Entrepreneurship

OVERVIEW

In-silico analysis enables the prediction and analysis of different states in a system with the help of computer simulations. Thus establishing realistic models was necessary to test and analyse the behaviour of the systems without the need to perform experiments. 

"APTASTELES" is a novel diagnostic kit for PCOS to detect the concentration of an array of biomarkers in a concentration-dependent manner by using aptamers. Our Diagnostic kit contains a microfluidic chip where biomarkers are detected through light-up aptamers, giving fluorescence output. We used modeling to learn about our system better. We divided our models into four parts to understand the intricacies of different systems in our project. We modeled different processes in each section and derived useful results from them. The modules in each section that we explored are:-

  1. Designing Aptamers 

  2. Reaction Kinetics

  3. Biomarker Analysis

DESIGNING APTAMERS

Aptamers are artificial short-chain oligonucleotides that can be selected against various targets. Structural motifs like G-quadruplexes, hairpins, bulges, loops, etc., help identify specific targets. (Click Background to know more about aptamers). 

Our detection system uses different kinds of Aptamers. We analysed the secondary structures of the aptamers using freely available online softwares before testing them experimentally. Based on this, we ordered sequences from Integrated DNA Technologies (IDT) and planned experiments to screen for the sequences that could work for our kit. (click here to see our experimental workflow)

Here, we discuss in detail how we designed aptamers for our detection systems (Click Blueprint to know about our detection systems).

FASTmiR Aptamer Design

STRATEGY

  1. Modify the available FASTmir sequence
    The FASTmiR sequence, which has already been characterised, is modified to accommodate the complementary sequence of the desired miRNA. This works as the RNA sensing region of the system. Moreover, the complementary sequence is split into two, with each part being added towards the 5’ region or the 3’ region of the sensor. The rest of the sensor sequence comprises a modified spinach aptamer (ModSpinach) and RNA motifs.

  2. Test out the different split positions of RNA sensing region
    The complementary regions’ split positions differ, so the number of bases at the 3’ and 5’ ends varies correspondingly, and we get various designs for the same miRNA. The free energy of these structures is calculated using RNAfold [8] and NUPACK [1] and compared to select the best designs among them.

  3. Check the ON-state in the presence of miRNA
    In the presence of miRNA, the FASTmiR sensor would shift to its ON state. So, we have co-folded the FASTmiR construct with the desired miRNA using RNAcofold [9] and NUPACK. The free energy of all these complexes is also calculated and compared with its OFF state. Desirably, the free energy of the ON state should be more negative than the OFF state, indicating more stability upon miRNA binding.

  4. Test out the in-silico data experimentally
    The designs selected with the data obtained from RNAfold, RNAcofold and NUPACK are tested experimentally to check out the fluorescence intensity of each of these designs in the presence of different concentrations of biomarkers.

PARAMETERS

  1. The free energy of the OFF structure was calculated to check the stability of the sensor in the absence of miRNA.
  2. Free energy of the co-folded FASTmiR-miRNA complex (ON state) to compare the energy with the OFF state

APPLICATION 

The FASTmiR sensors were designed specifically for each miRNA selected as a biomarker. miR-222 and miR-146a had five shortlisted designs, while miR-27a-5p and miR-30c had three designs each.  

 


EXAMPLE

FASTmiR-222

Design for miR-222 : FASTmiR-222 OFF

miR-222 SEQUENCE: Mature sequence hsa-miR-222-3p : 5’ AGCUACAUCUGGCUACUGGGU

Complementary : UCGAGUGAGACCGAUGACCCA 3’



Table 1. Represents split positions and energy values obtained from RNAcofold for miRNA-222 sequences

S.NO

POSITIONS

SELF

(Kcal/mol)

RNAcofold

(Kcal/mol)

Δ G 

(Kcal/mol)

REMARKS

1

3’ ACCCAGUAGCCA……GAGUGAGCU 5

-26.09

-58.24 

-22

Frequency High

2

3’ ACCCAGUAGCC…..AGAGUGAGCU 5’ 



-34.08

-58.43

-24.33 

Frequency High

3

3’ ACCCAGUAGC…..CAGAGUGAGCU 5’

-34.14 

-57.90

-23.75

Frequency low

4

3’ ACCCAGUAG……CCAGAGUGAGCU 5’ 

-32.27 

-57.21

-24.91

5

3’ ACCCAGUA……GCCAGAGUGAGCU 5’ 

