Key Features of Turing Systems are Determined Purely by Network Topology

Diego, Xavier; Marcon, Luciano; Müller, Patrick; Sharpe, James

doi:10.1103/physrevx.8.021071

Cited by 77 publications

(101 citation statements)

References 56 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While a large fraction of topologies exhibit a Turing I instability, this is again mainly for non-competitive rather than competitive interactions. This result stands in sharp contrast to the existing literature which mainly highlights the network topology as the important factor for TP capability (Diego et al, 2017). By contrast, our results suggest that network structure alone does not suffice but that the choice of regulatory function also critically determines a network's Turing capability.…”

Section: Turing Topologies Are Common But Sensitive To Regulatory Meccontrasting

confidence: 99%

Turing patterns are common but not robust

Scholes

Schnoerr

Isalan

et al. 2018

Preprint

View full text Add to dashboard Cite

Turing patterns (TPs) underlie many fundamental developmental processes, but they operate over narrow parameter ranges, raising the conundrum of how evolution can ever discover them. Here we explore TP design space to address this question and to distill design rules. We exhaustively analyze 2-and 3-node biological candidate Turing systems: crucially, network structure alone neither determines nor guarantees emergent TPs. A surprisingly large fraction (>60%) of network design space can produce TPs, but these are sensitive to even subtle changes in parameters, network structure and regulatory mechanisms. This implies that TP networks are more common than previously thought, and evolution might regularly encounter prototypic solutions. Importantly, we deduce compositional rules for TP systems that are almost necessary and sufficient (96% of TP networks contain them, and 95% of networks implementing them produce TPs). This comprehensive network atlas provides the blueprints for identifying natural TPs, and for engineering synthetic systems.

show abstract

Section: Turing Topologies Are Common But Sensitive To Regulatory Meccontrasting

confidence: 99%

Turing patterns are common but not robust

Scholes

Schnoerr

Isalan

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…To go further, we resorted to numerical methods because, unlike for the two-component system, there is not a well-established relationship between network topology and spatial patterning for 3-component networks (Scholes, Schnoerr, Isalan, & Stumpf, 2019 (Marcon et al, 2016) and general RD systems (Diego, Marcon, Müller, & Sharpe, 2018;Scholes et al, 2019). We systematically screened parameter ranges around known values published for biological RD systems (Nakamasu, Takahashi, Kanbe, & Kondo, 2009) and applied the White and Gilligan (White & Gilligan, 1998) parameters define specific topologies.…”

Section: Resultsmentioning

confidence: 99%

Perturbation analysis of a multi-morphogen Turing Reaction-Diffusion stripe patterning system reveals key regulatory interactions

Economou

Monk

Green

2019

Preprint

View full text Add to dashboard Cite

Periodic patterning is extremely widespread in developmental biology and is broadly modelled by Reaction-Diffusion (RD) processes. However, the minimal two-component RD system is vastly simpler than the multimolecular events that current biology is now able to describe. Moreover, RD models are typically underconstrained such that it is often hard to meaningfully relate the model architecture to real interactions measured experimentally. To address both these issues, we investigated the periodic striped patterning of the rugae (transverse ridges) in the roof of the mammalian palate. We experimentally implicated a small number of major signalling pathways and established theoretical limits on the number of pathway network topologies that can account for the stable spatial phase relationships of their observed signalling outputs. We further conducted perturbation analysis both experimentally and in silico, critically to assess the effects of perturbations on established patterns. We arrived at a relatively highly-constrained number of possible networks and found that these share some common motifs. Finally, we examined the dynamics of pattern appearance and discovered a core network consisting of epithelium-specific FGF and Wnt as mutually antagonistic "activators" and Shh as the "inhibitor", which initiates the periodicity and whose existence constrains the network topology still further. Together these studies articulate the principles of multi-morphogen RD patterning and demonstrate the utility of perturbation analysis as a tool for constraining networks in this and, in principle, any RD system.

show abstract

“…the number of parameter combinations) that allows for classical Turing patterns is extremely narrow, practically unrealistic. [73,74] This is in apparent contradiction with the high number of Turing mechanisms proposed to underlie natural patterns, and may also explain why the construction of synthetic Turing patterns has remained elusive thus far despite the growing interest of synthetic biologists in pattern formation. [8,[75][76][77] Recently, Karig and colleagues engineered a self-organizing synthetic system in which an isogenic population of bacteria produced a two-dimensional pattern of red fluorescent patches in a background of green fluorescence.…”

Section: The Elusive Turing Patternsmentioning

confidence: 99%

Using Synthetic Biology to Engineer Spatial Patterns

Santos‐Moreno

Schaerli

2018

Adv. Biosys.

View full text Add to dashboard Cite

Synthetic biology has emerged as a multidisciplinary field that provides new tools and approaches to address longstanding problems in biology. It integrates knowledge from biology, engineering, mathematics and biophysics to buildrather than to simply observe and perturbbiological systems that emulate natural counterparts or display novel properties. The interface between synthetic and developmental biology has greatly benefitted both fields and allowed us to address questions that would remain challenging with classical approaches due to the intrinsic complexity and essentiality of developmental processes. This Progress Report provides an overview of how synthetic biology can help us to understand a process that is crucial for the development of multicellular organisms: pattern formation. It reviews the major mechanisms of genetically-encoded synthetic systems that have been engineered to establish

show abstract

Key Features of Turing Systems are Determined Purely by Network Topology

Cited by 77 publications

References 56 publications

Turing patterns are common but not robust

Turing patterns are common but not robust

Perturbation analysis of a multi-morphogen Turing Reaction-Diffusion stripe patterning system reveals key regulatory interactions

Using Synthetic Biology to Engineer Spatial Patterns

Contact Info

Product

Resources

About