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Revision as of 13:18, 20 October 2019
Lead In
Introduction
1. The stakeholders informing us that benefits matter most in application, we realized that it’s
essential to make our anti-phage strains grow as robust as original strain under the current
fermentation conditions. However, it’ s difficult to tell which strain grows most suitable or which
device has the least impact on cell growth from the growth curve graph of all those strains (Figure 1).
Therefore, we established a mathematical model to evaluate the growth properties of strains containing
different parts for the most suitable device in future application.
2. The phage-induced promoters are vital in our genetic circuit, which would response the phage
stimulation and start transcription of antP and P-1 (antimicrobial peptide) against the phage
infection
(Figure 2). And the phage-induced promoters we used were selected from E. coli, so there might be
some
potential problems such as leakage and inclusion body due to the inappropriate promoter strength. Thus
we developed a quantitative design method for phage-induced promoters based on strength prediction using
artificial neural network, which allows us to choose or design promoters with desired strength without
extra experiments.
Assumption
(1) The promoter strengths in the training set are the same using various vectors.
(2) The impact of parts in cell growth are relatively the same under different situation.
GRA_EWM Model
Symbol Description
Weights Calculation of The Growth Curve
Analysis of The Weight Distribution
As showed in Figure 3, the OD600 measured after 4 hour to 10 hour were more useful. However, the
weight
given by the experts might consider plateau phase as an important period for industry fermentation.
Grey Relational Analysis for Picking The Most Suitable Strain
Conclusion
Figure 4 demonstrates that the impact of the distinguishing coefficient on the result of GRA is very
significant. In particular, for all tested distinguishing coefficients, part gntR always ranks
first,
which means it is the most suitable part for future application.
BP-ANN Model
Accurate and controllable regulatory elements like promoters are indispensable tools to quantitatively
regulate gene expression for rational pathway engineering, which means a promoter with proper strength
might be an easy solution to leakage and inclusion body. In order to select or design promoters with
desired strength without experiments, we developed a quantitative strength prediction method using
artificial neural network (ANN) with Neural Network Toolbox under Matlab environment.
Construction of Early Phage-induced Promoter Strength Library
The endogenous promoters in E.coli BL21(DE3) we selected to respond to phage infection might
start
transcription without phage stimulation, so we selected 19 promoters with distributed strength from T4
phage mentioned in previous study as the phage-induced promoter library. (attachment: T4PE.docx)
Construction of Early Phage-induced Promoter Strength Library
Matlab2019b (Mathworks Inc., https://www.mathworks.com/) ran on a personal laptop with Windows 10
64-bit(Microsoft Inc., http://www.microsoft.com/) operation system. Neural Network Toolbox within Matlab
served as the basic tool for artificial neural network (ANN) model construction, data fitting and
prediction. All programs used in this work were designed and run upon Neural Network Toolbox and Matlab
environment.
Construction And Training of ANN Predicting Models
The initial BP-ANN model was built by Neural Network Toolbox. The model contains four layers, including
an input layer, an output layer and two hidden layer. Neuron numbers of two hidden layer and a output
layer were 26, 5, 1 respectively. The initial weight for all neuron connections were randomly assigned
by Matlab functions. And other parameters were set as followed:
net = newff(minmax(p),[26,5,1],{'tansig', 'tansig', 'purelin'},'trainlm');
net.trainParam.epochs = 15000;
net.trainParam.mc = 0.98;
net.trainParam.goal = 1e-6;
net.trainParam.lr = 0.01;
And the original sequence data were translated to digital data and served as the input matrix according
to the following rules via python program (attachment: seqDigtal.py):
Among all generated models, NET_26_5 (attachment: NET_26_5.mat) shows the highest correlation
coefficient values of 0.93336 for test set prediction, and its correlation coefficient values for
fitting the training data set reaches up to 0.99104. (Figure 3)
And all the promoter prediction strengths were showed in Figure 4. (attachment:
pStrengthVsStrength.xlsx)
Conclusion
We could predict the designed promoter strength based on this BP-ANN model, which could save a lot time
for us to test millions of designed promoters.
