iGEM 2022 — Team william-and-mary

{% 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

[Spring 2022](#Software Contributions)
[Summer 2022](#Modeling Contributions)
[Fall 2022](#WetlabandData Contributions)

## Spring 2022

---

### 220130 - 220205:

* First meeting: discussed basic tenets and principles of synthetic biology
* Discussed past iGEM projects that included wetlab and modeling

### 220206 - 220212:

* Read hisat manual
* Looked into previous iGEM projects
* Prepared for lab safety check

### 220213 - 220219:

* Ran hisat program using DH5a genome
* Read papers on synthetic biology fiedability and B. subtilis engineering
* Brainstormed 2022 project ideas
* Had a team meeting

### 220227 - 220305:

* Looked into igem projects and papers about synthetic biology and fieldability
* Brainstormed project ideas
* prepared for an education event
* Meeting: discussed synbio fieldability

### 220306 - 220312:

* Review igem projects and papers about synthetic biology and fieldability, papers on synthetic biology chassis
* Brainstormed project ideas
* Meeting: discussed synbio fieldability

### 220313 - 220319:

* Spring Break

### 220320 - 220326

* Read papers and igem projects on synthetic biology bioremediation, soil contamination, biosensors
* Brainstormed project ideas
* Meeting: discussed papers and igem projects on synthetic biology bioremediation
  soil contamination, biosensors

### 220327 - 220402

* Looked into papers and igem projects on synthetic biology bioremediation, contamination in the environment
* Brainstormed project ideas
* Worked on team member introduction, photos, and science puns to introduce our team to people that pass the lab
* Meeting: discussed team member intro and synthetic biology bioremediation and environment pollution

### 220403 - 220409

* Did autoclave training for new members
* Did wet lab training
* Brainstormed project ideas
* Read synthetic biology in the environment
* Prepared for education event for William & Mary students(making kahoot games, cookie activity, advertising bioengineering minor program)

### 220410 - 220416

* Read papers on potential chassis, soil erosion, biofilm, environment pollution, bioremediation
* Held education event for students at W&M
* Brainstormed project ideas
* Meeting:
  talked about bioremediation, IHP, biosensing, and soil pollution

### 220417 - 220423

* Viewed papers on biodegradation, bioremediation, modeling on pollutants, soil bioengineering, pollutants in soil, chassis, circuit, and biofilm
* Downloaded files from galaxy
* Did safety training
* Viewed soil bioengineering papers
* Looked into modeling bacterial diffusion in soil
* Reviewed papers on chassis selection
* Had a team meeting

### 220424 - 220430

* Reviewed papers on soil bioengineering and synthetic biology circuits
* Miniprep practice for new members
* Meeting:

+ Talked about output measurement in soil
+ Discussed soil bioengineering

### 220501 - 220507

* Looked for biofilm producing circuits, bacterial genomes, chassis
* Did online safety training
* Meeting:

+ Talked about potential chassis
+ Discussed soil bioengineering

### 220508 - 220514

* Viewed circuit parts on different chassis, output measurements
* Did online safety training
* Meeting:

+ Talked about output measurement
+ Discussed soil bioengineering

### 220515 - 220521

* Reviewed chassis, biofilm, erosion, biodegradation, circuits, bacterial survival in the soil, bacterial readout, safety, and modeling
* Wet lab: minipreping and making glycerol stock for plasmid pZS1, pET-GFP, pDawn-Ag43
* IHP: conducted interview with Randolph Chambers

### 220522 - 220528

* Looked for sfgfp sequence in putida, putida plasmids, gene assembly technologies
* Reviewed articles about *P. putida*, *E. coli*, chassis selection, bacterial survival in the soil, bacterial readout, safety, and modeling
* Worked on IBC protocol and *M. smegmatis* transformation protocol
* Meeting:

+ Discussed plans for summer

## Summer 2022

---

### 220529 - 220604

* Worked on IBC protocol revision
* Reviewed materials on gene assembly technologies
* Sought IHP people
* Looked for primers for GFP in *P. putida*
* Looked into PAH degradation circuits
* Viewed PCR assaying methods
* Sought primers specific to bacterial genus, species, and strains
* Did safety training
* Wet lab: confirmed PCHERRY plasmid by inoculation, plate making, miniprep, PCR, making glycerol stock, made LB
* Conducted literature review on chassis selection, bacterial survival in the soil, bacterial readout, safety, and model
* Searched on rewriting biom-format to\_tsv()
* Wrote a script to determine stats about EMP dataset
* Worked on EMP data analysis
* Collaboration: picked up trash and took pictures
* Had a team meeting

### 220605 - 220611

* Reviewed articles on qRT PCR, soil experiments, soil kits, primers specific to bacterial genus, species, and strains, *M. Smegmatis* transformation, sfGFP primers
* Seek out local soil organizations and IHP people
* Designed primers for mcherry
* Wet lab: confirmed pTD-C\_sfYFPTwinStrep, pTDpelB-C\_sfYFPTwinstrep, pDCAF3-IS5 and pBAM1 by making glycerol stock and miniprep, inoculation, restriction digest; transformed pCHERRY into DH5a
* Reviewed requirements for impact grant
* Worked on *M. Smegmatis* transformation protocol
* Looked into growth model, models designed for food safety, and spatial models
* Worked on tensorflow program
* Assembled data into tables
* Looked at supplementing missing sample metadata with GIS information
* Tried to filter and export rebiom data
* Built coalesced spreadsheet of parameters to pull from QIITA metadata
* Worked on assembling QIITA data and augmenting with GIS
* Identified soil type parameters for chassis model
* Did CITI training:

+ Biomedical Research - Area III: Biomedical Research Investigators - PHSC (ID 42412)
+ AREA III Disciplines - Research Ethics (ID 68963)

* Had a team meeting

### 220612 - 220618

* Reviewed biosensors, paper strip testing, circuit design, bioremediation circuits,genome integration, chassis, primers
* Worked on *M. Smegmatis* transformation protocol
* Wetlab: made LB, midiprep PTDpelB-C\_sfYFPtwinStrep, ran a gel for the midiprep done this week
* Drafted IHP emails
* Made impact grant powerpoint
* Filled out the safety form
* Worked on finding GIS data sources
* Extracted qiita data
* Figured out modeling output
* Analyzed data
* Incorporated biotic factor
* Worked on metadata resolver
* IHP: conducted interview with Dan Schwartz and John Marken, joined Virginia Soil Health Coalition Quarterly Meeting
* Had a team meeting

### 220619 - 220625

* Conducted literature review on bioremediation circuits, paper strip, bacteria growth state, *P. putida* primers, *P. putida* circuit, bacteria growth model
* Drafted autoclave protocol, Taqman probe+primer protocol, and soil experiment protocol
* Wetlab: did restriction digest and gel analysis for pBAM-1,pTD-C, pDCAF, and pTDpelB-C, making 7h9 media, made plates, inoculated and minipreped PTD-C and pBAM-1, and measured OD for PTD-C and pBAM-1
* Wrote impact grant
* Did circuit analysis
* Analyzed midiprep results
* Revised *M. Smegmatis* transformation protocol
* Filled out the safety form
* Participated in the interlab study
* Tried to design paper strips experiment
* Wrote wiki page for project description
* Worked on getting WISE dataset integrated into datastream
* Tried to refetch samples from redbiom
* Figured out how to do non-linear regression in R
* Made the regression more accurate
* Analyzed regression results
* Exported data from bg7
* Added GIS sources
* Computed model
* Tried to visualize ML model on bg7
* Worked on a small GIS-based interface for interacting with the model
* IHP: conducted interview with Peter Savelyev, Alan Franzluebbers, Jessica Stephens
* Collaboration: Gaston Day School
* Had a team meeting

### 220626 - 220702

* Reviewed bacteria circuits, *P. putida* backbones, removing the insert from the backbone in pTD-C, bioremediation circuits
* Worked on impact grant
* Drafted 7H10 plate protocol, soil testing protocol, and 16S PCR protocols
* Searched the literature 16S primers
* Designed paper strip experiments
* Sought bacteria specific primers
* Attended Github igem session
* Ordered primers for mcherry and sfgfp
* Wet lab: midiprep pBAM-1 and pDCAF, making glycerol stocks of *M. Smegmatis*, making 7H10 plates, restriction digest, measuring OD of *M. Smegmatis*, miniprep on sfgfp e coli, wm21-014, inoculating *M. Smegmatis*, pTD-C, pTDpelB, restriction digest and gel analysis for pDCAF, pBAM-1, interlab study, soil collection, soil fluorescence testing
* Retrieved parameters from bg7
* Generated data subset
* Processed raw data
* Downloaded datasets
* Tried to process data for all BIOM files
* Figured out how to add a model response endpoint to the Flask chassis visualization server
* Worked on web visualization with GIS
* Collaboration: Biocrew and Hopkins
* Had a team meeting

### 220703 - 220709

* Reviewed paper strip testing, band pass, primer resuspension protocol, bioremediation circuits, metabolism circuits
* Worked on developing the 16s protocols and fluorescence testing protocol
* Wetlab: minipreped and running digest on WM21\_016 pDCAF3 and PTD-C\_sfYFPTwinStrep circuits, autoclaved soil, did interlab study, made 7H10 media and Electrocompetent *M. Smegmatis* cells, made PBS, transformed *M. Smegmatis*, made LB, resuspended mCherry primer, performed PCR to confirm efficacy of mCHerry Primers, did restriction digest and gel analysis on the PCR products, inoculated *M. Smegmatis*
* Tried to collected data analyzed by underscore+endswith script
* Worked on QIITA dedupe
* Implemented a micro-scale model of our bacterial system in soil
* Developed a micro-scale model using python
* Fixed data issues
* Built new macro model
* Had a team meeting

