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Chapter 7 - Computational analysis of high-throughput material screens

Published online by Cambridge University Press:  05 April 2013

By
Marc Hulsman ,
Liesbet Geris  and
Marcel J. T. Reinders
Edited by
Jan de Boer  and
Clemens A. van Blitterswijk
Jan de Boer
Affiliation:
University of Twente, Enschede, The Netherlands
Clemens A. van Blitterswijk
Affiliation:
University of Twente, Enschede, The Netherlands
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Summary

Scope

Computational and statistical tools play an important role in materiomics, to provide insights in the underlying processes that allow certain materials to outperform other materials. In this chapter, we discuss numerous methods that allow the analysis of materiomics data. Specifically, we describe the use of statistical tests, ranking and data mining approaches, model learning and testing, as well as experimental design and the exchange of experimental results. Also, we review some of the important publications in this field from the past 15 years, organizing them according to the type of material descriptors that were used.

Basic principles of data analysis

Computational methods play an ever more important role in the study of material function. Partly, this is due to the increased scale of the experiments being performed, with an accompanying need for automated analyses. But the move from low-throughput towards high-throughput experiments entails more than just testing more materials simultaneously. The extra information these experiments produce is slowly catalysing a transition to a more rational approach to material discovery, in which not just material screening plays a role but also material modelling. Materials and their environments are approached as systems that can be modelled and thus explored in silico. This ‘systems approach to material research’ has been termed materiomics. This transition is certainly needed given the size of the materiome that one wants to explore: many material parameters can be varied and combined into a practically infinite palette of combinations. This far surpasses even the reach of high-throughput screenings. The question that will be addressed in this chapter is: how can we efficiently make use of our capability to perform high-throughput experiments, to explore and characterize such a large search space?

Type
Chapter
Information
Materiomics
High-Throughput Screening of Biomaterial Properties
 , pp. 101 - 132
Publisher: Cambridge University Press
Print publication year: 2013

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References

Cranford, S, Buehler, M. Materiomics: biological protein materials, from nano to macro. Nanotechnol Sci Appl. 2010;3:127–48.Google Scholar
Fourches, D, Pu, D, Tassa, C et al. Quantitative nanostructure–activity relationship modeling. ACS Nano. 2010;4(10):5703–12.CrossRefGoogle Scholar
Kholodovych, V, Smith, J, Knight, D et al. Accurate predictions of cellular response using QSPR: a feasibility test of rational design of polymeric biomaterials. Polymer. 2004;45(22):7367–79.CrossRefGoogle Scholar
Kubinyi, H.From narcosis to hyperspace: the history of QSAR. Quant Struct Activ Relat. 2002;21(4):348–56.3.0.CO;2-D>CrossRef
Neuss, S, Apel, C, Buttler, P et al. Assessment of stem cell/biomaterial combinations for stem cell-based tissue engineering. Biomaterials. 2008;29(3):302–13.CrossRefGoogle Scholar
Tabachnick, B, Fidell, L, Osterlind, S. Using Multivariate Statistics 4th edn. Allyn & Bacon; 2001.
Noble, W. How does multiple testing correction work?Nat Biotechnol. 2009;27(12):1135–7.CrossRefGoogle Scholar
Benjamini, Y, Hochberg, Y.Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B.1995;57:289–300.Google Scholar
Swisher, J, Jacobson, S, Yücesan, E.Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Trans Modeling Comput Simul (TOMACS). 2003;13(2):134–54.CrossRefGoogle Scholar
Kim, S, Nelson, B.Selecting the best system. In Handbooks in Operations Research and Management Science Vol. 13: Simulation. North Holland; 2006. pp. 501–34.
