Difference between revisions of "Team:Virginia/Poster"

Latest revision as of 04:59, 11 November 2020

Manifold: Protein Shells with Encapsulated DNA Scaffolds for Increasing Efficiency of Biosynthetic Pathways

Presented by Team Virginia 2020

Team: J. Ball, V. Gutierrez, C. Haws, A. Kola, S. Link, C. Marino, E. Micklovic, D. Patel, J. Polzin, A. Pradhan, P. Revelli

Advisors: K. G. Kozminski, J. A. Papin

Abstract:
The lack of a versatile and reliable way to improve metabolic flux channeling, pathway orthogonality, and product yields is a major impediment to the expanded utilization of biosynthesis for the production of drugs and industrially valuable chemicals. Manifold, a platform technology that addresses this problem, consists of bacterial microcompartments (BMCs) with encapsulated dsDNA scaffolds that sequester and spatially organize, at fixed concentrations, biosynthetic enzymes presented as zinc-finger fusion proteins. Here we deliver the designs for an E. coli cell capable of synthesizing resveratrol using the Manifold platform. The Manifold platform will help lower costs and expand the applications of chemical biosynthesis.

Inspiration

scaffolding and bacterial microcompartments (BMCs).

Scaffolding has been shown to provide 5 fold increases in production of select chemicals in E. coli [1].

Figure 1 - Model of a DNA scaffold (green) with bound zinc-finger (purple) fusion enzymes (red).

Empty BMCs have been produced in E. coli suggesting their potential to serve as nanoreactors for biosynthesis [2].

Figure 2 - Model of BMC showing various hexameric proteins which assemble to create the icosahedron shell. Carboxysome BMCs visualized by electron micrography in the cell (A) and after isolation (B) with a scale bar representing 100 nm.

4 Big Problems in Biosynthesis

There are a number of limitations that must be overcome in prokaryotes to maximize biosynthesis efficiency, including four interrelated limitations that can all be tackled with one device [3].

Flux imbalances occur when the amount of substrate available does not match the efficiency of the enzyme. In multi-enzyme pathways, this results in an overabundance or lack of intermediates, even with careful promoter and ribosome binding site choice.
Loss of intermediates can occur as pathway intermediates cross a membrane or move to a region of the cell where pathway enzymes are not present and thus, overall yields are reduced.
Pathway competition also reduces yield. If an enzyme, substrate, intermediate, or coenzyme of the pathway of interest is utilized by a process native to the cell it will be less likely to be available for the desired pathway.
Toxic intermediates can cause harm or cell death before any product can be made making biosynthesis highly inefficient or impossible for many pathways.

Figure 3 - Animations of the four major issues 1) flux imbalances 2) loss of intermediates 3) pathway competition 4) toxic intermediates which poison the cell are show from left to right.

Proposed Solution

Manifold is a platform technology designed to improve the efficiency of biosynthesis in prokaryotes by combining DNA scaffolds with BMC shells.

Short double-stranded DNA scaffolds with zinc-finger binding motifs are produced in vivo by reverse transcriptases [4].
Scaffolds localized to the lumen of a PDU BMC shell via an interaction with a zinc-finger fusion shell protein.
As the shell assembles, zinc-finger fusions of the pathway enzymes bind to the scaffolds, allowing for localization of the enzymes to the BMC interior.
The combination of scaffolds and BMCs creates a comprehensive solution to the compartmentalization and organization needs.
Scaffolds provide a convenient way to target enzymes to the interior of the BMC in controllable ratios to prevent flux imbalances.
Proteinaceous BMC shells solve the problems of lost intermediates, pathway competition, and toxic intermediates by sequestering the pathway [5].

Manifold can optimize metabolic flux by creating pathway orthogonality,

Figure 4 - Animations of Manifold's ability to address the four major issues 1) flux imbalances 2) loss of intermediates 3) pathway competition 4) toxic intermediates are show from left to right. In 1 the light gray structure represents a DNA scaffold, and in 2-4 the light gray structures represent the proteinaceous shell of a BMC.

Design Cycle

Figure 5: This depicts the integration of modeling, part design, and project development into our holistic design cycle.