### 220710 - 220716

* Reviewed metabolism circuits, bioremediation circuits, paper strip, distribution kit, previous
  software projects, 16S, previous igem projects and medal requirements, papers and databases on metagenomic data in water, air, soil, and gut microbiome, ihp contacts
* Sought 16S data in gut microbiome, water, air, and soil
* Wet lab: did colony PCR on *M. Smegmatis* pCHERRY3, gel electrophoresis and analysis on the PCR, inoculated *M. Smegmatis*, interlab study, made 7H9, made LB plates
* Built on the genome-scale metabolic model
* Worked on gut microbiome model of the relative abundance models
* Reanalyzed QIITA data on HPC
* Implemented parameter resolver
* Revamped raw data processing
* Collaboration: Baltimore Biocrew and John Hopkins, VIT project
* Had a team meeting

### 220717 - 220723

* Reviewed plate reader operation, papers and databases on metagenomic data in water, air, soil, and gut microbiome, IHP contacts, isolating DNA for *M. Smegmatis*, 16S, circuits
* Worked on video script
* Sought 16S data in gut microbiome, water, air, and soil
* Drafted 16s PCR protocol
* Took team photos
* Made a calendar for upcoming tasks
* Wet lab: minipreped *M. Smegmatis*, tested out different PCR protocols, did gel electrophoresis and gel analysis for PCR products, minipreped pCHERRY3, made glycerol stocks of untransformed DH5a cells, made LB agar plates without antibiotics to have untransformed cells , resuspended 16S primers, did interlab study
* Built on microbiome analysis program
* Tried to understand genome-scale metabolic model concepts
* Attempted to solve the poetry error generated by HPC
* Established the concept of a "source data block" in the microbiome data processor
* Worked on growth and relative abundance and environment selection model
* Collaboration: Baltimore BioCrew education meeting
* Had a team meeting

### 220724 - 220730

* Sought 16S data in gut microbiome, water, air, and soil
* Worked on promotional video script
* Participated in the interlab study
* Reviewed papers on 16s sequencing and phage growth rate in relation to bacterial growth rate models
* Wet lab: tried out for the best PCR method, did gel analysis and PCR purification on the products
* Developed presentation to talk to funders of the university about our research
* Continued to work on microbiome analysis program
* Installed MPI in WSL to test concurrent dataset processing
* Built on relative abundance and growth rate model for soil, air, and water
* Worked on resolving genome-scale metabolic analysis of cinnamaldehyde system issues
* Solved a circular import issue related to units interacting with DataVal. Python-specific
* Created local venv to run cobra simulations
* Started on the system for more effectively implicitly assuming units
* IHP: conducted interview with Joseph Wood
* Had a team meeting

### 220731 - 220806

* Looked into mCherry codon optimization, incorporating plasmids into GEMS, phage growth rate in relation to bacterial growth rate, IHP contacts, interactions between inserted circuits and their chassis cell
* Worked on promotional video script
* Recorded and integrated interlab calibration data
* Wet lab: collected soil for 16s experiments, performed soil DNA extraction and purification, PCR, did restriction digest and gel analysis on the products
* Worked on software interface
* Looked into microbiome analysis program
* Generated data subset from individual studies
* Searched for Genome-scale metabolic models
* Collaboration: John Hopkins/Biocrew meeting, reach out to VIT Vellore, Gaston Day School
* Education: Terraforming Mars game expansion pack
* IHP: conducted interview with Ms Bouchra Ezzamouri, Dr. Bryn Adams, Dr. Salerno
* Had a team meeting

### 220807 - 220813

* Wrote Review of Chassis Selection
* Tried to incorporate genome-scale metabolic model
* Rebuilt software package for new platform
* Performed testing with multiple designs for sliders with explicit values + units
* Worked on promotional video
* Performed 16s PCR
* Coded and wrote for wiki
* Education: Booklet for children about chassis selection/associated games, terraforming Mars expansion pack, Re-Terraforming Earth Game
* Collaboration: Participated in Mid Atlantic Meetup with other iGEM teams
* IHP: conducted interview with Dr. Kunjapur
* Had a team meeting

### 220814 - 220820

* Worked on Terraforming Mars expansion pack
* Coded for wiki meet the team page
* Designed Re-terraforming Earth cards
* Recorded video sound track
* Ordered the updated primer set
* Conducted 16S sequencing experiments to test out our model
* Developed circuits to improve a part
* Created chassis selection software package
* Generated unfiltered water parameters set
* Collaboration: Hopkins/BioCrew, mid-Atlantic meetup, Gaston Day School
* Had a team meeting

### 220821 - 220827

* Translated video script into Chinese, Spanish, and portuguese
* Wrote notebook page for wiki
* Processed 16S data
* Worked on Terraforming Mars Expansion Pack
* Conducted 16S sequencing experiments
* Designed Re-Terraforming Earth Game
* Developed Promotional Vide
* Continued with developing circuits to improve a part
* Worked on genome-scale metabolic model
* Collaboration follow-up: Gaston Day School iGEM, UMaryland iGEM, PuiChing\_Macau iGEM
* Had a team meeting

## Fall 2022

---

### 220828 - 220903

* Looked into papers on validating our projects
* Worked on wiki pages writing & coding
* Sent out IHP emails
* Continued Writing chassis literature review
* Designed cards for Terraforming Mars expansion pack
* Developed the booklet
* Worked on the Re-Terraforming Earth game
* Filled out safety form and judging forms
* Continued with developing circuits to improve a part
* Drafted soil-drying protocols
* Wet lab: did DNA purification for 16s PCR gene from soil, imaged gel for the 16s purified PCR products
  Performed an analysis of the generic top bacteria in our datasets
* Implemented moisture level GIS from OpenLandMap and lembrechts temperature GIS
* Worked on a unified raster system
* Extracted regression info from R data snapshot to feed into gradient descent optimization
* Tried SGD optimization in tensorflow with regression equation
* Collaboration: Gaston Day School Model, VIT Vellore Model, John's Hopkins University Video, University of Maryland Storybook, PuiChing Macau Meetup, UNSW iGEM, GEMS Taiwan iGEM, NTHU Taiwan iGEM
* Had team meetings

### 220904 - 220910

* Looked into papers on validating our projects
* Worked on wiki pages writing & coding
* Sent out IHP emails
* Continued Writing chassis literature review
* Designed cards for Terraforming Mars expansion pack
* Developed the booklet
* Worked on the Re-Terraforming Earth game
* Filled out safety form and judging forms
* Determined which species to transform our improved part intp
* Crafted soil-drying protocols
* Wet lab: DNA purification for 16s PCR gene from soil, conducting PCRs/PCR purification, sfgfp circuits, mcherry circuit, and sent samples for sequencing
* Added scrollbox support
* Added a filter to the output list that selects Genus, Species, or both
* Added explicit value tags to parameters that are returned by the API on the JS-end
* Collaboration: Gaston Day School Model, VIT Vellore Model, John's Hopkins University Video, University of Maryland Storybook, PuiChing Macau Meetup, UNSW iGEM, GEMS Taiwan iGEM, NTHU Taiwan iGEM
* Had team meetings

### 220925 - 221001

* Worked on finalizing software
* Designed Expansion Pack Cards
* Assembled and tested our improving a part constructs
* Filmed and edited chassis selection video
* Worked on developing wiki
* Automated regression
* Gathered images for wiki
* Had team meetings

### 221002 - 221008

* Finalized software
* Developed wiki
* Tested our improving a part constructs
* Analyzed improve a part data
* Tested our Re-terraforming Earth game
* Designed Expansion Pack Cards
* Developed parts pages
* Had team meetings

### 221002 - 221008

* Developed wiki
* Had team meetings

## See our Benchling Pages:

* [Alana Thomas](https://static.igem.wiki/teams/4174/wiki/benchling/at-compressed.pdf)* [Avery Bradley](https://static.igem.wiki/teams/4174/wiki/benchling/ab-compressed.pdf)* [Beteel Abu-Ageel](https://static.igem.wiki/teams/4174/wiki/benchling/ba.pdf)* [Bjorn Shockey](https://static.igem.wiki/teams/4174/wiki/benchling/bs.pdf)* [Debby Zhong](https://static.igem.wiki/teams/4174/wiki/benchling/dz.pdf)* [Diego Morandi](https://static.igem.wiki/teams/4174/wiki/benchling/dmz.pdf)* [Krithika Layagala](https://static.igem.wiki/teams/4174/wiki/benchling/kl.pdf)* [Lin Fang](https://static.igem.wiki/teams/4174/wiki/benchling/lf.pdf)* [Megan Fleeharty](https://static.igem.wiki/teams/4174/wiki/benchling/mf.pdf)* [Walker Knapp](https://static.igem.wiki/teams/4174/wiki/benchling/wk.pdf)* [Zhe Liu](https://static.igem.wiki/teams/4174/wiki/benchling/zl.pdf)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Make a useful contribution for future iGEM teams. Use this page to document that contribution.{% endblock %}
{% block page\_content %}

# Engineering Success

[Overview](#Intro)
[Neural Network](#neural)
[Improvement of an Existing Part](#design)
[References](#sources)

As part of our project development, our team went through several iterations of the Engineering Design Cycle (design → build → test → learn), when (1) building our Neural Network, (2) developing our regression, and (3) designing our circuits.

We underwent iterations of the Engineering Design Cycle at two different scales. We designed versions of the software package and project as a whole, before building testing and ultimately learning by changing the direction of our software based on computed results, but we also underwent micro-iterations of the Engineering Design Cycle, where each component of the software and models were built iteratively, with knowledge from previous designs informing the next.

