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==Contact==
*[[Barry Canton]]
*[[Matt Gethers]]
*[[Heather Keller]]
==Overview==
==Overview==


===Furthering the Discipline===
===Motivation===
Biological engineering has great potential to solve many of the world's toughest problems. The ability to replicate, discern, measure, judiciously express chemicals, and evolve are all characteristics of engineered organisms. That said, our ability to reliably design and build such organisms is, to say the least, lacking. My project will explore one of many frontiers by attempting to establish a more concrete and quantitative relationship between the demand placed on an engineered organism by foreign DNA synthesis and cellular response and use this information to increase the efficiency of bioreactors.
Our ability to reliably engineer biological systems is very limited. One reason for this is because we have a crude understanding of the interaction between an engineered biological system and the cell that hosts it, here called a cellular chassis. It is my goal to elucidate and quantify the interaction between engineered biological system and cellular chassis.


===Translational Demand===
===Demand & chassis response===
The term "demand" refers to the many loads placed on a cell by an engineered biological system. Examples include the use of cellular machinery such as polymerases and ribosomes and/or "raw materials" such as nucleotides and amino acids. To measure these various demands are all singular challenges. For the purposes of this project, I've elected to focus on "translational demand," or the rate of foreign protein synthesis, because fluorescent proteins facilitate its measurement. The demand placed on cells by transcription and other processes that do not manifest themselves in polypeptide products are significant, but their measurements constitute separate experiments that I may decide to explore in the future.
An engineered biological system places many different demands on its cellular chassis.  For example, an engineered biological system will compete with chassis systems for machinery such as polymerases and ribosomes and for "raw materials" such as nucleotides and amino acids. When these demands are placed on a cellular chassis, it may lead to changes in the physiology of the chassis (e.g. growth rates, protein synthesis rates etc.). I would like to examine the relationship between applied demand and chassis response.


===Cellular Response===
===Goal===
As engineers ask cells to perform recombinant functions, one has to expect that the cells will respond to the foreign stressors. Just as demand has many facets to consider, so too does the cellular response. One can look at cell physiology, morphology, transcriptomes, proteomes, or any number of means of examining the cellular state while operating under the stress of recombinant DNA. Again, to narrow this term to a measurable quantity and to take advantage of the literature involving growth rates and stress responses, I've elected to examine the cellular growth rate.  As another, more qualitative means of measuring cellular response, I also intend to look at cell morphology through microscopy.
We expect that as translation demand increases, the chassis response will become more pronounced (decreasing growth rate, triggering of stress response pathways etc.).  I am interested in examining what range of translational demands can be applied to a cellular chassis before the chassis response to that demand adversely affects the performance of the engineered biological system.


===Main Goal===
You can read about the current status of this project [[Endy:Translation demand/Current status|here]].
The main goal in all of this is to tease out the point where foreign protein causes a significant growth hit in its bacterial host. Eliminating the growth hit is important both for industrial bioreactors and for engineering projects that are characterized by a need for reliable function. Bioreactors, or cultures of cells used to produce some protein product, are composed of subpopulations with varied characteristics. Some cells produce more of the protein product while others produce less for various reasons. After a certain protein synthesis threshold, the cells will take a growth hit. The problem is that in an assymetric culture (various production levels), some cells will take growth hits of various magnitudes while others will not. In a culture where metabolic resources are finite, those cells that grow and multiply more quickly will deplete the resources and soon represent the majority of the subpopulations. In effect, there is a selection for those cells that produce less of the desired product. The result is that a culture exhibiting a range of growth rates will naturally become less efficient in time. By eliminating the growth hit, we can eliminate one of many factors that tend towards inefficiency in bioreactors. Biological engineers, though concerned with efficiency, are equally concerned with reliable function. A growth hit is a macroscopic signal that the host cell has changed its state and that the engineered system may begin to act unreliably. Many changes in host cell metabolism and physiology will manifest themselves macroscopically as changes in growth rate, so by investigating mechanisms of avoiding growth hits, I am also developing means of maintaining the cellualar environment for optimum operation of an engineered system.
 