Watanabe, H, Khera, S, Vargas, M, Qian, F.Fracture toughness comparison of six resin composites. Dental Mater. 2008;24(3):418–25.CrossRefGoogle Scholar
Ashby, M. Multi-objective optimization in material design and selection. Acta Mater. 2000;48(1):359–69.CrossRefGoogle Scholar
Mutihac, L, Mutihac, R. Mining in chemometrics. Analyt Chim Acta. 2008;612(1):1–18.CrossRefGoogle Scholar
Kardamakis, A, Mouchtaris, A, Pasadakis, N. Linear predictive spectral coding and independent component analysis in identifying gasoline constituents using infrared spectroscopy. Chemomet Intell Lab Syst. 2007;89(1):51–8.CrossRefGoogle Scholar
Jain, A, Murty, M, Flynn, P. Data clustering: a review. ACM Comput Surveys (CSUR). 1999;31(3):264–323.CrossRefGoogle Scholar
Wilkinson, L, Friendly, M.The history of the cluster heat map. Am Statist. 2009;63(2):179–84.CrossRefGoogle Scholar
Handl, J, Knowles, J, Kell, D. Computational cluster validation in post-genomic data analysis. Bioinformatics. 2005;21(15):3201–12.CrossRefGoogle Scholar
Gubskaya, A, Kholodovych, V, Knight, D, Kohn, J, Welsh, W.Prediction of fibrinogen adsorption for biodegradable polymers: Integration of molecular dynamics and surrogate modelling. Polymer. 2007;48(19):5788–801.CrossRefGoogle Scholar
Mohamadi, F, Richards, N, Guida, W et al. MacroModel? an integrated software system for modeling organic and bioorganic molecules using molecular mechanics. J Comput Chem. 1990; 11(4): 440–67.CrossRefGoogle Scholar
Smith, J, Seyda, A, Weber, N et al. Integration of combinatorial synthesis, rapid screening, and computational modeling in biomaterials development. Macromol Rapid Commun. 2004;25(1):127–40.CrossRefGoogle Scholar
Goddard, W.A perspective of materials modelling. Handbook of Materials Modeling. Springer; 2005. pp. 2707–11.
Cranford, S, Buehler, M. Materiomics: biological protein materials, from nano to macro. Nanotechnol Sci Appl. 2010;3:127–48.Google Scholar
Cranford, S, Tarakanova, A, Pugno, N, Buehler, M.Nonlinear material behaviour of spider silk yields robust webs. Nature. 2012;482(7383):72–6.CrossRefGoogle Scholar
Wold, S, Hellberg, S, Dunn, IIIW.Computer methods for the assessment of toxicity. Acta Pharmacol Toxicol.1983;52:158–89.CrossRefGoogle Scholar
Alpaydin, E. Introduction to Machine Learning. MIT; 2004.
Byvatov, E, Fechner, U, Sadowski, J, Schneider, G.Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci. 2003;43(6):1882–9.CrossRefGoogle Scholar
Friedman, J, Hastie, T, Tibshirani, R. The Elements Of Statistical Learning Vol. 1. Springer; 2001.
Zweig, M, Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77.Google Scholar
Mason, S, Graham, N. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quart J Roy Met Soc. 2002;128(584):2145–66.CrossRefGoogle Scholar
Livingstone, D, Salt, D. Variable selection? Spoilt for choice? Rev Comput Chem. 2005: 287–348.
Guyon, I, Elissee, A. An introduction to variable and feature selection. J Machine Learning Res. 2003;3:1157–82.Google Scholar
Wessels, L, Reinders, M, Hart, A et al. A protocol for building and evaluating predictors of disease state based on microarray data. Bioinformatics. 2005;21(19):3755–62.CrossRefGoogle Scholar
Marée, A, Grieneisen, V, Hogeweg, P. The cellular potts model and biophysical properties of cells, tissues and morphogenesis. In Anderson, A, Rejniak, K, eds. Single-Cell-Based Models in Biology and Medicine. Birkhäuser; 2007. pp. 107–36.
