Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

Abstract

Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

Keywords: Genome Engineering; Metabolic Engineering; in-silico; metabolic models; whole-cell models.

Figure 1. A toy metabolic network

S_i are the substrates and r_i are the reaction rates. The network can be represented as a stoichiometric matrix (whose columns and rows correspond to reactions and metabolites, respectively), and a system of equations.

Figure 2. Creation and timeline of bacterial GEMs over the past two decades

More complex genome-scale computational models (such as metabolic and macromolecular expression (ME) models and the first whole-cell model), modelling automation tools (ModelSEED and CarveMe) and the ME software frameworks COBRAMe are also included. 2001 did not see any models created.

Figure 3. A schematic of the feasible region found through constraint-based modelling

Where v_i are fluxes of the system and form a flux polyhedron. The flux values that optimise the objective function can be found by looking at the extreme edges of the polyhedron, and selecting the point that fits the optimisation criteria.

Figure 4. Bilevel linear programming

The nested structure of the bilevel linear programming algorithms, where the inner problem optimises for a cellular objective function and the outer problem optimises for some metabolic engineering objective.

Figure 5. Comparison of metabolic engineering algorithms/frameworks features

Black rectangles indicate feature presence, white rectangles indicate absence.

Figure 6. An incomplete history of genome engineering in microorganisms