iGEM 2023 — Team iit-madras

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each
day for your project.{% endblock %}
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# Lab Notebook

## Dry Lab

### Expression Prediction and RBS optimization

**June**

|  |  |
| --- | --- |
| Week 1 | * Recruitment of the team completed * Review of the original work by Salis et al. for better technical understanding of the problem   statement * Review of other work on predictive design of ribosome binding sites - thermodynamic models, kinetic   models, stochastic models, machine learning models |
| Week 2 | * Review of other work on predictive design of ribosome binding sites - thermodynamic models, kinetic   models, stochastic models, machine learning models * Installing the ViennaRNA library for RNA sequence analysis using Python * Downloading a dataset of 1014 RBS sequences with experimentally determined expression levels from   Reis   and   Salis (2020) |
| Week 3 | * Defining the interaction between the rRNA and mRNA so as to have minimum hybridization energy * Including the effect of spacing between the SD sequence and the start codon * Defining the interaction between the rRNA and the ribosomal S1 protein * Accounting for the transient regional folding of the mRNA that can hinder RBS accessibility |
| Week 4 | * Including the presence of standby sites upstream of the SD sequence to which the ribosome can bind   if   the   SD sequence is transiently folded * Accounting for the effect of various start codons on translation efficiency by using data from Hecht   et   al. (2017) * Training and testing different machine learning models to see which works best for the data at hand |

**July**

|  |  |
| --- | --- |
| Week 1 | * Downloading a dataset of 16779 RBS sequences characterised by FlowSeq from Reis and Salis (2020) and   testing the model on it * Reading up on different optimization algorithms that can be used to "mutate" an RBS sequence in   silico   in order to achieve a desired expression level |
| Week 2 | * Simulated annealing and gradient descent optimisers were created and tested |
| Week 3 | * Optimiser performance was tested by varying its intrinsic parameters, work is ongoing * A partially working genetic algorithm was created, work is yet to be finished on this * The two working optimisers were used to generate RBS sequences with varying expression levels. These   sequences will be used in the wet lab using GFP-expressing Lactococcus lactis to see if the   optimization   was successful. |
| Week 4 | * All India iGEM Meet |

**August**

|  |  |
| --- | --- |
| Week 1 | * Genetic algorithm completed. Found to have the best performance. |
| Week 2 | * Attempted to combine different aspects to the Optimization algorithms. * Started work on adding new parameters to the model to improve accuracy of predictions. Literature   review on RNA folding kinetics and RNA degradation was initiated. |
| Week 3 | * Literature review continued |
| Week 4 | * Starting working with the Kinfold library of ViennaRNA to find the folding time. There was a need to   integrate command line operations with Python code. |

**September**

|  |  |
| --- | --- |
| Week 1 | * Kinfold used to generate folding time data for the dataset. * Prototype for degradation model built |
| Week 2 | * Folding kinetics and degradation did not improve model performance. * Model finalised without using these parameters. |

### Probiotic Treatment For Homocystonuria

**June**

|  |  |
| --- | --- |
| Week 1 | * Recruitment of the team completed * Installed and initialized MATLAB and SimBiology. Understood how to use SimBiology for solving ODEs   and running mass kinetic models |
| Week 2 | * Initial literature review needed before starting off with the modelling |
| Week 3 | * Identified the genes that must be inserted in *L. lactis* to help it successfully convert cysteine to   methionine and identified which gene must be knocked out. * Initial transcription modelling process started off by modelling the binding of the RNA polymerase   to the transcription factor |
| Week 4 | * Completed transcription modelling by modelling the rate of transcription by the RNA polymerase. * Started off with the translation modelling by modelling the binding of the Ribosome   to the RBS of the mRNA |

**July**

|  |  |
| --- | --- |
| Week 1 | * Completed translation modelling by modelling the rate of translation by the ribosome binding site. |
| Week 2 | * Literature review for what kill switches are ideal for the project |
| Week 3 | * Made a list of all possible kill switch designs that can be used. Identified that a glucose   concentration based kill switch is ideal in this case. * Started off with identifying the genetic circuit for the kill switch |
| Week 4 | * All India iGEM Meet |

**August**

|  |  |
| --- | --- |
| Week 1 | * Identified the most efficient promoter which is induced by the glucose concentration difference   between stomach an small intestine. |
| Week 2 | * With the kill switch circuit confirmed, we started off modelling the circuit on SimBiology |
| Week 3 | * Modelled the conversion of homocysteine to methionine by the engineered enzyme in *L. lactis* |
| Week 4 | * Compiled all the small processes modelled to obtain the final model for   the system |

## Wet Lab

**August**

18 Aug

* Randomized primers for both UTR regions received.
* Lyophilized primers made to 100uM conc and resuspended. Dilutions of 10uM are made.

