amara1

The SBML for this model was obtained from the BioModels database (BioModels ID: BIOMD0000000475) Biomodels notes: The paper has plots that correspond to different UV irradiation dose (5, 10, 50, 75J/m^2, etc.). The condition encoded in this model correspond to a UV irradiation of 5J/m^2, that should reproduce Figure 2B of the paper. Here, Figure 2B of the paper is reproduced. An assignment rule for PCNA_poly (i.e. PCNA_sum - the sum of every species in which PCNA isoforms (included complex) are involved) has been added to model to obtain the figure. The model time is in seconds and So, to run the simulation for 300mins, it has to be run for 18000(300x60) seconds. The model was simulated using Copasi v4.10 (Build 55) and the plots were generated using Gnuplot. JWS Online curation: This model was curated by reproducing Figure 2B PCNAonU.

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In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae.

  • Flavio Amara
  • Riccardo Colombo
  • Paolo Cazzaniga
  • Dario Pescini
  • Attila Csikász-Nagy
  • Marco Muzi Falconi
  • Daniela Besozzi
  • Paolo Plevani
BMC Syst Biol 2013; 7 : 24
Abstract
BACKGROUND: The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA).
RESULTS: We developed a novel protocol to measure the time-course ratios between mono-, di- and tri-ubiquitylated PCNA isoforms on a single western blot, which were used as the wet readout for PRR events in wild type and mutant S. cerevisiae cells exposed to acute UV radiation doses. Stochastic simulations of PCNA ubiquitylation dynamics, performed by exploiting a novel mechanistic model of PRR, well fitted the experimental data at low UV doses, but evidenced divergent behaviors at high UV doses, thus driving the design of further experiments to verify new hypothesis on the functioning of PRR. The model predicted the existence of a UV dose threshold for the proper functioning of the PRR model, and highlighted an overlapping effect of Nucleotide Excision Repair (the pathway effectively responsible to clean the genome from UV lesions) on the dynamics of PCNA ubiquitylation in different phases of the cell cycle. In addition, we showed that ubiquitin concentration can affect the rate of PCNA ubiquitylation in PRR, offering a possible explanation to the DNA damage sensitivity of yeast strains lacking deubiquitylating enzymes.
CONCLUSIONS: We exploited an in vivo and in silico combinational approach to analyze for the first time in a Systems Biology context the events of PCNA ubiquitylation occurring in PRR in budding yeast cells. Our findings highlighted an intricate functional crosstalk between PRR and other events controlling genome stability, and evidenced that PRR is more complicated and still far less characterized than previously thought.

Unit definitions have no effect on the numerical analysis of the model. It remains the responsibility of the modeler to ensure the internal numerical consistency of the model. If units are provided, however, the consistency of the model units will be checked.

Name Definition
1.0 dimensionless
1.0 dimensionless
1.0 second^(-1.0)
Id Name Spatial dimensions Size
compartment_1 compartment 3.0 1.0
Id Name Initial quantity Compartment Fixed
species_1 L 1001.0 compartment_1 (compartment)
species_10 Rad18:Rad18:PCNAon:Rad6U 0.0 compartment_1 (compartment)
species_11 Rad18:Rad18:PCNAonU 0.0 compartment_1 (compartment)
species_12 PCNAonU 0.0 compartment_1 (compartment)
species_13 Rad5 1520.0 compartment_1 (compartment)
species_14 Rad5:PCNAonU 0.0 compartment_1 (compartment)
species_15 Ubc13U:Mms2 0.0 compartment_1 (compartment)
species_16 Ubc13U:Mms2:Rad5:PCNAonU 0.0 compartment_1 (compartment)
species_17 Rad5:PCNAonU:U 0.0 compartment_1 (compartment)
species_18 Ubc13:Mms2 193.0 compartment_1 (compartment)
species_19 PCNAonU:U 0.0 compartment_1 (compartment)
species_2 PCNA 7480.0 compartment_1 (compartment)
species_20 Ubc13U:Mms2:Rad5:PCNAonU:U 0.0 compartment_1 (compartment)
species_21 Rad5:PCNAonU:U:U 0.0 compartment_1 (compartment)
species_22 PCNAonU:U:U 0.0 compartment_1 (compartment)
species_23 PCNAoff 0.0 compartment_1 (compartment)
species_3 PCNAon 0.0 compartment_1 (compartment)
species_4 Rad18:Rad18 0.0 compartment_1 (compartment)
species_5 Rad18 206.0 compartment_1 (compartment)
species_6 Rad6 194.0 compartment_1 (compartment)
species_7 Rad6U 0.0 compartment_1 (compartment)
species_8 U 8698.0 compartment_1 (compartment)
species_9 Rad18:Rad18:PCNAon 0.0 compartment_1 (compartment)

Initial assignments are expressions that are evaluated at time=0. It is not recommended to create initial assignments for all model entities. Restrict the use of initial assignments to cases where a value is expressed in terms of values or sizes of other model entities. Note that it is not permitted to have both an initial assignment and an assignment rule for a single model entity.

