The authors are grateful to Dr. Thomas Rademacher for providing the pPAM plant expression vector and Ibrahim Al Amedi for cultivating the tobacco plants used in this study. We would like to thank Dr. Richard M. Twyman for his assistance with editing the manuscript. This work was in part funded by the European Research Council Advanced Grant &#8220;Future-Pharma&#8221;, proposal number 269110 and the Fraunhofer Zukunftsstiftung (Fraunhofer&#160;Future Foundation).

1. Planning a DoE Strategy
<ol>
	<li>Identify relevant factors and responses for inclusion in the design.
	<ol>
		<li>Define one or several responses for measurement. Here, 2G12 and DsRed expression levels were used (&#956;g/ml), including the minimum detectable difference regarded as relevant (10 and 20 &#956;g /ml, respectively) and an approximate value for the estimated standard deviation of the system (4 and 8 &#956;g/ml, respectively) based on previous experiments.</li>
		<li>Use the available literature, data fro...

<ol>
	<li>Fischer, R., Emans, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+farming+of+pharmaceutical+proteins.">Molecular farming of pharmaceutical proteins.</a> Transgenic research. 9, 277-299 (2000).</li><li>Commandeur, U., Twyman, R. M., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biosafety+of+molecular+farming+in+plants.">The biosafety of molecular farming in plants.</a> AgBiotechNet. 5, 9 (2003).</li><li>Shoji, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+plant-based+system+for+rapid+production+of+influenza+vaccine+antigens.">A plant-based system for rapid production of influenza vaccine antigens.</a> Influenza Other Resp. 6, 204-210 (2012).</li><li>Goodin, M. M., Zaitlin, D., Naidu, R. A., Lommel, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nicotiana+benthamiana:+Its+history+and+future+as+a+model+for+plant-pathogen+interactions.">Nicotiana benthamiana: Its history and future as a model for plant-pathogen interactions.</a> Mol Plant Microbe In. 21, 1015-1026 (2008).</li><li>Berg, R. H., Beachy, R. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescent+protein+applications+in+plants.">Fluorescent protein applications in plants.</a> Method Cell Biol. 85, 153 (2008).</li><li>Chung, S. M., Vaidya, M., Tzfira, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Agrobacterium+is+not+alone:+gene+transfer+to+plants+by+viruses+and+other+bacteria.">Agrobacterium is not alone: gene transfer to plants by viruses and other bacteria.</a> Trends in plant science. 11, 1-4 (2006).</li><li>Sheludko, Y. V., Sindarovska, Y. R., Gerasymenko, I. M., Bannikova, M. A., Kuchuk, N. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+several+Nicotiana+species+as+hosts+for+high-scale+Agrobacterium-mediated+transient+expression.">Comparison of several Nicotiana species as hosts for high-scale Agrobacterium-mediated transient expression.</a> Biotechnology and Bioengineering. 96, 608-614 (2007).</li><li>Wydro, M., Kozubek, E., Lehmann, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+transient+Agrobacterium-mediated+gene+expression+system+in+leaves+of+Nicotiana+benthamiana.">Optimization of transient Agrobacterium-mediated gene expression system in leaves of Nicotiana benthamiana.</a> Acta Biochimica Polonica. 53, 289-298 (2006).</li><li>Buyel, J. F., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Processing+heterogeneous+biomass:+Overcoming+the+hurdles+in+model+building.">Processing heterogeneous biomass: Overcoming the hurdles in model building.</a> Bioengineered. 4, (2013).</li><li>Fischer, R., Schillberg, S., Hellwig, S., Twyman, R. M., Drossard, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GMP+issues+for+recombinant+plant-derived+pharmaceutical+proteins.">GMP issues for recombinant plant-derived pharmaceutical proteins.</a> Biotechnol Adv. 30, 434-439 (2012).</li><li>Daniel, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-at-a-time+plans.">One-at-a-time plans.</a> Journal of the American Statistical Association. 68, 353-360 (1973).</li><li>Czitrom, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-Factor-at-a-Time+versus+Designed+Experiments+The+American+Statistician.">One-Factor-at-a-Time versus Designed Experiments The American Statistician.</a> 53, 6 (1999).</li><li>Anderson, M. J., Kraber, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Keys+to+successful+designed+experiments.">Keys to successful designed experiments.</a> ASQ - The global voice of quality. 6, 6 (1999).</li><li>Montgomery, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Design and Analysis of Experiments. , (2007).</li><li>Myers, R. H., Montgomery, D. C., Anderson-Cook, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Response Surface Methodology: Process and Product Optimization Using Designed Experiments. , (2009).</li><li>Piepel, G. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Programs+for+generating+extreme+vertices+and+centroids+of+linearly+constrained+experimental+regions.">Programs for generating extreme vertices and centroids of linearly constrained experimental regions.</a> J Qual Technol. 20, 15 (1988).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FDA. , (2009).</li><li>Shivhare, M., McCreath, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Practical+Considerations+for+DoE+Implementation+in+Quality+By+Design.">Practical Considerations for DoE Implementation in Quality By Design.