iGEM 2020 — Team IIT_Roorkee

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:IIT Roorkee/Notebook

<!DOCTYPE html>

PYOMANCER

[PYOMANCER](https://2020.igem.org/Team%3AIIT_Roorkee)

* People
  + [Team](https://2020.igem.org/Team%3AIIT_Roorkee/Team)
  + [Attributions](https://2020.igem.org/Team%3AIIT_Roorkee/Attributions)
  + [Sponsors](https://2020.igem.org/Team%3AIIT_Roorkee/Sponsors)
* Project
  + [Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
  + [Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
  + [Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
  + [Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
  + [Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
  + [Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)
* Human Practices
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
  + [Science Communication & Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
  + [Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
  + [Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
  + [Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)
* Dry Lab
  + [Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
  + [Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)
* Wet Lab
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
  + [Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
  + [Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
  + [Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
  + [References](https://2020.igem.org/Team%3AIIT_Roorkee/References)
* Machine Learning
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
  + [Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
  + [Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)
* [Judges](https://2020.igem.org/Team%3AIIT_Roorkee/Judging)
* Notebook

# Notebook

Project

| Months | Work Done |
| --- | --- |
| January | 1. After exploring different ideas for the competition independently, first official team meet was organised 2. Prof Naveen K. Navani took hold as faculty advisor to the team to support administrative and research activities 3. Discussion on pyocins and phage therapy to treat bacterial infections 4. Analysis of TAU Israel Project 2019 |
| February | 1. Discussions with department professors about iGEM Competition 2. Presented road map to Head of Department (HoD), Department of Biotechnology, for iGEM 2020 3. Attended Antibiotic Resistance - One Health Perspective conference, IIT Roorkee 4. Prof Ranjana Pathania agreed to host our Team under her lab and provided assistance in understanding ***A. baumannii*** 5. Designed pyocin fusion through the assistance of researchers, Siva and Somok 6. Drafted proposal for institute committees, TIDES incubation and sponsorship support 7. Preliminary experiments were performed 8. Team recruitments were opened for undergraduate and masters’ students across disciplines 9. Curated human practices and fund raising strategies for March-April |
| March | \*Due to CoVID-19, all the team activities came to a halt and plans were suspended Attended Ambassador Calls to share the challenges faced by the team and seek guidance |
| April | 1. Established new format for working remotely and had a complete team meeting 2. Launched our Official Social Media Pages on Instagram, Facebook, and LinkedIn 3. Prepared pitch deck for the funding support at the university 4. Participated in the iBEC Competition |
| May | 1. Completed Registration and Jamboree Fee payment 2. Attended the iGEM 2020 Opening Weekend Festival 3. Re-designed project strategy and explored new areas to cover 4. Research on Machine Learning and biophysical modeling 5. Attended Webinar: Introduction to the Benchling Platform |
| June | 1. Focussed on human practices activities through multiple brainstorming sessions 2. Researched on educational tools to curate relevant & impactful content 3. Software team presented the design and framework for TailScout 4. Received mentorship from Computer Science students to understand the pipeline and resources required for our software TailScout 5. Collaborated with Design Studio, IIT Roorkee for assistance in team branding and designing 6. Attended Measurement Webinars Week 3: Modeling and DNA Assembly |

COVID impacted our project journey and roadmap significantly, as accessing funding, labs, experiments and Team Work became difficult to realise. Settling in the new format took us some time and by June 15 we had finalised the core team members. With brief plans to proceed in 5 main directions - Research Design, Software, Human Practices, Public Engagement, and Machine Learning, we formalised our subsystems and worked on the team constitution. The iGEM IIT Roorkee Constitution was drafted to lay down the team structure and guidelines for future teams from IIT Roorkee. We maintained a new lab notebook to track our progress and fulfillment of project goals in different categories.

WEEK 1 (
28th June to 4th July
)

#### Wet Lab

* Researched on microbiomes and thought of possible application of Pyocin in gut microbiome research
* Worked on design of alternate pyocins with tail fibers of other **A. baumannii** phages
* Considered the possibility to use iron in the formulation which prevents Ab infection
* Researched Urinary tract infection and wound infections as a possible target

#### Dry Lab

* Chartered pipeline for gene detection
* Coded for multiple sequences alignment.
* Computed a gene detection algorithm using python which works as the first process of the TailScout
* Read papers in machine learning for protein-protein interaction

#### Human Practices

* Applied for Official iGEM Mentor
* Mentorship meeting with IISER Bhopal to understand project planning
* Attended German iGEM Meetup
* Interviewed Dr. Anand Ratan Tiwari
* Worked out on the Webinar Brochure and mails
* Initiated AMR awareness campaign for the college students

WEEK 2 (
5th July to 11th July
)

