Lab Notebook

June

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

July

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

August

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

September

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

June

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

July

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

August

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

August

18 Aug

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

Sequences are referred to by serial numbers
Optimization Algorithms used to generate the sequence:
SA - Simulated Annealing
GD - Gradient Descent
GA - Genetic Algorithm

H, L = High, Low Expressing

19 Aug

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

21 Aug

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

23 Aug

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

24 Aug

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

25 Aug

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

27 Aug

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

28 Aug

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

30 Aug

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


• All fragments have been amplified!

31 Aug

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

September

2 Sep

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

3 Sep

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

4 Sep

• Glycerol added and strains stored in -80C

5 Sep

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

6 Sep

• No colonies observed

8 Sep

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

9 Sep

• No colonies observed

11 Sep

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

12 Sep

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

14 Sep

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

15 Sep

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

17 Sep

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

18 Sep

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

21 Sep

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

22 Sep

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

23 Sep

• Fluorescence Assay
• First batch of results obtained.

25 Sep

• Colonies picked from streaked plates and cultured overnight.

26 Sep

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

October

2 Oct

• All strained streaked from glycerol stocks.

3 Oct

• Colonies were picked from streaked plates and cultured overnight.

4 Oct

• Plasmid isolation (Qiagen Kit protocol).

5 Oct

• Samples sent for sequencing.