</a> BioProcess International. 8, 9 (2010).</li><li>Buyel, J. F., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+models+for+transient+protein+expression+in+tobacco+(Nicotiana+tabacum+L.)+can+optimize+process+time,+yield,+and+downstream+costs.">Predictive models for transient protein expression in tobacco (Nicotiana tabacum L.) can optimize process time, yield, and downstream costs.</a> Biotechnology and bioengineering. 109, 2575-2588 (2012).</li><li>Buyel, J. F., Kaever, T., Buyel, J. J., Fischer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+models+for+the+accumulation+of+a+fluorescent+marker+protein+in+tobacco+leaves+according+to+the+promoter/5'UTR+combination.">Predictive models for the accumulation of a fluorescent marker protein in tobacco leaves according to the promoter/5'UTR combination.</a> Biotechnology and bioengineering. 110, 471-482 (2013).</li><li>Anderson, M. J., Whitcomb, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> DOE Simplified: Practical Tools for Effective Experimentation. , (2000).</li><li>Anderson, M. J., Whitcomb, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Response Surface Methods Simplified. , (2005).</li><li>De Gryze, S., Langhans, I., Vandebroek, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+the+correct+intervals+for+prediction:+A+tutorial+on+tolerance+intervals+for+ordinary+least-squares+regression.">Using the correct intervals for prediction: A tutorial on tolerance intervals for ordinary least-squares regression.</a> Chemometr Intell Lab. 87, 147-154 (2007).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Plasmid DNA purification User manual. , (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PCR clean-up Gel extraction User manual. , (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Quick Ligation Protocol. 4, (2009).</li><li>Inoue, H., Nojima, H., Okayama, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-Efficiency+Transformation+of+Escherichia-Coli+with+Plasmids.">High-Efficiency Transformation of Escherichia-Coli with Plasmids.</a> Gene. 96, 23-28 (1990).</li><li>Main, G. D., Reynolds, S., Gartland, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electroporation+protocols+for+Agrobacterium.">Electroporation protocols for Agrobacterium.</a> Methods in Molecular Biology. 44, 405-412 (1995).</li><li>Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R., Hofacker, I. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Vienna+RNA+websuite.">The Vienna RNA websuite.</a> Nucleic acids research. 36, 70-74 (2008).</li><li>Howell, S., Kenmore, M., Kirkland, M., Badley, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-density+immobilization+of+an+antibody+fragment+to+a+carboxymethylated+dextran-linked+biosensor+surface.">High-density immobilization of an antibody fragment to a carboxymethylated dextran-linked biosensor surface.</a> J Mol Recognit. 11, 200-203 (1998).</li><li>Newcombe, A. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+a+biosensor+assay+to+quantify+polyclonal+IgG+in+ovine+serum+used+for+the+production+of+biotherapeutic+antibody+fragments.">Evaluation of a biosensor assay to quantify polyclonal IgG in ovine serum used for the production of biotherapeutic antibody fragments.</a> Process Biochem. 41, 842-847 (2006).</li><li>Peixoto, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+Variable+Selection+in+Polynomial+Regression-Models.">Hierarchical Variable Selection in Polynomial Regression-Models.</a> Am Stat. 41, 311-313 (1987).</li><li>Peixoto, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Property+of+Well-Formulated+Polynomial+Regression-Models.">A Property of Well-Formulated Polynomial Regression-Models.</a> Am Stat. 44, 26-30 (1990).</li><li>Sanders, P. R., Winter, J. A., Barnason, A. R., Rogers, S. G., Fraley, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+cauliflower+mosaic+virus+35S+and+nopaline+synthase+promoters+in+transgenic+plants.">Comparison of cauliflower mosaic virus 35S and nopaline synthase promoters in transgenic plants.</a> Nucleic acids research. 15, 1543-1558 (1987).</li><li>Ma, J. K. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation+and+Assembly+of+Secretory+Antibodies+in+Plants.">Generation and Assembly of Secretory Antibodies in Plants.</a> Science. 268, 716-719 (1995).</li><li>Wycoff, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Secretory+IgA+antibodies+from+plants.">Secretory IgA antibodies from plants.</a> Curr Pharm Design. 11, 2429-2437 (2005).</li><li>Pace, C. N., Vajdos, F., Fee, L., Grimsley, G., Gray, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+measure+and+predict+the+molar+absorption+coefficient+of+a+protein.">How to measure and predict the molar absorption coefficient of a protein.</a> Protein Sci. 4, 2411-2423 (1995).</li></ol>

Every experiment requires careful planning because resources are often scarce and expensive. This is particularly true for DoE strategies because errors during the planning phase (e.g. selecting a base model that does not cover all significant factor interactions) can substantially diminish the predictive power of the resulting models and thus devalue the entire experiment. However, these errors can easily be avoided by following basic procedures.
Considerations during DoE planning
<...

