iGEM 2023 — Team nju-china


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{% block title %}Engineering Success{% endblock %}
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ENGINEERING SUCCESS

responsible and good for the world

* [ ]
   [Origins](#s1)
* [ ]
   Overview
  + + [Dry Lab](#s211)
    + [Wet Lab](#s212)
* [ ]
   Dry Lab Cycle
  + + [Cycle 0](#s31)
    + [Cycle 1](#s32)
    + [Cycle 2](#s33)
* [ ]
   Wet Lab Cycle
  + + [Cycle 0](#s41)
    + [Cycle 1](#s42)
    + [Cycle 2](#s43)

Origins

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

AI for biology has become a new trend, especially in the field of
synthetic biology, because genes are the language of biology and are
particularly suitable for transfer learning using popular large language
models. However, through our interviews with relevant experts, we have
learned that the limited amount of data is a significant challenge for
AI for biology.

Overview

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

![](https://static.igem.wiki/teams/4815/wiki/new/new1.png)

Dry Lab

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Design1

We aim to design a pre-training + fine-tuning paradigm to improve the
fitting accuracy of AI models trained on small sample data and address
the issue of insufficient data for training good AI models.

Build1

We will experiment with different k-mers, optimizers, learning rates,
and pre-training models. We will then train AI models from scratch using
datasets of varying sizes: 300, 3,000, 30,000, 300,000, 3,000,000,
6,000,000, and 30,000,000. The details can be found in the dry lab
cycle.

Test1

We will test all AI models trained with different sample sizes using a
specifically low-throughput measurement test dataset with lower noise.
We will calculate the fitting accuracy and identify the AI model with
the highest fitting accuracy for each sample size. We will also train an
AI model based on the carefully designed model in a Nature article
using the same sample size. We will then test this AI model with the
specialized test dataset and calculate its fitting accuracy.

Learn1

We found that all models trained using the pre-training + fine-tuning
paradigm exhibited higher fitting accuracy compared to models built from
scratch. This validates the effectiveness of our pre-training +
fine-tuning paradigm. Additionally, after discussing with professors and
other teams, we realized that our model overlooked certain factors, such
as the occurrence of random mutations in promoter bases after multiple
cultivations. Mutations in the promoter can lead to changes in the
sequence and potentially lead to decreased expression levels.

Wet Lab

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Design2

To address the aforementioned issue, we plan to design a base mutation
model that simulates the process of base mutation. This will generate
mutated promoter sequences, which will be input into the AI model to
predict their expression levels. Finally, we will plot the expression
intensity changes of each generation of mutated promoters, fitting the
curve to illustrate the expression strength variation under multiple
generations of random mutations.

Build2

As many synthetically generated core promoter sequences lack standard
core promoter structures, it is not possible to apply existing core
promoter mutation hotspots and preferences to our base mutation model.
Therefore, we assume that mutations do not have any specific hotspots or
preferences but occur purely randomly. We found that some studies
constructing mutation models also used the same assumption. Based on
this assumption, we constructed the base mutation model and combined it
with the AI model to fit the expression intensity changes of 1000 highly
expressed promoters under multiple generations of random mutations. We
have also identified some mutation-resistant highly expressed promoters.

Test2

Wet lab experiments.

Learn2

Wet lab experiments confirmed that the AI model predictions were
relatively accurate. However, due to time constraints, we were unable to
measure the expression levels after multiple generations of cultivation
and mutation to verify their resistance to mutation. Given sufficient
time, we could further improve the fitting accuracy of the AI model and
the accuracy of the base mutation model by incorporating wet lab
experimental data. For example, we could identify mutation hotspots and
preferences for synthetically generated promoter sequences to optimize
our base mutation model. We could also integrate the promoter and
expression intensity data obtained from wet lab experiments into the
original dataset to further train our AI model and enhance its fitting
accuracy.

Dry Lab Cycle

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Cycle 0. Fine-tuning

Design0:

First, we load the DNABERT pre-trained model that
has generally shown the best performance. We identify the
important parameters and perform fine-tuning to achieve the best
fitting accuracy for our AI model.

