iGEM 2020 — Team TAU_Israel

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# Team:TAU Israel/Notebook

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[Home](https://2020.igem.org/Team%3ATAU_Israel)

Project

[Description](https://2020.igem.org/Team%3ATAU_Israel/Description)
[Design](https://2020.igem.org/Team%3ATAU_Israel/Design)
[Implementation](https://2020.igem.org/Team%3ATAU_Israel/Implementation)
[Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments)
[Results](https://2020.igem.org/Team%3ATAU_Israel/Results)
[Measurement](https://2020.igem.org/Team%3ATAU_Israel/Measurement)
[Proof of Concept](https://2020.igem.org/Team%3ATAU_Israel/Proof_Of_Concept)

Modelling

[Engineering](https://2020.igem.org/Team%3ATAU_Israel/Engineering)
[Model](https://2020.igem.org/Team%3ATAU_Israel/Model)
[Software](https://2020.igem.org/Team%3ATAU_Israel/Software)
[Contribution](https://2020.igem.org/Team%3ATAU_Israel/Contribution)

Lab

[Notebook](https://2020.igem.org/Team%3ATAU_Israel/Notebook)
[Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols)
[Safety](https://2020.igem.org/Team%3ATAU_Israel/Safety)

HP

[Integrated Human Practices](https://2020.igem.org/Team%3ATAU_Israel/Human_Practices)
[Inclusion](https://2020.igem.org/Team%3ATAU_Israel/Inclusion)
[Collaborations](https://2020.igem.org/Team%3ATAU_Israel/Collaborations)

Team

[Members](https://2020.igem.org/Team%3ATAU_Israel/Team)
[Our instructors](https://2020.igem.org/Team%3ATAU_Israel/Instructors)

Acknowledgements

[Sponsors](https://2020.igem.org/Team%3ATAU_Israel/Sponsors)
[Attributions](https://2020.igem.org/Team%3ATAU_Israel/Attributions)

Awards

[Awards](https://2020.igem.org/Team%3ATAU_Israel/Awards)
[Medals](https://2020.igem.org/Team%3ATAU_Israel/Medals)

☰

# Lab Notebook

We have conducted two experiments: the "Gene-SEQ" experiment (Measurement, large-scale of a Co-culture), and the "10-genes" experiment (POC, small-scale). For more information, visit our
[Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments) page.

The protocols we refer to in this notebook appear in our [Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols) page.

JANUARY

* Tuesday 28/01/2020 - 10 gene and Gene-SEQ

  We handled the yeast strain library that we received from Professor Maya Schuldiner's lab as written in the “Handling+Freezing the engineered yeast library - first time protocol”[![link to pdf](https://static.igem.org/mediawiki/2020/f/fa/T--TAU_israel--pdf_logo.jpg)](https://static.igem.org/mediawiki/2020/6/68/T--TAU_israel--protocol_start_libraries.pdf). The library contains 2 copies - GFP library + RFP library, each separated into 6 plates. Every strain position is explained according to the excel document “SWAT library”.

  In addition, the Gene-SEQ experiment started according to the “Starting the evolution experiment - Gene-SEQ” protocol. 96 Well Plate first Legend below.

  ![](https://static.igem.org/mediawiki/2020/4/49/T--TAU_israel--table_28-01.jpg)

  * Wednesday 29/01/2020 - 10 gene and Gene-SEQ

    We have conducted 2 fluorescence tests to analyze the strain's growth in the previous day.

    The first fluorescence reading was conducted using a fluorescence black plate 96-wells as shown in figure 1. Each well contains 100µL of the matching strain.

    The results of this test are summarized in folder “Results: Fluorescence Top Reading and Absorbance” and are divided into 3 sections:
    1. Green Fluorescence Top Reading (label 1)
    2. Red Fluorescence Top Reading (label 2)
    3. Absorbance\OD (label 3).![first_96wells](https://static.igem.org/mediawiki/2020/6/62/T--TAU_israel--96wells_1.jpg)

    **The results indicated that the YPD medium disrupted the measurements, because of its natural fluorescence.** Therefore we have decided to conduct another experiment and to dilute the strains with “SD” and “-Ura” solution
    .

    The second fluorescence reading was conducted using a fluorescence black plate 96-wells as shown in figure 2.
    Each well contains 1:10 dilution of the strains with SD and -Ura: We’ve transferred 50µL of the strain solution to a 200µL SD solution, and then transferred 50µL of the outcome solution to a 200µL -Ura solution.
    The results of this test are summarized in folder “Results: Fluorescence Top Reading and Absorbance” and are divided into 3 sections as mentioned above.

    1. Green Fluorescence Top Reading (label 1)
    2. Red Fluorescence Top Reading (label 2)
    3. Absorbance\OD (label 3).![first_96wells](https://static.igem.org/mediawiki/2020/4/4c/T--TAU_israel--96wells_2.jpg)

    **The results indicated that the use of SD lowered the noisy signals achieved from the
    YPD natural fluorescence.**

    Therefore we prepared SD complete solution (see media preparation) to be our new
    growing media and continued according to protocol with a new organization for the plates,
    in which all the empty wells is where the second experiment should be placed.

    ![first_96wells](https://static.igem.org/mediawiki/2020/2/20/T--TAU_israel--96wells_3.jpg)

    * Thursday 30/01/2020 - 10 gene and Gene-SEQ

      We followed the protocol “Updated Gene-SEQ protocol with SD media”. Results excel
      files were added to the results folder.

      While observing the fluorescence results, we noticed that the absorbance (OD) of
      plate “1” cells was lower than expected, compared to the test plate “2” OD:
      Approximately 0.2 average OD higher than plate “2” (multiplying the diluted cells OD in
      plate “1” by 5). It should be higher than that, considering the cells grow in plate “1” and
      not in plate “2”.

      **We assumed that this difference in expectations might be because we use different
      plates at the absorbance/fluorescence tests.** Plate “2” was tested in a transparent 96 well
      plate, and plate “1” was tested in a black 96 well plate.

      **Therefore, for the next test we do (sunday - 2/2) we will use 96 black plate for both tests.**

      * Friday 31/01/2020 - 10 gene and Gene-SEQ

        We detected a murky solution in column 12 of the infection plate (-leu) that may indicate the
        growth of infection. This may have been due to a mistake of the previous day- insertion of a
        little -ura substrate into the -leu substrate wells.

        We marked the plate and returned it to the incubator for a deeper test the following
        day and continued the experiment according to the protocol.

        We noticed that the experimental protocol could be improved by saving a 96 well plate and
        improving infection prevention. We updated the protocol and sent for approval of the lab manager .

FEBRUARY

* Saturday 01/02/2020 - 10 gene and Gene-SEQ

  Probably as a result of using the wrong media for the contamination plate, there was a
  considerable growth in this plate, we prepared a new contamination plate and the results tomorrow will indicate the reason for the growth, if because of wrong media or because a contamination.

  + We found a mistake in the genetic background of the negative control for the RFP library
    and therefore changed it for a different one from the RFP library.
  + **We added a WT as a negative control to the plate. This control should grow in the contamination plate as
    a positive control and should not be fluorescent in addition to serve as a baseline of the natural fluorescence of yeast.**

  * Sunday 02/02/2020 - 10 gene and Gene-SEQ

    We did everything as written in the protocol. There was not anything special in the results. We
    prepared new Glycerol plates and froze them as needed on Sunday.

    * Monday 03/02/2020 - Gene-SEQ

      We followed the “Updated Gene-SEQ protocol with SD media”

      .

      There was no remarkable
      growth on the contamination plate except for the control well. The results have been uploaded
      to the ‘Results’ folder.

      * Tuesday 04/02/2020 - 10 gene and Gene-SEQ

        The protocol was followed with no apparent problem.

        One of the pipettes in the multi pipette is not working properly, so whoever uses it should make
        sure that the liquids rise enough.

        When doing the OD test, we realized that the WT in H11 grew but not as much as expected, this
        could have been because the solution was not well mixed when collecting the sample, the day
        prior.

        Besides that we saw that in the results of the day before (3/2) A8 had an OD of 0.6 in
        comparison to an average of 0.1, on the other hand, the results today were normal and didn’t show
        signs of contamination.

        * Wednesday 05/02/2020 - 10 gene and Gene-SEQ

          The protocol was followed with no apparent problem.

          In the contamination plate, in 10B there was growth, we continued as usual and tomorrow based on
          the growth in the contamination plate, we will decide if the contamination was only in the
          contamination plate or also in the main plate and will behave accordingly.

          * Thursday 06/02/2020 - 10 gene and Gene-SEQ

            In the contamination plate, in 10B there was growth, we replaced 10B with 9B and we left
            9B as is.

            From the OD test we noticed that the WT in H11 grew but not as much as expected
            around (0.19). In the dilution plate in line B rows 7-12 the liquid was higher than the rest, so we
            filled H rows 1-6 instead.

            * Friday 07/02/2020 - 10 gene and Gene-SEQ

              We conducted the experiment as usual and no contamination has been observed. In the WT control in plate 2 growth was smaller than usual.

              * Saturday 08/02/2020 - 10 gene

                We conducted the experiment as usual and no contamination has been observed.

                EMS Calibration:

                In order to evaluate the correct concentration of EMS for our experiment, we constructed a calibration protocol. (see EMS Calibration protocol) EMS calibration link

                * Sunday 09/02/2020 - 10 gene and Gene-SEQ

                  There was a clear contamination in at-least four wells. We defrosted the plate from the 3/2/20 and continued according to the protocol.

                  * Monday 10/02/2020 - 10 gene and Gene-SEQ

                    All plates were apparently contaminated (including samples from the fridge/freezer). **We aborted the experiment in order to have a fresh start of the full experiment, including:**

                    1. Gene-SEQ Experiment.* Individual strains experiment.* EMS experiment.

                    * Tuesday 11/02/2020 - 10 gene

                      EMS Calibration results:

                      5μl of EMS in 500ml is enough to increase the mutation rate in 100-fold while causing 20% lethality or decrease in growth (as calculated from OD compared to a control that was not given EMS).

                      OD of cells:

                      + No ems -1.34+ 1μl ems - 1.1256+ 5μl ems-1.072+ 10μl ems- 0.93+ 25μl - No growth+ 50 μl - no growth

                      Mutation as measures on CAN plates:

                      + No ems -8+ 1μl ems - 379+ 5μl ems-852+ 10μl ems- 531+ 25μl - 0+ 50μl - 0

                      We started the full experiment according to the “Evolution experiment” protocol.

                      The strains selected by the modeling team are:

                      TEL2, SEC21, CEG1, KAP95, PMI40, GCD10, HCA4, CPC9, SQT1, TOL3.

                      GFP positive control - Ura+ - 1-27-41. (WT)

                      RFP positive control - his3d GFPdc - 1-5-40.

                      **All from the SWAT GFP/RFP library.**

                      * Wednesday 12/02/2020 - 10 gene and Gene-SEQ

                        We continued the full experiment according to the protocol and initiated the first contamination
                        plate.

                        Both "result" files (for plate “1” and “2”) contain the O.D, fluorescence and absorbance
                        measurements.

                        * Thursday 13/02/2020 - 10 gene and Gene-SEQ

                          Evolution experiment aborted due to a problem in the Gene-SEQ (including positive control
                          strains) and model changes, we realized we required different strains for the 10 individual strains experiment.

MARCH

* Thursday 05/03/2020 - 10 gene

  Evolution Experiment was re-initiated with 10 new individual strains chosen by our model team:
  According to “Evolution Experiment - Initiation Protocol”

  EMS experiment - WAS NOT STARTED .

  ![](https://static.igem.org/mediawiki/2020/1/14/T--TAU_israel--wells_05.03.jpg)

We started the daily protocol, and while transferring from plate “2” (Contamination control) to
the OD plate, we visualized turbidity in most of the wells in the first row, last column and a few
more turbid wells in the individual genes wells (GFP and RFP). All of which are negative control
for growth and therefore all contaminated.

**We decided to abort the experiment (Excluding EMS), restart fresh, and figure out a way to
make this experiment cleaner with less contaminants.**

MAY

* Tuesday 19/5/2020 - 10 gene(link missing)

  We had 2 goals:

  1. **validating the 10 chosen genes for our evolution experiment.* Creating a negative control for the GFP and for the RFP for the evolution experiment.**

  For the first, we used the correlated species we isolated before from Shuldiner yeast
  library. Then we followed the protocol for DNA extraction. We designed primers for each of the
  10 genes and we used them with the PCR protocol we followed.

  For the second goal we used primers we designed earlier (CNG - for the GFP, CNR- for the
  RFP). We needed to replace the Can1 gene with a GFP and RFP accordingly. In addition we
  inserted a stop codon to the primer, designed to enter after the GFP and the RFP, so that we will
  get the fluorescent protein (the fluorescent level was extremely low because the GFP/RFP are
  fused to the Can 1 gene). We followed the DNA extraction protocol and the PCR protocol.

