Abstract
When information flows through gene-regulatory networks, noise is introduced, and fidelity suffers. A cell unable to correctly infer the environment signals from the noisy inputs may be hard to make right responses. But there are so many “parallel circuit” in gene-regulatory networks where independently transcribed monomers assemble into functional complexes for downstream regulation. As we know, when we talking with two person at the same time, we could not fully understand them. But if things they talked about were more similar, we could understand their words much better. So that would tighter connection of monomers benefit the quality of gene-regulatory networks?
Inspired by these thoughts, we construct synthetic biology circuit by using split florescent proteins. And we add inteins to split florescent proteins to the make the connection tighter. Then, we quantitatively measure the capacity of these information channels. Computation and wet lab work are combined to optimize our understanding of such systems, and to interpret potential biological significance of reoccurring parallel designs in nature.
Introduction Story
Let us tell a simple but interesting story to help you understand what our project is about and also relax yourselves before you look into our project.I believe all of you know about a term called binaural effect which means you can judge where a thing is with less deviations when you use both of your ears. Similarly, here is the story.
Li lei is an undergraduate from Tsinghua University and Han Meimei is an undergraduate from Peking University. They met and knew each other when they attended the Jamboree of iGEM. After that they always communicated through WeChat and fell in love with each other. So Li lei often called Han Meimei to make sure that how she is going and wanted to make her feel happy. But he had a problem. The telephone line was very noisy and Han Meimei could not hear what he said clearly every time he called her. He knew that and he was very depressed because he worried about that Han Meimei would be angry with him. One day he came up with a good idea and he bought two. Since then he bought two amplifiers and used them both when he called her. Now Han Meimei can hear him clearly and this tells us that when you use two paths other than one to transmit your messages, the noises will be reduced observably. And now with the inspiration, come and see our project.
Description
From information theory to our project
As we can learn from Wikipedia, information theory studies the quantification, storage, and communication of information which was originally proposed by Claude E. Shannon in 1948. The theory has developed amazingly and has found applications in many areas. It’s not exaggerated to say that we can see the power of information theory all the time.
Based on this, we would like to explore something originally in biological pathway using information theory.
Our project has a close connection with two terms in information theory, mutual Information and channel capacity. And what are they?
In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as bits) obtained about one random variable, through the other random variable. The concept of mutual information is intricately linked to that of entropy of a random variable, a fundamental notion in information theory, that defines the "amount of information" held in a random variable. You can understand it trough from the figure below.
Formally, the mutual information of two discrete random variables X and Y can be defined as:\[I\left( {X;Y} \right) = \sum\limits_{y \in Y} {\sum\limits_{x \in X} {p\left( {x,y} \right)\log \left( {\frac{{p\left( {x,y} \right)}}{{p\left( x \right)p\left( y \right)}}} \right)} } \]
And the formula can also be proofed:\[I\left( {X;Y} \right) = H\left( X \right) - H\left( {X\left| Y \right.} \right) = H\left( Y \right) - H\left( {Y\left| X \right.} \right)\]
From the formula you can easily think that mutual information is the reduction of uncertainty in $X$ when you know $Y$.
And when you understand the mutual information, you can easily understand what the channel capacity is.
In electrical engineering, computer science and information theory, channel capacity is the tight upper bound on the rate at which information can be reliably transmitted over a communications channel. And by the noisy-channel coding theorem, the channel capacity of a given channel is the limiting information rate (in units of information per unit time) that can be achieved with arbitrarily small error probability. And you can also see the figure below.
The channel capacity is defined as:\[C = \mathop {\sup }\limits_{{p_X}\left( x \right)} I\left( {X;Y} \right)\]
where the supremum is taken over all possible choices of ${p_X}\left( x \right)$.
