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# Cell Division Cycle
*Stephan O. Adler, Ulrike Münzner, Thomas W. Spiesser, Friedemann Uschner*
[Module documentation](cdc-core)
***
* [Module description](#module-description)
1. [Update Jan 2014](#update-january-workshop-2014)
2. [Update Mar 2014](#update-march-workshop-2014)
3. [Update Feb 2016](#update-february-workshop-2016)
* [Other CC models](#other-cell-cycle-models)
***
## Module description
Cell cycle module delivers the machinery that coordinates duplication of cell components and DNA and finally cytokinesis. Input from other modules may interfere with the progression of the cell cycle leading to a temporary **cell cycle arrest**.
Change of morphology is also regulated by cell cycle.
Cell cycle arrest can be achieved (so far) by:
* Osmotic stress (Hog1)
* Pheromone treatment (Fus3)
see scheme below.
### Components
Components to be included in model:
* Cln2 (Cln1/2)
* Cln3
* Clb5 (Clb5/6)
* Clb3 (Clb3/4)
* Whi5
* SBF (Swi4-Swi6 complex)
* Far1
* MBF (Mbp1-Swi6 complex)
* Sic1
* Cdc14
* APC (Cdh1/Cdc20)
* Clb2 (Clb1/2)
* Swe1
* Mih1
* Mcm1
* Swi5
* **Cdc28: assumed to be abundant protein, implicitly part of the network**
Components excluded for now:
* Net1
* Cdc24
* Cdc42
* Fkh1/2
### Issues
* S/G2 transition: no gene expression during DNA replication (1 -> 2) OR delay through Clb-cascade (Clb5->Clb3->Clb2)? - resolved! delayed through Clb3.
* Need information/data from the GRN group about mRNA regulation
* Need information/data from the Met group on translation processes
* location issue of Cdc14: Cdc14 is in dephos state bound to Net1 and goes to the nucleulus (RENT), cannot use RENT_nuc and multiply with Clb2_cyt for example.
* Kinetics? Bistability?
* direct inhibition of mCLNs and mCLBs by Hog1PP?
* Sic1 multiple P-sites: Whi5, Cdh1, SBF and Net1 as well. Should we use Hill-eqs here as well?
* localization of components is inconsistent at the mo.
### Network
[[/pics/ycm_cell_cycle_scheme_20131011.png]]
We implemented this NW in python. Is running, pars arbitrary and not adjusted -> lot of flat-lines.
***
### Update January Workshop 2014:
* Clb3 in model, DNA out.
* Clb3 production is directly induced by Clb5 with a constant degradation and an APC directed degradation.
* This mechanism provides the time frame needed for DNA replication during each cycle
* Far1 mechanism updated/implemented -> arrest through pheromone signaling functional.
* All events removed, pure ODE model.
* Parameters adjusted -> protein concentrations still not correct, but stable oscillations.
Far1-mechanism (separate view):
[[/pics/ycm_cell_cycle_scheme_20140122_Far1.png]]
* We included two types of phosphorylations on Far1 that play a role in the mechanism:
* T306: Stabilizing phosphorylation by Fus3
* S86: Phosphorylation mediated by Cln2 leading to destruction of Far1
The updated version of the Network including the new Far1-mechanism (see above) looks as follows:
[[/pics/ycm_cell_cycle_scheme_20140124.png]]
#### Issues
* Sic1 level deceases while cell is arrested
* Some rate laws need to be adapted for consistency
* APC dependent degradation of Clb5 is missing in the model (solved!)
***
### Update March Workshop 2014:
* APC dependent degradation of Clb5 is missing in the model - 12.03.14 FIXED (is in now)
* units of species and most of the parameters in
* parameters converted to s^-1 from min^-1 (only in python file)
* simple cell growth mechanism implemented for testing before volume module is in place
* adds parameter to shut down all production and degradation reactions in case is simulated with GRN module
* Hog1 inhibition parameters set -> osmo-shock arrests cell cycle in different phases
***
### Update February Workshop 2016:
* Updated the (python) model to the latest Copasi model that is being published in the CC-bookchapter (Spiesser et.al.) -> inclusion of 6 more rates and 2 parameters
* Units for parameters have been updated and corrected
* Interface to DNA replication is being included (Stephan, Katja) -> mechanism of initiation depends on Clb5, cycling only continues when DNA is doubled (see [20151103_DNA_synthesis](http://wcmwiki.pbworks.com/w/page/102601597/20151103_DNA_synthesis) for that)
* Some testcases have been developed (conceptionally):
* Sequential activation of different cyclins can be checked for sanity
* DNA replication and continued cycling after doublication of DNA can be checked with this interface mechanism (does cycle stop if DNA takes longer to duplicate?)
* Stress induced arrest can be checked with a test for variance changes over time (depending on interface input levels: Hog1, Far1 so far)
***
### Update status 2017:
* For consistency, all species are now produced as well as degraded.
* CDC module was split into a regulating module and the GEX_TRX/TRL modules to represent the different functions.
* The current model structure looks like this:
[[/pics/ycm_cell_cycle_scheme_20170925.png]]
## Other Cell Cycle Models
**Barik et al. (2010) [20739927](http://www.ncbi.nlm.nih.gov/pubmed/20739927):**
In order for the cell's genome to be passed intact from one generation to the next, the events of the cell cycle (DNA replication, mitosis, cell division) must be executed in the correct order, despite the considerable molecular noise inherent in any protein-based regulatory system residing in the small confines of a eukaryotic cell. To assess the effects of molecular fluctuations on cell-cycle progression in budding yeast cells, we have constructed a new model of the regulation of Cln- and Clb-dependent kinases, based on multisite phosphorylation of their target proteins and on positive and negative feedback loops involving the kinases themselves. To account for the significant role of noise in the transcription and translation steps of gene expression, the model includes mRNAs as well as proteins. The model equations are simulated deterministically and stochastically to reveal the bistable switching behavior on which proper cell-cycle progression depends and to show that this behavior is robust to the level of molecular noise expected in yeast-sized cells (approximately 50 fL volume). The model gives a quantitatively accurate account of the variability observed in the G1-S transition in budding yeast, which is governed by an underlying sizer+timer control system.
**Chen et al (2004) [15169868](http://www.ncbi.nlm.nih.gov/pubmed/15169868):**
The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.