A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

The calculus of codes - From entropy, complexity, and information to life

Exploring the core components that define living systems and their operational mechanisms within emerging biological entities is a complex endeavor. In the realm of biological systems literature, the terms matter, energy, information, complexity, and entropy are frequently referenced. However, possessing these concepts alone does not guarantee a comprehensive understanding or the ability to reconstruct the intricate nature of life. This study aims to illuminate the trajectory of these organic attributes, presenting a theoretical framework that delves into the integrated role of these concepts in biology. We assert that Code Biology serves as a pivotal steppingstone for unraveling the mechanisms underlying life. Biological codes (BCs) emerge not only from the interplay of matter and energy but also from Information. Contrary to deriving information from the former elements, we propose that information holds its place as a fundamental physical aspect. Consequently, we propose a continuum perspective called Calculus of Fundamentals involving three fundamentals: Matter, Energy, and Information, to depict the dynamics of BCs. To achieve this, we emphasize the necessity of studying Entropy and Complexity as integral organic descriptors. This perspective also facilitates the introduction of a mathematical theoretical framework that aids in comprehending continuous changes, the driving dynamics of biological fundamentals. We posit that Energy, Matter, and Information constitute the essential building blocks of living systems, and their interactions are governed by Entropy and Complexity analyses, redefined as biological descriptors. This interdisciplinary perspective of Code Biology sheds light on the intricate interplay between the controversial phenomenon of life and advances the idea of constructing a theory rooted in information as an organic fundamental.

The modularity codes

The hypothesis presented here is that codes as described by Marcello Barbieri are the fundamental principle behind biological modularity. Modularity has been studied in different life science disciplines, especially in the fields of evolution and development, as well as in network biology, yet there is still no consensus on how modularity evolved itself. Modularity is basically the functional integrity of multiple molecular players involved in a common process. Codes as defined by Barbieri describe a tripartite relation involving an adapter molecule connecting two other independent types of molecules to each other in an arbitrary, but semantic manner. This form of interaction goes beyond predictable mere physical or chemical one-to-one interactions and always relates three molecules to each other. A code of three topologically related molecules interacting in a defined order may be considered a minimal module on its own, but when one regards a set of multiple, overlapping tripartite, coded interactions, this paves the way towards logically and functionally consistent coherence of multiple participants of a certain, modular process. A theoretical outline of how to identify and describe such modular structures is given.

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.