Toward design-based engineering of industrial microbes

Abstract

Engineering industrial microbes has been hampered by incomplete knowledge of cell biology. Thus an iterative engineering cycle of modeling, implementation, and analysis has been used to increase knowledge of the underlying biology while achieving engineering goals. Recent advances in Systems Biology technologies have drastically improved the amount of information that can be collected in each iteration. As well, Synthetic Biology tools are melding modeling and molecular implementation. These advances promise to move microbial engineering from the iterative approach to a design-oriented paradigm, similar to electrical circuits and architectural design. Genome-scale metabolic models, new tools for controlling expression, and integrated -omics analysis are described as key contributors in moving the field toward Design-based Engineering.

Figure 1. Transitioning from iterative to linear design-based engineering of industrial microbes

(a) Iterative engineering - Genome scale models describe the cellular processes under desired condition. These models guide implementation of engineering strategies. These implementations are characterized by –omics and integrated analysis tools, which are used to revise the model and improve predictive capability. (b) Design-based Engineering – Models and implementation tools are reliable enough that the expected outcome is usually achieved, as in civil and electrical engineering. Transitioning from iterative cycle to a linear design-based engineering can be achieved by advance by better modeling and implementation aided by enhanced analysis.

Figure 2. Biological ‘parts’ for controlling synthesis / degradation rates in the central dogma paradigm

Well-characterized biological components are necessary to control DNA, mRNA, and proteins in the cell. These biological components should be fine-tunable, independent of other implementations of the same control scheme, and robust in a variety of circumstances. The left column contains technologies that affect the synthesis rates, and the right column explains biological components that affect the degradation rates. Technologies are discussed in detail in the text. hRNA – Hammerhead RNA.

Figure 3. The conceptual work flow of integrated analysis of –omics data to formalize genotype-to-phenotype database

Advances in -omics measurement lead to the accumulation of high throughput -omics data. With systematic and integrative analysis tools, genotype-to-phenotype database are currently being developed under the core modules outlined in this figure. Standardized object models and data formats facilitate the exchange of information between different data systems and are essential for unambiguous transmission of data between computers. A well defined ontology enables the representation of domain-specific knowledge, as well as, the relationship between those domains and leads to powerful database searching. GUIDs solve data integration problems that result from ambiguity in name, or identity, of biological concepts and objects. Standard protocols for web service afford machine-to-machine interaction over the internet and simplify the task of exploring distributed data, forming the basis of the service-oriented architecture (SOA) [44].