At the core of our software and model development process, we created iterative revisions of key algorithms and packages in our software. For each of our predictive models, we designed initial exploratory algorithms, built agile implementations in R and python, evaluated these models by calculating the error between predictions and 16s data in metagenomic subsets, and then created new model revisions based on the information we learned. We repeated this process dozens of times, ultimately creating final versions of each model we use in the software.

To view our software toolkit, please visit the Gitlab: [chassEASE](https://gitlab.igem.org/2022/software-tools/william-and-mary).

## Neural Network

---

The Neural Network/Artificial Intelligence predictive model is one of the core algorithms we employed to create accurate relative abundance predictions. We chose to follow a common design for class inference, the fully-dense Artificial Neural Network (ANN) design. As implied by the name, its structure is inspired by the human brain, mimicking the way biological neurons send signals to each other. We designed our initial ANNs consisting of a layer of nodes corresponding to inputs, 6 fully connected hidden layers, and an output layer. Nodes are interconnected with a series of weights and biases. We planned on using the Tensorflow library to build the initial ANN with a subset of predictors.

#### Design:

We used python and Tensorflow to 6 fully connected hidden layers, and an output layer. We used cross-entropy for our loss function and the Adam function as our optimizer.

![](https://static.igem.wiki/teams/4174/wiki/neural-networks-structure-new.png)

#### Build:

We used python and Tensorflow to 6 fully connected hidden layers, and an output layer. We used cross-entropy for our loss function and the Adam function as our optimizer.

#### Test:

When training our neural network, we reached an accuracy of 71%. As shown in the graph, the accuracy of our model skyrocketed in the very early stages of our training process. Originally, in our dataset, to account for missing values, we replaced the blank measurements with zero. We suspect that this early jump in accuracy in the early iterations of our model was due to the large number of zero measurements in our initial dataset.

![](https://static.igem.wiki/teams/4174/wiki/model-accuracy.png)

#### Learn:

We learned that the data plays a crucial role in training an accurate and generalizing model. To avoid the problem with missing data, more data processing needs to be done so that our dataset can be as representative of the real data as possible. We also tested more loss functions and optimizers to see how the results change, and we have found that the best results came from our original loss function (binary cross-entropy) and optimizer (adam).

## Regression

---

### Goal

The goal of the regression was to create an interpretable equation that predicts the relative abundance of bacteria in soil.

### Iteration 1

#### Design:

In order to accurately predict the relative abundance of different bacterial genera, we built our initial regression to account for high degree polynomial terms and logarithmic independent variables. We hoped to find statistically significant coefficients that would allow us to compare how one unit change in the explanatory variable impacted the prediction of the dependant variable.

#### Build:

After our metadata table was imported to R studio we built the following code.

```
  >reg<-lm(Bacillus~poly(depth,4)+log(depth+.001)+poly(elevation,4)+log(elevation+.001)+poly(soil_temperature,4)
  +poly(ph,4)+log(ph+.001)+poly(respiration,4)+log(respiration+.001)+poly(total_carbon,4)+log(total_carbon+.001)
  +poly(organic_carbon,4)+log(organic_carbon+.001)+poly(total_nitrogen,4)+log(total_nitrogen+.001)+poly(carbon_nitrogen_ratio,4)
  +log(carbon_nitrogen_ratio+.001)+poly(aluminum,4)+log(aluminum+.001)+poly(soil_density,4)+log(soil_density+.001)
  +soil_sand+soil_clay+soil_silt, data=data)
  > summary(reg)
```

#### Test:

![](https://static.igem.wiki/teams/4174/wiki/regression1.png)

![](https://static.igem.wiki/teams/4174/wiki/regression-2.png)

#### Learn/Rebuild:

This regression taught us that a large amount of our soil parameters had almost no statistical significance. Additionally, the regression was only able to account for 8% of the variance in prediction. There were a few conclusions we initially made. Either, the soil parameters had very little effect on the relative abundance of our bacterial genera or something was wrong with our data. After running this regression we looked back on the assumptions we were making about normality and decided to build a new regression that used the logarithm of the dependent variable. This adjustment helped to normalize the data and fix issues with heteroskedasticity.

### Iteration 2

#### Design:

As we continued with our regressions we raised the sizes of the polynomial terms to account for higher derivative relationships. We knew that our data was slightly right skewed, so we decided to take the logarithm of our dependent variable. This helped mitigate error caused by lack of normality and homoscedasticity

#### Build:

```
  >reg <-lm(Bacillus~poly(depth,4)+log(depth+.001)+poly(elevation,4)+log(elevation+.001)+poly(soil_temperature,4)
  +poly(ph,4)+log(ph+.001)+poly(respiration,4)+log(respiration+.001)+poly(total_carbon,4)+log(total_carbon+.001)
  +poly(organic_carbon,4)+log(organic_carbon+.001)+poly(total_nitrogen,4)+log(total_nitrogen+.001)+poly(carbon_nitrogen_ratio,4)
  +log(carbon_nitrogen_ratio+.001)+poly(aluminum,4)+log(aluminum+.001)+poly(soil_density,4)+log(soil_density+.001)
  +soil_sand+soil_clay+soil_silt, data=data)
  >summary(reg)
```

#### Test:

![](https://static.igem.wiki/teams/4174/wiki/regression3.png)
![](https://static.igem.wiki/teams/4174/wiki/regression4.png)
![](https://static.igem.wiki/teams/4174/wiki/regression5-new.png)
![](https://static.igem.wiki/teams/4174/wiki/regression6.png)
![](https://static.igem.wiki/teams/4174/wiki/regression7.png)

#### Learn:

From this regression, we started to reduce the number of soil parameters that were consistently statistically relevant. This reduction led to interpretable coefficients in both sign and magnitude. We also realized that a huge source of error in previous regressions was due to the skewing of our data. By taking the logarithm of our dependent variables, we were able to greatly increase the accuracy of our regression. As the final step for the regression, we wanted to add interactive variables, automate the regression, and include species data in addition to genera.

### Iteration 3

#### Design:

We wanted to add interactive variables, species data, and automate the regression simultaneously. We created a python script that would build our regression. The Python code looked at the data spreadsheet and created a list of all of the possible input variables. Then it started to build an R-studio file that would be run in R. The first step was to download the necessary packages into R. Then we started building the regression. We raised each term to the seventh order. In the final regression we declined to use the poly function because it is not functional with stepwise dimensionality reduction. We then added logarithmic mappings for all of the non negative soil parameters. Lastly, we created a list of all possible interactive variables. This was then iterated for the set of all genera and species .

It would take the set of all possible input variables and regresses to the 7th order, if the variable was negative it would be excluded from the logarithm mapping, and would then multiply each variable by each other to get all possible interactive variables. We also added a forward selection dimensionality reduction to the code so it would only output statistically relevant variables.

#### Build:

```
        import pandas as pd
      import subprocess

      from microbiome_analysis.target.soil_mlr_target import SoilMlrTarget

      target = SoilMlrTarget()
      target.setup()

      params_to_model = target.params_sheet.loc[target.params_sheet["Model"] & (target.params_sheet["Units"] != "enum"), :]["Parameter"]

      output_columns = []
      with open("soil_genus.txt") as f:
          for genus in f.readlines():
              output_columns.append(genus.strip()[:1].upper() + genus.strip()[1:].lower())
      with open("soil_species.txt") as f:
          for species in f.readlines():
              output_columns.append(species.strip()[:1].upper() + species.strip()[1:].lower())

      subset_location = "../../william-and-mary-software/soil_genera_processed.csv"
      subset_for_stats = pd.read_csv(subset_location)

      for bacteria in output_columns:
          if bacteria != "Burkholderia":
              continue

          new_loc = "droppedsheet.csv"
          subset_for_stats.loc[subset_for_stats[bacteria] != 0, :]\
              .dropna(subset=[bacteria]).to_csv(new_loc)

          r_script_base = f"""
          install.packages('pacman');
          install.packages('leaps');
          library(pacman);
          pacman::p_load(leaps,pacman,dplyr,GGally,ggplot2,ggthemes,ggvis,httr,plotly,rio,rmarkdown,shiny,stringr,tidyr,MASS);
          rio_CSV<-import('./{new_loc}');
          """
          terms = []

            for parameter in params_to_model:
                if 'cobalt' in parameter or 'water_holding' in parameter or 'phenol' in parameter:
                    continue

                terms.append(f"{parameter}")
                for j in range(2, 7+1):
                    terms.append(f"{parameter}^{j}")

                if subset_for_stats[parameter].min() >= 0:
                    if "soil_temp" in parameter or "environment_temp" in parameter:
                        continue
                    terms.append(f"log({parameter}+0.0001)")

                for interactive_param in params_to_model:
                    if parameter == interactive_param:
                        continue
                    if 'cobalt' in interactive_param or 'water_holding' in interactive_param or 'phenol' in interactive_param:
                        continue

                    interactive_term = f"{parameter}*{interactive_param}"
                    terms.append(interactive_term)

            r_script = r_script_base + f"""
        reg<-lm({bacteria}+0.0001~{"+".join(terms)},data=rio_CSV);

        final_model <- stepAIC(reg, direction="forward", trace=FALSE);

        summary(final_model);
        """

            #r_script = r_script.replace("\n", "")

            with open("train.r", 'w') as f:
                f.write(r_script)

            print(r_script)
            print(len(r_script))

            process = subprocess.Popen(["wsl", "Rscript", f"./train.r"],
                                   stdout=subprocess.PIPE,
                                   universal_newlines=True)

            while True:
                output = process.stdout.readline()
                try:
                    print(output.strip())
                except:
                    pass
                    # Do something else
        return_code = process.poll()
        if return_code is not None:
            print('RETURN CODE', return_code)
            # Process has finished, read rest of the output
            for output in process.stdout.readlines():
                try:
                    print(output.strip())
                except:
                    pass
            break

    exit()
```

#### Test:

![](https://static.igem.wiki/teams/4174/wiki/engineering-success/i3-finalized.png)

#### Learn:

This is an output from Burkholderia, a random genera we selected to show the broad swath of our regression. We generated over 500 unique regressions for species and genera data of different accuracies and component variables. In this case, we have an adjusted R-squared of .15, meaning our metadata soil parameters were able to account for only 15% of the variance in Burkholderia relative abundance. This lower accuracy could be from a variety of possible errors, but most likely is due to the non-normality of the data or the incompleteness of our metadata. Additionally, due to the large set of input variables the forward and backward stepwise dimensionality reduction was not able to effectively reduce variables. This means we were unable to get interperatible coefficients when we were dealing with the larger metadata sets. In order to make assumptions about our soil parameters we reduced the number of interactive terms as well as input variables.