==Contact==
*[[Barry Canton]]
*[[Matt Gethers]]
*[[Heather Keller]]


==Approach==
==Approach==
Getting at these issues requires me to achieve two intermediate goals.  Firstly, I need to develop methods to place a specified range of demands on the chassis.  Secondly, I need to develop relevant measures of the chassis response to that applied demand.


===Placing a Range of Demands on the Cell===
===Placing a Range of Demands on the Chassis===
In order to develop a quantitative relationship between protein synthesis rate and cellular growth rate, I have to place a range of demands on the cell. I'm employing two methods in this experiment. First, I am using both a low and high copy vector. [http://parts.mit.edu/registry/index.php/Part:pSB4A3| pSB4A3], the low copy vector, produces approximately 10-12 copies for cell. The high copy vector, [http://parts.mit.edu/registry/index.php/Part:pSB1A3| pSB1A3], produces 100-300 copies per cell. This is a means of roughly but dramatically increasing the demand on a cell. In order to gain more intermediate steps in demand, I am also utilizing several different ribosome binding sites chosen to represent a range of strengths. I would expect that a stronger RBS would place a greater demand on the cell. The strenth of the RBS has been determined by the ranking system established by Ron Weiss, data taken from experiment conducted with the T7 Bacteriophage, and data from Heather Keller's work.
In order to develop a quantitative relationship between protein synthesis rate and cellular growth rate, I have to place a range of demands on the cell. I'm employing two methods in this experiment. First, I am using both a low and high copy vector. [http://parts.mit.edu/registry/index.php/Part:pSB4A3| pSB4A3], the low copy vector, produces approximately 10-12 copies for cell. The high copy vector, [http://parts.mit.edu/registry/index.php/Part:pSB1A3| pSB1A3], produces 100-300 copies per cell. This is a means of roughly but dramatically increasing the demand on a cell. In order to gain more intermediate steps in demand, I am also utilizing several different ribosome binding sites chosen to represent a range of strengths. I would expect that a stronger RBS would place a greater demand on the cell. The strenth of the RBS has been determined by the ranking system established by Ron Weiss, data taken from experiment conducted with the T7 Bacteriophage, and data from Heather Keller's work.


===Measuring Protein Synthesis and Growth Rates===
My experiment will utilize fluoroescent proteins to measure protein synthesis rate. By measuring GFP and Mcherry Counts with respect to time and determining the slope at selected points, I can determine the net synthesis rate of protein. By then taking optical density measurements, I can determine the number of cells at these selected times and determine rate of protein synthesis per cell. I will also use the OD vs. time data to determine the growth rate of the cells in culture. These analyses will allow me to directly compare the rate of protein synthesis and growth rate for a cell.  
My experiment will utilize fluoroescent proteins to measure protein synthesis rate. By measuring GFP and Mcherry Counts with respect to time and determining the slope at selected points, I can determine the net synthesis rate of protein. By then taking optical density measurements, I can determine the number of cells at these selected times and determine rate of protein synthesis per cell. I will also use the OD vs. time data to determine the growth rate of the cells in culture. These analyses will allow me to directly compare the rate of protein synthesis and growth rate for a cell.  


===Specifics===
===Measuring relevant chassis responses to an applied demand===
[[Image:PSB1A3 Scaffold.jpg|thumb|right|300px|Scaffold Map of High Copy Vector (Genbank Files to Follow)]]
I need to determine the appropriate metrics of the chassis response to an applied demand. For example, if the application of a demand on the chassis reduces chassis growth rate, then that will affect the behavior of the engineered biological system and hence growth rate would be a relevant chassis response.  Conversely, if an applied demand leads to an increase in the synthesis of some heat shock proteins but these merely serve to ensure that protein synthesis is unaffected by the increase in demand, then those heat shock proteins would not be a very relevant chassis response.


The fluoroescent proteins will be measured in the bacterial strain MG1655 (No, I did not discover the strain. I just happen to share my initials with a bacterium), though DB 3.1 will be used in an intermediary step to construct the recombinant plasmids.


