Lecture 6 and 7 Flashcards
Cell Cycle Checkpoint Kinases
- Cell cycle controlled by periodic synthesis and degradation of CDKs and cyclins
- CDKs active, if bound to specific cyclins
- here: focus on cyclin B - Cdk1 in frog egg cell cycle
-> also called MPF: Mitosis promoting factor
G1: Cdk6, cyclin D, Cdk4, Cyclin E, Cdk2
S: cyclin A, Cdk2
G2: Cdk1, CyclinA, CyclinB
MPF Control
- Cyclin degradation promoted by MPF -> negative feedback loop
- Inhibitory phosphorylation
-> phosphorylation by Wee1/Myt1 on threonine 14/tyrosine 15 blocks MPF
-> Cdc25 phosphatase activates MPF - CDKs active, if bound to specific cyclins
- Here: focus on cyclin B - Cdk1 in frog egg cell cycle
-> also calls MPF: Mitosis promoting factor
Hysteresis of MPF in Frog Eggs
- High cyclin needed to enter metaphase
- low cyclin sustains metaphase
MPF activity depends on cyclin / history
Hysteresis in Cell Cycle - Experimental Procedures
- Experiments performed in cell-free egg extracts
- Established by Murray & Kirshner 1998
- Xenopus eggs released from CSF-induced metaphase
- Washed in buer mimicking ionic composition of cytoplasm, crushed & fractionated
- Sperm nuclei & ATP regenerating system added
- 2-3 cell cycles for 35-55 minutes
- Endogenous Cyclin B degraded rapidly
- Exogenous Cyclin B ( cyclin B) added in non-degradable form & protein synthesis disabled via cyclohexamide (CHX)
- CHX added at 0min for activation
- CHX added at 60 min for inactivation threshold
Multiple Ways to the same Phenotype
- Dierentiation of Promyelocytes into Neutrophils
- Same phenotype upon dierent stimulation
Network Mutation Shifts Attractor
- Mutations lead to dierent phenotype upon same stimulation
- New attractors accessible
Mechanical Cell Fate Induction
1) same growth factors
2) same genes
3) same ECM
4) different geometry
5) different fate outcome
- Stimulus context dependent
- Different Stimuli can lead to the same phenotype
Attractor model - consequences
- finite number of stable cell states (Kaufman)
- cell fates mutually exclusive
- many routes / pathways / stimuli lead to same phenotype
Diffusion
- Important in the cell => non-homogenous environment
- So far neglected by us in Law-of-Mass action
- Need mathematical Description of diusion
The Flux
amount of a chemical which passes near a point in space per unit area per unit time (SI Units: mol/m2s) => Flux J is a vector
Turing model - Pattern Formation
- Generation of Turing Patterns
- Forest !re Model:
- Dry forest
- few randomly distributed fire fighters, but with helicopters
- fire starts at random locations & spreads
- fire fighters diuse fast via helicopters & kill fire
- Result: patches of burnt & green forrest
- Typical Patterns, depend on diffusion ratios
Patterning depends on Size
- Spotted animals can have striped tails
- Striped animals cannot have spotted tails
Hair pattern: Turing Example
- Head surface is a domains -> Turing pattern
- WNT and its inhibitor DKK determine spacing of hair follicles
Multiple Waves of Hair Development
- Multiple waves of Foxn1 activity successively create hair follicles
- Model shows secondary follicles (red) on prepatmtern (blue)
- If inhibitor insensitive regions exists -> Follicle clustering
Why do we have five fingers?
- The hand is a domain on which stripes emerge => fingers
- Science 2012: Sheth et al.
Hox Genes Regulate Digit Patterning by Controlling the Wavelength of a Turing-Type Mechanism - Reduction of Hox genes results in polydactyly
- Hox knockout induces more fingers!
- Can be modeled by Turing!
- Deletion of any of these genes => less digits, i.e. fingers
- Why we have exactly 5 !ngers => unknown, but our body is very plastic!
During limb development, digits emerge from the undifferentiated mesenchymal tissue that constitutes the limb bud. It has been proposed that this process is controlled by a self-organizing Turing mechanism, whereby diffusible molecules interact to produce a periodic pattern of digital and interdigital fates. However, the identities of the molecules remain unknown. By combining experiments and modeling, we reveal evidence that a Turing network implemented by Bmp, Sox9, and Wnt drives digit specification. We develop a realistic two-dimensional simulation of digit patterning and show that this network, when modulated by morphogen gradients, recapitulates the expression patterns of Sox9 in the wild type and in perturbation experiments. Our systems biology approach reveals how a combination of growth, morphogen gradients, and a self-organizing Turing network can achieve robust and reproducible pattern formation.
