系統生物學是一門以系統的角度探討生物元件之交互運作的學科，其發展有助於生物學之應用，如合成生物學及藥物開發。自 1990 年起，由高速度測量儀器所得的實驗數據使得此領域能夠快速發展。然而要從數據中得到有效的資訊，系統鑑別與數據處理是不可或缺的一環。在此，我們針對兩種不同的系統生物學主題進行資料處理以及參數估測，其中包含：對於 GAL1 合成基因電路之螢光顯微鏡數據的系統鑑別，以及用於命中選擇之蛋白質微陣列影像處理。

以三微分方程實現酵素內 GAL1 基因電路建模

背景：合成基因電路可以用來修改和控制現有的生物程序。目前，它們的應用及設計受到試誤法的阻礙。缺乏可靠的動態模型阻礙了控制理論於基因電路上的實際應用。

目標：我們在此研究 GAL1 合成基因電路，以做為創建自動化合成基因電路設計流程之第一步。

方法：我們從系統鑑別的角度研究 S. cerevisiae 的 yGIL337 菌株。當此菌株生長於半乳糖環境時，其會產生綠色螢光，以利我們獲得實驗數據，而當菌株於葡萄糖環境時則否。我們在重新針對顯微鏡之實驗數據進行數據預處理後，進行 Michaelis-Menten 三微分方程之參數鑑別。

結果與結論: 我們證明了我們的三微分方程模型的適合度檢定與過去發表的五個模型相當，並假設此系統為一自適應反饋系統。我們也展現了此數據並不具有足夠的資訊量來鑑別出最適當的模型，即使模型架構如此不同也無法辨別。

用於命中選擇之資料預處理與蛋白質微陣列之系統誤差

背景：蛋白質微陣列使得在單一芯片上同時進行數千種蛋白質分子結合實驗成為可能。然而在實際應用上，微陣列晶片常包含不可避免的實驗誤差，這使得可靠的命中選擇特別具有挑戰性。

目標：我們的目標是開發一種優化蛋白質微陣列命中選擇的自動程序，此程序包括數據預處理以及命中選擇。在此，我們專注於圖像數據預處理，以偵測、定量及消除蛋白質微陣列上的實驗雜訊。

方法：我們應用中心定位、蛋白質班點切割、背景雜訊擬合和溢出雜訊擬合以消除蛋白質微陣列上的雜訊。

結果與結論: 我們優化了蛋白質微陣列圖像中的蛋白質斑點、背景強度以及溢出強度的辨別能力。在此我們以 5 階多項式曲面於背景以及溢出雜訊進行曲面擬和。適合度檢定顯示背景擬合的 R2 為 0.559，溢出擬和的 R2 為 0.488。

Systems biology studies the interaction of biological components from a systematic point of view, which benefits applications, such as synthetic biology and drug discovery.
The field took off thanks to high-throughput measurement technologies in the late 1990s.
To extract useful information from the measurements, it is crucial to do system identification and data preprocessing.
Here we investigate data preprocessing and parameter estimation methods on two separate topics of systems biology, which are system identification of the GAL1 synthetic genetic circuit from fluorescence microscopy data and image analysis for hit selection based on protein microarray data.

Modelling of GAL1 genetic circuit in yeast using three equations

Background: Synthetic genetic circuits can be used to modify and control existing biological processes.
Currently, their use is largely hampered by the trial and error approach used to design them. Lack of reliable quantitative dynamical models of genetic circuits obstructs the use of well-established control design methods.

Aim: We investigate the GAL1 synthetic genetic circuit as a first step toward the creation of a pipeline for automated identification of synthetic genetic circuits.

Method: We study modelling from the system identification perspective on the yGIL337 strain of S. cerevisiae.
In the strain, expression of a fluorescent reporter can be turned on by growing the yeast in galactose and off by growing it in glucose.
We estimate the parameters of our three ordinary differential equations (ODE) of Michaelis-Menten type based on published data from an in vivo microfluidic experiment after redoing the data preprocessing.

Results and conclusion: We show that the goodness-of-fit of our three ODE model
is comparable to five previously proposed models and hypothesize that the system is
an adaptive feedback system.
We also show that the data is not informative enough to invalidate any of the alternative models despite significant difference in their structure.

Analysis of data preprocessing and systematic errors in protein microarray for hit se-
lection

Background: Protein microarrays allow rapid testing of molecular binding of thousands of proteins on a single chip.
However, in practice, the microarray chip contains artefacts due to inevitable experiment errors making hit selection challenging.

Aim: We aim to develop an automatic pipeline for hit selection optimised for protein microarrays, including data preprocessing.
In this project, we focused on image preprocessing to detect, quantify, and exclude artefacts of protein microarrays as the first
step.

Method: Center finding, spot segmentation, background surface fitting, and smear surface fitting are implemented to remove systematic errors.

Results and conclusion: We optimise the identification of protein spots, background intensities, and smear intensities in the protein microarray image.
A 5th order polynomial surface fitting is then applied on background and smear data, respectively.
The goodness-of-fit of background and smear give R2 equal to 0.559 and 0.488.

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