Competition for Catalytic Resources Alters Biological Network Dynamics

Rondelez, Yannick

doi:10.1103/physrevlett.108.018102

Cited by 96 publications

(82 citation statements)

References 32 publications

(34 reference statements)

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“…Notably, the competition over gene expression machinery is not necessary to achieve bistability, but expands the bistable region in parameter space ( Supplementary Fig. 6) 29 . We fabricated multiple arrays with identical lifetime τ ≈ 0.4 h, and different gene ratios 0.1 < α < 10, with 5-10% variation in lifetime and gene ratio 13 ( Supplementary Fig.…”

Section: Lettersmentioning

confidence: 99%

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

et al. 2015

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Living systems employ front propagation and spatiotemporal patterns encoded in biochemical reactions for communication, self-organization and computation 1-4 . Emulating such dynamics in minimal systems is important for understanding physical principles in living cells [5][6][7][8] and in vitro 9-14 . Here, we report a one-dimensional array of DNA compartments in a silicon chip as a coupled system of artificial cells, o ering the means to implement reaction-di usion dynamics by integrated genetic circuits and chip geometry. Using a bistable circuit we programmed a front of protein synthesis propagating in the array as a cascade of signal amplification and short-range di usion. The front velocity is maximal at a saddle-node bifurcation from a bistable regime with travelling fronts to a monostable regime that is spatially homogeneous. Near the bifurcation the system exhibits large variability between compartments, providing a possible mechanism for population diversity. This demonstrates that on-chip integrated gene circuits are dynamical systems driving spatiotemporal patterns, cellular variability and symmetry breaking.Sharp travelling fronts are prevalent in nature and emerge when nonlinear and dispersive effects combine, for example solitary waves in nonlinear optics, fluid convection, and chemical reactions 15 . Biological multicellular systems use travelling fronts of molecular signals as a means for computation and communication over long distances when signalling by diffusion is inefficient. In these systems, cells can be described as autocatalytic units that amplify and transmit signals beyond a threshold. Because signals dissipate in the medium over short distances, long-range information transmission is achieved by consecutive local cell-cell interactions. Signal propagation has been measured over orders of magnitudes in a variety of biological processes, ranging from fast action potentials in neuron networks with typical velocities 16 of v ≈ 10 7 mm h −1 , to slow gene expression fronts in development 3 with v ≈ 0.1 mm h −1 . Although some systems have a linearly unstable initial state, and thus can be treated as Fisher waves 17 , the majority of biological examples require more complex models, including the cable equation and Hodgkin-Huxley model 18 . These systems raise questions concerning the selected speed, bifurcations, fluctuations and stability in parameter space 15 , which are challenging to study in a living organism.Front propagation has been extensively studied in chemical reactions 16,19 and more recently in reconstituted biological systems, including the bacterial cell division network 10 , the Xenopus laevis cell cycle 11 , and RNA (ref. 20) and DNA (ref. 14) catalytic reactions. In the past decade cell-free transcription-translation reactions have been used to advance the design of programmable artificial gene systems, including bulk solution 21 , gels 9 , membrane vesicles 22 , water-in-oil drops 23 , microfluidic devices 24 and on-chip DNA compartments 13 . So far, however, a synthet...

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Section: Lettersmentioning

confidence: 99%

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

et al. 2015

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“…For instance, replacing a direct activation (A promotes B) by an indirect one (A promotes C which promotes B) will result in more than just some latency in the activation: there will also be a latency in all responses of the strand B, meaning that its concentration will also decrease slower (figure 3). Additionally, enzymes may get saturated, which would change the reaction rates of other parts of the system in ways difficult to apprehend for the human mind [21,33]. Those problems can be avoided by modifying parameters in the system (strand stability, template concentrations, enzyme activities, etc.…”

Section: The Dynamic Network Assembly Toolboxmentioning

confidence: 99%

“…The usual way to model such burden is to use a Michaelis -Menten term to quantify the enzymatic activity. Such term is further modified to take into account that multiple modules are competing for those resources [21]. Coaxial stacking, the collaborative stabilization that happens at a nick, and dangle are also considered by the model, as it affects the release speed of input and output.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Indeed, in well-mixed solutions, any molecular element can potentially interact with many others at any given time, and keeping track of all of these interactions and their kinetic consequences quickly becomes impossible for a human. It gets worse when counterintuitive effects, such as competition and saturation [19][20][21], have to be taken into account.…”

Section: Generalitymentioning

confidence: 99%

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Computer-assisted design for scaling up systems based on DNA reaction networks

Aubert

Mosca

Fujii

et al. 2014

J. R. Soc. Interface.

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In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the possibility to use CMA-ES, a state-of-the-art optimization algorithm that will automatically evolve parameters chosen by the user to try to match a specified behaviour. Finally, because all possible functionality cannot be captured by a single software, DACCAD includes the possibility to export a system in the synthetic biology markup language, a widely used language for describing biological reaction systems. DACCAD can be downloaded online at http://www.yannick-rondelez.com/downloads/.

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“…Competitive effects naturally appear in enzymatic systems, because enzymes are resources that are often shared by a great number of substrates [50][51][52][53]. Our classifiers are structurally similar to Hamming classifiers, in which a layer computes Hamming distances (the number of bits that are different) between stored and inputted patterns, and another layer selects the minimal distance [54].…”

Section: Introductionmentioning

confidence: 99%

Scaling down DNA circuits with competitive neural networks

Genot

Fujii

Rondelez

2013

J. R. Soc. Interface.

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DNA has proved to be an exquisite substrate to compute at the molecular scale. However, nonlinear computations (such as amplification, comparison or restoration of signals) remain costly in term of strands and are prone to leak. Kim et al. showed how competition for an enzymatic resource could be exploited in hybrid DNA/enzyme circuits to compute a powerful nonlinear primitive: the winner-take-all (WTA) effect. Here, we first show theoretically how the nonlinearity of the WTA effect allows the robust and compact classification of four patterns with only 16 strands and three enzymes. We then generalize this WTA effect to DNA-only circuits and demonstrate similar classification capabilities with only 23 strands.

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Competition for Catalytic Resources Alters Biological Network Dynamics

Cited by 96 publications

References 32 publications

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

Computer-assisted design for scaling up systems based on DNA reaction networks

Scaling down DNA circuits with competitive neural networks

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