Transcriptional processes: Models and inference.

Area of Science:

• Biochemistry
• Systems Biology
• Computational Biology

Background:

• Multistep reactions are fundamental to biochemical events, notably transcriptional processes.
• Accurate modeling of multistep reactions, especially transcriptional bursting, requires complex models with multiple states.
• Inferring model parameters that align with experimental data presents a significant computational challenge.

Purpose of the Study:

• To design a novel multistep promoter model for accurately characterizing transcriptional bursting.
• To develop a computational framework that integrates model and data for parameter inference.
• To validate the model's efficacy using real biological data.

Main Methods:

• Development of a promoter model featuring multiple OFF states and a single ON state, utilizing Erlang distribution.
• Integration of Monte Carlo Expectation Maximization (MCEM) with the delay Stochastic Simulation Algorithm (DSSA) to create the delay Bursty MCEM algorithm.
• Application of the delay Bursty MCEM algorithm to time-series data from the endogenous mouse glutaminase promoter.

Main Results:

• The proposed model with multiple OFF states demonstrates superior consistency with experimental data compared to simpler models.
• The delay Bursty MCEM algorithm proves to be an efficient method for inferring kinetic parameters.
• Successful validation of model assumptions and parameter inference for the mouse glutaminase promoter.

Conclusions:

• Multistep promoter models incorporating multiple OFF states are crucial for accurately representing transcriptional bursting.
• The delay Bursty MCEM algorithm offers an efficient and effective approach for parameter inference in complex biological models.
• This work provides a robust framework for analyzing gene expression dynamics and inferring regulatory mechanisms.