Main Cycle:
- Problem: Looking at biomanufacturing in general, we found four integrated problems we wanted to tackle: flux imbalances, loss of intermediates, pathway competition, and toxic intermediates.
- Research & Brainstorming: In the beginning stages, we found that BMCs showed great potential, but recruiting enzymes to the inside of the BMCs was one of the biggest challenges. Looking for more specific solutions led us to use DNA scaffolds, which can localize pathway enzymes to the inside of the BMCs.
- Design: We then designed a way to build and test this solution from available plasmids and synthesizable DNA. In order to do so, every part needed went through its own design cycle as detailed in the next section.
- Test & Assess: All of our testing was performed in silico through the help of computational models. The engineering design cycle played a large role in this process as well, and this is also detailed below.
Part Design Cycle:
- Part Function: For every part we needed to consider what its desired function was, how it would be assembled, where the assembly components were coming from.
- Research & Brainstorming: To answer these questions, various assembly methods were considered, and Golden Gate assembly, NEBuilder Assembly, and BioBrick Assembly became the most commonly used approaches.
- Design: After choosing a desired assembly method, the relevant parts were modeled in Benchling.
- Assess: The theoretical assembly products and components were then assessed for common issues, and any dysfunctional parts were modified. Some of the most common issues were illegal restriction sites, unwanted Type IIS restriction sites, primer complexity, primer specificity, and incompatibilities with downstream assemblies.
Modeling Cycle:
- Problem: In this stage we had two main questions: In what relative concentrations should the twelve parts of our system be expressed for optimized efficiency? And how much can a fully assembled MANIFOLD system be expected to increase resveratrol production when compared to a control system?
- Research: We consulted several research papers focused on computation of the bacterial microcompartment’s sub-components to answer our first question. We then analyzed prior in vitro studies to develop reaction kinetic equations pertaining to the biosynthesis network of our system. Then, we had to optimize and improve our approach to account for diffusion-related concerns when the reaction space was transitioned to a compartmentalized system.
- Run & Assess: Promoter/RBS pairings were generated for each of our system parts and validated these combinations through a model based on parameters from prior in vitro research. We used PyMOL to visualize BMC pore interactions and construct a mass action, three-compartment system used to quantify BMC concentrations at quasi-steady state. Lastly, we used Michaelis-Menten equations to determine the theoretical increase in resveratrol production over time in our BMC system when compared to a shell-free system.

Figure 6: Key for the diagram on the top left. This figure shows the four components needed for the Manifold platform including the bacterial microcompartment, the HIV reverse transcriptase (HIV-RT) and Murine leukemia reverse transcriptase (ML-RT), a scaffold DNA template, and scaffold pathway enzymes.

Modeling

Optimizing Part Expression
We used the Anderson RBS/Promoter libraries and the Salis Lab RBS Calculator to effectively control part expression [6]. This process directly assisted our wet lab team by using quantification to determine the RBS/Promoter sequences for each part. We utilized a mass action model to validate the RBS/Promoter combinations for parts required in creation of DNA Scaffolds.

Figure 7 - Results from the relative expression model which show ideal and computed ratios for individual part expression relative to PduJ. The bar graph represents the differences between ideal and computationally-derived ratios for each part. Ideal ratios are derived from stoichiometric comparisons between parts in the idealized system derived from literature. Computational ratios are derived from the model output using the product of Anderson promoter transcriptional rates and translation initiation rates of Anderson RBS sequences.

Uncovering BMC Pore Dynamics
We used PyMOL to visualize BMC pore interactions for acetate in comparison to natural BMC metabolites. These pore energies were then modeled using a mass action, three-compartment system to quantify BMC concentrations at quasi-steady state. This process generated a bounded estimation for the acetate concentration available for our compartmentalized pathway. The compartment model is derived from Arrhenius and Fick’s Law equations and uses pore energies determined in silico [7]. Further validation of this approach will require in vitro testing.

Figure 8 - Graph: The change in concentration of propionaldehyde and 1,2-propanediol in the three-compartment system. Curves represent the BMC concentrations of 1,2-propanediol and propionaldehyde based on predetermined pore energies after 7 milliseconds. All constants including energies and other important parameters are found here. Note that due to the computational burden by using a mandatory dt of 50 picoseconds, 7 milliseconds becomes the assumed quasi-steady-state of the system. Circles: Visual of molecules with observed hydrogen bond interactions in the PduA pore. Molecular representations of propionaldehyde (left), acetate (middle), and 1,2-propanediol (right) with serine residues on the PduA protein. Hydrogen bonds less than 4 angstroms are expressed as yellow dashed lines. Note all molecules were constructed and inserted into the pore along the same plane and at the same point in space. These molecular conformations in the pore represent a sample of possible arrangements.

Investigating the Reaction Kinetics
We used Michaelis-Menten equations to determine theoretical resveratrol concentration over time in our BMC system and a shell-free system. This process quantified the expected increase of resveratrol production in the BMC system compared to a shell-free system. MANIFOLD improves the resveratrol titer by a factor between 206.5 and 434.2 compared to a shell-free system after 12 hours. Reaction kinetic models utilize parameters established in vitro and shell-free titer appears consistent with previous studies [6]. The model relies on inputs from the pore dynamics methodology suggesting the necessity of in vitro studies for further model validation.

Figure 9 - Resveratrol concentrations in a free enzyme and BMC system over 12 hours. All metabolite concentrations measured per BMC reactor. The comparative resveratrol yields displayed over 12 hours in a bulk cell population. Blue lines represent the lower and upper bound estimates for resveratrol production based on pore calculated concentrations of reactants p-coumaric acid and acetate at t=0. The green line (B) represents the free enzyme production of resveratrol after 12 hours. The resveratrol yield was computed as the resveratrol concentration over time (in mM) multiplied by the cell density (1.0 ⋅ 10¹¹ cells/L) and molecular weight of resveratrol (228.25 g/L) as well as the assumed presence of 5 BMCs per E. coli cell to achieve the resveratrol titer (in mg/L) in the BMC system. Note the cell density was adjusted in accordance to prior work displaying OD600 changes upon BMC protein expression when compared to a control (1.0 ⋅ 10¹¹ cells/L compared to 4.8 ⋅ 10¹¹ cells/L).