  Later we tested the PCR products by gel electrophoresis, here are the results:

  ![](https://static.igem.org/mediawiki/2020/5/53/T--TAU_israel--Gel_electrophoresis1.jpg)

JUNE

* Tuesday 2/6/2020 - 10 gene

  Because we did not get a product after 3 PCR runs of CDC9 strain for GFP, we decided to re-seed the strain onto an agar plate and re-extract DNA according to the attached protocol. Then we froze the DNA products.

  * Tuesday 9/6/2020 - 10 gene

    After we re-extracted the DNA from the CDC9 strain, we ran a PCR and got no product at all. We assume that this strain is not CDC9. in order to validate this we ordered different primers that will give a different PCR product in different cases:

    1. If it is the CDC9 strain, we get a 4KB PCR product containing the GFP fused to CDC9 gene* If it isn’t the CDC9 strain, we get a 1KB PCR product containing the GFP only.

    Update: we performed the PCR and got 1KB product. We successfully confirmed that this specific strain is not CDC9. Unfortunately we can not know which strain it is at this stage.

    ![CDC9 gel verification](https://static.igem.org/mediawiki/2020/0/01/T--TAU_israel--CDC_verification.jpg)

    * Wednesday 10/6/2020 - 10 gene

      We performed calibrations for OD measurements and GFP measurements of our plate reader.
      We used the iGEM official measurement protocols (with measurement kit from previous year).
      [https://2020.igem.org/Measurement/Protocols#kit\_protocols](url)
      We will order an RFP calibration material for future experiments that we intend to do with mcherry protein.

      * Wednesday 10/6/2020 - 10 gene

        We performed another PCR for the negative control of our 10 genes experiment, using the new primers we designed. Unfortunately we still didn’t get any PCR product. Again, we decided to redesign new primers.

        * Monday 15/6/2020 - 10 gene

          We seeded the ten strains (of the 10 gene experiment, RFP/GFP) from our frozen stock to -ura plate (GFP) and not+ plate (RFP) , in order to have fluorescent cells available for FACS analysis. The goal is to eventually make a FACS analysis of the Gene-SEQ frozen stock, and a FACS separation of fluorescent cells for further experiments.

          * Thursday 18/6/2020 - 10 gene

            We took the ten strains of GFP that were seeded and started the evolution experiment on those strains. This time the experiment will be strictly inside a biological hood to further prevent any contamination. We also initiated the RFP strains although we still do not have a negative control for it.

            first FACS analysis of 10 genes (GFP/RFP) were done. Details added later.

            * Friday and Saturday 19-20/6/2020 - 10 gene

              **Contaminations arose in some of the wells of the RFP strains.** So, the experiment was stopped, we decided to do an isolation seeding to those strains again in order to get rid of the contaminations.

              * Sunday 21/6/2020 - 10 gene

                We did not detect any growth in the wells of the WT, so we decided to take new colonies for the WT control as it is only used as a measurement base control for the fluorescent emission.
                No contaminations whatsoever.

                * Monday 22/6/2020 - 10 gene and Gene-SEQ

                  We noticed that the measurement fluorescence protocol of Schuldiner's Lab is not suitable for the plate reader we have, so the results were not good enough. We changed the protocol and stopped the experiment because of technical reasons.

                  * Thursday 25/6/2020 - Gene-SEQ

                    First FACS sorting - mixed cultures. Results will be shown later.

JULY

* Friday 10/7/2020 - 10 gene

  **The 10 genes experiment was started today with the new protocol for fluorescence.**

  * Saturday until Wednesday 11-15/7/2020 - 10 gene

    The experiment went smooth with no contamination. The experiment was stopped once again due to technical issues. We froze the strains and will continue the experiment on Sunday.

    * Monday 13/7/2020- 10 gene

      Starters for tomorrow’s FACS were made.

      After isolating all the 10 genes from frozen stock we took a picture of them with fluorescent microscopy, they were all fluorescent (at different levels).

      RFP

      ![](https://static.igem.org/mediawiki/2020/6/64/T--TAU_israel--fluo1.jpg)

AUGUST

* 28/07-9/08/2020 - 10 gene

  The experiment continued smoothly without contaminations so far. the measurements were uploaded to the results files in the drive.

  * Monday 10/8/2020 - Co cultures

    In the previous day we have made starters of GFP/RFP co cultures, the GFP in SD-URA and RFP
    in SD-complete + NAT (1:1000). **That might be the reason previous experiments went wrong.**

    The RFP culture has a NAT resistance selection, which was not used before. **The major mistake
    was growing the RFP culture in -Leu media.** After reviewing articles about those yeast strains,
    the BY4741 strain (which is used as control and as basis of the libraries).

    The libraries have a +Ura strain which should grow on -Ura media, this should affect the results, meaning that when RFP culture is transferred to -Ura media as negative control, the +Ura strain can grow!.

    * 10/8-16/8/2020 - 10 gene

      Up until now the experiment went well. Today a contamination occurred in all the wells.

      **After investigation we concluded the plate itself was not washed thoroughly.** Unfortunately, we had to
      start the experiment again from a frozen stock of day 10.

      * 13/8/2020 - 10 gene

        We want to send samples from the 10 gene experiment for sequencing, so today we started preparing them, we did DNA extraction, PCR and clean up.

        * Sunday 17-23/8/2020 - 10 gene

          The experiment was going as should be with some changes. **From now on, the plates will be washed
          with water thoroughly until no residue is observed and soaked in bleach overnight, then put to
          dry until the next cycle of autoclave, in addition, the breathable stickers will be changed every
          day. For a final precaution, a batch of each strain will be stock in the refrigerator everyday in
          case of contaminations on the next day.**

          * Monday 31/8/2020 - Gene-SEQ:

            Apparently, Matan informed today that our frozen stock of the Gene-SEQ contains two distinct copies, one of them is the “Old” one, which is the first one we made with all the controls. We have used this one for all experiments until this day. The other one is the “New” one which is a copy that from where Matan tried to remove some of the controls, this stock might be contaminated according to Matan.

            We have made starters for GFP/RFP co cultures each from the New/Old, 2 repetitions for each one, total of 8 tubes (RFP- comp+nat media | GFP- -Ura media). In addition, we have made negative controls for each GFP/RFP/New/Old - Total of 4 controls. Contained -Leu(old)/-His(new) media - The cells should not grow.

SEPTEMBER

* Tuesday 1/9/2020 - Gene-SEQ

  There was some growth in the -His negative controls (New Gene-SEQ). We assumed that some negative controls (with the ability to synthesize Histidine) remained in the New culture and decided to use it anyway, assuming that the controls were not harmful for the results.

  **Started the Chi-Bio Gene-SEQ experiment.** 2 vials of each GFP/RFP New Gene-SEQ. We added a small amount of cells to start the experiment. The machine is connected to a 1 liter bottle of media

  * Wednesday 2/9/2020 - 10 gene

    We prepared 8 of 10 genes for sequencing. The genes were divided into 4 groups:

    1. GFP with reverse primer.

    2. GFP with forward primer.

    3. RFP with reverse primer.

    4. RFP with forward primer.

    The prepared samples were frozen.

    * Thursday 3/9/2020 - 10 gene

      The preparation for sequencing was successful on almost all the genes, so we sent them for sequencing and prepared again the ones that didn't succeed in addition to the 2 that were not prepared yesterday (the initial primers we use for the 2 genes did not work properly).

      * Thursday 3/9/2020 - 10 gene

        In order to get the best results out of the plate reader, our instructor Dan advised us to precipitate the yeasts and change their solution to PBS before checking the fluorescence levels.

        * Thursday 3/9/2020 - 10 gene

          In order to get the best results out of the plate reader, our instructor Dan advised us to precipitate the yeasts and change their solution to PBS before checking the fluorescence levels.

          * Monday 7/8/2020 - Gene-SEQ

            Doron connected a 4 liter bottle of SD complete media to the Chi-Bio, should last for 3-4 days.

            * Tuesday 8/9/2020 - Gene-SEQ

              One of the GFP vials might be contaminated, the machine wasted 4 liters of media (prepared yesterday) trying to resolve the high OD. Another explanation might be that cells were stuck to glass vial and interrupted the OD reading, thus maintaining a false and a very high OD read.

              Both the GFP (contaminated) and RFP (clean) remain at the vials without media. Tomorrow we will examine the GFP for contamination and connect a new media bottle for the 2 RFP vials.

              * Sunday 9/9/2020 - Gene-SEQ

                We found out that the negative control for the RFP library (RFP not fused to any gene) is not fluorescent and as such should not be used in the experiment and we decided to clone him from the original. To do this we ordered primers that attach on both sides of the RFP construct (with the selection marker and the promoter) and added in the primers homology to the locus we want to insert the construct, and because we took the construct from a strain in which the RFP is fused to a gene, we added a stop codon to the RFP. we run the PCR, got the band in the size that we expected.

                The primers used:

                ![](https://static.igem.org/mediawiki/2020/a/a0/T--TAU_israel--gelsmall.jpg)
                ![](https://static.igem.org/mediawiki/2020/1/15/T--TAU_israel--sequence.jpg)
                + F- AGATAAGTAGATAAGAGAATGATACGAGATAAAGCACAAAcgtacgctgcaggtcgacggtggcggttctggcggtggcggatcc+ R- TACATTAGTATTAGCGTGAAAATGTACACATATACATATAaggaacc**TTA**cttgtacagctcgtccatgc

                In capital letters - created homology, in bold, mutation (inserted stop codon)

                We did a transformation to yeast according to the protocol described in the protocols section, but for some reason did not get any colonies (while getting colonies in the positive control.) because of time consideration and the corona lockdown we decided to not try again.

                * Monday 21/9/2020 - Gene-SEQ

                  Because there are no suitable optional primers for deep sequencing of the GFP/RFP library, if we want to also sequence the ORF the SWAT marker is attached to, we had to find a better solution. **We decided to cut the DNA with restriction enzymes that the engineering team found for us.** That were present in all the SWAT library (fluorescent protein and its promoter and contained enough of the ORF in order to identify it.), **then we will ligate the fragment to create a circular DNA fragment and by using primers from L2 linker we can now PCR all the fragments and send them to sequencing.**

                  Afterward we learned that in order to achieve good sequencing we need to ligate the PCR fragment so we will be able to do a RCA reaction on it (rolling circle amplification), in this manner we will get a concatemer with many repeats of the PCR fragment, and in such manner we can distinguish between mutation and sequencing errors.

                  Restriction enzyme illustration:

                  ![](https://static.igem.org/mediawiki/2020/7/7c/T--TAU_israel--restricion_enz.jpg)

                  Ligation illustration

                  ![](https://static.igem.org/mediawiki/2020/6/6a/T--TAU_israel--ligation.jpg)

                  PCR amplification illustration

                  ![](https://static.igem.org/mediawiki/2020/a/a3/T--TAU_israel--PCRamp.jpg)

                  Table showing the amount of DNA achieved per microliter after DNA extraction from yeast in nano grams (protocol in protocols ) after checking in nano-drop.

                  ![](https://static.igem.org/mediawiki/2020/2/22/T--TAU_israel--table_seq.jpg)

                  Then we used the resection protocol on all the samples and left for overnight in 37 degrees Celsius. We also conducted the same protocol on a control of one of the 10 genes (sec2), so we can check if the protocol was successful.

                  * Tuesday 22/9/2020 - Gene-SEQ

                    We finished the reaction by incubating in 65 degrees for 20 minutes. Then we performed overnight ligation protocol as stated in protocols.

                    * Wednesday 23/9/2020 - Gene-SEQ

                      We stopped the ligation reaction by incubating in 65 degrees for 20 minutes and then we used all volume from the ligation protocol for a PCR reaction. We first performed the PCR on the control, after validating that we got the expected band size (2.4kb)

                      ![](https://static.igem.org/mediawiki/2020/7/73/T--TAU_israel--gel_seq.jpg)

                      We decided to move on to the rest of the DNA samples. Because the band was weak, we decided to increase the number of cycles to 50 (instead of 40). Afterward we validated the control by further sending it for sequencing, and came back with 300bp inside the GFP in very high assurance level (2e-58). We then did the PCR reaction on the rest of the sample, cleaned the PCR product, checked the amount of DNA in nanogram again in the nanodrop machine and did a ligation protocol as stated in the protocol. Added a table with the amount of DNA in nanogram per microliter in each sample.

                      ![](https://static.igem.org/mediawiki/2020/f/f9/T--TAU_israel--gel_seq2.jpg)

                      * Wednesday 23/9/2020 - 10 gene

                        We extracted DNA, did PCR and cleaning from the latest samples on the experiment until today, to send for sequencing.

                        this will most probably be the latest results we will get until the competition.

                        * Thursday 24/9/2020 - Gene-SEQ

                          **We stopped the reaction as stated in the protocol and send the DNA the Netherlands for sequencing at Cyclomics(Alessio Marcozzi PH.D is a Co-founder and former IGEM competitor ).**

                          * Thursday 24/9/2020 - 10 gene

                            We perform another PCR for the RFP negative control of our 10 genes experiment, using the new primers we designed. Unfortunately we still didn’t get any PCR product. **We decided to postpone the RFP 10 genes experiment and continue only with the GFP.**

                            * Friday 25/9/2020 - 10 gene

                              We followed the sanger sequencing protocol and performed it on the 10 strains of our experiment (except CDC9 strain). Those are the strains of day 0 of the experiment.

