Communication to the editor. Application of the cross-regulation system as a metabolic switch

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Living organism is an intelligent system coded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al., PNAS, 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.

MoVE identifies metabolic valves to switch between phenotypic states

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Cellular checkpoint control using programmable sequential logic

Biological processes that require orderly progression, such as growth and differentiation, proceed via regulatory checkpoints where the cell waits for signals before continuing to the next state. Implementing such control would allow genetic engineers to divide complex tasks into stages. We present genetic circuits that encode sequential logic to instruct Escherichia coli to proceed through a linear or cyclical sequence of states. These are built with 11 set-reset latches, designed with repressor-based NOR gates, which can connect to each other and sensors. The performance of circuits with up to three latches and four sensors, including a gated D latch, closely match predictions made by using nonlinear dynamics. Checkpoint control is demonstrated by switching cells between multiple circuit states in response to external signals over days.

Rethinking the Hierarchy of Sugar Utilization in Bacteria

Bacteria are known to consume some sugars over others, although recent work reported by Koirala and colleagues in this issue of the Journal of Bacteriology (S. Koirala, X. Wang, and C. V. Rao, J Bacteriol 198:386-393, 2016, http://dx.doi.org/10.1128/JB.00709-15) revealed that individual cells do not necessarily follow this hierarchy. By studying the preferential consumption of l-arabinose over d-xylose in Escherichia coli, those authors found that subpopulations consume one, the other, or both sugars through cross-repression between utilization pathways. Their findings challenge classic assertions about established hierarchies and can guide efforts to engineer the simultaneous utilization of multiple sugars.

Digital and analog gene circuits for biotechnology

Biotechnology offers the promise of valuable chemical production via microbial processing of renewable and inexpensive substrates. Thus far, static metabolic engineering strategies have enabled this field to advance industrial applications. However, the industrial scaling of statically engineered microbes inevitably creates inefficiencies due to variable conditions present in large-scale microbial cultures. Synthetic gene circuits that dynamically sense and regulate different molecules can resolve this issue by enabling cells to continuously adapt to variable conditions. These circuits also have the potential to enable next-generation production programs capable of autonomous transitioning between steps in a bioprocess. Here, we review the design and application of two main classes of dynamic gene circuits, digital and analog, for biotechnology. Within the context of these classes, we also discuss the potential benefits of digital-analog interconversion, memory, and multi-signal integration. Though synthetic gene circuits have largely been applied for cellular computation to date, we envision that utilizing them in biotechnology will enhance the efficiency and scope of biochemical production with living cells.

Design of expression systems for metabolic engineering: coordinated synthesis and degradation of glycogen

In metabolic engineering, systems which allow coordinated control of two metabolic pathways can be useful. We designed two expression systems and demonstrated their application by coordinating glycogen synthesis and degradation. The first expression vector pMSW2 expressed the glycogen synthesis genes in one operon and the glycogen degradation gene in a separate, coordinately regulated operon. The plasmid was designed to switch off expression of the first operon and activate expression of the second operon on addition of IPTG. As an alternative means to control glycogen synthesis and degradation pathways, we constructed expression vector pGTSD100, which contains the native Escherichia coli glycogen synthesis and degradation operon under control of the tac promoter. Both expression vectors work successfully to control the net synthesis and degradation of glycogen. In cultures of the E. coli strain TA3476 carrying the plasmid pMSW2, before the addition of IPTG, glycogen continued to accumulate in the culture. About three hours after IPTG was added, glycogen levels began to decrease. When no IPTG was added to cultures of TA3476:pMSW2, glycogen accumulated in the cells as before but the rate of degradation of glycogen was much lower. When IPTG was added to TA3476:pMSW2, the total cell protein at the end of batch cultivation was approximately 15% higher compared to cultures without IPTG addition. The extra biomass was formed during the glycogen degradation phase. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 419-426, 1997.

Genetic network driven control of PHBV copolymer composition

We developed a detailed mathematical model describing the coupling between the molecular weight distribution dynamics of poly(3-hydroxybutyrate-co-3hydroxyvalerate) (PHBV) copolymer chains with those of hydroxybutyrate (HB) and hydroxyvalerate (HV) monomer formation. Sensitivity analysis of the model revealed that both the monomer composition and the molecular weight distribution of the copolymer chains are strongly affected by the ratio between the rates at which the two-monomer units are incorporated into the chains. This ratio depends on the relative HB and HV availability, which in turn is a function of the expression levels of genes encoding enzymes that catalyze monomer formation. Regulation of gene expression was accomplished through the aid of an artificial genetic network, the patterns of expression of which can be controlled by appropriately tuning the concentration of an extracellular inducer. Extensive simulations were used to study the effects of operating conditions and parameter uncertainties on the range of achievable copolymer compositions. Since the predicted conditions fell in the range of feasible bioprocessing manipulations, it is expected that such strategy could be successfully employed. Thus, the presented model constitutes a powerful tool for designing genetic networks that can drive the formation of PHBV copolymer structures with desirable characteristics.

A memorial review of Jay Bailey's contribution in prokaryotic metabolic engineering

When mentioning prokaryotic metabolic engineering, most people will immediately think of Jay Bailey. Jay's contribution to this fast-growing field is evident and familiar to many. Therefore, instead of a detailed technical review, we attempt in this article to summarize his contribution and dissect reasons for his success in this area from a standpoint of one of his former students (VH) and of a colleague in the field (JCL). This short review is by no means complete and provides only a partial view of Jay's contribution to the metabolic engineering of prokaryotes.