![](https://static.igem.wiki/teams/4931/wiki/wetlab.png)

Sequences are referred to by serial numbers

Optimization Algorithms used to generate the sequence:

SA - Simulated Annealing

GD - Gradient Descent

GA - Genetic Algorithm

H, L = High, Low Expressing

19 Aug

* **Colony PCR (Attempt 1) :** Template solution was made by picking colonies and dispersing in aq medium.

21 Aug

* **Gel Check :** All PCR products ran on agarose gel; 6, 8, 13, 16 amplified

23 Aug

* **Colony PCR (Attempt 2)** (unamplified sequences) : Reduced annealing temperature to 54, 53
* **Gel Check :**
  + 9, 10, 15: Strong bands
  + 7, 11: weak bands

24 Aug

* **PCR Attempt 3 (All Samples) :** 1uL pure plasmid taken as template

25 Aug

* **Gel Check :** 4, 5, 7, 8, 9 14 Not amplified

27 Aug

* **PCR Attempt 4 (Unamplified samples) :** Reduced annealing temperature to 53, 52
* **Gel Check :** only 9 Amplified

28 Aug

* **PCR Attempt 5 (Unamplified samples) :** Touchdown PCR 56-52C
* **Gel Check :** 4, 5 Unamplified

30 Aug

* **PCR Attempt 6 (Unamplified samples) :** 50uL reaction separated into 2 tubes of 25uL
* **Gel Check :** both fragments (4, 5) amplified

* **All fragments have been amplified!**

31 Aug

* 0.4uL Dpn1 added to all samples
* **Gel Extraction :** (Qiagen Kit protocol)
  + Plasmid conc :
    - 3,4,5,6,7,8,9,10 - around 10ng/uL.
    - 11,12,13,14,15,16 - around 30-40 ng/uL
    - 5 - 2ng/ul

**September**

2 Sep

* Transformation + Plating

+ Electroporation was done for all the samples in L. lactis competent cells.
+ Samples were incubated for 1hr after which they were spread plated and incubated for 24 hours.

3 Sep

* Colonies in all plates except samples 4 and 14. 10 colonies were observed in plate 3.
* Colonies were picked and cultured overnight. (2 cultures from each non-randomized sample, all 10 cultures of
  each colony from the randomized sample 3.)

4 Sep

* Glycerol added and strains stored in -80C

5 Sep

* Transformation and plating (Attempt 2) (Samples 3,4,14) (Working to obtain more strains of randomized sample
  3)

6 Sep

* No colonies observed

8 Sep

* Transformation and plating (Attempt 4) (Samples 3,4,14)

9 Sep

* No colonies observed

11 Sep

* Transformation and plating (Attempt 5) (Samples 3,4,14) (Slightly reduced antibiotic concentration)

12 Sep

* Golden Gate Assembly set up from left over plasmid for 3,4,14

14 Sep

* Transformation and plating (Attempt 6) (Samples 3,4,14)

15 Sep

* 1 colony for sample 3. None for 4 and 14.

17 Sep

* Transformation and plating (Attempt 7) (Samples 3,4,14)

18 Sep

* 2 colonies in sample 3. None for 4 and 14.

21 Sep

* All strains (13 variants from randomized sample 3, 11 model generated sequences) streaked on agar plates

22 Sep

* Colonies picked and Overnight 1ml cultures made and incubated for 14 hrs

23 Sep

* Fluorescence Assay
* **First batch of results obtained.**

25 Sep

* Colonies picked from streaked plates and cultured overnight.

26 Sep

* Plasmid isolation (Qiagen Kit protocol)
* Plasmid concentration obtained were very poor.

**October**

2 Oct

* All strained streaked from glycerol stocks.

3 Oct

* Colonies were picked from streaked plates and cultured overnight.

4 Oct

* Plasmid isolation (Qiagen Kit protocol).

5 Oct

* Samples sent for sequencing.