                              **Update: The sequencing results successfully validate the 10 strains (except CDC9) - in each one of them we got GFP fused to the gene we have expected.**

OCTOBER

* Sunday 04/10/2020 - 10 gene

  We created starters for each strain, using samples we took from day 26 of the experiment.

  * Monday 05/10/2020 - 10 gene

    We performed DNA extraction for each of the strains in day 26 of the experiment (except CDC9). We then confirmed the extraction by PCR followed by electrophoresis. We documented the results in our “results” wiki Page.

    * Tuesday 06/10/2020 - 10 gene

      We followed Promega Protocol and performed a PCR clean-up to the PCR products we got yesterday. We then followed the Sanger sequencing protocol and sent the samples to the sequencing lab.

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# Team:TAU Israel/Engineering

sTAUbility

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[Home](https://2020.igem.org/Team%3ATAU_Israel)

Project

[Description](https://2020.igem.org/Team%3ATAU_Israel/Description)
[Design](https://2020.igem.org/Team%3ATAU_Israel/Design)
[Implementation](https://2020.igem.org/Team%3ATAU_Israel/Implementation)
[Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments)
[Results](https://2020.igem.org/Team%3ATAU_Israel/Results)
[Measurement](https://2020.igem.org/Team%3ATAU_Israel/Measurement)
[Proof of Concept](https://2020.igem.org/Team%3ATAU_Israel/Proof_Of_Concept)

Modelling

[Engineering](https://2020.igem.org/Team%3ATAU_Israel/Engineering)
[Model](https://2020.igem.org/Team%3ATAU_Israel/Model)
[Software](https://2020.igem.org/Team%3ATAU_Israel/Software)
[Contribution](https://2020.igem.org/Team%3ATAU_Israel/Contribution)

Lab

[Notebook](https://2020.igem.org/Team%3ATAU_Israel/Notebook)
[Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols)
[Safety](https://2020.igem.org/Team%3ATAU_Israel/Safety)

HP

[Integrated Human Practices](https://2020.igem.org/Team%3ATAU_Israel/Human_Practices)
[Inclusion](https://2020.igem.org/Team%3ATAU_Israel/Inclusion)
[Collaborations](https://2020.igem.org/Team%3ATAU_Israel/Collaborations)

Team

[Members](https://2020.igem.org/Team%3ATAU_Israel/Team)
[Our instructors](https://2020.igem.org/Team%3ATAU_Israel/Instructors)

Acknowledgements

[Sponsors](https://2020.igem.org/Team%3ATAU_Israel/Sponsors)
[Attributions](https://2020.igem.org/Team%3ATAU_Israel/Attributions)

Awards

[Awards](https://2020.igem.org/Team%3ATAU_Israel/Awards)
[Medals](https://2020.igem.org/Team%3ATAU_Israel/Medals)

☰

# Engineering Success

# Introduction

Our work in sTAUbility is divided into four main planes:

1. Model – predict the stability of different constructs and decide upon the best conjugated gene and linker for a given target gene, considering expression levels.
2. Software – a recommendation engine that combines the developed models and outputs the best construct for increased stability and expression levels. The software is our way to communicate our solution for potential users.
3. Measurement – quantify the stability of each variant in large-scale libraries or part collections; the data from such experiments can be used to train our models further and create a more generalized software.
4. Validation – make sure that the basic idea is valid and reliable.

This modular design enables us to develop and improve each element separately, and quickly adapt to changes according to feedbacks we receive. However, some of these steps required preliminary assessment.

![](https://static.igem.org/mediawiki/2020/d/d4/T--TAU_Israel--engineering_cycle.svg)

**Figure 1.** Our design cycle, including preliminary-assessment projects as part of the engineering approach.

In order to solve some of our project's problems we used synthetic biology tools to generate expected results. We tested design principles through modelling and statistical analysis. **In this page, we will elaborate on five different projects that are part of our engineering success:**

* **[Probability analysis of overlapping frame-shifted sequences](#First)**, that helped us analyze our initial vision and improve our design.
* **[Co-culture statistical analysis](#Second)**, to define the fitness-related limitations of using FACS in the initial Gene-SEQ experimental design.
* **[Chi.Bio setup](#Third)**, to be able to use the sophisticated hardware for our Gene-SEQ experiment.
* **[Restriction enzyme analysis](#Forth)**, to choose the restriction enzymes that will enable us to identify the different strains in the co-culture, while preserving as much of the target gene (GFP or RFP) as possible for mutational-footprint analysis.
* **[Selection of 10 essential genes](#Fifth)**, for the Proof-of-concept experiment. The experiment's strategy includes measuring stability of 10 diverse essential genes interlocked to GFP, in comparison to GFP alone, thus proving that essential genes affect the stability of the GFP, but not in the same way. The selection model helped us define what is meant by "diverse genes".

# Probability analysis of overlapping frame-shifted sequences

Our initial idea for the project (see [inspiration page](https://2020.igem.org/Team%3ATAU_Israel/Description)) was inspired by the [2013 Lethbridge team](https://2013.igem.org/Team%3ALethbridge), who introduced the use of pseudoknots as a frame changer. Pseudoknots are RNA secondary structural elements that cause the ribosome to “slip” backwards by one nucleotide, thus switching between translational frames and reading the protein that is encoded in the -1 reading frame.

One application for Pseudoknot-Induced Frameshifting is **Dual-coding**: By utilizing dual-coding sequences downstream of a pseudoknot, the ORFs of two proteins can be **overlapped** and placed in different reading frames, thereby reducing the coding space required to produce a construct.

Our team came up with another possible way of using this pseudoknot’s quality, by encoding a target gene and an essential gene in different frames, and thus increasing the stability of the gene of interest. A proof of concept already exised in literature [1], that showed when a sequence of interest was co-encoded with an essential gene in a living bacterium, its evolutionary stability substantially increased. We wished to develop an automated system that will recommend such a contrast for a given target gene, without changing the amino-acid sequence.

![](https://static.igem.org/mediawiki/2020/e/e4/T--TAU_Israel--Engineering_Figure_1.png)

**Figure 2.** Scheme of the reading frames. In the 0 frame, the encoded protein is the target construct. In the -1 frame, the sequence encodes an essential protein.

As a first step, we decided to run a **feasibility test** and analyze the probability of a given target gene to overlap a frame-shifted essential gene in a meaningful way.

For that, we **developed a generic algorithm** that calculates the probability that a given target gene will have an overlap longer than 5 AA (amino acids) with any frame shifted ORF in the given reference genome, with the shift being -1 nucleotides.

The function receives a target gene sequence (for instance GFP or insulin) in an amino-acid alphabet, and a path to a FASTA file that contains the reference genome (for example, a yeast’s whole genome) in amino-acid alphabet. The reference genome can also consist of a specific set of genes such as essential genes. The last input 'n' is the number of iterations.

In each iteration 'j' the function randomizes the nucleotide sequences of the genome (preserving the amino acids) and then shifts the reading frame by +1 nucleotides. This results in a new set of nucleotide sequences, or a “frame-shifted genome”, that is translated back to amino acid sequences (different from the original input sequences).  We call the translated set $FS\_{AA}$, as in “frame-shifted amino acid set”.

The search for overlap is based on a unique data structure called “**Suffix Array**” (SA) that was first presented here [2] and is used in the context engineering gene expression and analyzing the adaptability of an ORF to a reference genome [3]. SA is basically a sorted list of all the suffixes of the reference genome, without storing the actual strings. Thus, it does not require a lot of memory and is easily searched. SA can be used as an efficient way to find the longest substring (or overlap) starting at position ‘i’ in the target gene, that appears somewhere in the reference genome [3].

Sorted arrays, such as suffix arrays, can be searched efficiently, using **binary search** - a search algorithm that compares the target value to the middle element of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found.

So, after generating a suffix array from $FS\_{AA}$, the function calculates the length of the longest common prefix between the frame-shifted genome and the so-called "key" (the target gene starting from position 'i' in the sequence). Thus, for each position 'i' in the target gene we get a **score** of the longest common prefix, marked $S\_{ij}$ where ‘i’ is the position and ‘j’ is the iteration.

![](https://static.igem.org/mediawiki/2020/4/4c/T--TAU_Israel--Engineering_Figure_2.png)

**Figure 3.** An illustration of the search for overlap (based on the chimeraARS algorithm [3]). To compute the probability to overlap between a target gene and a frame-shifted reference genome, we find for each ‘i’ position in the ORF the longest substring that starts in this position, but also appears in one of the ORFs of the genome.

The summarized **"iteration score"** $S\_{j}$ for the specific 'j' iteration is calculated as the number of prefixes longer than 5. Mathematically:$S\_{j}=count(S\_{ij}>5)$ , where $S\_{ij}$ is the longest prefix starting from the i-th position in the j-th iteration as described above.

The **final probability** is calculated as sum on the iteration scores, divided by the number of iterations and the length of the target gene. Mathematically:

$$(1) p=\frac{1}{n\*L}\sum\_{j=1}^{n}S\_{j}$$

where $S\_{j}$ is the iteration score, 'n' number of iterations, 'L' length of target gene (in AA).

For our analysis, we inserted the following inputs:

1. Yeast essential genes
2. GFP as a target gene

We also used e-coli whole genome and insulin as a gene of interest but achieved similar results, therefore we will only show the results of the yeast analysis.

After 50 iterations, the calculated probability was 0.01297.

The longest overlap was of 9 amino acids. Also, there is a maximum of 7 substrings per iteration that produce overlap longer than 5 amino acids, as can be seen in the histogram below, which is hardly enough to maintain a stable construct and form a meaningful overlap between the two genes.

![](https://static.igem.org/mediawiki/2020/d/d4/T--TAU_Israel--Engineering_Figure_3.png)

**Figure 4.** A histogram presenting the distribution of the iteration scores.

This analysis led us to the conclusion that our current solution (see figure 2) has a major limitation, since we will not be able to produce a generalized construct for any given target gene.

Thus, we came up with other possible ways to interlock a target gene to an essential gene, as described in our [project description](https://2020.igem.org/Team%3ATAU_Israel/Description) page.

This kind of process reflects the engineering cycle:

* We **researched** and read about other iGEM teams, and this research inspired us to use the pseudoknot to solve the stability problem of genetically modified constructs.
* We **Imagined** how it can be used in a system by creating an overlap with an essential gene
* We **designed** an algorithm to test the feasibility of the solution
* We **built** a platform that does the calculations
* We **tested** this platform with many target genes and reference genomes.
* We **learned** that the probability of an overlap between gene of interest and an essential gene is not as high as we wanted
* We **improved** our methods accordingly and designed new ways to interlock the essential and target genes by using many different linkers.

# Gene-SEQ statistical analysis

One of the experiments planned by our team is the gene-SEQ experiment. In this experiment, we used 6-7k variants of *saccharomyces* *cerevisiae*, each variant, has a fluorescent gene (GFP/RFP) was attached attachedN-terminally linked to another known yeast gene. All variants were grown as a mixed culture in the Chi.Bio machine, and fluorescence was monitored for 300 generations. This allowed us to analyze both the fluorescent gene’s stability, and the variant’s fitness. We were able to conduct this experiment due to the works of Prof. Schuldiner's lab, which timeprovided us their SWAT-GFP/RFP yeast libraries.

Initially, we planned to manually dilute the co-cultures every day. We referred to the volume sampled (in microliters) each day for dilution purposes as the "sample size". At the end of this evolution experiment we planned to use FACS to classify the different strains according to their fluorescence level.

In this analysis, we attempted to confirm the statistical viability and limitations of the usage of FACS for the gene-SEQ experiment, and quantify the sample size that will enable us to keep most of the variants in the population, regardless of their fitness. As it was done in early stages of our design and modelling, we did not have access to gene features or metadata and based our analysis on various simplifying assumptions. We tried different assumptions and monitored their effect on our results.

**Probability of capturing all genes**

In the previously described library of yeast variants, the quantity of each variant was different. We found empirically that the most prevalent variants are a hundredfold more frequent than the least prevalent ones. We wanted to estimate the effect of sample size on the probability of “losing a construct” between samples in the co-culture experiment.

We estimated the probability of not capturing all variants as a function of sample size. Our estimation was based on different assumptions of prior distribution on variant population quantities.

As a baseline, we calculated this probability for equal population sizes, in which case we can easily use Poisson’s formula. The probability of finding k samples of a specific variant in a sample is:

$$(2) P(k)=\frac{\lambda^{k}\exp (\lambda)}{k!}$$

In our case, $\lambda=\frac{sample\,size}{6000}$, as we assume 6k genes and equal populations, the probability of missing a gene is $1-P(0)$, and the probability of missing any gene is $(1-P(0))^{6000}$. Thus, our probability equals to:

$$(3) P(missing\:any\:gene)=(1-\exp(\frac{sample\:size}{6000}))^{6000}$$

The second distribution we tested is a uniform distribution on population sizes, along a 100-fold factor. We carried out a similar calculation, where we assumed the ratios between populations fit a uniform distribution, plugged the appropriate $\lambda$ for each population size, and found $P(missing\:any\:gene)$.