Build0:

We identify the important parameters, including the
tokenization length (kmer), optimizer, and learning rate. We
appropriately combine and test these important parameters.

Test0:

We train different variations of the AI model using different
parameter combinations. At this stage, we use 10% of the original
data, randomly splitting three million data points into a 70:30
ratio for training and testing sets respectively. The training set
is used to train the AI model, while the testing set is used to
assess the model's fitting accuracy.

Learn0:

After training the AI models, we find that the best
Pearson correlation coefficient achieved is 0.84, which is
significantly lower than the well-constructed AI model mentioned
in reference. Next, we plan to train the models using the
entire dataset. However, after discussing with our teacher, we
discover that several other pre-trained models are also suitable
for our project, especially the recently released DNABERT2 model,
which may potentially yield better results.

Cycle 1. Pre-trained Model

Design1:

After considering our teacher's advice and
conducting literature research, we have identified three
additional pre-trained models: DNABERT2, BioBERT, and RNAprob. To
improve the fitting accuracy of our model, we will load each of
these models and perform fine-tuning.

Build1:

We will load each of these pre-trained models, download the
corresponding packages, and modify the code for the fully
connected layer. The modified code will take the output from the
pre-trained models and pass it through a linear layer to produce a
single numerical output. We will then perform fine-tuning cycles
to achieve the highest fitting accuracy for our AI model.

Test1:

We will compare the highest fitting accuracies
achieved by the four pre-trained models and observe that the AI
model trained with the DNABERT pre-trained model has the best
fitting accuracy. We will then use the entire dataset of 30
million samples to train the best pre-trained model and
fine-tuning combination. We find that the Pearson correlation
coefficient can reach a maximum of 0.85, with only slight
improvement.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering/engineer1.png)

In addition, we plotted the predicted expression levels obtained
from the model trained with a dataset of 30 million raw data
against the existing measured expression levels. The x-axis
represents the predicted expression levels generated by the model,
while the y-axis represents the actual measured expression levels.
Upon observing the generated graph, it became apparent that many
horizontal lines were present, indicating that the model exhibits
significant variations in predicting expression levels for integer
sequences. This suggests a limitation in the model's ability to
accurately predict expression levels for integer values.
Additionally, we noticed that data values, apart from integers,
predominantly had a precision of up to five decimal places.
Consequently, obtaining precisely integer values from measurements
in such a high precision scenario becomes challenging. Moreover,
around three-quarters of the data points are integers, further
adding to the perplexity of the situation, indicating a potential
underlying issue.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering/engineer2.png)

Learn1:

In order to ascertain the source of the data, we
decided to email the first author of the Nature article. In
response, the author informed us that they conducted measurements
of expression levels across multiple culture vessels. They found
that for sequences where the expression levels were measured as
integers, it indicated that the sequence was only detected in a
single culture vessel. Consequently, the presence of integer data
introduced significant errors, as it was greatly affected by
noise. Moreover, they employed a separate high-precision,
low-throughput test dataset specifically for measuring the model's
goodness of fit, aiming to minimize the interference of noise
during testing.

Cycle 2. Dataset

Design2:

In order to further investigate the impact of small
sample sizes, we conducted simulations by removing integer data
from the original dataset comprising 30 million data points. This
step aimed to reduce noise and simulate scenarios characterized by
low-throughput and high-precision. Subsequently, we randomly drew
samples of 300, 3,000, 30,000, 300,000, 3,000,000, and 6,000,000
instances from the dataset for training purposes. Additionally, we
assessed the model's goodness of fit using a dedicated test
dataset.

Build2:

During the random sample selection process, we observed a tendency
for small sample data to exhibit overfitting. To mitigate this, we
applied weight decay treatment and performed fine-tuning,
incorporating optimizers and adjusting the learning rate. These
modifications were crucial in achieving optimal goodness of fit.
Furthermore, to ensure the reliability of our findings, we
employed a specialized test dataset to test the model's
performance, and the results were subsequently visualized.