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{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a part of your project by going through at least one iteration of the
engineering design cycle.{% endblock %}
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# Engineering Success

**WET LAB**

In the lab, designing each minor experiment itself is an experience that takes one through the DBTL cycle. As
outlined in our Lab Notebook, even staple molecular biology techniques like PCR and electroporation can prove to
be challenging when approaching it for the first time with new organisms and parts to test.

The first wall we hit in our experimental journey was the amplification of our ordered sequences. After
thoroughly checking the primers and matching them with the plasmid we were using, we were dejected to get our
amplified products. Our first thought was to reduce the annealing temperature since the temperature calculated
from the sequence information could have a slight deviation. After multiple iterations of this, we were able to
get satisfactory results only by implementing touchdown PCR with a range of annealing temperatures. Finally, we
were able to obtain all the amplified products by reducing our reaction volume which would have aided in better
conduction of heat during the process.

Our next hurdle was the electroporation. Electroporation is shown to be the standard approach in transforming
our recombinant DNA into *L. lactis*. However, we weren't able to obtain many colonies after the first attempt.
This could have been due to the presence of chemicals in the cuvettes after disinfection from previous attempts,
which could have degraded DNA. But even after thorough washing steps, we were able to only add 3 strains to our
initial library of 10 strains. This issue is something we continued to struggle with as we couldn't successfully
obtain variants of the larger natural RBS sequence that we randomized.

Despite these setbacks, we were able to generate an RBS library with sufficient variation for further metabolic
engineering needs. This also shows that apart from the large-scale hypothesis testing-based DBTL cycles that one
goes through in research, there are also the everyday decisions to contend with on how to run experiments and
troubleshoot when things don't go your way. Being able to produce results both in the wet lab and dry lab and in
the process learning how to circumvent/solve problems of daily research work is our engineering success.

**MODEL TRAINING AND TESTING**

DBTL Iteration 1

We designed and trained commonly used regression-based machine learning models such as a Linear Regressor, a
Support Vector Machine Regressor, a k-Nearest Neighbour Regressor, and a Random Forest Regressor. We did not
tune their hyperparameters and just measured their base performance. On testing these models, we found that the
Random Forest performs best.

DBTL Iteration 2

We then compared the Random Forest to other ensemble-based models that use "boosting" techniques, such as an
Adaptive Boosting Regressor and a Gradient Boosting Regressor. Suprisingly, the Random Forest still performed
better, despite the use of boosting in the other models.

DBTL Iteration 3

We finally fine-tuned the hyperparameters of the Random Forest Regressor by performing grid search
cross-validation. We found that the combination of parameters that work best were max\_depth=80, max\_features=3,
min\_samples\_leaf=3, min\_samples\_split=8, n\_estimators=50 for random\_state=23. We compared the performance of our
model against already existing ones, which showed very favourable results. This comparison is described in
detail under the 'Software' page.

Thus, through the Design-Build-Test-Learn methodology, we identified the best performing machine learning to
predict the relative expression of a gene given data about the chassis, temperature and the sequences of the RBS
and the coding region.

**OPTIMIZATION ALGORITHM**

We employed the same strategy to evaluate different optimization algorithms that can be used to modify the RBS
sequence to achieve a desired relative expression. We designed, implemented, and tested a Gradient Descent
optimizer, a Simulated Annealing optimizer, and a Genetic Algorithm optimizer. Our testing indicated that the
Genetic Algorithm yielded the most favorable results among the three models.

This strategy encompassed the following steps:

1. Design: The formulation of each optimization model, such as Gradient Descent, Simulated Annealing, and
Genetic Algorithm, tailored to the specific requirements of the protein expression optimization task.

2. Build: The designed models were implemented into code, thus translating the theoretical constructs
into functional algorithms.
For Gradient Descent, we adjusted learning rates and convergence criteria, while for Simulated Annealing,
temperature schedules and acceptance criteria were carefully optimized. In the case of the Genetic Algorithm, we
fine-tuned parameters like population size, mutation rates, and selection strategies.

3. Test: We rigorously tested the optimization algorithms, considering time efficiency and correlation
with target expression levels. Simulations were conducted to compare their performance. The Genetic Algorithm
emerged as the optimal choice due to its efficient resource utilization, high correlation with target levels,
and consistent performance across various scenarios.

4. Learn: The results obtained from this Design-Build-Test process indicated that the Genetic Algorithm
produced the most favorable results among the three algorithms. This suggested to us that, in the context of
optimizing gene expression, the Genetic Algorithm demonstrated superior performance in reaching the desired
target, leading us to select it as the main optimization algorithm.

