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New Advances in Biocomplexity
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New Advances in Biocomplexity

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 25516

Special Issue Editor

Prof. Dr. Mikhail Prokopenko

E-Mail Website
Guest Editor
Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Interests: self-organisation; information theory; complex systems; artificial life; computational epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Salient evolutionary transitions, such as the emergence of genetic coding, multicellularity or language, generate higher levels of organisation, with qualitatively new properties that are not fully predictable or explainable in reductionist terms. The higher levels of biological and/or social organisation are typically associated with increased complexity, resulting from dynamic interactions between the system, its constituent parts, and the external environment.

Biocomplexity is the multidisciplinary study of macroscale complex structures and collective behaviours that arise from microscale interactions of relatively simple biological agents, across multiple levels ranging from molecules and cells to organisms and ecosystems. The key concepts and features include emergence, self-organisation, feedbacks, nonlinearity, sensitivity to initial conditions, critical dynamics, resilience, as well as adaptation and evolution. Information theory, probability theory, and complex network theory provide rigorous frameworks to study these concepts quantitatively.

The aim of this Special Issue, aligned with a topical workshop, as well as an international symposium, is to highlight advances in biocomplexity achieved both in terms of the state-of-the-art and state-of-the-practice. Several areas are of special interest: Computational epidemiology and disease control, microbial ecology and biosecurity, functional genomics and bioinformatics, systems biology and artificial life, swarm intelligence and active matter, computational neuroscience and neuro-engineering, cognitive modelling and machine learning.

Prof. Mikhail Prokopenko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial life
  • biosecurity
  • collective behavior
  • critical dynamics
  • ecology
  • emergence
  • epidemiology
  • evolution
  • neuroscience
  • self-organization
  • systems biology

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Published Papers (5 papers)