#### Wet Lab

* Engineered plasmid using gibson assembly
* Faced issues with sequence complexities that required expert opinion and guidance
* Decided to target pneumonia under all possible Ab infections
* Devised the possibility of aerosol based delivery of our drugs directly to the site if infection
* Researched and conceptualised inhaled protein therapeutics

#### Dry Lab

* Debugged errors in the gene detection algorithm and saw research about Biopython library
* Scouted deep models in Machine learning
* Identified the possibility  for including games on AMR in the wiki

#### Human Practices

* Compiled the School database for the Webinar
* Outlined the Presentation for School Webinars
* Rolled out  mails to school
* Formulated the resource booklets (according to different learning standards)
* Collaboration meeting with team iGEM Montpellier

WEEK 3 (
12th July to 18th July
)

#### Wet Lab

* Opted for DNA Synthesis instead of cloning
* Plasmid sent for verification
* Validated gene fragments
* Communicated with IDT Customer service for optimizing sequences for synthesis

#### Dry Lab

* Orchestrated the software development workflow
* Catalogued various machine learning based approaches in AMR
* Formulated modelling methodologies for the fusion protein
* Decided on bioinformatics modeling due to lack of lab access in COVID

#### Human Practices

* Hosted a webinar in St. Anne's Convent School, Chandigarh.
* Collaborated with National Service Scheme IIT Roorkee
* Hosted a webinar in ‘Srijan - Ek Soch’  NGO
* Interviewed Dr. NandKishore Joshi
* Released AMR Mythbusters and Team Introduction on social media handles

WEEK 4 (
19th July to 25th July
)

#### Wet Lab

* Read papers on HAI’s and Ventilator-Associated Pneumonia caused mainly by ***A. baumannii***

#### Dry Lab

* Prepared the phage tail fibers database of their protein sequence. I.e, phage library
* Started with structural modeling, energy minimization, and docking studies for proof of concept

#### Human Practices

* Hosted a Webinar at Delhi Public School, Ghaziabad Vasundhara
* Stimulated All India iGEM Meet 2020
* Formulated content for ML workshop in All India iGEM Meet

WEEK 5 (
26 July to 01 August
)

#### Wet Lab

* Studied VAP for Proposed Implementation
* Meeting with Dr. Kulkarni helped set the points and made us focus on VAP.

#### Dry Lab

* Searched for the Clustal Omega MSA and Jpred Secondary Structure Prediction REST Api’s
* Decided to try all the three modeling methods and compare results

#### Human Practices

* Fabricated our Presentation and Poster for AIIM
* Consulted Dr. Rajesh Kulkarni to understand VAP infections
* Conducted All India iGEM Meet

WEEK 6 (
02 August to 08 August
)

#### Wet Lab

* Conducted in depth research and analysis Ventilator Associated Pneumonia
* Designed the delivery system based on nebuliser based-therapeutics

#### Dry Lab

* Read papers for using Clustal Omega on the web server-based application
* Founded error in first run for the models due to large size of model protein

#### Human Practices

* Initiated collaboration activities for other iGEM teams
* Project discussion with IISER Tirupati
* Created Team Spirit Poster for Global iGEM Meetup

WEEK 7 (
09 August to 15 August
)

#### Wet Lab

* Switched from nebuliser system to Bolus delivery system

#### Dry Lab

* Tested the individual codes on the system with different phages fibres
* DId literature review for the model and found out that N-truncated fragment was sufficient for modeling

#### Human Practices

* Prepared and rolled out emails to stakeholders (Clemedi, APT)
* Attended Global iGEM Meet

WEEK 8 (
16 August to 22 August
)

#### Wet Lab

* Worked on Final Plasmid assembly with the help of Twist Biosciences

#### Dry Lab

* ML approach discussions with Keerat and Harkirat
* Added a more bacteriophages in the library
* Identified the fusion protein sequence by crossing over AP22 bacteriophage with R2-NTF pyocin tail fiber instead of the complete R2 pyocin tail fiber

#### Human Practices

* First phase meeting with Clemedi
* Proctored patient Interview
* Received inputs from the iGEM judging committee that guided our further work

WEEK 9 (
23 August to 29 August
)

#### Wet Lab

#### Dry Lab

* Preliminary ML Analysis
* Made Github repo for the WebServer and committed initial codes
* Constructed the new sequence for the fusion protein - for 3 modeling methods

#### Human Practices

* Second Phase meeting with Clemedi
* Finalised our Team Merchandise

WEEK 10 (
30 August to 05 September
)

#### Wet Lab

* Worked on safety forms

#### Dry Lab

* Tried to integrate the python code and REST Api’s but failed
* Once the models were built, identified parameters that would help in comparing these models
* Decided to compare alignment RMSD values for alignment with R2-NTF pyocin and AP22 phage