This thorough and systematic approach allowed us to gain a comprehensive understanding of the strengths and
weaknesses of each algorithm in the context of protein expression optimization. It also highlighted the
adaptability and robustness of the Genetic Algorithm in achieving the desired protein expression targets.

Our optimization algorithm was put to a final test by generating different RBS sequences for GFP expression in *L.
lactis*. This were then implemented in the wet lab. The results of this testing reflected a good differentiation
between high and low expressing RBS variants. The results are described in detail under the 'Results' page.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
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# Experimental Design

![First Image for ED page](https://static.igem.wiki/teams/4931/wiki/ed1.png)

![Second image of ED page](https://static.igem.wiki/teams/4931/wiki/ed2.jpg)

![Third Image for ED page](https://static.igem.wiki/teams/4931/wiki/ed3.jpg)

Plasmid vector being used : **pNZ8148**

# Protocols

## PCR

![Third Image for ED page](https://static.igem.wiki/teams/4931/wiki/ed4.png)

**Intermediate steps repeated for 35 cycles**

**Mix:**

* Template solution - 10uL
* FWD primer - 2.5uL
* Rev primer - 2.5uL
* HF buffer - 10uL
* dNTPs - 1uL
* Polymerase - 0.5uL
* dH2O - 23.5 uL

**Total - 50uL**

## Golden Gate Assembly

* Fragment + dH20 -> 16uL
* Bsa1-HF -> 0.5uL
* T4 Ligase -> 0.5uL
* T4 Ligase Buffer - 2uL

**Run :**

* 37C -> 5min
* 37C -> 5min
* 16C -> 5min
* Repeat above 2 steps for 30 cycles
* 60C -> 15min

## Gel Extraction: QIAquick® Gel Extraction Kit

1. Excise the DNA fragment from the agarose gel with a clean, sharp scalpel.
2. Weigh the gel slice in a colorless tube. Add 3 volumes of Buffer QG to 1 volume of gel (100
   mg gel ~100 μl). Note: For >2% agarose gels, add 6 volumes of Buffer QG.
3. Incubate at 50°C for 10 min (or until the gel slice has completely dissolved). Vortex the
   tube every 2–3 min to help dissolve the gel. After the gel slice has dissolved completely, check that the color of
   the mixture is yellow (similar to Buffer QG without dissolved agarose). If the color of the mixture is orange or
   violet, add 10 μl of 3 M sodium acetate, pH 5.0, and mix. The mixture turns yellow.
4. Add 1 gel volume of isopropanol to the sample and mix.
5. Place a QIAquick spin column in a provided 2 ml collection tube or into a vacuum manifold.
   To bind DNA, apply the sample to the QIAquick column and centrifuge for 1 min or apply vacuum to the manifold
   until all the samples have passed through the column. Discard flow-through and place the QIAquick column back into
   the same tube. For sample volumes >800 μl, load and spin/apply vacuum again.
6. If DNA will subsequently be used for sequencing, *in vitro* transcription, or microinjection,
   add 500 μl of Buffer QG to the QIAquick column and centrifuge for 1 min or apply vacuum. Discard flow-through and
   place the QIAquick column back into the same tube.
7. To wash, add 750 μl of Buffer PE to the QIAquick column and centrifuge for 1 min or apply
   vacuum. Discard flow-through and place the QIAquick column back into the same tube.
8. Place the QIAquick column into a clean 1.5 ml microcentrifuge tube.
9. To elute DNA, add 50 μl of Buffer EB (10 mM Tris·Cl, pH 8.5) or water to the center of the
   QIAquick membrane and centrifuge the column for 1 min. For increased DNA concentration, add 30 μl of Buffer EB to
   the center of the QIAquick membrane, let the column stand for 1 min, and then centrifuge for 1 min. After the
   addition of Buffer EB to the QIAquick membrane, increasing the incubation time to up to 4 min can increase the
   yield of purified DNA.