Test2:

To assess the performance of our model, we trained the
meticulously developed AI model from the Nature article using the
preprocessed data. Remarkably, our model outperformed the Nature
model across all dataset sizes. Notably, our model demonstrated a
pronounced advantage in handling small sample data. Additionally,
we discovered that models trained on the randomly partitioned
original dataset, which yielded the highest goodness of fit during
the evaluation using the test set, also delivered superior
performance when evaluated on the specialized test dataset. This
confirms the efficacy of employing a dedicated test dataset for
the evaluation process.

Wet Lab Cycle

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Cycle 0. Build Promoter Library

Design0:

How does the 80bp promoter sequence generated by
Pymaker drive actual protein expression? Firstly, we gained an
understanding of the biological significance of the 80bp promoter
sequence through literature review. The length of 80bp is
considered an appropriate length for RNA polymerase binding.
Therefore, we placed it at the -160 to -80 position within the
entire promoter framework, which is the assumed transcription
start site (TSS) and the initial binding site for RNA polymerase.
We expect it to play an important role. Additionally, besides the
AI-generated sequence, are there any other components required to
form a complete and functional promoter?

We conducted in-depth discussions and consultations with external
expert Hu Yilin. Under his guidance, we designed the sequence
within the pT and pA sequences of the ADH1 promoter framework,
which had all possible cis-acting elements removed. This resulted
in the formation of a complete promoter.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering/engineer3.png)

Build0:

Based on the aforementioned requirements and the
subsequent need for a fluorescence reporting system, we
constructed a complete fluorescent reporter vector that contains
20 AI-derived promoter sequences. To facilitate subsequent
experimental procedures, we designed primers targeting the
conserved pA and pT sequences at both ends for selective
amplification and extraction of the promoter sequences.
Additionally, we introduced XhoI and BamHI restriction enzyme
recognition sites for future enzymatic digestion if needed.

Success:

we extract promoter sequences and test them using agarose gel electrophoresis.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-1.png)

The figure illustrates that we successfully extract designed promoter sequences from the dual-fluorescence reporter plasmids.

Test0:

To confirm the adequacy of the promoter framework we used in driving downstream gene expression and the functionality of the complete promoter, we conducted preliminary experiments. We used an empty promoter framework and a promoter framework containing the desired sequences to drive the expression of a fluorescent gene. We obtained appropriate results that provided evidence for the effectiveness of our promoter constructs.

Success:

We observed yeast smear results under fluorescence microscope.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-0.jpg)

Learn0:

We have obtained an effective framework that can be
used to experimentally test the function of transcription factor
cis elements.

Cycle 1. Establish Reporter System

Design1:

We have obtained functional promoter sequences, and
now we need to quantify their expression strength and further
compare them. We engaged in discussions with Professor Mali Jia
from West Lake University. Prof. Jia specializes in functional
genomics and has constructed gene libraries for human regulatory
sequences, providing extensive experience in experimentally
characterizing the strength of regulatory sequences. Based on this
collaboration and a thorough review of relevant literature, we
explored the feasibility and ultimately decided to employ a
dual-fluorescence reporter system for characterization (the model is shown in the figure below)..

The advantage of this system is its ability to eliminate the
influence of plasmid copy number and the growth status of the
bacterial host, thereby providing a more direct measurement of the
relative expression strength of the designed promoters.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-11.png)

Build1:

We used a synthetic promoter to drive the expression
of the YeGFP gene on the same plasmid, while the TEF1 promoter was
used in the reverse orientation to drive the expression of the
mCherry gene. Additionally, we incorporated a lactose-inducible
switch to enhance safety.

The plasmid structure is as follows

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-2.png)

Success:

Spacers H1, H2, H3, H4, H5, H6, H7 means our Pymaker generated generated high expression rate promoters and L1, L2, L3 means low expression rate ones.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-3.png)

Test1:

We utilized flow cytometry to monitor the two
fluorescence signals excited by different light channels and
analyzed the corresponding data. We plotted the natural logarithm
of the ratio of GFP to mCherry (ln(GFP/mCherry)) as a frequency
distribution graph to showcase the relative expression strength of
different promoters in yeast.

Success:

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-4.png)

Learn1:

We have established a high-throughput experimental
system to monitor the strength of promoter sequences and applied
it in practice.

