Abstract

The multi-level organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism, while in the same time each protein (a single node of such interaction network) is itself a network of interacting amino-acid residues allowing coordinated motion of the macromolecule and systemic effect as allosteric behavior. Similar pictures can be drawn for structure and function of cells, organs, tissues, and ecological systems. The majority of biologists are used to think that causally relevant events originate from the lower level (the molecular one) in the form of perturbations, that “climb up” the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such causative model, stemming from the usual genotype-phenotype distinction, is not the only one. As a matter of fact, one can observe top-down, bottom-up, as well as middle-out perturbation/control trajectories. The recent complex network studies allow to go further the pure qualitative observation of the existence of both non-linear and non-bottom-up processes and to uncover the deep nature of multi-level organization. Here, taking as paradigm protein structural and interaction networks, we review some of the most relevant results dealing with between networks communication shedding light on the basic principles of complex system control and dynamics and offering a more realistic frame of causation in biology.

Highlights

  • Specialty section: This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in GeneticsReceived: 07 May 2021 Accepted: 31 May 2021 Published: 21 June 2021Citation: Uversky VN and Giuliani A (2021) Networks of Networks: An Essay on Multi-Level Biological Organization.Front

  • Protein molecules are the most elementary complex systems, lying in the borderline between simple and complex systems physics (Frauenfelder and Wolynes, 1994), they present the basic features of “Weaver organized complexity” (Weaver, 1948): multiple stable states, wiring structure changing in time, adaptation to changing environmental conditions

  • All these features are acquired by means of biodynamic interfaces (Arora et al, 2020) that, in the case of protein molecules, can be traced down to “high-P” residues

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Summary

INTRODUCTION

Specialty section: This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics. The network formalism is probably the most natural way to represent biological systems. Even if in the last decades the analysis of complex networks became a very widespread paradigm to face problems going from macromolecular structures (Di Paola et al, 2013) to genetic regulation circuits (Lopez-Kleine et al, 2013), neuroscience (Petersen and Sporns, 2015), and ecological systems (Bascompte, 2010), this is not a new idea. In 1948 Warren Weaver (1948), one of the fathers of mathematical information theory, sketched a very intriguing synthetic tripartite description of science into problems of “organized simplicity,” “disorganized complexity,” and “organized complexity” with biology located in the last class. Class 1 problems allow for an extreme abstraction (e.g., a planet can be thought as a dimensionless ‘material point”). The possibility to take into consideration only very few basic (and object independent) features, such as mass and distance, is at the basis of the extreme precision and generality of classical mechanics

Networks of Networks
BIODYNAMIC INTERFACES
THE MIDDLE WAY
INFORMATION FLUXES ACROSS NETWORKS
Findings
CONCLUSION
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