Table 1.
Families of methods for constraint-based models. Broad classes of methods are described, along with references to some individual implementations or studies.
| Method Family | Description | Benefits | Caveats | Solver Type | Notes | Refs |
|---|---|---|---|---|---|---|
| FBA | Flux Balance Analysis: Linear programming applied to the model. | Usually very fast and simple to use, especially when a biomass pseudo-objective is available. | Arguably has more limited use in non-microbial models. Only simple objectives or sequential (e.g. bi-level) optimization is practical. | Linear | Often constraint-based modeling (CBM) in general may be referred to as FBA, though this is not technically correct. | 16 |
| MOMA | Minimization of Metabolic Adjustment | Usually very fast and simple to use, especially when a reference or wild-type flux is available; useful for simulating mutations. | It has been argued that the closest distance to a flux doesn’t represent mutation as well as simulating the least number of flux changes (ROOM). | Linear, Quadratic Convex | Related, but slightly more sophisticated methods are being used to estimate flux profiles from expression data. | 123,124 |
| DFBA | Dynamic FBA: incorporates a step-wise simulation of FBA, along with update rules that relate biomass to uptake rate, solving for extracellular concentrations. | Allows for some non-steady state observations | Small timescale dynamics and intracellular dynamics may be difficult to model. | Linear (Iterative) | Other, but infrequently used (due to difficulty) methods involving regulation (rFBA) or multi-scale models of tissues build on this approach. | 11 |
| EBA | Energy Balance Analysis: FBA, but also incorporates thermodynamic constraints | Incorporates thermodynamic information, prevents futile cycles. | Usually much slower than LP methods like FBA. | nonlinear, MILP, or Monotropic | A highly active research area. | 10,15,17,19 |
| Tissue-specific Model Creation | Requires expression data for tissue of interest. | Tissues have vastly different regulatory schemes; these methods take this into account by finding which metabolic genes are likely to be expressed in a given tissue. | Still requires some other method and objective to estimate flux or do pathway analysis. | MILP | A highly active research area. | 28–30 |
| Expression-Flux mapping | Takes ideas from MOMA and tissue-specific model creation to estimate fluxes. | Unlike tissue-specific models, will actually estimate the flux since a MOMA-like objective is employed. | Requires high-quality (e.g. RNA-Seq) expression data, or for PROM, abundant microarray data from different conditions. | Linear optimization, but moderate number of simulations or preprocessing required. | Highly accurate predictions can be obtained. | 125,126 |
| Interaction Search | Epistasis, or genetic interactions, come up in many contexts, but are also important in energy metabolism, since energy is often related to very important phenotypes including growth, proliferation, and survival. | For such analyses, convex optimization may offer the only tractable method. | Simulating pairwise epistasis in the general case requires pairwise simulation of all double mutants of interest, which can be very time-consuming at the genome scale when different mutations in each gene, or different environments, are considered | Linear optimization, but often many simulations required. Min Cuts (exponential). | The sign of weak epistasis is difficult to predict, due to error propogation in growth rates | 127–130 |