Complex Systems Biophysics

Introduction

Complex Systems Biophysics () or Complex systems biology () is generally described as a non-reductionist, mathematical theory of emergent living organisms or biosystems in terms of a network, graph or category of integrated interactions between their structural and functional components or subsystems. This is often abbreviated to systems biology in entries that should be described in fact as complex systems biology.

Categorical ontology: theories of existence levels

Definition 1.1   A categorical ontology theory of levels is often defined as the classification of ontology, or theory of existence of items (objects–in the mathematical sense) by means of the mathematical theory of categories into three levels of dynamic systems pertaining to: the physical/chemical level, the biological level, and the psychological level (or human mind). Connections between the three levels of reality and their transformations are represented, respectively, by morphisms/ and natural transformations defined for categories of molecular sets, categories of -systems and organismic supercategories.

From a categorical ontology theory of levels viewpoint, however, the term complex is misplaced because systems with chaos, or chaotic dynamics, are currently defined by physicists as `complex systems', which may have placed a role in the emergence of living systems that are, in fact, super-complex. Therefore, the more appropriate classification of this relatively new area in mathematical or theoretical biology and Biophysics is super-complex systems biology, -complex systems biology, or simply “systems biology”–as a more general approach where the focus may be not on the super-complexity aspects of living systems but on computer modeling of physiological, or functional genomics, integration of physiological flows, signaling pathways or interactomics. However, unlike the case of purely functional -systems theory in abstract relational biology (ARB), complex systems biology (or systems biology) proponents are primarily concerned with the integration of data from a multitude of bioinformatics and genomic/proteomic/post-genomic (primarily structural) data; scientists also aim to study interactomics or metabolomics primarily through computer-based data analysis, and often Bayesian-based attempts at integration. branches of mathematics that find applications in are, for example: computer modeling, colored graphs, graph-theoretical based approaches, biotopology, genetic, metabolic and signaling network theories, Bayesian models, biostatistics, correlation techniques, and less frequently: abstract algebra, group theory, groupoid and category theory modeling of cell-cell interactions and biodynamics.