#### Human Practices

* Dispatched Learning material to Anushruti Academy for the Deaf via NSS
* Participated in the survey Collaboration by IISER Berhampur and CCU iGEM

WEEK 11 (
06 September to 12 September
)

#### Wet Lab

* Submitted Safety forms

#### Dry Lab

* Discussion with Mentor to finalize ML approach
* For the next iteration for integration, learnt through videos on youtube of Django
* Meetings with Institute Seniors for software integration

#### Human Practices

* Project Discussion Meeting with Mentor
* Designed survey for gauging the problem of HAI through medical staff
* Conducted Medical Staff Survey

WEEK 12 (
13 September to 19 September
)

#### Wet Lab

#### Dry Lab

* Discussion with institute administration for providing high computing PC
* Made a presentation for running all the codes locally on an individual system
* Decided to compare the Ramachandran plots of the models as well

#### Human Practices

* Built storyline script with Subtitles for Project promotion video

WEEK 13 (
20 September to 26 September)

#### Wet Lab

#### Dry Lab

* Collection of the dataset for ML
* Meeting with the mentor for discussion on the integration part and sent out a document explaining our problems and issues to relevant contacts
* Discarded all the models obtained via threading from consideration because of their monomeric structure

#### Human Practices

* Organised Awareness Quiz in AAD in collaboration with NSS
* Wrapped up the production of Project Promotional Video

WEEK 14 (
27 September to 03 October)

#### Wet Lab

#### Dry Lab

* Preprocessed the dataset collected
* Django integration completely (i.e. Backend)
* Set weightages for the comparison parameters andgave Ramachandran plot data the highest weightage

#### Human Practices

* Collaborated with AUC Egypt - “Thank You Mentor”
* Launched our Project Logo on Social Media platforms

WEEK 15 (
04 October to 10 October)

#### Wet Lab

#### Dry Lab

* Machine learning experiments completed
* Carried out energy minimization for the best chosen model using Yasara energy minimisation toolkit

#### Human Practices

* SDG Collaboration with iGEM UPCH Peru
* Participated in the Collaboration “Envirospeaks” by iGEM RUM

WEEK 16 (
11 October to 17 October)

#### Wet Lab

* Developed Protocols
* Collected the data of preliminary experiments for the wiki

#### Dry Lab

* Working on the deployment of the web server
* Studied protein-protein interaction through docking
* Searched the structure of the LPS receptor and the fusion protein but it wasn’t available in existing databases so, couldn’t perform docking

#### Human Practices

* Co-produced Policy Review Report on AMR (with NSS IITR)
* Ideation on hosting webinars in collaboration with NSS for underprivileged students of Roorkee schools

WEEK 17 (
18 October to 24 October)

#### Wet Lab

#### Dry Lab

* Collected available results of all the modeling
* Sorted the Machine learning experiment results

#### Human Practices

* Finalised all collaborations
* Started our iGEM⤬SDG Collaboration - “SMILE”

![](https://static.igem.org/mediawiki/2020/f/fb/T--IIT_Roorkee--images--icons--next.svg "Next section")

iGEM IIT Roorkee

People

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

Project

[Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
[Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
[Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
[Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
[Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
[Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)

Human Practices

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
[Science Communication and Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
[Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
[Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
[Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)

Dry Lab

[Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
[Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)

Wet Lab

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
[Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
[Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
[Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
[References](https://2020.igem.org/Team%3AIIT_Roorkee/References)

Machine Learning

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
[Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
[Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)

The Department of Biotechnology,
Indian Institute of Technology, Roorkee
Roorkee, Uttarakhand,
India - 247667

![](https://static.igem.org/mediawiki/2020/a/a2/T--IIT_Roorkee--images--images--james_thomason_building.svg)

Contact us:

+011-2875468
+91-76487699
igem@iitr.ac.in

Follow us on:

[![Instagrm](https://static.igem.org/mediawiki/2020/7/78/T--IIT_Roorkee--images--icons--instagram.svg)](https://www.instagram.com/igem_iitr/)
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[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:IIT Roorkee/Engineering

<!DOCTYPE html>

PYOMANCER

[PYOMANCER](https://2020.igem.org/Team%3AIIT_Roorkee)

* People
  + [Team](https://2020.igem.org/Team%3AIIT_Roorkee/Team)
  + [Attributions](https://2020.igem.org/Team%3AIIT_Roorkee/Attributions)
  + [Sponsors](https://2020.igem.org/Team%3AIIT_Roorkee/Sponsors)
* Project
  + [Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
  + [Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
  + [Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
  + Engineering
  + [Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
  + [Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)
* Human Practices
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
  + [Science Communication & Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
  + [Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
  + Sustainable Development Goals
  + [Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)
* Dry Lab
  + [Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
  + [Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)
* Wet Lab
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
  + [Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
  + [Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
  + [Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
  + [References](https://2020.igem.org/Team%3AIIT_Roorkee/References)
* Machine Learning
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
  + [Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
  + [Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)
* [Judges](https://2020.igem.org/Team%3AIIT_Roorkee/Judging)
* [Notebook](https://2020.igem.org/Team%3AIIT_Roorkee/Notebook)

## Project

## Description

Description

To realise our iGEM Project we followed a middle out approach to the achieve goal of creating biological systems that can be used to solve a problem at hand. Maintaining proper engineering principles at different stages of project planning provided us clarity on our work and it's impact.