The third distribution we tested was a Gaussian distribution on population sizes. We randomly generated population sizes 100 times, with mean of 50 and STD of 13.6 – values which as expected value allow one variant to have a population size of 100 and one to have population size of 1. We used these generated population ratios, calculated appropriate values of $\lambda$, and calculated $P(missing\:any\:gene)$.

In our final and most realistic assumption, we assumed that the original populations were equal, co-culture experiment had **started** already in Shuldiner’s lab, and this caused the population size discrepancies. We assumed naively that the populations were not yet competing for resources (multiplying in 2 every generation), and thus their population size followed the following ratios:

$$(3) population\:ratio=2^{fitness\*number\:of\:generations}$$

We assumed that their fitness is uniformly distributed between 0.75 and 1, based on a simplifying description of empirical data. Thus our 100-fold factor in population sizes allows us to estimate how many generations have passed:

$$2^{1\*number\:of\:generations}=100\*2^{0.7\*number\:of\:generations}\rightarrow number\:of\:generations \approx 26.6 $$

Using the number of generations found and a uniform distribution on fitness, we generated population ratios and estimated $P(missing\:any\:gene)$.

The results are summarized in the following graph, for different population sizes.

![](https://static.igem.org/mediawiki/2020/a/af/T--TAU_Israel--Engineering_Figure_4.png)

**Figure 5.** Probability of not catching all types of variants as a function sample size. [View image in full-size.](https://static.igem.org/mediawiki/2020/a/af/T--TAU_Israel--Engineering_Figure_4.png)

**Probability of all genes’ survival after 300 generations**

We discovered an even more fundamental issue while calculating the above results. Given a co-culture experiment, we would not keep variants of none-similar fitness even with very large sample sizes.

To demonstrate this point, we once again assumed a uniform distribution of fitness scores among variants. We calculated the probability of survival of all constructs of fitness above 0.99, thus almost equal fitness.

We demonstrated mathematically that the probabilities of keeping almost all variants (losing just a few) is of a similar scale to keeping all variants, allowing us to estimate only the probability of keeping all variants. We generated the population sizes after 300 generations if no sampling is done along the way, and using our previous mechanism, calculated the probability of survival of all variants to the 301st generation. As one can see in the following graph, the likelihood of success is very small even for very large sample sizes, and we are ignoring the probability of losing populations **until** the 300th generation.

![](https://static.igem.org/mediawiki/2020/0/09/T--TAU_Israel--Engineering_Figure_5.png)

**Figure 6.** Survival probability of all constructs of fitness 0.99 and above after 300 generations, assuming uniform fitness distribution. [View image in full-size.](https://static.igem.org/mediawiki/2020/0/09/T--TAU_Israel--Engineering_Figure_5.png)

Due to these results, we realized both the possible importance of separating co-culture experiments, and the need to use very large samples in order to prevent losing variants with low fitness.
This last conclusion led us to reconsider the use of FACS to separate samples. Therefore, following this analysis, we **improved our design** - we decided to switch to using a sophisticated robotic system called Chi.Bio. Instead of FACS separation, we will grow the cultures in the Chi.Bio machine and at the end of the experiment we will sequence them to find the mutational footprints. For more information, visit our [Measurement](https://2020.igem.org/Team%3ATAU_Israel/Measurement) page.

# Chi.Bio setup

[Chi Bio](http://chi.bio/) is an open-source robotic platform for experimental automation in biological science research and education. The system contains a control computer, main reactor and liquid handling (pumps and tubes). During our experimental work, we conducted a co-culture experiment using the Chi Bio system.

The engineering approach also means adopting given systems to our needs. For this purpose, we needed to make changes in the system’s main operation code. This included:

* Changing the default code that controls the liquid handling, so that we can split the pumps and use them for two different purposes. In addition, we changed the frequency of the pumps.
* Designing caps with nozzles that allows sterility and avoidance of leaks.

You can read more about the Chi.Bio system and how we implemented it in our [Experiments page](https://2020.igem.org/Team%3ATAU_Israel/Experiments#Gene-SEQ).

# Restriction enzyme analysis

At the end of the Gene-SEQ experiment (i.e. the evolution experiment with the GFP and RFP co-cultures) we used Nanopore deep sequencing to divide fluorescent strains and non-fluorescent strains. We estimated that we were going to face 2 main challenges at that point:

1. Identify a specific strain from the 6000 strains in the library.
2. Identify the mutations that occurred in the fluorescent gene (RFP or GFP) and in the gene attached to it.

Unfortunately, the SWAT library we received from Schuldiner labs (Schuldiner, [SWAT](http://mayaschuldiner.wixsite.com/schuldinerlab/s-w-a-t)) [4,5] did not contain a bar code that allowed a relatively simple identification of the strains. We concluded that the best way to meet both challenges is to use restriction enzymes. Our approach using the restriction enzymes wasis to make a cut in the promoter of the fluorescent gene sequence, and another cut as far as possible in the conjugated gene, so the extracted sequence would consist the complete fluorescent gene and some of the conjugated gene. Using the same restriction enzyme for both ends would allow self-ligation of the cut sequence to create a circular DNA. This way, we could use the linker between the two genes (florescent and conjugated) to design one primer that fits to all the strains in the library and eventually make a deep-sequencing of these sequences (fig. 7).

![](https://static.igem.org/mediawiki/2020/b/bc/T--TAU_Israel--Engineering_Figure_17.png)

**Figure 7.** The SWAT cassette harbouring the NOP1 constitutive promotor, GFP gene and conjugated gene (ORF) with a linker between them.

To meet economic and biological constraints, we chose to use one or a maximum of two restriction enzymes, which would give the optimal result. Our model team had designed and implemented an algorithm that would compare and display the optimal set of enzymes.

The process

As soon as we realized the need, we started searching the internet for a tool that could help us accomplish the task. We had a specific need, going over all possible enzymes for all possible constructs and collecting statistics to find an optimal enzyme. As we had not found a method that would do this efficiently, we decided to design such a tool.

We designed a tool that would search for an optimal enzyme with the following properties:

1. There is no cutting site within the promoter or fluorescent gene, which are identical for the 6000 yeast variants.
2. Each restriction enzyme cuts a segment of the yeast variant, which includes the promoter, fluorescent gene, and some of the conjugated gene. Our method optimizes for a minimal amount of none-unique sequences cut by the enzyme, among the 6000 variants. This maximizes our differentiation capabilities.
3. In addition, beyond a certain sequence length, the PCR may yield inaccurate results. Thus, we optimized for a minimal number of yeast variants which, when cut, create a sequence of length over 3K BP.

![](https://static.igem.org/mediawiki/2020/9/98/T--TAU_Israel--Engineering_Figure_18.png)

**Figure 8.** Restriction sites within the different strains of the SWAT library cassette.

At first, we attempted two approaches:

1. Search for a suitable restriction enzyme from a database of commercially available restriction enzymes [6].
2. Search for a custom designed restriction site.

After comparing the results of the two approaches, we realized that there is no significant advantage to either one. Thus, so we chose to focus on commercially available enzymes. This would, of course, reduce costs significantly.

![](https://static.igem.org/mediawiki/2020/5/57/T--TAU_Israel--Engineering_Figure_19.png)

**Figure 9.** Comparison between custom designed restriction site to a single or a pair of commercially available Restriction enzymes.

Later, we simulated the use of 2 different restriction enzymes. We saw some minor improvement of the results, but not enough to warrant the financial and biological cost. Thus, we decided to stick with one enzyme.

Instead of trying to optimize between two parameters where we do not know the relative weight (number of none-unique sequences vs. number of too-long sequences), we found the enzymes which are Pareto efficient [7]. An enzyme would be Pareto efficient if there is no way to reduce the percent of none-unique sequences without increasing the percent of sequences over length 3000. Pareto efficiency is a necessary but not enough condition for being an optimal solution. In addition, we used the fact that not many enzymes are Pareto efficient. We compared these enzymes manually and found BsrI to be the optimal enzyme for RFP fluorescent gene attached to conjugated genes in yeast. Still, due to difficulties in supply caused by the COVID-19 situation, we had to consider not only the efficiency but also the availability of the chosen enzymes.

Finally, we chose the enzymes that showed good performance in this analysis and also were availble in the lab. You can see the results in our [Results](https://2020.igem.org/Team%3ATAU_Israel/Results) page.

# Selection of 10 genes for single gene experiment

During an evolution experiment, GFP (or mCherry) proteins conjugated to essential genes with certain properties will show greater preservation at the genetic sequence level than GFP expressed without being coupled to another protein. The evolution experiment was conducted by using the SWAT-tagged library of yeast genes, containing $\sim 5,500$ strains each tagged with GFP or mCherry at the N-terminus of a protein (Schuldiner, [SWAT](http://mayaschuldiner.wixsite.com/schuldinerlab/s-w-a-t)).

The purpose of the following model is to select 10 inviable genes that are as diverse as possible, in order to understand (when crossed with empirical information) which features helped maintain genomic stability of GFP or RFP proteins that are transcriptionally interlocked to it. This way, we could **prove that distinctive essential genes contribute differently to the stability of the fused construct.**

To achieve this goal, the model focuses on the characterization of inviable genes based on available databases and sequence analysis, followed by the clustering of genes into clusters.

The work process

Since the goal of this model is to choose ten **inviable** diverse genes, the first thing we had to do was to collect only the inviable genes out of the tagged genes that are available in Shuldiner's library. Thus, for the first filter we use the definition of "inviable" gene from the SGD website, and filter the genes accordingly.

Then, since the ten genes are about to be used for evolution experiment, we needed them to show adequte **flourescence** (so that it will be detected in a plate reader). Thus, in the second filter we took all the inviable genes and choose only the one that are flourecent above a threshold of 40 (see explanation below).

After filtering all the genes in the yeast library, the genes that remained are inviable and flourecent, and so match our purpose.

To each gene in this filtered database we generate a set of features, and then choose the 10 most diverse genes as described below.

Step 1: Filtering

1. *Filtering inviable with no overexpression genes from SGD*

**Source:** The initial database is from  [Yeastmine search engine](http://yeastmine.yeastgenome.org/yeastmine/begin.do), a collaboration between SGD and the Intermine project.

Tab: PHENOTYPES.

Query: Gene >> Phenotype.

Gene list:  ALL\_Verified\_Uncharacterized\_Dubious\_ORFs

**Filters:** keep only genes with:

(a): Observable: inviable, (b) Mutant Type: no overexpression

Those filters were chosen to fit the conditions of the evolution experiment.

2. *Filtering fluorescent genes based on empiric database Loqate*

**Source:** [Link](http://www.weizmann.ac.il/molgen/loqate/search/localization)

Loqate is an online database that includes measurements of localization and abundance of 5,330 proteins of the yeast (single cell resolution) from two libraries:

1. The GFP-tagged [yeast library](http://www.marcottelab.org/users/CH391L/Handouts/nature02026.pdf) (C’-terminal GFP under natural promoter).
2. The [SWAT library](http://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_eb01667b976f4728bcafdd1b8de007cb.pdf) (N’-terminal GFP under NOP1 promoter).

For the fluorescence filtering we used the measurements of the SWAT library, because this is the library we eventually use in our experiment.

**Condition:** N’ NOP1pr-GFP in SD from SWAT library, and N' TEF2pr-mCherry in SD from SWAT library. All measurements also appear on the SWAT website.

**Localization:** All. We downloaded a separate file for each location in the cell and combined all files, as it gave a much more complete list. Each file includes the intensity (fluorescence) and the localization under each condition. The intensity is given in arbitrary units (a.u.).

**Filters:** keep only genes with above threshold fluorescence intensity in both conditions. The threshold is determined as 40, because it resulted in enough genes for the analysis, and appeared to take only those with observable intensity as can be seen in the database’s images.

Figure 10 shows the distribution of genes with a given intensity (a histogram of intensity vs. number of genes). It can be observed that below the threshold level of 40 is the 'center of mass' of the distribution, such that above this value are genes with significant fluorescence.

![](https://static.igem.org/mediawiki/2020/5/53/T--TAU_Israel--Engineering_Figure_6.png)

**Figure 10.** The distribution of genes with a given intensity. [View image in full-size.](https://static.igem.org/mediawiki/2020/5/53/T--TAU_Israel--Engineering_Figure_6.png)

Figure 11 shows the genes that remain for the analysis after choosing a specific threshold. Thus we ensure that the threshold chosen does not leave us with too few genes.

For future acknowledgement, we may consider checking more thresholds, but since the goal of this model is to collect initial empirical data in an unsupervised way, and since the 10-genes evolution experiment will be followed by more experiments, this threshold is sufficient for our needs.

![](https://static.igem.org/mediawiki/2020/e/e1/T--TAU_Israel--Engineering_Figure_7.png)

Step 2: Feature Generation

The filtering resulted in 600 genes. Now, for each gene we generated descriptive features, in order to divide them to "families" with distinct characteristics.

