Table 2.

A summary of various categories of community modeling approaches using genome-scale metabolic models.

Modeling formalism	Modeling condition	Type of optimization problem	Reference
Compartmentalized community- level metabolic models based on FBA	Steady-state	Linear programming	Stolyar et al 181, Shoaie et al 184, Heinken and Thiele 186, Bordbar et al 187, Klitgord and Segre 56, Gomes de Oliveira Dal’Molin et al 189, Bizukojc et al 190, Merino et al 191, Nagarajan et al 192
Compartmentalized community- level metabolic models based on MOMA	Steady-state	Quadratic programming	Wintermute and Silver 22
(De-)Compartmentalized community-level metabolic models based on elementary mode analysis	Steady-state	NA	Taffs et al 195,
Analysis of metabolic model- derived metrics quantifying the degree of cooperation and/or competition	Steady-state	NA	Zelezniak et al 196, Kreimer et al 197, Levy et al 198; 200, Borenstein and Feldman 199
Community FBA based on the balanced growth of microorganisms	Steady-state	Linear/Nonlinear programming	Khandelwal et al 194
Multi-level and multi-objective modeling	Steady-state	Nonlinear programming	Zomorrodi and Maranas 201, El-Semman et al 202
Dynamic multi-species metabolic modeling based on the extension of dynamic FBA 211 for single species	Dynamic	Linear programming	Zhuang et al 203, Salimi et al 204, Hanly and Henson 206; 207,209, Tzamali et al 208, Chiu et al 212
Multi-level and multi-objective dynamic metabolic modeling	Dynamic	Nonlinear programming	Zomorrodi et al 214
Direct integration of community-level dynamic FBA and diffusion models	Spatiotemporal	Linear programming	Harcombe et al 215, Cole et al 216