Cickovski, T, Huang, C, Chaturvedi, R et al. A framework for three-dimensional simulation of morphogenesis. IEEE/ACM TransComput Biol Bioinformat. 2005;2(4):273–88.CrossRefGoogle Scholar
Rejniak, K, Anderson, A. Hybrid models of tumor growth. WIRes Syst Biol Med. 2011;3(1):115–25.CrossRefGoogle Scholar
Stéphanou, A, McDougall, S, Anderson, A, Chaplain, M. Mathematical modeling of the influence of blood rheological properties upon adaptative tumour-induced angiogenesis. Math Comput Modeling. 2006;44(1):96–123.CrossRefGoogle Scholar
Myers, R, Montgomery, D, Anderson-Cook, C. Response Surface Methodology: Process and Product Optimization using Designed Experiments Vol. 705. Wiley; 2009.
Yeten, B, Castellini, A, Guyaguler, B, Chen, W. A comparison study on experimental design and response surface methodologies. SPE Reservoir Simulation Symposium. Society of Petroleum Engineers Inc.; 2005. Available at
Desai, K, Survase, S, Saudagar, P, Lele, S, Singhal, R. Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochem Eng J. 2008;41(3):266–73.CrossRefGoogle Scholar
Harmon, L. Experiment planning for combinatorial materials discovery. J Mater Sci. 2003;38(22):4479–85.CrossRefGoogle Scholar
Hong, L, Nelson, B. A brief introduction to optimization via simulation. Proc 2009 Winter Simulation Conf (WSC). IEEE; 2009. pp. 75–85.
Settles, B. Active learning literature survey. Computer Sciences Technical Report 1648. University of Wisconsin-Madison; 2009.
Adams, N, Murray-Rust, P.Engineering polymer informatics: Towards the computer-aided design of polymers. Macromol Rapid Commun. 2008;29(8):615–32.CrossRefGoogle Scholar
Berners-Lee, T, Hendler, J. Scientific publishing on the semantic web. Nature. 2001;410:1023–4.CrossRefGoogle Scholar
Blake, J, Bult, C. Beyond the data deluge: data integration and bio-ontologies. J Biomed Informat. 2006;39(3):314–20.CrossRefGoogle Scholar
Bodenreider, O, Stevens, R. Bio-ontologies: current trends and future directions. Briefings Bioinformat. 2006;7(3):256–74.CrossRefGoogle Scholar
Xiang, X, Sun, X, Briceno, G et al. A combinatorial approach to materials discovery. Science. 1995;268(5218):1738.CrossRefGoogle Scholar
Danielson, E, Golden, J, McFarland, E et al. A combinatorial approach to the discovery and optimization of luminescent materials. Nature. 1997;389(6654):944–8.CrossRefGoogle Scholar
Jandeleit, B, Schaefer, D, Powers, T, Turner, H, Weinberg, W. Combinatorial materials science and catalysis. Angew Chem Int Ed. 1999;38(17):2494–532.3.0.CO;2-#>CrossRefGoogle Scholar
Pérez-Luna, V, Horbett, T, Ratner, B. Developing correlations between fibrinogen adsorption and surface properties using multivariate statistics. J Biomed Mater Res. 1994;28(10):1111–26.CrossRefGoogle Scholar
Urquhart, A, Taylor, M, Anderson, D et al. ToF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettability. Analyt Chem. 2008;80(1):135–42.CrossRefGoogle Scholar
Taylor, M, Urquhart, A, Anderson, D et al. Partial least squares regression as a powerful tool for investigating large combinatorial polymer libraries. Surf Interface Anal. 2009;41(2):127–35.CrossRefGoogle Scholar
Chilkoti, A, Schmierer, A, Pérez-Luna, V, Ratner, B. Relationship between surface chemistry and endothelial cell growth: Partial least-squares regression of the static secondary ion mass spectra of oxygen-containing plasma-deposited films. Analyt Chem. 1995;67(17):2883–91.CrossRefGoogle Scholar