## Plasmid Isolation: QIAprep® Spin Miniprep Kit

1. Resuspend pelleted bacterial cells in 250 μl Buffer P1 and transfer to a microcentrifuge
   tube. Ensure that RNase A has been added to Buffer P1. No cell clumps should be visible after resuspension of the
   pellet. If LyseBlue reagent has been added to Buffer P1, vigorously shake the buffer bottle before use to ensure
   LyseBlue particles are completely dissolved. The bacteria should be resuspended completely by vortexing or
   pipetting up and down until no cell clumps remain.
2. Add 250 μl Buffer P2 and mix thoroughly by inverting the tube 4–6 times. Mix gently by
   inverting the tube. Do not vortex, because this will result in shearing of genomic DNA and contamination of
   plasmid. Continue inverting the tube until the solution becomes viscous and slightly clear. Do not allow the lysis
   reaction to proceed for more than 5 min. If LyseBlue has been added to Buffer P1, the cell suspension will turn
   blue after addition of Buffer P2. Mixing should result in a homogeneously colored suspension.
3. Add 350 μl Buffer N3. Mix immediately and thoroughly by inverting the tube 4–6 times. To
   avoid localized precipitation, mix the solution thoroughly, immediately after addition of Buffer N3. Large culture
   volumes (e.g., ≥5 ml) may require inverting up to 10 times. The solution should become cloudy. If LyseBlue reagent
   has been used, the suspension should be mixed until all trace of blue is gone and the suspension is colorless.
4. Centrifuge for 10 min at 13,000 rpm (~17,900 x g) in a table-top microcentrifuge. A compact
   white pellet will form.
5. Apply 800 μl of the supernatant from step 4 to the QIAprep 2.0 Spin Column by pipetting.
6. Centrifuge for 30–60 s. Discard the flow through.
7. Recommended: Wash the QIAprep 2.0 Spin Column by adding 0.5 ml Buffer PB and centrifuging
   for 30–60 s. Discard the flow through. This step is necessary to remove trace nuclease activity when using endA+
   strains, such as the JM series, HB101 and its derivatives, or any wild-type strain, which have high levels of
   nuclease activity or high carbohydrate content. Host strains, such as XL-1 Blue and DH5α, do not require this
   additional wash step.
8. Wash QIAprep 2.0 Spin Column by adding 0.75 ml Buffer PE and centrifuging for 30–60 s.
9. Discard the flow through, and centrifuge at full speed for an additional 1 min to remove
   residual wash buffer. Important: Residual wash buffer will not be completely removed unless the flow through is
   discarded before this additional centrifugation. Residual ethanol from Buffer PE may inhibit subsequent enzymatic
   reactions.
10. Place the QIAprep 2.0 Spin Column in a clean 1.5 ml microcentrifuge tube. To elute DNA, add
    50 μl Buffer EB (10 mM Tris·Cl, pH 8.5) or water to the center of each QIAprep 2.0 Spin Column, let stand for 1
    min, and centrifuge for 1 min.

## Fluorescence Assay

* Overnight cultures were used for creating 1/100 times diluted subcultures in biological
  duplicates. The Lactococcus strains were grown at optimal conditions reported for them.
* These subcultures were grown till the end of their exponential phase. The samples were then
  centrifuged. The media was discarded and the pellets were washed with NaCl (0.9%) solution.
* The pellets were re-homogenized in 0.9% NaCl and 100uL was taken from each sample (duplicate
  sample measurement for each biological replicate) in a clear bottomed 96 well plate.
* Fluorescence/OD values were measured after subtraction of the negative control. The mean from
  all the samples and biological replicates was used for the creation of the respective graph.

## Electroporation

* Golden gate reaction mixture and electro-competent L.Lactis cells were mixed and added into
  an electroporation cuvette (0.2cm) and allowed to homogenize for 20-30 minutes.
* A 2500 Volt pulse is passed through the cuvette and 1ml SGM17 media is added to the mixture
  immediately for 5 minutes.
* The cultures are transferred to 1.5ml tubes and incubated for 1 hour.
* The cultures were spread plated on M17 Agar with appropriate antibiotics and incubated for 24
  hours.
* The colonies were picked for overnight cultures with appropriate antibiotics and used for
  creating glycerol stocks.

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{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
{% block page\_content %}

# Results

The aim of this wet lab project was to construct a library of RBS parts for *L. lactis*. With enough variation in the expression of each strain with a particular sequence, we can enable fine-tuning of genes for multiple metabolic engineering goals using this organism.

We also intended to test out computationally generated sequences based on our very own predictor-optimizer model and understand if we are able to predict expression levels reliably.