The production of biopharmaceutical proteins in plants is advantageous because plants are inexpensive to grow, the platform can be scaled up just by growing more plants, and human pathogens are unable to replicate 1,2. Transient expression strategies based for example on the infiltration of leaves with Agrobacterium tumefaciens provides additional benefits because the time between the point of DNA delivery and the delivery of a purified product is reduced from years to less than 2 months 3. Transient expression is also used for functional analysis, e.g. to test genes for their ability to complement loss-of-function mutants or to investigate protein interactions 4-6. However, transient expression levels tend to show greater batch-to-batch variation than expression levels in transgenic plants 7-9. This reduces the likelihood that biopharmaceutical manufacturing processes based on transient expression will be approved in the context of good manufacturing practice (GMP) because reproducibility is a critical quality attribute and is subject to risk assessment 10. Such variation can also mask any interactions that researchers intend to investigate. Therefore, we set out to identify the major factors that affect transient expression levels in plants and to build a high-quality quantitative predictive model.
The one-factor-at-a-time (OFAT) approach is often used to characterize the impact (effect) of certain parameters (factors) on the outcome (response) of an experiment 11. But this is suboptimal because the individual tests (runs) during an investigation (experiment) will be aligned like pearls on a string through the potential area spanned by the factors that are tested (design space). The coverage of the design space and hence the degree of information derived from the experiment is low, as shown in Figure 1A 12. Furthermore, interdependencies among different factors (factor interactions) can remain concealed resulting in poor models and/or the prediction of false optima, as shown in Figure 1B 13.
The drawbacks described above can be avoided by using a design of experiments (DoE) approach in which the runs of an experiment are scattered more evenly throughout the design space, meaning that more than one factor is varied between two runs 14. There are specialized designs for mixtures, screening factors (factorial designs) and the quantitation of factor impacts on responses (response surface methods, RSMs) 15. Furthermore, RSMs can be realized as central-composite designs but can also be achieved effectively by using specialized software that can apply different criteria for the selection of runs. For example, the so called D-optimality criterion will select runs so to minimize the error in the coefficients of the resulting model, whereas the IV-optimality criterion selects runs that achieve the lowest prediction variance throughout the design space 15,16. The RSM we describe here allows the precise quantitation of transient protein expression in plants, but it can easily be transferred to any system involving several (~5-8) numeric factors (e.g. temperature, time, concentration) and a few (~2-4) categoric factors (e.g. promoter, color) in which a mechanistic description is unavailable or too complex to model.
The DoE approach originated in the agricultural sciences but has spread to other areas because it is transferable to any situation where it is useful to reduce the number of runs necessary to obtain reliable data and generate descriptive models for complex processes. This in turn has led to the inclusion of DoE in the &#34;Guidance for Industry, Q8(R2) Pharmaceutical Development&#34; published by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) 17. DoE is now used widely in scientific research and industry 18. However, care must be taken during the planning and execution of the experiment because selecting an improper polynomial degree for the multiple-linear-regression model (base model) can introduce a need for additional runs to model all factor effects correctly. Furthermore, corrupted or missing data generate incorrect models and flawed predictions, and may even prevent any model building attempt as described in the protocol and discussion sections 18. In the protocol section, we will initially set out the most important planning steps for a RSM-based experiment and then explain the design based on the DoE software DesignExpert v8.1. But similar designs can be built with other software including JMP, Modde, and STATISTICA. The experimental procedures are followed by instructions for data analysis and evaluation.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51216/51216fig1highres.jpg" src="/files/ftp_upload/51216/51216fig1.jpg" /> 
Figure 1. Comparison of OFAT and DoE. A. Sequential variation of one factor at a time (OFAT) in an experiment (black, red and blue circles) achieves a low coverage of the design space (hatched regions). In contrast, the variation of more than one factor at a time using the design of experiments (DoE) strategy (green circles) enhances the coverage and thus the precision of the resulting models. B. The biased design space coverage means that OFAT experiments (black circles) can also fail to identify optimal operating regions (red) and predict sub-optimal solutions (large black circle), whereas DoE strategies (black stars) are more likely to identify preferable conditions (large black star).

