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Cellular Computing$
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Martyn Amos

Print publication date: 2004

Print ISBN-13: 9780195155396

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195155396.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 25 February 2021

Proteins and Information Processing

Proteins and Information Processing

Chapter:
2 Proteins and Information Processing
Source:
Cellular Computing
Author(s):

Ray Paton

Michael Fisher

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195155396.003.0006

This chapter reviews and briefly discusses a set of computational methods that can assist biologists when seeking to model interactions between components in spatially heterogeneous and changing environments. The approach can be applied to many scales of biological organization, and the illustrations we have selected apply to networks of interaction among proteins. Biological populations, whether ecological or molecular, homogeneous or heterogeneous, moving or stationary, can be modeled at different scales of organization. Some models can be constructed that focus on factors or patterns that characterize the population as a whole such as population size, average mass or length, and so forth. Other models focus on values associated with individuals such as age, energy reserve, and spatial association with other individuals. A distinction can be made between population (p-state) and individual (i-state) variables and models. We seek to develop a general approach to modeling biosystems based on individuals. Individual-based models (IBMs) typically consist of an environment or framework in which interactions occur and a number of individuals defined in terms of their behaviors (such as procedural rules) and characteristic parameters. The actions of each individual can be tracked through time. IBMs represent heterogeneous systems as sets of nonidentical, discrete, interacting, autonomous, adaptive agents (e.g., Devine and Paton [5]). They have been used to model the dynamics of population interaction over time in ecological systems, but IBMs can equally be applied to biological systems at other levels of scale. The IBM approach can be used to simulate the emergence of global information processing from individual, local interactions in a population of agents. When it is sensible and appropriate, we seek to incorporate an ecological and social view of inter-agent interactions to all scales of the biological hierarch. In this case we distinguish among individual “devices” (agents), networks (societies or communities), and networks in habitats (ecologies). In that they are able to interact with other molecules in subtle and varied ways, we may say that many proteins have social abilities . This social dimension to protein agency also presupposes that proteins have an underlying ecology in that they interact with other molecules including substrates, products, regulators, cytoskeleton, membranes, water, and local electric fields.

Keywords:   Agent, Bacteriorhodopsin, Cascade, Effector, Fatty acid, Glue, Hormone, Inhibitor, Membrane

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