IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

1. Network Physics: A Vital Interlinked Anatomy and Physiology

Gabrielli, Andrea, et al. Network Renormalization. arXiv:2412.12988. Enrico Fermi Research Center, Rome, IMT School for Advanced Studies, Lucca, University of Leiden and Universitat de Barcelona physicists including Ángeles Serrano begin to methodically scope out how this reliable physical approach can now be effectively applied to life’s many complex network vitalities, which has mostly eluded prior success. Their contribution so far involves a new informational content and the presence of chimeras and criticalities.

Renormalization group (RG) theories were developed to describe system configurations with many degrees of freedom, along with the associated model parameters and coupling constants. They also can identify critical points of phase transitions. Usually, the RG builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining. However, the strong heterogeneity of real-world networks complicates renormalization procedures. In this review, we discuss past attempts, the important advances, and the ochallenges on the road to network renormalization. (Excerpt)

gallo, Luca, et al. Gallo, Luca, et al. Higher-order correlations reveal complex memory in temporal hypergraphs. Nature Communications. 15/4754, 2024. Central European University, Vienna and Queen Mary University, London theorists including Vito Latora and Federico Battiston continue to explicate the many structural, informative, knowledgeable qualities of nature’s brain-like anatomy and physiology.

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics based on pairwise interactions only. Here, we introduce a framework for higher-order correlations to characterize their temporal organization. We use a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data. (Excerpt)
In conclusion, our work sheds light on the multifaceted nature of memory that emerges at different scales in real-world interacting systems. Beyond the scope of network science, we hope that our framework can open new avenues to reveal the higher-order dynamics of coherent structures in a variety of physical systems, from multi-fragmentation in nuclear physics to vortex-vortex interaction in the atmosphere or other fluid dynamical systems. (4)

Garcia-Perez, Guillermo, et al. Multiscale Unfolding of Real Networks by Geometric Renormalization. Nature Physics. 14/6, 2018. University of Barcelona systems theorists Garcia-Perez, Marian Boguna, and Angeles Serrano find this physical and mathematical theory helps tease out inherent regularities across multiplex webworks. This deep conception, while naturally apt, does strain attempts to explain it. A Critical History of Renormalization by Kerson Huang at arXiv:1310.5533, written as a tribute to Nobel laureate Kenneth Wilson (1936-2013), is a good entry. See also Mutual Information, Neural Networks and the Renormalization Group by Koch-Janusz and Ringel (2018 search). We also quote from Wikipedia.

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group. Here, we provide a framework for the investigation of complex networks at different resolutions. We find that real scale-free networks show geometric scaling under this renormalization group transformation. This in turn offers a basis for exploring critical phenomena and universality in complex networks. (Abstract)

In theoretical physics, the renormalization group refers to a mathematical apparatus that allows systematic investigation of the changes of a physical system as viewed at different scales. In particle physics, it reflects the changes in the underlying force laws (codified in a quantum field theory) as the energy scale at which physical processes occur varies, energy/momentum and resolution distance scales being effectively conjugate under the uncertainty principle. A change in scale is called a scale transformation. The renormalization group is intimately related to scale invariance and conformal invariance, symmetries in which a system appears the same at all scales (self-similarity). (Wikipedia)

Gershenson, Carlos and Mikhail Prokopenko. Complex Networks. Artificial Life. Online July, 2011. An Introduction for a forthcoming issue of eight articles on the title subject drawn from the 2010 ALife XI conference in Odense, Denmark. Click on Early Access at the journal’s MIT Press site to view abstracts. Indeed in the past decade a sudden, revolutionary realization has occurred that every physical, organismic, neural, ecological, linguistic and societal domain is distinguished by the same vital nested, invariant, systemic networks. In this regard, the authors cite number of recent books in further support of this natural propensity: Reuven Cohen and Shlomo Havlin Complex Networks: Structure, Robustness and Function (Cambridge, 2010); Mark Newman Networks: An Introduction (Oxford, 2011); one could add Mark Buchanan, et al, eds. Networks in Cell Biology (Cambridge, 2010).

Gosak, Marko, et al. Network Science of Biological Systems at Different Scales. Physics of Life Reviews. Online November, 2017. Akin to Khaluf 2017 who cite a common scale invariance, seven University of Maribor, Slovenia researchers from physics, mathematics, physiology and medical departments including Matjaz Perc report a similar maturation of network studies whence multiplex entity nodes and relational links are found across life’s bacterial, cellular and multicellular phases. Along with all the discrete components (mitochondria, eukaryotes, organisms), equally present interconnections serve an integral communicative physiologies. Once again, the same universal, independent geometries and dynamics animate everywhere.

Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks. Second, we focus on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems. Third, we concentrate on the emerging field of multilayer networks and review the first endeavors and novel perspectives offered by this framework in exploring biological complexity. (Abstract excerpts)

Gysi, Deisy and Katja Nowick. Construction, Comparison and Evolution of Networks in Life Sciences and Other Disciplines. Journal of the Royal Society Interface. May, 2020. University of Leipzig and Free University of Berlin bioinformatic scholars (View GDs website, who is now with AL Barabasi’s group at Northeastern University) offer a broad survey of the 21st century network revolution that ALB and Reka Albert (search) initiated around 2000. Through the 2010s, almost every physical, biological and social phase has become reconceived, filled out and invigorated by these scale-free connective dynamics. The paper opens with glossary terms such as centrality and clustering to an extent that the common multiplex linkages appear to actively exist on their independent own.