Loqate features

**Source:** [Loqate website](http://www.weizmann.ac.il/molgen/loqate/). The database: [Link](http://www.weizmann.ac.il/molgen/loqate/sites/molgen.loqate/files/proteomesummarylatestversion.xlsx)

 As mentioned before, Loqate is an online database that includes measurements of localization and abundance of 5,330 proteins of the yeast (single cell resolution) [8],[9]. The experiments were conducted using the GFP-tagged yeast library (C’-terminal GFP under natural promoter).

![](https://static.igem.org/mediawiki/2020/b/be/T--TAU_Israel--Engineering_Figure_8.png)

**Figure 12.** GFP-tagged yeast library (C’-terminal GFP under natural promoter), used for measurements of localization and abundance of proteins.

Loqate started as a project that aimed to describe the dynamics of proteins in response to external stimuli, considering that regulation of protein levels and localization is an essential part of such responses. In case of starvation, the protein can change its subcellular localization and expression in comparison to its control condition.

The data collected from this database is the result of systematically tracking the localization and abundance of proteins under:

* Normal conditions (control) - The growth experiments were conducted under normal conditions (no stress) on an SD medium (synthetic medium with dextrose) and the fluorescence intensity was measured.
* Three stress conditions (external perturbations) -

  1. Dithiothreitol (DTT): reducing stress, can influence gene cloning, growth and function.
  2. Hydrogen Peroxide (H2O2): oxidative stress.
  3. Nitrogen starvation: the yeast consumes a variety of nitrogen and carbon sources. Without it, the yeast starves.* Two genetic mutations -
    1. CCT mutant (MA3): MA3 is a mutation library first presented in [this article](http://www.pnas.org/content/pnas/109/46/18833.full.pdf) [10].

    ![](https://static.igem.org/mediawiki/2020/f/f6/T--TAU_Israel--Engineering_Figure_9.png)

    **Figure 13.** MA3 mutation library.

    * Pup2-DAmP: Localization shift under hypomorphic mutation (partial loss of gene function) in the proteasome core, which is a protein complex in cells containing proteases; it breaks down proteins that have been tagged by ubiquitin.

**Features:**Under each condition mentioned above besides Pup2-DAmP, there are features:

1. Median abundance: based on the fluorescence intensity of the GFP. in arbitrary units.
2. Standard deviation (std) of the abundance: based on the fluorescence intensity of the GFP. in arbitrary units.
3. Localization: classified proteins into 13 localization categories and detected hundreds of proteins that shift between different cellular locals under stress.

![](https://static.igem.org/mediawiki/2020/0/02/T--TAU_Israel--Engineering_Figure_10.png)

**Figure 14.** Screenshot of the localization search-engine available in Loqate website, including the 13 localization categories.

In order to implement the clustering algorithm, we have decided to convert the categorial feature of localization to 13 numerical features (one hot encoding) and compare it to control localization. Those 13 features were assigned with low weight, so that their influence will not be too high when clustering.

4. Fold change: For each stress condition, the fold change value is calculated as the ratio of median GFP intensity measured under stress to the median GFP intensity under reference conditions.

   For example:  Say control median intensity = 26.65.

   Say DTT median intensity = 22.74.

   Then fold change value = 22.74/26.65 = 0.85.

SWAT features

**Source:** [S-W-A-T website](http://mayaschuldiner.wixsite.com/schuldinerlab/s-w-a-t). The database: [Link](http://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_38c3c91d7774413bba3906f628502af1.xlsx?dn=WeillEtAlSupplementaryTable1.xlsx)

The SWAp-Tag (SWAT) library is a genome-wide library of $\sim 5,500$ strains carrying the SWAT NOP1promoter-GFP module at the N terminus of proteins. This is the first whole-genome SWAT library with an N′ tag, covering $\sim 90\%$ of yeast genes. The imaging of this library allows to determine the localization of 796 yeast proteins that could not be visualized before with a C′ fluorescent tag. The researchers also constructed six additional libraries to explore many aspects of yeast cell biology: the role of promoters in regulation of protein expression, the mitochondrial protein roster, protein interactions on a whole-organelle level, and systematic assessment of protein topology.

The full libraries were first presented here [4]. The construction of this library involved using the SWAp-Tag method that was first introduced here [5], and allowed rapid creation of yeast libraries.

![](https://static.igem.org/mediawiki/2020/7/74/T--TAU_Israel--Engineering_Figure_11.png)

**Figure 15.** The SWAp-Tag (SWAT) libraries.

The database includes Information on protein localization and fluorophore intensity from this study [8] for every yeast strain in the N’ SWAT libraries: NOP1pr-GFP, NOP1pr-MTS-GFP, NOP1pr-SP-GFP, TEF2pr-mCherry, TEF2pr-MTS-mCherry, TEF2pr-SP-mCherry, NATIVEpr-GFP and TEF2pr-VC/cyto-VN.

Additional information for each yeast strain from relevant literature: protein localization and fluorophore intensity of images of C' GFP tagged strains (Breker et al., 2013; Huh et al., 2003), Flow cytometry data of the C’ GFP tagged library (Newman et al., 2006), Ribosome Profiling (Ingolia et al., 2009), Mass Spectroscopy (Picotti et al., 2013), Protein Half-life (Belle et al., 2006), mRNA abundance (Weinberg et al., 2016) and mRNA Half-life (Neymotin et al., 2014).

We haven’t used all additional information, because we wished to focus only on the relevant ones and not to overflow the feature space, but we may use the rest of them in the next model.

**Features:**

1. NOP1pr-GFP intensity and localization: Median of GFP intensity of strains tagged with basic SWAT, SWAT-MTS or SWAT-SP cassettes (URA-Nop1p-GFP).
2. TEF2pr-mCherry intensity and localization: Median of mCherry intensity of SWAT strains after swap withTef2-mCherry donor (NAT-Tef2-mCherry).
3. NATIVEpr-GFP intensity and localization: Median of GFP intensity of SWAT strains after swap with Seamless GFP donor (Native promoter-GFP)
4. Tef2pr-VC and Cyto-VN intensity and localization: Median of GFP intensity of SWAT strains after swap with Tef2-VC donor (Hygro-Tef2-VC), and mating with a cytosolic -VN marker strain.
5. mRNA: mRNA abundance data from Weinberg et al., 2016.
6. mRNA half life: mRNA turnover is the rate at which a mRNA is degraded intracellularly and is described in terms of mRNA half-life, which is the time required to degrade 50% of the mRNA. mRNA Half life data from Neymotin et al., 2014

Rates of protein evolution

**Source:** the article “Functional genomic analysis of the rates of protein evolution” [11]. Supporting Table 4. [Link](http://www.pnas.org/content/suppl/2005/03/29/0501761102.DC1/01761Table4.html).

The above article aims to estimate the evolutionary rates over 3000 proteins in four species of the yeast genus Saccharomyces and to investigate their relationship with levels of expression and protein dispensability.

Nonsynonymous and synonymous substitution rates were obtained using multiple orthologous sequences and the following phylogeny ((*S. paradoxus*, *S. cerevisiae*), *S. mikatae*, *S. bayanus*). Codon bias (CAI) was used to derive an adjusted measure of synonymous rate that does not contain a selection effects from translational accuracy or efficiency.

**Features:**

1. CAI - codon adaptation index, an estimation to codon bias. The CAI is a measure of synonymous codon usage bias in the direction of the bias seen in a reference set of 24 S. cerevisiae genes having high protein levels [12]. CAI takes values from 0.0 (no bias) to 1.0 (maximum bias). This estimation looks at the distribution of codons in a specific gene in relation to the reference genome, and from it learns about the similarity of the gene to the reference genome. CAI is twice as strongly associated with evolutionary rate as is mRNA abundance. This finding is consistent with the hypothesis that CAI reflects historical expression levels relevant to protein evolution, rather than expression levels in the laboratory [11].
2. dS - Synonymous divergence, or synonymous substitutions per synonymous site. This is the mean amount of evolutionary silent mutations in comparison to Saccharomyces bayanus, Saccharomyces mikatae, Saccharomyces paradoxus, and S. cerevisiae.
3. dN - nonsynonymous divergence.
4. dN/dS - The ratio serves as a measure of evolutionary rate. High ratio means that the gene is less conserved.
5. dS adjusted for codon bias - to correct for selection on synonymous sites, the writers use a measure of dS-adjusted according to the gene’s level of codon bias. Thus they derive a measure of synonymous rate that does not contain selectional effects from translational accuracy or efficiency.
6. dN/dS adjusted - computes the ratio with dS adjusted.

Sequence analysis

The sequences were downloaded from the [SGD archive](http://sgd-archive.yeastgenome.org/sequence/S288C_reference/orf_dna/).

File: orf\_coding\_all.fasta.gz. Includes a FASTA file with systemic name, description and sequence of all genes.

**Source**[**:** Biopython library, package SeqUtils](http://biopython.org/DIST/docs/api/Bio.SeqUtils-module.html).

The Biopython Project is an international association of developers of freely available Python tools for computational molecular biology. The [Biopython web site](http://www.biopython.org) provides an online resource for modules, scripts, and web links for developers of Python-based software for bioinformatics use and research. Basically, if you like to program in Python, this library makes it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and scripts. Biopython features include parsers for various Bioinformatics file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...), interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...), a standard sequence class, various clustering modules, a KD tree data structure etc. and even documentation.

The package “seqUtils” includes miscellaneous functions for dealing with sequences.

**Features:**

1. GC content total: the total appearances of nucleotides 'G' and 'C' divided by the length of the sequence.
2. Gc\_content\_1: GC content of first codon position, affects gene expression ( a measure of codon usage bias).
3. Gc\_content\_2: GC content of second codon position.
4. Gc\_content\_3: GC content of third codon position.
5. Molecular\_weight: Molecular weight of the protein
6. MeltingPoint\_NN: Melting point of the protein. Calculation based on nearest neighbor thermodynamics. Several tables for DNA/DNA, DNA/RNA and RNA/RNA hybridizations are included. Correction for mismatches, dangling ends, salt concentration and other additives are available in this module.
7. Local\_complexity\_ coefficient\_simple: Local compositional complexity is a numerical measure of repetitiveness of sequences of symbols from a finite alphabet. Highly repetitive sequences are considered simple, whereas highly non-repetitive sequences are considered complex.
8. Percent\_ duplicate\_dna: Calculates the percentage of two-letters duplications in a sequence (two following letters are identical).

Protein analysis:

**Source:** [Biopython library, package SeqUtils, Module ProtParam](http://biopython.org/DIST/docs/api/Bio.SeqUtils.ProtParam.ProteinAnalysis-class.html).

The “ProteinAnalysis” class contains methods for simple protein analysis.

**Features:**

1. Aromaticity: calculates the relative frequency of aromatic amino-acids (called aromaticity) according to Lobry, 1994 [13]. It is simply the relative frequency of Phe+Trp+Tyr. In the context of genetic stability, aromatic amino-acids represent a group of amino-acids which frequency is highly variable among proteins. An interpretation is that the biosynthesis of these aminoacids is expensive for the cell, so that there is a selective pressure to reduce the aromaticity of proteins. The fact that these aminoacids are rare is consistent with this hypothesis. However, these amino-acids do not completely disappear, so that there should be an inverse tendency to maintain them in proteins. This inverse tendency could be attributed either to a simple mutational drift or more likely to a selective advantage due to a contribution to the stabilization of the three-dimensional structure of the protein.
2. Isoelectric\_point: calculates the isoelectric point of the protein, which is defined as the pH at which the protein carries no net electrical charge or is electrically neutral in the statistical mean.
3. Helix\_perc: calculates the percentage of Alpha-helix secondary structure in the protein. Amino acids in helix: V, I, Y, F, W, L
4. Turn\_perc: calculates the percentage of Turn secondary structure in the protein. Amino acids in Turn: N, P, G, S.
5. Sheet\_perc: calculates the percentage of beta-sheet secondary  structure in the protein. Amino acids in sheet: E, M, A, L.
6. molar\_extinction \_coefficient\_reduced: A measurement of how strongly a protein with reduced cysteines absorbs light at a given wavelength, per molar concentration.
7. molar\_extinction \_coefficient\_oxidized: A measurement of how strongly a protein with disulfide bridges and cystine residues (oxidized form, Cys-Cys-bond) absorbs light at a given wavelength, per molar concentration.

Chromosomal location analysis (folder: length analysis)

**Source:**  [Yeastmine search engine](http://yeastmine.yeastgenome.org/yeastmine/begin.do)

Tab: GENOME.

Query: Gene >> Chromosomal location.

Gene list:  ALL\_Verified\_Uncharacterized\_Dubious\_ORFs

A table containing 6,600 genes of yeast and their chromosomal coordinates (location on the chromosome). We used this data to calculate the distance of each gene from the nearest and farthest chromosome end. For that purpose, we combined this data with [data of chromosome length from NCBI](http://www.ncbi.nlm.nih.gov/genome/?term=Saccharomyces%20cerevisiae%5bOrganism%5d&cmd=DetailsSearch%5d).

