臺灣博碩士論文加值系統

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.

Table of Contents

Chinese abstract i

Abstract iii

Acknowledgment v

Table of Contents vi

List of Tables viii

List of Figures x

List of Symbols xii

1 Introduction 1
1.1 Introduction of synthetic and systems biology . . . . . . . . . . . . . . . . 1
1.2 Introduction of hit selection . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Organization of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Modelling of GAL1 genetic circuit 12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3 Analysis of data preprocessing and systematic error 23
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.5 Discussion and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 35

References 36

A Modelling of the GAL1genetic circuit 42
A.1 Measures of goodness-of-fit . . . . . . . . . . . . . . . . . . . . . . . . . 42
A.2 Examination of Fiore et al. 2013 . . . . . . . . . . . . . . . . . . . . . . . 43
A.3 Detailed description of all models, including ones not included in the article 46
A.4 Additional details on reproduction of Fiore et al. 2013 . . . . . . . . . . . 79

B Supplementary results of image preprocessing 86
B.1 Center finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
B.2 Spot segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
B.3 Background surface fitting . . . . . . . . . . . . . . . . . . . . . . . . . . 95
B.4 Smear image processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Algar, R. J. R., Ellis, T., and Stan, G.-B. (2014). Modelling essential interactions between synthetic genes and their chassis cell. In 53rd IEEE Conference on Decision and Control, pages 5437–5444. IEEE.

Anderson, J. C., Voigt, C. A., and Arkin, A. P. (2007). Environmental signal integration by a modular AND gate. Molecular systems biology, 3:133.

Balsa-Canto, E., Henriques, D., Gabor, A., Banga, J. R., Gábor, A., and Banga, J. R. (2016). AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology. Bioinformatics, 32(21):3357–3359.

Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., Schinzel, A. C., Sandy, P., Meylan, E., Scholl, C., Fröhling, S., Chan, E. M., Sos, M. L., Michel, K., Mermel, C., Silver, S. J., Weir, B. A., Reiling, J. H., Sheng, Q., Gupta, P. B., Wadlow, R. C., Le, H., Hoersch, S., Wittner, B. S., Ramaswamy, S., Livingston, D. M., Sabatini, D. M., Meyerson, M., Thomas, R. K., Lander, E. S., Mesirov, J. P., Root, D. E., Gilliland, D. G., Jacks, T., and Hahn, W. C. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(7269):108–112.

Bayer, T. S. and Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3):337–343.

Bennett, M. R., Pang, W. L., Ostroff, N. A., Baumgartner, B. L., Nayak, S., Tsimring, L. S., and Hasty, J. (2008). Metabolic gene regulation in a dynamically changing environment. Nature, 454(7208):1119.

Berset, Y., Merulla, D., Joublin, A., Hatzimanikatis, V., and van der Meer, J. R. (2017).
Mechanistic Modeling of Genetic Circuits for ArsR Arsenic Regulation. ACS Synthetic
Biology, 6(5):862–874.

Birmingham, A., Selfors, L. M., Forster, T., Wrobel, D., Kennedy, C. J., Shanks, E., Santoyo-Lopez, J., Dunican, D. J., Long, A., Kelleher, D., Smith, Q., Beijersbergen, R. L., Ghazal, P., and Shamu, C. E. (2009). Statistical methods for analysis of high-throughput RNA interference screens. Nature Methods, 6(8):569–575.

Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P. (2004). Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Letters, 573(1-3):83–92.

Broach, J. R. and Thorner, J. (1996). High-throughput screening for drug discovery.
Caraus, I., Alsuwailem, A. A., Nadon, R., and Makarenkov, V. (2015). Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions. Briefings in Bioinformatics, 16(6): 974–986.

Cardinale, S. and Arkin, A. P. (2012). Contextualizing context for synthetic biology identifying causes of failure of synthetic biological systems. Biotechnology Journal, 7(7): 856–866.

Chandran, D., Bergmann, F. T., and Sauro, H. M. (2009). TinkerCell: modular CAD tool for synthetic biology. Journal of Biological Engineering, 3(1):19. Chandran, D. and Sauro, H. M. (2012). Hierarchical Modeling for Synthetic Biology. ACS Synthetic Biology, 1(8):353–364.

Chen, C.-S., Korobkova, E., Chen, H., Zhu, J., Jian, X., Tao, S.-C., He, C., and Zhu, H.
(2008). A proteome chip approach reveals new DNA damage recognition activities in
Escherichia coli. Nature Methods, 5(1):69–74.