-33.08

-58.10

-24.91

6

3’ ACCCAGU……AGCCAGAGUGAGCU 5’ 

-32.91

-58.23

-25.93 

7

3’ ACCCAG……UAGCCAGAGUGAGCU 5’ 

-33.05

-58.19

-25.12

Toehold : No

Number of split positions = Number of designs 

DESIGN 1 : Split position : 3’  ACCCAGUAGCCA……GAUGUAGCU 5’ 

OFF : 5’ GGGACCCAGUAGCCAAAAGGGUACUUGUUGAGUAGAGUGUGAGCUCCGUAACUAGUCGCGUCUUC
GGACGCACUGAAUGAAAUGGUGAAGGACGGGUCCAGUGCCUAGAUGUAGCUA


ON : 5’ GGGACCCAGUAGCCAAAAGGGUACUUGUUGAGUAGAGUGUGAGCUCCGUAACUAGUCGCGUCUUC
GGACGCACUGAAUGAAAUGGUGAAGGACGGGUCCAGUGCCUAGAUGUAGCUAAGCUACAUCUGGCUACUGGGU3’

Figure 1. NUPACK A) FASTmiR-222 OFF state B) FASTmiR-222 ON state

Figure 2. A)RNAfold FASTmiR-222 OFF state B)RNAcofold FASTmiR-222 ON state

FASTmiR sensors with different split positions specific for miR-146a, miR-27a-5p, miR-30c and other miR-222 were similarly made with the same strategy in mind. Here are the other designed sequences.

Figure 1. A) FASTmiR-222-D1 OFF state B) FASTmiR-222-D1 ON state


Dual Aptamer Design

STRATEGY

  1. Choose an aptamer (single-stranded RNA or DNA) that has the following:
    • • high affinity (low dissociation constants; aptamers can bind to even low amounts of biomarkers)
    • • high specificity (prevents cross-reactivity with other molecules present in the sample) and
    • • high selectivity (binds maximally to the desired biomarker and folds lesser to non-target molecules)
  2. Using literature, obtain the potential target-binding segments within the aptamer
  3. Aptamer complementary sequence: 
    • • Analyse the secondary structure of aptamer using DuplexFold, UNAFold, NUPACK, etc. [1-3]
    • • Design different sequences, blocking whole binding sites, regions adjacent to binding sites or overlapping binding and non-binding regions of the aptamer. If these sites are not known or aptamer is not well-characterised, go by hit and trial method by analysing secondary structures and minimum free energy values.
    • • Vary the length of the complementary sequences. Some mismatched bases can also be incorporated.
  4. Experimentally, with different concentrations of biomarkers, test the displacement response to the aptamer’s conformational change when the biomarker is recognised.

PARAMETERS

  1. The biomarker and its aptamer should be more stable than the aptamer-trigger complex. Only then all the triggers can get displaced, ensuring that the reaction goes in one direction.
  2. The affinity of the aptamer-trigger should be strong enough to remain in annealed form in the absence of a biomarker and weak enough so that trigger gets easily displaced in the presence of a biomarker. The system's sensitivity will depend on the feasibility of biomarker-induced trigger displacement.
  3. The target should be designed so that all the displaced triggers bind to the target after the sequential addition of the amplification module. This can be achieved by creating the target sequence entirely complementary to the trigger sequence.

APPLICATION

We designed two modules to detect two of our biomarkers, testosterone and C-reactive protein (CRP). For proof-of-concept, we used quinine as it has well-characterised aptamer (cocaine binding aptamer; MN4) and progesterone. Progesterone is a steroidal hormone and quinine is an alkaloid.

  • • Testosterone
  • • Progesterone
  • • Quinine
  • • C-reactive protein 

Aptamers that were chosen for the above molecules

Table 2. Information about the chosen aptamers

S.NO

MOLECULE

APTAMER

KD 

METHOD

REFERENCES

1.

Testosterone

BBa_K4438600

(1.8 ± 0.3) nM

Apta-PCR affinity assay (APAA)

[4]

2.

Progesterone

BBa_K4438700

17 nM

electrochemical impedance spectroscopy (EIS), fluorometric assay

[5]

3.

Quinine

BBa_K4438800

(0.20 ± 0.05)μM*

Isothermal Titration Calorimetry (ITC)

[6]

4.

C-reactive protein

BBa_K4438900

Not reported

      —

[7]

*at 140 nM NaCl concentration


EXAMPLE


Testosterone aptamer sequence (T6)

The dissociation constant, reported in the literature, lies in the low nanomolar range and was calculated using apta-PCR affinity assay (APAA) [10].

