Endy:Translation demand
Overview
Furthering the Discipline
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.
Translational Demand
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.
Cellular Response
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.
Main Goal
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
Approach
Placing a Range of Demands on the Cell
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.
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.
Specifics
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 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.
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.
Future Pursuits
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
Data
Graphs and Images
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.
Modeling
References
| Date | Raw Data | Data Processing script (.m) | Description | |
| MG-GFP-1series-2.csv | translationdemand1trial1.m | First Run of the Primary Exeperiment | ||
| TranslationDemandRun1growthrate.m | Growth Rate Generator for the First Run of the Primary Exeperiment | |||
| Plate_Layout_Run_1.xls | First Run Plate Layout | |||
| MG-GFP-1series-3.csv | translationdemand1trial2.m | Second Run of the Primary Exeperiment | ||
| TranslationDemandRun2growthrate.m | Growth Rate Generator for the Second Run of the Primary Exeperiment | |||
| Plate_Layout_Run_2.xls | Second Run Plate Layout | |||
| MG-GFP-1series-4.csv | translationdemand1trial3.m | Third Run of the Primary Exeperiment | ||
| TranslationDemandRun3growthrate.m | Growth Rate Generator for the Third Run of the Primary Exeperiment | |||
| Plate_Layout_Run_3.xls | Third Run Plate Layout | |||
| Morevariablesblanktestall.csv | TranslationDemandmvblanktest.m | First "Blank" Test | ||
| Full_Plate_Same_Media_Blank_Test.csv | TranslationDemandplategradientFullPlate.m | Second "Blank" Test | ||
| Empty_Plate_Test.csv | TranslationDemandplategradientEmpty.m | Empty Plate Test | ||
| GFPandMcherrytrial.xls | translationDemand2trial1.m | First Run "Voltmeter" | ||
| TranslationDemand2C86andX90growthraterun1.m | Growth Rate Generator for the First Run of the "Voltmeter" | |||
| Plate_Layout_X90_and_C86_Run_1.xls | First Run "Voltmeter" Plate Layout | |||
| GFPandMcherrytrial2.xls | translationDemand2trial2.m | Second Run "Voltmeter" | ||
| TranslationDemand2C86andX90growthraterun2.m | Growth Rate Generator for the Second Run of the "Voltmeter" | |||
| Plate_Layout_X90_and_C86_Run_1.xls | Second Run "Voltmeter" Plate Layout |
