iGEM 2024 — Team sjtu-software

{% 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 %}
{% block page\_content %}

# Notebook

## Notebook

---

---

Here is an overview over the full timeline of our HEATMAP project.

November
January
February
March
April
May
June
July
August
September
October

November

Week 1

January

Week 1
Week 2
Week 3
Week 4

February

Week 5
Week 6
Week 7
Week 8

March

Week 9
Week 10
Week 11
Week 12
Week 13

April

Week 14
Week 15
Week 16
Week 17

May

Week 18
Week 19
Week 20
Week 21

June

Week 22
Week 23
Week 24
Week 25
Week 26

July

Week 27
Week 28
Week 29
Week 30

August

Week 31
Week 32
Week 33
Week 34
Week 35

September

Week 36
Week 37
Week 38
Week 39

October

Week 40
Week 41
Week 42
Week 43

## 14th, November to 20th, November

---

### Project

Recruitment:

1. collect the registration forms

2. on-site interview

3. confirm all members

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/team.png)

## 01st, January to 06th, January

---

### Project

1. hold the first ice-breaking meeting

Maxence: Finance/Modelo
Margaux: Entrepreneurship
Romane: Wiki/Modelo
Thomas: Experimentation /Wiki
Romain: Human Practices
Camille: Organisation/Communication

## 07th, January to 13th, January

---

### Project

We continued the brainstorming effort and began holding more meetings per week.

## 14th, January to 20th, January

---

### Project

1. One online meeting:

note sharing
 topic discussion and decision

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w3-1.jpg)

2. Meeting with supervisors:

experience sharing and matters reminding

## 21st, January to 27th, January

---

### Project

1. One online meeting:

note sharing
 topic discussion and decision

2. Online Group meeting:

ice-breaking
 work assigning

## 28th, January to 03rd, February

---

### Project

1. One online meeting:

note sharing
 topic discussion and decision

2. Team Registration and Members Registration:

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w4-1.jpg)

## 04th, February to 10th, February

---

### Project

1. One online meeting:

note sharing
 topic discussion and decision
 arrange the communication with teachers

2. Education:

set up our own WeChat public account

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w5-1.jpg)

## 11th, February to 17th, February

---

We took the Spring Festival break!

## 18th, February to 24th, February

---

### Project

1. One offline meeting:

conclude the notes made before
 put foward possible topics by members
 group discussion

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w8-1.jpeg)

2. Integrated Human Practice -- Consulting with professors:

consult Professor Wei Ting for advice

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w8-2.jpeg)

consult Professor Feng Yan for advice

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w8-3.jpeg)

design questionnaires

3. Education:

publish an article to introduce our team

## 25th, February to 02nd, March

---

### Project

1. One offline meeting:

put foward possible topics by members
 consult with advisors

2. Four offline group meeting:

Back-end group: Topic sharing and discussing
 Front-end group: Excellent wiki sharing and discussing
 Human practice group: timeline drawing of HP works for the new semester
 Design group: Excellent logo sharing and discussing

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w9-1.jpg)

3. Education:

design questionnaires
 consult Professor Wang Tao for advice

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w9-2.jpeg)

consult Professor Xue Xiaoli for advice
 consult Professor Bai Linquan for advice

## 03rd, March to 09th, March

---

### Project

1. Search for informations about previous possible topics

2. One offline meeting:

discuss the communication with teachers
 one team meeting to discuss previous possible topics

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w10-1.jpg)

3. Integrated Human Practice -- Consulting with professors:

consult Professor Chen Haifeng for advice

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w10-2.jpeg)

## 10th, March to 16th, March

---

### Project

1. One offline meeting:

confirm our final topic -- HEATAP!

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w11-1.png)

learn about relavant literature to confirm the feasibility of the topic

2. Three offline group meetings:

Back-end group: Project designing
 Human Practice group: SJTUer's Day planning
 Design group: Stamp books, keychains designing

3. Integrated Human Practice:

find out enterprise that is relevant to our project
 distribute questionnaire at the school's party

4. Education:

distribute questionnaire at the school's party

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w11-2.jpg)

## 17th, March to 23rd, March

---

### Project

1. Integrated Human Practice -- Consulting with professors:

communicate with the professor in relavant field teacher Lu Hongzhong
 communicate with team tutor teacher Zhang Yue

![]()

send messages to relevant enterprise to understand the circumstance in the real producing progress

![]()

learn about relavant literature to confirm the feasibility of the topic

2. Project Implementation:

study relavant knowledge
 search for relevant documents

3. Four offline group meeting:

Human Practice group: SJTUer's Day planning
 Design group: Memory cards, and various cartoon characters designing
 Back-end group: Share relevant documents
 Front-end froup: Learn relevant wiki developing methods

## 24th, March to 30th, March

---

### Project

1. Project Implementation:

communicate with wetlab group and get the genome
 communicate with wetlab group and learn about basic information of the bacteria
 search for relevant documents

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w13-1.png)

2. One offline meeting:

team plan fomulating for this semester
 project designing
 document sharing

3. Two offline froup meetings:

Human Practice group: Prepare for host the visit of Shanghai Shixi Middle School
 Design group: Prepare for the SJTUer's Day

4. Education:

get in touch with some iGEM teams for collaboration

## 31st, March to 06th, April

---

### Project

1. One offline meeting:

ask tutors for advice
 plan confirming

2. Project Implementation:

communicate with professeor Bai Linquan to formulate the final plan
 search for relevant documents

3. Collaboration:

communicate with Jiuzhou Pharmaceutical Company

4. Education:

produce an article to promote our project through the WeChat public account
 participate in SJTUer's Day