<table border="0" cellpadding="0" cellspacing="0" width="946">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Design-Expert(R) 8</td>
			<td style="width:195px;">Stat-Ease, Inc.</td>
			<td style="width:119px;">n.a.</td>
			<td style="width:272px;">DoE software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Tryptone</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">8952.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Yeast extract</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">2363.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Sodium chloride</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">P029.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Ampicillin</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">K029.2</td>
			<td>Antibiotic</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Agar-Agar</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">5210.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Escherichia coli K12 DH5a</td>
			<td style="width:195px;">Life technologies</td>
			<td style="width:119px;">18263-012</td>
			<td>Microorganism</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">pPAM</td>
			<td style="width:195px;">GenBank</td>
			<td style="width:119px;">AY027531</td>
			<td>Cloning/expression vector;&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">NucleoSpin Plasmid&#160;</td>
			<td style="width:195px;">MACHEREY-NAGEL GmbH</td>
			<td style="width:119px;">740588.250</td>
			<td>Plasmid DNA isolation kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">NucleoSpin Gel and PCR Clean-up</td>
			<td style="width:195px;">MACHEREY-NAGEL GmbH</td>
			<td style="width:119px;">740609.250</td>
			<td>Plasmid DNA purification kit</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">NanoDrop 2000</td>
			<td style="width:195px;">Thermo Scientific</td>
			<td style="width:119px;">n.a.</td>
			<td>Spectrophotometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">NcoI</td>
			<td style="width:195px;">New England Biolabs Inc.</td>
			<td style="width:119px;">R3193L</td>
			<td>Restrictionendonuclease</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">EcoRI</td>
			<td style="width:195px;">New England Biolabs Inc.</td>
			<td style="width:119px;">R3101L</td>
			<td>Restrictionendonuclease</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">AscI</td>
			<td style="width:195px;">New England Biolabs Inc.</td>
			<td style="width:119px;">R0558L</td>
			<td>Restrictionendonuclease</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">NEB 4</td>
			<td style="width:195px;">New England Biolabs Inc.</td>
			<td style="width:119px;">B7004S</td>
			<td>Restrictionendonuclease buffer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">TRIS</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">4855.3</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Disodium tetraborate</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">4403.3</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">EDTA</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">8040.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Agarose</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">6352.4</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Bromophenol blue</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">A512.1</td>
			<td>Color indicator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Xylene cyanol</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">A513.1</td>
			<td>Color indicator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Glycerol</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">7530.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Mini-Sub Cell GT Cell</td>
			<td style="width:195px;">BioRad</td>
			<td style="width:119px;">170-4406</td>
			<td>Gel electrophoresis chamber</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Agrobacterium tumefaciens strain GV3101:pMP90RK</td>
			<td style="width:195px;">DSMZ</td>
			<td style="width:119px;">12365</td>
			<td>Microorganism</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Electroporator 2510</td>
			<td style="width:195px;">Eppendorf</td>
			<td style="width:119px;">4307000.658</td>
			<td>Electroporator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Beef extract</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">X975.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Peptone</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">2365.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Sucrose</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">4621.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Magnesium sulfate</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">0261.3</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Carbenicillin</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">6344.2</td>
			<td>Antibiotic</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Kanamycin</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">T832.3</td>
			<td>Antibiotic</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Rifampicin</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">4163.2</td>
			<td>Antibiotic</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">FWD primer</td>
			<td style="width:195px;">Eurofins MWG Operon</td>
			<td style="width:119px;">n.a.</td>
			<td>CCT CAG GAA GAG CAA TAC</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">REV primer</td>
			<td style="width:195px;">Eurofins MWG Operon</td>
			<td style="width:119px;">n.a.</td>
			<td>CCA AAG CGA GTA CAC AAC</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">2720 Thermal cycler</td>
			<td style="width:195px;">Applied Biosystems</td>
			<td style="width:119px;">4359659</td>
			<td>Thermocycler</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">RNAfold webserver</td>
			<td style="width:195px;">University of Vienna</td>
			<td style="width:119px;">n.a.</td>
			<td>Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Ferty 2 Mega</td>
			<td style="width:195px;">Kammlott</td>
			<td style="width:119px;">5.220072</td>
			<td>Fertilizer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Grodan Rockwool Cubes 10x10cm</td>
			<td style="width:195px;">Grodan</td>
			<td style="width:119px;">n.a.</td>
			<td>Rockwool block</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Greenhouse</td>
			<td style="width:195px;">n.a.</td>
			<td style="width:119px;">n.a.</td>
			<td>For plant cultivation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Phytotron</td>
			<td style="width:195px;">Ilka Zell</td>
			<td style="width:119px;">n.a.</td>
			<td>For plant cultivation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Omnifix-F Solo</td>
			<td style="width:195px;">B. Braun</td>
			<td style="width:119px;">6064204</td>
			<td>Syringe</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Murashige and Skoog salts</td>
			<td style="width:195px;">Duchefa</td>
			<td style="width:119px;">M 0222.0010</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Glucose</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">6780.2</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Acetosyringone</td>
			<td style="width:195px;">Sigma-Aldrich</td>
			<td style="width:119px;">D134406-5G</td>
			<td>Phytohormon analogon</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">&#160;BioPhotometer plus</td>
			<td style="width:195px;">Eppendorf</td>
			<td style="width:119px;">&#160;6132 000.008</td>
			<td>Photometer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Osram cool white 36 W</td>
			<td style="width:195px;">Osram</td>
			<td style="width:119px;">4930440</td>
			<td>Light source</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Disodium phosphate</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">&#160;4984.3&#160;</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Centrifuge 5415D</td>
			<td style="width:195px;">Eppendorf</td>
			<td style="width:119px;">5424 000.410</td>
			<td>Centrifuge</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Forma -86C ULT freezer</td>
			<td style="width:195px;">ThermoFisher</td>
			<td style="width:119px;">88400</td>
			<td>Freezer</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Synergy HT</td>
			<td style="width:195px;">BioTek</td>
			<td style="width:119px;">SIAFRT</td>
			<td>Fluorescence plate reader</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Biacore T200</td>
			<td style="width:195px;">GE Healthcare</td>
			<td style="width:119px;">n.a.</td>
			<td>SPR device</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Protein A</td>
			<td style="width:195px;">Life technologies</td>
			<td style="width:119px;">10-1006</td>
			<td>Antibody binding protein</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">HEPES</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">9105.3</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">Tween-20</td>
			<td style="width:195px;">Carl Roth GmbH</td>
			<td style="width:119px;">9127.3</td>
			<td>Media component</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:360px;">2G12 antibody</td>
			<td style="width:195px;">Polymun</td>
			<td style="width:119px;">AB002</td>
			<td>Reference antibody</td>
		</tr>
	</tbody>
</table>

