Phenomenological modeling of gene transcription by approximating cooperativity of transcription factors improves prediction and reduces complexity in gene regulatory network models.

Several computational models are available for representing the gene expression process, with each having their advantages and disadvantages. Phenomenological models are widely used as they make appropriate simplifications that aim to find a middle ground between accuracy and complexity. The existing phenomenological models compete in terms of how the transcription initiation process is approximated, to achieve high accuracy while having the lowest complexity possible. However, most current models still suffer from high parameter complexity in the case of complex promoters. Herein, we formally derive a phenomenological approach to model RNA polymerase recruitment, stating approximations on cooperativity between transcription factors that are applicable to promoters requiring multifactorial input, which reduces parameter complexity. We then apply this method to biologically relevant networks of varying complexities to show that the approximations improved predictive ability compared to existing models. In summary, our reduced parameter model (RPM) had lower complexity while maintaining high accuracy, which leads to better scalability for complex networks.