Inferring biochemical reaction rates from stochastic fluctuations in growing and dividing cells

We previously established a novel approach to mechanistically interpret stochastic fluctuations in partially observed complex interactions networks. We illustrated the approach with numerical proof-of-principle examples in which we translated the covariability of components into biochemical reaction rates without perturbing the system or utilizing temporal information. However, these examples considered stationary state fluctuations of models that ignore cell division and approximate cellular growth with first-order dilution. Here we generalize our approach to nonstationary models of growing and dividing cells. We present numerical examples in which non-stationary system fluctuations were used to successfully infer rate functions between stochastically interacting components.