Cycle 2. Constitute Express System

Design2:

We have encountered challenges in accurately and
rapidly quantifying the expression levels of the produced
products, despite being able to transfer the expression of the
target gene using a dual-fluorescence reporter system.

Firstly, due to the unique nature of yeast cell walls, it is not
possible to obtain secreted proteins directly. Therefore, we need
to lyse the yeast strain and perform purification to obtain the
protein of interest. In the case of LTB (Lytic transglycosylase
B), there is no readily available and affordable antibody. After
reviewing literature on LTB expression, we found that the main
approach for expressing LTB in yeast is through fusion proteins.
To facilitate purification and enable multi-level detection, we
designed a fusion protein strategy by fusing LTB with GFP.

Build2:

Using a high-fidelity PCR system, we extracted the
AI-optimized promoter sequence from the commercially synthesized
complete dual-reporter system and introduced specific restriction
enzyme sites. Subsequently, we employed double digestion and
ligation methods to connect the promoter to the LTB-YeGFP plasmid
framework

Test2:

We initially conducted shake flask cultivation and
plasmid extraction

We extracted total RNA from yeast and performed real-time
quantitative fluorescence PCR to measure the expression of the
plasmid. Furthermore, we lysed the yeast cells to extract total
protein and selectively purified the protein using an EGFP
antibody. Subsequently, we conducted Western blot analysis.

Success:

Western blot using rabbit anti-GFP antibody shows that LTB-eGFP fusion protein is successfully expressed in yeasts.

![](https://static.igem.wiki/teams/4815/wiki/engineer/engineering-5.png)

We intend to use flow cytometry to record the GFP density, while we come across a failure in getting data from the flow cytometer, which may result from too much cell fragments that block the cytometer. However, the remain result we get is in correspond with that of our western blot result.

![](https://static.igem.wiki/teams/4815/wiki/result/15-1.png)

Learn2:

The strength of the promoter sequence is closely
related to the expression of downstream genes. The absolute
quantitative data obtained for LTB expression will be used to
construct a new dataset. This data can be utilized to further
optimize our AI model using our paradigm, enabling us to generate
promoter sequences that specifically drive high expression of LTB.

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Experiment

* [ ]
   [Introduction](#s1)
* [ ]
  [Dry Lab](#s2)
* [ ]
  [Wet Lab](#s3)

Introduction

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

During the April to June, we collected the data, constructed the
model and trained the model. By using our model, we predicted the
high-expression synthesized promoter and began to test it by wet
lab. We conducted the wet lab from July to November.

Dry Lab

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Wet Lab

![](https://static.igem.wiki/teams/4815/wiki/devider.png)

Wet Lab

1

Preliminary Experiment

Episomal single reporter system expressing a
constitutive red fluorescent protein (mcherry)
wasdescribed and was uesd to create plasmid.

Episomal dual reporter system expressing a constitutive
red fluorescent protein (mcherry) and a variable yellow
fluorescent protein (YeGFP) were described and were uesd
to create plasmid.

The plasmids were stored at -20℃(the plasmids were
introduced with the

AmpR gene and the URA3 gene)

Made 20 YPD culture medium added Amp

Made the yeast-competent cells and store at -80℃

7.14-7.20

Inserted the mcherry plasmid and mcherry+YeGFP plasmid
into the INVSC1 yeasts

The INVSC1 yeasts were streaked with a disposable loop
on the YPD solid medium(adding the amp) and grown at 30
°C for 72 h. Screen the colonies on the YPD solid medium
with amp.

2

Made the YPD and Sc-Ura fluid nutrient medium and
autoclaved it for 20 minutes.

After the yeast grown in single colony, picking out the
single colony into the YPD fluid nutrient medium and
putting into the 30℃ horizontal rotators.

During this time, yeasts failed to grow on some of the
YPD fluid nutrient medium and we inserted the plasmid
again.

7.21-7.27

Extracted the plasmids from the yeast and store it at
-80℃.

After Diluted the yeast for four times in 48h, with the
first and the second time using the YPD,while the third
and the fourth time using the Sc-Ura.

After the OD600 reached 0.4-0.6, we can dilute it with
new fluid nutrient medium.