5’-TAGGGAAGAGAAGGACATATGATTCCTGTCGAATTCAAATCGAACTAGCCTCATCTCAGCTCGTTGACTAGTACATGACCACTTGA-3’

The sequence motifs for testosterone aptamer T6 can form the potential binding site for the target molecule. 


Table 3. Represents data obtained from NUPACK for the trigger sequences designed for testosterone aptamer T6 (Minimum free energy at 37° C is -3.88 kcal/mol; KD# = 1.8 ± 0.3 nM)

S.NO.

Trigger sequence

(5’ end to 3’ end)

MFE* at 37° C (kcal/mol) 

MFE* of hybridised trigger and aptamer at 37° C (kcal/mol)

Sequence length (number of bases)

Remarks

1.

BBa_K4438601

-1.93

-15.74

27

Designed for approach 1~; sequence motif is blocked; mismatched bases present

2.

BBa_K4438603

-2.65

-13.48

30

Designed for approach 1; sequence motif is blocked; mismatched bases present; few bases of antisense T7 promoter sequence present at 5’ end

3.

BBa_K4438605

-0.07

-16.74

29

Designed for approach 2+; sequence motif is blocked; mismatched bases present; few bases of sense T7 promoter present at 3’ end

# KD refers to dissociation constant

* MFE stands for minimum free energy

~ Approach that requires phi 29 DNA polymerase

+ Approach that does not require phi 29 DNA polymerase

 


Figure 1. A) T6 Aptamer structure B) Trigger 1 design C) Target 1 design D) T6 Aptamer-Trigger complex

Figure 2. A) T6 Aptamer structure B) Trigger 2 design C) Target 2 design D) T6 Aptamer-Trigger complex

Figure 3. A) T6 Aptamer structure B) Trigger 3 design C) Target 3 design D) T6 Aptamer-Trigger complex

Slide 4



PARTS MODIFICATION

Towards the modification of parts from the Registry of Standard Biological Parts (RSBP), we aimed to design investigations that would enhance the fluorescence intensity of Spinach2.1, a light-up aptamer [17]. In order to achieve this, we altered the sequence by introducing mutations (single, double), inversions, and deletions at different regions of the Spinach2.1 aptamer sequence to increase its relative fluorescence intensity than the wild-type. We employed two approaches in which, firstly, the sequence in the tetraloop (UUCG or TTCG) was mutated and inversion near this tetraloop sequence [10, 12]. Secondly, point mutations (A12G and U86C) were made at different places in the Spinach2.1 sequence. These mutations were chosen by a thorough inspection of the Spinach2.1 3D structure (PDB entry: 4TS2) with the help of Dr Hussain Bhukya, IISER Tirupati, and Dr Harikrishna S, Senior Scientist at Syngene.

All possible single-point mutations (SPMs) were made in the tetraloop using combinatorics and inversions. The free energy of the thermodynamic ensembles, frequency of the minimum free energy (MFE) structure, and the ensemble diversity were predicted using RNAfold. Based on these predictions, we chose a few sequences that displayed the best parameters. The mutated sequences and their corresponding RNAfold structures are given Figure 1.



Mutations and Inversion: 

The free energy of the thermodynamic ensemble are predicted to be -34.44 kcal/mol, -34.44 kcal/mol, -34.80 kcal/mol and -35.50 kcal/mol for the sequences (modified Spinach2.1) of Spinach2.2, Spinach2.3, Spinach2.4 and Spinach2.5 respectively.



Figure 1. Sequence and structures of the RNA Aptamers. The DNA sequence and the RNAfold structures of Spinach2 and Spinach2.1 are adopted from the parts registry made by DTU, Denmark. The Spinach2 sequences highlighted in bold are the aptamer sequence, and those flanking are the tRNA scaffold sequence, tRNALys3. The yellow colored box highlights the tetraloop sequence (colored red) and the bases adjacent (colored blue) to the tetraloop. The bases colored green are mutated from A/T and T/A to give a modified Spinach2 version, Spinach2.1. The other Spinach aptamer versions are derived from the Spinach2.1 sequence by base mutations in the tetraloop and the region adjacent to it (highlighted in the yellow colored boxes) and keeping the rest of the sequence unchanged. These mutants correspond to Spinach2.2, Spinach2.3, Spinach2.4 and Spinach2.5. The mutations in the DNA sequence are displayed on the RNAfold structures of the RNA aptamers (Spinach). The tetraloop region is indicated by a box colored blue for Spinach2 and the other mutations are circled in blue for other modified Spinach aptamers.



