Isotope-assisted metabolic flux analysis (isotope MFA) combines experimental and computational techniques to analyze complex metabolic networks and thus to evaluate intracellular carbon flow and partitioning. However, the use of this methodology to increasingly complex networks can compromise flux identifiability and precision. In this thesis, we investigate two unique techniques that hold promise in overcoming this drawback.

The first of these is multi-element isotope MFA. Traditionally, isotope MFA has traditionally utilized only 13C. Multi-element isotope MFA is a novel and untapped field of research, with less than a handful of studies which have utilized tracer elements other than 13C. This thesis explores the possibility of tracking different elements simultaneously fed either as a mixture or as multiple

labels within the same molecule (multi-element tracer). Our analysis shows that multi-element tracking within a network allows for better information yield, which is necessary for resolution of fluxes more accurately for the network model. Also, our study shows that there are advantages to using several multi-element isotope labeling experiments in collaboration with parallel labeling experiments to provide better flux resolution at relatively less expensive costs.

Instationary analysis is another topic of interest for this thesis. In this study, we create an instationary framework in our native flux analysis software adapting prior work done in the field. We also implement the method based on existing guidelines on a Pt diatom to attempt to explain an existing controversy regarding its carbon concentrating mechanism. Our brief work supports earlier work claiming that Pt operates a C4 mechanism.