characterization of complex systems using the design of experiments approach: transient protein expression in tobacco as a case study

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

A descriptive model for DsRed accumulation during transient expression using different promoters and 5&#39;UTRs
DsRed fluorescence in leaf extracts was used to indicate the expression level of the recombinant protein and thus was used as the response in the DoE strategy. The minimum detectable difference we considered relevant was 20 &#956;g/ml&#160;and the estimated standard deviation of the system was 8 &#956;g/ml&#160;based on initial experiments. Factors included in the tr...

Watch this Scientific Journal Video about Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study at JoVE.com

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

We describe a design of experiments approach that can be used to determine and model the influence of transgene regulatory elements, plant growth and development parameters, and incubation conditions on the transient expression of monoclonal antibodies and reporter proteins in plants.

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the ...

characterization-complex-systems-using-design-experiments-approach

Research

JoVE Journal

Bioengineering

16.4K ビュー.  RWTH Aachen University. 我々は、植物におけるモノクローナル抗体およびレポータータンパク質の一過性発現に対する導入遺伝子の調節エレメント、植物の成長および発達パラメータ、およびインキュベーション条件の影響を決定し、モデル化するのに使用することができる実験アプローチの設計を記載している。 

ビデオ: Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

High-throughput Protein Expression Generator Using a Microfluidic Platform

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and high-fidelity measurements of large systems. Microfluidics promises to fulfill many of these requirements, such as performing high-throughput screening experiments on-chip, encompassing biochemical, biophysical, and cell-based assays1. Since the early days of microfluidics devices, this field has drastically evolved, leading to the development of microfluidic large-scale integration2,3. This technology allows for the integration of thousands of micromechanical valves on a single device with a postage-sized footprint (Figure 1). We have developed a high-throughput microfluidic platform for generating in vitro expression of protein arrays (Figure 2) named PING (Protein Interaction Network Generator). These arrays can serve as a template for many experiments such as protein-protein 4, protein-RNA5 or protein-DNA6 interactions.
The device consist of thousands of reaction chambers, which are individually programmed using a microarrayer. Aligning of these printed microarrays to microfluidics devices programs each chamber with a single spot eliminating potential contamination or cross-reactivity Moreover, generating microarrays using standard microarray spotting techniques is also very modular, allowing for the arraying of proteins7, DNA8, small molecules, and even colloidal suspensions. The potential impact of microfluidics on biological sciences is significant. A number of microfluidics based assays have already provided novel insights into the structure and function of biological systems, and the field of microfluidics will continue to impact biology.

We present a microfluidic approach for the expression of protein arrays. The device consists of thousands of reaction chambers controlled by micro-mechanical valves. The microfluidic device is mated to a microarray-printed gene library. These genes are then transcribed and translated on-chip, resulting in a protein array ready for experimental use.

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and ...