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w14-2.jpeg)

receive students from Shanghai Shixi Middle School

## 07th, April to 13th, April

---

### Project

1. One offline meeting:

work deviding and distributing

![]()

2. Project Implementation:

collect protein data from multiple resouces

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/uniprot.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/brenda.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/document.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/temperature-curve.png)

search for possible deep learning models

3. Collaboration:

present project plan and goal to Jiuzhou Pharmaceutical Company

## 14th, April to 20th, April

---

### Project

1. Project Implementation:

further search for more data
 further search for more models

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/esm.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/hyena.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/proteinbert.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/proteinnpt.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/pmlm.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/prottrans.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/rosettafold.jpg)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/protflash.jpg)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/saport.png)

2. One offline group meeting:

Back-end group: Share and dicuss the results of data and model

3. Collaboration:

sign a contract with Jiuzhou Pharmaceutical Company

## 21st, April to 27th, April

---

### Project

1. Project Implementation:

data processing
 futher research on information of models

2. One offline group meeting:

Back-end group: Conclude data and possible model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/esm.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/hyena.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/proteinbert.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/protflash.jpg)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/saport.png)

3. Integrated Human Practice:

get in touch with other iGEM teams

## 28th, April to 04th, May

---

### Project

1. Project Implementation:

yield over 8,000 raw data points after the initial screening process
 start to conduct the construction of deep learning models

2. One offline group meeting:

Back-end group: Share the information of models

## 05th, May to 11th, May

---

### Project

1. Project Implementation:

construct the ProtFlsh based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w19-1.jpg)

consult teacher Lu Hongzhong about GEM model

2. One offline group meeting:

Back-end group: Discuss the methods used to process data in the next step

3. Education:

plan and draw a picture book with five iGEM teams

## 12th, May to 18th, May

---

### Project

1. Project Implementation:

construct and test the ProtFlsh based model
 reconstruct the GEM model
 fill out safety form

2. Two offline group meetings:

Front-end group: Make a schedule for the wiki development
 Design froup: design our team uniform

## 19th, May to 25th, May

---

### Project

1. Project Implementation:

optimize the ProtFlsh based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w21-1.png)

reconstruct the GEM model
 check the safety form

2. Integrated Human Practice:

communicate with ECUST

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w21-2.jpg)

3. Education:

produce an article to introduce Spinosade in the WeChat public account

## 26th, May to 01st, June

---

### Project

1. Project Implementation:

construct the ESM2 based model
 reconstruct and test the GEM model
 collect more data from literature
 further processing of data

2. One offline group meeting:

Back-end group: Share the progress of processing data

3. Education:

do researches on picture book

## 02nd, June to 08th, June

---

### Project

1. Project Implementation:

test the ESM2 based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w23-1.jpg)

construct Saprot based model
 reconstruct and test the GEM model
 use custom scripts to further process data

2. Integrated Human Practice:

look for possible and available enterprise that we can visit

3. One offline group meeting:

Back-end group: Share the progress of processing data
 Human Practice Group: Share information about possible and available enterprise

## 09th, June to 15th, June

---

### Project

1. Project Implementation:

collect 6,090 valid data points eventually
 construct and test Saprot based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w24-1.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w24-2.png)

construct Hyena based model
 test the GEM model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w24-3.jpg)

2. Integrated Human Practice:

get in touch to available enterprise
 make the schedule of enterprise visit

## 16th, June to 22nd, June

---

### Project

1. Project Implementation:

optimize the ESM2 based model
 test Hyena based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w25-1.png)

construct ecGEM model

2. Select the village -- Software&AI!

3. Integrated Human Practice:

discuss with Professor Jiang Qun about ethics in synthetic biology

## 23rd, June to 29th, June

---

### Project

1. One online meeting:

set up plan for summer break
 integrate progress of each group
 ask tutors and advisors for suggestion

2. Project Implementation:

test the ESM2 based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/esm2.png)

optimize Hyena based model
 construct ecGEM model

3. One offline group meeting:

Back-end group: Integrate each members' progress
 Design group: confirm our team logo, project logo, and team pannant

4. Education:

volunteer in student enrollment
 make a promotion video of actinomycetes

## 30th, June to 6th, July

---

### Project

1. One online meeting:

mid-term presentation of HEATMAP

2. Project Implementation:

test optimized Hyena based model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/hyena2.jpg)

construct ecGEM model

3. Integrated Human Practice:

visit Mengniu diary company

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w27-2.jpeg)

## 7th, July to 13th, July

---

### Project

1. Project Implementation:

test ecGEM model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w28-1.png)

search for methods of verifying the deep learning model

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w28-2.png)

2. Integrated Human Practice & Education:

participate in CCiC
 do project presentation to domestic iGEM teams
 communicate with domestic iGEMers

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w28-3.jpg)

## 14th, July to 20th, July

---

### Project

1. Project Implementation:

construct etcGEM model
 prepare meterials for experiment

2. One online group meeting:

Back-end group: Select and optimize best wet lab method
 Human Pratice group: work distribution

3. Integrated Human Practice:

send emails to the United Nations to consult with agricultural problems worldwide

## 21st, July to 27th, July

---

### Project

1. Project Implementation:

optimize etcGEM model
 conduct wetlab experitment

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w30-1.png)

construct the website of deep learning

2. Education:

do investigation and survey about picture books among young children in Anhui Tongdu Bookstore
 do investigation and survey about picture books among young children in Anhui Xinhua Bookstore
 Human Pratice group: work distribution

3. Collaboration:

reach a cooperation with State Key Laboratory of Microbial Metabolism- Bai group

## 28th, July to 3rd, August

---

### Project

1. Project Implementation:

test etcGEM model

![]()

conduct wetlab experitment
 construct the website of database

2. Four Online group meeting:

Front-end group: Confirm the style and essencial element of wiki
 Back-end group: Summerize works that have done this month and make a future plan
 Human Practice group: Arrange the work that have done logically and distribute wiki writting tasks
 Design group: Summerize and discuss the design of elements

