James M. Carothers

Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids

Microbial production of chemicals is now an attractive alternative to chemical synthesis. Current efforts focus mainly on constructing pathways to produce different types of molecules. However, there are few strategies for engineering regulatory components to improve product titers and conversion yields of heterologous pathways. Here we developed a dynamic sensor-regulator system (DSRS) to produce fatty acid–based products in Escherichia coli, and demonstrated its use for biodiesel production. The DSRS uses a transcription factor that senses a key intermediate and dynamically regulates the expression of genes involved in biodiesel production. This DSRS substantially improved the stability of biodiesel-producing strains and increased the titer to 1.5 g/l and the yield threefold to 28% of the theoretical maximum. Given the large number of natural sensors available, this DSRS strategy can be extended to many other biosynthetic pathways to balance metabolism, thereby increasing product titers and conversion yields and stabilizing production hosts.

Informational Complexity and Functional Activity of RNA Structures

Very little is known about the distribution of functional DNA, RNA, and protein molecules in sequence space. The question of how the number and complexity of distinct solutions to a particular biochemical problem varies with activity is an important aspect of this general problem. Here we present a comparison of the structures and activities of eleven distinct GTP-binding RNAs (aptamers). By experimentally measuring the amount of information required to specify each optimal binding structure, we show that defining a structure capable of 10-fold tighter binding requires approximately 10 additional bits of information. This increase in information content is equivalent to specifying the identity of five additional nucleotide positions and corresponds to an ∼1000-fold decrease in abundance in a sample of random sequences. We observe a similar relationship between structural complexity and activity in a comparison of two catalytic RNAs (ribozyme ligases), raising the possibility of a general relationship between the complexity of RNA structures and their functional activity. Describing how information varies with activity in other heteropolymers, both biological and synthetic, may lead to an objective means of comparing their functional properties. This approach could be useful in predicting the functional utility of novel heteropolymers.

Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression

Evidence is presented for the feasibility of computer-aided design of biological circuits for regulation of gene expression. The models and simulation tools available to design functionally complex synthetic biological devices are very limited. We formulated a design-driven approach that used mechanistic modeling and kinetic RNA folding simulations to engineer RNA-regulated genetic devices that control gene expression. Ribozyme and metabolite-controlled, aptazyme-regulated expression devices with quantitatively predictable functions were assembled from components characterized in vitro, in vivo, and in silico. The models and design strategy were verified by constructing 28 Escherichia coli expression devices that gave excellent quantitative agreement between the predicted and measured gene expression levels (r = 0.94). These technologies were applied to engineer RNA-regulated controls in metabolic pathways. More broadly, we provide a framework for studying RNA functions and illustrate the potential for the use of biochemical and biophysical modeling to develop biological design methods.

Measurement and modeling of intrinsic transcription terminators

The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

Chemical synthesis using synthetic biology

An immense array of naturally occurring biological systems have evolved that convert simple substrates into the products that cells need for growth and persistence. Through the careful application of metabolic engineering and synthetic biology, this biotransformation potential can be harnessed to produce chemicals that address unmet clinical and industrial needs. Developing the capacity to utilize biology to perform chemistry is a matter of increasing control over both the function of synthetic biological systems and the engineering of those systems. Recent efforts have improved general techniques and yielded successes in the use of synthetic biology for the production of drugs, bulk chemicals, and fuels in microbial platform hosts. Synthetic promoter systems and novel RNA-based, or riboregulator, mechanisms give more control over gene expression. Improved methods for isolating, engineering, and evolving enzymes give more control over substrate and product specificity and better catalysis inside the cell. New computational tools and methods for high-throughput system assembly and analysis may lead to more rapid forward engineering. We highlight research that reduces reliance upon natural biological components and point to future work that may enable more rational design and assembly of synthetic biological systems for synthetic chemistry.

Functional information and the emergence of biocomplexity

Complex emergent systems of many interacting components, including complex biological systems, have the potential to perform quantifiable functions. Accordingly, we define “functional information,” I(Ex), as a measure of system complexity. For a given system and function, x (e.g., a folded RNA sequence that binds to GTP), and degree of function, Ex (e.g., the RNA–GTP binding energy), I(Ex) = −log2[F(Ex)], where F(Ex) is the fraction of all possible configurations of the system that possess a degree of function ≥ Ex. Functional information, which we illustrate with letter sequences, artificial life, and biopolymers, thus represents the probability that an arbitrary configuration of a system will achieve a specific function to a specified degree. In each case we observe evidence for several distinct solutions with different maximum degrees of function, features that lead to steps in plots of information versus degree of function.

