A stochastic hybrid model of a biological filter
This work addresses inference challenges in genetic circuits for systems biology, but it is incremental as it builds on existing hybrid modeling approaches.
The authors tackled the problem of statistical inference and parameter learning for a hybrid model of a biological filter, representing a feed-forward loop motif with continuous protein concentrations and binary gene promoter states, and demonstrated an efficient algorithm on simulated data.
We present a hybrid model of a biological filter, a genetic circuit which removes fast fluctuations in the cell's internal representation of the extra cellular environment. The model takes the classic feed-forward loop (FFL) motif and represents it as a network of continuous protein concentrations and binary, unobserved gene promoter states. We address the problem of statistical inference and parameter learning for this class of models from partial, discrete time observations. We show that the hybrid representation leads to an efficient algorithm for approximate statistical inference in this circuit, and show its effectiveness on a simulated data set.