**Features:**

1. Gene length (in nucleotides): defined as the difference between the end and start coordinate of the gene.
2. Gene distance to the nearest end of chromosome (in nucleotides)
3. Gene distance to the farthest end of chromosome (in nucleotides)
4. Gene distance to the nearest end of chromosome (in percentage from total chromosome length)
5. Gene distance to the farthest end of chromosome (in percentage from total chromosome length)

STRING features: Protein-Protein Interaction Networks

**Source:** [STRING website.](http://string-db.org)

The data we used (version 10.5, organism Saccharomyces cerevisiae): [Link](http://version-10-5.string-db.org/download/protein.links.v10.5/4932.protein.links.v10.5.txt.gz)

The STRING database [14] aims to collect and integrate information regarding functional interactions between expressed proteins, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. The 2017 version introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution

The data file we used includes protein network data of Saccharomyces cerevisiae (scored links between proteins). The score is the weight of the interaction between the proteins.

**Feature:** For each protein we extracted its rank, which is defined as the number of interactions the protein is involved in.

Step 3: Feature standardization

In order to standardize the features, we performed feature normalization followed by the filling of missing values with a representative value: We applied standard-score normalization to the features (minus the average, divided by STD), since the database is largely sparse.

Empty features' values were filled with the average feature value.

Step 4: Convergence: K-means

As mentioned above, the purpose of this model is to select 10 inviable genes that are as diverse as possible, in order to understand which features helped maintain genomic stability of GFP or RFP proteins that are transcriptionally interlocked to it.

To achieve this goal, we would like to represent the inviable genes space as 10 groups (clusters) that are as distinct as possible, and with as much similarity within each of the 10 groups. This dividing process is called clustering, and can be accomplished through two main approaches:

1. Partitional clustering approach (left image): division of data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset.
2. Hierarchical clustering (right image): A set of nested clusters organized as a hierarchical tree.

To conclude, in our model each gene is represented as a single dot in the multi-dimensional feature-space, and we would like to cluster them into 10 representative subsets.

![](https://static.igem.org/mediawiki/2020/b/bb/T--TAU_Israel--Engineering_Figure_12.png)

# References (APA)

[1] Blazejewski, T., Ho, H. I., & Wang, H. H. (2019). Synthetic sequence entanglement augments stability and containment of genetic information in cells. Science, 365(6453), 595-598.‏

[2] Manber, U., & Myers, G. (1993). Suffix arrays: a new method for on-line string searches. siam Journal on Computing, 22(5), 935-948.‏

[3] Zur, H., & Tuller, T. (2015). Exploiting hidden information interleaved in the redundancy of the genetic code without prior knowledge. Bioinformatics, 31(8), 1161-1168.‏

[4] Weill U, Yofe I, Sass E, Stynen B, Davidi D, Natarajan J, Ben-Menachem R, Avihou Z, Goldman O, Harpaz N, Chuartzman S, Kniazev K, Knoblach B, Laborenz J, Boos F, Kowarzyk J, Ben-Dor S, Zalckvar E, Herrmann JM, Rachubinski RA, Pines O, Rapaport D, Michnick SW, Levy ED, Schuldiner M. (2018) Genome-wide SWAp-Tag yeast libraries for proteome exploration. Nature methods, DOI: 10.1038/s41592-018-0044-9. [Pdf](http://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_eb01667b976f4728bcafdd1b8de007cb.pdf).

[5] Yofe I, Weill U, Meurer M, Chuartzman S, Zalckvar E, Goldman O, Ben-Dor S, Schütze C, Wiedemann N, Knop M, Khmelinskii A, Schuldiner M. (2016) One library to make them all: streamlining the creation of yeast libraries via a SWAp-Tag strategy. Nature methods, DOI: doi.org/10.1038/nmeth.3795. [Pdf](http://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_eae6d15f80a84f9f972c5010c7c55895.pdf).

[6] Roberts, R. J., & Macelis, D. (n.d.). Monthly Formats. Retrieved from Rebase: <http://rebase.neb.com/rebase/rebase.files.html>

[7] CFI. (n.d.). Pareto Efficiency. Retrieved from CFI (TM): https://corporatefinanceinstitute.com/resources/knowledge/economics/pareto-efficiency/

[8] Breker M., Gymrek M. & Schuldiner M. (2013) A novel single-cell screening platform reveals proteome plasticity during yeast stress responses. Journal of Cell Biology, DOI: 10.1083/jcb.201301120. [Pdf](http://www.weizmann.ac.il/molgen/loqate/sites/molgen.loqate/files/j_cell_biol-2013-breker-839-501.pdf).

[9] Breker M., Gymrek M., Moldavski O. & Schuldiner M. (2013) LoQAtE—Localization and Quantitation ATlas of the yeast proteomE. A new tool for multiparametric dissection of single-protein behavior in response to biological perturbations in yeast. Nucleic acids research, DOI: 10.1093/nar/gkt933. [Pdf](http://www.weizmann.ac.il/molgen/loqate/sites/molgen.loqate/files/nucl._acids_res.-2013-breker-nar-gkt933.pdf).

[10] Nadler M\*., Breker M\*., Gruber R., Azia A., Gymrek M., Eisenstein M., Willison K.R., Schuldiner M. & Horovitz A. (2012) Interactions of subunit CCT3 in the yeast chaperonin CCT/TRiC with Q/N-rich proteins is revealed by high-throughput microscopy analysis. PNAS, DOI: 10.1073/pnas.1209277109. [Pdf](http://www.weizmann.ac.il/molgen/loqate/sites/molgen.loqate/files/pnas.pdf).

[11] Wall D, Hirsh A, Fraser H, Kumm J, Giaever G, Eisen M, Feldman M. (2005) Functional genomic analysis of the rates of protein evolution. PNAS, DOI: 10.1073/pnas.0501761102. [Pdf](http://www.pnas.org/content/pnas/102/15/5483.full.pdf).

[12] Coghlan A, Wolfe KH. (2000) Relationship of codon bias to mRNA concentration and protein length in Saccharomyces cerevisiae. Yeast. DOI:10.1002/1097-0061(20000915)16:12<1131::AID-YEA609>3.0.CO;2-F. [Pdf.](http://onlinelibrary.wiley.com/doi/pdf/10.1002/1097-0061%2820000915%2916%3A12%3C1131%3A%3AAID-YEA609%3E3.0.CO;2-F)

[13] Lobry, J. R., & Gautier, C. (1994). Hydrophobicity, expressivity and aromaticity are the major trends of amino-acid usage in 999 Escherichia coli chromosome-encoded genes. Nucleic acids research, DOI:10.1093/nar/22.15.3174. [Pdf](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC310293/pdf/nar00039-0320.pdf).

[14] Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., Jensen, L. J., & von Mering, C. (2017). The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic acids research, 45(D1), D362–D368. DOI: 10.1093/nar/gkw937. [Pdf](http://watermark.silverchair.com/gkw937.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAlYwggJSBgkqhkiG9w0BBwagggJDMIICPwIBADCCAjgGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMLpMlQqVDoto1l5uEAgEQgIICCQUh8BWkRY-sgBetj01AP3cTFdXezIP8ovrBi550Y-Q_EjNnl_QTrn1z4u5HCAFtVl7ntWfxepmFkoB-pYvYo5C-W7h2YCuc7uqQECFadsg5g2iYltascy6l--3KIrNR0KqYDhAyxTxcUPj5F2DYXcLLnTVdCvi-qvKQEBHj6iNvovK_dKvcGZ7w_2ZHQyWcsvp4mEDBtCPaOFVh9g2kmyU0ZaCgETEhD08DH0xWWTH25Yg1a_O_jr5fO76SmiIFBmpQWc9Y1IoA3aWQN8-aaSqeNaLaWlH12WqvpyvKjXW-OUWcapHo1SnSqOrXE7ufjtWypqgyP0tyF8FmnwycXgXv09oDjtgNsM1vSCPdfCKWxS8KxJPz8CAI7gkeIgyF3KkPEFynnN1aaWocPQ7u5fg83olbMKiIyUa_L59LrSV7qCTxA4havX2AG_e2G3xSIZ5izWw6pC6kxdxNhs3t-B80uCcZ1rF9l6why-qZ6jTXRBmQkJ1Av-QB1pQI87Y3ypNnwBtd5rjRXiLfQPXFiuXmuBKWk5aNCQOmZBvVHzCXbroPl2mOXxFbiQLv3Gzh1N30LFaKwkVauMXRHJUKh10wMdjQDPdxNDh-hqkwCL3j5RHxS2L6fMtizv9E7X-VNn7JYWQieTOTfaiVx_8Bp15gUEnGTxvrs3_x8-se_ZLx6E84bOxcv-CZ).

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# Team:TAU Israel/Experiments

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Project

[Description](https://2020.igem.org/Team%3ATAU_Israel/Description)
[Design](https://2020.igem.org/Team%3ATAU_Israel/Design)
[Implementation](https://2020.igem.org/Team%3ATAU_Israel/Implementation)
[Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments)
[Results](https://2020.igem.org/Team%3ATAU_Israel/Results)
[Measurement](https://2020.igem.org/Team%3ATAU_Israel/Measurement)
[Proof of Concept](https://2020.igem.org/Team%3ATAU_Israel/Proof_Of_Concept)

Modelling

[Engineering](https://2020.igem.org/Team%3ATAU_Israel/Engineering)
[Model](https://2020.igem.org/Team%3ATAU_Israel/Model)
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[Notebook](https://2020.igem.org/Team%3ATAU_Israel/Notebook)
[Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols)
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HP

[Integrated Human Practices](https://2020.igem.org/Team%3ATAU_Israel/Human_Practices)
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☰

# Experiments

# Evolution experiment – what does it mean?

In an evolution experiment, a particular genetically modified strain (or several strains in parallel) that expresses an inserted construct is grown over time. An expression measure is defined beforehand, for example, a fluorescent reporter gene in the construct. The evolutionary stability of that construct is measured at different time points, as individuals/populations adapt to new environmental conditions by natural selection. If the pre-defined measure indicates a significant decrease or a total disappearance of the construct from the population (depending on its location, viability, and other features), the experiment is aborted.

Based on the defined parameter, one can conclude the evolutionary half-life: the amount of time that the construct's frequency in the population is reduced by half.

We have conducted two evolution experiments to prove our hypothesis and gather mutational data.

**Our experiments** included measuring genetic stability of constructs composed of an exogenous gene (that adds no value to the fitness) attached to the "N" terminus of an endogenous open reading frame (ORF) (figure 1). We conducted two separate experiments: The Gene-SEQ experiment and the POC experiment (10 gene experiment).

# Libraries

For each experiment we used two unique Saccharomyces cerevisiae libraries, a GFP- and an RFP-based, provided by Prof. Maya Schuldiner from the Weizmann Institute. These libraries contain about 6000 yeast variants, one for each ORF in the yeast genome, with a distinct promotor per library. Each variant has a fluorescent gene (either GFP or RFP) fused to the N-terminus of a different gene[1]. [![link to library PDF](https://static.igem.org/mediawiki/2020/f/fa/T--TAU_israel--pdf_logo.jpg)](https://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_eae6d15f80a84f9f972c5010c7c55895.pdf)

This means the library contains a variant with a fluorescent-tagged gene for every known yeast gene. The fluorescent protein-ORF conjugate is translated together, resulting a fused protein.

We used these libraries as a basis for both our experiments since they allowed us to conveniently test our hypothesis and produce our Gene-SEQ measurement protocol. This was possible due to the unique design of the library constructs - As you can see in figure 1, each variant contains a construct composed of GFP/RFP, a linker, and a specific ORF. This design fits our proposed idea - to interlock a target gene (in this case, GFP or RFP), to an essential gene of the host organism. Thus, we can use the libraries to collect empirical data for our models and understand the behavior of such constructs in evolution experiments.

[![SWAT library pic](https://static.igem.org/mediawiki/2020/e/e1/T--TAU_israel--SWATpic.jpg)](https://mayaschuldiner.wixsite.com/schuldinerlab/s-w-a-t)
![physical libraries](https://static.igem.org/mediawiki/2020/c/cb/T--TAU_israel--physical_libraries.jpg)
**Figure 1.** The libraries (right) and construct (left) obtained from Prof. Schuldiner's lab.

# Proof of Concept (POC) Experiment

This experiment also referred to as the "10-gene experiment", was conducted as a **preliminary proof of concept**.

**Our hypothesis** hypothesis is that by interlocking a target gene upstream to an essential gene will prolong the evolutionary half-life of the target gene. However, each target gene can be linked with a limited number of essential genes, regarding the conjugate’s folding and translation. Those protein conjugates need to be as stable as possible and this could be achieved through tailoring target and essential linkage.

This experiment had **two main goals**:

1. Validate that fusion to an essential gene likely prolongs a target gene’s evolutionary half-life, in comparison to the target gene alone (as the negative control for the stability).
2. Demonstrate that different essential genes provide varying stability levels.

For this purpose, we compared the evolutionary half-life of ten diverse constructs against the half-life of the unattached target gene, by measuring their **relative preservation of fluorescence, which is used as a measure of stability**.. Each construct from the ten constructs that were tested is derived from the GFP library described above, thus it is composed of GFP, a fixed linker, and an essential gene. The GFP serves as the target gene for this experiment.