## Improvement of an Existing Part

---

### Overview and Biological Relevance

The ability to assay whether a chassis is actively transcribing its circuit to make proteins is crucial for testing the efficacy of a fieldable construct. Bacteria have two main life states: exponential growth, during which they reproduce and express their circuits with ease, and stationary phase, during which they cease most non-essential metabolic activity. Since stationary phase is induced by inopportune environments, such as metabolite shortage, most bacteria in nature exist in stationary phase (Jaishankar 2000). This is a major problem for fieldable synthetic biology, as constructs that work perfectly in the lab may stop expressing their circuits when introduced into their deployment sites. In order to assay how a circuit will behave in nature, constructs should be tested in the lab while in stationary phase. To this end, William and Mary has designed two stationary phase detection constructs, using red and green fluorescence as outputs. These composite parts are an improvement of MIT iGEM 2006’s composite part BBa\_J45995, and are included in the iGEM Registry as parts BBa\_K4174001 (red fluorescence) and BBa\_K4174002 (green fluorescence), where you can find their full sequences. Below we have outlined information about [1] the original MIT iGEM 2006 composite part, [2] our improved parts, [3] our protocol for testing our constructs, and [4] results demonstrating our constructs exhibiting stronger fluorescence than the original construct.

### Design:

#### osmY-sfGFP Stationary Phase Detection Construct (BBa\_K4174002)

![](https://static.igem.wiki/teams/4174/wiki/osmy-sfgfp-final.png)

We improved this part by [a] increasing fluorescence by replacing GFP with superfolder GFP (sfGFP), [b] removing non-functional scar sequences, and [c] making the sequence compatible with a new assembly method (Type IIS).

* Replacing GFP with sfGFP and Changing the RBS

+ Superfolder GFP (sfGFP) is an improved version of GFP which folds more readily and precisely in *Escherichia coli*, allowing for more efficient, bright, and accurate assays (Pédelacq 2006). We sourced our sfGFP sequence from Ceroni *et al.* (2015). This sfGFP sequence was designed for high-level expression in *E. coli* (Ceroni et al. 2015). In order to ensure that our ribosome binding sequence (RBS) was compatible with our coding region, we also replaced the original RBS with an RBS used by Ceroni *et al.* (2015) with sfGFP.

* Removing Non-functional Scar Sequences

+ The original construct sequence has scar sequences present from assembly. These nonfunctional units were removed by our team as they are purely artifacts of biological assembly.

* Making the Construct Type IIS Assembly Compatible

+ All constructs uploaded to the iGEM Registry that are considered for the iGEM competition must be compatible with either Type IIS Assembly or RFC 10 Assembly. The original MIT iGEM 2006 construct was only compatible with RFC 10, but after altering the sequence as described above to create our composite part, it is now compatible with both Type IIS Assembly and RFC 10 Assembly. Increasing the options for assembly methods compatible with our construct will help make its construction more accessible to researchers.

#### osmY-mRFP1 Stationary Phase Detection Construct (BBa\_K4174001)

![](https://static.igem.wiki/teams/4174/wiki/osmy-mrfp1-final.png)

We improved this part by [a] offering a new fluorescence option to researchers by replacing GFP with monomeric red fluorescence protein (mRFP1) and [b] making the sequence compatible with a new assembly method (Type IIS).

* Replacing GFP with mRFP1

+ Creating alternative versions of fluorescent bioreporters using proteins with different excitation and emission wavelengths allows researchers to assay multiple parameters at a time, as different fluorescent assays conducted simultaneously must use proteins with different absorption spectra in order for researchers to differentiate between them. We replaced GFP with mRFP1 in order to give researchers a red fluorescence protein for detecting stationary phase in bacteria. mRFP1 is a monomer, has a rapid maturity rate, and has minimal spectral overlap with GFP compared to the wild-type red fluorescent protein DsRed (Campbell et al. 2002). However, other DsRed variants have a much higher fluorescence quantum yield and extinction coefficient than mRFP1 (Campbell et al. 2002).

* Making the Construct Type IIS Assembly Compatible

+ All constructs uploaded to the iGEM Registry that are considered for the iGEM competition must be compatible with either Type IIS Assembly or RFC 10 Assembly. The original MIT iGEM 2006 construct was only compatible with RFC 10 Assembly, but after altering the sequence as described above to create our composite part, it became compatible with both Type IIS Assembly and RFC 10 Assembly. Increasing the options for assembly methods compatible with our construct will help make its construction more accessible to researchers.

### Build:

In order to assemble the original MIT iGEM 2006 construct along with our improved constructs, we selected to use Gibson Assembly. The inserts were synthesized by IDT and were flanked by unique nucleotide sequences (UNSs) 1 and 10 (Torella *et al.* 2014). Our backbone, pSB1C3, was also flanked by UNSs 1 and 10. We isolated this backbone from a 2018 William and Mary iGEM construct using PCR primers that anneal to the UNS sequences, then performed a gel extraction and PCR purification before utilizing the backbone for Gibson Assembly. Please download our protocols for PCR, gel extraction, PCR purification, transformation of *E. coli* and Gibson Assembly here:

Download Protocols Below:

* [PCR Protocol](https://static.igem.wiki/teams/4174/wiki/pcr-neb-q5-220707-1.pdf)
* [Gel Electrophoresis Protocol](https://static.igem.wiki/teams/4174/wiki/gelelectrophoresisprotocol.pdf)
* [Gel Extraction Protocol](https://static.igem.wiki/teams/4174/wiki/copy-of-gel-extraction-190628jau-docx.pdf)
* [PCR Purification Protocol](https://static.igem.wiki/teams/4174/wiki/neb-pcr-dna-clean-up-kit-protocol.pdf)
* [Transformation Protocol](https://static.igem.wiki/teams/4174/wiki/transformation-e-coli-1.pdf)
* [Gibson Assembly Protocol](https://static.igem.wiki/teams/4174/wiki/copy-of-gibson-assembly-1.pdf)

### Test:

#### Protocol for Testing Parts

To test the effectiveness of our improved parts, our team grew all three constructs in E. coli NEB5-α cells in our plate reader. They were grown at 37°C, and were continuously shaking in a plate reader (with OD600 and fluorescence measurements taken every 10 minutes) with the following exceptions: the cultures were not shaking while measurements were taken, and the cultures were placed at around room temperature outside the plate reader for about two hours during the experiment (during which no measurements were taken). Although the cultures were grown for around a total of 19 hours and 35 minutes, the graphs shown below only include measurements taken starting from around the 10 hour and 5 minute mark. In addition, the graphs below represent the averages of fluorescence measurements (normalized to OD600) taken from two biological replicates of our experiment. However, the inoculation process differed between these two replicates. For one of our replicates, we diluted a culture grown overnight in 4 mLs of LB to an OD of 0.1, then loaded the culture into wells to grow overnight. For the other replicate, we inoculated into 1 mL of culture, waited roughly 30 minutes, and loaded the culture into the well plates to grow. For both replicates, 200 uLs of culture was loaded into each well. Also, for both replicates, green fluorescence was measured using an excitation wavelength of 485 nm and an emission wavelength of 528 nm, and red fluorescence was measured using an excitation wavelength of 584 nm and an emission wavelength of 610 nm. Please download our raw fluorescence data and protocols for preparation of LB media, inoculation, and plate reader testing below.

Download Raw Fluorescence Data Below:

* [Raw Fluorescence Data (CSV)](https://static.igem.wiki/teams/4174/wiki/improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-xlsx-plate-1-sheet1.csv)* [Raw Fluorescence Data (PDF)](https://static.igem.wiki/teams/4174/wiki/improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-xlsx-plate-1-sheet1.pdf)* [Key: Raw Fluorescence Data](https://static.igem.wiki/teams/4174/wiki/key-improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-sheet1.csv)

Download Protocols Below:

* [Preparation of LB Media Protocol](https://static.igem.wiki/teams/4174/wiki/lb-210226-3.pdf) * [Inoculation Protocol](https://static.igem.wiki/teams/4174/wiki/inoculations-for-miniprep-220424-3.pdf)* [Plate Reader Testing Protocol](https://static.igem.wiki/teams/4174/wiki/final-fluorescence-intensity-testing-protocol-1.pdf)

      #### Results

      ##### BBa\_K417002 Characterization (osmY-sfGFP Construct)

      To test the effectiveness of our osmY-sfGFP construct (BBa\_K4174002), our team transformed the original MIT iGEM 2006 construct, our improved osmY-sfGFP construct, and our improved osmY-mRFP1 construct (BBa\_K4174001) in E. coli NEB5-α in a plate reader. The various transformants were grown at 37°C. For green fluorescence, we used an excitation wavelength of 485 nm and an emission wavelength of 528 nm. The values for green fluorescence intensity normalized to OD600 values are reported below.