What we learned from our results is that research is always a mixed bag. We win some and lose some. When we ‘lose’, it indicates that we need to go back to the drawing board, figure out what went wrong, and try again. This cycle of design-build-test-learn happens both at the small scale of day-to-day experiments and at the large scale of testing out new hypotheses. This is illustrated clearly in our lab notebook and our engineering success.

**Fluorescence Assay Results**

* Fluorescence readings are adjusted for OD and negative control values.
![Results](https://static.igem.wiki/teams/4931/wiki/results.png)* The sequencing data for each of these sequences is provided below:

| Sample | RBS Sequence |
| --- | --- |
| PJ102 | CGAAGGAACTACAAAATAAATTATAAGGAGGCACTCA |
| 5 | CTTTAGCAATATACAGGGGGTCCGAAGGAGGTGATTA |
| 6 | TCCTCCCGAATCGCCGCGCTCATCAAGGAGGAGTTTG |
| 7 | CTGCCGCTGCGGACATAAGGCCCTAAGGAGGTGAGCT |
| 8 | GGCGAGGTTTGTCATACCCGCTGAAAGGAGGCCCTCG |
| 9 | TGAGGGAAACGCAATAGAAGTTACAAGGAGGCACTCC |
| 10 | CGAACGAACTACAAAATAATTTCGAAGGAGGCCTACA |
| 11 | ATAAACGGGTTAAAGCGTAAGGTCAGTCCTAATGGAGGTTACAT |
| 12 | TATTAAGATAGGTATCCGACCCCAAGTGCGAATGGAGGCCGTGT |
| 13 | ATATTTTAAAGACTCGCAGACGCAACTTAGAATGGAGGCAGTTA |
| 15 | AGGAAAAAATAATCAAAAAAGATTGTGTCTAAAGGAGGAAAAAA |
| 16 | AGGAGCAAATAATCATAGGAGATTGTGTCTAATCGTAGACAATT |
| 3.1 | CGTCAATTTGGTCGTTTGGTAGTAAAAGGAGGGGCTT |
| 3.2 | GGTGGCCTACCAAAGGCTCTTGATCAAGGAGGTTCCA |
| 3.3 | GAACAGAACATTTCTCGACTTGGAGAAGGAGGTTTAT |
| 3.4 | AGTTTAATCTTCGGCGGTCTTCGATAAGGAGGCCGTT |
| 3.5 | GACTTGACATTGGTCAGAATCAAACAAGGAGGGTATC |
| 3.6 | TACTAGGCAGTCGTTTCGCTAAATAAAGGAGGTGATT |
| 3.7 | CGTCAATTTGGTCGTTTGGTAGTAAAAGGAGGGGCTT |
| 3.9 | AACCCCAGGGCCTAGAGTCGGGGAGAAGGAGGTAGGT |
| 3.10 | GGTGGCCTACCAAAGGCTCTTGATCAACGAGGTTCCA |
| 3.11 | ATTCGAGAAAGTGACGCTCATTCTAAAGGAGGGATCT |
| 3.12 | GGTACTCGTTCTAGATATACTTAGGAAGGAGGCGGAA |
| 3.6 | TACTAGGCAGTCGTTTCGCTAAATAAAGGAGGTGATT |

* Looking at our results, we have been able to successfully characterize a library of 24 RBS sequences with a good variable range of expression.
* Most of the sequences that were computationally generated were quite low expressing compared to our positive control (PJ102 - natural RBS of *L. lactis*).
* The strains generated through randomization of the native RBS have shown a large steady range of variation from low to medium to similar expression levels as the positive control.
* One surprise for us was the RBS15 from the computationally generated set that had twice the expression level of the control.
* Future work would include integrating the sequencing data from the randomized strains into the predictor-optimizer model and attempting to predict the expression of the computationally generated sequences using the data from the randomized variants. Characterizing a larger randomized library including sequences that also show higher expression than that of the control will also give us higher tuning ability.
* This would be beneficial to attempt to correct the bias in the original dataset used to train the model and also train the model on *L. lactis*-specific data to ascertain if better predictions can be made on the computationally generated sequences.
* Our predictor-optimizer model showed good performance on the test set which was similar to the dataset it was trained on. We suspect that due to the dearth of gram-positive bacteria data in the dataset, our model hasn't been able to generalize well to accurately predict expression in *Lactococcus lactis*.
* This work can also be carried forward by future iGEM teams from our institute, setting a foundation for next year’s project.

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