## Research

We started brainstorming on wide range of topics to allow good breadth of research and ideas. In the process, we came across an interesting molecule, Pyocin, which possessed unique properties and distinctive features from the current solutions like bacteriophages, antibiotics etc. Next, we followed an extensive literature review for Pyocins, thoroughly understanding their evolutionary relationships, mechanisms, and positioning in the environment. Our further research was guided by the idea to re-apply or tweak these features in a more directed & controlled manner.

## Rational Design

Based on the scientific literature analysis and on-ground exploration of the problem of antibiotic resistance, we funnelled down to 3 key questions that determined our further course of action further:

1. Selecting the Pyocin
2. Selecting the Bacteriophage
3. Approach for Fusion

Refer to our [Design Page](https://2020.igem.org/Team%3AIIT_Roorkee/Design) that elucidates the principles adopted and techniques used to design the complete system for the production of chimeric pyocins.

## Experimental Design

Without access to lab and testing facilities, it was challenging to interpret the functioning of the antimicrobial complexes our project is based on. We focussed on laying out a robust design of the experiments that will indicate the success & failure of our systems once we get access to the lab. The experiments are designed to test all our hypotheses and assumptions and provide meaningful data that will be utilised in project phase II. Refer to our [Experiments Page](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments) for more details

## *In-Silico* Testing

Bioinformatics analysis and protein modelling allowed us to make calculated predictions and formulate a primary proof of concept. We followed a three-pronged approach to develop the best model of the fusion protein, Seekercin, and results from biophysical testing improved our understanding of our fusion protein structure. Refer to [Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model) and [Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results) for more details.

## Community Feedback

We continued to reach out to the stakeholders and experts in the field that helped us in aligning and realigned our project goals and focus areas. While we started with the global problem of AMR, our interviews and research directed our objectives towards Hospital Acquired Infections. We penetrated through multiple layers and targeted one specific species, *A. baumannii*. This allowed us to have a well thought out solution, which now can be replicated to other areas by future iGEM Teams.

## Making Useful Tools

While researching, designing and implementing various techniques to curate our project, we decided to standardize and automate the process that will make it easier for other teams to build further on this concept. This will provide them with space to explore other areas of analysis and applications, which was not possible within the frame of our work. Refer to our [Software page](https://2020.igem.org/Team%3AIIT_Roorkee/Software) for further details. We intend to provide this modular system to the students and researchers so that it can be improved, and collaboratively reach a higher impact level. The lack of therapeutic interventions for handling resistant species is a grave concern, and through this work, we aim to popularise a new way of antimicrobials.

![](https://static.igem.org/mediawiki/2020/f/fb/T--IIT_Roorkee--images--icons--next.svg "Next section")

iGEM IIT Roorkee

People

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

Project

[Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
[Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
[Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
[Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
[Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
[Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)

Human Practices

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
[Science Communication and Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
[Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
Sustainable Development Goals
[Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)

Dry Lab

[Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
[Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)

Wet Lab

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
[Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
[Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
[Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
[References](https://2020.igem.org/Team%3AIIT_Roorkee/References)

Machine Learning

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
[Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
[Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)

The Department of Biotechnology,
Indian Institute of Technology, Roorkee
Roorkee, Uttarakhand,
India - 247667

![](https://static.igem.org/mediawiki/2020/a/a2/T--IIT_Roorkee--images--images--james_thomason_building.svg)

Contact us:

+011-2875468
+91-76487699
igem@iitr.ac.in

Follow us on:

[![Instagrm](https://static.igem.org/mediawiki/2020/7/78/T--IIT_Roorkee--images--icons--instagram.svg)](https://www.instagram.com/igem_iitr/)
[![Facebook](https://static.igem.org/mediawiki/2020/1/1c/T--IIT_Roorkee--images--icons--facebook.svg)](https://www.facebook.com/IGEM-IIT-Roorkee-111403760578466)
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# Team:IIT Roorkee/Experiments

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PYOMANCER

[PYOMANCER](https://2020.igem.org/Team%3AIIT_Roorkee)

