Biology subjects

Klavins, E.

Publications and source records attributed to Klavins, E..

4 recordsLinked to original sources

bioRxiv2017

Synthetic hormone-responsive transcription factors can monitor and re-program plant development

Developmental programs continuously sculpt plant morphology to meet environmental challenges, and these same programs have been manipulated to increase agricultural productivity1,2. Small molecule phytohormones act as signals within these programs creating chemical circuitry3 that, in many cases, has been represented in mathematical models4,5. To date, model validation and forward engineering of plant morphology has been largely restricted to adding or subtracting genes, as more nuanced tools to modulate key control parameters identified by such models in vivo are severely limited6,7. Here, we use Arabidopsis thaliana to validate a novel set of synthetic and modular hormone activated Cas9-based repressors (HACRs) that respond to three phytohormones: auxin, gibberellins and jasmonates. We demonstrate that HACRs can regulate genes in response to both exogenous hormone treatments, as well as in response to local differences in endogenous hormone levels associated with developmental events. We further show that HACRs can be used to reprogram the agriculturally relevant traits of shoot branching and phyllotaxy by tuning canalization strength, a critical control parameter predicted by mathematical models. By deploying a HACR to re-parameterize the threshold for induction of the auxin transporter PIN-FORMED1 (PIN1), we observed a decrease in shoot branching and phyllotactic noise as predicted by existing models4,5. The approach described here provides a framework for improved mapping of developmental circuitry, as well as a means to better leverage model predictions to engineer development.

plant biology↗

bioRxiv2017

Accelerating Evolutionary Hill Climbs in Parallel Turbidostats

Evolution has been used to address many engineering problems. Within the context of metabolic engineering and synthetic biology, directed evolution has natural appli-cations. However, most research concerning optimizing microbial evolution has been focused on library generation and screening, while accelerating evolutionary hill climbs and been largely ignored. Here, we develop a model to explore how population struc-ture can accelerate evolutionary hill climbs. We show that by adjusting the population size, environmental challenge, and meta-population dynamics that the rate of evolution can be accelerated in parallel turbidostats. Our analyses leads to two surprising results: small populations are favored over conventionally large microbial populations, and propagating modest fitness improvements is favored over propagating mutants with large beneficial mutations. When combined with rational design and other optimization techniques our theory can accelerate strain development for applications such as consolidated bioprocessing, and bioremidation systems.

synthetic biology↗

bioRxiv2017

High-Throughput Characterization of Protein-Protein Interactions by Reprogramming Yeast Mating

High-throughput methods for screening protein-protein interactions (PPIs) enable the rapid characterization of engineered binding proteins and interaction networks. While existing methods are powerful, none allow quantitative library-on-library characterization of PPIs in a modifiable extracellular environment. Here, we show that sexual agglutination of S. cerevisiae can be reprogrammed to link PPI strength with mating efficiency using yeast synthetic agglutination (YSA). Validation of YSA with 96 previously characterized interactions shows a strong log-linear relationship between mating efficiency and PPI strength for interactions with KDs ranging from 500 pM to 25 M. Using induced chromosomal translocation to pair barcodes representing interacting proteins, thousands of distinct interactions can be screened in a single pot. YSA binding interactions occur in a controllable extracellular environment, and thus studying the effects of environmental factors on PPI networks is possible. YSA enables the high-throughput, quantitative characterization of PPI networks in a fully defined extracellular environment at a library-on-library scale.

synthetic biology↗

bioRxiv2016

Self-destructive altruism in a synthetic developmental program enables complex feedstock utilization

Stochastic differentiation and programmed cell death are common developmental processes in microbes, driving diverse altruistic behaviors that promote cooperation. Utilizing cell death in developmental programs requires control over the rate of differentiation to balance cell proliferation against the utility of sacrifice. However, the regulatory networks that control these behaviors are often complex and have yet to be successfully harnessed as biotechnology. Here, we engineered a synthetic developmental gene network that couples stochastic differentiation with programmed cell death to implement a two-member division of labor. Progenitor cellobiose consumer cells were engineered to grow on cellobiose and differentiate at a controlled rate into self-destructive altruists that release an otherwise sequestered cellulase enzyme payload through autolysis to form a developmental Escherichia coli consortium that utilizes cellulose for growth. We used an experimentally parameterized model of task switching, payload delivery and cellulose conversion to nutrients to set key parameters to achieve overall population growth supported by cellulase release, liberating 14-23% of the available carbon. An inevitable consequence of engineering altruistic developmental behaviors is the emergence of cheaters that undermine cooperation. We observed cheater phenotypes for consumers and altruists, identified mutational hotspots and constructed a predictive model of circuit longeivity based on mutation rate estimates for each mode of evolutionary escape. This work introduces the altruistic developmental program as a new tool for synthetic biology, demonstrates the utility of population dynamics models to engineer complex phenotypes and provides a testbed for probing the evolutionary biology of self-destructive altruism.

synthetic biology↗