The G1-S transition phase in colorectal cells is a complex process involving numerous protein interactions. Certain reactions within this network are particularly critical, as mutations affecting the function of specific proteins can compromise regulatory mechanisms and contribute to the onset of diseases such as cancer. A well-designed Chemical Reaction Network (CRN) can describe how these proteins and their complexes interact, offering a structured representation of cellular signaling dynamics. From a mathematical perspective, CRNs can be formulated as systems of autonomous ordinary differential equations (ODEs). Computing their steady states is essential for understanding how mutations alter the cellular behavior and for identifying potential therapeutic targets capable of mitigating the effects of specific alterations. This talk presents a review of recent developments in the analysis and construction of CRNs for the study of cancer signaling. First, the Non-Linearly Projected Combined (NLPC) algorithm, an efficient and accurate method for computing CRNs' nonnegative steady states, combining Newton’s method with gradient descent, is introduced. A local sensitivity analysis framework is then described, with the aim of identifying the most influential parameters affecting the network dynamics. Additionally, a strategy for reconstructing microscopic rate constants of enzyme-catalyzed reactions from Michaelis–Menten parameters is proposed, thus enabling the integration of experimental data into mass-action-based models. This method has been successfully applied to extend the existing CRN for the colorectal cell by incorporating the mTOR pathway, a key regulator of metabolism and therapy resistance. Together, these tools form a coherent and modular framework for the construction and analysis of biologically consistent CRNs, offering new insights into the mathematical modeling of oncogenic pathways and the design of targeted therapeutic strategies.