The mutational landscape of a gene or a genome is an abstractive multidimensional space in which mutations are measured according to their contribution to fitness. Such landscapes are case-specific and are commonly portrayed as three dimensional for convenience purposes. The aim of bioengineers is to find the global maximum, i.e., the highest peak within the landscape while avoiding local maxima “traps”. Since it is impossible to evaluate complete landscapes experimentally, methods are developed to increase the chances of finding the best performing mutants. Three approaches will be discussed: reducing the screening load of semi-rationally designed libraries, systematically scanning through single point mutations, and increasing mutational accessibility by refactoring the genetic code.

Adaptive laboratory evolution is a powerful tool for strain development. In an evolving population, individuals with beneficial mutations are selected for, and become enriched, in the environment. However, the rate of adaptation can be limited by the frequency of beneficial mutations; and competition amongst co-occurring beneficial mutations can lead to a loss of information. Here we describe the use of horizontal gene transfer (HGT) in conjunction with tunable mutation rate to more rapidly develop complex phenotypes in E. coli. We have previously developed a “genderless” strain of E. coli proficient in continuous HGT during normal culturing conditions. In this work, we introduced an inducible mutator system to the genderless strain to allow modulation of mutation rate to enhance the supply of mutations during ALE. The system was characterized to determine the influence of HGT and mutation rate. Our results indicate HGT and increasing mutation rate can act together to speed the rate of adaptive laboratory evolution. The system was further leveraged to more rapidly combine different complex phenotypes, to help expedite strain development of more industrially relevant phenotypes.

Microbial chemical factories increasingly rely on biosynthetic pathways incorporating toxic intermediates that inhibit cellular growth and product formation. A promising strategy to overcome this challenge is dynamic regulation of production pathways to limit toxic intermediate accumulation, and maintain cells at optimal health. While dynamic control may be implemented with natural transcription factors as sensors that recognize and respond to a given metabolite, sensors for many intermediates are largely unknown. Here, we develop modular transcriptional regulators that consist of a tunable elastin-like polypeptide (ELP) biosensor and transcription factor to directly respond to general indicators of cellular stress for dynamic control of any biosynthetic pathway. ELPs are engineered proteins that reversibly self-assemble at a critical temperature, pH, or ionic strength programmed by their tunable primary sequence. When fused to transcription factors such as orthogonal sigma factors and tetR, ELP self-assembly suppresses gene transcription at targeted promoters in a reversible switch-like fashion. We show that ELP-transcription factor fusions operate as precise temperature repressible switches over a range of programmed physiological temperatures. Similarly, we demonstrate that these regulators respond to small variations in intracellular pH that precede significant cellular damage. The tunable responsive nature of these synthetic regulators make them ideal regulators for pathway optimization, with potentially broad utility across organisms with appropriate transcription factor design.

Inspired by the exponential growth of the microelectronic industry, synthetic biologists have been attempting to build biological foundries for rapid genetic design and cellular prototyping. In this talk, I will briefly discuss the challenges and opportunities in synthetic biology and highlight our recent work on the development and application of novel foundational synthetic biology tools. Specifically, I will introduce the Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB) that we have been establishing to automate the design-build-test-analyze cycle and discuss its three potential biotechnological applications. The first is the rapid and high throughput synthesis of transcription activator-like effector nucleases (TALENs) for genome editing applications. The second is the discovery, characterization, and engineering of novel natural product biosynthetic pathways for drug discovery and development. The third is the design, construction and optimization of biochemical pathways and microbial factories for economic production of chemicals and fuels.

Fundamental unsolved problem in genomics is the need for high-throughput approaches to bridge the gap between the availability of DNA sequence data and our ability to assign biological function to it. The DOE JGI’s niche is the development of a diversity of large-scale experimental and computational capabilities to link sequence to biological insights relevant to energy and environmental research. This will range from preparing material and applying functional capabilities prior to genomic analysis to post-sequence processing and manipulations to enable the Institute’s users to carry out sequence-to-function studies that are beyond the capabilities of individual laboratories. Here, I will discuss new sequence-to-function capabilities that have been established at the DOE JGI and some scientific examples enabled with these capabilities (Schwander et. al. Science 2016 and Tsementzi et al. Nature 2016).