Achievements

Models validated that the Manifold design can provide a 206 fold increase in resveratrol production when compared to a free enzyme system.
A provisional patent has been filed and preliminary market research into resveratrol has been conducted to facilitate a future transition into the start-up space.
The “Resource Hub” contains tools for collaboration and organization and will be shared via the iGEM Foundation website after the competition.
The Code of Ethical Conduct was constructed describing standards of behavior and just implementation of Manifold, and it served to establish a team culture of respect and inclusion.
A proof of concept utilizing the resveratrol biosynthesis pathway was designed and is being implemented.

Future Directions

We will implement our extensive laboratory plans to develop and test our proof of concept, as well as redesign some of our enzymatic ratios based on a late-stage collaboration we did with the UChicago iGEM Team and the model they developed to optimize enzymatic ratios for flux imbalance.
We will take this device beyond the world of iGEM by filing for a full patent following a successful in vivo proof-of-concept. This patent will outline the manufacturing process that Manifold will have pioneered, and define its framework and applications.
We will also expand Manifold's use to new biosynthesis pathways by continuing to work with experts in the industry to determine the full scope of Manifold’s applications to industry and pharmaceuticals. Much of this work will involve utilizing cutting edge BMC research to expand the possibilities of Manifold, such as looking at controlled variation of pore size and charge.
By following through on these three action plans, Manifold can be implemented to improve biosynthesis for established and new pathways and to revolutionize the biomanufacturing industry.

Integrated Human Practices

Collaborating with University of Chicago

Code of Ethical Conduct

Establishing an Entrepreneurship committee

Understanding the content better

References

Cited Sources
[1] R. J. Conrado et al., “DNA-guided assembly of biosynthetic pathways promotes improved catalytic efficiency,” Nucleic Acids Res., vol. 40, no. 4, pp. 1879–1889, Feb. 2012, doi: 10.1093/nar/gkr888

[2] J. B. Parsons et al., “Synthesis of Empty Bacterial Microcompartments, Directed Organelle Protein Incorporation, and Evidence of Filament-Associated Organelle Movement,” Mol. Cell, vol. 38, no. 2, pp. 305–315, Apr. 2010, doi: 10.1016/j.molcel.2010.04.008

[3] A. H. Chen and P. A. Silver, “Designing biological compartmentalization,” Trends Cell Biol., vol. 22, no. 12, pp. 662–670, Dec. 2012, doi: 10.1016/j.tcb.2012.07.002.

[4] J. Elbaz, P. Yin, and C. A. Voigt, “Genetic encoding of DNA nanostructures and their self-assembly in living bacteria,” Nat. Commun., vol. 7, no. 1, p. 11179, Apr. 2016, doi: 10.1038/ncomms11179.

[5] S. D. Axen, O. Erbilgin, and C. A. Kerfeld, “A Taxonomy of Bacterial Microcompartment Loci Constructed by a Novel Scoring Method,” PLoS Comput. Biol., vol. 10, no. 10, Oct. 2014, doi: 10.1371/journal.pcbi.1003898.

[6] Salis, Mirsky, and Voigt, “Automated Design of Synthetic Ribosome Binding Sites to Control Protein Expression.” 2009.

[7] J. Park, S. Chun, Thomas. A. Bobik, K. N. Houk, and T. O. Yeates, “Molecular Dynamics Simulations of Selective Metabolite Transport across the Propanediol Bacterial Microcompartment Shell,” J Phys Chem B, vol. 121, no. 34, pp. 8149–8154, Aug. 2017, doi: 10.1021/acs.jpcb.7b07232.

Image Sources
Figure 2. (Right) Toyeates, English: Stylized view of the carboxysome and related bacterial structures such as the propanediol utilization (Pdu) and ethanolamine utilization (Eut) microcompartments. Distinct hexameric BMC shell proteins carrying out different functions in the shell are shown in different shades of blue. Pentameric vertex proteins are shown in magenta. Encapsulated enzymes are shown in green, organized in layers. [Image: T. Yeates]. 2013.
(Left) Y. Tsai et al., “Structural Analysis of CsoS1A and the Protein Shell of the Halothiobacillus neapolitanus Carboxysome,” PLoS Biol., vol. 5, no. 6, Jun. 2007, doi: 10.1371/journal.pbio.0050144.

All other figures were made by the researchers.