7.28-8.3

Measured OD600 of this diluted yeast suspension.
Centrifuged at 4 °C at 3,500 rpm for 7 min and washed it
with PBS for two times , useing flow cytometry to
reportthemcherry or the GFP expression signal

3

Made the slide using the INVSC1 yeasts cell suspension
and then oberserved the slides under the fluorescent
microscope.

Formal experiment

8.4-8.10

Created the plasmid with the episomal dual reporter
system and the library ofDNA sequences was inserted
within a promoter scaffold. We got a series of plasmid
totally 20 plasmids.

Made 40 YPDA culture mediums added Amp

Cultivated the INVSC1 yeasts for three days and made the
INVSC1 yeast-competent cells and store at -80℃

8.11-8.17

Inserted the plasmids into the INVSC1 yeast, all the
yeastswere streaked with a disposable loop on the YPD
solid medium and grown at 30 °C for 72 h.

Made the YPD and Sc-Ura fluid nutrient medium.

After the INVSC1 yeast grown in single colony, picking
out the single colony into the YPD fluid nutrient medium
and putting into the 30℃ horizontal rotators.

4

The INVSC1 yeasts which were inserted with H1, ADH
plasmid failed to grow on the culture medium.

8.18-8.24

Diluted the INVSC1 yeasts for four times in 48h, the
first and the second dilution used the Sc-Ura fluid
nutrient medium while the third and the forth used YPDA.

Extracted the plasmids from the yeasts and store it at
-80℃

During the dilution,the INVSC1 yeasts which were
inserted with H2,H4,H6,H7,L3,TEF,LTB-eGFP were polluted.

We inserted these plasmids into the INVSC1 yeasts
renewedly.

8.25-8.31

After inserting the plasmid renewedlly, incubating the
plates at 30 degrees

Extracted all the plasmids from the targeted
microorganism and stored it at -80℃ to backup

After diluted the INVSC1 yeast for four times, measuring
OD600 of this diluted INVSC1 yeast suspension.
Centrifuged at 4 °C at 3,500 rpm for 7 min and washed it
with PBS for two times , useing flow cytometry to
reportthemcherry and

5

GFP expression signal

LTB-yeGFP

9.1-9.7

Extracted all the plasmids from the targeted
microorganism and stored it at -80℃ renewedly to get the
synthesized promoter and the LTB-eGFP plasmid frame.

Using double enzymes to perform sequential DNA cleavage
and ran the agarose gel to confirmand the PCR to
amplify. Ligated the LTB-eGFP plasmid frame with the
synthesized promoter and the nature promoter one by one.

9.8-9.14

After we got the synthesized plasmid, inserted these
plasmids into the INVSC1 yeasts and cultivated with the
YPD/YPDA fulid nutrient medium.

Extracted the synthesized plasmids from the yeasts and
store it at -80℃ to backup.

During the extraction, we failed to extract the plasmids
and we extracted renewedly. Made 40 YPDA culture mediums
added Amp.

Made the YPD and Sc-Ura fluid nutrient medium and
autoclaved it for 20 minutes.

6

9.15-9.21

The INVSC1 yeasts with synthesized plasmids were
streaked with a disposable loop on the YPD solid
medium(adding the amp) and grown at 30 °C for 72 h.
Screen the colonies on the YPD solid medium with amp.
Each sample took four single conoly and cultivated
individually with four 50ml centrifuge tube.

Diluted the INVSC1 yeasts with synthesized plasmids for
four times, measuring OD600 of this diluted INVSC1 yeast
suspension. Centrifuged at 4 °C at 3,500 rpm for 7 min
and washed it

with PBS for two times , useing flow cytometry to
reporttheLTB-eGFP expression signal.

9.22-9.28

Extract the total RNA from the INVSC1 yeasts (totally 50
samples)and stored for QRT-PCR.

Extract the total protein from the INVSC1 yeasts(totally
30 samples and stored at -80℃)

Ran the SDS gel of the INVSC1 yeasts and chose a
suitable WB internal reference.

7

9.29-11.5

We used the Gapdh as the internal reference and ran the
SDS gel, we also ran the Q-PCR to detect the gene
expression.

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