It is becoming increasingly clear that making non-trivial predictions of phenotypic functions from genomic information requires a thorough understanding of the molecular processes involved in the expression of genes into functional proteins, the organization of these proteins with lipids and nucleic acids in spatiotemporal structures, and the assessment of the role of such structures in carrying out metabolic processes, creating ion currents involved in membrane excitability, maintaining electro-neutrality and osmotic homeostasis, and performing other cellular functions. This statement represents a grand challenge for biological and medical research for the twenty-first century. The COBRA methods covered in this text can address some of these issues. They represent a first step in addressing this grand challenge.

The Brief History of COBRA

Constraint-based reconstruction and analysis has been applied to biochemical reaction networks for over two decades. Research articles that utilize COBRA methods for interpreting and predicting biological phenotypes appearing from 1986 to mid-2013 have been collected and the cumulative rate of progress in the field can be graphed (Figure 29.1). The analysis of this published literature shows that the history of deployment of COBRA methods can be divided into four phases [52].

Initial studies (1986–1998) Early interest in constraint-based models was directed towards the determination of theoretical pathway yields and metabolite overflows [121, 258]. Then, experimental metabolic fluxes and growth rates were found to be consistent with computation based on optimization of physiologically meaningful objective functions, including minimal production of reactive oxygen species for hybridoma cells [373] and maximal growth rate for laboratory strains of E. coli [438]. The quantitative match between model predictions and measured cellular behavior opened up the possibility of predicting cellular phenotypes from a biochemically reconstructed network.