## 4th, August to 10th, August

---

### Project

1. Project Implementation:

construct the website

2. Integrated Human Practice:

visit Chenghua Biology

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w32-1.jpg)

## 11th, August to 17th, August

---

### Project

1. Project Implementation:

construct the gitlab work
 refine the files of backend

## 18th, August to 24th, August

---

### Project

1. Project Implementation:

construct the website

2. One online meeting:

summerize and integrate each groups' work

3. Collaboration:

give a project presentation to BGI genomics company and receive precious suggestions

## 25th, August to 31st, August

---

### Project

1. Project Implementation:

make the readme files

2. Integrated Human Practice:

communicate with Imperial-College

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w35-1.png)

3. Education:

do investigation and survey about picture books among young children in Sichuan Children's Press

## 1st, September to 7th, September

---

### Project

1. Project Implementation:

fill out the safety form
 communicate with other group members to make wiki

## 8th, September to 14th, September

---

### Project

1. Project Implementation:

combine the work files with wiki
 write relavant articles

2. Integrated Human Practice:

consult with Qilu Pharma

![](https://static.igem.wiki/teams/5085/wiki/pages/notebook/w37-1.jpg)

## 15th, September to 21st, September

---

### Project

1. Project Implementation:

check the igem forms

2. Integrated Human Practice:

produce the picture book

## 22nd, September to 28th, September

---

### Project

1. One offline meeting:

give a presentation of HEATMAP
 ask tutors and advisors for suggestion
 take team photos

2. Project Implementation:

check all the works

## 29th, September to 5th, October

---

### Project

check all the works

![](https://static.igem.wiki/teams/5085/wiki/pages/back/2.svg)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle. This achievement should be distinct from your Contribution for Bronze.{% endblock %}
{% block page\_content %}

# Engineering

ecGEM-Learn

ecGEM-Design

ecGEM-Build

ecGEM-Test

etcGEM-Learn

etcGEM-Design

etcGEM-Build

etcGEM-Test

Reference

Regarding the issues mentioned before, our proposed technical approach primarily revolves around the classic design, construction, testing, and learning cycle system in synthetic biology, which is DBTL in short.

On the one hand, as for the etcGEM model, we did a DBTL cycle surrounding dry lab. On the other hand, in terms of the HEATMAP-AI model, the core part of our project, we engineered a comprehensive DBTL model involving wet lab, which you can see detailedly in [AI section](https://2024.igem.wiki/sjtu-software/ai).

1 ecGEM-Learn

The use of genome-scale models was a valuable approach to mathematically explore metabolism. A conventional genome-scale metabolic model (GEM) was
reconstructed from all metabolic reactions of the organism of interest and converted into a computational format. By employing GEM models, we were
able to numerically represent the metabolic pathways of \( Saccharopolyspora\:spinosa \), which allowed us to better identify key enzymes in the spinosad
biosynthetic pathway for yield optimization. However, GEMs were limited by their lack of kinetic and omics data, which meant they could not accurately
simulate the specific physiological state of the organism. Therefore, we used the GECKO3 software to integrate protein data and enzymatic parameters,
applying enzyme constraints to construct an ecGEM model.

In the building-ecGEM module, we integrated protein data and enzyme information downloaded from the UniProt database with KEGG information for the organism
retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the previously obtained GEM model. This process involved expanding from a starting
metabolic model to an ecModel structure. Next, we incorporated kcat values matched with the EC numbers from the BRENDA database, as well as kcat values
predicted by the DLKcat package that were independent of EC numbers,, into the model. We then added the protein cost of enzymes in the form of MW / kcat
to all enzyme-catalyzed reactions in the model. Finally, enzyme constraints were imposed using methods such as setting a maximum constraint on the protein
pool. The model parameters were adjusted according to physiological data, resulting in a functional ecModel. This model was then used to predict key
enzymes affecting spinosad yield, and combined with deep learning modeling results, it guided wet lab modifications of key enzymes whose optimal and
growth temperatures were mismatched.

2 ecGEM-Design

When importing the \( Saccharopolyspora\:spinosa \) GEM provided by our collaborator, Prof. Hongzhong Lu, into GECKO, we also completed the necessary
adapters for constructing the ecGEM, with specific details provided in the table below (adapter for building the ecGEM). Notably, the parameters
related to KEGG information and UniProt information were of particular importance.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/adapter.png)

**Table 1：**adapter for building the ecGEM for Saccharopolyspora spinosa

The KEGG database was used to assign EC number annotations. First, we located \( {Actinomycetota} \), \( {Saccharopolyspora} \) in the Category section on
the KEGG official website (https://www.genome.jp/kegg/catalog/org\_list.html). We then selected \( {Saccharopolyspora sp. ASAGF58} \) from the Organisms list.
This strain, isolated and analyzed by Chao Guo and colleagues from Zhejiang Province, is used industrially to produce dodecane antibiotics,
and its complete genome sequence is stored in GenBank. Although it differs from our wet lab strain, it was selected as the source of KEGG data
for its industrial relevance. We set the KEGG ID to 'sacg' and KEGG gene ID to 'kegg', directing us to the necessary Organism ID in the KEGG
database.

