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Mixed microbial communities exhibit emergent biochemical properties not found in clonal monocultures. We report a new type of synthetic genetic interaction, synthetic mutualism in trans (SMIT), in which certain pairs of auxotrophic Escherichia coli mutants complement one another's growth by cross‐feeding essential metabolites. We find significant metabolic synergy in 17% of 1035 such pairs tested, with SMIT partners identified throughout the metabolic network. Cooperative phenotypes show more growth on average by aiding the proliferation of their conjugate partner, thereby expanding the source of their own essential metabolites. We construct a quantitative, predictive, framework for describing SMIT interactions as governed by stoichiometric models of the metabolic networks of the interacting strains.

Massive Open Online Course (MOOC) platforms incorporate large course catalogs from which individual students may register multiple courses. We performed a network-based analysis of student achievement, considering how course-course interactions may positively or negatively affect student success. Our data set included 378,000 users and 1,000,000 unique registration events in France Université Numérique (FUN), a national MOOC platform. We adapt reliability theory to model certificate completion rates with a Weibull survival function, following the intuition that students “survive” in a course for a certain time before stochastically dropping out. Course-course interactions are found to be well described by a single parameter for user engagement that can be estimated from a user’s registration profile. User engagement, in turn, correlates with certificate rates in all courses regardless of specific content. The reliability approach is shown to capture several certificate rate patterns that are overlooked by conventional regression models. User engagement emerges as a natural metric for tracking student progress across demographics and over time.

The purpose of biomedicine is to serve society, yet its hierarchical and closed structure excludes many citizens from the process of innovation. We propose a collection of reforms to better integrate citizens within the research community, reimagining biomedicine as more participatory, inclusive, and responsive to societal needs.

Whole-cell screening for Mycobacterium tuberculosis ( Mtb ) inhibitors is complicated by the pathogen’s slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli . We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb -derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb . In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets. Whole-cell screening for Mycobacterium tuberculosis inhibitors is complicated by the pathogen’s slow growth and biocontainment requirements. Here the authors develop engineered E. coli as a synthetic biology tool to express and screen metabolic targets from Mycobacterium tuberculosis .

Background The engineering of metabolism holds tremendous promise for the production of desirable metabolites, particularly alternative fuels and other highly reduced molecules. Engineering approaches must redirect the transfer of chemical reducing equivalents, preventing these electrons from being lost to general cellular metabolism. This is especially the case for high energy electrons stored in iron-sulfur clusters within proteins, which are readily transferred when two such clusters are brought in close proximity. Iron sulfur proteins therefore require mechanisms to ensure interaction between proper partners, analogous to many signal transduction proteins. While there has been progress in the isolation of engineered metabolic pathways in recent years, the design of insulated electron metabolism circuits in vivo has not been pursued. Results Here we show that a synthetic hydrogen-producing electron transfer circuit in Escherichia coli can be insulated from existing cellular metabolism via multiple approaches, in many cases improving the function of the pathway. Our circuit is composed of heterologously expressed [Fe-Fe]-hydrogenase, ferredoxin, and pyruvate-ferredoxin oxidoreductase (PFOR), allowing the production of hydrogen gas to be coupled to the breakdown of glucose. We show that this synthetic pathway can be insulated through the deletion of competing reactions, rational engineering of protein interaction surfaces, direct protein fusion of interacting partners, and co-localization of pathway components on heterologous protein scaffolds. Conclusions Through the construction and characterization of a synthetic metabolic circuit in vivo , we demonstrate a novel system that allows for predictable engineering of an insulated electron transfer pathway. The development of this system demonstrates working principles for the optimization of engineered pathways for alternative energy production, as well as for understanding how electron transfer between proteins is controlled.

Background FeFe-hydrogenases are the most active class of H 2 -producing enzymes known in nature and may have important applications in clean H 2 energy production. Many potential uses are currently complicated by a crucial weakness: the active sites of all known FeFe-hydrogenases are irreversibly inactivated by O 2 . Results We have developed a synthetic metabolic pathway in E. coli that links FeFe-hydrogenase activity to the production of the essential amino acid cysteine. Our design includes a complementary host strain whose endogenous redox pool is insulated from the synthetic metabolic pathway. Host viability on a selective medium requires hydrogenase expression, and moderate O 2 levels eliminate growth. This pathway forms the basis for a genetic selection for O 2 tolerance. Genetically selected hydrogenases did not show improved stability in O 2 and in many cases had lost H 2 production activity. The isolated mutations cluster significantly on charged surface residues, suggesting the evolution of binding surfaces that may accelerate hydrogenase electron transfer. Conclusions Rational design can optimize a fully heterologous three-component pathway to provide an essential metabolic flux while remaining insulated from the endogenous redox pool. We have developed a number of convenient in vivo assays to aid in the engineering of synthetic H 2 metabolism. Our results also indicate a H 2 -independent redox activity in three different FeFe-hydrogenases, with implications for the future directed evolution of H 2 -activating catalysts.

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network.

The purpose of biomedicine is to serve society, yet its hierarchical and closed structure excludes many citizens from the process of innovation. We propose a collection of reforms to better integrate citizens within the research community, reimagining biomedicine as more participatory, inclusive, and responsive to societal needs.

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (ALR) revealed benazepril as a targeted inhibitor. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical ALR inhibitors. This is the first report of an antimicrobial activity in an approved Angiotensin Converting Enzyme (ACE) inhibitor and may explain clinical data associating use of ACE inhibitors with reduced Mtb infection risk. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets. Competing Interest Statement The authors have declared no competing interest.

Microbial metabolism is well characterized, conserved in evolution, and complex. Thus it is an excellent model for the systems approach to biology, which connects adaptive biological functions to the networks from which they emerge. This thesis will explore the complementary principles of optimality and plasticity in microbial metabolism, taking investigative strategies from systems and synthetic biology. Chapter 2 reviews metabolic interactions in mixed microbial communities. Basic cellular metabolism extends into cross-feeding, quorum-sensing, and communication with neighboring strains. Such interactions emerge from the metabolic network, yet represent a unique set of highly refined dynamic behaviors. Systems biology is the natural discipline for investigations in this field. Chapter 3 reports and characterizes widespread metabolic cooperation between auxotrophic strains of E. coli. We examine a library of laboratory-engineered deletion strains with no coevolutionary history and no selective pressure favoring cooperation. Yet we show they are often able to grow cooperatively in conditions where neither could grow alone. A quantitative model is developed to show how this behavior emerges from the inherrent flexibility of the metabolic network. Chapters 4 and 5 bring the tools of synthetic biology to the problem of metabolic engineering. We present the design and optimization of a system for the biological production of molecular hydrogen. Our efforts employ a range of techniques in synthetic biology: gene synthesis, standardization, protein fusion, scaffolding, and directed evolution. The flexibility of biological function is found to play an essential role in this work, as biological components are mixed and matched far from their native contexts. Chapter 6 revisits endogenous cellular metabolism with a new method for predicting the behavior of metabolic mutants. This method extends flux-balancing stoichiometric models by explicity representing ambiguity in our ability to predict fluxes. Counterintuitively, accounting for inherrent uncertainty in metabolism allows more accurate and precise predictions of metabolic behavior. The success of this method suggests a highly plastic model of metabolic fluxes that may be adaptive in the face of intracellular and environmental perturbation.