Chapter 7
Agents in Biology
7.1
Example Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.1
Molecular Systems Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.2
Tissue and Organ Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
7.1.3
Ecological Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
7.1.4
Industrial Applications of Agent-Based Modeling with
FLAME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.2
Modeling Epithelial Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
7.2.1
Merging with Other Toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.3
Modeling Drosophila Embryo Development . . . . . . . . . . . . . . . . . . . . . 187
7.3.1
Stochastic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7.3.2
Converting to an Agent-Based Model . . . . . . . . . . . . . . . . . . . 188
7.3.3
Find Optimum Model Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 196
7.4
Output Files for Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
7.5
Modeling Pharaoh’s Ants (Monomorium pharaonis) . . . . . . . . . . . . 202
7.6
Model Drug Delivery for Cancer Treatment . . . . . . . . . . . . . . . . . . . . . 224
7.6.1
Using Multiple Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Biological systems are often a collection of multiple complex systems. These
systems range from small bacterial models or large cell tissue models and
their behavior with other organs. Complex systems display adaptive behavior
to continually changing environment, coping to survive. These systems are extremely robust. Studying these systems is extremely cumbersome, due to their
complexity, size and capability of collecting data at minute scales. Simulation,
however, allows biologists to conceptually visualize how these systems function and what factors affect them. Having described these system as a series of
steps in models, the biologists can then test the data produced through simulation with real data, inherently matching their predictions and understanding
to the real systems.
Describing a biological system virtually thus involves the following:
• Make design decisions: Identifying the system functions of the model being simulated. Following agile methods, this process involved repeated
conversations between domain experts (i.e. biologists) and computer scientists (i.e. programmers).
• List agents and functions: Identify agent states and the order in which
they function during one iteration in the simulation.
175
Agents in Biology
7.1
Example Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.1
Molecular Systems Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.2
Tissue and Organ Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
7.1.3
Ecological Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
7.1.4
Industrial Applications of Agent-Based Modeling with
FLAME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.2
Modeling Epithelial Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
7.2.1
Merging with Other Toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.3
Modeling Drosophila Embryo Development . . . . . . . . . . . . . . . . . . . . . 187
7.3.1
Stochastic Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7.3.2
Converting to an Agent-Based Model . . . . . . . . . . . . . . . . . . . 188
7.3.3
Find Optimum Model Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 196
7.4
Output Files for Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
7.5
Modeling Pharaoh’s Ants (Monomorium pharaonis) . . . . . . . . . . . . 202
7.6
Model Drug Delivery for Cancer Treatment . . . . . . . . . . . . . . . . . . . . . 224
7.6.1
Using Multiple Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Biological systems are often a collection of multiple complex systems. These
systems range from small bacterial models or large cell tissue models and
their behavior with other organs. Complex systems display adaptive behavior
to continually changing environment, coping to survive. These systems are extremely robust. Studying these systems is extremely cumbersome, due to their
complexity, size and capability of collecting data at minute scales. Simulation,
however, allows biologists to conceptually visualize how these systems function and what factors affect them. Having described these system as a series of
steps in models, the biologists can then test the data produced through simulation with real data, inherently matching their predictions and understanding
to the real systems.
Describing a biological system virtually thus involves the following:
• Make design decisions: Identifying the system functions of the model being simulated. Following agile methods, this process involved repeated
conversations between domain experts (i.e. biologists) and computer scientists (i.e. programmers).
• List agents and functions: Identify agent states and the order in which
they function during one iteration in the simulation.
175