      ![](https://static.igem.wiki/teams/4174/wiki/normalized-green-fluorescence-graph-final-larger.png)

      *Graph Label: Please note that the data represented in the graph above only includes measurements taken starting from around the 10 hour and 5 minute mark (out of a total growth time of about 19 hours and 35 minutes). In addition, the data shown represents the averages of fluorescence measurements (normalized to OD600) taken from two biological replicates of our experiment. Please note that the inoculation process differed between these two replicates (see the protocol section of this page for details)*

      As seen in the graph above, both the osmY-sfGFP and MIT iGEM 2006 GFP constructs enter stationary phase right before 14 hours, but our improved osmY-sfGFP construct is much more fluorescent. The other constructs are our osmY-RFP construct and untransformed E. coli cells, both of which serve as negative controls for green fluorescence.

      ![](https://static.igem.wiki/teams/4174/wiki/improveapart-smaller.png)

      As seen in the image above, qualitative results reveal that our improved constructs are more fluorescent than the original construct. Here, bacterial cells transformed with our osmY-sfGFP construct are on the far right, and are visibly more green than cells transformed with the original MIT iGEM 2006 construct.

      ##### BBa\_K417001 Characterization (osmY-mRFP1 Construct)

      To test the effectiveness of our osmY-mRFP1 construct (BBa\_K4174001), our team transformed the original MIT iGEM 2006 construct (BBa\_J45995), our osmY-mRFP1 construct, and our osmY-sfGFP construct (BBa\_K4174001) into E. coli NEB5-α cells and grew the various transformants in a plate reader. For red fluorescence measurements, we used an excitation wavelength of 584 nm and an emission wavelength of 610 nm. The values for red fluorescence intensity normalized to OD600 values are reported below.

      ![](https://static.igem.wiki/teams/4174/wiki/normalized-red-fluorescence-graph-final-larger.png)

      *Graph Label: Please note that the data represented in the graph above only includes measurements taken starting from around the 10 hour and 5 minute mark (out of a total growth time of about 19 hours and 35 minutes). In addition, the data shown represents the averages of fluorescence measurements (normalized to OD600) taken from two biological replicates of our experiment. Please note that the inoculation process differed between these two replicates (see the protocol section of this page for details).*

      Based on the graph above, the bacterial cells engineered with our osmY-mRFP1 construct appear to have entered stationary phase around 16 hours. As seen in the graph, our osmY-mRFP1 construct produces more red fluorescence than the original construct (as expected since the original construct was a GFP construct). The other measurements taken are for our osmY-sfGFP construct and untransformed *E. coli* NEB5-α cells, both of which serve as negative controls for red fluorescence.

      The fluorescence intensity of the untransformed cells appears higher than that of cells transformed with our osmY-mRFP1 construct due to our normalization process. Although the raw fluorescence values of the untransformed cells were consistently lower than the raw fluorescence values of the osmY-mRFP1 circuit, the OD600 values of the untransformed cells were much lower than the OD600 values of the cells transformed with osmY-mRFP1. Therefore, when we normalized by dividing raw fluorescence by OD600, the fluorescence intensity of the untransformed cells appeared to be higher than that of the osmY-mRFP1 transformants.

      ![](https://static.igem.wiki/teams/4174/wiki/improveapart-smaller.png)

      As seen in the image above, qualitative results reveal that our osmY-mRFP1 construct produces more red fluorescence than the original construct. Here, bacterial cells transformed with our osmY-mRFP1 construct are on the far left, and are visibly red, unlike the other two cultures transformed with the osmY-sfGFP and MIT iGEM 2006 constructs.

      ### Learn:

      Through our testing process, we learned that these two combinations of parts lead to an optimized construct.

## References

---

Campbell, R. E., Tour, O., Palmer, A. E., Steinbach, P. A., Baird, G. S., Zacharias, D. A., & Tsien, R. Y. (2002). A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7877–7882. https://doi.org/10.1073/pnas.082243699

Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5):415-418. Doi: 10.1038/nmeth.3339

Chang, D. E., Smalley, D. J., & Conway, T. (2002). Gene expression profiling of Escherichia coli growth transitions: an expanded stringent response model. Molecular microbiology, 45(2), 289-306.

Jaishankar, J., & Srivastava, P. (2017). Molecular basis of stationary phase survival and applications. Frontiers in microbiology, 8, 2000.

Pédelacq, J. D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2006). Engineering and characterization of a superfolder green fluorescent protein. Nature biotechnology, 24(1), 79-88.

Torella, J. P., Boehm, C. R., Lienert, F., Chen, J. H., Way, J. C., & Silver, P. A. (2014). Rapid construction of insulated genetic circuits via synthetic sequence-guided isothermal assembly. Nucleic acids research, 42(1), 681–689. doi.org/10.1093/nar/gkt860

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Make a useful contribution for future iGEM teams. Use this page to document that contribution.{% endblock %}
{% block page\_content %}

# Experiments

[Overview](#Intro)
[1) Improvement of an Existing Part](#Software Contributions)
[2) Experiments for 16S rRNA Sequencing](#2) Experiments for 16S rRNA Sequencing )
[References](#References)

&nbspThis season, our team conducted experiments for 1) improvement of an existing part and 2) for our proof of concept.

## 1) Improvement of an Existing Part

---

### Original Part:

#### Original osmY Stationary Phase Detection Construct from MIT iGEM 2006

![](https://static.igem.wiki/teams/4174/wiki/osmy-mit-2006-new.png)

 &nbspThe original composite part, which was designed by MIT iGEM 2006, can be found on the iGEM Registry as part BBa\_J45995. This construct consists of the following basic parts: an osmY promoter, ribosome binding sequence BBa\_B0030, a green fluorescence protein (GFP) coding region, and terminator BBa\_B0015. Between the promoter and RBS, RBS and coding region, and coding region and terminator, the DNA sequence contains scar sequences from assembly.

 &nbspThe promoter selected was an osmY promoter, as this promoter is induced by the cell’s entry into stationary phase. In typical *E. coli* cells, osmY, which helps cells transition into stationary phase when under osmotic or metabolic stress, is not produced during exponential growth phase but is produced during stationary phase. Specifically, the osmY promoter is induced by rpoS (ribosome polymerase sigma S) at the onset of stationary phase (Chang 2002). Because the coding region in this construct is *GFP*, this construct fluoresces green once the cell enters stationary phase.

### Design:

#### A. osmY-sfGFP Stationary Phase Detection Construct (BBa\_K4174002)

![](https://static.igem.wiki/teams/4174/wiki/osmy-sfgfp-final.png)

 &nbspWe improved this part by [a] increasing fluorescence by replacing GFP with superfolder GFP (sfGFP), [b] removing non-functional scar sequences, and [c] making the sequence compatible with a new assembly method (Type IIS).

##### a. Replacing GFP with sfGFP and Changing the RBS

 &nbspSuperfolder GFP (sfGFP) is an improved version of GFP which folds more readily and precisely in *Escherichia coli*, allowing for more efficient, bright, and accurate assays (Pédelacq 2006). We sourced our *sfGFP* sequence from Ceroni et al. (2015). This *sfGFP* sequence was designed for high-level expression in *E. coli* (Ceroni et al. 2015). In order to ensure that our ribosome binding sequence (RBS) was compatible with our coding region, we also replaced the original RBS with an RBS used by Ceroni et al. (2015) with their *sfGFP* sequence.

##### b. Removing Non-functional Scar Sequences

 &nbspThe original construct sequence has scar sequences present from assembly. These nonfunctional units were removed by our team as they are purely artifacts of biological assembly.

##### c. Making the Construct Type IIS Assembly Compatible

 &nbspAll constructs uploaded to the iGEM Registry that are considered for the iGEM competition must be compatible with either Type IIS assembly or RFC 10 Assembly. The original MIT iGEM 2006 construct (BBa\_J45995) was only compatible with RFC 10, but after altering the sequence as described above to create our composite part, it is now compatible with both Type IIS Assembly and RFC 10 Assembly. Increasing the options for assembly methods compatible with our construct will help make its construction more accessible to researchers.

#### B. osmY-mRFP1 Stationary Phase Detection Construct (BBa\_K4174001)

![](https://static.igem.wiki/teams/4174/wiki/osmy-mrfp1-final.png)

 &nbspWe improved this part by [a] offering a new fluorescence option to researchers by replacing GFP with monomeric red fluorescence protein (mRFP1) and [b] making the sequence compatible with a new assembly method (Type IIS).

##### a. Replacing GFP with mRFP1

 &nbspCreating alternative versions of fluorescent bioreporters using proteins with different excitation and emission wavelengths allows researchers to assay multiple parameters at a time, as different fluorescent assays conducted simultaneously must use proteins with different absorption spectra in order for researchers to differentiate between them. We replaced GFP with mRFP1 in order to give researchers a red fluorescence protein for detecting stationary phase in bacteria. mRFP1 is a monomer, has a rapid maturity rate, and has minimal spectral overlap with GFP compared to the wild-type red fluorescent protein DsRed (Campbell et al. 2002).

##### b. Making the Construct Type IIS Assembly Compatible

 &nbspAll constructs uploaded to the iGEM Registry that are considered for the iGEM competition must be compatible with either Type IIS Assembly or RFC 10 Assembly. The original MIT iGEM 2006 construct (BBa\_J45995) was only compatible with RFC 10 Assembly, but after altering the sequence as described above to create our composite part, it became compatible with both Type IIS Assembly and RFC 10 Assembly. Increasing the options for assembly methods compatible with our construct will help make its construction more accessible to researchers.

