An explanatory evo-devo model for the developmental hourglass

Akhshabi, Saamer; Sarda, Shrutii; Dovrolis, Constantine; Yi, Soojin V.

doi:10.12688/f1000research.4583.2

Cited by 16 publications

(3 citation statements)

References 40 publications

(68 reference statements)

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“…The three most relevant studies about the hourglass effect focused on the special case of layered and acyclic directed networks in which edges can only exist between successive layers (Akhshabi & Dovrolis, 2011;Akhshabi et al, 2014;Friedlander et al, 2015). In those studies, the hourglass effect is defined in terms of the number of vertices at each layer, and a network is referred to as an hourglass if the width of the intermediate layers is much smaller relative to the width of the input and output layers.…”

Section: Related Workmentioning

confidence: 99%

“…The first study (Akhshabi & Dovrolis, 2011) proposed an evolutionary model (called EvoArch) for the emergence of the hourglass effect in computer networking protocol stacks; EvoArch captures the creation and competition between modules that perform similar functions and it may be also applicable in other layered technological systems. The second study (Akhshabi et al, 2014) made the case that the topological structure of developmental regulatory networks (namely that the specificity of regulatory interactions increases during embryogenesis) is sufficient for the emergence of the hourglass effect in that context. The third study (Friedlander et al, 2015) showed that a layered and directed network can evolve to a bow-tie structure if the relation between inputs and outputs can be represented with a rank-deficient matrix, and if the mutations in the intensity (weights) of module interactions (network edges) can be modeled as products by a random number (rather than sums).…”

Section: Related Workmentioning

confidence: 99%

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The hourglass effect in hierarchical dependency networks

Sabrin¹,

Dovrolis²

2017

Net Sci

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Many hierarchically modular systems are structured in a way that resembles an hourglass. This "hourglass effect" means that the system generates many outputs from many inputs through a relatively small number of intermediate modules that are critical for the operation of the entire system, referred to as the waist of the hourglass. We investigate the hourglass effect in general, not necessarily layered, hierarchical dependency networks. Our analysis focuses on the number of source-to-target dependency paths that traverse each vertex, and it identifies the core of a dependency network as the smallest set of vertices that collectively cover almost all dependency paths. We then examine if a given network exhibits the hourglass property or not, comparing its core size with a "flat" (i.e., non-hierarchical) network that preserves the source dependencies of each target in the original network. As a possible explanation for the hourglass effect, we propose the Reuse Preference (RP) model that captures the bias of new modules to reuse intermediate modules of similar complexity instead of connecting directly to sources or low complexity modules. We have applied the proposed framework in a diverse set of dependency networks from technological, natural and information systems, showing that all these networks exhibit the general hourglass property but to a varying degree and with different waist characteristics.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The hourglass effect in hierarchical dependency networks

Sabrin¹,

Dovrolis²

2017

Net Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…This hypothesis has been supported by genetic approaches (Galis and Metz 2001), suggesting that novel genes are more prevalent during early and late development, while older genes are expressed during the phylotypic stage (Domazet-Lošo and Tautz 2010, Akhshabi et al 2014).…”

Section: Ancient Synapomorphy and The Adaptive Decoupling Hypothesismentioning

confidence: 92%

The characterization of larval homology: transcriptomic insights into the origin and evolution of animal life cycles

Hatleberg¹

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Emergence and Evolution of Hierarchical Structure in Complex Systems

Siyari

Dilkina

Dovrolis

2019

Dynamics on and of Complex Networks III

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It is well known that many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. What is not well understood however is how this hierarchical structure (which is fundamentally a network property) emerges, and how it evolves over time. We propose a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. Evo-Lexis models the most elementary modules of the system as symbols ("sources") and the modules at the highest level of the hierarchy as sequences of those symbols ("targets"). Evo-Lexis computes the optimized adjustment of a given hierarchy when the set of targets changes over time by additions and removals (a process referred to as "incremental design"). In this paper we use computation modeling to show that:-Low-cost and deep hierarchies emerge when the population of target sequences evolves through tinkering and mutation. -Strong selection on the cost of new candidate targets results in reuse of more complex (longer) nodes in an optimized hierarchy. -The bias towards reuse of complex nodes results in an "hourglass architecture" (i.e., few intermediate nodes that cover almost all source-target paths). -With such bias, the core nodes are conserved for relatively long time periods although still being vulnerable to major transitions and punctuated equilibria. -Finally, we analyze the differences in terms of cost and structure between incrementally designed hierarchies and the corresponding "clean-slate" hierarchies which result when the system is designed from scratch after a change.arXiv:1805.04924v2 [cs.NE] 4 Aug 20182. Under what conditions do the emergent hierarchies exhibit the so called "hourglass effect"? Why are few intermediate modules reused much more than others? 3. Do intermediate modules persist during the evolution of hierarchies? Or are there "punctuated equilibria" where the highly reused modules change significantly? 4. Which are the differences in terms of cost and structure between the incrementally designed and the corresponding clean-slate designed hierarchies?The structure of the paper is as follows: In Section 2, we present an overview of Lexis, the static optimization framework that serves as the main building block in Evo-Lexis. 1 In Section 3, we present the components of the Evo-Lexis framework, along with the metrics that we use for the analysis of evolving hierarchies. In Section 4, we evaluate the evolution of hierarchies under different target generation models. Sections 5 and 6 present further analysis regarding the evolvability and major transitions in hierarchies produced using the most full-fledged (MRS) target generation model. Finally, Section 7 focuses on the comparison between clean-slate and incremental design in terms of cost and structure. In Section 8, we review related work in the context of Evo-Lexis. Section 9 discusses the results and p...

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An explanatory evo-devo model for the developmental hourglass

Cited by 16 publications

References 40 publications

The hourglass effect in hierarchical dependency networks

The hourglass effect in hierarchical dependency networks

The characterization of larval homology: transcriptomic insights into the origin and evolution of animal life cycles

Emergence and Evolution of Hierarchical Structure in Complex Systems

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