Team:Shenzhen BGIC ATCG/modeling

Blueprint

Our project based a lot on cell cycle, especially the cyclin-promoters and cyclin-degradation tags. Through modelling Cell cycle is one of the most complex network in biology world. Better understanding of cell cycle and it’s regulation, to some extent, faciliate the fermentation industry because we can easily accelarate or decelarate a cell cycle or even one phase in the cycle which are important for metabolism product synthesis. In order to simulation and predict the experimets of the effeciency of Sic1, alternative splicing and degradation tags in the whole cell cycle, we build tree ordinary differential equation system models.

Cell Cycle

Cell Synchronization

Previously study reported the introduction of sic1p could prevent the cell to enter S phase. Based on the sic1 system in yeast, we developed an artificial sic1 system (SIC1_Art). By adding galactose or modifying the phosphorylated sites, we can regulate the synthesis (Ka) and degradation (Kd) rates of the sic1_Art. We are trying to utilize this artificial system to precisely regulate the phase in yeast cell cycle, and our goal is to understand the synchronization behavior in yeast.

SIC1_Art on G1 stage

G1 length:

To understand the temporal effect of SIC1_Art on the length G1 phase, we performed parameter scan on the amount of time of adding SIC1_Art (DeltaT). By setting Ka=0.12 and Kd=0.016, we estimated the relationship between DeltaT and the length of G1 phase. Our computation simulation showed that, as we added SIC1_Art into the yeast, the amount of SIC1_Art will increase at first, and then it will enter a plateau stage. After the plateau stage, SIC1_Art will decrease gradually, which subsequently followed by the leaving of G1 stage (900 min). Our result suggests a positive correlation between DeltaT and the length of G1 phase in the first 900 min, and we discovered an upper bound at the length of G1 phase.

两幅图

Plateau, the definition:

Based on the relation between DeltaT and the cellular level of SIC1_Art, we defined plateau stage as the time space within which the amount of SIC1_Art is less than the maximum SIC_Art amount during the G1 phase (SIC1_Amax) and greater than (SIC1_Amax - Kd*5min). During this time space, the temporal difference of SIC1_Art degradation is less than 5 min, which we considered the minimum requirement of synchronization.

Ka/Kd, Entering Plateau:

To examine the relation between the synthesis and degradation rate and the timing of entering plateau stage (Tp), we performed parameter scan on Ka and Kd. We found that the Tp is negatively correlated with Ka and positively correlated with Kd.

Ka/kd, Length of Plateau:

To further explore the optimal Ka/Kd in SIC1_Art system, we investigated the influence of Ka/Kd on the length of plateau domain (Lp).

Synchronization in yeast cell cycle

Using the result of above analysis, we chose Ka=0.219, Kd=0.048 as the optimal parameters, which give rise to short entering plateau time and long enough plateau stage (Tp = 61 min, Lp=355 min ).

We simulated the multi-cell behavior in yeast cell cycle: we started with cells in different phases, Art_time = 990 min, we added galactose into the systems, which initiated the synthesis of SIC1_Art and subsequently stopped all the cells at G1 phase. At time=1030 min, we stopped adding galactose and subsequently caused fast degradation of SIC1_Art. This process made all the yeasts into the phase and consequently achieved synchronization.

Alternative Splicing by CRISPRi

To predict the alternative outcome, we also made an intron model to show different results due to incubating in different media. In our project, intron can be spliced in two different ways, providing a completely different outcome because of frame-shift, and this result is not a change like 1-0 to 0-1, but somehow more like a change between 0.4-0.6 and 0.8-0.2.

Reactions:

Parameter Table:

Degradation Rate

Degradation tags were also obtained from cyclins because cyclins should degrade fast enough to avoid binding to cdc28 and delaying its own phase. From our simulation we can find out that transformed proteins can also be degraded at a convenient speed.

Parameter Table

As Degradation tags could not fully help tell apart each phase by the light of XFP, we built targeting peptide into model to make a more distinguishable visual result. As shown here, we present a 3D simulation result by adding another axis to specify different organelles.