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31 pages, 3797 KiB  
Article
Genomic Intelligence as Über Bio-Cybersecurity: The Gödel Sentence in Immuno-Cognitive Systems
by Sheri M. Markose
Entropy 2021, 23(4), 405; https://doi.org/10.3390/e23040405 - 29 Mar 2021
Cited by 6 | Viewed by 5241
Abstract
This paper gives formal foundations and evidence from gene science in the post Barbara McClintock era that the Gödel Sentence, far from being an esoteric construction in mathematical logic, is ubiquitous in genomic intelligence that evolved with multi-cellular life. Conditions uniquely found in [...] Read more.
This paper gives formal foundations and evidence from gene science in the post Barbara McClintock era that the Gödel Sentence, far from being an esoteric construction in mathematical logic, is ubiquitous in genomic intelligence that evolved with multi-cellular life. Conditions uniquely found in the Adaptive Immune System (AIS) and Mirror Neuron System (MNS), termed the genomic immuno-cognitive system, coincide with three building blocks in computation theory of Gödel, Turing and Post (G-T-P). (i) Biotic elements have unique digital identifiers with gene codes executing 3D self-assembly for morphology and regulation of the organism using the recursive operation of Self-Ref (Self-Reference) with the other being a self-referential projection of self. (ii) A parallel offline simulation meta/mirror environment in 1–1 relation to online machine executions of self-codes gives G-T-P Self-Rep (Self-Representation). (iii) This permits a digital biotic entity to self-report that it is under attack by a biotic malware or non-self antigen in the format of the Gödel sentence, resulting in the “smarts” for contextual novelty production. The proposed unitary G-T-P recursive machinery in AIS and in MNS for social cognition yields a new explanation that the Interferon Gamma factor, known for friend-foe identification in AIS, is also integral to social behaviors. New G-T-P bio-informatics of AIS and novel anti-body production is given with interesting testable implications for COVID-19 pathology. Full article
(This article belongs to the Special Issue New Advances in Biocomplexity)
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22 pages, 4643 KiB  
Article
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network
by Hyobin Kim, Stalin Muñoz, Pamela Osuna and Carlos Gershenson
Entropy 2020, 22(9), 986; https://doi.org/10.3390/e22090986 - 4 Sep 2020
Cited by 6 | Viewed by 5021
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue New Advances in Biocomplexity)
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17 pages, 651 KiB  
Article
Mutual Information as a General Measure of Structure in Interaction Networks
by Gilberto Corso, Gabriel M. F. Ferreira and Thomas M. Lewinsohn
Entropy 2020, 22(5), 528; https://doi.org/10.3390/e22050528 - 7 May 2020
Cited by 8 | Viewed by 3830
Abstract
Entropy-based indices are long-established measures of biological diversity, nowadays used to gauge partitioning of diversity at different spatial scales. Here, we tackle the measurement of diversity of interactions among two sets of organisms, such as plants and their pollinators. Actual interactions in ecological [...] Read more.
Entropy-based indices are long-established measures of biological diversity, nowadays used to gauge partitioning of diversity at different spatial scales. Here, we tackle the measurement of diversity of interactions among two sets of organisms, such as plants and their pollinators. Actual interactions in ecological communities are depicted as bipartite networks or interaction matrices. Recent studies concentrate on distinctive structural patterns, such as nestedness or modularity, found in different modes of interaction. By contrast, we investigate mutual information as a general measure of structure in interactive networks. Mutual information (MI) measures the degree of reciprocal matching or specialization between interacting organisms. To ascertain its usefulness as a general measure, we explore (a) analytical solutions for different models; (b) the response of MI to network parameters, especially size and occupancy; (c) MI in nested, modular, and compound topologies. MI varies with fundamental matrix parameters: dimension and occupancy, for which it can be adjusted or normalized. Apparent differences among topologies are contingent on dimensions and occupancy, rather than on topological patterns themselves. As a general measure of interaction structure, MI is applicable to conceptually and empirically fruitful analyses, such as comparing similar ecological networks along geographical gradients or among interaction modalities in mutualistic or antagonistic networks. Full article
(This article belongs to the Special Issue New Advances in Biocomplexity)
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18 pages, 1407 KiB  
Article
Phase Transitions in Spatial Connectivity during Influenza Pandemics
by Nathan Harding, Richard Spinney and Mikhail Prokopenko
Entropy 2020, 22(2), 133; https://doi.org/10.3390/e22020133 - 22 Jan 2020
Cited by 14 | Viewed by 4034
Abstract
We investigated phase transitions in spatial connectivity during influenza pandemics, relating epidemic thresholds to the formation of clusters defined in terms of average infection. We employed a large-scale agent-based model of influenza spread at a national level: the Australian Census-based Epidemic Model (A [...] Read more.
We investigated phase transitions in spatial connectivity during influenza pandemics, relating epidemic thresholds to the formation of clusters defined in terms of average infection. We employed a large-scale agent-based model of influenza spread at a national level: the Australian Census-based Epidemic Model (AceMod). In using the AceMod simulation framework, which leverages the 2016 Australian census data and generates a surrogate population of ≈23.4 million agents, we analysed the spread of simulated epidemics across geographical regions defined according to the Australian Statistical Geography Standard. We considered adjacent geographic regions with above average prevalence to be connected, and the resultant spatial connectivity was then analysed at specific time points of the epidemic. Specifically, we focused on the times when the epidemic prevalence peaks, either nationally (first wave) or at a community level (second wave). Using the percolation theory, we quantified the connectivity and identified critical regimes corresponding to abrupt changes in patterns of the spatial distribution of infection. The analysis of criticality is confirmed by computing Fisher Information in a model-independent way. The results suggest that the post-critical phase is characterised by different spatial patterns of infection developed during the first or second waves (distinguishing urban and rural epidemic peaks). Full article
(This article belongs to the Special Issue New Advances in Biocomplexity)
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12 pages, 243 KiB  
Perspective
Emergence of Organisms
by Andrea Roli and Stuart A. Kauffman
Entropy 2020, 22(10), 1163; https://doi.org/10.3390/e22101163 - 16 Oct 2020
Cited by 21 | Viewed by 6360
Abstract
Since early cybernetics studies by Wiener, Pask, and Ashby, the properties of living systems are subject to deep investigations. The goals of this endeavour are both understanding and building: abstract models and general principles are sought for describing organisms, their dynamics and their [...] Read more.
Since early cybernetics studies by Wiener, Pask, and Ashby, the properties of living systems are subject to deep investigations. The goals of this endeavour are both understanding and building: abstract models and general principles are sought for describing organisms, their dynamics and their ability to produce adaptive behavior. This research has achieved prominent results in fields such as artificial intelligence and artificial life. For example, today we have robots capable of exploring hostile environments with high level of self-sufficiency, planning capabilities and able to learn. Nevertheless, the discrepancy between the emergence and evolution of life and artificial systems is still huge. In this paper, we identify the fundamental elements that characterize the evolution of the biosphere and open-ended evolution, and we illustrate their implications for the evolution of artificial systems. Subsequently, we discuss the most relevant issues and questions that this viewpoint poses both for biological and artificial systems. Full article
(This article belongs to the Special Issue New Advances in Biocomplexity)
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