10 research outputs found
Prediction of Cellular Burden with Host--Circuit Models
Heterologous gene expression draws resources from host cells. These resources
include vital components to sustain growth and replication, and the resulting
cellular burden is a widely recognised bottleneck in the design of robust
circuits. In this tutorial we discuss the use of computational models that
integrate gene circuits and the physiology of host cells. Through various use
cases, we illustrate the power of host-circuit models to predict the impact of
design parameters on both burden and circuit functionality. Our approach relies
on a new generation of computational models for microbial growth that can
flexibly accommodate resource bottlenecks encountered in gene circuit design.
Adoption of this modelling paradigm can facilitate fast and robust design
cycles in synthetic biology
Dynamic metabolic control: towards precision engineering of metabolism
Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the âpushâpull-blockâ strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production
Predictive biology: modelling, understanding and harnessing microbial complexity
Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.Defence Threat Reduction Agency (Grant HDTRA1-15-1-0051