This thesis discusses six scientific works within the fields of systems biology and bioinformatics. These works are unified in their conception of the cell as a system of integrated fluxes of mass and information, in the application of computational approaches to answer the questions at hand and in their aim for computation to drive new biological discoveries. The overarching theme is that by bringing computational methodologies in contact with quantitative experimental data, new principles can be proposed and/or tested that would not otherwise have been discovered. The first three chapters of this thesis focus on (the cell cycle of) budding yeast. Specifically, Ch. 2 deals with kinetic models for waves-of-cyclins. Ch. 3 concerns analysis of ChIP-exo experiments to retrieve the specific binding sites at gene promoters where Forkhead transcription factors Fkh1 and Fkh2 bind. Ch. 4 presents a web-based database and visualization tool which integrates a variety of sources of information concerning all protein-coding genes and allows users to craft specific and visualizations of the topology of interaction networks. The last three chapters of this thesis focus on that other process by which life produces more of itself: metabolism. Specifically, our focus is on thermodynamics and metabolic networks in acetogenic bacteria (Ch. 5) and human liver (Ch. 6-7). Ch. 5 is concerned with the concept of gear-shifting: an organism’s hypothetical ability to express metabolic enzymes that result in different stoichiometric yields in order to navigate a trade-off between rate and yield. In Ch. 6 and 7 we discuss two approaches to deal partially with concentrations in flux balance analysis, i.e. in terms of serum concentrations of biomarkers (Ch. 6) and in terms of uptake fluxes and concentrations of medium metabolites and metabolic enzymes (Ch. 7). The six chapters present new datasets, provide novel tools, develop new models, propose novel (extensions of) computational methodologies and rationalize and assess existing methodologies. As such, this thesis provides a glance into the cutting-edge of biomedical research in this data-driven, computation-assisted age.