Characterization and mitigation of gene expression burden in mammalian cells

Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells

Mitigation of ribosome competition through distributed sRNA feedback (extended version)

This paper is an extended version of a paper of the same title accepted to Proceedings of the 55th IEEE Conference on Decision and Control (2016).A current challenge in the robust engineering of synthetic gene networks is context dependence, the unintended interactions among genes and host factors. Ribosome competition is a specific form of context dependence, where all genes in the network compete for a limited pool of translational resources available for gene expression. Recently, theoretical and experimental studies have shown that ribosome competition creates a hidden layer of interactions among genes, which largely hinders our ability to predict design outcomes. In this work, we establish a control theoretic framework, where these hidden interactions become disturbance signals. We then propose a distributed feedback mechanism to achieve disturbance decoupling in the network. The feedback loop at each node consists of the protein product transcriptionally activating a small RNA (sRNA), which forms a translationally inactive complex with mRNA rapidly. We illustrate that with this feedback mechanism, protein production at each node is only dependent on its own transcription factor inputs, and almost independent of hidden interactions arising from ribosome competition.AFOSR grant FA9550-12-1-0129 and ONR grant N00014131007

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits

MIRELLA: a mathematical model explains the effect of microRNA-mediated synthetic genes regulation on intracellular resource allocation

Competition for intracellular resources, also known as gene expression burden, induces coupling between independently co-expressed genes, a detrimental effect on predictability and reliability of gene circuits in mammalian cells. We recently showed that microRNA (miRNA)-mediated target downregulation correlates with the upregulation of a co-expressed gene, and by exploiting miRNAs-based incoherent-feed-forward loops (iFFLs) we stabilise a gene of interest against burden. Considering these findings, we speculate that miRNA-mediated gene downregulation causes cellular resource redistribution. Despite the extensive use of miRNA in synthetic circuits regulation, this indirect effect was never reported before. Here we developed a synthetic genetic system that embeds miRNA regulation, and a mathematical model, MIRELLA, to unravel the miRNA (MI) RolE on intracellular resource aLLocAtion. We report that the link between miRNA-gene downregulation and independent genes upregulation is a result of the concerted action of ribosome redistribution and ‘queueing-effect’ on the RNA degradation pathway. Taken together, our results provide for the first time insights into the hidden regulatory interaction of miRNA-based synthetic networks, potentially relevant also in endogenous gene regulation. Our observations allow to define rules for complexity- and context-aware design of genetic circuits, in which transgenes co-expression can be modulated by tuning resource availability via number and location of miRNA target sites

Analysis of transcriptional feedback strategy for reducing interaction in gene expression processes

Advances in genetic manipulation have allowed the overexpression of proteins and insertion of circuits in cells. However, the expected behaviour can be altered by the internal competition for the limited amount of cellular resources. In this work we analyse a feedback strategy based on transcription inhibition that aims to reduce the interaction in a two-protein expression system. The results allow interpreting the effects of negative feedback on the steady-state protein levels and how the realizable protein region is affected by the feedback loop.Fil: Nuñez, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Picó, Jesús. Universidad Politécnica de Valencia. Departamento de Ingeniería de Sistemas y Automática; EspañaFil: de Battista, Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentin

Robust set-point regulation of gene expression using resource competition couplings in mammalian cells

Gene expression depends on the cellular con-text. One major contributor to gene expression variability is competition for limited transcriptional and translational re-sources, which may induce indirect couplings among otherwise independently-regulated genes. Here, we apply control theoretical concepts and tools to design an incoherent feedforward loop (iFFL) biomolecular controller operating in mammalian cells using translational-resource competition couplings. Harnessing a resource-aware mathematical model, we demonstrate analytically and computationally that our resource-aware design can achieve near-constant set-point regulation of gene expression whilst ensuring robustness to plasmid uptake variation. We also provide an analytical condition on the model parameters to guide the design of the resource-aware iFFL controller ensuring robustness and performance in set-point regulation. Our theoretical design based on translational-resource competition couplings represents a promising approach to build more sophisticated resource-aware control circuits operating at the host-cell level

Effective interaction graphs arising from resource limitations in gene networks

Protein production in gene networks relies on the availability of resources necessary for transcription and translation, which are found in cells in limited amounts. As various genes in a network compete for a common pool of resources, a hidden layer of interactions among genes arises. Such interactions are neglected by standard Hill-function-based models. In this work, we develop a model with the same dimension as standard Hill-function-based models to account for the sharing of limited amounts of RNA polymerase and ribosomes in gene networks. We provide effective interaction graphs to capture the hidden interactions and find that the additional interactions can dramatically change network behavior. In particular, we demonstrate that, as a result of resource limitations, a cascade of activators can behave like an effective repressor or a biphasic system, and that a repression cascade can become bistable.United States. Air Force Office of Scientific Research (FA9550-12-1-0129)National Institute of General Medical Sciences (U.S.) (P50 GMO98792

Engineering Robust and Programmable Biological Systems

The ability to engineer programmable biological systems using complex artificial gene networks has great potential to unlock important innovative solutions to many biotechnological challenges. While cells have been engineered to implement complex information processing algorithms and to produce food, materials, and pharmaceuticals, many innovative applications are yet to be realized due to our poor understanding of how robust, reliable, and predictable artificial gene circuits are built. In this work, we demonstrate that robust complex cellular behaviors (e.g., bistability and gene expression dynamics) can be achieved by engineering gene regulatory architecture and increasing the complexity of genetic networks. We further demonstrate that increasing demand for cellular resources causes resource-associated interference among noninteracting genetic devices of various complexities. Importantly, we show that feedback systems can be engineered to enhance the robustness and reliability of genetic circuits by reducing such resource-associated interference among independent circuits. Taken together, this work contributes to understanding the design principles that govern biological robustness and represents an important step towards construction of robust, tunable, reliable, and predictable complex artificial genetic circuits for a wide range of biotechnological applications

Robustness of networked systems to unintended interactions with application to engineered genetic circuits

A networked dynamical system is composed of subsystems interconnected through prescribed interactions. In many engineering applications, however, one subsystem can also affect others through "unintended" interactions that can significantly hamper the intended network's behavior. Although unintended interactions can be modeled as disturbance inputs to the subsystems, these disturbances depend on the network's states. As a consequence, a disturbance attenuation property of each isolated subsystem is, alone, insufficient to ensure that the network behavior is robust to unintended interactions. In this paper, we provide sufficient conditions on subsystem dynamics and interaction maps, such that the network's behavior is robust to unintended interactions. These conditions require that each subsystem attenuates constant external disturbances, is monotone or "near-monotone", the unintended interaction map is monotone, and the prescribed interaction map does not contain feedback loops. We employ this result to guide the design of resource-limited genetic circuits. More generally, our result provide conditions under which robustness of constituent subsystems is sufficient to guarantee robustness of the network to unintended interactions