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

Kim, Hyobin; Sayama, Hiroki

doi:10.1162/artl_a_00262

Cited by 10 publications

(4 citation statements)

References 54 publications

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“…Indeed, the recent advances in information theories around quantifying causal emergence [ 99 , 100 ] show how the same Boolean network can compute different functions simultaneously, depending on the level of analysis chosen by an observer [ 99 ]. This has interesting biological implications, since such networks are a popular model for understanding the functional relationships between genes [ 101 , 102 , 103 ].…”

Section: Current Debates: Dissolving Dichotomous Thinkingmentioning

confidence: 99%

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

Bongard

Levin

2023

Biomimetics

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The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

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Section: Current Debates: Dissolving Dichotomous Thinkingmentioning

confidence: 99%

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

Bongard

Levin

2023

Biomimetics

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“…Although RBNs are highly simplified models, they can greatly explain relevant properties of life and its possibilities. Accordingly, they have been actively used in many fields such as systems biology and artificial life [31][32][33][34][35][36]. In this study, a single-layer RBN represents a GRN at a single cell level, a multilayer RBN indicates coupled GRNs at a multicellular level.…”

Section: Measurement Of Antifragility In Single-layer and Multilayer ...mentioning

confidence: 99%

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

2019

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Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems.

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“…The definition of robustness and evolvability has been applied to a number of studies adopting Boolean network models in artificial life and systems biology [ 18 , 19 , 20 ]. However, the calculations for finding all attractors of the networks are computationally expensive.…”

Section: Introductionmentioning

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

Cited by 10 publications

References 54 publications

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models

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

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