@@ Line 3: / Line 3: @@
 <html>
+<head>
+	<style>
+		#section_title{
+			color: #EB3349;
+		}
+	</style>
+</head>
 <div class="page_wrapper">
@@ Line 12: / Line 18: @@
 <!---Place the content of your poster inside this div--->
 <div class="poster_w content" style="
-     background-image: url(https://static.igem.org/mediawiki/2020/thumb/4/4c/T--Virginia--Poster_background.png/720px-T--Virginia--Poster_background.png);
+     background-image: url(https://static.igem.org/mediawiki/2020/4/4c/T--Virginia--Poster_background.png);
-     background-size: 99%;
+     background-size: 99% 100%;
      background-repeat: no-repeat;
 ">
@@ Line 90: / Line 96: @@
 		<!--Info: Place the title and text for this section, it will appear when the user clicks on it-->
 		<div class="info">
 			<!--Write the title of the section -->
 			<div class="title"> 4 Big Problems in Biosynthesis </div>
@@ Line 97: / Line 102: @@
 				<ul>
 					<b>There are a number of limitations that must be overcome in prokaryotes to maximize biosynthesis efficiency, including four interrelated limitations that can all be tackled with one device [3].</b><br><br>
-<li><b>Flux imbalances</b> occur when the amount of substrate available does not match the efficiency of the enzyme. In multi-enzyme pathways, this results in an overabundance or lack of intermediates, even with careful promoter and ribosome binding site choice.</li>
+					<li><b>Flux imbalances</b> occur when the amount of substrate available does not match the efficiency of the enzyme. In multi-enzyme pathways, this results in an overabundance or lack of intermediates, even with careful promoter and ribosome binding site choice.</li>
 					<li><b>Loss of intermediates</b> can occur as pathway intermediates cross a membrane or move to a region of the cell where pathway enzymes are not present and thus, overall yields are reduced. </li>
 					<li><b>Pathway competition</b> also reduces yield. If an enzyme, substrate, intermediate, or coenzyme of the pathway of interest is utilized by a process native to the cell it will be less likely to be available for the desired pathway. </li>
 					<li><b>Toxic intermediates</b> can cause harm or cell death before any product can be made making biosynthesis highly inefficient or impossible for many pathways. </li><br>
 				</ul>
-			<img src="https://static.igem.org/mediawiki/2020/2/27/T--Virginia--Poster_Solutions_2.gif">
+			<img src="https://static.igem.org/mediawiki/2020/5/5c/T--Virginia--Poster_Problems.gif">
 			<i><b>Figure 3</b> - Animations of the four major issues 1) flux imbalances 2) loss of intermediates 3) pathway competition 4) toxic intermediates which poison the cell are show from left to right.</i><br><br>
 			</div>
@@ Line 127: / Line 132: @@
 				<li>As the shell assembles, zinc-finger fusions of the pathway enzymes bind to the scaffolds, allowing for localization of the enzymes to the BMC interior. </li>
 				<li>The combination of scaffolds and BMCs creates a comprehensive solution to the compartmentalization and organization needs.</li>
-				<li>Proteinaceous BMC shells solve the problems of lost intermediates, pathway competition, and toxic intermediates by sequestering the pathway [5].</li>
+				<li>Scaffolds provide a convenient way to target enzymes to the interior of the BMC in controllable ratios to prevent flux imbalances. </li>
-				<li>Scaffolds provide a convenient way to target enzymes to the interior of the BMC in controllable ratios to prevent flux imbalances. </li><br>
+				<li>Proteinaceous BMC shells solve the problems of lost intermediates, pathway competition, and toxic intermediates by sequestering the pathway [5].</li><br>
 				<b>Manifold can optimize metabolic flux by creating pathway orthogonality,</b> which will actualize potential efficiency of existing biosynthetic pathways and allow for the development of previously impossible ones.<br><br>
 				</ul>
@@ Line 143: / Line 148: @@
 		<!--Info: Place the title and text for this section, it will appear when the user clicks on it-->
 		<div class="info">
+		<!--Write the title of the section -->
-			<!--Write the title of the section -->
+		<div class="title"> Design Cycle </div>
-			<div class="title"> Design Cycle </div>
+		<!--Write the text explaining this section -->
-			<!--Write the text explaining this section -->
+		<div class="text">
-			<div class="text">
+			<img src="https://static.igem.org/mediawiki/2020/0/0d/T--Virginia--Poster_DesignCycleImage_%281%29.png"><br>
-			<ul>
+			<ul style="list-style-type:none;">
-			<b>Resveratrol biosynthesis was chosen for a proof of concept due to its simplicity and capacity to be improved through scaffolding [1].</b><br><br>
+<i><b>Figure 5:</b> This depicts the integration of modeling, part design, and project development into our holistic design cycle.</i><br><br>
-			<li>Biosynthesis of resveratrol from p-Coumaric acid requires two enzymes, 4CL and STS, and the coenzyme malonyl-CoA [6]. </li>
+				<li>
-			<img src="https://static.igem.org/mediawiki/2020/8/8b/T--Virginia--Poster_Resveratrol.png" /><br><br>
+					<b>Main Cycle:</b><br>
-			<i><b>Figure 5</b> - Resveratrol biosynthesis pathway shown alongside the competing fatty acid biosynthesis pathway.</i><br><br><br>
+					<ul>
-			<li>Steric hindrance of malonyl-CoA makes it unlikely to diffuse into the BMC through a pore.</li><br>