* People
  + [Team](https://2020.igem.org/Team%3AIIT_Roorkee/Team)
  + [Attributions](https://2020.igem.org/Team%3AIIT_Roorkee/Attributions)
  + [Sponsors](https://2020.igem.org/Team%3AIIT_Roorkee/Sponsors)
* Project
  + [Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
  + [Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
  + [Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
  + [Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
  + [Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
  + [Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)
* Human Practices
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
  + [Science Communication & Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
  + [Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
  + [Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
  + [Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)
* Dry Lab
  + [Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
  + [Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)
* Wet Lab
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
  + [Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
  + [Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
  + [Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
  + [References](https://2020.igem.org/Team%3AIIT_Roorkee/References)
* Machine Learning
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
  + [Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
  + [Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)
* [Judges](https://2020.igem.org/Team%3AIIT_Roorkee/Judging)
* [Notebook](https://2020.igem.org/Team%3AIIT_Roorkee/Notebook)

# Wet Lab

## Overview

Overview

# Construction

In the Design section, we designed Seekercin-encoding sequences by fusing the tail fiber sequence of a
phage to that of an R-type pyocin. In this section, we describe how we plan to assemble the DNA correctly and
introduce our constructed expression plasmid into a competent host. We used Gibson assembly to
assemble the fragments.

## Gibson Assembly

We choose to assemble our ordered gBlocks gene fragments into the expression plasmid using Gibson Assembly, as we have to
assemble a total of 9 fragments. Other reasons we chose Gibson are its lower relative cost and number of steps compared to
conventional cloning and because it doesn't leave restriction site scars. The need for a PCR extension step of the ordered fragments
is eliminated as they come with the necessary overlaps. However, the overlaps between the terminal fragments and the plasmid
backbone were not included. We would linearize the pET-28a plasmid with BamHI and use PCR extension to add the overlap
sequences with appropriate primers.

![](https://static.igem.org/mediawiki/2020/a/a8/T--IIT_Roorkee--images--images--WL_Overview_1.jpg)

As we want to assemble more than 5 parts simultaneously, it was recommended that we follow the protocol for two-step Gibson
Assembly. It is a one-pot reaction that requires two separate additions of master mix; one for the exonuclease and annealing step
and second for the DNA polymerase and ligation steps. For the two-step approach, different incubation temperatures are used in the
two steps.

## Transformation

Transformation of plasmids into chemically competent *E. coli* BL21 DE3 cells would be done by heat shock method. Prior to this, the
mixture would be digested by BamHI to linearize the plasmid backbones that did not undergo successful Gibson, ensuring that only
correctly assembled plasmids get transformed. The transformed cells would be selected for by growing on media containing
kanamycin. The plasmid would be extracted after agarose gel electrophoresis and sent for sequence verification.

# Production

Here we describe the process for expressing our Seekercins in *E. coli* and producing high-purity solutions
with appreciable yield.

## Expression and Purification

In 2011, Williams et al demonstrated that functional pyocins could be produced in E. coli with appreciable yield. Transformed *E. coli*
BL21 ED3 cells will be grown overnight in a tryptic soy broth till it reaches an OD600 of 1.0. Expression would be induced by
addition of IPTG. This would activate the T7-lac promoter of the pET28a plasmid and transcription of the several protein subunits of
pyocins would begin. After roughly 3 hours, complete cell lysis of the culture should occur due to the presence of a lysis cassette in
the gene cluster. As shown in the literature, the Seekercins would be purified by three step high-speed ultracentrifugation at
increasing speeds and resuspension in TN 50 buffer. This should achieve approximately 90% Seekercin purity and net approximately
10^10 killing units per ml. Since *E. coli* lysates do not typically have anti- Acinetobacter properties, this purity should be sufficient for
antimicrobial activity tests. We would perform the same purification procedure on non-transformed *E. coli* lysates as well, to
provide a negative control solution for the coming tests.

## Yield Assessment

To assess the yield of our Seekercins in comparison to other cell proteins, we would be using the Bradford assay. This is a simple and
accurate spectroscopic method which relies on measuring the absorbance of a protein solution dyed with Coomassie Brilliant Blue
G-250 dye (Bradford reagent) at 595 nm. The assay would be performed on both the crude lysate as well as the centrifuge-purified
solution, so as to calculate the ratio between Seekercin to total protein produced, as a simple indicator of yield.

## Advanced purification

Performing protein characterization assays like CD spectroscopy require a highly pure protein solution. To achieve this purity, we
plan to use an alternate dialysis method described by Scholl et al. The impure solution would be ultracentrifuged, resuspended in a
Tris buffer and subjected to repeated filtration through 100kDa Amicon molecular filters. The resultant purity should be sufficient for
working with a spectrophotometer.

# Testing

Here, we attempt to validate the design of our Seekercins by testing their effectiveness against
*Acinetobacter baumannii* clinical strains.

## Antibacterial Spectrum Determination

Semiquantitative assays for determination of strain spectrum and specificity would
be performed. The purified Seekercins would be tested against 50+ Acinetobacter
baumannii isolates, *E. coli* lab strains, Pseudomonas aeruginosa PAO1 and other
common pathogenic bacteria. The Pathania lab (our PI’s!) hosts a large library of
*Acinetobacter baumannii* clinical strains ranging from MDR to XDR in nature.

