Processes 2018, 6,56
simulated cellular and molecular pathways may govern morphogenesis and inflammation
in healthy and disease ileal mucosal dynamics. The system includes individual agents
representing five different cell types, each with multiple independently acting instantiations
at different physical locations. Cell agents have specific behaviors (proliferation, death,
anoikis, etc.) and can influence each other’s decision-making process. Inside each agent,
there is also a simulated signaling network. The system uses algebraic rules to simulate
most of the different components, including a representation of extracellular paracrine
signaling between cells (with the addition of a grid-based partial differential equation to
simulate consequences of diffusion), the dynamics of the simulated intracellular signaling
networks and (using the current values of key intracellular signaling components as a
basis) the likelihood of cell agents exhibiting each possible behavior. By simulating cell
behavior in a virtual world that is analogous to biological microenvironments, the system
can generate measurable phenomena (predictions) at multiple levels. Simulations provide
insight into plausible pathological processes, including crosstalk between morphogenesis
and inflammation and the effects of cell death on tissue health.
Example VII.3: Changes to savanna ecosystems related to climate change and land use
practices are linked to fluctuations in savanna bird community structures, functional
traits, and risk of extinction. Better, more insightful models of explanation are needed
to support policy changes. However, detailed species-specific data for a given ecosystem
are often limited. As a method test case for overcoming such limitations, Scherer et al. [42]
used an agent-oriented approach (implemented in NetLogo) that merged trait-based and
individual-based simulation methods to predict how different bird functional types might
change in response to concurrent alterations to savanna rangeland from a combination of
climate change and land use. The entire simulated ecosystem operates during execution as
a Model Mechanism. Contained within are all of the features listed in Table 1. The system
includes a spatial and stochastically varying set of entities representative of the type
of individual, home range, vegetation, landscape, and environment. Each entity was
characterized by a set of state variables, examples of which include age and reproductive
status, or grasses, shrubs, or trees. Executions advance in uniform steps that map to an
interval of up to 100 years and progress by randomly selecting, calculating and updating
properties that control the spatial composition and configuration of simulated habitat
and animals. Simulation results provided possible explanations for why simulated extinction
risks for simulated larger- bodied insectivores, omnivores and small-bodied species were
impacted differently by changes in simulated shrub-grass ratio and clumping intensity of
shrub patches. Such predictions could prove essential for identifying better policies for
conservation management.
6. Relevant Information, Multiple Sources
Essential relevant information from a variety of sources is needed to establish and enhance
the credibility of improved insights derived from IV–VII. The Figure 1b,c spectra characterize two
important sources. The three Figure 5 spectra identify additional information sources and types.
The Figure 5 spectra are more closely linked to methodology than are the workflow characterizations
in Figures 3–5. Having available sufficient information enables authors and readers to identify
approximate locations on all six spectra, which improves clarity and brings into focus the characteristics
that distinguish among IV–VII.
197
simulated cellular and molecular pathways may govern morphogenesis and inflammation
in healthy and disease ileal mucosal dynamics. The system includes individual agents
representing five different cell types, each with multiple independently acting instantiations
at different physical locations. Cell agents have specific behaviors (proliferation, death,
anoikis, etc.) and can influence each other’s decision-making process. Inside each agent,
there is also a simulated signaling network. The system uses algebraic rules to simulate
most of the different components, including a representation of extracellular paracrine
signaling between cells (with the addition of a grid-based partial differential equation to
simulate consequences of diffusion), the dynamics of the simulated intracellular signaling
networks and (using the current values of key intracellular signaling components as a
basis) the likelihood of cell agents exhibiting each possible behavior. By simulating cell
behavior in a virtual world that is analogous to biological microenvironments, the system
can generate measurable phenomena (predictions) at multiple levels. Simulations provide
insight into plausible pathological processes, including crosstalk between morphogenesis
and inflammation and the effects of cell death on tissue health.
Example VII.3: Changes to savanna ecosystems related to climate change and land use
practices are linked to fluctuations in savanna bird community structures, functional
traits, and risk of extinction. Better, more insightful models of explanation are needed
to support policy changes. However, detailed species-specific data for a given ecosystem
are often limited. As a method test case for overcoming such limitations, Scherer et al. [42]
used an agent-oriented approach (implemented in NetLogo) that merged trait-based and
individual-based simulation methods to predict how different bird functional types might
change in response to concurrent alterations to savanna rangeland from a combination of
climate change and land use. The entire simulated ecosystem operates during execution as
a Model Mechanism. Contained within are all of the features listed in Table 1. The system
includes a spatial and stochastically varying set of entities representative of the type
of individual, home range, vegetation, landscape, and environment. Each entity was
characterized by a set of state variables, examples of which include age and reproductive
status, or grasses, shrubs, or trees. Executions advance in uniform steps that map to an
interval of up to 100 years and progress by randomly selecting, calculating and updating
properties that control the spatial composition and configuration of simulated habitat
and animals. Simulation results provided possible explanations for why simulated extinction
risks for simulated larger- bodied insectivores, omnivores and small-bodied species were
impacted differently by changes in simulated shrub-grass ratio and clumping intensity of
shrub patches. Such predictions could prove essential for identifying better policies for
conservation management.
6. Relevant Information, Multiple Sources
Essential relevant information from a variety of sources is needed to establish and enhance
the credibility of improved insights derived from IV–VII. The Figure 1b,c spectra characterize two
important sources. The three Figure 5 spectra identify additional information sources and types.
The Figure 5 spectra are more closely linked to methodology than are the workflow characterizations
in Figures 3–5. Having available sufficient information enables authors and readers to identify
approximate locations on all six spectra, which improves clarity and brings into focus the characteristics
that distinguish among IV–VII.
197