Creating a computer model for processes as complex as photosynthesis, photorespiration, and dark respiration has proven to be an exceptional challenge. The various feedbacks that are engaged within the processes at specific metabolic steps, and that are triggered by specific sets of conditions, render the numerical modeling framework most applicable. Within the numerical framework, processes can be modeled as a series of sequential biochemical reactions, which can then be integrated across finite time intervals to produce pathway fluxes. While numerical solutions have their advantages, the path to the ultimate outcome tends to be less transparent than the alternative, an analytical solution. Numerical models are seldom amenable to complete mathematical closure, requiring that approximations be made to close the numerical iteration and reach a stable, steady-state, solution. An analytical solution is not constructed from sequential, time-dependent steps, but rather represents a closed set of equations (i.e., no variables left to approximation) that fully accounts for all components of a process or problem. For processes as complex as metabolic pathways, however, analytical solutions are difficult. Thus, even for this approach, researchers have sought approximations and simplifications that must be accommodated in order to achieve a stable, steady-state solution. The danger of relying on such approximations, as stated in the quote above by Graham Farquhar, a noted analyst in the field of photosynthetic modeling, is that these simplifications tend be forgotten and the analytical models migrate with time toward uncritical acceptance.