In silico feedback for in vivo regulation of a gene expression circuit

Milias-Argeitis, Andreas; Summers, Sean; Stewart-Ornstein, Jacob; Zuleta, Ignacio A.; Pincus, David; El-Samad, Hana; Khammash, Mustafa; Lygeros, John

doi:10.1038/nbt.2018

Cited by 274 publications

(319 citation statements)

References 13 publications

Supporting

Mentioning

317

Contrasting

Unclassified

Order By: Relevance

“…We show that gene expression can be controlled by repeatedly stimulating a native endogenous promoter over many cell generations (>15 h) for both time-constant and timevarying target profiles and at both the population and single-cell levels. Recently, Milias-Argeitis et al (19) also proposed an approach for feedback control of gene expression in yeast. In contrast to their work, we propose a method that is effective at the single-cell level, for time-varying target profiles, and robust despite the presence of strong internal feedback loops.…”

mentioning

confidence: 99%

Long-term model predictive control of gene expression at the population and single-cell levels

Uhlendorf

Miermont

Delaveau

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

220

237

View full text Add to dashboard Cite

Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here, we show that, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmostress responsive promoter and playing with the osmolarity of the cells environment, we show that long-term control can, indeed, be achieved for both time-constant and time-varying target profiles at the population and even the single-cell levels. Importantly, we provide evidence that real-time control can dynamically limit the effects of gene expression stochasticity. We anticipate that our method will be useful to quantitatively probe the dynamic properties of cellular processes and drive complex, synthetically engineered networks.model based control | computational biology | high osmolarity glycerol pathway | quantitative systems biology U nderstanding the information processing abilities of biological systems is a central problem for systems and synthetic biology (1-6). The properties of a living system are often inferred from the observation of its response to static perturbations. Timevarying perturbations have the potential to be much more informative regarding the dynamics of cellular functions (7-12). Currently, it is not possible to precisely perturb protein levels in an analogous manner, even though this perturbation would be instrumental in our understanding of gene regulatory networks. Indeed, despite the development of novel regulatory systems, including various RNA-based solutions (13), transcriptional control by means of inducible promoters is still the preferred method for manipulating protein levels (14, 15). Unfortunately, inducible promoters have several generic limitations. First, there is a significant delay between gene expression activation and effective protein synthesis. Second, many cellular processes can interfere with gene expression through internal feedback loops whose effects are hard to predict. Third, the process of gene expression shows significant levels of noise (16)(17)(18). Given these limitations, novel experimental strategies are required to gain quantitative, real-time control of gene expression in vivo.Here, we see the...

show abstract

mentioning

confidence: 99%

Long-term model predictive control of gene expression at the population and single-cell levels

Uhlendorf

Miermont

Delaveau

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

220

237

View full text Add to dashboard Cite

show abstract

“…Ultimately, it should be possible to implant intracellular, human designed control devices that will override or replace the natural control and signalling mechanisms. An example of work in this area relates to in silico feedback in the control of a gene expression circuit [36].…”

Section: Examples Of Applicationsmentioning

confidence: 99%

Synthetic biology – the state of play

Kitney

Freemont

2012

FEBS Letters

View full text Add to dashboard Cite

a b s t r a c tJust over two years ago there was an article in Nature entitled ''Five Hard Truths for Synthetic Biology''. Since then, the field has moved on considerably. A number of economic commentators have shown that synthetic biology very significant industrial potential. This paper addresses key issues in relation to the state of play regarding synthetic biology. It first considers the current background to synthetic biology, whether it is a legitimate field and how it relates to foundational biological sciences. The fact that synthetic biology is a translational field is discussed and placed in the context of the industrial translation process. An important aspect of synthetic biology is platform technology, this topic is also discussed in some detail. Finally, examples of application areas are described. It is now just over two years since the article 5 Hard Truths for Synthetic biology was published [1]. The article looked at some of the issues relating to synthetic biology at that time. As we will show, many things have changed in the interim period. Two years ago there was still considerable doubt about whether or not synthetic biology was likely to have significant industrial impact. It is now pretty clear that the case has been made and that synthetic biology will have very significant impact in a range of fields -leading to the development of new, major industries. The evidence for this statement can be found in a number of sources. For example the bcc report [2] which predicts that:The global value of the synthetic biology market reached $1

show abstract

“…The development of microfluidics technologies over the recent years [11] has allowed researchers to apply control in other cellular systems as well, such as microbe and yeast cells, in order to affect their metabolic states usually by manipulating gene expression. For example Milias-Argeitis et al [12] used a fourth-order linear model describing a light-responsive genetic network in order to control the gene expression of a microbial population, around a reference value, using optogenetics. Uhlendorf et al [13] used a two-variable delay differential equation (DDE) model to capture the dynamics of the yeast hyperosmotic stress response and compute inputs to make a population of cells to follow a time-varying signal.…”

Section: Introductionmentioning

confidence: 99%

“…To study the feasibility of linear control techniques over a population of bacterial cells part of a microfluidics platform we extend the spatially resolved ABM presented in Mina et al [21] to study open loop (nonfeedback) and closed loop (feedback) control strategies in silico. Thus, unlike most work in control of cellular populations [12][13][14], we also consider the spatial dependencies of coupled cells undergoing global control; in this case classical P-control, PI-control and PID-control [1].…”

Section: Introductionmentioning

confidence: 99%

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model

Mina¹,

Tsaneva-Atanasova

Bernardo

2016

ACS Synth. Biol.

View full text Add to dashboard Cite

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms AbstractAs control in cellular populations is becoming more common we extend a spatially explicit agent based model (ABM), developed previously to investigate population emergent behaviour of synchronized oscillating cells in a microfluidic chamber, to include control. Thus, unlike most of the work in models that deal with control of biological systems, we model individual cells with spatial dependencies that may contribute to certain behavioural responses. We use the model to test whether linear control methods can be used to tame the collective behaviour of a bacterial population as recently suggested in the literature. We compare and contrast open and closed loop control in a spatially explicit model of control (specifically proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control) as can be applied in a microfluidic chamber setting) and show when entrainment to a non-natural oscillating period is possible, using an increasing bacterial population size. Results indicate that during open loop control entrainment is only possible in a subset of forcing periods, unlike closed loop control, and a wide variety of dynamical behaviours is obtained outside the regions of entrainment which in a physical setting may be undesirable. However, even with closed loop control, fixed gains cannot harness a population that keeps growing beyond a certain size.

show abstract

In silico feedback for in vivo regulation of a gene expression circuit

Cited by 274 publications

References 13 publications

Long-term model predictive control of gene expression at the population and single-cell levels

Long-term model predictive control of gene expression at the population and single-cell levels

Synthetic biology – the state of play

Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model

Contact Info

Product

Resources

About