### Build:

 &nbspIn order to assemble the original MIT iGEM 2006 construct (BBa\_J45995) along with our improved constructs, we selected to use Gibson Assembly. Our inserts (described in the design section above) were synthesized by IDT and were flanked by unique nucleotide sequences (UNSs) 1 and 10 (Torella *et al.* 2014). Our backbone, pSB1C3, was also flanked by UNSs 1 and 10. We isolated this backbone from a 2018 William and Mary iGEM construct using PCR primers that anneal to the UNS sequences, then performed a gel extraction and PCR purification before utilizing the backbone for Gibson Assembly. Please download our protocols for PCR, gel extraction, PCR purification, transformation of *E. coli* and Gibson Assembly below.

Download Protocols Below:

* [PCR Protocol](https://static.igem.wiki/teams/4174/wiki/pcr-neb-q5-220707-1.pdf)
* [Gel Electrophoresis Protocol](https://static.igem.wiki/teams/4174/wiki/gelelectrophoresisprotocol.pdf)
* [Gel Extraction Protocol](https://static.igem.wiki/teams/4174/wiki/copy-of-gel-extraction-190628jau-docx.pdf)
* [PCR Purification Protocol](https://static.igem.wiki/teams/4174/wiki/neb-pcr-dna-clean-up-kit-protocol.pdf)
* [Transformation Protocol](https://static.igem.wiki/teams/4174/wiki/transformation-e-coli-1.pdf)
* [Gibson Assembly Protocol](https://static.igem.wiki/teams/4174/wiki/copy-of-gibson-assembly-1.pdf)

### Test:

#### Protocol for Testing Parts

To test the effectiveness of our improved parts, our team grew all three constructs in *E. coli* NEB5-α cells in our plate reader. They were grown at 37°C, and were continuously shaking in a plate reader (with OD600 and fluorescence measurements taken every 10 minutes). Although the cultures were grown for around a total of 19 hours and 35 minutes, the graphs shown below only include measurements taken starting from around the 10 hour and 5 minute mark. In addition, the graphs below represent the averages of fluorescence measurements (normalized to OD600) taken from two experiments. However, the inoculation process differed between these two experiments. For one of our experiments, we diluted a culture grown overnight in 4 mLs of LB to an OD of 0.1, then loaded the culture into wells to grow overnight. For the other experiment, we inoculated into 1 mL of LB, waited roughly 30 minutes, and loaded the culture into the well plates to grow. For both experiments, 200 uLs of culture was loaded into each well. Also, for both experiments, green fluorescence was measured using an excitation wavelength of 485 nm and an emission wavelength of 528 nm, and red fluorescence was measured using an excitation wavelength of 584 nm and an emission wavelength of 610 nm. Please download our raw fluorescence data and protocols for preparation of LB media, inoculation, and plate reader testing below.

Download Raw Fluorescence Data Below:

+ [Raw Fluorescence Data (CSV)](https://static.igem.wiki/teams/4174/wiki/improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-xlsx-plate-1-sheet1.csv)+ [Raw Fluorescence Data (PDF)](https://static.igem.wiki/teams/4174/wiki/improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-xlsx-plate-1-sheet1.pdf)+ [Key: Raw Fluorescence Data](https://static.igem.wiki/teams/4174/wiki/key-improving-a-part-raw-fluorescence-overnight-plate-reader-experiment-221001-sheet1.csv)

Download Protocols Below:

+ [Preparation of LB Media Protocol](https://static.igem.wiki/teams/4174/wiki/lb-210226-3.pdf) + [Inoculation Protocol](https://static.igem.wiki/teams/4174/wiki/inoculations-for-miniprep-220424-3.pdf)+ [Plate Reader Testing Protocol](https://static.igem.wiki/teams/4174/wiki/final-fluorescence-intensity-testing-protocol-1.pdf)

### Results:

#### BBa\_K417002 Characterization (osmY-sfGFP Construct)

 &nbspTo test the effectiveness of our osmY-sfGFP construct (BBa\_K4174002), our team transformed the original MIT iGEM 2006 construct, our improved osmY-sfGFP construct, and our improved osmY-mRFP1 construct (BBa\_K4174001) in *E. coli* NEB5-α in a plate reader. The various transformants were grown at 37°C with continuous shaking. For green fluorescence, we used an excitation wavelength of 485 nm and an emission wavelength of 528 nm. The values for green fluorescence intensity normalized to OD600 values are reported below.

![](https://static.igem.wiki/teams/4174/wiki/normalized-green-fluorescence-graph-final-larger.png)

*The data represented in the graph above only includes measurements taken starting from around the 10 hour and 5 minute mark (out of a total growth time of about 19 hours and 35 minutes). In addition, the data shown represents the averages of fluorescence measurements (normalized to OD600) taken from two experiments. Please note that the inoculation process differed between these two experiments (see the protocol section of this page for details).*

 &nbspAs seen in the graph above, both the osmY-sfGFP (BBa\_K4174002) and MIT iGEM 2006 osmY (BBa\_J45995) constructs enter stationary phase right before 14 hours, but our improved osmY-sfGFP construct (BBa\_K4174002) is much more fluorescent. The other constructs are our osmY-RFP construct (BBa\_K4174001) and untransformed *E. coli* cells, both of which serve as negative controls for green fluorescence.

![](https://static.igem.wiki/teams/4174/wiki/improveapart-smaller.png )

As seen in the image above, qualitative results reveal that our improved constructs are more fluorescent than the original construct (BBa\_J45995). Here, bacterial cells transformed with our osmY-sfGFP (BBa\_K4174002) construct are on the far right, and are visibly more green than cells transformed with the original MIT iGEM 2006 construct (BBa\_J45995).

#### BBa\_K417001 Characterization (osmY-mRFP1 Construct)

 &nbspTo test the effectiveness of our osmY-mRFP1 construct (BBa\_K4174001), our team transformed the original MIT iGEM 2006 construct (BBa\_J45995), our osmY-mRFP1 construct, and our osmY-sfGFP construct (BBa\_K4174001) into *E. coli* NEB5-α cells and grew the various transformants in a plate reader. The various transformants were grown at 37°C with continuous shaking. For red fluorescence measurements, we used an excitation wavelength of 584 nm and an emission wavelength of 610 nm. The values for red fluorescence intensity normalized to OD600 values are reported below.

![](https://static.igem.wiki/teams/4174/wiki/normalized-red-fluorescence-graph-final-larger.png)

*The data represented in the graph above only includes measurements taken starting from around the 10 hour and 5 minute mark (out of a total growth time of about 19 hours and 35 minutes). In addition, the data shown represents the averages of fluorescence measurements (normalized to OD600) taken from two experiments. Please note that the inoculation process differed between these two experiments (see the protocol section of this page for details).*

Based on the graph above, the bacterial cells engineered with our osmY-mRFP1 construct (BBa\_K4174001) appear to have entered stationary phase around 16 hours. As seen in the graph, our osmY-mRFP1 construct (BBa\_K4174001) produces more red fluorescence than the original construct (BBa\_J45995) (as expected since the original construct was a GFP construct). The other measurements taken are for our osmY-sfGFP construct (BBa\_K4174002) and untransformed *E. coli* NEB5-α cells, both of which serve as negative controls for red fluorescence.

The fluorescence intensity of the untransformed cells appears higher than that of cells transformed with our osmY-mRFP1 construct due to our normalization process. Although the raw fluorescence values of the untransformed cells were consistently lower than the raw fluorescence values of the osmY-mRFP1 circuit, the OD600 values of the untransformed cells were much lower than the OD600 values of the cells transformed with osmY-mRFP1. Therefore, when we normalized by dividing raw fluorescence by OD600, the fluorescence intensity of the untransformed cells appeared to be higher than that of the osmY-mRFP1 transformants.

Based on the graph above, the bacterial cells engineered with our osmY-mRFP1 (BBa\_K4174001) construct appear to have entered stationary phase around 16 hours. As seen in the graph, our osmY-mRFP1 (BBa\_K4174001) construct produces more red fluorescence than the original construct (BBa\_J45995) (as expected since the original construct was a GFP construct). The other measurements taken are for our osmY-sfGFP construct (BBa\_K4174002) and untransformed *E. coli* NEB5-α cells, both of which serve as negative controls for red fluorescence.

![](https://static.igem.wiki/teams/4174/wiki/improveapart-smaller.png)

As seen in the image above, qualitative results reveal that our osmY-mRFP1 construct (BBa\_K4174001) produces visibly red flourescence. Here, bacterial cells transformed with our osmY-mRFP1 construct are on the far left alongside the other two cultures transformed with the osmY-sfGFP (BBa\_K4174002) and MIT iGEM 2006 osmY (BBa\_J45995) constructs.

## 2) Experiments for 16S rRNA Sequencing

---

### Overview

 &nbspThe majority of the data used to train and validate our software was obtained from online databases and individual studies. As we were searching for our data, we noticed that much of this sequencing data lacks corresponding environmental parameters (ex. soil temperature, moisture-content, and carbon-content). For our team’s proof of concept, we went to the field and collected 12 soil samples along with their associated parameters. After DNA soil extraction, the soil samples were sent to Dr. Randolph Chambers to have specific parameters analyzed and recorded. 16S PCR was performed on these soil extractions. Primer pair (f 5’- CCTACGGGNGGCWGCAG –3’) and (r 5’- GACTACHVGGGTATCTAATCC -3’) were used targeting the 16s rRNA V3-V4 region. These PCR products were then sent out for sequencing. Combining the sequencing results and the soil parameters, we tested our software’s functionality. In addition to using this data to test our software, we also plan to make it available to other researchers.