The GFP and Mcherry scaffolds I'm using were constructed by [[Heather Keller]] of the [[Endy Lab]]. They include a LacI regulated version of the [http://parts.mit.edu/registry/index.php/Part:BBa_R0011| lambda pL promoter (BBaR0011)], two hair pins on either side of the coding sequence to increase stability, GFP (BBa0040) or [http://parts.mit.edu/registry/index.php/Part:BBa_J06504| Mcherry (BBaJ06504)], and an RBS that I intend to vary. As I stated earlier, I'm using the Biobricks vectors [http://parts.mit.edu/registry/index.php/Part:pSB1A3| pSB1A3] and [http://parts.mit.edu/registry/index.php/Part:pSB4A3| pSB4A3]. For those unfamiliar with Biobricks and the [http://parts.mit.edu/registry/index.php/Main_Page| Registry for Standard Biological Parts], they are both very successful efforts to make biological engineering more standardized and modular by making and recording various interchangable parts to be used in the construction of DNA. It is this modularity that allows me to place Heather's scaffolds on both the high and low copy vectors and switch RBS's without having to syntheisize each construct from scratch.
==Materials & Methods==
===Demand Constructs===
[[Image:PSB1A3 Scaffold.jpg|thumb|right|300px|Scaffold Map of High Copy Vector (Genbank Files to Follow)]]
The GFP and Mcherry scaffolds I'm using were constructed by [[Heather Keller]] of the [[Endy Lab]]. They include a LacI regulated version of the [http://parts.mit.edu/registry/index.php/Part:BBa_R0011 lambda pL promoter (BBaR0011)], two hair pins on either side of the coding sequence to increase stability, GFP (BBa0040) or [http://parts.mit.edu/registry/index.php/Part:BBa_J06504 Mcherry (BBaJ06504)], and an RBS that I intend to vary. As I stated earlier, I'm using the Biobricks vectors [http://parts.mit.edu/registry/index.php/Part:pSB1A3 pSB1A3] and [http://parts.mit.edu/registry/index.php/Part:pSB4A3 pSB4A3]. For those unfamiliar with Biobricks and the [http://parts.mit.edu/registry/index.php/Main_Page Registry for Standard Biological Parts], they are both very successful efforts to make biological engineering more standardized and modular by making and recording various interchangable parts to be used in the construction of DNA. It is this modularity that allows me to place Heather's scaffolds on both the high and low copy vectors and switch RBS's without having to syntheisize each construct from scratch.


As I build my constructs and grow cells, I'll use [[LB]] with [[Ampicillin]] for both cultures and plates. In preparation for the plate reader, however, I'll innoculate culture in [[Neidhardt EZ Rich Defined]] and induce with [[IPTG]]. The plate will have samples of each RBS on the high and low copy vectors, and as controls I will have "empty" vectors (just the vector without the scaffold), RBSes constructed to be insignificantly weak, the untranformed strain MG1655, and blanks of the EZ media with both [[Ampicillin]] and [[IPTG]].
===Bacterial strains===
My initial characterization work will be done in the ''E. coli'' strain MG1655 (No, I did not discover the strain. I just happen to share my initials with a bacterium), though DB 3.1 will be used in an intermediary step to construct the recombinant plasmids.  


===Future Pursuits===
X90/C86
The major thrust of this project is to better characterize the relationship between recombinant load and growth rate and eventually tease out the point at which the strain MG1655 takes a significant growth hit. A natural progression of this project would be to examine the variables that determine this point. Perhaps MG1655 and a host of other strains could be "refactored" simliar to T7 for optimum production capacity [[Rebuilding T7]].


