CRISPR

We decided to create a model of the CRISPR system for two main reasons:

• Identifying the parts of the network that could be targeted by our lab team to improve repression efficiency
• To approximate time-series mecA repression data for use in modelling the overall vulnerability of a S. aureus population

Model Formation

After a literature review we were able to construct the CRISPR interference system network. The targeted single guide RNA (sgRNA) associates with nuclease-deficient Cas9 protein (dCas9) to form a complex that binds with the DNA complementary to the sgRNA target . The bound complex prevents transcription elongation by RNA polymerase, repressing YFP mRNA expression . The chemical network is shown below:

Using standard mass-action kinetics, the network simplifies into the following set of differential equations:

MISSINGEQUATION

We chose the model kinetics to be largely first-order; this decision was supported by the findings of several recent studies . To simplify the model, we assumed that the formation of the dCas9-sgRNA complex ($b$ in Figure xyz) is in made a quasi-steady-state. That is, we assume that the association/dissociation of dCas9 and sgRNA occurs on a faster timescale than the other reactions in the network (i.e. transcription, translation and the binding of the complex to the DNA), allowing us to assume that the complex is always at steady-state, relative to the other time-dependent species concentrations. This same assumption was made in previous modelling efforts, e.g. .

Under this quasi-steady state assumption, the differential expression for the complex is given by:

MISSINGEQUATION

Our model then simplifies to:

MISSINGEQUATION

This is the same assumption made by previous teams.

Modelling Incomplete Repression

A recent study by Bikard et al. found that maximal repression (on the order of 100 fold) was achieved when the promoter was targeted. However, targeting the promoter is not viable in this project since an essential promoter from elsewhere in the genome has been harnessed to produce the fluorescent promoter. Instead, we model the incomplete repression (ranging from 6-fold to 35-fold) observed when the off-promoter regions, specifically on the non-coding strand, are targeted.

There are two possible approaches for modelling the incomplete repression, each reflecting a different physical mechanism that allows leaky YFP expression. In the first mechanism, RNA polymerase is sometimes able to cleave the bound dCas9-sgRNA complex from the DNA. In the second mechanism, the complex binds inefficiently and is sometimes separated from the DNA, permitting transcription to continue.

We assumed that the incomplete repression is accounted for by the first mechanism. This assumption was based on several studies showing radically different repression rates if the complex targets the promoter, preventing transcription initiation, rather than targeting the DNA further downstream and impeding transcription elongation. The differences in the system behaviour depending on whether or not RNA polymerase has the opportunity to bind suggest that the “cleavage” mechanism may more closely resemble the chemical reality.

Consequently, we modelled incomplete repression using a leaky expression term proportional to the expected YFP expression when the complex is saturated. The differential equation model was updated with a repression term dependent on the fold reduction FR and the initial concentration of YFP mRNA, Y0:

MISSINGEQUATION

This equation was derived using two boundary conditions. Before repression, when the concentration of the complex is zero, YFP mRNA is produced at the rate expected from the sarA promoter, α. After repression has reached its steady state, the YFP mRNA production has been reduced by FR fold, to Y0/FR.

Parameters

Production of dCas9 from dCas9 mRNA

Degradation rate of dCas9

mRNA production from the sarA promoter

Initial Model Results

Updating mRNA Production Rates

Sensitivity Analysis

sRNA

Relevant Biology

Model Formation

Conjugation