Table 2.

Characteristics and limitations of ALE methods outlined in this study.

Method	Characteristics	Limitations
Classical ALE	- ALE with the primary objective function designed to select for end-point strains with improved adaptive fitness under a selective environment.	- Maximization of strain fitness (i.e. specific growth rate) as the primary objective function does not necessarily correlate with maximization of target metabolite production. Target compound production can be selected against when target products are metaboically costsly.
Metabolic ALE	- Implementation of metabolic engineering schemes to couple target compound production with adaptive phenotypes. May help circumvent negative selection against the production of metabolically costly chemicals. Effectively selects for the end-point adaptive strains with metabolism rewired for the production of target chemicals.	- Metabolic perturbation may impose severe fitness defects in the engineered strain. - Requires a priori knowledge on the metabolic network for the effective coupling of adaptation and target compound production. May limit the application to a relatively few well-known targets
Systems metabolic ALE	- Use of genome-scale metabolic models (GEMs) to probe for non-intuitive metabolic engineering targets that may be more effective (in metabolite-growth coupling) compared to traditional targets. - Simulation of growth rate following gene knockouts can help inform the user the feasibility of metabolic engineering designs (i.e. growth defects). - Integration of multi-omic data along with GEM can help infer metabolic bottlenecks (for target compound production) for consideration in ALE design.	- May not be applicable for non-model strains lacking a high-quality GEM. - The limitations in linear programming may preclude accurate representation of strain metabolism and knockout simulations, resulting in disparities between in silico simulations and empirical results.
Biosensor-assisted ALE	- Use of a genetic circuit that sense the target compound (using an orthogonal sensor protein) and respond by controlling the expression of growth-associated genes.	- Non-target 'escapee' mutations can render the biosensor-imposed selection pressure obsolete.