==Current Status==
===Media & growth conditions===
 
As I build my constructs and grow cells, I'll use [[LB]] with [[Ampicillin]] for both cultures and plates. In preparation for the plate reader, however, I'll innoculate culture in [[Neidhardt EZ Rich Defined]] and induce with [[IPTG]]. The plate will have samples of each RBS on the high and low copy vectors, and as controls I will have "empty" vectors (just the vector without the scaffold), RBSes constructed to be insignificantly weak, the untransformed strain MG1655, and blanks of the EZ media with both [[Ampicillin]] and [[IPTG]].
===8/11/06===
Cells expressing [[Ampicillin]] resistance do so by excreting Beta-Lactamase into the extracellular environment to destroy the antibiotic. This allows one cell to "protect" another by creating a pocket free of antibiotic. When one combines the growth advantage given to plasmid free cells with this abilitiy to survive without providing resistance for itself, the possibilty of losing the plasimd generationally becomes stronger. In order to test for plasmid instability, I've grown cultures and plated them in media both with and without antibiotics. The idea is that if subpopulations of cells within the culture have lost the plasmid, they will grow only on the plate without antibiotics. In my first run of this plating experiment, I plated high copy mCherry constructs in C86, a strain of bacteria with a GFP chromosomal insert. The result was as expected; the constructs with stronger RBS's grew more colonies on the LB plate (no antibiotic) than the Ampicillin plate meaning that conditions were present to select for plasmid free populations. A disparity in the number of colonies became indistinguishable in the mid and low strength RBS's. When this experiment was repeated with high copy GFP constructs in MG1655, there seemed to be little or no difference in the number of colonies suggesting a more stable plasmid situation.
 
Though plasmid loss is perhaps the most striking manifestation instability, there are other, more subtle forms. The mechanism of plasmid segregration in bacteria isn't clearly undestood. [http://web.mit.edu/prathergroup/| Kristala Jones Prather] has suggested that especially when using high copy vectors (100 - 300 copies/cell), it is very possible that there is an asymmeteric segregation of plasmids during cell division. Until daughter cells are receiving roughly equal amounts of the plasmid, the variation in copy number will produce too many unique subpopulations to pin down a pattern in growth rates for a particular construct. I'll be reading some literature on the mechanism of plasmid segregation to see if I can somehow maintain the range of demands allowed by using the high copy vector but reduce the variability of copy number in a particular cell.
 
==To Dos==
#Test for plasmid instability
#*Grow cultures used for plate reader expts. 1-3 and plate them on Amp and on LB plates. 
#*Use the same growth protocol as used for the full experiments.
#*Do this for the 5 RBSs.
#Select non-pink colonies from the voltmeter plates and see if some of those have the F-plasmid.


==Future work==
The major thrust of this project is to better characterize the relationship between recombinant load and growth rate and eventually tease out the point at which the strain MG1655 takes a significant growth hit. A natural progression of this project would be to examine the variables that determine this point. Perhaps MG1655 and a host of other strains could be "refactored" simliar to T7 [[Rebuilding T7]] for optimum production capacity and reliable function.