In silico Analysis: Molecular Dynamics (MD) Simulation of RNA-ligand interactions

Inspired by the fluorescence property of the DFHBI bound Spinach2.1 aptamer complex, we aimed to enhance the detection limits of this aptamer by modifying its sequence. To check whether the proposed modifications would enhance the fluorescence intensity, we performed in-silico studies by simulating the native DFHBI-Spinach aptamer complex (PDB entry: 4TS2) to validate the forcefield and other parameters for running Umbrella Sampling MD in Gromacs [13]. Later, the modified Spinach structure was generated in X3DNA[16] where the base mutations, A12G and U86C, are incorporated in the 4TS2 to give the DFHBI bound Spinach aptamer structure, Spinach2.6. Both the native and modified Spinach structures were subjected to identical simulation parameters in order to estimate the binding energy (ΔGbind) using the potential of mean force (PMF). The PMF was extracted from a series of umbrella sampling simulations and these values are anticipated to provide insights into the affinity of DFHBI binding to the G-quadruplex, a part of the aptamer structure [11,13,14].




Figure 2. Structures of DFHBI-Spinach RNA aptamer complex and Spinach2.6 complex generated in X3DNA are displayed in the left and right panels, respectively. The bases highlighted in spacefill model are the mutation points. The RNA sequence corresponding to the structures are displayed below and the bases mutated are shown in blue. 



GROMACS simulations

The input file for both the structures were generated using CHARMM-GUI, where the RNA aptamer and ligand (DFHBI) complex reside in the water (TIP3P water system) box consisting of the defined periodic boundaries. The Amber99-OL3 RNA force field was used to perform the energy minimization and equilibration (frame rate, 125000 ns steps x 2 fs) by normal volume and temperature (NVT) method for 250 ps to get the equilibrated structures of both the native and modified RNA aptamers. Steer MD simulations were performed on these equilibrated complex structures where an imaginary spring force was used to pull the ligand out of the binding site. The RNA was restrained in the course of pulling the ligand out of the G-quadraplex structure, where the trajectories of this course were plotted. Later, a window spacing was performed to generate configurations to help visualize the unbinding and rebinding . This was followed by adding-up the configuration free energies to obtain the final histogram and the total binding energy. 




Figure 3. Flowchart for GROMACS input file preparation.


The energy minimization results were plotted using the steepest descent algorithm and it was observed that the native structure converged in almost 200 steps (Figure 4a) while for Spinach2.6 converged in nearly 180 steps (Figure 4d). An NVT equilibration was performed and trajectory files were generated to visualise the simulation. It was found that the system reached equilibrium at less than 20 ps (Figure 4b, e). This equilibrated system was subjected to the Steer MD simulations to dislocate the ligand from the G-quadruplex binding site with spring force 10000 KJ/mol/nm2, see the Figure 4c, d. The force applied was able to pull the ligand apart from the binding site of the RNA aptamer as seen in the animation below. Overall, the energy minimization plot suggests that the Spinach2.6 complex structure is relatively stable when compared to the native aptamer structure. Moreover, the Steer MD plots for both these complexes are significantly different suggesting the binding affinities of the ligand to RNA aptamer structure are different. However, additional simulations and experimental confirmations need to performed to strengthen this observation.



Figure 4.Results of MD simulations of native and modified RNA aptamers:a, b) Energy minimization and equilibration plots of native (4TS2) complexes respectively. c) Steer MD simulation plot for the native structure. d, e) Energy minimization and equilibration plots of modified Spinach RNA aptamer, Spinach2.6 complex structure respectively and f) Steer MD simulation plot for the Spinach2.6 complex structure



Steer MD trajectory visualisation was done using pull.gro and pull.xtc files.






REACTION KINETICS

One of the primary goals of modelling is to understand the behaviour of the system and check whether the system is behaving as expected. In this section, we shall be reviewing the detection systems by building rate law models and analysing the results through graphs.

Introduction

The concentration of miRNA in our sample is quite low hence we proposed to amplify our miRNA through a process called miRPA [18] (Check Blueprint to know more about miRPA). Using the data from this model, we could predict the concentration of miRNA and miDNA that has been amplified for a given time and resources. Based on the amplified concentration, the amount of FASTmiR aptamers required was calculated and used to find out the final concentration of FASTmiR aptamer that is bound to the dye. 