### 1) Selection of 16S PCR Primers

Picking the optimal primer set is a crucial step when setting up 16S rRNA gene sequencing. For our project, we depended heavily on our review of the literature when deciding which primer set to utilize. Among the 22 papers we read, 16 of them conducted 16S rRNA gene sequencing specifically for the purpose of bacterial diversity determination in soil. The purpose of these papers varies from comparing 16S primer pairs in silico to actually utilizing 16S technology in soil samples. After carefully studying these papers, we believe that our primer set should aim to amplify the V3-V4 amplification region. The V3-V4 hypervariable region has been mentioned by 17 out of 22 papers we read on testing soil samples obtained from various regions using different sequencing platforms. In an evaluation of four 16S primers pairs, one paper concluded that “341f/785r [primers targeting the V3-V4 region] detected the highest bacterial diversity, broadest taxonomic coverage, and provided the most reproducible results” (Thijs et al. 2017). Other researchers have also seemed to reach a consensus that primer pairs amplifying the V3-V4 region are “preferred” as they are accurate and have been used extensively across the field (Wang et al. 2018, Xia et al. 2019). Orwin et al. also stated that due to the popularity of these primer pairs, “the extensive reference databases for the V3-V4 region of the microbial 16S rRNA gene have allowed information about soil bacterial communities to be obtained at a much finer taxonomic resolution” (Orwin et al. 2018). Using a widely used primer pair not only provides us with many protocols from scientific literature to reference for our experimental design, but also aids our data analysis through the existing reference databases for our region of interest. Among the primer pairs we found targeting the V3-V4 region, the primer pair (f 5’- CCTACGGGNGGCWGCAG –3’) and (r 5’- GACTACHVGGGTATCTAATCC -3’) was used by 10 papers in addition to the Illumina 16S metagenomic sequencing library preparation protocol. Due to the accuracy of these primers and their consistent use in the literature, we selected a primer pair amplifying the V3-V4 region of the 16S rRNA gene for our own 16S rRNA sequencing experiments.

Download Sources from our 16S PCR Literature Review Below:

* [16S Literature Review Sources](https://static.igem.wiki/teams/4174/wiki/sources-16s-rrna-gene-sequencing-1.pdf)

  ### 2) Soil Sample Collection

  1. The soil samples were collected along the Matoaka Trails surrounding Lake Matoka, Williamsburg, Virginia. These trails cover a range of soil types including Alluvium, Norfolk Formation, Windsor Formation, Bacon’s Castle Formation, Sedley Formation, Yorktown Formation, and Saint Marys Formation.

  2. At each soil digging site, soil parameters including coordinates, sample collection time, latitude and longitude (GPS), sample depth, elevation, altitude, humidity, soil temperature, air temperature, and pH were collected.

  3. Using a washed shovel, dig and collect the soil into a plastic collection bag. Collect soil horizontally to ensure the entire soil sample came from a uniform depth.

  4. Moisture content was measured using an oven-baked method. Weights of the samples were measured before and after oven-drying.

  5. Portions of the samples were sent to Dr. Randy Chambers for measuring the sample’s organic content.

  Please download our protocol for soil sample collection below:

  + [Soil Sample Collection Protocol](https://static.igem.wiki/teams/4174/wiki/soil-collection-protocol.pdf)

    ### 3) Soil DNA Extraction

    1. Sieve soil

    2. Two replicates for each soil sample, one extracted using  **Qiagen DNAeasy Soil Extraction Kit**, one extracted using  **Qiagen DNeasy PowerSoil Pro Kit**

    3. Nanodrop/Gel Confirmation of DNA Extraction

    Please download our protocols for soil DNA extraction below:

    + [Soil DNA Extraction Protocol - Qiagen DNAeasy Soil Extraction Kit](https://static.igem.wiki/teams/4174/wiki/qiagen-dnaeasy-soil-extraction-kit-1.pdf)+ [Soil DNA Extraction Protocol - Qiagen DNeasy PowerSoil Pro Kit](https://static.igem.wiki/teams/4174/wiki/qiagen-dneasy-powersoil-pro-kit-quick-start-protocol-1-1.pdf)

        ### 4) 16S PCR

        1. Two settings were used (see Table 1 below)

        |  |  |  |  |  |  |
        | --- | --- | --- | --- | --- | --- |
        | Source | Initial Denaturation | Denaturation | Primer Annealing | Extension | Final Extension |
        | Settings from Sinclair, Bertilsson, & Eiler 2015 | 95°C for 5s | 20 cycles at 95°C for 40 s | 53°C for 40s | 72°C for 60s | 72 °C for 7 min |
        | Modified Settings | 98°C for 30s | 20 cycles at 98°C for 10s | 53°C for 40s | 72°C for 60s | 72 °C for 7 min |

        *Table 1: Top row contains PCR settings from Sinclair Sinclair, Bertilsson, & Eiler 2015; bottom row contains PCR settings modified from Sinclair, Bertilsson, & Eiler 2015.*

        2. PCR Purification with NEB Monarch Kit.
        3. Purified PCR products were sent for sequencing.

        Please download our protocol for 16S PCR here: [PCR for 16S rRNA Sequencing Protocol](https://static.igem.wiki/teams/4174/wiki/16s-pcr-protocol-finalized.pdf)

## References

---

Campbell, R. E., Tour, O., Palmer, A. E., Steinbach, P. A., Baird, G. S., Zacharias, D. A., & Tsien, R. Y. (2002). A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7877–7882. https://doi.org/10.1073/pnas.082243699

Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5):415-418. Doi: 10.1038/nmeth.3339

Chang, D. E., Smalley, D. J., & Conway, T. (2002). Gene expression profiling of Escherichia coli growth transitions: an expanded stringent response model. Molecular microbiology, 45(2), 289-306.

Illumina. (n.d.). 16S Metagenomic Sequencing Library Preparation. Illumina.com. Retrieved October 9, 2022, from https://support.illumina.com/documents/documentation/chemistry\_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf

Jaishankar, J., & Srivastava, P. (2017). Molecular basis of stationary phase survival and applications. Frontiers in microbiology, 8, 2000.

Orwin, K.H., Dickie, I.A., Holdaway, R., & Wood, J.R. (2018). A comparison of the ability of PLFA and 16S rRNA gene metabarcoding to resolve soil community change and predict ecosystem functions. Soil Biology and Biochemistry, 117, 27-35. https://doi.org/10.1016/j.soilbio.2017.10.036.

Pédelacq, J. D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2006). Engineering and characterization of a superfolder green fluorescent protein. Nature biotechnology, 24(1), 79-88.

Sinclair, L., Osman, O. A., Bertilsson, S., & Eiler, A. (2015). Microbial community composition and diversity via 16S rRNA gene amplicons: evaluating the illumina platform. PloS one, 10(2), e0116955. https://doi.org/10.1371/journal.pone.0116955

Thijs, S., Op De Beeck, M., Beckers, B., Truyens, S., Stevens, V., Van Hamme, J. D., Weyens, N., & Vangronsveld, J. (2017). Comparative Evaluation of Four Bacteria-Specific Primer Pairs for 16S rRNA Gene Surveys. Frontiers in microbiology, 8, 494. https://doi.org/10.3389/fmicb.2017.00494

Wang, F., Men, X., Zhang, G., Liang, K., Xin, Y., Wang, J., Li, A., Zhang, H., Liu, H., & Wu, L. (2018). Assessment of 16S rRNA gene primers for studying bacterial community structure and function of aging flue-cured tobaccos. AMB Express, 8(1), 182. https://doi.org/10.1186/s13568-018-0713-1

Xia, X., Zhang, P., He, L., Gao, X., Li, W., Zhou, Y., Li, Z., Li, H., & Yang, L. (2019). Effects of tillage managements and maize straw returning on soil microbiome using 16S rDNA sequencing. Journal of integrative plant biology, 61(6), 765–777. https://doi.org/10.1111/jipb.12802

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# Results

[Overview](#Intro)
[Metagenomic Data Analysis Results](#Metagenomic Data Analysis)
[Neural Network Results](#Neural Networks)
[KNN Results](#KNN)
[Random Forest Results](#Random Forest)
[Regression Results](#Regression)
[GEMs Results](#GEMS)
[Gut Microbiome Results](#Gut)
[Conclusion](#conclusion)

This page details the results we have achieved with our software design: 1) The results of our metagenomic data analysis are our complete GIS dataset 2) Our neural network results explain the accuracy that our neural network achieved and how it was optimized during the training process. 3) The KNN regression results show how well our trained KNN model fits the validation data. 4) The Random Forest regression results show how well our trained Random Forest model fits the validation data. 5) The linear regression results shows the in depth analysis of our dataset and how the linear model fits our dataset. 6) Our GEM models results demonstrate how the GEM model successfully predicts metabolic behavior. 7) Our gut microbiome database is complete and ready for users.

To view our software toolkit, please visit the Gitlab: [chassEASE](https://gitlab.igem.org/2022/software-tools/william-and-mary).

## 1. Metagenomic Data Analysis Results

---

We managed to create a large database from many different sources including bacterial data from different environments: air, water and soil. Although the database does not include a model that gives a statistical output, it is useful for researchers to have bacterial data to make predictions of survival for their desired environment. Additionally, a complete meta dataset serves as the foundation for the next parts of our software, such as our prediction models.

Snapshot of our dataset:

![](https://static.igem.wiki/teams/4174/wiki/result-1.png)

## 2. Neural Network Results

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These results show the accuracy of our neural network on the training and validation data. Our model reached an accuracy of 71% after training with training data. To optimize our prediction accuracy, we used the optimizer “adam”. We used 1000 epochs, giving the model plenty of space to optimize from losses.

The following graph shows the training accuracy and validation accuracy of our neural network during the training process. The results indicate that the accuracy of our model increases over time. At the end of training, our results demonstrate that the validation accuracy is higher than the training accuracy, meaning that our model does not have the problem of overfitting.

