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Entropy
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Complexity, Complex Networks and Its Applications in Biological Systems
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Complexity, Complex Networks and Its Applications in Biological Systems

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

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 6105

Special Issue Editor

Dr. Yves De Saá Guerra

E-Mail
Guest Editor
Palestra Drago, Research and Sport Training Center, Las Palmas de Gran Canaria, 35010, Canary Islands, Spain
Interests: complex networks; complex systems; complexity theory; data analysis; holistic training; kinesiology

Special Issue Information

Dear Colleagues,

In our effort to understand reality, we ought to question everything we consider true or static. This leads us, inexorably, to try to isolate a phenomenon in order to a better understand. But we realize that we fall into the contradiction of trying to study a phenomenon by isolating it from its environment. It is not possible to understand reality by isolating the constituent elements of their relationships with their own universe. There is a duality element–environment; and from this nonlinear local relationships, new behaviors emerge. For that reason, researchers apply the concept of complex network.

Complex networks appear in many biological and technological contexts, indicating universality of certain functional and organizational principles in complex systems. In fact, a long-standing problem in biological and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. In nature, there are many examples of opposition or conflict which drive an improvement in an individual or in a group of individuals. The struggle for resources or survival have marked significant changes in the human body and other species, and in the way they relate. Darwin's ideas and the study of evolution have focused on competition as a driving force for evolutionary change.

This special issue aims display the new and improved techniques of information theory for complex networks and its applications in biological systems, ecology, economics, sport sciences, etc. in order to a better understand of natural reality.

Dr. Yves De Saá Guerra
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

  • self-organization
  • nonlinear
  • criticality
  • emergence
  • chaos
  • uncertainty

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Published Papers (2 papers)

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Research

15 pages, 3071 KiB  
Article
The Complex Structure of the Pharmacological Drug–Disease Network
by Irene López-Rodríguez, Cesár F. Reyes-Manzano, Ariel Guzmán-Vargas and Lev Guzmán-Vargas
Entropy 2021, 23(9), 1139; https://doi.org/10.3390/e23091139 - 31 Aug 2021
Cited by 3 | Viewed by 2434
Abstract
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. [...] Read more.
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures). Full article
(This article belongs to the Special Issue Complexity, Complex Networks and Its Applications in Biological Systems)
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19 pages, 10665 KiB  
Article
A Transcriptome Community-and-Module Approach of the Human Mesoconnectome
by Omar Paredes, Jhonatan B. López, César Covantes-Osuna, Vladimir Ocegueda-Hernández, Rebeca Romo-Vázquez and J. Alejandro Morales
Entropy 2021, 23(8), 1031; https://doi.org/10.3390/e23081031 - 11 Aug 2021
Cited by 5 | Viewed by 2944
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen [...] Read more.
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network Full article
(This article belongs to the Special Issue Complexity, Complex Networks and Its Applications in Biological Systems)
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