A Systems’ Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

Abstract

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.

Systems-level regulation of microRNA networks by miR-130/301 promotes pulmonary hypertension

“…Higher-order regulation of subordinate miRNAs by upstream "master miRNAs" has begun to be described in cardiac tissue (1) and cancer (2) and may underlie the robustness of certain miRNA-based actions in health and disease. Computational approaches have been proposed to decipher systems-level regulatory motifs involving miRNAs (3,4), but these theories have been challenging to study experimentally (as reviewed by ref. 5).…”

Understanding microRNA-mediated gene regulatory networks through mathematical modelling

“…cooperative miRNA regulation) can induce stronger repression of the gene ( 88 , 89 ). Based on this evidence, we developed an ODE model to investigate the regulation of CDKN1A by multiple and potentially cooperative miRNAs ( 90 , 91 ). Model simulations showed that selective expression of CDKN1A-targeting cooperative miRNAs can fine-tune its protein expression level, thereby resulting in tightly regulated p21 expression in various cellular processes, such as cell cycle and apoptosis.…”

Neural fate decisions mediated by combinatorial regulation of Hes1 and miR-9

“…The parameters related to the linear form of miR-9 synthesis are chosen according to [24], while the Michaelis-Menten form is adopted according to [10,22]. The parameter d related to the degradation of the RNA complex is chosen according to [25], which is experimentally validated. All these parameters related to miR-9 are chosen to make the period of Hes1 oscillations close to 2-3 h. The standard parameter values are shown in Table 1, unless otherwise indicated.…”