Optimal Regulatory Circuit Topologies for Fold-Change Detection

Adler, Miri; Szekely, Pablo; Mayo, Avi; Alon, Uri

doi:10.1016/j.cels.2016.12.009

Cited by 71 publications

(81 citation statements)

References 69 publications

(90 reference statements)

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“…S4D) and two negative-feedback loops type (Fig. S4E), which are consistent with recent analytical search of a topology space (46). The basic network models could be slightly modified to take into account the nonlinear input-output relation of the cAMP relay response (Fig.…”

Section: Resultssupporting

confidence: 80%

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Fold-change detection and scale invariance of cell–cell signaling in social amoeba

Kamino

Kondo

Nakajima

et al. 2017

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

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Cell-cell signaling is subject to variability in the extracellular volume, cell number, and dilution that potentially increase uncertainty in the absolute concentrations of the extracellular signaling molecules. To direct cell aggregation, the social amoebae Dictyostelium discoideum collectively give rise to oscillations and waves of cyclic adenosine 3′,5′-monophosphate (cAMP) under a wide range of cell density. To date, the systems-level mechanism underlying the robustness is unclear. By using quantitative live-cell imaging, here we show that the magnitude of the cAMP relay response of individual cells is determined by fold change in the extracellular cAMP concentrations. The range of cell density and exogenous cAMP concentrations that support oscillations at the population level agrees well with conditions that support a large fold-change-dependent response at the singlecell level. Mathematical analysis suggests that invariance of the oscillations to density transformation is a natural outcome of combining secrete-and-sense systems with a fold-change detection mechanism.fold-change detection | oscillations | collective behavior | Dictyostelium | robustness C ell-cell signaling lies at the basis of development and maintenance of multicellular forms of life. Extracellular signals are often subject to greater fluctuations in the size of extracellular space and the number of cells (Fig. 1A), not to mention nonspecific binding to other molecules, degradation, and dilution. These factors introduce an uncertainty to the detectable number of extracellular ligand molecules, thus posing a threat to the fidelity of cell-cell communication. One of the means by which cells could cope with such uncertainties is to base their behavioral decisions on temporal changes in the extracellular signals. Persistent stimuli are often ignored while their changes in time elicit transient responses-a property collectively called adaptation (1-3). Recent studies have highlighted cellular response whose magnitude appears to be dictated by the fold change in the input stimuli-a property referred to as "fold-change detection" (FCD) (4, 5). In bacterial chemotaxis, cells respond adaptively to a fold change in chemoattractant concentration (6) so that their search patterns depend only on the spatial profiles of the chemoattractant irrespective of its absolute level. Fold-change dependence is also implied in eukaryotic chemotactic response (7, 8) as well as cell fate control and gene regulation in Xenopus embryo (9), Drosophila imaginal disk (10), and mammalian cells (11). These studies have shed light on the role of FCD for a simple unidirectional signal transduction from an extracellular ligand-receptor interaction (input) to a cellular response (output). However, cell-cell signaling and multicellular systems as a whole often use secretion and sensing of the same molecules (12), whereby the output is fed back to the responding cell itself in addition to the neighboring cells, thus forming a complex bidirectional signal transduction system. Th...

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Section: Resultssupporting

confidence: 80%

“…2 and Fig. 5C), a computational search for minimal networks with two nodes and three links (3,45,46) was performed (Fig. S4 A-C).…”

Section: Resultsmentioning

confidence: 99%

Fold-change detection and scale invariance of cell–cell signaling in social amoeba

Kamino

Kondo

Nakajima

et al. 2017

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

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“…One possibility is that the RAF/MEK/ERK cascade could act as fold-change detector for variation in EGFR activity (Cohen-Saidon et al, 2009). Fold-change detector models are expected to incorporate motifs such as an incoherent feedforward loop or a non-linear integral feedback loop (Adler et al, 2017). An alternative model is that the pathway is arranged for dose-response alignment (Brent, 2009), which ideally employs push-pull mechanisms or combines negative feedback with a comparator (Andrews et al, 2016).…”

Section: The Mapk Pathway As a Robust Interpreter Moderating Mutant Rmentioning

confidence: 99%

Oncogenic mutant RAS signaling activity is rescaled by the ERK/MAPK pathway

Gillies

Pargett

Silva

et al. 2020

Preprint

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Activating mutations in RAS are present in ~30% of human tumors, and the resulting aberrations in ERK/MAPK signaling play a central role in oncogenesis. However, the form of these signaling changes is uncertain, with activating RAS mutants linked to both increased and decreased ERK activation in vivo.Rationally targeting the kinase activity of this pathway requires clarification of the quantitative effects of RAS mutations. Here, we use live-cell imaging in cell lines expressing only one RAS isoform to quantify ERK activity with a new level of accuracy. We find that despite large differences in their biochemical activity, mutant KRAS isoforms within cells have similar ranges of ERK output. We identify roles for pathway-level effects, including variation in feedback strength and feedforward modulation of phosphatase activity, that act to rescale pathway sensitivity independent of expression level, ultimately resisting changes in the dynamic range of ERK activity while preserving responsiveness to growth factor stimuli. Our results reconcile seemingly inconsistent reports within the literature and imply that the initial signaling changes induced by RAS mutations in oncogenesis are subtle.

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“…These observations led to the important insight that signaling networks have evolved sophisticated feedback control mechanisms that confer robustness, similar to those developed for classical engineering systems [8][9][10][11][12]. To this end, understanding the architecture and constraints of these regulatory processes is essential both to assessing the range of biological functions that they can implement and to building functional synthetic systems [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Though this model is more complex and nonlinear than those we have discussed so far, we will see that the same essential theoretical approach applies. A) The circuit diagram for the dynamics described in equation (15). This circuit controls the growth of a bacterial population via the toxin CcdB.…”

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confidence: 99%

Hard Limits and Performance Tradeoffs in a Class of Sequestration Feedback Systems

Olsman

Baetica

Xiao

et al. 2017

Preprint

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A common feature of both biological and man-made systems is the use of feedback to control their behavior. In this paper, we explore a particular model of biomolecular feedback implemented using a sequestration mechanism. This has been demonstrated to implement robust perfect adaption, often referred to as integral control in engineering. Our work generalizes a previous model of the sequestration feedback system and develops an analytical framework for understanding the hard limits, performance tradeoffs, and architectural properties of a simple model of biological control. We find that many of the classical tools from control theory and dynamical systems can be applied to understand both deterministic and stochastic models of the system. Our work finds that there are simple expressions that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and noise. These findings yield a holistic picture of the general behavior of sequestration feedback, and will hopefully contribute to a more general theory of biological control systems.

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Optimal Regulatory Circuit Topologies for Fold-Change Detection

Cited by 71 publications

References 69 publications

Fold-change detection and scale invariance of cell–cell signaling in social amoeba

Fold-change detection and scale invariance of cell–cell signaling in social amoeba

Oncogenic mutant RAS signaling activity is rescaled by the ERK/MAPK pathway

Hard Limits and Performance Tradeoffs in a Class of Sequestration Feedback Systems

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