In the past decades, an extensive mathematical literature was developed to model and analyze gene networks under both deterministic and stochastic formalisms. However, such literature is predominantly focused to deal with the modeling of transcriptional and translational regulation, but results related to post-transcriptional regulation via splicing and its connection with transcriptional and translational regulation are almost missing in literature. However, it is becoming of paramount importance the need for modeling post-transcriptional regulation via splicing especially for minor organisms (e.g., bacteria) or viruses, and in the field of deep sequencing, as well as single-cell sequencing.The aim of this work is to theoretically study a general basic modeling scheme of gene expression via alternative splicing and its connection with transcription and translation.This study showed the pivotal role of the splicing conversion rates capable to both increase or decrease the stochastic noise, as well as their interconnection with the stochastic bursts in gene expression. The study also shows when it is important to model the pre-mRNA degradation or, at least, to account for the conversion rate for more than two mRNA isoforms.