TY - CHAP
T1 - Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host
AU - Saula, Aminat Yetunde
AU - Rowlatt, Christopher
AU - Bowness, Ruth
PY - 2024/7/2
Y1 - 2024/7/2
N2 - To model complex systems, individual-based models (IBMs), sometimes called "agent-based models" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.
AB - To model complex systems, individual-based models (IBMs), sometimes called "agent-based models" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.
KW - Agent-based modeling
KW - Antibiotic resistance
KW - Differential equations
KW - Mathematical modeling
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85197652688&partnerID=8YFLogxK
U2 - 10.1007/978-1-0716-3981-8_10
DO - 10.1007/978-1-0716-3981-8_10
M3 - Other chapter contribution
C2 - 38949704
AN - SCOPUS:85197652688
SN - 9781071639801
T3 - Methods in molecular biology (Clifton, N.J.)
SP - 93
EP - 108
BT - Antibiotic Resistance Protocols
A2 - Gillespie, S. H.
PB - Humana Press
CY - New York, U. S. A.
ER -