![](https://static.igem.wiki/teams/4174/wiki/modeling/neural-networks-model-accuracy.png)

The major advantage of our neural network model is the ability of ANNs to learn and organize complex models. In reality, the relationships between various inputs and outputs are interpersonal and complex. After training our neural network model, it can not only predict bacteria reads but also give a generalization of the pattern of the dataset.

### Proof of Prediction

To validate the accuracy of the chassEASE software package, we first generated a metagenomic subset of QIITA using the standard process which contained parameters we accumulated: metagenome\_type, sample\_time, season, latitude, longitude, depth, elevation, biome\_class, land\_use, moisture, humidity, soil\_temperature, environment\_temperature, ph, total\_carbon, total\_nitrogen, total\_phosphorus, and carbon\_nitrogen\_ratio.

We then used our AI/Artificial Neural Network model to generate predicted relative abundance values of bacteria that were found in our 16S sequencing results. These predictions were evaluated by determining the likelihood that predictions with equal or higher accuracy would be generated by an “educated blind algorithm,” a hypothetical algorithm that is aware of overall trends in bacterial abundance but unaware of specific environmental conditions.

To model the probability of this hypothetical algorithm generating a certain value, we created a normal distribution of relative abundance for each target chassis (a bacterial genre or species) using the overall QIITA database, under the assumption that the probability of a blind algorithm creating a certain prediction would follow a normal curve.

Using these normal curves, we determined the probability that a random algorithm would have made a prediction as effective or better than our algorithm by finding the area under the curve between the chassEASE prediction and the real value, and adding it to the area under the curve between the real value, and a prediction of equal accuracy in the opposite direction of the real value. This will generate a measure of accuracy for every genus and species prediction varying between 0 (perfectly matches real data) and 1 (indistinguishable from random guessing).

To create an overall evaluation, we then took the average of these accuracy measurements across each species and genus predicted for one of our samples.

![](https://static.igem.wiki/teams/4174/wiki/percent-chance-of-random-prediction.png)

We calculated the likelihood that our software is randomly guessing its output results for predictions of individual species and genera. Averaged across our 15 sequence samples and all bacterial genera present, there is a 15.9% chance that our software is randomly guessing. This value increases slightly to 28.3% when predicting individual species. These results indicate that our software program is predicting the presence of these bacterial species as opposed to simply guessing.

To learn more about the components of our software, please see our  [Proof of Concept](https://2022.igem.wiki/william-and-mary/proof-of-concept)  page

## 3. K-Nearest Neighbor (KNN) Results

---

This results section shows how our KNN regression model performs on the validation dataset. For KNN results, there is not a percentage to indicate the accuracy. Instead, several statistical error functions are used to give an understanding of the accuracy of the predictions.

Our validation results show:

* *Pseudomonas*: MAE = 0.024, RMSE= 0.073 MSE = 0.005 R-Squared = 0.192
* *Mycobacterium*:MAE = 0.001, RMSE= 0.0026, MSE = 6.85e-6, R-Squared = 0.326
* *Bacillus*: MAE = 0.009, RMSE= 0.027, MSE = 0.0007, R-Squared = 0.033

For  *Pseudomonas*  prediction, the MAE is 0.024, meaning the absolute values of the  *Pseudomonas*  abundance prediction errors on overall instances in the test set is 0.024. RMSE is 0.073, meaning that our standard deviation of the *Pseudomonas* prediction errors is 0.073. MSE = 0.005 indicates that the average of the squares on the errors on *Pseudomonas* prediction is 0.005. And 0.192 R-Squared means 19.2% of the variance of *Pseudomonas* abundance is explained by the predictors (independent variables). For *Mycobacterium*  prediction, the MAE is 0.001, meaning the absolute values of the *Mycobacterium*  abundance prediction errors on over all instances in the test set is 0.001. RMSE is 0.0026, meaning that our standard deviation of the *Mycobacterium*  prediction errors is 0.0026. MSE = 6.85e-6 indicates that the average of the squares on the errors on *Mycobacterium*  prediction is 6.85e-6. And 0.326 R-Squared means 32.6% of the variance of  *Mycobacterium*  abundance is explained by the predictors (independent variables). For *Bacillus*  prediction, the MAE is 0.009, meaning the absolute values of the *Bacillus*  abundance prediction errors over all instances in the test set is 0.009. RMSE is 0.027, meaning that our standard deviation of the *Bacillus* prediction errors is 0.027. MSE = 0.0007 indicates that the average of the squares on the errors on *Bacillus*  prediction is 0.0007. And 0.033 R-Squared means 3.3% of the variance of *Bacillus*  abundance is explained by the predictors (independent variables).

KNN models can serve as a model to estimate a value without expecting large error. KNN is relatively a mature theory which can be used to do regression. The training time complexity is lower than that of algorithms like supervised learning. Compared to algorithms like Naive Bayes, no assumptions on the data are needed. Moreover, it provides high accuracy and is less sensitive to outliers. This is achieved through the KNN method mainly depending on the limited neighboring samples around the data point being predicted, rather than the method of discriminating class domain to determine the category.

## 4. Random Forest Results

---

This part shows how our Random forest regression model performs on the validation dataset. For Random forest results there is not a percentage to indicate the accuracy. Instead, several statistical error functions are used to give an understanding of how well the predictions are..

* *Pseudomonas*: MAE = 0.024, RMSE= 0.071 MSE = 0.005 R-Squared = 0.243
* *Mycobacterium*: MAE = 0.001, RMSE= 0.0025, MSE = 6.35e-6, R-Squared = 0.376
* *Bacillus*: MAE = 0.006, RMSE= 0.023, MSE = 0.0005, R-Squared = 0.302

For *Pseudomonas prediction* , the MAE is 0.024, meaning the absolute values of the *Pseudomonas*  abundance prediction errors on overall instances in the test set is 0.024. RMSE is 0.071, meaning that our standard deviation of the  *Pseudomonas*  prediction errors is 0.071. MSE = 0.005 indicates that the average of the squares on the errors on *Pseudomonas*  prediction is 0.005. And 0.243 R-Squared means 24.3% of the variance of *Pseudomonas* abundance is explained by the predictors (independent variables). For *Mycobacterium*  prediction, the MAE is 0.001, meaning the absolute values of the *Mycobacterium*  abundance prediction errors over all instances in the test set is 0.001. RMSE is 0.0025, meaning that our standard deviation of the  *Mycobacterium*  prediction errors is 0.0025. MSE = 6.35e-6 indicates that the average of the squares on the errors on *Mycobacterium* prediction is 6.35e-6. And 0.376 R-Squared means 37.6% of the variance of *Mycobacterium* abundance is explained by the predictors (independent variables). For *Bacillus*  prediction, the MAE is 0.006, meaning the absolute values of the *Bacillus* abundance prediction errors over all instances in the test set is 0.006. RMSE is 0.023, meaning that our standard deviation of the Bacillus prediction errors is 0.023. MSE = 0.0005 indicates that the average of the squares on the errors on *Bacillus* prediction is 0.0005. And 0.302 R-Squared means 30.2% of the variance of *Bacillus*  abundance is explained by the predictors (independent variables).

Random Forest has great advantages over other algorithms on many current data sets and performs well. It can handle very high dimensional data without feature selection (feature subsets are randomly selected). After training, it can give which features are more important. When creating random forest, unbiased estimation is used for Generalization Error, and the model has strong generalization ability. Random forest training is fast and easy to parallelize (trees are independent of each other during training). For an imbalanced dataset like ours, it balances the error. Accuracy can be still maintained if a significant portion of the features are missing.

## 5. Regression Results

---

After successfully collecting 16S data from soil samples collected by our team, we began testing our regression model for predicting *Nitrospira* with the 16s data. We built a linear regression model using our 16S data from soil samples as training data, leading to a 0.91 R-Squared, which is a high accuracy. Yet when testing the model with a subset of GIS data as testing data, we found the accuracy is low. Therefore the model using 16s dataset as training data is overfit. Here are the regressions side by side.

![](https://static.igem.wiki/teams/4174/wiki/re-9.png)

![](https://static.igem.wiki/teams/4174/wiki/lastpic.png)

We created linear regression models for the first 50 bacteria sequenced, and gained the following results:

This is yet another example of how good data is necessary for the accuracy of a regression, but also a copious amount of it. Without both quality and quantity of data we wont be able to make complete and statistically relevant conclusions.

![](https://static.igem.wiki/teams/4174/wiki/re-8.png)

Lastly, we decided to create regressions for the first 50 genera and record our R^2 values. The plot above is meant to exemplify that with accurate measurements our regression accuracy improves, but without more metadata they are still incomplete. We hope that as these values increase the mean adjusted R^2 will increase and overall predictability will increase.

## 6. GEM Results

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## 7. Gut Microbiome Results

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We successfully collected Metadata for in total 353 projects, containing 71,642 runs (samples) are available in GMrepo. We eliminated samples in which there is no information of genus or species, leaving us about 37,000 samples. Each sample contains information about the age, BMI, country of origin and the relative abundance of genus and species in his or her gut. We used a self-developed data scraper to collect the data in all of 353 projects and successfully designed algorithms that take human conditions as input such as country, age and BMI, and give users the ranking of both the most dominant species and genus of samples that meets the conditions given.

Snapshot of our dataset collected:

![]( https://static.igem.wiki/teams/4174/wiki/results-table-1.png)

## 8. Conclusion

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Overall, our software can predict bacterial relative abundance based on environmental parameters better than random chance. However, there is still much to improve on. Our project also serves as a call to action in the field in order to stress the importance of collecting and providing metadata with 16s data. One of our challenges with making accurate models was the difficulty of finding 16s data with available metadata. We will continue to strive towards making our software more accurate in the future.

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