Improve speed of numpy.random functions

I am writing a simulation of cancer cell population growth, and I am using numpy.random functions to model the cells acquiring or losing mutations. I have determined through profiling that the bottleneck in the code (around 70% of runtime) is the first few lines that contain the numpy.random functions. Here the variable num_steps is a large number, around one million:

def simulate(mu, gamma, beta, num_steps, threshold):
    mutation_num = 0 # the index of the mutation (we assume each mutation only occurs once)
    population = {() : 1} # represents population: tuple of mutations and number of cells with those mutations
    for epoch in range(num_steps):
        next_population = {}
        for mutations, size in population.items():      
            born = np.random.binomial(size, birth_rate)
            if np.random.binomial(born, gamma):
                return True 
            mut_loss = 0 # initializing in case variable is not created
            if mutations:
                mut_gain, mut_loss, mut_same = np.random.multinomial(born, [mu, beta, 1-mu-beta])
            else:
                mut_gain, mut_same = np.random.multinomial(born, [mu, 1-mu])
.....

Is there a way to make the np.random.binomial and np.random.multinomial functions run faster? I tried using Cython, but that did not help.