We chose the 10 essential genes based on a hierarchical clustering algorithm and many descriptive features we created to characterize the genes and define their "diversity". The selection procedure is described in the [Engineering Success](https://2020.igem.org/Team%3ATAU_Israel/Engineering) Page.

![](https://static.igem.org/mediawiki/2020/f/f8/T--TAU_Israel--CEG1_GFP_benchling.svg)
**Figure 2.** One of the 10 selected genes for the experiment, with the sfGFP (lilac) attached to its N terminal, the NOP1 promoter (orange) and the selective marker.

**Measurement Considerations**

We grew triplicates of each construct separately, where the unattached GFP was the negative control for stability. Every day, we repeatedly transferred them to fresh media for overnight growth, measuring fluorescence and optical density (OD) for 180 generations.

In every ongoing evolution experiment, which spans over days, weeks and even months, the problem of **sterility** arises. Therefore, in the 10-gene experiment that lasted for months, we had to work in a very rigorous and sterile method in a biological hood at all times.

Handling contaminations:  To make sure that our biological repeats did not become contaminated, we transferred samples from the previous day to another plate of selective media (lacking amino acids). This media would not allow the library’s yeast growth. If high turbidity was observed the next day, it was indication that the previous day's experiment went wrong, and we would have to abort the experiment, to avoid dragging the mistake into the next day. To prevent starting the experiment all over again in case of contamination, we froze samples from each day that would serve as a backup when necessary.

Throughout the experiment, we measured fluorescence values and turbidity **accurately and uniformly**. This was achieved by eliminating possible variables that could disrupt the results, such as:

1. Calibration of the plate reader.
2. Sterile and uniform work by one staff member throughout the whole experiment.
3. Precipitate the yeasts and replacing the old substrate with transparent liquid (PBS) before measuring the fluorescence values, to prevent GFP residues and other proteins and waste in the substrate from interfering with the reading.
4. Perform the experiment at a constant time each day.
5. Use of two controls for the experiment: wild-type (WT) control of the library's background strain (i.e. yeast) to serve as a negative review for the fluorescence test.
   On the other hand, we have transformed the same species with a GFP that is not fused with any gene in order to serve as a positive control for the fluorescence test.

The protocols and results can be seen in their respective pages.

[Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols)
[Results](https://2020.igem.org/Team%3ATAU_Israel/Results)

**Raw Data Analysis**

The triplicates of each of the ten variants were grown in 96-well plates, with three positive controls for fluorescence (GFP alone), and two negative controls for fluorescence (WT). As mentioned, we measured fluorescence and OD for 180 generations. At the same plates, there were also 2 wells with media only, as described in the protocol.

The analyzed raw data consisted of fluorescence and OD measurements for the start (day one) and the end of the experiment, after 180 generations.

The analysis included the following steps:

1. 1. Averaging the fluorescence values of the two media-only wells and reducing the average value from the fluorescence levels of the other wells. This way we take into consideration differences between media in the triplicates since sometimes the liquid itself appears to be turbid regardless of growth or contaminations.
2. Normalization of the reduced values with the OD values of the same strains. The OD reflects the amount of yeast in the media. By normalizing it, we consider the possibility that some strains are reproducing faster than others, thus they will show higher fluorescence without it indicating actual stability.
3. Calibrating the normalized values according to the [plate-reader calibration protocol.](http://www.protocols.io/view/calibration-protocol-plate-reader-fluorescence-cal-6zrhf56?step=21)
4. Normalization of the fluorescence values of the fluorescent variants (10 construct's triplicates and the negative control) with the fluorescence value of the WT. The reason behind this normalization is to take into account inaccuracies of the plate reader. The difference between the WT and the fluorescent variants should remain the same regardless of variations resulting from the plate reader errors.
5. Averaging the reads from the triplicates of each strain, resulting in average fluorescence value for the 10 constructs and the negative control for the first and last day of the experiment.
6. 6. Normalization of the average fluorescence value from the last day with the values from day one. Thus, we receive relative values that indicate the preservation of fluorescence – if this value is high, it means that this variant kept expressing the fluorescent protein.

You can see that the results matched our hypothesis in the Proof of Concept Page!

[Proof of Concept](https://2020.igem.org/Team%3ATAU_Israel/Proof_Of_Concept)

# Gene-SEQ experiment

This experiment is **the first test of our large-scale stability measurement technique**, called Gene-SEQ (Stability Enhancing Quantifier).

It has **two main goals:**

1. Large-scale testing of our hypothesis, that the target-essential gene linkage prolongs the half-life of the target gene.
2. Analyzing the mutational footprints of each construct, in order to eventually predict the preservation of the target gene.

As an evolution experiment that is meant to test the genomic stability of any gene coupling over time, we had to grow the yeast variants for many generations. We could not grow thousands of different variants separately because of time and logistics limitations – this kind of procedure would have required the use of thousands of test tubes, plates, tens of thousands of tips, tens of liters of media, and perishable equipment every day.

Therefore, we decided to grow all the strains together as a "co-culture" – a mix of all strains – in a **reactor/chemostat.** But the existing devices on the market are expensive, large, and cumbersome. Fortunately, **Chi.Bio** was recently developed for exactly this purpose. It is an autonomous, compact, simple-to-use, and even programmable and upgradeable turbidostat (robotic growth platform). This machine monitors OD and Fluorescence (if needed) and transmits the data directly to the computer. It constantly dilutes the culture with fresh media, to always maintain an OD of choice.

![](https://static.igem.org/mediawiki/2020/a/a2/T--TAU_Israel--measurement_3.svg)

**Figure 3.** Chi.Bio illustration. (from [https://chi.bio/](http://chi.bio/))

The device is made of three units:

1. A reactor into which a glass test tube is inserted. That is capable of mixing and heating the solution at a fixed rate and a constant temperature. In addition, the reactor contains sensors for measuring turbidity, fluorescence, temperature and more.
2. A set of peristaltic pumps that attach to the reactor and pump a new substrate into it and an old substrate out of it.
3. A control panel that is connected to the pumps and the reactor and gives commands through a computer.

You can read more about chi-bio in the following [link](http://chi.bio/).

![](https://static.igem.org/mediawiki/2020/8/8d/T--TAU_Israel--chi_bio_gif.gif)
**Figure 4.** The Chi.Bio

Since it is a DIY device, it had to be **adapted to our experiment**. To avoid problems of sterility and leaks, we designed caps with nozzles for both test-tubes and media bottles inspired by the optional cap offered on the chi-bio website. Regarding this design, we would like to thank Ben Oz who designed the caps fully voluntarily according to our needs. In fact, the new caps allowed us total autonomy without having to regularly test the experiment except to change the media every few days.

![](https://static.igem.org/mediawiki/2020/thumb/8/83/T--TAU_Israel--chi_bio_caps.jpeg/450px-T--TAU_Israel--chi_bio_caps.jpeg)
**Figure 5.** The designed caps for the Chi.Bio

The yeast cultures were monitored continuously and were grown in constant turbidity while monitoring fluorescence levels for about 300 generations. Then, we extracted and prepared sequencing samples with our novel Gene-SEQ protocol that is aimed to measure stability in a larger-scale. The protocol and a detailed explanation of our planned data-analysis can be found in our Measurement Page.

[Measurement](https://2020.igem.org/Team%3ATAU_Israel/Measurement)

More protocols and the results of the Gene-SEQ preliminary experiment can be observed in the respective pages.

[Protocols](https://2020.igem.org/Team%3ATAU_Israel/Protocols)
[Results](https://2020.igem.org/Team%3ATAU_Israel/Results)

# references

[1] Yofe I, Weill U, Meurer M, Chuartzman S, Zalckvar E, Goldman O, Ben-Dor S, Schütze C, Wiedemann N, Knop M, Khmelinskii A, Schuldiner M. (2016) One library to make them all: streamlining the creation of yeast libraries via a SWAp-Tag strategy. Nature methods, DOI: doi.org/10.1038/nmeth.3795. [Pdf](http://08a4b8fa-f0e6-42d4-b61f-31e9aaae326f.filesusr.com/ugd/67f9a9_eae6d15f80a84f9f972c5010c7c55895.pdf).

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# Team:TAU Israel/Results

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# Results

# Table of contents

This page includes the following results:

1. [POC experiment](#POC_experiment) - raw data, plate-reader calibration and validation
2. [Restriction enzymes analysis results](#restriction)
3. [Gene-SEQ experiment](#Gene-SEQ) - validation and sequencing results
4. [EFM optimizer](#EFM) - POC
5. [Feature selection for stability predictor](#feature_selection)
6. [Stability predictor performances on a test set](#Predictor)

# POC experiment (or "10 Genes experiment")

Our wet-lab Proof of Concept experiment and its raw-data analysis process is described in the [Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments#poc) page. In short, we compared the evolutionary half-life of ten diverse constructs against the half-life of the unattached target gene (GFP), by measuring their relative preservation of fluorescence, which is used as a measure of stability.

**Validation**

The ten genes experiment was conducted as detailed in the [Lab Notebook](https://2020.igem.org/Team%3ATAU_Israel/Notebook)  and [Experiments](https://2020.igem.org/Team%3ATAU_Israel/Experiments#poc) pages.

Since we used 10 variants from a large-scale library, there is a chance for a mix-up between the variants, meaning that what we think is strain X is in fact strain Y. This could be the result of an error within the library, or a human mistake from our side.

In order to prevent these mistakes, we validated the ten variants using both PCR and sanger sequencing, in detail in the lab notebook. Additionally, to avoid any mix-up that could happen during the evolution experiment itself, which could highly influence our results, we also conducted the same validation process to the strains in the end of the experiment. Altogether, nine out of the ten genes were validated using both PCR and sequencing at the beginning and end of the experiment.

The sequencing results are below.

![](https://static.igem.org/mediawiki/2020/0/0f/T--TAU_Israel--results_figure1.svg)

**Figure 1.** Sanger sequencing results.

**Plate reader calibration**

We followed the [plate-reader calibration protocol](http://www.protocols.io/view/calibration-protocol-plate-reader-fluorescence-cal-6zrhf56?step=21) and recieved the results attached in [**this file**](https://static.igem.org/mediawiki/2020/3/38/T--TAU_Israel--results_calibration.docx).

**Raw Data**

The raw data from the first and last day of the experiment is summarized in the following [zip file](https://static.igem.org/mediawiki/2020/b/b2/T--TAU_Israel--results_poc_raw_data.zip). It contains the plate-reader measurements of fluorescence and OD (absorbance). Following the steps of the raw-data analysis described in the Experiments page, we received the final results that are documented in our POC page. Visit us there and see how we proved our hypothesis!

# Restriction enzymes analysis

For our [Gene-SEQ experiment](https://2020.igem.org/Team%3ATAU_Israel/Measurement#our_experiment) we used DNA constructs composed of a promoter, fluorescent gene (GFP or RFP), a linker and an ORF of the yeast genome (details in measurement page). When we extracted DNA from the cultures, these constructs were embedded in the yeast genome. Thus, we needed to cut these fragments with sticky-ends using restriction enzymes, both before the promoter and as upstream as possible in the conjugated gene (ORF), so that would be enough nucleotides to allow recognition of the conjugated ORF when sequencing.

The analysis is described in our [Engineering Success](https://2020.igem.org/Team%3ATAU_Israel/Engineering#Forth) page. In summary, the analysis is composed of the following steps:

1. Search for a custom designed restriction enzyme.
2. Search for a suitable restriction enzyme from a database of commercially available restriction enzymes.
3. Test pairs of commercial enzymes (instead of a single enzyme) that can work in parallel to create the desired construct.
4. In-depth view of the final candidates to reach a final decision.

The results of each step are summarized in a different file. Finally, 8 result files were extracted – 4 for a GFP target gene, and 4 for an RFP target gene. **All files are available in the following [zip file](https://static.igem.org/mediawiki/2020/0/09/T--TAU_Israel--results_restriction_enzymes.zip).**

**Step 1: Custom restrictor sites**

Filenames: custom\_sites\_gfp/rfp

In these files, custom built sites for restriction enzymes are tested.

All possible custom sites were generated from the respective target gene, up to length 8 bp.

For each custom site, statistics were extracted for all genes in *S. Cerevisiae*.

Columns:

* site: the custom site tested
* median\_total\_length: median of construct length
* percent\_gene\_none\_unique: the percentage of constructs whose sequence is not unique. We aim to minimize this.
* percent\_total\_over\_3000/5000: the percentage of constructs whose length is over 3000/5000. We aim to minimize this as well; in our computations we minimized the 3000 value.
* percent\_gene\_under\_50/25: the percentage of cut ORFs (in the conjugated genes) with length under 25/50.

We filtered the sites presented by the Pareto efficiency principle – a site is discarded if there is another site with smaller percent\_gene\_none\_unique and also smaller percent\_total\_over\_3000.

**Step 2: Commercial enzymes**

Filenames: commercial\_gfp/rfp

In these files, commercial enzymes are tested.

All enzymes from rebase which cut the target gene in a legal region are analyzed.

For each enzyme, statistics were extracted for all genes in *S. Cerevisiae*.