The UniProt database provided enzyme molecular weights, amino acid sequences, and other protein information. To ensure consistency between
UniProt and KEGG data, we searched for the organism-specific proteome datasets on UniProt (https://www.uniprot.org/proteomes) and successfully
retrieved the reference proteome for \( {Saccharopolyspora sp. ASAGF58} \). We set the UniProt ID to 'UP000501808' and UniProt type to 'proteome' to
achieve complete gene coverage in the ecModel. Note: Initially, in constructing the ecGEM, we set the UniProt ID to 'UP000233786' in an attempt
to match the variant, which led to mismatches between the two database files. This issue was subsequently optimized in the species design phase.

After importing the GEM model using the code '**model = loadConventionalGEM()**', we discovered that the gene ID systems used in the GEM model’s
workspace variables '**model.genes**' and '**model.grRules**' did not match the gene ID system in UniProt. To resolve this issue, where the gene names
in the FASTA sequence file used for modeling did not match those in the database, we utilized DIAMOND to perform a BLAST alignment between the
FASTA file ('019D.fasta') of the modeling strain and the genome in UniProt. Based on the alignment results, we could
replace the gene ID systems in '**model.genes**' and '**model.grRules**'. According to its GitHub description, DIAMOND is a sequence aligner designed
for high-performance analysis of large sequence datasets, offering pairwise alignment of proteins and translated DNA at speeds 100x-10,000x
faster than BLAST with low resource requirements, allowing it to run on a standard laptop. Specific instructions for installing and running
DIAMOND in WSL are detailed in the attached document ([diamond.dox](https://static.igem.wiki/teams/5085/wiki/pages/engineering/diamond.pdf)). We then modified the information format in Excel
([matches.xlsx](https://static.igem.wiki/teams/5085/wiki/pages/engineering/diamond-matches.pdf)) retaining only the original gene ID numbers in the first column (e.g., '1\_2') and the corresponding UniProt gene
ID numbers in the second column (e.g., 'A0A6H1RF29'). We used the functions '[replaceGenesWithMap.m](https://static.igem.wiki/teams/5085/wiki/pages/engineering/replacegeneswithmap.pdf)' and '[replaceGrRulesWithMap.m](https://static.igem.wiki/teams/5085/wiki/pages/engineering/replacegrruleswithmap.pdf)'
to make the gene ID systems in '**model.genes**' and '**model.grRules**' with the UniProt format starting with 'A0A6'.

Next, we addressed other discrepancies between the original GEM model and the adapter, such as standardizing the 'Compartment name in which the
added enzymes should be located' to 'cytoplasm' and 'extracellular' in the adapter. After completing these adjustments, our GEM model and
adapter met the basic requirements for constructing the ecGEM. We then used '**[ecModel, noUniprot] = makeEcModel(model, false);**' to build the
ecGEM model. During this construction, the 'loadDatabases' function was called to gather enzyme information via UniProtDB from the adapter and
parse reactions associated with genes. Finally, proteins and protein pools were added as pseudometabolites to the GEM model, expanding it to an
ecGEM matrix by incorporating protein usage reactions and protein pool reactions.It was observed that the constructed ecGEM model was a structure
containing 28 fields, with specific variable information stored in '[ecGEM\_1.yml](https://static.igem.wiki/teams/5085/wiki/pages/engineering/ecmodel-1.pdf) '.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/figure1.png)

**Figure 1:** Basic information of \( Saccharopolyspora\:sp.\:ASAGF58 \) on the KEGG website, with its KEGG ID being 'sacg'.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/figure2.png)

**Figure 2:** Basic information of \( Saccharopolyspora\:sp.\:ASAGF58 \) on the UniProt website, with its Proteomics UniProt ID being 'UP000501808'.

3 ecGEM-Build

The main task at this step was the integration of 'kcat' into the 'ecModel' structure, specifically by incorporating 'kcat' values obtained from
the BRENDA database or predicted by the DLKcat package into the empty 'ecModel'. The BRENDA Enzyme Database (https://www.brenda-enzymes.org/)
originated from the National Center for Biotechnology Research (GBF) established in Braunschweig, Germany, in 1987 and is currently operated
by the Institute of Biochemistry, University of Cologne. It serves as a comprehensive database of enzymes, metabolic pathways, structures,
functions, and reactions. BRENDA contains extensive information on enzyme functions and properties, including enzyme classification (EC
numbers), functional data (reactions, substrates and products, kinetic parameters), and the sources of enzymes. This information allows GECKO
to directly retrieve 'kcat' values for target gene enzymes from the BRENDA database. If the enzyme's EC number and reaction substrates match
those in BRENDA, the 'kcat' value is copied directly. If some parameters differ, the 'phylogenetically closest organism, any alternative
substrate, or a fuzzy EC number' is selected as a substitute. The specific process involves first using '**getECfromGEM()**' and
'**getECfromDatabase()**' to assign EC numbers to reactions. After populating the variable '**ecModel.ec.eccodes**' with the corresponding EC
values, we gathered 'kcat' values from BRENDA using '**kcatList\_fuzzy = fuzzyKcatMatching(ecModel);**'.

Since there were discrepancies between the strain used in GEM modeling and the strains in the UniProt database, some reactions still lacked
'kcat' values. Therefore, we used the DLKcat package to predict 'kcat' values for all reactions. DLKcat, developed by Feiran Li et al. in 2022
in Nature Catalysis, predicts high-throughput 'kcat' values for metabolic enzymes of any organism using only substrate structures and protein
sequences. DLKcat’s deep learning approach combines graph neural networks (GNN) for substrates with convolutional neural networks (CNN) for
proteins. DLKcat relies on the simplified molecular-input line-entry system (SMILES) format to gather 'kcat' through deep learning predictions.
We used the '**[ecModel, noSMILES] = findMetSmiles(ecModel);**' function to query PubChem and collect these annotations, creating the
'**data/smilesDB.tsv**' file. DLKcat predictions do not run natively in MATLAB but require Python. We prepared the initial files with
'**writeDLKcatInput(ecModel, [], [], [], [], true);**' to create the '**DLKcat.tsv**' file. Once all initial files were ready, we called 'runDLKcat();'
, which executes the DLKcat algorithm via a Docker image. Docker is a platform that helps developers build, share, and run applications using
containers. After DLKcat completes, it fills the predicted 'kcat' values into '**DLKcat.tsv**', and finally, we read the DLKcat predicted 'kcat' values into the model using 'kcatList\_DLKcat = readDLKcatOutput(ecModel);'.