Chung, N., Zhang, X. D., Kreamer, A., Locco, L., Kuan, P.-F., Bartz, S., Linsley, P. S., Ferrer, M., and Strulovici, B. (2008). Median Absolute Deviation to Improve Hit Selection for Genome-Scale RNAi Screens. Journal of Biomolecular Screening, 13(2):149–158.

Cobelli, C., Lepschy, A., and Jacur, G. R. (1979). Identifiability of compartmental systems and related structural properties. Mathematical Biosciences, 44(1-2):1–18.

Del Vecchio, D., Dy, A. J., and Qian, Y. (2016). Control theory meets synthetic biology.
Journal of the Royal Society, Interface, 13(120):20160380.

Del Vecchio, D., Ninfa, A. J., and Sontag, E. D. (2008). Modular cell biology: retroactivity and insulation. Molecular Systems Biology, 4(1):161.

Douglas Zhang, X., Yang, X. C., Chung, N., Gates, A., Stec, E., Kunapuli, P., J Holder, D.,
Ferrer, M., and S Espeseth, A. (2006). Robust statistical methods for hit selection in RNA interference high-throughput screening experiments. Pharmacogenomics, 7(3):299–309.
Elowitz, M. B. and Leibler, S. (2000). A synthetic oscillatory network of transcriptional
regulators. Nature, 403(6767):335–338.

Fiore, G., Menolascina, F., di Bernardo, M., and di Bernardo, D. (2013). An experimental approach to identify dynamical models of transcriptional regulation in living cells. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(2):025106.

Fiore, G., Perrino, G., di Bernardo, M., and di Bernardo, D. (2016). In Vivo Real-Time
Control of Gene Expression: A Comparative Analysis of Feedback Control Strategies in
Yeast. ACS synthetic biology, 5(2):154–62.

Flores Bueso, Y. and Tangney, M. (2017). Synthetic Biology in the Driving Seat of the
Bioeconomy. Trends in Biotechnology, 35(5):373–378.

Fu, P. and Panke, S. (2009). Systems Biology and Synthetic Biology. John Wiley ＆ Sons.
Gardner, T. S., Cantor, C. R., and Collins, J. J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature, 403(6767):339–342.

Guiziou, S., Ulliana, F., Moreau, V., Leclere, M., and Bonnet, J. (2018). An Automated
Design Framework for Multicellular Recombinase Logic. ACS Synthetic Biology, 7(5):
1406–1412.

Haseltine, E. L. and Arnold, F. H. (2007). Synthetic Gene Circuits: Design with Directed
Evolution. Annual Review of Biophysics and Biomolecular Structure, 36(1):1–19.
Hsu, C.-C., Wu, Y.-H., Menolascina, F., and Nordling, T. E. M. (2018). Modelling of the
GAL1 Genetic Circuit in Yeast Using Three Equations. In 10th IFAC Symposium on Ad-
vanced Control of Chemical Processes (ADCHEM2018), Shenyang, China. International Federation of Automatic Control (IFAC).

Hu, C.-J., Song, G., Huang, W., Liu, G.-Z., Deng, C.-W., Zeng, H.-P., Wang, L., Zhang,
F.-C., Zhang, X., Jeong, J. S., Blackshaw, S., Jiang, L.-Z., Zhu, H., Wu, L., and Li, Y.-
Z. (2012). Identification of new autoantigens for primary biliary cirrhosis using human
proteome microarrays. Molecular ＆ cellular proteomics : MCP, 11(9):669–80.

Huynh, L. and Tagkopoulos, I. (2015). Fast and Accurate Circuit Design Automation through Hierarchical Model Switching. ACS Synthetic Biology, 4(8):890–897.

Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Eng, J. K., Bumgarner, R.,
Goodlett, D. R., Aebersold, R., and Hood, L. (2001). Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic Network. Science, 292(5518):929–934.

Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., and Collins, J. J.
(2004). Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnology, 22(7):841–847.

Jacob, F. and Monod, J. (1961). Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology, 3(3):318–356.

Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., and Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407(6804):651–4.