+						<li><b>Problem:</b> Looking at biomanufacturing in general, we found four integrated problems we wanted to tackle: flux imbalances, loss of intermediates, pathway competition, and toxic intermediates.</li>
-			<img src="https://static.igem.org/mediawiki/2020/5/57/T--Virginia--Poster_Pore.jpg" /><br><br>
+						<li><b>Research & Brainstorming:</b> In the beginning stages, we found that BMCs showed great potential, but recruiting enzymes to the inside of the BMCs was one of the biggest challenges. Looking for more specific solutions led us to use DNA scaffolds, which can localize pathway enzymes to the inside of the BMCs.</li>
-			<i><b>Figure 6</b> - Visual of malonyl-coa with observed hydrogen bond interactions in the PduA pore. The protein structure (green) of the PduA residues surround the main chain of a malonyl-CoA molecule (predominantly light blue). Yellow dashed lines represent hydrogen bonds between malonyl-CoA and the serine residue of the PduA shell protein. Note this image presents one possible conformation of the malonyl-CoA molecule in the BMC pore. The image is purposely chaotic to prove the predicted minimal diffusion of malonyl-CoA due to molecule size and other steric hindrances which increase the energy barrier for particle entry.</i><br><br><br>
+						<li><b>Design:</b> We then designed a way to build and test this solution from available plasmids and synthesizable DNA. In order to do so, every part needed went through its own design cycle as detailed in the next section. </li>
-			<li>Malonyl-CoA must be available within the BMC to allow for resveratrol production.</li>
+						<li><b>Test & Assess:</b> All of our testing was performed in silico through the help of computational models. The engineering design cycle played a large role in this process as well, and this is also detailed below. </li>
-			<li>ACC and ACS produce malonyl-CoA from acetate.</li><br>
+						<i>A simplification of the final design is shown in Figure 6.</i><br><br>
-			<img src="https://static.igem.org/mediawiki/2020/b/b1/T--Virginia--Poster_MalonylCoA.png" /><br><br>
+					</ul>
-			<i><b>Figure 7</b> - Malonyl-CoA biosynthesis pathway shown. </i><br><br>
+				</li>
-			<li>Introduction of an additional scaffold with ACC and ACS fusion enzymes allows malonyl-CoA recycling within the BMC [7].</li>
+				<li>
+					<b>Part Design Cycle:</b><br>
+					<ul>
+						<li><b>Part Function:</b> For every part we needed to consider what its desired function was, how it would be assembled, where the assembly components were coming from.</li>
+						<li><b>Research & Brainstorming:</b> To answer these questions, various assembly methods were considered, and Golden Gate assembly, NEBuilder Assembly, and BioBrick Assembly became the most commonly used approaches.</li>
+						<li><b>Design:</b> After choosing a desired assembly method, the relevant parts were modeled in Benchling. </li>
+						<li><b>Assess:</b> The theoretical assembly products and components were then assessed for common issues, and any dysfunctional parts were modified. Some of the most common issues were illegal restriction sites, unwanted Type IIS restriction sites, primer complexity, primer specificity, and incompatibilities with downstream assemblies.</li><br>
+					</ul>
+				</li>
+				<li>
+					<b>Modeling Cycle:</b><br>
+					<ul>
+						<li><b>Problem:</b> In this stage we had two main questions: In what relative concentrations should the twelve parts of our system be expressed for optimized efficiency? And how much can a fully assembled MANIFOLD system be expected to increase resveratrol production when compared to a control system? </li>
+						<li><b>Research:</b> We consulted several research papers focused on computation of the bacterial microcompartment’s sub-components to answer our first question. We then analyzed prior in vitro studies to develop reaction kinetic equations pertaining to the biosynthesis network of our system. Then, we had to optimize and improve our approach to account for diffusion-related concerns when the reaction space was transitioned to a compartmentalized system.</li>
+						<li><b>Run & Assess:</b> Promoter/RBS pairings were generated for each of our system parts and validated these combinations through a model based on parameters from prior in vitro research. We used PyMOL to visualize BMC pore interactions and construct a mass action, three-compartment system used to quantify BMC concentrations at quasi-steady state. Lastly, we used Michaelis-Menten equations to determine the theoretical increase in resveratrol production over time in our BMC system when compared to a shell-free system. </li><br>
+					</ul>
+				</li>
 			</ul>
-			 </div>
+			<img src="https://static.igem.org/mediawiki/2020/2/28/T--Virginia--Poster_combineddesign.png"><br>
+<i><b>Figure 6:</b> Key for the diagram on the top left. This figure shows the four components needed for the Manifold platform including the bacterial microcompartment, the HIV reverse transcriptase (HIV-RT) and Murine leukemia reverse transcriptase (ML-RT), a scaffold DNA template, and scaffold pathway enzymes.</i>
 		</div>
-	</div>
+		</div>
+	</div>
 </div>
@@ Line 181: / Line 203: @@
 			<!--Write the text explaining this section -->
 			<div class="text">