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, each offering its own advantages and shortcomings5,6. Most prominently, due to the microscale device dimensions, a high modulus is required to facilitate implantation into living tissue. Conversely, the stiffness of the device should match the surrounding tissue to minimize induced local strain7-9. Therefore, we recently developed a new class of bio-inspired materials to meet these requirements by responding to environmental stimuli with a change in mechanical properties10-14. Specifically, our poly(vinyl acetate)-based nanocomposite (PVAc-NC) displays a reduction in stiffness when exposed to water and elevated temperatures (e.g. body temperature). Unfortunately, few methods exist to quantify the stiffness of materials in vivo15, and mechanical testing outside of the physiological environment often requires large samples inappropriate for implantation. Further, stimuli-responsive materials may quickly recover their initial stiffness after explantation. Therefore, we have developed a method by which the mechanical properties of implanted microsamples can be measured ex vivo, with simulated physiological conditions maintained using moisture and temperature control13,16,17. 
To this end, a custom microtensile tester was designed to accommodate microscale samples13,17 with widely-varying Young's moduli (range of 10 MPa to 5 GPa). As our interests are in the application of PVAc-NC as a biologically-adaptable neural probe substrate, a tool capable of mechanical characterization of samples at the microscale was necessary. This tool was adapted to provide humidity and temperature control, which minimized sample drying and cooling17. As a result, the mechanical characteristics of the explanted sample closely reflect those of the sample just prior to explantation.
The overall goal of this method is to quantitatively assess the in vivo mechanical properties, specifically the Young's modulus, of stimuli-responsive, mechanically-adaptive polymer-based materials. This is accomplished by first establishing the environmental conditions that will minimize a change in sample mechanical properties after explantation without contributing to a reduction in stiffness independent of that resulting from implantation. Samples are then prepared for implantation, handling, and testing (Figure 1A). Each sample is implanted into the cerebral cortex of rats, which is represented here as an explanted rat brain, for a specified duration (Figure 1B). At this point, the sample is explanted and immediately loaded into the microtensile tester, and then subjected to tensile testing (Figure 1C). Subsequent data analysis provides insight into the mechanical behavior of these innovative materials in the environment of the cerebral cortex.

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

A method is discussed by which the in vivo mechanical behavior of stimuli-responsive materials is monitored as a function of time. Samples are tested ex vivo using a microtensile tester with environmental controls to simulate the physiological environment. This work further promotes understanding the in vivo behavior of our material.

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, ...

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in vitro analysis are well suited to study of large numbers of cells (&ge;105) in milliliter-scale volumes (&ge;0.1 ml). However, there are many instances in which it is necessary or desirable to scale down culture size to reduce consumption of the cells of interest and/or reagents required for their culture, stimulation, or processing. Unfortunately, conventional approaches do not support precise and reproducible manipulation of micro-scale cultures, and the microfluidics-based automated systems currently available are too complex and specialized for routine use by most laboratories. To address this problem, we have developed a simple and versatile technology platform for automated culture, stimulation, and recovery of small populations of cells (100 - 2,000 cells) in micro-scale volumes (1 - 20 &mu;l). The platform consists of a set of fibronectin-coated microcapillaries ("cell perfusion chambers"), within which micro-scale cultures are established, maintained, and stimulated; a digital microfluidics (DMF) device outfitted with "transfer" microcapillaries ("central hub"), which routes cells and reagents to and from the perfusion chambers; a high-precision syringe pump, which powers transport of materials between the perfusion chambers and the central hub; and an electronic interface that provides control over transport of materials, which is coordinated and automated via pre-determined scripts. As an example, we used the platform to facilitate study of transcriptional responses elicited in immune cells upon challenge with bacteria. Use of the platform enabled us to reduce consumption of cells and reagents, minimize experiment-to-experiment variability, and re-direct hands-on labor. Given the advantages that it confers, as well as its accessibility and versatility, our platform should find use in a wide variety of laboratories and applications, and prove especially useful in facilitating analysis of cells and stimuli that are available in only limited quantities.

We have developed an automated cell culture and interrogation platform for micro-scale cell stimulation experiments. The platform offers simple, versatile, and precise control in cultivating and stimulating small populations of cells, and recovering lysates for molecular analyses. The platform is well suited to studies that use precious cells and/or reagents.

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in ...

Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor

F&#246;rster Resonance Energy Transfer (FRET) has become a powerful tool for monitoring protein folding, interaction and localization in single cells. Biosensors relying on the principle of FRET have enabled real-time visualization of subcellular signaling events in live cells with high temporal and spatial resolution. Here, we describe the application of a genetically encoded Bile Acid Sensor (BAS) that consists of two fluorophores fused to the farnesoid X receptor ligand binding domain (FXR-LBD), thereby forming a bile acid sensor that can be activated by a large number of bile acids species and other (synthetic) FXR ligands. This sensor can be targeted to different cellular compartments including the nucleus (NucleoBAS) and cytosol (CytoBAS) to measure bile acid concentrations locally. It allows rapid and simple quantitation of cellular bile acid influx, efflux and subcellular distribution of endogenous bile acids without the need for labeling with fluorescent tags or radionuclei. Furthermore, the BAS FRET sensors can be useful for monitoring FXR ligand binding. Finally, we show that this FRET biosensor can be combined with imaging of other spectrally distinct fluorophores. This allows for combined analysis of intracellular bile acid dynamics and i) localization and/or abundance of proteins of interest, or ii) intracellular signaling in a single cell.