This heretofore unknown anatomy and physiology is then noted from protein, metabolic, genomic, and neural realms onto an evolutionary presence and role so as to join living systems in modular scales. A further topical series covers science learning, cultural media, finance and more. It closes by saying that the same forms and functions can now be seen to repeat in kind at every stage which Geoffrey West cited as a “Universality of Networks” in Niall Ferguson’s Networld 2020 TV special.

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through disciplines such as social sciences, finance and computational gastronomy to present commonalities and differences in how networks change and can be analysed. (Abstract)

Halu, Arda, et al. The Multiplex Network of Human Diseases. bioRxiv. Online January 18, 2017. A posting on this new e-print site for biological and genetic sciences where Harvard Medical School and Universitat Rovira i Virgili, Spain researchers survey these latest network theories so as to gain novel insights into palliative epidemiology control and cure.

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers. (Abstract)

Harush, Uzi and Baruch Barzel. Dynamic Patterns of Information Flow in Complex Networks. Nature Communications. 8/2181, 2017. We cite this entry by Bar-Ilan University, Israel mathematicians because after some 20 years of node/link multiplex network studies, the detection of common recurrences everywhere must imply “universal laws” in effect. The paper was cited by Paul Davies in The Demon in the Machine as proof that the generative “informative patterns” he and colleagues propose are indeed “coherent things with an independent existence.” (101)

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit different flow patterns, driven by other interaction mechanisms. To uncover a network’s actual flow patterns, we use a perturbative formalism, tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic flow for a broad range of nonlinear systems. We find that the diversity of patterns can be mapped into a single universal function, characterizing the interplay between the system’s topology and its dynamics. (Abstract excerpt)

Our results show that despite the diversity of potential interaction mechanisms, the patterns of information flow are governed by universal laws that can be directly linked to the system’s microscopic dynamics. (2) From neuronal signals to gene regulation, complex networks unction by enabling the flow of information between nodes. Understanding the rules that govern this flow is a crucial step toward establishing a theory of network dynamics. (10) In a broader perspective, our predicted universality indicates that the macroscopic flow patterns of complex systems are controlled by only a few relevant parameters of the system’s microscopic dynamics. (10)

Havlin, Shlomo, et al. Challenges in Network Science: Applications to Infrastructures, Climate, Social Systems and Economics. European Physical Journal Special Topics. 214/1, 2012. In this FuturICT issue, a dozen systems thinkers from Israel, Germany, Switzerland and Hungary, including Eshel Ben-Jacob and Jurgen Kurths, provide a tutorial about nature’s universally evident animate topologies and dynamical interactions and how they might be intentionally availed to better effect these vital areas.

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. (Abstract)

Network theory has revolutionized our understanding of complex systems in diverse areas and offers a deeper understanding on how e.g., people, computers, or proteins are connected among their kind. Many systems can be efficiently modeled using a network structure where the system entities are the network nodes and the relations between the entities are the network links. The universal appeal of the field led researchers from different disciplines to embrace network theory as a common paradigm of true inter-discipliner nature. (276)

Holme, Petter. Modern Temporal Network Theory. European Physical Journal B. 88/9, 2016. An introduction to a special collection in this regard such as From Calls to Communities: A Model for Time-varying Social Networks, and Temporal Fidelity in Dynamic Social Networks. For a 5 year update see Networks of Climate Change by PH and Jaun Rocha (2105.12537)

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more.

Hristova, Desislava, et al. A Multilayer Approach to Multiplexity and Link Prediction in Online Geo-Social Networks. EPJ Data Science. Online July, 2016. Since circa 2010 complexity theorists have increasingly realized that nature’s pervasive webworks are distinguished by many nested, scintillating iterative scales. This advanced extension of their study has been given a multiplexity moniker. Here Cambridge University computer scientists show how Twitter and Foursquare media exhibit these intricate, animate features.

Iniquez, Gerardo, et al. Universal Dynamics of Ranking. arXiv:2104.13439. GI, Central European University, Vienna, Carlos Pineda and Carlos Gershenson, National Autonomous University Mexico, and Albert-Laszlo Barabasi, Northeastern University theorists extend their complexity studies to discern how constant changes in subject rank listings, as an example of our penchant to arrange everything into a relative order, can exhibit common, temporal patterns and features. A universality is then depicted across many social instances. An allusion to statistical, many-body physical phenomena in effect could also be made.

Virtually anything can be and is ranked; people and animals, universities and countries, words and genes. Rankings reduce the components of highly complex systems into ordered lists, aiming to capture the fitness or ability of each element to perform relevant functions. Far less is known, however, about ranking dynamics, when the elements change their rank in time. Here we explore the dynamics of 30 ranking lists in natural, social, economic, and infrastructural systems, comprising millions of elements, whose temporal scales span from minutes to centuries. We find that the flux governing the arrival of new elements into a ranking list reveals systems with identifiable patterns of stability: in high-flux systems only the top of the list is stable, while in low-flux systems the top and bottom are equally stable.

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