Abstract

The fall of reductionist approaches to explanation leaves biology with an unescapable challenge: how to decipher complex systems. This entails a number of very critical questions, the most basic ones being: “What do we mean by ‘complex’?” and “What is the system we should look for?” In complex systems, constraints belong to a higher level that the molecular one and their effect reduces and constrains the manifold of the accessible internal states of the system itself. Function is related but not deterministically imposed by the underlying structure. It is quite unlikely that such kind of complexity could be grasped by current approaches focusing on a single organization scale. The natural co-emergence of systems, parts and properties can be adopted as a hypothesis-free conceptual framework to understand functional integration of organisms, including their hierarchical or multilevel patterns, and including the way scientific practice proceeds in approaching such complexity. External, “driving” factors – order parameters and control parameters provided by the surrounding microenvironment – are always required to “push” the components’ fate into well-defined developmental directions. In the negative, we see that in pathological processes such as cancer, organizational fluidity, collapse of levels and dynamic heterogeneity make it hard to even find a level of observation for a stable explanandum to persist in scientific practice. Parts and the system both lose their properties once the system is destabilized. The mesoscopic approach is our proposal to conceptualizing, investigating and explaining in biology. “Mesoscopic way of thinking” is increasingly popular in the epistemology of biology and corresponds to looking for an explanation (and possibly a prediction) where “non-trivial determinism is maximal”: the “most microscopic” level of organization is not necessarily the place where “the most relevant facts do happen.” A fundamental re-thinking of the concept of causality is also due for order parameters to be carefully and correctly identified. In the biological realm, entities have relational properties only, as they depend ontologically on the context they happen to be in. The basic idea of a relational ontology is that, in our inventory of the world, relations are somehow prior to the relata (i.e., entities).

Highlights

  • Specialty section: This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology

  • The fall of reductionist approaches to explanation leaves biology with an unescapable challenge: how to decipher complex systems. This entails a number of very critical questions, the most basic ones being: “What do we mean by ‘complex’?” and “What is the system we should look for?” In complex systems, constraints belong to a higher level that the molecular one and their effect reduces and constrains the manifold of the accessible internal states of the system itself

  • Besides the existence of several excellent operational definitions of complexity elaborating on different notions of the amount of correlation among parts of a system, we sketch three relevant “signatures” of complex systems: 1. A complex system includes a vast number of components, linked each other through dynamic relationships, enabling its representation in terms of a correlation network

Read more

Summary

THE MESOSCOPIC APPROACH

The described situation highlights the importance of identifying an explanatory level that is adequate to the observed phenomenon, by finding relational structures that are able to relate microscopic elements and macroscopic phenomena and parameters. Complex networks (whichever level of biological organization they belong to) allow for a natural convergence between top-down and bottom-up approaches for the simple fact the computation of network invariants encompasses the simultaneous consideration of microscopic (single node), mesoscopic (cluster of nodes) and macroscopic (entire network) features (Csermely et al, 2013) This allows the environmental effects at different levels of definition to be tracked (Masiello et al, 2018). Quantification Analysis (RQA) (Marwan et al, 2007) is a nonlinear signal analysis technique focusing on the search of “nontrivial determinism” that here takes the form of “sojourn points,” i.e., areas of the phase space that are visited by different system trajectories corresponding to “stable configurations” of the temporal organization The onset of such “deterministic islands” in a time course is instrumental to detecting emerging properties of the system as a whole: e.g., the onset of “fatigue” (a global system feature), corresponds to the observation of a drastic increase in determinism of EMG time series (a mesoscopic (muscle fibers) level feature) (Liu et al, 2004)

Conceptualizing the Mesoscopic Way
INVESTIGATING THE MESOSCOPIC WAY
THE DYNAMICS OF MESOSCOPIC PARAMETERS
RELATIONAL ONTOLOGY FOR BIOLOGICAL PROPERTIES
CONCLUSION
AUTHOR CONTRIBUTIONS
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.