After obtaining 'kcat' values through fuzzy matching from the BRENDA database and deep learning predictions from DLKcat, we needed to merge DLKcat
and BRENDA structures. The merging rule was to assign a single 'kcat' value to each reaction, prioritizing 'kcat' values obtained from BRENDA through
complete EC number matches. If no complete match was available, 'kcat' values derived from DLKcat were used. If DLKcat could not assign 'SMILES' due
to missing substrate information, fuzzy matching with EC number wildcards was used. This resulted in '**kcatList\_merged**'. Since the previous GEM model
did not have standard 'kcat' values set, we used the '**selectKcatValue();**' function to fill '**kcatList\_merged**' as 'kcat' information into the model,
expanding the protein 'kcat' information. Subsequently, 'kcat' and MW were combined to form the stoichiometric coefficient: \( {-MW/k\_{cat}} \), representing the
flux used by the enzyme in reactions. After the Build step, the ecGEM model with added protein usage information was saved with specific variable
information stored in '[ecModel\_2.yml](https://static.igem.wiki/teams/5085/wiki/pages/engineering/ectestgemadapter.pdf)'.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/figure3.png)

**Figure 3:** Compilation of 'kcat' values through fuzzy matching from the BRENDA database and deep learning predictions from DLKcat, resulting in
'**kcatList\_merged**'. A. Basic information contained in the '**kcatList\_merged**' structure variable. B. 'kcat' data in '**kcatList\_merged**', arranged
according to the order of reactions in 'rxns'.

4 ecGEM-Test

In the 'Build' module, the ecGEM model constructed was merely a listing of protein costs and other parameters along with metabolic reaction information.
It was not able to accurately replicate the physiological conditions under which \( Saccharopolyspora\:spinosa \) grows, with its growth rate being only
\( 0.027674 /hour \), significantly lower than the standard growth rate for \( Saccharopolyspora\:spinosa \). The specific testing approach involved checking
whether the model could achieve its maximum growth rate under unlimited nutrient supply conditions. For unlimited nutrient conditions, constraints
on nutrient exchange were removed, and biomass production was maximized using the 'RAVEN toolbox'. The RAVEN (Reconstruction, Analysis and Visualization
of Metabolic Networks) Toolbox is a software suite for MATLAB that allows for semi-automated reconstruction of genome-scale models (GEMs), and it has
quality control features and capabilities for system-wide data analysis in metabolic environments.According to the DBLT model, we needed to find and
learn other constraints that could optimize the ecGEM model, design them as specific parameters, incorporate them into the model's build, and perform
functionality tests on the optimized ecGEM. The iterative adjustment of parameters and addition of new parameters to better simulate real-world
conditions effectively demonstrated the Design-Build-Test-Learn Cycle of synthetic biology.

To achieve a resulting 'functional ecModel', the model's parameters were tuned based on physiological data if available, such as imposing a maximum
constraint on the protein pool. For the maximum constraint on the protein pool, following Sánchez, B. J. et al.'s definition in the construction of
yeast ecGEM models, the value of the maximum constraint was calculated by \( {\sigma \times f\times P\_{tot}} \). Here, \( σ \) represents the average saturation of all enzymes in the
model, \( f \) is the mass fraction of metabolic enzymes in the total proteome, and \( P\_{tot} \) is the total protein mass (often the ratio of protein mass to cell
dry weight). In the 'Build' module, all three variables were set to 0.5, resulting in a default maximum constraint of 0.5 × 0.5 × 0.5 = 0.125.

Relaxing constraints on protein pool usage effectively removed all enzyme constraints. Specifically, this was done by using the RAVEN toolbox to set
'**prot\_pool\_exchange**' with the lower bound to -1,000. After relaxing the protein pool constraint, the model had no protein pool or nutrient constraints.
The minimum protein pool usage needed to support the observed maximum growth rate was then calculated using the '**strcmp()**' function from the RAVEN
toolbox, and this predicted protein pool usage was used as the new parameter value. However, this global relaxation approach might result in unrealistic
high protein pools due to some key enzymes having very low or incorrect 'kcat' values, and thus might not significantly optimize the model.

Furthermore, sensitivity tuning of 'kcat' values was performed. Unlike relaxing the maximum constraint on the protein pool, sensitivity tuning involved
making small incremental adjustments to 'kcat' values, iteratively increasing the 'kcat' values of growth-limiting enzymes until the desired growth rate
was achieved. During each deterministic iteration, the enzyme with the highest constraint (which corresponds to the enzyme that requires the maximum
proportion of the protein pool) was identified, and its 'kcat' value, which contributes most to overconstraining the model, was increased. This step
was performed using the '**sensitivityTuning()**' function. The ecGEM model constructed with these adjustments and its specific variable information were
stored in '[ecModel\_3.yml](https://static.igem.wiki/teams/5085/wiki/pages/engineering/draft-model-3-019d-gem.pdf)'.

Both methods described above are automated, meaning they do not manually specify significant metabolic reactions for the growth state of the organism as
starting points for adjustments. Sometimes, manually increasing 'kcat' values by 1-10 times for specific reactions can significantly enhance model
accuracy. This approach can be applied in our future research process after obtaining sufficient wet lab data to target specific enzyme functions.