Shen, M, Wagner, M, Castner, D, Ratner, B, Horbett, T. Multivariate surface analysis of plasma-deposited tetraglyme for reduction of protein adsorption and monocyte adhesion. Langmuir. 2003;19(5):1692–9.CrossRef
Taylor, M, Elhissi, A. Predicting the physical properties of tablets from atr-ftir spectra using partial least squares regression. Pharm Devel Technol. 2011;16(2):110–7.CrossRefGoogle Scholar
Yang, L, Shard, A, Lee, J, Ray, S. Predicting the wettability of patterned ito surface using tof-sims images. Surf Interface Anal. 2010;42(6–7):911–15.Google Scholar
Wagner, M, Tyler, B, Castner, D. Interpretation of static time-of-flight secondary ion mass spectra of adsorbed protein films by multivariate pattern recognition. Analyt Chem. 2002;74(8):1824–35.CrossRefGoogle Scholar
Kubinyi, H. From narcosis to hyperspace: the history of QSAR. Quant Struct–Activity Relat. 2002;21(4):348–56.3.0.CO;2-D>CrossRefGoogle Scholar
Todeschini, R, Consonni, V. Handbook of Molecular Descriptors Vol. 79. Wiley; 2008.
Tseng, Y, Hopfinger, A, Esposito, E. The great descriptor melting pot: mixing descriptors for the common good of qsar models. J Computer-aided Mol Design. 2012;26:39–43.CrossRefGoogle Scholar
Chemical Computing Group Inc. Montreal Q. Molecular operating environment. 2012. .
Vilar, S, Cozza, G, Moro, S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Topics Med Chem. 2008;8(18):1555–72.CrossRefGoogle Scholar
Karelson, M, Maran, U, Wang, Y, Katritzky, A. QSPR and QSAR models derived using large molecular descriptor spaces. a review of codessa applications. Collection of Czechoslovak Chem Commun. 1999;64(10):1551–71.Google Scholar
Talete, . DRAGON. 2012. .
Smith, J, Kholodovych, V, Knight, D, Kohn, J, Welsh, W. Predicting fibrinogen adsorption to polymeric surfaces in silico: a combined method approach. Polymer. 2005;46(12):4296–306.CrossRefGoogle Scholar
Kholodovych, V, Smith, J, Knight, D et al. Accurate predictions of cellular response using qspr: a feasibility test of rational design of polymeric biomaterials. Polymer. 2004;45(22):7367–79.CrossRefGoogle Scholar
Li, X, Petersen, L, Broderick, S, Narasimhan, B, Rajan, K. Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods. ACS Combinat Sci. 2011;13(1):50–8.CrossRefGoogle Scholar
Landrum, G, Penzotti, J, Putta, S. Machine-learning models for combinatorial catalyst discovery. Measurement Sci Technol. 2005;16:270.CrossRefGoogle Scholar
Lyne, P. Structure-based virtual screening: an overview. Drug Discovery Today. 2002;7(20):1047–55.CrossRefGoogle Scholar
Cruz, V, Ramos, J, Martinez, S et al. Structure–activity relationship study of the metallocene catalyst activity in ethylene polymerization. Organometallics. 2005;24(21):5095–102.CrossRefGoogle Scholar
Linati, L, Lusvardi, G, Malavasi, G et al. Qualitative and quantitative structure-property relationships analysis of multicomponent potential bioglasses. J Phys Chem B. 2005;109(11):4989–98.CrossRefGoogle Scholar
Roy, N, Potter, W, Landau, D. Polymer property prediction and optimization using neural networks. IEEE Trans Neural Netw. 2006;17(4):1001–14.CrossRefGoogle Scholar