Main goals

  1. Determine the concentration of the amplified miRNA(and miDNA) in PCOS and non PCOS people and find the fold change.
  2. Predict the concentration of FASTmiR aptamers required.
  3. Find out the total concentration of FASTmiR sequences that is bound to the dye.

Assumptions

  1. Most reactions have been considered to be irreversible since forward rate of reaction >> reverse rate
  2. Some parametric values have been assumed because some reactions are specific to our system.

Reaction System


Figure 1. Flowchart Representing the Reaction System


Reactions

Equations

Parameters

ParameterDescriptionValue Units Source
k1 Rate of probe 1 binding to miRNA 106M-1s -1Assumed
k2Rate of probe 2 binding to complex 1 (miRNA - probe 1)106M-1s-1Assumed
Vmax3Rate of ligase activity (kcat*enzyme concentration)2.4x10-6Ms-1Assumed
kM3Michelis Menten constant of ligase 10-9s-1Assumed
k4Rate of forward primer binding to duplex 106M-1s-1Assumed
Vmax5Rate of Strand displacing polymerase activity2.4x10-6Ms-1Assumed
kM5Michaelis Menten constant of strand displacing polymerase43x10-6s-1Assumed
k6Rate of forward primer and recombinase4.6x10-3s-1[19]
k7Rate of forward primer-recombinase and miDNA 108M-1s-1[19]
k8miRNA- FASTmiR aptamer Binding rate106M-1s-1Assumed
k9FASTmiRoff-dye Binding rate106M-1s-1Assumed

Tunable components:

By tuning the following components

  1. Concentration of miRNA 
  2. Concentration of Probes
  3. Concentration of Forward primer
  4. Concentration of recombinase

Simulation Results

Figure 2: a) Rate of formation of miDNA for PCOS (2x10-14 M) and Normal(1x10-14 M) condition. b) Rate of formation of FASTmiRon aptamers for PCOS (9nM) vs Normal (4nM) condition

Results and Inferences:

Using these graphs we were able to find that after 30 minutes of amplification, which is the standard time of reaction of miRPA, we could reach a considerable concentration of miDNA. The miRNA is amplified to approximately 4nM and 9nM respectively for normal and PCOS case. It was noted that the fold change was nearly maintained at the same level before amplification. We could also find the time at which the maximum concentration of FASTmiR - dye complex is reached. This graph can be used to fix a threshold for PCOS and non-PCOS conditions. The plateauing of the concentration of the FASTmiR on-state that happens at the same concentration level, indicates that there is a one to one correspondence between miRNA concentration and the FASTmiR-dye complex concentration which verifies our biosensor working. Initially as the concentration of the miRNA increases there is a drastic increase in the concentration of the FASTmiR on-state. After a timepoint, it reaches a saturation point, after which, there is no further increase in the concentration of the FASTmiR on-state, which indicates the equilibrium state where most of the concentration of miDNA and miRNA is detected

Improvements:

In the model, we have assumed that non-specific interactions between miRNA and FASTmiR to be negligible. Since it can have a significant effect in the real system our model can be updated based on experimental data about the Kd values specific to our biosensors.




BIOMARKER ANALYSIS

A person might not be positive for all the biomarkers. Hence we needed to identify the combination of biomarkers that gives the most precise results in the given samples. Also, since we are detecting our biomarkers in a concentration-dependent manner, we needed to set a threshold to distinguish between PCOS and non-PCOS people. These results will be used to analyse the results by the Arduino (Click Hardware to know more about Arduino). We consulted with Dr Shibdas Banerjee and decided to take a machine-learning approach to classify PCOS and non-PCOS. 

Main goals

  1. Build a machine-learning model for our biomarkers using a testing set.
  2. Find out the best hyperplane which separates the two classes of our data. 
  3. Validate it using the test set.
  4. Predict the best combinations and thresholds for the biomarkers

We aimed to create a dataset for the biomarkers and build the model. However, due to a lack of experimental data, we envision a build a model in the future. We believe this model allows us to test the specificity and sensitivity of the biomarker combinations for detecting PCOS.

REFERENCES

  1. Bellaousov, S., Reuter, J. S., Seetin, M. G., & Mathews, D. H. (2013). RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic acids research, 41(W1), W471-W474.