Finally, to assess the model’s ability to replicate physiological states, we simulated 'exchange fluxes at various growth rates'. The approach involved
loading our constructed 'ecModel' to simulate the impact of protein pool constraints on metabolism at different growth rates. During the depiction of
'exchange fluxes', we discovered that the naming of metabolic reactions in the original GEM model did not match standard reaction names. For example,
the standard system describes the Reaction ID for biomass pseudoreaction as 'r\_4041', while the corresponding reaction ID in the original GEM data was
'BIOMASS\_SCO'. We manually replaced reaction IDs, such as changing '**EX\_o2\_e**', '**EX\_co2\_e**', '**EX\_glc\_\_D\_e**', and '**EX\_etoh\_e**' to '**r\_1992**', '**r\_1672**', '**r\_1714**',
and '**r\_1761**' respectively. We then used the '**plotCrabtree()**' function to predict the chemical reaction fluxes for \( Saccharopolyspora\:spinosa \) at different
growth rates and plotted the results.

This demonstrated that the model was able to simulate the organism’s growth conditions to some extent. The remaining discrepancies between the simulated
and real conditions might be due to minor differences between the original GEM and our target strain, as well as incomplete annotation of the strain’s
proteome and insufficient wet lab data for different physiological conditions. Despite these challenges, we finally successfully built an
enzyme-constrained metabolic model for \( Saccharopolyspora\:spinosa \) through the DBLT modeling process. The ecGEM model provided genomic and metabolic
pathway guidance for predicting and modifying enzymes with mismatched optimal temperatures and growth temperatures for spinosad production pathways.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/figure4.png)

**Figure 4:** Simulation of growth conditions for \( Saccharopolyspora\:spinosa \) using the ecGEM model. A. Simulation of exchange fluxes at various growth rates using the ecGEM model. B. Proportional usage of protein pools in the ecModel as growth rate changes (final constructed ecGEM model). C. Proportional usage of protein pools in the ecModel as growth rate changes (ecGEM model without adjusted protein pool constraints). D. Proportional usage of protein pools in the ecModel as growth rate changes (ecGEM model without adjustments to kcat values).

5 etcGEM-Learn

During the constructing the etcGEM of \( Saccharopolyspora\:spinosa \), we found our ecGEM can not be recognized by cobrapy in python, which may be caused by the lack of reaction data. To make HEATMAP project complete, we build etcGEM based on the ecGEM of \( Actinoplanes\:sp.SE50/110 \), which is construct by IGEM 2022 SJTU-software team (https://gitlab.igem.org/2022/software-tools/sjtu-software).

Most of the data used in constructing etcGEM have been elaborated in the Learn phase in ecGEM, except optimal temperature and melting temperature.On the basis of ecGEM, the consruction of etcGEM requires six unknown parameters: \(\bigtriangleup H\_{T\_{0} }^{\ddagger } \), \(\bigtriangleup S\_{T\_{0} }^{\ddagger } \), \(\bigtriangleup C\_{p }^{\ddagger } \), \( \bigtriangleup H^{\ast } \), \( \bigtriangleup S^{\ast } \), \( \bigtriangleup C\_{p,u } \) for each enzyme. Considering the thermodynamic data are hard to collect, we can calculate them through basic protein properties such as optimal temperature and melting temperature. Considering the low accuracy of existing models, we used our HEATMAP-AI to make high accuracy prediction of \( T\_{opt} \) and used the deep learning model DARWINS which is built by IGEM 2023 SJTU-software team (https://gitlab.igem.org/2023/software-tools/sjtu-software) to predict \( T\_{m} \).

After the Test phase, we can collect the data of the growth situation of the \( Actinomycetes \), which can instruct the improvement of both etcGEM and HEATMAP-AI. In the future, to resolve the challenges arising from the uncertainties in the parameter values, Approximate Bayesian Computation can be used in the improvement of etcGEM, which is a probabilistic framework that has been successfully applied for quantifying and reducing uncertainties in various fields, including deep learning, ordinary differential equations, and biochemical kinetic models. The approach uses experimental observations (\( D \)) to update Prior distributions (\( P\left ( \theta \right ) \)) of model parameters to Posterior ones (\( P\left ( \theta |D \right ) \)).

6 etcGEM-Design

To built etcGEM, optimal temperature and melting temperature should be added into the data of enzymes. If it is hard to search, we will use deeplearning model to predict as mentioned before. After that, With the help of cobrapy, we changed the primary matrix of metabolism in ecGEM in python. first, we replace the previous \( k\_{cat} \) with \( k\_{cat} \left ( T \right ) \) to simulate the change in enzyme activity. The value is caculated by Macromolecular rate theory. Second, we calculate \( fN\left ( T \right ) =\left [ E \right ] \_{N,i} /\left [ E \right ] \_{t,i} \) by the Two-state model to simulate the situation of denaturation, after which we will increase the uasge of the protein in protein pool according to \( fN\left ( T \right ) \).

7 etcGEM-Build

Different from ecGEM, the construction of etcGEM is mainly based on python language, instead of MATLAB, so the first step is to save the ecGEM model with matlab file and then load it in the python project with cobrapy.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg1.png)

Subsequently, for each enzyme involved in ecGEM, we will search its optimal temperature and melting temperature in the database based on the sequence information. If the information cannot be retrieved, we will supplement it through HEATMAP-AI and DARWINS.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg2.png)

With \( T\_{opt} \), \( T\_{m} \), \( k\_{cat} \), \( fN\left ( T \right ) \) and \( k\_{cat} \left ( T \right ) \) of each enzyme in any given temperature T can be calculated through specific functions.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg3.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg4.png)

Calculated \( fN\left ( T \right ) \) and \( k\_{cat} \left ( T \right ) \) will be used to replace the coefficient and \( k\_{cat} \) of each protein in every reaction in ecGEM. A simple schematic diagram helps to explain it clearly.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg5.png)

8 etcGEM-Test

In the buiding phase, we have successfully construct an etcGEM model. In order to test the model, we used etcGEM to simulate the growth situation in multiple temperture with function in cobrapy.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg6.png)

The results are close to what we have assumed before, which shows a trend that rises first and then falls.

