- FBA = flux balance analysis; an optimization problem that uses a linear combination of metabolic rates, either to be maximized (e.g. biomass) or minimized (e.g. total sum of cellular fluxes).
*I’m not clear on why these need be linear functions… ?*

Orth, J. D., Thiele, I., and Palsson, B. O. (2010). What is flux balance analysis? Nat. Biotechnol. 28, 245–248. doi: 10.1038/nbt.1614

- GEM = genome-scale models; used to address the need for a (theoretical) model in which, for example, every enzyme within each metabolic network influenced by a genome is described, e.g. with respect to its function and kinetic parameters. One option is to reconstruct from annotated genomic data using network models. [Researchers need to be mindful of limitations in annotations and inaccurate and incomplete knowledge of how metabolic activities are influenced by individual genes or genomic regions.] The general idea of GEMs is a set of differential equations (representing enzymatic reactions) set into a matrix* representing the metabolic network, with that matrix bound by a set of specific constraints, e.g. reaction minima/maxima, ir/reversibility, that reduce the space of possible solutions. These possibilities must then be reduced to a single solution, for a given set of parameters and constraints; in this context, a common approach is the
**flux balance analysis**. GEMs are typically compartmentalized by cellular type or structure, e.g. cytosolic versus external; or by different tissues.

**Note that this is much like the model I worked with for my Master’s thesis, in which four differential equations for a SI [Susceptible-Infected] population were embedded within a 2-by-2 matrix describing reciprocal interactions between males and females. This matrix was then allowed to vary using an adaptive dynamics approach… which now that I’m at it, means I need to go back and review that study, because I can’t quite explain very well what, exactly, we did! The external constraints were things like population limitations, e.g. 0 to 1 rather than 0 to infinity; and using parameters derived from HIV research, and specific functions for transmission and virulence, and resistance and tolerance, based on a collection of infectious disease studies that helped us to better understand the general relationships between these factors.*

**Quotes**

“For plants to access recalcitrant soil-borne nutrients, they are dependent upon the metabolic activities of soil microbiota.”

“We know that although soil is the major determinant of the microbial community associated with plant roots, plants have a significant effect on taxonomic assembly.”

“Particularly interesting are genes in plant metabolic pathways that affect the composition of root exudates and thus the actual signals in the rhizosphere.”

“Recent genomic studies are beginning to reveal the specific microbial strains that contain metabolic pathways favorable for plant nutrition (Muller et al. 2016). The big question, however, remains: to what extent can plants attract specific microbes for specific environmental/ nutritional conditions?”

“…there is still an enormous knowledge gap regarding a sound theory of plant-microbe interactions. The huge volume of data obtained from the characterization of the microbiome is clearly calling for a modeling approach, e.g. to construct nutritional networks integrating metabolic pathways of plants and microbiota.”