The virtual cell-a candidate co-ordinator for 'middle-out' modelling of biological systems

TY - JOUR

T1 - The virtual cell-a candidate co-ordinator for 'middle-out' modelling of biological systems

AU - Walker, Dawn C.

AU - Southgate, Jennifer

PY - 2009/7

Y1 - 2009/7

N2 - Understanding the functioning of biological systems depends on tackling complexity spanning spatial scales from genome to organ to whole organism. The basic unit of life, the cell, acts to co-ordinate information received across these scales and processes the myriad of signals to produce an integrated cellular response. Cells interact with and respond to other cells through direct or indirect contact, resulting in emergent structure and function of tissues and organs. Systems biology has traditionally used either a 'top-down' or 'bottom-up' approach. However, neither approach takes account of heterogeneity or 'noise', which is an inherent feature of cellular behaviour and may have significant impact on system level behaviour. We review existing approaches to modelling that use cellular automata or agent-based methodologies, where individual cells are represented as equivalent virtual entities governed by simple rules. These paradigms allow a direct one-to-one mapping between real and virtual cells that can be exploited in terms of acquiring parameters from experimental systems, or for model validation. Such models are inherently extensible and can be integrated with other modelling modalities (e.g. partial or ordinary differential equations) to model multi-scale phenomena. Alternatively, hierarchical agent models may be used to explore the functions of biological systems across temporal and spatial scales. This review examines individual-based models and the application of the paradigm to explore multi-scale phenomena in biology. In so doing, it demonstrates how cellular-based models have begun to play an important role in the development of 'middle-out' models, but with considerable potential for future development.

AB - Understanding the functioning of biological systems depends on tackling complexity spanning spatial scales from genome to organ to whole organism. The basic unit of life, the cell, acts to co-ordinate information received across these scales and processes the myriad of signals to produce an integrated cellular response. Cells interact with and respond to other cells through direct or indirect contact, resulting in emergent structure and function of tissues and organs. Systems biology has traditionally used either a 'top-down' or 'bottom-up' approach. However, neither approach takes account of heterogeneity or 'noise', which is an inherent feature of cellular behaviour and may have significant impact on system level behaviour. We review existing approaches to modelling that use cellular automata or agent-based methodologies, where individual cells are represented as equivalent virtual entities governed by simple rules. These paradigms allow a direct one-to-one mapping between real and virtual cells that can be exploited in terms of acquiring parameters from experimental systems, or for model validation. Such models are inherently extensible and can be integrated with other modelling modalities (e.g. partial or ordinary differential equations) to model multi-scale phenomena. Alternatively, hierarchical agent models may be used to explore the functions of biological systems across temporal and spatial scales. This review examines individual-based models and the application of the paradigm to explore multi-scale phenomena in biology. In so doing, it demonstrates how cellular-based models have begun to play an important role in the development of 'middle-out' models, but with considerable potential for future development.

KW - cellular automata

KW - cell biology

KW - computational modelling

KW - emergent behaviour

KW - middle-out modelling

KW - software agent

KW - systems biology

KW - AGENT-BASED SIMULATION

KW - MATHEMATICAL-MODEL

KW - TUMOR-GROWTH

KW - MULTICELLULAR PATTERNS

KW - COLLAGEN DEPOSITION

KW - COMPLEX AUTOMATA

KW - CLINICAL-TRIALS

KW - DYNAMICS

KW - CANCER

KW - BEHAVIOR

UR - http://www.scopus.com/inward/record.url?scp=67449089661&partnerID=8YFLogxK

U2 - 10.1093/bib/bbp010

DO - 10.1093/bib/bbp010

M3 - Article

C2 - 19293250

SN - 1477-4054

VL - 10

SP - 450

EP - 461

JO - Briefings in bioinformatics

JF - Briefings in bioinformatics

IS - 4

ER -