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg7.png)

![](https://static.igem.wiki/teams/5085/wiki/pages/engineering/eg8.png)

The simulation shows that the growth situation differs in different temperature. What’s more, It can be observed that the most suitable growth temperature for the bacterial strain is around 56 degrees Celsius. When the temperature rises or falls, the activity of its internal proteins decreases, thereby affecting the growth and development of the entire organism.

9 Reference

[1]  Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. Current status and applications of genome-scale metabolic models. Genome Biol. 2019 Jun 13;20(1):121. doi: 10.1186/s13059-019-1730-3. PMID: 31196170; PMCID: PMC6567666.

[2]  Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine. Annu Rev Microbiol. 2021 Oct 8;75:199-222. doi: 10.1146/annurev-micro-060221-012134. Epub 2021 Jul 27. PMID: 34314593.

[3]  Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012 Feb 27;10(4):291-305. doi: 10.1038/nrmicro2737. PMID: 22367118; PMCID: PMC3536058.

[4]  UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D523-D531. doi: 10.1093/nar/gkac1052. PMID: 36408920; PMCID: PMC9825514.

[5]  Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021 Jan 8;49(D1):D545-D551. doi: 10.1093/nar/gkaa970. PMID: 33125081; PMCID: PMC7779016.

[6]  Li, G., Hu, Y., Jan Zrimec et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism. Nat Commun 12, 190 (2021). https://doi.org/10.1038/s41467-020-20338-2

[7]  Stourac, J., Dubrava, J., Musil, M., Horackova, J., Damborsky, J., Mazurenko, S., Bednar, D., 2020: FireProtDB: Database of Manually Curated Protein Stability Data. Nucleic Acids Research 49: D319-D324

[8]  Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Neumann-Schaal M, Jahn D, Schomburg D. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 2021 Jan 8;49(D1):D498-D508. doi: 10.1093/nar/gkaa1025. PMID: 33211880; PMCID: PMC7779020.

[9]  Guo C, Guo W, Liu Y, Wang C. Complete genome sequence of butenyl-spinosyn-producing Saccharopolyspora strain ASAGF58. Ann Microbiol. 2020;70:46. doi:10.1186/s13213-020-01587-4.

[10]  Buchfink B, Reuter K, Drost HG, "Sensitive protein alignments at tree-of-life scale using DIAMOND", Nature Methods 18, 366–368 (2021). doi:10.1038/s41592-021-01101-x

[11]  Docker Inc, https://www.docker.com/

[12]  Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, Nielsen J, Kerkhoven EJ. (2018) RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol 14(10): e1006541. doi:10.1371/journal.pcbi.1006541.

[13]  Jeschek M, Gerngross D, Panke S. Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort. Nat Commun. 2016 Mar 31;7:11163. doi: 10.1038/ncomms11163. PMID: 27029461; PMCID: PMC4821882.

[14]  Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010 Mar;28(3):245-8. doi: 10.1038/nbt.1614. PMID: 20212490; PMCID: PMC3108565.

![](https://static.igem.wiki/teams/5085/wiki/pages/back/2.svg)

{% endblock %}

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

## What should this page contain?

---

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](https://parts.igem.org/Main_Page).

## Inspirations

---

* [2019 Nantes](https://2019.igem.org/Team%3ANantes/Experiments)
* [2019 TU Eindhoven](https://2019.igem.org/Team%3ATU_Eindhoven/Experiments)
* [2019 Mingdao](https://2019.igem.org/Team%3AMingdao/Demonstrate)
* [2020 Amsterdam](https://2020.igem.org/Team%3AAmsterdam/Experiments)
* [2020 NCTU Formosa](https://2020.igem.org/Team%3ANCTU_Formosa/Experiments)
* [2020 USAFA](https://2020.igem.org/Team%3AUSAFA/Experiments)

{% endblock %}

{% 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

Data Results

AI Results

ecGEM&etcGEM Results

Wetlab results

1 Data Results

To train HEATMAP-AI for enzyme temperature prediction, we collected and organized data from public databases and scientific literature, which will be a valuable resource for future researchers.

To download the data, please visit [Website](https://gitlab.igem.org/2024/software-tools/sjtu-software/-/tree/main/HEATMAP-DL/data?ref_type=heads).

Additionally, to make our dataset more accessible, we have created a platform where users can easily search for and download enzyme data.

To access the database, please visit our [Website](http://121.199.165.251:8000/).

2 HEATMAP-AI Results

To evaluate the performance of our model, we utilized the cross-validation method. This robust statistical technique assesses how well the results of a model generalize to an independent dataset. Below are the results obtained from our cross-validation process.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re1.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/result/re2.png)

The Pearson correlation coefficient of both are up to 0.89, which proves that our model not only has the advantage of high accuracy, but also has excellent robustness, which indicates that our model has very strong universality.