-		<b>Optimizing Part Expression</b><br>We used the Anderson RBS/Promoter libraries and the Salis Lab RBS Calculator to effectively control part expression [8]. This process directly assisted our wet lab team by using quantification to determine the RBS/Promoter sequences for each part. We utilized a mass action model to validate the RBS/Promoter combinations for parts required in creation of DNA Scaffolds.
+		<b>Optimizing Part Expression</b><br>We used the Anderson RBS/Promoter libraries and the Salis Lab RBS Calculator to effectively control part expression [6]. This process directly assisted our wet lab team by using quantification to determine the RBS/Promoter sequences for each part. We utilized a mass action model to validate the RBS/Promoter combinations for parts required in creation of DNA Scaffolds.
 		<br> <img src="https://static.igem.org/mediawiki/2020/2/2a/T--Virginia--Poster_Model1.png" />
-		<i><b>Figure 8</b> -  Results from the relative expression model which show ideal and computed ratios for individual part expression relative to PduJ. The bar graph represents the differences between ideal and computationally-derived ratios for each part. Ideal ratios are derived from stoichiometric comparisons between parts in the idealized system derived from literature. Computational ratios are derived from the model output using the product of Anderson promoter transcriptional rates and translation initiation rates of Anderson RBS sequences. </i><br><br>
+		<i><b>Figure 7</b> -  Results from the relative expression model which show ideal and computed ratios for individual part expression relative to PduJ. The bar graph represents the differences between ideal and computationally-derived ratios for each part. Ideal ratios are derived from stoichiometric comparisons between parts in the idealized system derived from literature. Computational ratios are derived from the model output using the product of Anderson promoter transcriptional rates and translation initiation rates of Anderson RBS sequences. </i><br><br>
-		<b>Uncovering BMC Pore Dynamics</b><br>We used PyMOL to visualize BMC pore interactions for acetate in comparison to natural BMC metabolites. These pore energies were then modeled using a mass action, three-compartment system to quantify BMC concentrations at quasi-steady state. This process generated a bounded estimation for the acetate concentration available for our compartmentalized pathway. The compartment model is derived from Arrhenius and Fick’s Law equations and uses pore energies determined in silico [9]. Further validation of this approach will require in vitro testing.
+		<b>Uncovering BMC Pore Dynamics</b><br>We used PyMOL to visualize BMC pore interactions for acetate in comparison to natural BMC metabolites. These pore energies were then modeled using a mass action, three-compartment system to quantify BMC concentrations at quasi-steady state. This process generated a bounded estimation for the acetate concentration available for our compartmentalized pathway. The compartment model is derived from Arrhenius and Fick’s Law equations and uses pore energies determined in silico [7]. Further validation of this approach will require in vitro testing.
 		<br> <img src="https://static.igem.org/mediawiki/2020/c/ca/T--Virginia--Poster_Model2.png" />
-		<i><b>Figure 9</b> -  <b>Graph:</b> The change in concentration of propionaldehyde and 1,2-propanediol in the three-compartment system. Curves represent the BMC concentrations of 1,2-propanediol and propionaldehyde based on predetermined pore energies after 7 milliseconds. All constants including energies and other important parameters are found here. Note that due to the computational burden by using a mandatory dt of 50 picoseconds, 7 milliseconds becomes the assumed quasi-steady-state of the system. <b>Circles:</b> Visual of molecules with observed hydrogen bond interactions in the PduA pore. Molecular representations of propionaldehyde (left), acetate (middle), and 1,2-propanediol (right) with serine residues on the PduA protein. Hydrogen bonds less than 4 angstroms are expressed as yellow dashed lines. Note all molecules were constructed and inserted into the pore along the same plane and at the same point in space. These molecular conformations in the pore represent a sample of possible arrangements.</i><br><br>
+		<i><b>Figure 8</b> -  <b>Graph:</b> The change in concentration of propionaldehyde and 1,2-propanediol in the three-compartment system. Curves represent the BMC concentrations of 1,2-propanediol and propionaldehyde based on predetermined pore energies after 7 milliseconds. All constants including energies and other important parameters are found here. Note that due to the computational burden by using a mandatory dt of 50 picoseconds, 7 milliseconds becomes the assumed quasi-steady-state of the system. <b>Circles:</b> Visual of molecules with observed hydrogen bond interactions in the PduA pore. Molecular representations of propionaldehyde (left), acetate (middle), and 1,2-propanediol (right) with serine residues on the PduA protein. Hydrogen bonds less than 4 angstroms are expressed as yellow dashed lines. Note all molecules were constructed and inserted into the pore along the same plane and at the same point in space. These molecular conformations in the pore represent a sample of possible arrangements.</i><br><br>
