# 5. Complexity and self-organization in biology and physiology

Workshop organized by: P. Paradisi and R. Metzler

The investigation of living biological systems is probably the most important field of applications of the “complexity paradigm”, which essentially denotes the emergence of self-organized structures in multi-component systems. A complex system is not only composed of many units, but the set of strong nonlinear interactions linking these same units have a cooperative nature, thus displaying the crucial property of triggering self-organization.

In the case of living systems, self-organized structures can be identified and modelled not only by means of geometrical and structural concepts (e.g., pattern formation, network topology) but also through the emergence of a specific function in the living organism, such as the response to a environmental stimulus (e.g., cellular homeostasis, event related potential in the brain). This highlights the metastability of self-organized structures, which is an important feature of complex systems allowing to reach a optimal compromise between the efficiency of response to environmental changes and the need of maintaining low levels of disorder (entropy).

Over the past few decades, the increasing power of computer technology and the increased spatial and temporal accuracy of novel instruments has dramatically increased data collection and the ability of data manipulation. This aspect characterizes not only current research in biology and physiology, but also many other research fields (e.g., sociology, economy) and is known as the “Big Data” problem. Important examples of Big Data are given by complex biological networks, usually denoted as “-omics” data: genomics, transcriptomics, proteomics, metabolomics, connectomics (e.g., neural systems in vitro or in vivo, the brain).

The availability of a large amount of very accurate data does not actually correspond to a real understanding of the problem under study (e.g., the capability of predicting relationships among different variables), as compact representations of the data are needed. Consequently, the main focus is on the extraction of useful synthetic information (regularities, patterns or rules) from large data sets.

In order to face with the need of reducing the system and data complexity to a small set of statistical (complexity) indicators, novel tools of statistical data analysis are being developed (e.g., data mining,

bioinformatics, network analysis, signal processing, pattern recognition). Associated to this aspect there is also the need for modeling approaches being able to interpret the experimental data in terms of this small set of indicators (e.g., stochastic models).

In this workshop we encourage the submission of works concerning the investigation, either theoretical, methodological, or experimental, of complexity features in biological systems, thus associated with the emergence of self-organization, also in the form of specific functions emerging in the biological system.

The range of applications spans from the molecular and cellular levels (e.g., anomalous transport and crowding in the cell, bacteria foraging strategies) to the physiological level, i.e., tissues and organs (e.g., brain dynamics, heart beating).

Methods and models may include (but are not limited to): multi-scale modelling, scaling analysis, fractals and multi-fractals, long-range memory (non-Markovian processes, Lévy flights and walks), anomalous diffusion, fractional models, critical phenomena, Self-Organized Criticality (SOC), stochastic processes (e.g., Master equations, Gaussian and non-Gaussian noise), nonlinear dynamical systems, complex network analysis, data mining, bioinformatics, non-extensive entropy.