Chapter 15
Multi-agent Simulations of Population Behavior:
A Promising Tool for Systems Biology
Alfredo Colosimo
Abstract
This contribution reports on the simulation of some dynamical events observed in the collective behavior of
different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated
brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS)
paradigm as incorporated in the NetLogo™interpreter, is a direct consequence of the cornerstone that
simple, individual actions within a population of interacting agents often give rise to complex, collective
behavior.
The discussion will mainly focus on the emergence and spreading of synchronous activities within the
population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A
relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network
models. In such a general framework some recent applications taken from the direct experience of the
author, and exploring the activation patterns characteristic of specific brain functional states, are described,
and their impact on the Systems-Biology universe underlined.
KeywordsMulti agent systems, Complex adaptive systems, Dynamic simulations, Ising models,
Brain networks1 Introduction
1.1 Generalities Little doubt remains that the emergence of complexity in most
natural phenomena [1, 2] is not an exception but a rule imposing,
as a corollary, a severe limit to their quantitative prediction on the
long term: hence, asystemicperspective relying upon statistical [3]
and simulation studies [4, 5] could open new avenues in the field.
Moreover, any reliable approach to the simulation of population
dynamics stemming from the interactions among individuals as well
as produced by environmental factors, in the last fifty years should
be most welcome.
The left panel of Fig.1 (modified from [6]) shows, in the
evolving landscape of modern informatics, the relatively recent
Mariano Bizzarri (ed.),Systems Biology, Methods in Molecular Biology, vol. 1702,
https://doi.org/10.1007/978-1-4939-7456-6_15,©Springer Science+Business Media LLC 2018
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