Quantitative Modelling Approaches

The increased accessibility of experimental high-throughput omics data, such as genomics, proteomics, and metabolomics, has significantly contributed to the advancement of theoretical biology. This wealth of data is being examined using well-established bioinformatics tools developed over the past decades, alongside innovative approaches rapidly devised by the scientific community. The quantitative modelling of biological phenomena encompasses a wide range of techniques borrowed from other disciplines such as physics, mathematics, computer science, and engineering. These methods are employed to understand and tackle the complex nature of biological systems. Due to the immense diversity observed in biological systems, obtaining accurate quantitative estimations of parameters of interest can be challenging. Additionally, results often rely on the underlying modelling assumptions. As a result, these methods span the spectrum from purely qualitative to highly quantitative. In this article, we will provide an overview of the most commonly utilised quantitative modelling approaches, highlighting their advantages and disadvantages.