Engineering ecosystems and synthetic ecologies

Abstract

Microbial ecosystems play an important role in nature. Engineering these systems for industrial, medical, or biotechnological purposes are important pursuits for synthetic biologists and biological engineers moving forward. Here we provide a review of recent progress in engineering natural and synthetic microbial ecosystems. We highlight important forward engineering design principles, theoretical and quantitative models, new experimental and manipulation tools, and possible applications of microbial ecosystem engineering. We argue that simply engineering individual microbes will lead to fragile homogenous populations that are difficult to sustain, especially in highly heterogeneous and unpredictable environments. Instead, engineered microbial ecosystems are likely to be more robust and able to achieve complex tasks at the spatial and temporal resolution needed for truly programmable biology.

Figure 1

Development of synthetic ecology requires insights gained through manipulating simple biological systems and analyzing complex ecological systems. Evolution must be factored into these pursuits, not only as a destabilizing force but also as a means to optimize our engineered designs.

Figure 2

A summary of the crucial parameters that impact a microbial ecosystem. These parameters determine the ecosystem’s ability to convert an energy source into biomass and waste, and are prime targets for engineering and optimization. Metabolic capabilities are distributed across different members as defined by metabolotypes (shaded and colored ovals). Metabolic exchange can occur via metabolite transport across cellular membranes or through intercellular bridges. Community structure can be tuned by adjusting the degree of aggregation and formation of extracellular structures such as biofilms. Horizontal gene transfer enables genomic innovation and the rise of new capabilities within the population.

Figure 3

Diversity of amino acid biosynthetic capabilities across all sequenced organisms from the Integrated Microbial Genomes (IMG) database, separated based on the three domains (Bacteria, red, top panel; Archaea, blue, middle panel; Eukarya, orange, bottom panel). (a.) Predicted frequencies at which species have the ability to synthesize zero to all 20 standard amino acids. (b.) For each amino acid, frequencies at which complete biosynthetic pathways are found across each domain are shown in solid colored bars (Bacteria, red, top panel; Archaea, blue, middle panel; Eukarya, orange, bottom panel). White bars indicate fractions in each domain where one or more biosynthetic gene is missing. Gray bars indicate unknown annotations.

Figure 4

The four main classes of quantitative models that are used to study microbial ecosystems. (a.) Kinetic models describe changes in system variables (e.g. abundance) with simple differential equations that can exhibit interesting dynamics such as oscillations and limit cycles. (b.) Stoichiometric models can be applied to study optimal metabolic flux using objective functions to guide the design of intercellular metabolite exchange. (c.) Evolutionary games can be used to analyze phenotypic strategies within a microbial community using payoff calculations. These models aid in elucidating key variables that influence the domination or coexistence of microbial strategies. (d.) Digital evolution systems help to simulate microbial evolution, traversal of fitness landscapes, development of complex traits, and contributions of epistatic and pleiotropic effects to fitness.

Figure 5

Experimental tools enable engineering of microbial ecosystems from the population level down to the DNA level. In vitro tools such microfluidics and microchambers or in vivo mice models enable precise control of the environment. High-throughput sequencing and transcriptomics enable parallel interrogation of phylogeny, composition, and gene expression of cell populations. Techniques such as multiplexed genome engineering and transposon mutagenesis enable forward engineering and accelerated evolution of cell populations at the genetic level. New genetic circuitry and synthetic biology frameworks enable the development of multi-component genetic programs that are executed across populations of cells.