Selecting RNA aptamers for synthetic biology: investigating magnesium dependence and predicting binding affinity

The ability to generate RNA aptamers for synthetic biology using in vitro selection depends on the informational complexity (IC) needed to specify functional structures that bind target ligands with desired affinities in physiological concentrations of magnesium. We investigate how selection for high-affinity aptamers is constrained by chemical properties of the ligand and the need to bind in low magnesium. We select two sets of RNA aptamers that bind planar ligands with dissociation constants (Kds) ranging from 65 nM to 100 μM in physiological buffer conditions. Aptamers selected to bind the non-proteinogenic amino acid, p-amino phenylalanine (pAF), are larger and more informationally complex (i.e., rarer in a pool of random sequences) than aptamers selected to bind a larger fluorescent dye, tetramethylrhodamine (TMR). Interestingly, tighter binding aptamers show less dependence on magnesium than weaker-binding aptamers. Thus, selection for high-affinity binding may automatically lead to structures that are functional in physiological conditions (1–2.5 mM Mg2+). We hypothesize that selection for high-affinity binding in physiological conditions is primarily constrained by ligand characteristics such as molecular weight (MW) and the number of rotatable bonds. We suggest that it may be possible to estimate aptamer–ligand affinities and predict whether a particular aptamer-based design goal is achievable before performing the selection.

Designing RNA-based genetic control systems for efficient production from engineered metabolic pathways.

Engineered metabolic pathways can be augmented with dynamic regulatory controllers to increase production titers by minimizing toxicity and helping cells maintain homeostasis. We investigated the potential for dynamic RNA-based genetic control systems to increase production through simulation analysis of an engineered p-aminostyrene (p-AS) pathway in E. coli. To map the entire design space, we formulated 729 unique mechanistic models corresponding to all of the possible control topologies and mechanistic implementations in the system under study. Two thousand sampled simulations were performed for each of the 729 system designs to relate the potential effects of dynamic control to increases in p-AS production (total of 3 × 10(6) simulations). Our analysis indicates that dynamic control strategies employing aptazyme-regulated expression devices (aREDs) can yield >10-fold improvements over static control. We uncovered generalizable trends in successful control architectures and found that highly performing RNA-based control systems are experimentally tractable. Analyzing the metabolic control state space to predict optimal genetic control strategies promises to enhance the design of metabolic pathways.

Design-driven, multi-use research agendas to enable applied synthetic biology for global health

Many of the synthetic biological devices, pathways and systems that can be engineered are multi-use, in the sense that they could be used both for commercially-important applications and to help meet global health needs. The on-going development of models and simulation tools for assembling component parts into functionally-complex devices and systems will enable successful engineering with much less trial-and-error experimentation and laboratory infrastructure. As illustrations, I draw upon recent examples from my own work and the broader Keasling research group at the University of California Berkeley and the Joint BioEnergy Institute, of which I was formerly a part. By combining multi-use synthetic biology research agendas with advanced computer-aided design tool creation, it may be possible to more rapidly engineer safe and effective synthetic biology technologies that help address a wide range of global health problems.

Dual-Selection for Evolution of In Vivo Functional Aptazymes as Riboswitch Parts

Both synthetic biology and metabolic engineering are aided by the development of genetic control parts. One class of riboswitch parts that has great potential for sensing and regulation of protein levels is aptamer-coupled ribozymes (aptazymes). These devices are comprised of an aptamer domain selected to bind a particular ligand, a ribozyme domain, and a communication module that regulates the ribozyme activity based on the state of the aptamer. We describe a broadly applicable method for coupling a novel, newly selected aptamer to a ribozyme to generate functional aptazymes via in vitro and in vivo selection. To illustrate this approach, we describe experimental procedures for selecting aptazymes assembled from aptamers that bind p-amino-phenylalanine and a hammerhead ribozyme. Because this method uses selection, it does not rely on sequence-specific design and thus should be generalizable for the generation of in vivo operational aptazymes that respond to any targeted molecules.