Group 1: Session 1
Q1: Regulatory Goals & Issues
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Depends on type of assessment, i.e., site-specific vs. Widespread regulatory (e.g.,
pesticides).
Defined by legal requirements program falls under.
Interest and focus on developing models in general frameworks that can benefit both
diverse methods and apply to multiple species.
Regulators overall aim to be protective: transparent; streamlined; clearly communicated;
reduce/low uncertainty; validated process
Communication with risk managers in right way is key
o Peer reviewed, accepted guidelines and transparent use of them
o Provide reasoning, methods, and why - Translate to audience of stakeholders
o Reduce and better define uncertainties in assessment
Note: indirect effects and ecosystem services likely not adequately addressed; modeling
could improve that.
Fitting modeling into existing process is a challenge; validated and ‘trusted’ documented
models provide the best chance of being incorporated.
Key is to bring forward a framework and a framework for validation
General use tools & framework that can be tweaked vs. different models for site-specific
issues more likely to be incorporated.
Q2: Challenges that could be addressed?
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Models may help address needs that aren’t defined yet (see-ahead sensitivities)
Models can define question being asked (most sensitive processes/links)
Models can inform data collection so we get data that are useful to advancing the science.
Better than current process re: dealing with mixtures; effects-based monitoring; e.g.,
endangered species – look at model and identify type of data that would be most helpful
(vice versa).
Identify key parameters.
Models are helping regulators refine their understanding of what they want to protect and
how best to do that.
Research needs.
Q3: Where and how in the process could models that integrate across levels of organization
help?
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Screening multiple chemicals to focus on.
Reducing need for animal testing; reduce false positives; increase available effects data in
cost-effective manner (QSAR)
Chemical grouping behaviors?
More educated extrapolation between species – molecular relationships may be more
translatable.
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Higher-level models should inform outputs of lower-level models – informing each
other?
Model focus – physiological process in normal state can look at resilience in parameter;
how much room is there for process impact (can reduce animal use).
Link microbial interactions with molecular observations because the cell is the organism;
key community
Effects-based monitoring – models could help e.g., biomarkers.
Q4: Where would models not be useful at this point?
Can’t ask questions that model can’t do.
Don’t waste time modeling if observed effects are adverse and obvious
Cost/benefit analysis and risk/benefit analysis – done by economists, separate from risk assessors
What are the protection goals? Depends on amount and type of refinements/communications/etc.
Group 1: Session 2
Molecule-to-organism modeling
1. What are the advantages and limitations of different classes of predictive systems models for
linking responses to toxic chemicals across different levels of biological organization - from
molecular to ecological levels?
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Analytical (process-based) advantages over statistical approaches. Incorporation of
feedbacks is one of them.
Using in vitro data to parameterize process-based models requires more integration
Defining links between model parameters (e.g., DEB) with gene expressions would be an
important step, but results may not be very useful. Broad gene expression responses
related to processes such as metabolic rates.
ODEs as the main mathematical approach to model processes (numerical simulations)
Mix of linear and non-linear rate equations
Balancing complexity
Do we need to model systems details (for scaling up to populations)? Yes, to understand
how different chemicals affect different processes. But it is data limited. Assumptions
must be clear.
Process-based allows transfer of information between species
Problems with over-parameterization
DEB as a framework for a common model structure across groups of animals
Common structure versus system-specific structure: the first is relevant for linking to
higher levels of organization
Homeostasis as a constraint justifying simpler, more general, model structures
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Incorporating trade-offs between processes (and also feedbacks) is facilitated by common
model structures
Allocation decisions is still complicated to model (e.g., DEB for plants)
It is important to show that AOP modeling adds some value to ERA (which has relevance
for justifying the relevance of process-based approaches in general)
EPA recognizes the value of process-based modeling approaches, but still rely on in vitro
tests (particularly for humans).
However, modeling is already being used to screen for potential chemicals (e.g., decision
tree approaches applied to identify relevant estrogen-binding components)
Connection between DEB and more detailed physiological models needs careful
consideration
Much of discussion of molecules-to-organism scaling also hinges on the organisms-toecosystem problems
Key-point: Contrast between GENERAL FRAMEWORK (e.g. DEB) versus SYSTEMSPECIFIC MODELS
2. What are the most important knowledge gaps that act as barriers to developing an integrated
predictive systems modeling framework to assess risks of exposure to toxic chemicals and
how can they be filled?
3. What are the major scientific challenges that need to be addressed in order for the models to be
used in practice for ecological risk assessment?
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Lack of knowledge for several systems (e.g., invertebrates)
Lack of mechanistic knowledge of the links between gene expression and physiological
processes
Finding genes that mater for different processes is important
But could be ignored depending on modeling objectives (e.g., knowing protein activity
and ignoring details on underlying gene expression)
Complexity issues again
Collaboration between math-oriented people (e.g., system engineers) and biologists helps
balancing complexity towards optimal levels
Analysis of model properties can indicate the effective dimensionality (complexity) of
the system being studied.
What comes first? Model or data? Models can be useful to identify potential key events
(upstream in the AOP framework) from known initiating events (e.g., death of brain cells
and behavior), and to help defining potential data to be collected
Adaptive model selection based on initiating events and potential key events (instead of
predetermined model choice)
Developing a library of models
Identifying rate-limiting processes to narrow down complexity
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Comparing models of different complexity using objective techniques (e.g., statistical,
numerical) would be really valuable
Key-point: FINDING INTERMEDIATE (OPTIMAL) COMPLEXITY
Group 1: Session 3
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Link between individuals and populations is better defined than between molecules and
individuals
Link AOP to DEB models?
We should use needs of ecological models to inform measurements at
molecular/biochemical level.
AOP framework – idea is to move beyond individual, but thus far work appears to be at
lower levels.
Can biomarkers fit into the framework?
Use of standard models at certain levels
Exposure-suborganismal, quantifying effects need to include higher-level effects; avoid
false negatives
Use high throughput models to screen; work backwards and forwards; prioritize based on
potential
Need to define what you are going to protect from a regulatory context (e.g., survival,
growth, reproduction); ultimately, how does that translate to populations.
Can you use simulations to predict effects at higher levels without doing experiments?
Depends on variability of system and ability to characterize; applicability of standard
guidelines and validated models; ability to characterize abundance and population size is
limited in many cases – how to predict effects, also for different life stages?
No single best population endpoint
What can you measure to calibrate model?
Suite of potential parameters – who values what?
Can you work backwards from ecosystem services to population level endpoint that is
relevant?
Connecting ecosystem services
Connecting species specific data to data for sensitive species/populations
Need set of sub-models that can be applied for site-specific and chemical-specific
assessments, can mix and match to fit.
Translating correlations to mechanistic explanation (e.g., vitellogenin and fecundity)
Try to use field data to validate effects