Systems biology has emerged recently as the field aspiring to integrate meaningfully vast amounts of data and information collected by modern high throughput methods on a cell-wide and genome-wide basis. Most methods used to-date make heavy use of modeling and approaches developed in classical chemical reaction engineering. As such, they benefit greatly from the seminal contributions of R. Aris to this field. On the other hand, success of system biology in elucidating cellular behavior has been mixed depending heavily on the complexity of the system investigated, quality and completeness of collected data and prior knowledge of basic mechanisms about the system.

In this talk we will illustrate these points through the analysis of global regulation of yeast physiology. We examine, in particular, the integration of cell-wide measurements such as gene expression with networks of protein-protein interactions and transcription factor binding that has previously revealed critical insights into cellular behavior. However, the potential of these approaches is limited by difficulties in correlating transcriptional data with metabolic measurements despite their being most closely linked to cellular phenotype. To address this limitation, we introduce here a new approach to modeling metabolic flux dependence on transcriptional state. To this end, we quantified 5764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of the global regulator Gcn4p. While mRNA expression alone did not directly predict metabolic response, this correlation improved substantially through incorporating a network-based model of amino-acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model also provides evidence of general biological principles: rewiring of metabolic flux by transcriptional regulation and the emergence of metabolite-enzyme interaction density as a key biosynthetic control determinant. The predicted flux rewiring was further validated with additional 13C-based flux measurements in follow-on studies with knocked out transcriptional regulators.

These results suggest that while systems biology has the potential to enhance our understanding of global cellular behavior, progress will be generally slow, ad hoc and case-dependent. There are certainly no tools that can directly convert “omic” data to cellular knowledge, as it was initially hoped and widely expected. As such, results from comprehensive investigations are likely to not meet expectations. On the other hand, such results can only be obtained from the integrated mindframe of systems biology leading, eventually, to more realistic models of cellular regulation for understanding diseases as well as engineering strains for industrial applications.