==Data==
All raw data and data processing scripts can be found [[Endy:Translation demand/Data|here]].
==Modeling==
==Modeling==
[[Endy:Translation demand/Code|Code modules]]
[[Endy:Translation demand/Code|Code modules]]
Line 60: Line 54:
*[[Endy:Translation demand/References.tex|LaTex code to generate list of references]]
*[[Endy:Translation demand/References.tex|LaTex code to generate list of references]]
*[[Endy:Translation demand/Demand.bib|References in bibtex format]]
*[[Endy:Translation demand/Demand.bib|References in bibtex format]]
==Data==
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Date'''
| align="center" style="background:#f0f0f0;"|'''Raw Data'''
| align="center" style="background:#f0f0f0;"|'''Data Processing script (.m)'''
| align="center" style="background:#f0f0f0;"|'''Description'''
|-
| 6/23/06||[[:image:MG-GFP-1series-2.csv|MG-GFP-1series-2.csv]]||[[:image:translationDemand1trial1.m|translationdemand1trial1.m]] ||First Run of the Primary Exeperiment
|-
| ||||[[:image:TranslationDemandRun1growthrate.m|TranslationDemandRun1growthrate.m]] ||Growth Rate Generator for the First Run of the Primary Exeperiment
|-
| ||[[:image:Plate_Layout_Run_1.xls|Plate_Layout_Run_1.xls]]||  ||First Run Plate Layout
|-
| 6/30/06||[[:image:MG-GFP-1series-3.csv|MG-GFP-1series-3.csv]]||[[:image:translationDemand1trial2.m|translationdemand1trial2.m]]  ||Second Run of the Primary Exeperiment
|-
| ||||[[:image:TranslationDemandRun2growthrate.m|TranslationDemandRun2growthrate.m]] ||Growth Rate Generator for the Second Run of the Primary Exeperiment
|-
| ||[[:image:Plate_Layout_Run_2.xls|Plate_Layout_Run_2.xls]]|| ||Second Run Plate Layout
|-
| 7/20/06||[[:image:MG-GFP-1series-4.csv|MG-GFP-1series-4.csv]]||[[:image:translationDemand1trial3.m|translationdemand1trial3.m]]  ||Third Run of the Primary Exeperiment
|-
| ||||[[:image:TranslationDemandRun3growthrate.m|TranslationDemandRun3growthrate.m]] ||Growth Rate Generator for the Third Run of the Primary Exeperiment
|-
| ||[[:image:Plate_Layout_Run_3.xls|Plate_Layout_Run_3.xls]]|| ||Third Run Plate Layout
|-
| 7/13/06||[[:image:Morevariablesblanktestall.csv|Morevariablesblanktestall.csv]]||[[:image:TranslationDemandmvblanktest.m|TranslationDemandmvblanktest.m]] ||First "Blank" Test
|-
| 7/17/06||[[:image:Full_Plate_Same_Media_Blank_Test.csv|Full_Plate_Same_Media_Blank_Test.csv]]||[[:image:TranslationDemandplategradientFullPlate.m|TranslationDemandplategradientFullPlate.m]]|| Second "Blank" Test
|-
| 7/17/06||[[:image:Empty_Plate_Test.csv|Empty_Plate_Test.csv]]||[[:image:TranslationDemandplategradientEmpty.m|TranslationDemandplategradientEmpty.m]] ||Empty Plate Test
|-
| 7/11/06||[[:image:GFPandMcherrytrial.xls|GFPandMcherrytrial.xls]]|| [[:image:translationDemand2trial1.m|translationDemand2trial1.m]]|| First Run "Voltmeter"
|-
| ||||[[:image:TranslationDemand2C86andX90growthraterun1.m|TranslationDemand2C86andX90growthraterun1.m]] ||Growth Rate Generator for the First Run of the "Voltmeter"
|-
| ||[[:image:Plate_Layout_X90_and_C86_Run_1.xls|Plate_Layout_X90_and_C86_Run_1.xls]]|| || First Run "Voltmeter" Plate Layout
|-
| 7/31/06||[[:image:GFPandMcherrytrial2.xls|GFPandMcherrytrial2.xls]]|| [[:image:translationDemand2trial2.m|translationDemand2trial2.m]]||Second Run "Voltmeter"
|-
| ||||[[:image:TranslationDemand2C86andX90growthraterun2.m|TranslationDemand2C86andX90growthraterun2.m]] ||Growth Rate Generator for the Second Run of the "Voltmeter"
|-
| ||[[:image:Plate_Layout_X90_and_C86_Run_1.xls|Plate_Layout_X90_and_C86_Run_1.xls]]|| ||Second Run "Voltmeter" Plate Layout
|-}
<br style="clear:both" />
===Graphs and Images===
[[Image:All_RBS's-1A3_GFP_Growth_Rate_Trial_2.jpeg|thumb|left|300px|OD vs. Dilution Rate]]
[[Image:RBS_2_GFP_Trial_3_Fig_1.jpeg|thumb|right|300px|OD vs. Time and GFP Counts vs. OD]]
[[Image:071706_Media_Image_scaled.jpeg|thumb|left|300px|OD vs. Location on 96 Well Plate]]
[[Image:2-4A3t.jpg|thumb|right|300px|Bacterial Morphology While Expressing Foreign Protein]]
<br style="clear:both" />

Latest revision as of 17:17, 4 September 2006

Contact

Overview

Motivation

Our ability to reliably engineer biological systems is very limited. One reason for this is because we have a crude understanding of the interaction between an engineered biological system and the cell that hosts it, here called a cellular chassis. It is my goal to elucidate and quantify the interaction between engineered biological system and cellular chassis.

Demand & chassis response

An engineered biological system places many different demands on its cellular chassis. For example, an engineered biological system will compete with chassis systems for machinery such as polymerases and ribosomes and for "raw materials" such as nucleotides and amino acids. When these demands are placed on a cellular chassis, it may lead to changes in the physiology of the chassis (e.g. growth rates, protein synthesis rates etc.). I would like to examine the relationship between applied demand and chassis response.