We provide a detailed protocol to study bile acid dynamics in living cells using a genetically encoded BAS FRET sensor. This Bile Acid Sensor represents a unique tool to study (regulation of) bile acid transport and FXR activation in a wide range of cell types.

F&#246;rster Resonance Energy Transfer (FRET) has become a powerful tool for monitoring protein folding, interaction and localization in single cells. ...

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate (LAL) Assays

When present in pharmaceutical products, a Gram-negative bacterial cell wall component endotoxin (often also called lipopolysaccharide) can cause inflammation, fever, hypo- or hypertension, and, in extreme cases, can lead to tissue and organ damage that may become fatal. The amounts of endotoxin in pharmaceutical products, therefore, are strictly regulated. Among the methods available for endotoxin detection and quantification, the Limulus Amoebocyte Lysate (LAL) assay is commonly used worldwide. While any pharmaceutical product can interfere with the LAL assay, nano-formulations represent a particular challenge due to their complexity. The purpose of this paper is to provide a practical guide to researchers inexperienced in estimating endotoxins in engineered nanomaterials and nanoparticle-formulated drugs. Herein, practical recommendations for performing three LAL formats including turbidity, chromogenic and gel-clot assays are discussed. These assays can be used to determine endotoxin contamination in nanotechnology-based drug products, vaccines, and adjuvants.

Detection of Endotoxin in Nano-formulations Using Limulus Amoebocyte Lysate LAL Assays

Detection of endotoxins in engineered nanomaterials represents one of the grand challenges in the field of nanomedicine. Here, we present a case study that describes the framework composed of three different LAL formats to estimate potential endotoxin contamination in nanoparticles.

When present in pharmaceutical products, a Gram-negative bacterial cell wall component endotoxin (often also called lipopolysaccharide) can cause ...

Transient Expression in Red Beet of a Biopharmaceutical Candidate Vaccine for Type-1 Diabetes

Plant molecular farming is the use of plants to produce molecules of interest. In this perspective, plants may be used both as bioreactors for the production and subsequent purification of the final product and for the direct oral delivery of heterologous proteins when using edible plant species. In this work, we present the development of a candidate oral vaccine against Type 1 Diabetes (T1D) in edible plant systems using deconstructed plant virus-based recombinant DNA technology, delivered with vacuum infiltration. Our results show that a red beet is a suitable host for the transient expression of a human derived autoantigen associated to T1D, considered to be a promising candidate as a T1D vaccine. Leaves producing the autoantigen were thoroughly characterized for their resistance to gastric digestion, for the presence of residual bacterial charge and for their secondary metabolic profile, giving an overview of the process production for the potential use of plants for direct oral delivery of a heterologous protein. Our analysis showed almost complete degradation of the freeze-dried candidate oral vaccine following a simulated gastric digestion, suggesting that an encapsulation strategy in the manufacture of the plant-derived GAD vaccine is required.

Here, we present a protocol to produce an oral vaccine candidate against Type 1 diabetes in an edible plant.

Plant molecular farming is the use of plants to produce molecules of interest. In this perspective, plants may be used both as bioreactors for the ...

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

The limitations of cell-based synthetic biology are becoming increasingly apparent as researchers aim to develop larger and more complex synthetic genetic regulatory circuits. The analysis of synthetic genetic regulatory networks in vivo is time consuming and suffers from a lack of environmental control, with exogenous synthetic components interacting with host processes resulting in undesired behavior. To overcome these issues, cell-free characterization of novel circuitry is becoming more prevalent. In vitro transcription and translation (IVTT) mixtures allow the regulation of the experimental environment and can be optimized for each unique system. The protocols presented here detail the fabrication of a multilayer microfluidic device that can be utilized to sustain IVTT reactions for prolonged durations. In contrast to batch reactions, where resources are depleted over time and (by-) products accumulate, the use of microfluidic devices allows the replenishment of resources as well as the removal of reaction products. In this manner, the cellular environment is emulated by maintaining an out-of-equilibrium environment in which the dynamic behavior of gene circuits can be investigated over extended periods of time. To fully exploit the multilayer microfluidic device, hardware and software have been integrated to automate the IVTT reactions. By combining IVTT reactions with the microfluidic platform presented here, it becomes possible to comprehensively analyze complex network behaviors, furthering our understanding of the mechanisms that regulate cellular processes.

The fabrication process of a PDMS-based, multilayer, microfluidic device that allows in vitro transcription and translation (IVTT) reactions to be performed over prolonged periods is described. Furthermore, a comprehensive overview of the hardware and software required to automate and maintain these reactions for prolonged durations is provided.