-		<b>Investigating the Reaction Kinetics</b><br>We used Michaelis-Menten equations to determine theoretical resveratrol concentration over time in our BMC system and a shell-free system. This process quantified the expected increase of resveratrol production in the BMC system compared to a shell-free system. MANIFOLD improves the resveratrol titer by a factor between 206.5 and 434.2 compared to a shell-free system after 12 hours. Reaction kinetic models utilize parameters established in vitro and shell-free titer appears consistent with previous studies [8]. The model relies on inputs from the pore dynamics methodology suggesting the necessity of in vitro studies for further model validation.
+		<b>Investigating the Reaction Kinetics</b><br>We used Michaelis-Menten equations to determine theoretical resveratrol concentration over time in our BMC system and a shell-free system. This process quantified the expected increase of resveratrol production in the BMC system compared to a shell-free system. MANIFOLD improves the resveratrol titer by a factor between 206.5 and 434.2 compared to a shell-free system after 12 hours. Reaction kinetic models utilize parameters established in vitro and shell-free titer appears consistent with previous studies [6]. The model relies on inputs from the pore dynamics methodology suggesting the necessity of in vitro studies for further model validation.
 		<br> <img src="https://static.igem.org/mediawiki/2020/5/58/T--Virginia--Poster_Model3.png">
-		<i><b>Figure 10</b> - Resveratrol concentrations in a free enzyme and BMC system over 12 hours. All metabolite concentrations measured per BMC reactor. The comparative resveratrol yields displayed over 12 hours in a bulk cell population. Blue lines represent the lower and upper bound estimates for resveratrol production based on pore calculated concentrations of reactants p-coumaric acid and acetate at t=0. The green line (B) represents the free enzyme production of resveratrol after 12 hours. The resveratrol yield was computed as the resveratrol concentration over time (in mM) multiplied by the cell density (1.0 ⋅ 1011cells/L) and molecular weight of resveratrol (228.25 g/L) as well as the assumed presence of 5 BMCs per <i>E. coli</i> cell to achieve the resveratrol titer (in mg/L) in the BMC system. Note the cell density was adjusted in accordance to prior work displaying OD600 changes upon BMC protein expression when compared to a control (1.0 ⋅ 1011cells/L compared to 4.8 ⋅ 1011cells/L).</i><br><br>
+		<i><b>Figure 9</b> - Resveratrol concentrations in a free enzyme and BMC system over 12 hours. All metabolite concentrations measured per BMC reactor. The comparative resveratrol yields displayed over 12 hours in a bulk cell population. Blue lines represent the lower and upper bound estimates for resveratrol production based on pore calculated concentrations of reactants p-coumaric acid and acetate at t=0. The green line (B) represents the free enzyme production of resveratrol after 12 hours. The resveratrol yield was computed as the resveratrol concentration over time (in mM) multiplied by the cell density (1.0 ⋅ 10<sup>11</sup> cells/L) and molecular weight of resveratrol (228.25 g/L) as well as the assumed presence of 5 BMCs per <i>E. coli</i> cell to achieve the resveratrol titer (in mg/L) in the BMC system. Note the cell density was adjusted in accordance to prior work displaying OD600 changes upon BMC protein expression when compared to a control (1.0 ⋅ 10<sup>11</sup> cells/L compared to 4.8 ⋅ 10<sup>11</sup> cells/L).</i><br><br>
 		</div>
 		</div>
@@ Line 229: / Line 251: @@
 				<div class="text">
 <ul>
-<li>We will implement our extensive laboratory plans to <b>develop and test our proof of concept,</b> working off of the flowcharts we designed.</li>
+<li>We will implement our extensive laboratory plans to <b>develop and test our proof of concept,</b> as well as redesign some of our enzymatic ratios based on a late-stage collaboration we did with the UChicago iGEM Team and the model they developed to optimize enzymatic ratios for flux imbalance.</li>
-<li><b>Ideal enzymatic ratios will continue to be adapted</b> based on the model developed by the UChicago iGEM team to optimize enzymatic ratios for flux imbalance. </li>
+<li>We will take this device beyond the world of iGEM by <b>filing for a full patent</b> following a successful in vivo proof-of-concept. This patent will outline the manufacturing process that Manifold will have pioneered, and define its framework and applications. </li>
-<li>Following a successful in vivo proof-of-concept, <b>a full patent application will be filed next year</b>. We also plan to build on our market research, find ways to test Manifold on a large scale, and work with connections we made this year in the Charlottesville start-up community. </li>
+<li>We will also <b>expand Manifold's use to new biosynthesis pathways</b> by continuing to work with experts in the industry to determine the full scope of Manifold’s applications to industry and pharmaceuticals. Much of this work will involve utilizing cutting edge BMC research to expand the possibilities of Manifold, such as looking at controlled variation of pore size and charge.</li>
-<li>We will also <b>expand Manifold's use to new biosynthesis pathways</b> through utilization of cutting edge BMC research namely controlled variation of pore size. </li>