Goal

We expect that as translation demand increases, the chassis response will become more pronounced (decreasing growth rate, triggering of stress response pathways etc.). I am interested in examining what range of translational demands can be applied to a cellular chassis before the chassis response to that demand adversely affects the performance of the engineered biological system.

You can read about the current status of this project here.

Approach

Getting at these issues requires me to achieve two intermediate goals. Firstly, I need to develop methods to place a specified range of demands on the chassis. Secondly, I need to develop relevant measures of the chassis response to that applied demand.

Placing a Range of Demands on the Chassis

In order to develop a quantitative relationship between protein synthesis rate and cellular growth rate, I have to place a range of demands on the cell. I'm employing two methods in this experiment. First, I am using both a low and high copy vector. pSB4A3, the low copy vector, produces approximately 10-12 copies for cell. The high copy vector, pSB1A3, produces 100-300 copies per cell. This is a means of roughly but dramatically increasing the demand on a cell. In order to gain more intermediate steps in demand, I am also utilizing several different ribosome binding sites chosen to represent a range of strengths. I would expect that a stronger RBS would place a greater demand on the cell. The strenth of the RBS has been determined by the ranking system established by Ron Weiss, data taken from experiment conducted with the T7 Bacteriophage, and data from Heather Keller's work.

My experiment will utilize fluoroescent proteins to measure protein synthesis rate. By measuring GFP and Mcherry Counts with respect to time and determining the slope at selected points, I can determine the net synthesis rate of protein. By then taking optical density measurements, I can determine the number of cells at these selected times and determine rate of protein synthesis per cell. I will also use the OD vs. time data to determine the growth rate of the cells in culture. These analyses will allow me to directly compare the rate of protein synthesis and growth rate for a cell.

Measuring relevant chassis responses to an applied demand

I need to determine the appropriate metrics of the chassis response to an applied demand. For example, if the application of a demand on the chassis reduces chassis growth rate, then that will affect the behavior of the engineered biological system and hence growth rate would be a relevant chassis response. Conversely, if an applied demand leads to an increase in the synthesis of some heat shock proteins but these merely serve to ensure that protein synthesis is unaffected by the increase in demand, then those heat shock proteins would not be a very relevant chassis response.


Materials & Methods

Demand Constructs

Scaffold Map of High Copy Vector (Genbank Files to Follow)

The GFP and Mcherry scaffolds I'm using were constructed by Heather Keller of the Endy Lab. They include a LacI regulated version of the lambda pL promoter (BBaR0011), two hair pins on either side of the coding sequence to increase stability, GFP (BBa0040) or Mcherry (BBaJ06504), and an RBS that I intend to vary. As I stated earlier, I'm using the Biobricks vectors pSB1A3 and pSB4A3. For those unfamiliar with Biobricks and the Registry for Standard Biological Parts, they are both very successful efforts to make biological engineering more standardized and modular by making and recording various interchangable parts to be used in the construction of DNA. It is this modularity that allows me to place Heather's scaffolds on both the high and low copy vectors and switch RBS's without having to syntheisize each construct from scratch.

Bacterial strains

My initial characterization work will be done in the E. coli strain MG1655 (No, I did not discover the strain. I just happen to share my initials with a bacterium), though DB 3.1 will be used in an intermediary step to construct the recombinant plasmids.

X90/C86

Media & growth conditions

As I build my constructs and grow cells, I'll use LB with Ampicillin for both cultures and plates. In preparation for the plate reader, however, I'll innoculate culture in Neidhardt EZ Rich Defined and induce with IPTG. The plate will have samples of each RBS on the high and low copy vectors, and as controls I will have "empty" vectors (just the vector without the scaffold), RBSes constructed to be insignificantly weak, the untransformed strain MG1655, and blanks of the EZ media with both Ampicillin and IPTG.

Future work

The major thrust of this project is to better characterize the relationship between recombinant load and growth rate and eventually tease out the point at which the strain MG1655 takes a significant growth hit. A natural progression of this project would be to examine the variables that determine this point. Perhaps MG1655 and a host of other strains could be "refactored" simliar to T7 Rebuilding T7 for optimum production capacity and reliable function.

Data

All raw data and data processing scripts can be found here.

Modeling

Code modules

References

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