EscapeSimulator

EscapeSimulator is a pop-gen simulator for Julia. This is a derivative of the Tomoko.jl package but with differences: 1. Genotypes are represented as unsigned integers (UInt64) instead of BitVectors which have unlimited length. This means that genotypes have a maximum length of 64, thus the maximal number of escape sites in the simulation is 64. The benefit is that UInt64 is a static memory object which leads to significant speed-ups. 2. The mutation model is much more flexible in that mutations can occur at different forward and backward rates µ ≠ µ_dagger, and they can differ based on the site of interest. 3. Other functions related to the data analysis and discrepancy are also included

Basic Operations

Fitting fitness ratios

See the file: bayes_posterior.jl in HIVTreatmentOptimization/JuliaScripts for example implementations. At each time-point for each patient in the longitudinal-deep-sequencing patients we construct an object, a LikelihoodSample, which contains the importance-sampling estimator for the likelihood at different values for the observable fitness difference ratio rs = σ/θ = (f_wt - f_mut)/μ.

Afterward we can combine likelihood estimates using different averaging procedures

posterior_sample = baysian_pop_rs(listof_listof_LikelihoodSamples; 
	samples = 2*10^3, burn_in = 10^2, avg = avg)

avg is a keyword between 0 and 1 that toggles between independent avg = 0 and patient averaged avg = 1.0 samples.

Running Simulations

To run simulations and calculate and estimate a rebound time, you must first create a viral popualtion with initialize_viral_population which has the following signiture

vp = initialize_viral_population(θ, ab_profile_list;
		# θ is the diversity of the viral population at treatment initiation, and sets the effective carrying capacity via θ = 2 N_e μ
		# ab_profile_list consists of a list of ab_profiles. Each profile is a list of site profiles, which itself is a list cosisting of 
		# [forward mutation rate, backward mutation rate, fitness cost]
		mut_per_gen = 3*1.1*10^(-5), # the base (avg'd) transition rate measured in growth rate
		decayrate = 0.31, # the rate of removal 
        λ = 2.0, # total noise rate of the populaiton
		f = 1.0/3, # absolute growth rate
        mutations = true, # whether or not to include post-treatment mutations
        antibody = true)

We then can evolve a population forward in time or get a rebound time using several functions with increasing sophistication

virus_time_trace generates a time trace from 0-56 days recording mutant and wildtype fractions

rebound_time generates a truncated trace up until breakpoint = 0.8, a threshold for the mutant fraction to acieve afterwhich the dynamics are deterministic. It returns (reboundtime,trace)

viral_rebound_times efficiently samples multiple rebound times with the same parameters by restarting the viral popuplation after every breakpoint

trial_rebound_times multi-threaded version which simulates the rebound times of a trial with a constant antibody profile, but which includes diversity variation.

Calculating Discrepancies

This includes one all purpose function which calculates the discrepancy between the reference data and the simulated data.

	observed_discrepancy(reference_data, simulated_data; min = 0.001, max = 56)`

The keys min and max indicate truncation points for the data categories (in this case the minimum and maximum rebound times)