Columns:

* enzyme: which commercial enzyme is tested
* generalized\_site: what site does this enzyme recognize
* overhang\_length: how much overhang does this enzyme create. We aim to maximize this.
* median\_total\_length: median of construct length
* percent\_gene\_none\_unique: the percentage of constructs whose sequence is not unique. We aim to minimize this.
* percent\_total\_over\_3000/5000: the percentage of constructs whose length is over 3000/5000. We aim to minimize this as well; in our computations we minimized the 3000 value.
* percent\_gene\_under\_50/25: the percentage of cut ORFs (in the conjugated genes) with length under 25/50.

We filtered the sites presented by the Pareto efficiency principle – a site is discarded if there is another site with smaller percent\_gene\_none\_unique, smaller percent\_total\_over\_3000, and larger overhang\_length.

**Step 3: Two commercial enzymes**

Filenames: commercial\_2\_enzymes\_gfp/rfp

In these files, pairs of commercial enzymes are tested.

All pairs of enzymes from rebase that meets the following criteria were analyzed:

* At least one enzyme cuts the target gene in a legal location.* None of the enzymes cut the target gene in an illegal location.

For each pair, statistics were extracted for all genes in *S. Cerevisiae*.

Columns:

* Enzyme\_1/2: which commercial enzymes we tested
* generalized\_site\_1/2: what sites do these enzymes recognize
* overhang\_length\_1/2: how much overhang do these enzymes create. We aim to maximize this.
* median\_total\_length: median of construct length
* percent\_gene\_none\_unique: the percentage of constructs whose sequence is not unique. We aim to minimize this.
* percent\_total\_over\_3000/5000: the percentage of constructs whose length is over 3000/5000. We aim to minimize this as well; in our computations we minimized the 3000 value.
* percent\_gene\_under\_50/25: the percentage of cut ORFs (in the conjugated genes) with length under 25/50.

In the case of RFP, we filtered the sites presented by the Pareto efficiency principle – a site is discarded if there is another site with smaller percent\_gene\_none\_unique, smaller percent\_total\_over\_3000, and larger overhang\_length.

In the case of GFP, we realized that it was infeasible to order 2 enzymes due to schedule limitations during the Covid-19 crisis. Thus, the data uploaded was not filtered.

**Step 4: Commercial enzymes: a more in-depth view**

Filenames: commercial\_enzyme\_summary\_gfp/rfp

In these files, each of the final commercial enzymes is presented more in depth.

In order to make a more educated decision regarding which enzyme to choose, we created a clearer view of the final commercial enzymes. This is a PDF with a summary page per enzyme. The page title includes the statistics presented in commercial\_gfp/rfp.

In addition, two graphs were added per enzyme:

1. Total Length for PCR – displays the construct length distribution per enzyme.
2. Group on None-unique Sizes – displays what are the sizes of sets of constructs with the same sequence after applying this enzyme. This helps to estimate the difficulty in differentiating none-unique genes.

An example of the RFP graphs is found below.

Commercial Enzymes Summary RFP

Due to supply difficulties caused by the COVID-19 situation, we had to consider not only the efficiency but also the availability of the chosen enzymes. Finally, **the chosen restriction enzymes are NSP1 for RFP, and DRA1 for GFP**.

If you wish to know how we used them, visit our  [Measurement page!](https://2020.igem.org/Team%3ATAU_Israel/Measurement)

# Gene-SEQ experiment

The Gene-SEQ measurement is conducted using a new protocol that we designed, thus it had to be validated before each new step.

The best way to ensure that we followed the protocol correctly is to prove that we received the desired results from the Nanopore deep-sequencing, by analyzing the sequencing data. Unfortunately, due to Covid-19 related issues, we had some major delays in the sequencing process. We received the results next to the competition deadlines, leaving us with insufficient time to analyze them. As we were concerned that the pandemic could cause such a delay, we planned another experiment to validate our Gene-SEQ method.

Performing PCR on the mixed cultures of the experiment would not serve as a proper validation of the results, because it requires running the samples on gel. Since there are millions of constructs with different sizes, it will be difficult to differentiate between them and to determine that the process was successful.

Thus, while performing the Gene-SEQ protocol on our mixed cultured libraries, as elaborated in Measurement page, we performed the same sample preparation protocol for one of the genes used in the 10 genes experiments – the SEC2 gene. This way, we could validate our method using both PCR and SANGER sequencing - as opposed to performing PCR on the mixed culture, following the same protocol for only one gene is much easier to analyze.

Be verifying the right size in the PCR, and by further validating in SANGER sequencing, we can answer the following questions:

1. Did the restriction enzyme model work?
2. Is our novel protocol valid and working?
3. Is the product of our protocol in an adequate state for sequencing?

The expected size of the PCR should be around 2.5KB, as demonstrated below since Nsp1 enzyme was used in the protocol. As you can see on the right, we received a band in the expected size.

![](https://static.igem.org/mediawiki/2020/8/8a/T--TAU_Israel--results_figure2.svg)

**Figure 2.** PCR results for SEC2, as a validation for the Gene-SEQ protocol.

We then sent the PCR fragments for SANGER sequencing. The sequence was compared against the sequence that it should contain - NOP1 promotor, GFP, and part of SEC2 - using the BLAST tool. The alignment showed a similarity in significance level of 2E-58. This means our protocol is valid and working.

In figure 3 there is an example of the deep sequencing results – one of many FASTQ files as explained in our Measurement page. We are currently analyzing the data, and hoping to get results soon!

![](https://static.igem.org/mediawiki/2020/7/7c/T--TAU_Israel--results_figure3.png)

**Figure 3.** Example of the Nanopore sequencing data in FASTQ format, that is currently being analyzed.

# EFM optimizer - POC

To prove the efficiency and robustness of our [EFM optimizer](https://2020.igem.org/Team%3ATAU_Israel/Contribution), we wanted the check the evolutionary stability of residues marked as unstable by our software.

For this purpose, we calculated conservation score for each nucleotide in many different genes of *Saccharomyces cerevisiae,*, by utilizing ConSurf program [1], developed at Tel - Aviv University. This software creates multiple sequence alignment (MSA) out of all the orthologous genes it can find for a given sequence. Then, it sums the appearance of each nucleotide in a specific position across all the orthologous genes.

A well- conserved nucleotide will appear across all the different orthologs genes, while an evolutionary unstable nucleotide will appear significantly less. The number of times the nucleotide appears across the MSA is counted and calculated so that every nucleotide is given a conservation score.

For this analysis, we selected genes that are evolutionary conserved based on the onservation score, and inserted them in the EFM optimizer. The conservation requirement is crucial for the ConSurf software, because if the gene is not well-conserved the software will not be able to find many orthologous genes or to assign reliable conservation score. We also selected genes with different functions in the cell and in different length.

We calculated the average conservation score at each position for each gene we picked, and compared this value to:

* The average of the lowest conservation score from each area predicted as unstable by our EFM's SSR calculator.
* The average score from all the areas predicted by the RMD calculator.

When addressing SSR we used only the lowest conserved nucleotide. The reason for this is because a mutation mediated by SSR will cause a single nucleotide level event (deletion, insertion of substation), while RMD mediated mutational event will influence all the predicted sites (large scale deletion of insertion).

Conservation score was calculated for Pol30, Pol3, Cdc9, Tor2, Mgs1 and Rad52, all are evolutionary conserved genes from *saccharomyces cerevisiae* that vary in length and function. The significancy of the difference between the conservation of all the genes and the areas marked by the EFM optimizer varied between 0.0016 in Pol30 to 0.0004 in Cdc9.

![](https://static.igem.org/mediawiki/2020/0/00/T--TAU_Israel--results_figure4.png)

**Figure 4.** An illustration of the conservation scores of Pol30 sequence.

In total, we found that there is a significant difference between the conservation score of the entire gene and the conservation scores of the areas predicated by the EFM (whether it is SSR or RMD). The results prove that the EFM software our team has devised successfully predicts areas that are evolutionary unstable and automatically offers a new, optimized sequence with enhanced evolutionary stability.

The optimization process is robust and applied on multiple sequences at once with great ease for the user. We believe that this software is highly beneficial for any IGEM team, lab, or biotechnology company.

# Feature Selection for the Stability Predictor

As described in our [Model](https://2020.igem.org/Team%3ATAU_Israel/Model) page, we developed a predictor of genetic stability, based on many descriptive features we generated for each of the ORFs of yeast.

We ended up with approximately 2,000 features. To reduce the model's dimensionality, we selected a small set from these features based on a heuristic approach that is explained in the "feature selection" sector of the Model page. In short, this small set is composed of 300 features that are highly correlated to the labels (the predicted values), but are less correlated with one another.

The 300 chosen features appear in the file above. The first 40 features are described within this file, so one could get a sense of the importance of some features. The other features are elaborated on the Model page.

Chosen Features

# Stability Predictor Performances on a Test Set

We evaluated the performances of our [stability prediction model](https://2020.igem.org/Team%3ATAU_Israel/Model)  when implemented on a test set. The target values $y\_{true}$ , to which we compare the predicted results, are the fluorescence measurements of each tested variant in Prof. Schuldiner's libraries (referring to GFP and RFP as target genes). The fluorescence predictions resulted from the model are marked $y\_{pred}$ accordingly.

Our metrics of success are: $R^2$, accuracy, and agreement on 20 leading candidates.

**$R^2 $ ("Coefficient of determination"):**

In linear regression, we try to explain the observed target data by a set of features generated for this purpose. We would like to find the linear line that minimizes the squared distance between it and the true data. This squared distance is called the residual sum of squares $u=\sum{(y\_{true}-y\_{pred})^2}$.

However, minimizing u is not enough. Part of the variance in the target data $y\_{true}$ is due to varying features' values, but some of it is due to reasons that are unknown to us. Thus, the variance of $y\_{true}$ can be divided to **explained variance** and **unexplained variance**. Our goal in the regression process is that most of the variance of $y\_{true}$ will be the explained variance, because it will indicate that our features characterize the data efficiently. This explained variance is called $R^2 $ or the Coefficient of determination.

Mathematically, $R^2 $ is defined as:

$$R^2 = 1-\frac{u}{v}.$$

where $u$ is the residual sum of squares and v is the total sum of squares $v=\sum{(y\_{true}-\hat{y\_{true}})^2}$.

This measure denotes the proportion of variance of $y\_{true}$ that has been explained by the features in the model. For example, a constant model that always predicts the expected value of y, disregarding the input features, has no variance. Therefore, it will be assigned with $R^2=0$. Similarly, the best possible score of $R^2 $ is 1.0 and it can be negative (because the model can be arbitrarily worse).

**Accuracy and median diff:**

As mentioned in our Model page, our model is trying to predict fluorescence values from Prof. Schuldiner SWAT database. For each gene in the test set, we calculate the absolute prediction error $e=|y\_{true}-y\_{pred}|$. We then define accuracy as the number of genes with an error within a threshold of 10au, divided by the size of the test set:

$$accuracy = \frac{number-of-genes-with-e<10}{|test-set|}.$$

Also, we define the "median diff" as:

$$ Median-diff = median(e) $$

For example, if the median diff is 5, it means that for about half of the genes in the test set, the prediction error is of 5au. Similarly, if the accuracy is 0.6, it means that the predicted value of about 60% of the genes in the test set is close to the target value within a range of 10au.

**Agreement:**

We use the predictor to rank the co-stability of the genes in yeast when interlocked to a target gene. The ranking will be presented to the users in order to make an intelligent decision regarding the conjugated gene they prefer.

Thus, the most important measure for our software is the agreement, indicating the predictor's agreement with the original ranking of the leading candidates.

For the GFP test set, the results are:

|  |  |  |  |
| --- | --- | --- | --- |
| R-sqaure | Accuracy | Median diff | Agreement on 20 leading candidates |
| 0.5499 | 0.6487 | 5.63 | 0.55 |

The agreement graph:

![](https://static.igem.org/mediawiki/2020/1/13/T--TAU_Israel--predictor_results.svg)
**Figure 5.** Predicted ranks of the leading candidates in comparison to the ground-true ranks.
For example, the gene that was originally ranked as 2nd- is predicted to be the 5th best gene.

As you can see, 11 genes that originally showed high fluorescence levels are also predicted to be of 20 leading candidates.

In order to test the generalization of our model, we also tested the performances on RFP as a new target gene. For this purpose, we used only the agreement measure since it best reflects the suitability of the model to our end goal – recommend the best candidates for increasing the target gene's stability. We received agreement on 4 out of 10 leading candidates!

We believe that following the [analysis of the sequencing data from our Gene-SEQ experiment](https://2020.igem.org/Team%3ATAU_Israel/Measurement#data_analysis), and its integration to the prediction model, we will be able to further improve the results.

# References

[1] Ashkenazy H., Erez E., Martz E., Pupko T. and Ben-Tal N. 2010. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids.
*Nucl. Acids Res.* 2010; DOI: 10.1093/nar/gkq399; PMID: 20478830 [[ABS]](http://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkq399), [[PDF]](http://nar.oxfordjournals.org/content/38/suppl_2/W529.full.pdf)