Gonzalez-Carrasco, I, Garcia-Crespo, A, Ruiz-Mezcua, B, Lopez-Cuadrado, J.An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios. Appl Intell. 2010:36:1–18.Google Scholar
Fourches, D, Pu, D, Tassa, C et al.Quantitative nanostructure- activity relationship (QNAR) modelling. ACS Nano. 2010;4(10): 5703–12.CrossRefGoogle Scholar
Ghosh, J, Lewitus, D, Chandra, P et al Computational modeling of in vitro biological responses on polymethacrylate surfaces. Polymer. 2011;52(12):2650–60.Google Scholar
Tu, Le, Epa, V, Burden, F, Winkler, D. Quantitative structure–property relationship modeling of diverse materials properties. Chem Rev. 2012;112(5):2889–919.CrossRefGoogle Scholar
Lampin, M, Warocquier-Clérout, R, Legris, C, Degrange, M, Sigot-Luizard, M.Correlation between substratum roughness and wettability, cell adhesion, and cell migration. J Biomed Mater Res. 1997;36(1):99–108.3.0.CO;2-E>CrossRefGoogle Scholar
Miller, C, Shanks, H, Witt, A, Rutkowski, G, Mallapragada, S. Oriented schwann cell growth on micropatterned biodegradable polymer substrates. Biomaterials. 2001;22(11):1263–9.CrossRefGoogle Scholar
Hatano, K, Inoue, H, Kojo, T et al. Effect of surface roughness on proliferation and alkaline phosphatase expression of rat calvarial cells cultured on polystyrene. Bone. 1999;25(4):439–45.CrossRefGoogle Scholar
Meredith, J, Sormana, J, Keselowsky, B et al. Combinatorial characterization of cell interactions with polymer surfaces. J Biomed Mater Res A. 2003;66(3):483–90.CrossRefGoogle Scholar
Anselme, K, Bigerelle, M, Noel, B et al. Qualitative and quantitative study of human osteoblast adhesion on materials with various surface roughnesses. J Biomed Mater Res. 2000;49(2):155–66.3.0.CO;2-J>CrossRefGoogle Scholar
Benhamou, C, Lespessailles, E, Jacquet, G et al. Fractal organization of trabecular bone images on calcaneus radiographs. J Bone Miner Res. 1994;9(12):1909–18.CrossRefGoogle Scholar
Bigerelle, M, Anselme, K. Statistical correlation between cell adhesion and proliferation on biocompatible metallic materials. J Biomed Mater Res Part A. 2005;72(1): 36–46.CrossRefGoogle Scholar
Zapata, P, Su, J, García, A, Meredith, J. Quantitative high-throughput screening of osteoblast attachment, spreading, and proliferation on demixed polymer blend micropatterns. Biomacromolecules. 2007;8(6):1907–17.CrossRefGoogle Scholar
Su, J, Zapata, P, Chen, C, Meredith, J. Local cell metrics: a novel method for analysis of cell-cell interactions. BMC Bioinformat. 2009;10(1):350.CrossRefGoogle Scholar
Groeber, M, Ghosh, S, Uchic, M, Dimiduk, D. A framework for automated analysis and simulation of 3D polycrystalline microstructures. Part 1: Statistical characterization. Acta Mater. 2008;56(6):1257–73.Google Scholar
Qidwai, M, Lewis, A, Geltmacher, A. Using image-based computational modeling to study microstructure-yield correlations in metals. Acta Mater. 2009;57(14):4233–47.CrossRefGoogle Scholar
Fullwood, D, Niezgoda, S, Adams, B, Kalidindi, S. Microstructure sensitive design for performance optimization. Prog Mater Sci. 2010;55(6):477–562.CrossRefGoogle Scholar
Unadkat, H, Hulsman, M, Cornelissen, K et al. An algorithm-based topographical biomaterials library to instruct cell fate. Proc Natl Acad Sci. 2011;108(40):16565–70.CrossRefGoogle Scholar
Bohner, M, Loosli, Y, Baroud, G, Lacroix, D. Commentary: deciphering the link between architecture and biological response of a bone graft substitute. Acta Biomater. 2011;7(2):478–84.CrossRefGoogle Scholar