  2. Markham, N. R., & Zuker, M. (2008). UNAFold. In Bioinformatics (pp. 3-31). Humana Press.

  3. J. N. Zadeh, C. D. Steenberg, J. S. Bois, B. R. Wolfe, M. B. Pierce, A. R. Khan, R. M. Dirks, N. A. Pierce. NUPACK: analysis and design of nucleic acid systems. J Comput Chem, 32:170–173, 2011. (pdf)

  4. Skouridou, V., Jauset-Rubio, M., Ballester, P., Bashammakh, A. S., El-Shahawi, M. S., Alyoubi, A. O., & O’Sullivan, C. K. (2017). Selection and characterization of DNA aptamers against the steroid testosterone. Microchimica Acta, 184(6), 1631-1639.

  5. Contreras Jiménez, G., Eissa, S., Ng, A., Alhadrami, H., Zourob, M., & Siaj, M. (2015). Aptamer-based label-free impedimetric biosensor for detection of progesterone. Analytical chemistry, 87(2), 1075-1082.

  6. Neves, M. A., Slavkovic, S., Churcher, Z. R., & Johnson, P. E. (2017). Salt-mediated two-site ligand binding by the cocaine-binding aptamer. Nucleic acids research, 45(3), 1041-1048.

  7. Liu, Z., Luo, D., Ren, F., Ran, F., Chen, W., Zhang, B., ... & Chen, Q. (2019). Ultrasensitive fluorescent aptasensor for CRP detection based on the RNase H assisted DNA recycling signal amplification strategy. RSC advances, 9(21), 11960-11967.

  8. Denman, R. B. (1993). Using RNAFOLD to predict the activity of small catalytic RNAs. Biotechniques, 15(6), 1090-1095.

  9. Bernhart, S. H., Tafer, H., Mückstein, U., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2006). Partition function and base pairing probabilities of RNA heterodimers. Algorithms for Molecular Biology, 1(1), 1-10.

  10. Ketterer, S., Fuchs, D., Weber, W., & Meier, M. (2015). Systematic reconstruction of binding and stability landscapes of the fluorogenic aptamer spinach. Nucleic acids research, 43(19), 9564–9572. https://doi.org/10.1093/nar/gkv944

  11. Yildirim, I., Park, H., Disney, M. D., & Schatz, G. C. (2013). A dynamic structural model of expanded RNA CAG repeats: a refined X-ray structure and computational investigations using molecular dynamics and umbrella sampling simulations. Journal of the American Chemical Society, 135(9), 3528-3538.

  12. Autour, A., Westhof, E., & Ryckelynck, M. (2016). iSpinach: a fluorogenic RNA aptamer optimized for in vitro applications. Nucleic acids research, 44(6), 2491–2500. https://doi.org/10.1093/nar/gkw083

  13. http://www.mdtutorials.com/gmx/umbrella/07_analysis.html

  14. Brooks, B. R., Bruccoleri, R. E., Olafson, B. D., States, D. J., Swaminathan, S. A., & Karplus, M. (1983). CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. Journal of computational chemistry, 4(2), 187-217.

  15. Warner, K. D., Chen, M. C., Song, W., Strack, R. L., Thorn, A., Jaffrey, S. R., & Ferré-D'Amaré, A. R. (2014). Structural basis for activity of highly efficient RNA mimics of green fluorescent protein. Nature structural & molecular biology, 21(8), 658–663. https://doi.org/10.1038/nsmb.2865

  16. Colasanti, A. V., Lu, X. J., & Olson, W. K. (2013). Analyzing and building nucleic acid structures with 3DNA. JoVE (Journal of Visualized Experiments), (74), e4401.

  17. http://parts.igem.org/Part:BBa_K1330000

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  19. Clint Moody, Heather Newell, Hendrik Viljoen, A mathematical model of recombinase polymerase amplification under continuously stirred conditions, Biochemical Engineering Journal, Volume 112, 2016, Pages 193-201, ISSN 1369-703X, https://doi.org/10.1016/j.bej.2016.04.017.

  20. Stögbauer T, Windhager L, Zimmer R, Rädler JO. Experiment and mathematical modeling of gene expression dynamics in a cell-free system. Integrative Biology. 2012 May 1;4(5):494-501.

  21. Morin JA, Cao FJ, Lázaro JM, Arias-Gonzalez JR, Valpuesta JM, Carrascosa JL, Salas M, Ibarra B. Mechano-chemical kinetics of DNA replication: identification of the translocation step of a replicative DNA polymerase. Nucleic acids research. 2015 Apr 20;43(7):3643-52.

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