The Role of Criticality of Gene Regulatory Networks in Morphogenesis

Kim, Hyobin; Sayama, Hiroki

doi:10.1109/tcds.2018.2876090

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…By casting the emergent dynamics at multiple scales in the circuit space, this approach brings us a step closer to closing the gap between the structure and the function of a complex dynamical system. In this regard, our method also contributes to the theory of complex systems by complementing and potentially generalizing existing approaches to characterizing canalization [ 88 ], control [ 92 ], collectivity [ 70 , 93 ], coarse-graining [ 94 , 95 , 96 ] and criticality in complex nonlinear dynamical systems [ 97 , 98 ].…”

Section: Discussionmentioning

confidence: 99%

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

Manicka

Levin

2022

Entropy

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What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

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Section: Discussionmentioning

confidence: 99%

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

Manicka

Levin

2022

Entropy

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“…Boolean networks were proposed by Stuart Kauffman as gene regulatory network models [ 25 , 26 , 27 ]. They have been extensively used in many areas including artificial life, robotics, and systems biology [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. They consist of nodes and links, where nodes represent genes, and the links represent interactions between genes.…”

Section: Methodsmentioning

confidence: 99%

“…Comparing the attractors between the original and mutated networks, we classify the properties of biological networks [ 66 ]. Our classification is extended from the definition of robust and evolvable network in Aldana et al’s work [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

Kim

Muñoz

Osuna³

et al. 2020

Entropy

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Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here, we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.

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“…As mentioned in Introduction (Section 1), we have presented a new GRN-based morphogenetic model and revealed the role of the criticality of GRNs in morphogenesis [21,22]. To make our finding more solid from an evolutionary viewpoint, we included genetic perturbations occurring in real living organisms in our morphogenetic model.…”

Section: Perturbations To Grns and Robust And Evolvable Grnsmentioning

confidence: 99%

“…They all used a discrete space like Cellular Automata, which is not realistic as a model of morphogenetic processes. To investigate a potential role of criticality of GRNs at a multicellular level which is hard to uncover through the single-cell-level studies, we have recently proposed GRN-based morphogenetic systems operating in a continuous space and revealed that the criticality of GRNs facilitated the formation of nontrivial morphologies [21,22]. To make our finding more solid from an evolutionary viewpoint, in this study, we include an evolutionary process in our morphogenetic model by introducing genetic perturbations (e.g., mutations) to GRNs.…”

Section: Introductionmentioning

confidence: 99%

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

Kim

Sayama

2018

Artificial Life

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Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.

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The Role of Criticality of Gene Regulatory Networks in Morphogenesis

Cited by 5 publications

References 30 publications

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

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