+<li>By following through on these three action plans, Manifold can be implemented to improve biosynthesis for established and new pathways and to <b>revolutionize the biomanufacturing industry.</b></li>
 </ul>
   </div>
@@ Line 255: / Line 277: @@
 				UChicago’s R-based software utilizes reaction parameters from a multi-enzyme pathway to find the most efficient enzyme stoichiometry, which was used to optimize enzyme concentrations.<br><br>
 				<img style="max-width:10%;" src="https://static.igem.org/mediawiki/2020/3/3b/T--Virginia--Poster_IHPCOEC.png"/><b>Code of Ethical Conduct</b><br>
-				Our Code of Ethical Conduct establishes a framework for our projects implementation, and the guidelines we choose to abide by in our effort to maximize good and mitigate risk.<br><br>
+				Our Code of Ethical Conduct establishes a framework for our project's implementation, and the guidelines we choose to abide by in our effort to maximize good and mitigate risk.<br><br>
 				<img style="max-width:10%;" src="https://static.igem.org/mediawiki/2020/d/de/T--Virginia--Poster_IHPentrprenuership.png"/><b>Establishing an Entrepreneurship committee</b><br>
 				Industry research helped us contextualize the use and applications of Manifold in the chemical manufacturing industry. To further our vision, our team came up with an all-encompassing business plan to map out the steps required to establish a start-up. Most importantly, our Provisional Patent Application was reviewed and accepted by the USPTO.<br><br>
@@ Line 282: / Line 304: @@
 			<!--Write the text explaining this section -->
 			<div class="text">
-<b>Cited Sources</b><br><br>
+<b>Cited Sources</b><br>
 [1] R. J. Conrado et al., “DNA-guided assembly of biosynthetic pathways promotes improved catalytic efficiency,” Nucleic Acids Res., vol. 40, no. 4, pp. 1879–1889, Feb. 2012, doi: 10.1093/nar/gkr888<br><br>
 [2] J. B. Parsons et al., “Synthesis of Empty Bacterial Microcompartments, Directed Organelle Protein Incorporation, and Evidence of Filament-Associated Organelle Movement,” Mol. Cell, vol. 38, no. 2, pp. 305–315, Apr. 2010, doi: 10.1016/j.molcel.2010.04.008<br><br>
@@ Line 288: / Line 310: @@
 [4] J. Elbaz, P. Yin, and C. A. Voigt, “Genetic encoding of DNA nanostructures and their self-assembly in living bacteria,” Nat. Commun., vol. 7, no. 1, p. 11179, Apr. 2016, doi: 10.1038/ncomms11179.<br><br>
 [5] S. D. Axen, O. Erbilgin, and C. A. Kerfeld, “A Taxonomy of Bacterial Microcompartment Loci Constructed by a Novel Scoring Method,” PLoS Comput. Biol., vol. 10, no. 10, Oct. 2014, doi: 10.1371/journal.pcbi.1003898.<br><br>
-[6]  C. G. Lim, Z. L. Fowler, T. Hueller, S. Schaffer, and M. A. G. Koffas, “High-Yield Resveratrol Production in Engineered Escherichia coli,” Applied and Environmental Microbiology, vol. 77, no. 10, pp. 3451–3460, 2011.<br><br>
+[6] Salis, Mirsky, and Voigt, “Automated Design of Synthetic Ribosome Binding Sites to Control Protein Expression.” 2009.<br><br>
-[7] W. Zha, S. B. Rubin-Pitel, Z. Shao, and H. Zhao, “Improving cellular malonyl-CoA level in Escherichia coli via metabolic engineering,” Metab. Eng., vol. 11, no. 3, pp. 192–198, May 2009, doi: 10.1016/j.ymben.2009.01.005.<br><br>
+[7] J. Park, S. Chun, Thomas. A. Bobik, K. N. Houk, and T. O. Yeates, “Molecular Dynamics Simulations of Selective Metabolite Transport across the Propanediol Bacterial Microcompartment Shell,” J Phys Chem B, vol. 121, no. 34, pp. 8149–8154, Aug. 2017, doi: 10.1021/acs.jpcb.7b07232.<br><br>
-[8] Salis, Mirsky, and Voigt, “Automated Design of Synthetic Ribosome Binding Sites to Control Protein Expression.” 2009.<br><br>
-[9] J. Park, S. Chun, Thomas. A. Bobik, K. N. Houk, and T. O. Yeates, “Molecular Dynamics Simulations of Selective Metabolite Transport across the Propanediol Bacterial Microcompartment Shell,” J Phys Chem B, vol. 121, no. 34, pp. 8149–8154, Aug. 2017, doi: 10.1021/acs.jpcb.7b07232.<br><br>
 <b>Image Sources</b><br>
 <b>Figure 2.</b> (Right) Toyeates, English: Stylized view of the carboxysome and related bacterial structures such as the propanediol utilization (Pdu) and ethanolamine utilization (Eut) microcompartments. Distinct hexameric BMC shell proteins carrying out different functions in the shell are shown in different shades of blue. Pentameric vertex proteins are shown in magenta. Encapsulated enzymes are shown in green, organized in layers. [Image: T. Yeates]. 2013.<br>
 (Left) Y. Tsai et al., “Structural Analysis of CsoS1A and the Protein Shell of the Halothiobacillus neapolitanus Carboxysome,” PLoS Biol., vol. 5, no. 6, Jun. 2007, doi: 10.1371/journal.pbio.0050144. <br><br>
-<b>Figure 5.</b> C. Lim, Z. Fowler, T. Hueller, S. Schaffer, and M. Koffas, “High-Yield Resveratrol Production in Engineered Escherichia coli,” Appl. Environ. Microbiol., vol. 77, pp. 3451–60, Mar. 2011, doi: 10.1128/AEM.02186-10.<br><br>
+<b>All other figures</b> were made by the researchers.
-<b>Figure 7.</b> W. Zha, S. B. Rubin-Pitel, Z. Shao, and H. Zhao, “Improving cellular malonyl-CoA level in Escherichia coli via metabolic engineering,” Metab. Eng., vol. 11, no. 3, pp. 192–198, May 2009, doi: 10.1016/j.ymben.2009.01.005.<br><br>
-Figures 1, 3, 4, 6, 8, 9, 10. Made by the researchers.
   </div>
 		</div>
@@ Line 321: / Line 337: @@
 <li>NEB</li>
 <li>TWIST</li>
-<li>Cville BioHub</li>
 <li>AddGene</li>
 <li>MatLab</li>