Jeong, J. S., Jiang, L., Albino, E., Marrero, J., Rho, H. S., Hu, J., Hu, S., Vera, C., Bayron-
Poueymiroy, D., Rivera-Pacheco, Z. A., Ramos, L., Torres-Castro, C., Qian, J., Bonaven-
tura, J., Boeke, J. D., Yap, W. Y., Pino, I., Eichinger, D. J., Zhu, H., and Blackshaw, S.
(2012). Rapid identification of monospecific monoclonal antibodies using a human proteome microarray. Molecular ＆ cellular proteomics : MCP, 11(6):O111.016253.

Kevorkov, D. and Makarenkov, V. (2005). Statistical Analysis of Systematic Errors in High-Throughput Screening. Journal of Biomolecular Screening, 10(6):557–567.

Klumpp, S., Zhang, Z., and Hwa, T. (2009). Growth Rate-Dependent Global Effects on Gene Expression in Bacteria. Cell, 139(7):1366–1375.

König, R., Chiang, C.-y., Tu, B. P., Yan, S. F., DeJesus, P. D., Romero, A., Bergauer, T., Orth, A., Krueger, U., Zhou, Y., and Chanda, S. K. (2007). A probability-based approach for the analysis of large-scale RNAi screens. Nature Methods, 4(10):847–849.

Kotz, S. and Nadarajah, S. (2000). Extreme Value Distributions. PUBLISHED BY IMPE-
RIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO.

Lang, G. I. and Botstein, D. (2011). A Test of the Coordinated Expression Hypothesis for the Origin and Maintenance of the GAL Cluster in Yeast. PLoS ONE, 6(9):e25290.
Leonhardt, N., Kwak, J. M., Robert, N., Waner, D., Leonhardt, G., and Schroeder, J. I. (2004). Microarray Expression Analyses of Arabidopsis Guard Cells and Isolation of a Recessive Abscisic Acid Hypersensitive Protein Phosphatase 2C Mutant. The Plant Cell, 16(3):596–615.

Levy, S. and Barkai, N. (2009). Coordination of gene expression with growth rate: A feedback or a feed-forward strategy? FEBS Letters, 583(24):3974–3978.

Ljung, L. (1999). System identification - Theory for the User. Prentice hall PTR, Upper
Saddle River, New Jersey 07458, USA, 2nd edition.

Madec, M., Pecheux, F., Gendrault, Y., Rosati, E., Lallement, C., and Haiech, J. (2016).
GeNeDA: An Open-Source Workflow for Design Automation of Gene Regulatory Net-
works Inspired from Microelectronics. Journal of Computational Biology, 23(10):841–
855.

Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J., and Nadon, R. (2006). Statistical practice in high-throughput screening data analysis. Nature Biotechnology, 24(2):167–175.

Manly, K. F., Nettleton, D., and Hwang, J. T. G. (2004). Genomics, prior probability, and statistical tests of multiple hypotheses. Genome research, 14(6):997–1001.

Marchisio, M. and Stelling, J. (2008). Computational design of synthetic gene circuits with composable parts. Bioinformatics, 24(17):1903–1910.

Menolascina, F., Fiore, G., Orabona, E., De Stefano, L., Ferry, M., Hasty, J., di Bernardo, M., and di Bernardo, D. (2014). In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS computational biology, 10(5):e1003625.

Moon, T. S., Lou, C., Tamsir, A., Stanton, B. C., and Voigt, C. A. (2012). Genetic programs constructed from layered logic gates in single cells. Nature, 491(7423):249–253.

Müller, P., Kuttenkeuler, D., Gesellchen, V., Zeidler, M. P., and Boutros, M. (2005). Identification of JAK/STAT signalling components by genome-wide RNA interference. Nature, 436(7052):871–875.

Otero-Muras, I., Henriques, D., and Banga, J. R. (2016). SYNBADm: a tool for optimization-based automated design of synthetic gene circuits. Bioinformatics, 32(21):3360–3362.

Paddon, C. J. and Keasling, J. D. (2014). Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development. Nature Reviews Microbiology, 12(5): 355–367.

Patel, D. A., Patel, A. C., Nolan, W. C., Huang, G., Romero, A. G., Charlton, N., Agapov,
E., Zhang, Y., and Holtzman, M. J. (2014). High-Throughput Screening Normalized to
Biological Response. Journal of Biomolecular Screening, 19(1):119–130.

Popescu, S. C., Snyder, M., and Dinesh-Kumar, S. (2007). Arabidopsis protein microarrays for the high-throughput identification of protein-protein interactions. Plant signaling ＆ behavior, 2(5):416–20.