After you know about all the concept above, now we are glad to tell you that you can easily follow us and find out many interesting and inspiring things in our project. Congratulations!
miRNA as Potential Biomarkers
Studies have confirmed that miRNA expression is highly concordant cross individuals. And aberrant expression of miRNA may relate to diseases. Some studies have reported that specific miRNAs in tissue and plasma can be discriminatory bio-makers for detecting cancers. However, getting access to tissue of plasma means physical harm to the human body. So we turn to the easily accessible saliva. Since saliva is considered to be a terminal product of blood circulation, components like proteins and RNAs which are present in plasma are also present in saliva. In fact, both coding RNAs and non-coding RNAs, including some miRNAs, have been found in human saliva. Although mRNAs are highly degraded in saliva, miRNAs are stably and abundantly present in saliva. Recently, there are many reports on cancer-related miRNA expression in saliva. Featured miRNA expression are reported to be found in oral squamous cell carcinoma, parotid gland tumors and esophageal cancer, indicating potential salivary miRNA to be biomarkers for detecting these diseases. According to the test done by Zijun Xie group, there are three type of miRNAs significantly upregulated in the whole saliva from the esophageal cancer patient group in contract to normal control group – miR-10b, miR-144, and miR-451 (p value 0.001, 0.012 and 0.002, respectively; AUC 0.762, 0.706 and 0.756, respectively). And four miRNAs are significantly upregulated in saliva supernatants from the esophageal cancer patient group – miR-10b, miR-144, miR-21 and miR-451. Among them, miR-21 is the most frequently reported one for its high performance in specific expression level related to esophageal cancer (according to one of the reports, p < 0.05, AUC = 0.8820, sensitivity = 90.20% and specificity 70.69%; different tests may report different results).
Comprehensively considering the performance of each miRNA, we finally chose miR-144 as our biomarkers for esophageal cancer. To make our detection quick and convenient, we designed synthetic gene pathways based on paper. It will only require some saliva to complete the detection, which will do no harm to human body. And this techniques can be expanded to be used in the detection of many other diseases which has specific miRNA expression pattern in saliva.
To make our detection quick and convenient, we designed synthetic gene pathways. It will only require some saliva to complete the detection, which will do no harm to human body. And this technique can be expanded to be used in the detection of many other diseases which has specific miRNA expression pattern in saliva.
What is Toehold Switch
We choose toehold switch as our miRNA detector. The structure of toehold switch is similar to hairpin, except it has a loop at the top as ‘toehold’. Toehold switch functions as riboregulator through linear-linear interaction between RNAs. When target RNA appears, it will bind one of the toehold switch stems and open the loop, exposing the RBS.
Toehold switch systems are composed of two RNA strands referred to as the switch and trigger. The switch RNA contains the coding sequence of the gene being regulated. Upstream of this coding sequence is a hairpin-based processing module containing both a strong RBS and a start codon that is followed by a common 21 nt linker sequence coding for low-molecular-weight amino acids added to the N terminus of the gene of interest. A single-stranded toehold sequence at the 50 end of the hairpin module provides the initial binding site for the trigger RNA strand. This trigger molecule contains an extended single-stranded region that completes a branch migration process with the hairpin to expose the RBS and start codon, thereby initiating translation of the gene of interest.
Our Circuit to Detect miRNA-144
There are 3 parts in our circuit in total.
Part 1 includes toehold switch for miRNA-144 and GFP coding sequence. When miRNA-144 exists, the switch is on and mRNA for GFP is transcribed.
Part 2 contains toehold switch for GFP mRNA and T3 RNA polymerase coding sequence (BBa_K346000). Note that, since the maximum length of trigger RNA for toehold switch is about 25nt, so we analyzed GFP mRNA’s structure and choose a small piece from it which ensures binding specificity and stability.
Part 3 is simply a GFP generator (BBa_E0840), with T3 promoter. As we know, T3 promoter can only function when bound with T3 RNA polymerase.
So now it’s clear that, part 2 and part 3 are designed for amplification. They form a positive feedback loop. So the whole process is as follow: when miRNA-144 exists, it triggers part 1 and GFP mRNAs are transcribed. These mRNA on the one hand can be directly translated to GFP and show green fluorescence, on the other hand, can trigger part 2 and T3 RNA polymerase is transcribed and translated, which enables part 3 to work, thus transcribe more GFP mRNAs. And these GFP mRNAs can also be used to active more part 2.