Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model thes...Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organ- ism＇s lifetime. In this work, we focus on modeling GRN state transition （edge） variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing addi- tional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.展开更多
文摘Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organ- ism＇s lifetime. In this work, we focus on modeling GRN state transition （edge） variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing addi- tional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.