Potvin-Trottier, L., Lord, N. D., Vinnicombe, G., and Paulsson, J. (2016). Synchronous long-term oscillations in a synthetic gene circuit. Nature, 538(7626):514–517.

Reuveni, S., Meilijson, I., Kupiec, M., Ruppin, E., and Tuller, T. (2011). Genome-Scale
Analysis of Translation Elongation with a Ribosome Flow Model. PLoS Computational
Biology, 7(9):e1002127.

Rodrigo, G. and Jaramillo, A. (2013). AutoBioCAD: Full Biodesign Automation of Genetic Circuits. ACS Synthetic Biology, 2(5):230–236.

Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., and Hwa, T. (2010). Interdependence of cell growth and gene expression: origins and consequences. Science (New York, N.Y.), 330(6007):1099–102.

Sellick, C. A., Campbell, R. N., and Reece, R. J. (2008). Galactose metabolism in yeast-
structure and regulation of the leloir pathway enzymes and the genes encoding them. International review of cell and molecular biology, 269:111–150.

Slusarczyk, A. L., Lin, A., and Weiss, R. (2012). Foundations for the design and implementation of synthetic genetic circuits. Nature Reviews Genetics, 13(6):406–420.

Stricker, J., Cookson, S., Bennett, M. R., Mather, W. H., Tsimring, L. S., and Hasty, J. (2008). A fast, robust and tunable synthetic gene oscillator. Nature, 456(7221):516–519.

Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess,
S., Jiang, T., Paige, E., Surendran, P., Oliver-Williams, C., Kamat, M. A., Prins, B. P.,
Wilcox, S. K., Zimmerman, E. S., Chi, A., Bansal, N., Spain, S. L., Wood, A. M., Morrell,
N. W., Bradley, J. R., Janjic, N., Roberts, D. J., Ouwehand, W. H., Todd, J. A., Soranzo, N., Suhre, K., Paul, D. S., Fox, C. S., Plenge, R. M., Danesh, J., Runz, H., and Butterworth, A. S. (2018). Genomic atlas of the human plasma proteome. Nature, 558(7708):73–79.

Szymański, P., Markowicz, M., and Mikiciuk-Olasik, E. (2012). Adaptation of high-
throughput screening in drug discovery-toxicological screening tests. International journal of molecular sciences, 13(1):427–52.

Trosset, J.-Y. and Carbonell, P. (2015). Synthetic biology for pharmaceutical drug discovery. Drug design, development and therapy, 9:6285–302.

Westerhoff, H. V. and Palsson, B. O. (2004). The evolution of molecular biology into systems biology. Nature Biotechnology, 22(10):1249–1252.

Yu-Heng Wu, Ryu Yamanaka, N. H. N. T. E. M. (2018). Image analysis for cell adaptation against environmental stress. Poster presented at CELLAB-SOCU summer symposim.

Zhang, X. D., Ferrer, M., Espeseth, A. S., Marine, S. D., Stec, E. M., Crackower, M. A.,
Holder, D. J., Heyse, J. F., and Strulovici, B. (2007). The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments. Journal of Biomolecular Screening, 12(4):497–509.

Zhang, X. D., Kuan, P. F., Ferrer, M., Shu, X., Liu, Y. C., Gates, A. T., Kunapuli, P., Stec,
E. M., Xu, M., Marine, S. D., Holder, D. J., Strulovici, B., Heyse, J. F., and Espeseth, A. S.
(2008). Hit selection with false discovery rate control in genome-scale RNAi screens.
Nucleic Acids Research, 36(14):4667–4679.

Zhang, X. D., Santini, F., Lacson, R., Marine, S. D., Wu, Q., Benetti, L., Yang, R., McCampbell, A., Berger, J. P., Toolan, D. M., Stec, E. M., Holder, D. J., Soper, K. A., Heyse, J. F., and Ferrer, M. (2011). cSSMD: assessing collective activity for addressing off-target
effects in genome-scale RNA interference screens. Bioinformatics, 27(20):2775–2781.
Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P., Lan, N., Jansen,
R., Bidlingmaier, S., Houfek, T., Mitchell, T., Miller, P., Dean, R. A., Gerstein, M., and
Snyder, M. (2001). Global analysis of protein activities using proteome chips. Science
(New York, N.Y.), 293(5537):2101–5.