def Initialize_Classes(gillespie_parameters):
[alpha, beta, yr, r0, c0, mu, cv] = gillespie_parameters
dilution0 = Classy.Reaction(np.array([-1, 0, 0], dtype=int), 0,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution1 = Classy.Reaction(np.array([0, -1, 0], dtype=int), 1,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution2 = Classy.Reaction(np.array([0, 0, -1], dtype=int), 2,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
degradation0 = Classy.Reaction(np.array([-1, 0, 0], dtype=int), 0,
'mobius_sum_propensity', [0, yr, r0, 1], 1,
[0])
degradation1 = Classy.Reaction(np.array([0, -1, 0], dtype=int), 1,
'mobius_sum_propensity', [0, yr, r0, 1], 1,
[0])
degradation2 = Classy.Reaction(np.array([0, 0, -1], dtype=int), 2,
'mobius_sum_propensity', [0, yr, r0, 1], 1,
[0])
production1 = Classy.Reaction(np.array([0, 1, 0], dtype=int), 0,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production0 = Classy.Reaction(np.array([1, 0, 0], dtype=int), 2,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production2 = Classy.Reaction(np.array([0, 0, 1], dtype=int), 1,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
reaction_list = np.array([
production1, production0, production2, degradation0, degradation1,
degradation2, dilution0, dilution1, dilution2
])
return reaction_list
def Initialize_Classes(gillespie_parameters):
[alpha, beta, yr, r0, c0, mu, cv] = gillespie_parameters
dilution = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, beta, 1, 0], 1, [0])
enzymatic_degradation = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, yr, r0, 1], 1, [0])
production = Classy.Reaction(np.array([1], dtype=int), 0, 1, [alpha, c0, 2], 0, [mu, mu * cv])
reaction_list = np.array([production, enzymatic_degradation, dilution])
return reaction_list
def add_reaction(queue, schedule_time, next_reaction):
reaction = Classy.ScheduleChange(schedule_time, next_reaction.change_vec)
if len(queue) == 0:
return queue.append(reaction)
else:
for k in range(len(queue)):
if reaction.comp_time < queue[k].comp_time:
return queue.insert(k, reaction)
return queue.append(reaction)
def Initialize_Reactions(delay_parameters):
[mu, cv] = delay_parameters
factor = 2
alpha_r = 5000
alpha_a = 2 * alpha_r
beta = .1
gamma_r = 200
gamma_a = 440
r0 = 1
c0 = c1 = 50
initial_vector = np.array([0, 0], dtype=int)
dilution0 = Classy.Reaction(np.array([-1, 0], dtype=int), 0,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution1 = Classy.Reaction(np.array([0, -1], dtype=int), 1,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
degradation0 = Classy.Reaction(np.array([-1, 0], dtype=int), 0,
'mobius_propensity', [0, gamma_r, r0, 1], 1, [0])
degradation1 = Classy.Reaction(np.array([0, -1], dtype=int), 1,
'mobius_propensity', [0, gamma_a, r0, 1], 1, [0])
production0 = Classy.Reaction(np.array([1, 0], dtype=int), 0,
'dual_feedback_decreasing_hill_propensity', [alpha_r, c0, c1, factor, 2],
'gamma_distribution', [mu, mu * cv])
production1 = Classy.Reaction(np.array([0, 1], dtype=int), 1,
'dual_feedback_increasing_hill_propensity', [alpha_a, c0, c1, factor, 2],
'trivial_distribution', [0])
reaction_list = [production0, production1,
degradation0, degradation1,
dilution0, dilution1]
return [reaction_list, initial_vector]
def gillespie_sim(mu, cv, alpha, beta, R0, C0, yr,param,par,dilution,enzymatic_degradation):
#model parameters
init_Protein = (alpha - yr) * ( mu - C0 * (math.sqrt(alpha / yr) - 1) / yr) # calculate the avg peak to initialize at a peak
production = Classy.Reaction(np.array([1], dtype=int), 0, 1, [alpha, C0, 2], 0, [mu, mu * cv])
timeRun = 4000
#Naming files and paths
path1 = 'PostProcessing/Simulations/{}{}'.format(param,par)
file_name = '{}/mean={}_CV={}.csv'.format(path1,mu,cv)
#Gillespie
time_series = gillespie(np.array([production, enzymatic_degradation, dilution]), timeRun,
np.array([init_Protein], dtype=int))
pd.DataFrame(time_series).to_csv(file_name, header=False, index=False)
return file_name
def Initialize_Reactions(delay_parameters):
[mu, cv] = delay_parameters
alpha = 250
beta = .1
gamma_r = 150
r0 = 1
c0 = 10
initial_vector = np.array([0, 500, 1000], dtype=int)
dilution0 = Classy.Reaction(np.array([-1, 0, 0], dtype=int), 0,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution1 = Classy.Reaction(np.array([0, -1, 0], dtype=int), 1,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution2 = Classy.Reaction(np.array([0, 0, -1], dtype=int), 2,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
degradation0 = Classy.Reaction(np.array([-1, 0, 0],
dtype=int), 0, 'mobius_propensity',
[0, gamma_r, r0, 1], 1, [0])
degradation1 = Classy.Reaction(np.array([0, -1, 0],
dtype=int), 1, 'mobius_propensity',
[0, gamma_r, r0, 1], 1, [0])
degradation2 = Classy.Reaction(np.array([0, 0, -1],
dtype=int), 2, 'mobius_propensity',
[0, gamma_r, r0, 1], 1, [0])
production1 = Classy.Reaction(np.array([0, 1, 0], dtype=int), 0,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production0 = Classy.Reaction(np.array([1, 0, 0], dtype=int), 2,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production2 = Classy.Reaction(np.array([0, 0, 1], dtype=int), 1,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
reaction_list = [
production1, production0, production2, degradation0, degradation1,
degradation2, dilution0, dilution1, dilution2
]
return [reaction_list, initial_vector]
def Initialize_Classes(gillespie_parameters):
[alpha, beta, yr, r0, c0, mu, cv] = gillespie_parameters
dilution0 = Classy.Reaction(np.array([-1, 0, 0], dtype=int), 0,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution1 = Classy.Reaction(np.array([0, -1, 0], dtype=int), 1,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
dilution2 = Classy.Reaction(np.array([0, 0, -1], dtype=int), 2,
'mobius_propensity', [0, beta, 1, 0], 1, [0])
degradation0 = Classy.Reaction(
np.array([-1, 0, 0],
dtype=int), 0, 'mobius_propensity', [0, yr, r0, 1], 1,
[0]) # need to rewrite this so that all molecules are in the base
degradation1 = Classy.Reaction(
np.array([0, -1, 0],
dtype=int), 1, 'mobius_propensity', [0, yr, r0, 1], 1, [0]
) # need to make it such that denominator is R 0 +r 1 (t)+r 2 (t)+r 3 (t)
degradation2 = Classy.Reaction(np.array([0, 0, -1], dtype=int), 2,
'mobius_propensity', [0, yr, r0, 1], 1, [0])
production1 = Classy.Reaction(np.array([0, 1, 0], dtype=int), 0,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production0 = Classy.Reaction(np.array([1, 0, 0], dtype=int), 2,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
production2 = Classy.Reaction(np.array([0, 0, 1], dtype=int), 1,
'decreasing_hill_propensity', [alpha, c0, 2],
0, [mu, mu * cv])
reaction_list = np.array([
production1, production0, production2, degradation0, degradation1,
degradation2, dilution0, dilution1, dilution2
])
return reaction_list