![](https://static.igem.org/mediawiki/2020/1/17/T--IIT_Roorkee--images--images--WL_Overview_2.png)

Serially diluted samples of Seekercins would be spotted on lawns of bacteria and
incubated overnight. Activity would be shown by a circular clear zone of inhibition
with a sharp edge. Solutions of purified *E. coli* lysates would also be spotted as a
negative control.

Mueller-Hinton agar plates will be used for growing the lawns, as they are optimal
for antimicrobial testing. It contains starch which is known to absorb toxins
released from bacteria, so that they cannot interfere with antimicrobials. Secondly,
it is a loose agar which allows for better diffusion than most other plates, which
would show a truer zone of inhibition.

A meta analysis of antibiotic resistance patterns along with those of pyocin
susceptibility of tested strains would hopefully give us some new insights.

## Quantitative assays

#### Broth microdilution

Broth microdilution method is widely used because of its accuracy and clear results. In this method, wells are filled with broth
containing different concentrations of the antibiotic. These are then inoculated with bacteria and incubated overnight. The next day,
the contents of the wells are extracted and plated on agar, followed by counting of surviving cells using a colony counter. This would
allow us to determine the minimum inhibitory concentration as well as the number of bactericidal events per ml.

It has been shown by Kageyama et al that one pyocin molecule is capable of killing one bacterial cell, and that is how we define a
bactericidal event. Probabilistically, bacterial cells may be met by multiple pyocins, one pyocin or no pyocins at all. We must
account for this variability by relating the number of bactericidal events to a certain measurable quantity. It was suggested by our
advisor that we employ a Poisson distribution, a discrete probability distribution that expresses the probability of a given number of
events occurring in a fixed volume if these events occur with a known constant mean rate and independently of the time since the
last event, which is exactly the case for pyocins.

![](https://static.igem.org/mediawiki/2020/8/8a/T--IIT_Roorkee--images--images--WL_Overview_3.jpg)

Based on the work of Scholl et. al, the number of bactericidal events are related to the fraction of bacterial survivors in a Poisson
distribution, m = −lnS, where m is the average number of bactericidal events per bacterial cell, and S is the fraction of survivors. This
would allow us to calculate the total number of bactericidal events per ml, which equals m times the number of bacterial cells per ml.

#### Time-Kill Assay

An effective antimicrobial should have a known and reliable duration required for it to take effect. We plan to observe the
bactericidal effect of Seekercins over time in-vitro. This would give us an idea of the time-frame required for sufficient 3-log
reduction of cell count. Seekercins at twice the calculated value of MIC would be added to a susceptible bacterial culture of 10^8
CFU/100 μl. Samples would be extracted and the number of surviving cells counted at regular intervals.

We felt this was a necessary experiment, as it is important to characterize the time-activity of our designed antimicrobials. Future
iGEM teams may use pyocins for many applications that we may have not thought of yet, and this would give them a fair idea of how
fast their designs might take to work, and if it would make sense in their application.

#### Cell line antimicrobial assay

As a proof of concept for the application of treating ventilator-associated pneumonia, an experiment to test the efficacy of
Seekercins on lung epithelial cell lines would be conducted. Activity in vitro vs in cells could differ due to a multitude of factors
including but not limited to diffusion in the medium, cell permeability, inactivation due to native proteins, and activation of virulence
factors. Evidence of antimicrobial action on the intended medium would be a good validation of our project in the Therapeutics track.

MIC determinations will be performed on lung epithelial cell line (A549) monolayers in 96-well plates infected with **A. baumannii**
test strains at MOI of 100 – 1000. Prior to infection, epithelial cells will be maintained in antimicrobial-free growth medium based on
DMEM. On the day of infection, 2-fold serial dilutions of pyocins in antibiotic-free DMEM will be added to each well. After incubation,
bacterial cell numbers will be calculated by serially diluting and spotting the dilutions on LB agar plates. Epithelial cell viability would
be calculated by performing an MTT assay using a cell proliferation kit (as per manufacturer’s instructions).

# Biochemical Characterization

The characterization of Seekercins under different environmental conditions is crucial for determining the
feasibility of a proposed implementation, for our project as well as future projects. Observing the structure
of the complexes over a range of physical factors would allow for development of optimal delivery media
or optimal storage conditions.

## Temperature and pH stability test by CD spectroscopy

Circular Dichroism is an absorption spectroscopy method based on the differential absorption of left and right circularly polarized light, known as ellipticity. It is widely used in identifying conformational changes of proteins. The peptide bonds in proteins are optically active and secondary structures such as α-helices and β-sheets exhibit unique spectra that form the basis for protein secondary structure analysis. A change in these secondary structures would produce a change in the ellipticity recorded by the instrument.