Definition
Id Name Objective coefficient Reaction Equation and Kinetic Law Flux bounds
reaction_1 r01 species_2 + species_1 > species_3

compartment_1 * k1 * species_2 * species_1
reaction_10 r10 species_11 > species_4 + species_12

compartment_1 * k1 * species_11
reaction_11 r11 species_8 + species_18 > species_15

compartment_1 * k1 * species_8 * species_18
reaction_12 r12 species_12 + species_13 > species_14

compartment_1 * k1 * species_12 * species_13
reaction_13 r13 species_14 > species_12 + species_13

compartment_1 * k1 * species_14
reaction_14 r14 species_14 + species_15 > species_16

compartment_1 * k1 * species_14 * species_15
reaction_15 r15 species_16 > species_14 + species_15

compartment_1 * k1 * species_16
reaction_16 r16 species_16 > species_18 + species_17

compartment_1 * k1 * species_16
reaction_17 r17 species_17 > species_13 + species_19

compartment_1 * k1 * species_17
reaction_18 r18 species_13 + species_19 > species_17

compartment_1 * k1 * species_13 * species_19
reaction_19 r19 species_15 + species_17 > species_20

compartment_1 * k1 * species_15 * species_17
reaction_2 r02 {2.0}species_5 > species_4

compartment_1 * k1 * pow(species_5, 2)
reaction_20 r20 species_20 > species_15 + species_17

compartment_1 * k1 * species_20
reaction_21 r21 species_20 > species_18 + species_21

compartment_1 * k1 * species_20
reaction_22 r22 species_21 > species_13 + species_22

compartment_1 * k1 * species_21
reaction_23 r23 species_12 > species_8 + species_23

compartment_1 * k1 * species_12
reaction_24 r24 species_19 > {2.0}species_8 + species_23

compartment_1 * k1 * species_19
reaction_25 r25 species_22 > {3.0}species_8 + species_23

compartment_1 * k1 * species_22
reaction_3 r03 species_4 > {2.0}species_5

compartment_1 * k1 * species_4
reaction_4 r04 species_6 + species_8 > species_7

compartment_1 * k1 * species_6 * species_8
reaction_5 r05 species_3 + species_4 > species_9

compartment_1 * k1 * species_3 * species_4
reaction_6 r06 species_9 > species_3 + species_4

compartment_1 * k1 * species_9
reaction_7 r07 species_7 + species_9 > species_10

compartment_1 * k1 * species_7 * species_9
reaction_8 r08 species_10 > species_7 + species_9

compartment_1 * k1 * species_10
reaction_9 r09 species_10 > species_6 + species_11

compartment_1 * k1 * species_10

Global parameters

Id Value
parameter_1 0.0

Local parameters

Id Value Reaction
k1 0.078 reaction_19 (r19)
k1 0.0000000001 reaction_20 (r20)
k1 0.005 reaction_21 (r21)
k1 0.005 reaction_22 (r22)
k1 0.00000003 reaction_23 (r23)
k1 0.0008 reaction_24 (r24)
k1 0.005 reaction_25 (r25)
k1 0.000000015 reaction_1 (r01)
k1 0.01 reaction_2 (r02)
k1 1000.0 reaction_3 (r03)
k1 0.00000025 reaction_4 (r04)
k1 100000.0 reaction_5 (r05)
k1 1000.0 reaction_6 (r06)
k1 0.0351 reaction_7 (r07)
k1 0.01 reaction_8 (r08)
k1 0.01 reaction_9 (r09)
k1 1.0 reaction_10 (r10)
k1 100000.0 reaction_11 (r11)
k1 0.000005 reaction_12 (r12)
k1 0.005 reaction_13 (r13)
k1 0.078 reaction_14 (r14)
k1 0.0000000001 reaction_15 (r15)
k1 0.05 reaction_16 (r16)
k1 0.0000075 reaction_17 (r17)
k1 0.000005 reaction_18 (r18)

Assignment rules

Definition
parameter_1 = species_12 + species_19 + species_22 + species_11 + species_14 + species_17 + species_21 + species_20 + species_16

Rate rules

Definition

Algebraic rules

Definition
Trigger Assignments