Carlier, A, Chai, Y, Moesen, M et al. Designing optimal calcium phosphate scaffold-cell combinations using an integrative model-based approach. Acta Biomater. 2011;7(10):3573–85.CrossRefGoogle Scholar
Hartman, E, Vehof, J, Spauwen, P, Jansen, J. Ectopic bone formation in rats: the importance of the carrier. Biomaterials. 2005;26(14):1829–35.CrossRefGoogle Scholar
Roberts, S, Geris, L, Kerckhofs, G et al. The combined bone forming capacity of human periosteal derived cells and calcium phosphates. Biomaterials. 2011;32(19):4393–405.CrossRefGoogle Scholar
Geris, L, Gerisch, A, Maes, C et al. Mathematical modeling of fracture healing in mice: comparison between experimental data and numerical simulation results. Med Biol Eng Comput. 2006;44(4):280–9.CrossRefGoogle Scholar
Isaksson, H, van Donkelaar, C, Huiskes, R, Yao, J, Ito, K. Determining the most important cellular characteristics for fracture healing using design of experiments methods. J Theor Biol. 2008;255(1):26–39.CrossRefGoogle Scholar
Urdy, S.On the evolution of morphogenetic models: mechano-chemical interactions and an integrated view of cell differentiation, growth, pattern formation and morphogenesis. Biol Rev. 2012;87(4):786–803.CrossRefGoogle Scholar
Jiang, Y, Bauer, A, Jackson, T.Cell-based models of tumor angiogenesis. In Modeling Tumor Vasculature. Springer; 2012. pp. 135–50.
Kenwright, J, Gardner, T.Mechanical influences on tibial fracture healing. Clin Orthopaed Related Res. 1998;355:S179.Google Scholar
Geris, L, Sloten, J, Oosterwyck, H.Connecting biology and mechanics in fracture healing: an integrated mathematical modeling framework for the study of nonunions. Biomech Modeling Mechanobiol. 2010;9(6):713–24.CrossRefGoogle Scholar
Keten, S, Buehler, M.Nanostructure and molecular mechanics of spider dragline silk protein assemblies. J Roy Soc Interface. 2010;7(53):1709–21.CrossRefGoogle Scholar
Nova, A, Keten, S, Pugno, N, Redaelli, A, Buehler, M. Molecular and nanostructural mechanisms of deformation, strength and toughness of spider silk fibrils. Nano Lett. 2010;10(7):2626–34.CrossRefGoogle Scholar
Lépinoux, J.Multiscale Phenomena in Plasticity: From Experiments to Phenomenology, Modeling and Materials Engineering Vol. 367. Springer; 2000.
Ghoniem, N, Cho, K.The emerging role of multiscale modeling in nano-and micro-mechanics of materials. Comput Modeling Eng Sci. 2002;3(2):147–74.Google Scholar
Curtin, W, Miller, R. Atomistic/continuum coupling in computational materials science. Modeling Simul Mater Eci Eng. 2003;11:R33.Google Scholar
Phillips, R.Multiscale modeling in the mechanics of materials. Curr Opin Solid State Mater Sci. 1998;3(6):526–32.CrossRefGoogle Scholar
Bock, H.On some significance tests in cluster analysis. J Classif. 1985;2(1):77–108.CrossRefGoogle Scholar
Čopíková, J, Barros, A, Šmídová, I et al. Influence of hydration of food additive polysaccharides on FT-IR spectra distinction. Carbohyd Polym. 2006;63(3):355–59.CrossRefGoogle Scholar
Keten, S, Xu, Z, Ihle, B, Buehler, M.Nanoconfinement controls stiffness, strength and mechanical toughness of [beta]-sheet crystals in silk. Nat Mater. 2010;9(4):359–67.CrossRefGoogle Scholar

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