Biochemical networks can be modeled either quantitatively—when kinetic data are available—or qualitatively, based on network topology and stoichiometry. This chapter outlines the fundamental stages involved in the development of quantitative models and examines analysis methods applicable to qualitative models. We explore various modeling frameworks, including deterministic, stochastic, and fuzzy approaches, and discuss criteria for selecting the optimal modeling paradigms for a given biological system, based on scale-density analysis and comparative evaluation of simulation data. Additionally, we address strategies for validating models against biological data. A brief overview of biological databases and computational tools utilized in the case studies presented in this book is also included. The chapter further presents key analysis techniques such as reachability and coverability tree methods, invariant-based methods, and transformation techniques. Overall, the structure of this chapter is designed to maintain a methodological balance between quantitative and qualitative modeling perspectives, laying the groundwork for the applied case studies presented in Part II of this book.