The limitations of cell-based synthetic biology are becoming increasingly apparent as researchers aim to develop larger and more complex synthetic genetic ...

In Vesiculo Synthesis of Peptide Membrane Precursors for Autonomous Vesicle Growth

Compartmentalization of biochemical reactions is a central aspect of synthetic cells. For this purpose, peptide-based reaction compartments serve as an attractive alternative to liposomes or fatty acid-based vesicles. Externally or within the vesicles, peptides can be easily expressed and simplify the synthesis of membrane precursors. Provided here is a protocol for the creation of vesicles with diameters of ~200 nm based on the amphiphilic elastin-like polypeptides (ELP) utilizing dehydration-rehydration from glass beads. Also presented are protocols for bacterial ELP expression and purification via inverse temperature cycling, as well as their covalent functionalization with fluorescent dyes. Furthermore, this report describes a protocol to enable the transcription of RNA aptamer dBroccoli inside ELP vesicles as a less complex example for a biochemical reaction. Finally, a protocol is provided, which allows in vesiculo expression of fluorescent proteins and the membrane peptide, whereas synthesis of the latter results in vesicle growth.

Presented here are protocols for the creation of peptide-based small unilamellar vesicles capable of growth. To facilitate in vesiculo production of the membrane peptide, these vesicles are equipped with a transcription-translation system and the peptide-encoding plasmid.

Compartmentalization of biochemical reactions is a central aspect of synthetic cells. For this purpose, peptide-based reaction compartments serve as an ...

High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

Tomato is an agronomically important crop that can be infected by Pseudomonas syringae, a Gram-negative bacterium, resulting in bacterial speck disease. The tomato-P. syringae pv. tomato pathosystem is widely used to dissect the genetic basis of plant innate responses and disease resistance. While disease was successfully managed for many decades through the introduction of the Pto/Prf gene cluster from Solanum pimpinellifolium into cultivated tomato, race 1 strains of P. syringae have evolved to overcome resistance conferred by the Pto/Prf gene cluster and occur worldwide.
Wild tomato species are important reservoirs of natural diversity in pathogen recognition, because they evolved in diverse environments with different pathogen pressures. In typical screens for disease resistance in wild tomato, adult plants are used, which can limit the number of plants that can be screened due to their extended growth time and greater growth space requirements. We developed a method to screen 10-day-old tomato seedlings for resistance, which minimizes plant growth time and growth chamber space, allows a rapid turnover of plants, and allows large sample sizes to be tested. Seedling outcomes of survival or death can be treated as discrete phenotypes or on a resistance scale defined by amount of new growth in surviving seedlings after flooding. This method has been optimized to screen 10-day-old tomato seedlings for resistance to two P. syringae strains and can easily be adapted to other P. syringae strains.

High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

The seedling flood assay facilitates rapid screening of wild tomato accessions for the resistance to the Pseudomonas syringae bacterium. This assay, used in conjunction with the seedling bacterial growth assay, can assist in further characterizing the underlying resistance to the bacterium, and can be used to screen mapping populations to determine the genetic basis of resistance.

Tomato is an agronomically important crop that can be infected by Pseudomonas syringae, a Gram-negative bacterium, resulting in bacterial speck disease. ...

Layer Microdissection of Tricuspid Valve Leaflets for Biaxial Mechanical Characterization and Microstructural Quantification

The tricuspid valve (TV) regulates the unidirectional flow of unoxygenated blood from the right atrium to the right ventricle. The TV consists of three leaflets, each with unique mechanical behaviors. These variations among the three TV leaflets can be further understood by examining their four anatomical layers, which are the atrialis (A), spongiosa (S), fibrosa (F), and ventricularis (V). While these layers are present in all three TV leaflets, there are differences in their thicknesses and microstructural constituents that further influence their respective mechanical behaviors. 
This protocol includes four steps to elucidate the layer-specific differences: (i) characterize the mechanical and collagen fiber architectural behaviors of the intact TV leaflet, (ii) separate the composite layers (A/S and F/V) of the TV leaflet, (iii) carry out the same characterizations for the composite layers, and (iv) perform post-hoc histology assessment. This experimental framework uniquely allows the direct comparison of the intact TV tissue to each of its composite layers. As a result, detailed information regarding the microstructure and biomechanical function of the TV leaflets can be collected with this protocol. Such information can potentially be used to develop TV computational models that seek to provide guidance for the clinical treatment of TV disease.

This protocol describes the biaxial mechanical characterization, polarized spatial frequency domain imaging-based collagen quantification, and microdissection of tricuspid valve leaflets. The provided method elucidates how the leaflet layers contribute to the holistic leaflet behaviors.

The tricuspid valve (TV) regulates the unidirectional flow of unoxygenated blood from the right atrium to the right ventricle. The TV consists of three ...