Multiple-Molecule drug of Skin Aging. 52 Chapter 5. Conclusion 58 References 59

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