Why is the context of biological information difficult to represent in mathematical models?

In the field of systems biology, representing biological information in mathematical models is challenging primarily because of the complex, multifaceted context in which that information operates.1

While researchers utilize information theory and other mathematical frameworks to study biological phenomena, these tools currently lack a fully realized theoretical basis for capturing the environmental and internal conditions that influence biological processes.1

The difficulty is well-illustrated by the process of gene expression. The production of a protein from genetic information is not an isolated event; it is influenced by a wide array of dynamic factors, including:
* The specific type of cell involved.1
* External signals received by the cell.1
* The current metabolic state of the cell.1
* Preexisting states of gene expression.1

Because these variables constantly impinge upon one another, creating a clear and useful mathematical representation of this context remains a significant hurdle. Consequently, while scientists have successfully modeled some small, well-characterized networks, capturing the broader, interconnected nature of biological systems remains an ongoing area of research.1