We aim to use this method to learn the range of pH and temperature over which the Seekercins maintain their native conformation. High purity Seekercins suspended in a special CD buffer would be pipetted into a quartz cuvette. Measurements of ellipticity would be taken by a calibrated spectrophotometer in units of millidegrees in the range of 0-80 ℃.

For pH range, a similar experiment would be conducted with multiple cuvettes, each containing a highly pure pyocin solution with different pH buffers in the range of 4-10. The data would be analysed using the DichroWeb interface to find out the limits of stability.

In reporting CD data for large biomolecules such as pyocins it is necessary to convert the data into a normalized value that is independent of molecular length. To do this the molar ellipticity is divided by the number of residues or monomer units in the molecule.

# Resistance development studies

A concern when designing any antimicrobial agent is the potential for development of resistant
strains. Wild-type R pyocins have been shown to have extremely low frequency of resistance when
used against their natural host, at the order of 10^-8. Here, we try to assess the frequency of
resistance to Seekercins by *Acinetobacter baumannii*, a bacteria known to develop rapid resistance
to all classes of antibiotics.

## Spontaneous mutation frequency assay(aka. Frequency of Resistance analysis)

An experiment to calculate the frequency at which resistant mutants emerge when
exposed to inhibitory concentrations of Seekercins. The first step would be to expose a
culture of susceptible A baumannii to a high dose, about 4 times the calculated MIC,
so as to ensure that all bacterial cells come into contact with at least one pyocin molecule
. This would ensure that only true resistant strains survive in the culture.This would be followed
by plating the cultures and counting the remaining number of colony forming units.

## LPS analysis of resistant mutants and susceptible strains

Seekercins bind specifically to surface receptors on the Gram negative cell wall, which are often crucial
virulence or fitness factors. The development of resistance against pyocins is thought to be associated with
modification or loss of these receptors. The lipooligosaccharide of *Acinetobacter baumannii* has been shown
to be an important virulence and antibiotic resistance factor. Further, it has been shown that LPS-deficient
**A. baumannii** have increased permeability and show higher sensitivity to certain antibiotics and antimicrobial
peptides.

We would attempt to demonstrate that pyocin-resistant
**A. baumannii** mutants show phenotype for reduced LPS/LOS as
compared to susceptible strains. LPS would be extracted using
the Bacterial lipopolysaccharide extraction kit from iNtRON Biotech.
THe LPS preparations would be separated on a 15% SDS-PAGE and visualized
using 2D-Silver Stain Reagent II. Lack of a band in comparison to LPS band
of susceptible bacteria would indicate loss of LPS.

![](https://static.igem.org/mediawiki/2020/f/fb/T--IIT_Roorkee--images--icons--next.svg "Next section")
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iGEM IIT Roorkee

People

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

Project

[Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
[Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
[Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
[Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
[Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
[Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)

Human Practices

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
[Science Communication and Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
[Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
[Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
[Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)

Dry Lab

[Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
[Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)

Wet Lab

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
[Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
[Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
[Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
[References](https://2020.igem.org/Team%3AIIT_Roorkee/References)

Machine Learning

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
[Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
[Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)

The Department of Biotechnology,
Indian Institute of Technology, Roorkee
Roorkee, Uttarakhand,
India - 247667

![](https://static.igem.org/mediawiki/2020/a/a2/T--IIT_Roorkee--images--images--james_thomason_building.svg)

Contact us:

+011-2875468
+91-76487699
igem@iitr.ac.in

Follow us on:

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# Team:IIT Roorkee/Results

<!DOCTYPE html>

PYOMANCER

[PYOMANCER](https://2020.igem.org/Team%3AIIT_Roorkee)

* People
  + [Team](https://2020.igem.org/Team%3AIIT_Roorkee/Team)
  + [Attributions](https://2020.igem.org/Team%3AIIT_Roorkee/Attributions)
  + [Sponsors](https://2020.igem.org/Team%3AIIT_Roorkee/Sponsors)
* Project
  + [Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
  + [Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
  + [Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
  + [Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
  + [Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
  + [Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)
* Human Practices
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
  + [Science Communication & Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
  + [Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
  + [Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
  + [Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)
* Dry Lab
  + [Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
  + [Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)
* Wet Lab
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
  + [Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
  + [Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
  + [Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
  + [References](https://2020.igem.org/Team%3AIIT_Roorkee/References)
* Machine Learning
  + [Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
  + [Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
  + [Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
  + [Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)
* [Judges](https://2020.igem.org/Team%3AIIT_Roorkee/Judging)
* [Notebook](https://2020.igem.org/Team%3AIIT_Roorkee/Notebook)

# Wet Lab

## Results

Results

# Dry Lab

[Click here for the
 Dry Lab Results Page](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)

# Wet Lab

As our institute remains closed to students due to the coronavirus pandemic, we would not be able to
perform the experiments we have planned within time for iGEM 2020. This section is a discussion about
what results we might expect from our experiments and our contingency plans in case some of them
don’t go as planned.

