These files and simulations are part of the following publication: B. Schnitzer, J. Borgqvist, M.Cvijovic The Synergy of Damage Repair and Retention Promotes Rejuvenation and Prolongs Healthy Lifespans in Cell Lineages. (2020) PLoS Comput Biol 16(10):e1008314 https://doi.org/10.1371/journal.pcbi.1008314
population.jl (Julia file, https://julialang.org) is the main file that includes all functions needed to simulate populations.
The most important functions for simulating single-cells are:
damageAccumulation defines the model.
division defines the distribution of proteins between mother and daughter.
retention defines the retention factor, potentially dynamically.
singleCell solves the single-cell model for given parameters and initial conditions.
The most important functions for creating populations are:
initializeEmptyPopulation creates a population structure with population parameters.
setResources sets the start value for the resources (currently the resources do not change over time, but a constant should be set in the beginning).
addCell adds a cell with certain initial conditions to a population.
evolvePopulation solves the population model that grows in size with each cell division until a certain time point.
evolveUncoupledPopulation solves the population model and creates the lineage until a certain generation.
analyse produces statistics of cell properties in populations that were created by the evolveUncoupledPopulation function.
In all folders there are examples how to use the model. Results are typically saved in txt files and visualised with gnuplot (gnu file, http://www.gnuplot.info).
CompareDynamics shows how to generate single-cell dynamics with manually set parameters (compareDynamics.jl).
WtSurface shows how to find parameters for cells with a specific replicative lifespan (wtSurface.jl).
RetentionPopulation shows how to find parameters (findParamters.jl) and generate populations for different retention factors and analyse the population-based behaviour (comparePopulations.jl).
WtPopulations shows how to generate cell lineages up to a few generations given a parameter set with focus on all individual cells (populationAnalysis.jl), and how to use the data for analysis and plotting (generatePlotData.jl). It is divided into the four data sets corresponding to four parameter sets representing unlimited repair capacity (UnlimCap) vs decline in repair capacity (DecCap) both in combination with retention (HighRe) vs no retention (NoRe), as focus of the publication.
GrowthRate shows an example of solving the dynamically growing system until a certain time point (growthRate.jl). In this particular case the function uses a file that defines the distribution of the initial conditions of the population's founder cells, which could have also been defined manually. Again the four cases UnlimCap and DecCap in combination with HighRe and NoRe are used.
The folder PublicationData includes the data underlying the figures in the publication in txt files as well as Julia functions that generate the data (note that since there is stochasticity in the model, it is not possible to generate exactly the same data set used in the publication again, however the conclusions stay the same).