On top of this, HEATMAP-AI is used to build etcGEM model. The successful construction of etcGEM also means the model is excellent.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re3.png)

In the wet-lab section, we validated the accuracy of the HEATMAP model's predicted optimal temperature for enzymes by investigating the catalytic efficiency of trypsin in hydrolyzing casein at different temperatures. Based on the amino acid sequence of trypsin, the HEATMAP model predicted an optimal temperature of 43.71°C. Consequently, we designed three reaction systems at 33.5°C, 43.5°C, and 53.5°C, respectively. The catalytic efficiency under these temperature conditions was characterized by measuring the absorbance of the reaction product, L-tyrosine, at its characteristic absorption peak of 275 nm, using a Thermo Scientific NanoDrop spectrophotometer to monitor the trypsin cleavage of α-casein.

The absorbance values of the 43.5°C reaction group at wavelengths of 270 nm, 275 nm, and 280 nm were 0.243, 0.285, and 0.283, respectively, which were higher than those of the 33.5°C reaction group (0.221, 0.265, 0.270) and the 53.5°C reaction group (0.236, 0.277, 0.280). This likely indicates that more L-tyrosine was produced at 43.5°C, suggesting that the catalytic efficiency of trypsin was highest at this temperature, potentially corresponding to its optimal temperature. This observation aligns with the HEATMAP model's predicted optimal temperature of 43.71°C, thereby validating the HEATMAP model's accuracy through wet-lab experiments.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re4.svg)

3 ecGEM & etcGEM Results

To better explore the key enzymatic systems involved in the production of Spinosad by \( Saccharopolyspora\:spinosa \), we constructed an enzyme-constrained genome-scale metabolic model (ecGEM) using mathematical modeling techniques, integrating \( k\_{cat} \) deep learning predictions and protein pool flux constraints to characterize its overall metabolic network.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re5.png)

Figure A shows the simulation of exchange fluxes at various growth rates using the ecGEM model.
 Figure B illustrates the proportional usage of protein pools in the ecModel as the growth rate changes for the final constructed ecGEM model.
 Figure C presents the proportional usage of protein pools in the ecModel as the growth rate changes for the ecGEM model without adjusted protein pool constraints.
 Figure D depicts the proportional usage of protein pools in the ecModel as the growth rate changes for the ecGEM model without adjustments to kcat values.

The results demonstrate that the final ecGEM model adequately simulates the normal physiological state of \( Saccharopolyspora\:spinosa \) and progressively achieves a matched proportion of protein usage corresponding to the growth rates.

Here, you can click the corresponding link to download our constructed [ecGEM model](https://gitlab.igem.org/2024/software-tools/sjtu-software).

This model can be optimized further or applied to investigate the specific functions of the \( Saccharopolyspora\:spinosa \) metabolic network.

To access modeling data results, including DLKcat predicted kcat values and ecGEM simulations of the growth state of \( Saccharopolyspora\:spinosa \), visit the [Model section](https://2024.igem.wiki/sjtu-software/model).

In order to explore the effects of temperature on biological growth. Based on the ecgem model in 2022 SJTU-software, we constructed etcGEM model with HEATMAP-AI and DARWINS in 2023 SJTU-software.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re6.png)
![](https://static.igem.wiki/teams/5085/wiki/pages/result/re7.png)

The simulation shows that the growth situation differs in different temperature. What's more, It can be observed that the most suitable growth temperature for the bacterial strain is around 56 degrees Celsius. When the temperature rises or falls, the activity of its internal proteins decreases, thereby affecting the growth and development of the entire organism.

Here, you can click the corresponding link to download our constructed [etcGEM model](https://gitlab.igem.org/2024/software-tools/sjtu-software).

4 Wetlab results

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re-8.png)

**Figure 1:** HEATMAP Prediction of the Optimal Working Temperature for Proteinase K.
Absorbance Results of Enzyme Reaction Systems at Different Temperature Gradients:
We conducted full-wavelength absorbance scans on the supernatants from the reaction systems at 33.5°C, 43.5°C, and 53.5°C. Notably, all three groups exhibited a significant absorbance peak between 250 nm and 290 nm, with a peak position around 275 nm. This corresponds to the characteristic absorption peak of L-tyrosine, indicating that Proteinase K successfully cleaved α-casein in all three experimental groups.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re9.png)

**Figure 2:** Full Wavelength Absorbance Scan of the Supernatant from the Enzyme Reaction System at 33.5°C, covering the range from 190.0 nm to 850.0 nm.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re10.png)

**Figure 3:** Full Wavelength Absorbance Scan of the Supernatant from the Enzyme Reaction System at 43.5°C, covering the range from 190.0 nm to 850.0 nm.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re11.png)

**Figure 4:** Full Wavelength Absorbance Scan of the Supernatant from the Enzyme Reaction System at 53.5°C, covering the range from 190.0 nm to 850.0 nm.

Absorbance Values at 270 nm, 275 nm, and 280 nm under Different Temperature Gradients:
The enzyme reaction at 43.5°C showed absorbance values of 0.243, 0.285, and 0.283 at 270 nm, 275 nm, and 280 nm, respectively. These values were higher than those observed at 33.5°C (0.221, 0.265, 0.270) and 53.5°C (0.236, 0.277, 0.280). This strongly suggests that the 43.5°C reaction group produced more L-tyrosine, indicating that Proteinase K likely exhibits its highest catalytic efficiency at this temperature, which may represent its optimal temperature. This finding aligns with the HEATMAP model's predicted optimal temperature, thereby validating the model's accuracy based on the wet experimental data.temperature and confirming the model's accuracy through wet experiments.

![](https://static.igem.wiki/teams/5085/wiki/pages/result/re4.svg)

**Figure 5:** Comparison of Absorbance at 270 nm, 275 nm, and 280 nm in the Supernatants of Enzyme Reaction Systems at 33.5°C, 43.5°C, and 53.5°C.

![](https://static.igem.wiki/teams/5085/wiki/pages/back/2.svg)

{% endblock %}