We had initially performed some preliminary experiments in March to verify the function of wild-type pyocins against
susceptible P aeruginosa strains and their effect(or lack thereof) on **A. baumannii**. However, evacuation of the campus
was ordered shortly after planning of this. The experiment ended up failing, as we tried to rush through them. Inducing
expression of pyocins in PAO1 is done by addition of mitomycin C, a mutagen that triggers the SOS pathway. The order
for the reagent did not arrive in time, and so we tried to induce expression through oxidative stressor methylviologen.
Pelleting the proteins from the PAO1 lysates showed that there was no expression induced.

## Assembly/Cloning

Gibson Assembly being a highly reliable process should not pose any problems in terms of results. We chose to use the two-step
Gibson protocol, which has been shown to reliably assemble upto 15 fragments. However, since this is our first time doing Gibson,
we contemplate the possibility of a learning curve and optimization.

In the unlikely case that cloning the synthetic DNA fails, we would circumnavigate this by cloning the native genes from *P. aeruginosa*.
PCR cloning of the pyocin gene cluster from PAO1 followed by replacement of the tail fiber gene with a fusion tail fiber
gene by restriction-ligation would be done. The gene products would be identical to those from the synthetic DNA.

However, the native gene cluster contains many illegal restriction sites which would have to be edited through site-directed
mutagenesis. This is a rather lengthy process which we hope to avoid, and hence our primary plan is the synthesis method.

## Antimicrobial tests

In the case that the produced Seekercins do not show positive results on any of the *A. baumannii* isolates, we would proceed by producing pyocins with alternate fusion tail fibers made from other Acinetobacter-specific phages. A new *A. baumannii* phage is characterized in the literature every few months, and selecting new candidates may be possible.
Another approach will be to try other fusion sequences of the AP22 tail fiber .We would use different lengths of N-terminal pyocin tail fiber and C-terminal AP22 tail fiber in combination and perform the spot test to determine which of those would be viable.

![](https://static.igem.org/mediawiki/2020/f/fb/T--IIT_Roorkee--images--icons--next.svg "Next section")

iGEM IIT Roorkee

People

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

Project

[Description](https://2020.igem.org/Team%3AIIT_Roorkee/Description)
[Problem](https://2020.igem.org/Team%3AIIT_Roorkee/Problem)
[Design](https://2020.igem.org/Team%3AIIT_Roorkee/Design)
[Engineering](https://2020.igem.org/Team%3AIIT_Roorkee/Engineering)
[Implementation](https://2020.igem.org/Team%3AIIT_Roorkee/Implementation)
[Contributions](https://2020.igem.org/Team%3AIIT_Roorkee/Contribution)

Human Practices

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Human_Practices)
[Science Communication and Public Engagement](https://2020.igem.org/Team%3AIIT_Roorkee/Education)
[Integrated Human Practices](https://2020.igem.org/Team%3AIIT_Roorkee/Integrated_HP)
[Sustainable Development Goals](https://2020.igem.org/Team%3AIIT_Roorkee/Sustainable)
[Collaborations](https://2020.igem.org/Team%3AIIT_Roorkee/Collaborations)

Dry Lab

[Modelling](https://2020.igem.org/Team%3AIIT_Roorkee/Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/DryLab_Results)
[Software](https://2020.igem.org/Team%3AIIT_Roorkee/Software)

Wet Lab

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/Experiments)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/Results)
[Protocols](https://2020.igem.org/Team%3AIIT_Roorkee/Protocols)
[Parts](https://2020.igem.org/Team%3AIIT_Roorkee/Parts)
[Safety](https://2020.igem.org/Team%3AIIT_Roorkee/Safety)
[References](https://2020.igem.org/Team%3AIIT_Roorkee/References)

Machine Learning

[Overview](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Overview)
[Model](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Model)
[Results](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Results)
[Impact](https://2020.igem.org/Team%3AIIT_Roorkee/ML_Impact)

The Department of Biotechnology,
Indian Institute of Technology, Roorkee
Roorkee, Uttarakhand,
India - 247667

![](https://static.igem.org/mediawiki/2020/a/a2/T--IIT_Roorkee--images--images--james_thomason_building.svg)

Contact us:

+011-2875468
+91-76487699
igem@iitr.ac.in

Follow us on:

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[![Facebook](https://static.igem.org/mediawiki/2020/1/1c/T--IIT_Roorkee--images--icons--facebook.svg)](https://www.facebook.com/IGEM-IIT-Roorkee-111403760578466)
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