Group 3: Session 1
1) What are regulatory goals?
1) Minimize false negative and positive decision errors with respect to specific regulatory
decision criteria.
2) Preserve ecosystem services (e.g., pollination, fishing)
3) Specific ecological assessment endpoints - e.g., Populations, functional groups, individual
survival, sublethal effects
What are regulatory issues?
1) Criteria map to specific regulatory laws.
2) Europe v US differences (regulatory language, pesticides, endangered species, testing
methods).
3) Where exposure occurs (on-field versus off-field in Europe)
4) Endpoints selection/data collection
a) Reproductive (#eggs, thinning, viability)
b) Survival endpoint
c) Sublethal effects - how are they assessed (sublethal/reproductive birds exist, honeybees in
process, aquatic growth endpoints, particulars of the experimental design)
5) Valuation of species/ecosystem services
6) Jeopardy for a population/individuals for endangered species? Julann
7) Climate change impacts on fisheries, in general.
8) Pollination services provided by e.g., honeybees.
9) International borders - climate change, migratory species, cross-border pollution transport
10) Mixtures
11) Data submittal packages, lot of talk
Avian - Endangered species act, Migratory bird treaty act 1918,
FIFRA versus Endangered Species Act interpretation
Lead, pesticides, automobile coolant
Specific questions concerning data submittal under FIFRA.
AOPs for mixtures.
Genetic engineering …
TSCA pmns/REACH
Gene array/QSAR
AOP as a means to infer TSCA chemicals.
Brominated flame retardants/halogens
Endocrine disruptors
2) From a regulatory perspective, what are the challenges that can be addressed using
modeling?
1) Endpoints can be modeled
a) Fecundity
b) Survival
c) Sublethal effects -> Population models
2) Exposure concentrations as a function of space and time
3) Indirect effects
4) Risks from genetic engineering/evolution of resistance
5) Size-specific mortality/JulAnn
6) Climate change impact on fisheries
7) Mixture effects based on AOPs, metabolomic maps
8) International - migratory songbirds, climate change, ocean fish, marine mammals
9) Evaluating multiple endpoints at the same time
10) Toxicity extrapolation (between species, endpoints, time spans)
11) UA/SA
3) Where and how in the process could models that integrate across levels of organization
help?
1) Potential toxicity, modes of action based on chemical structure
2) Sublethal (endocrine/sex ratio, fecundity, foraging success, behavior) -> population outcomes
3) Behavior -> Exposure
4) Evolution of resistance
5) Tritrophic interactions
6) Bioaccumulation
Group 3: Session 2
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
- what are predictive system models? mechanistic, emergence; some phenomenological
representation is inevitable (even the rates at the molecular pathway models are phen.). I think
Irv’s example describing their need for more information on how vitellogenin is incorporated
into cells is a good example of how what was phenomenological model required more
mechanistic information to adequately parameterize the process.
- stochastic vs. deterministic models - stochastic models will end up with statistical predictions,
i.e. distributions
- is there a difference between stochasticities at the molecular and higher level - there is a lot of
stochasticity at the molecular level, but not all of it - question is how much to include in the
model? is it a question of the time scale - e.g. not looking to each tiny timestep, but look at the
higher level that is of interest - maybe not so relevant if we are talking about ecosystem services
- at which levels should we then worry about stoch.? only demographic and environmental? maybe we should go a level below (why?)
NOT TOXINS, BUT TOXICANTS or CONTAMINANTS!!! - pollutants, contaminants,
toxicants (what are EDCs?)
- can we learn from multiscale models in physics or applied math - epidemiology and
immunology (cross scale models in epidemiology) - within-host, transmissions, dynamics - don’t
really go to more than 2 scales
- are there any techniques we could apply from - age-dependent branching theory, how the
reproductive output changes over lifetime - sophisticated matrix model formulation
- molec. pathways models - big mistakes with arbitrary parameters, need to put in realistic,
biological parameters
- a question on the relevant level, then figure out what you need to get that - e.g. a detailed mol.
pathway can be simplified - methods to simplify the low level complexity that will reduce the
computational intensity
- how to select how many levels of hierarchy you need to model phenomena - different
techniques, POM, AIC - this will also depend on extrapolation (do we want it or not?)
- cheap screening levels are at a molecular level, need techniques to translate these into more
relevant level - this is the reason why phenomenological approaches might not work, but still
maybe room to manipulate this, e.g. if there is no effect, then we don’t need higher level
representations
- if there is a % change at the molecular level, what does it mean and how to translate it? building models or looking at tox tests relevant to the endpoint - you need to have resources for
that! - need for a framework that would give as an estimate of what does an effect at the
molecular level mean for more relevant endpoints - reproduction, growth etc.
- systems biology approaches usually lack biological processes - no feedbacks included - for a
bottom-up approach, you need feedbacks from the top-down too
- sometimes changes in behaviour cannot be detected only by molecular levels - metals change
behaviour before effects are detected at the suborganismal level - BU poses constraints to the
lower levels
- both approaches are needed - TD and BU!
- the predictive systems models framework should include both directions, feedbacks are very
important!
- every step in the AOP is a model in itself, and each of the steps need to be verified
- focusing on single levels of biological organisation is the limitation of our understanding of
biological systems
- data?
- in this context we have to remember this is for RA, where protection goals, in theory, are
defined - this can limit the number of ways and approached we could go
- how important are these feedbacks from the environment to the population - they can be very
important, e.g. reduction of bioavailability with higher amounts of resources - biological entities
are changing the environment
- to what level are we trying to preserve genetic diversity? organisms can dapat to toxicants - we
can ignore it
- are there chemical initiating events that AOP does not
- sometimes we can choose how low to go in modelling - if there is narcosis, then maybe we
don’t need a detailed molecular model
- how do we optimize the level of complexity we need for a RA question?
- we need solid theory before we answer this question
Group 3: Session 3
2. 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.
3. 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?
4. 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?
- use POM to see what type of information is needed to reproduce/predict/validate trends
- use information from different levels (for POMing) to decide if the model is making good
enough predictions
- not catching model behaviour tells you something about the model and about the system
- model selection exercises - have a number of models to test against a certain data set, way to
approach the question of the sweet-sour spot
- how to know if the data is noisy and there is error in understanding or ignoring some factor vs.
absolute trust in data and trying to understand why the model does not reproduce
- can use MLA to assess which model reproduces some pattern in data best, i.e. which
parameters reproduce the data - you can get relative likelihood
- used in turbulence studies
- PO model selection
- often for a given question, need to mix model types - especially if modelling different scales
- you want to chose the model that minimizes the output error
- model choice and complexity is going to depend very much on the question - maybe DEBis not
the most relevant model for swarming of bacteria in the Petri dish - however, models need to be
consistent with the conservation of mass and energy (if some sorto for energy transfer is being
modeled)
- DEB discussion - a lot of behaviour is beyond DEB, e.g. migration, movement, behaviour however for toxicological questions, energy budgets are necessary to model
- predictive systems model framework - are we really aiming to link dynamically all these levels,
or inform in a simpler way higher level models, e.g. molecular pathways informing DEB and
above - however, what about novel conditions and feedbacks from the top?
- no matter which level, we have to ground truth it with data
- challenge to find data for higher level organisms to parameterize and validate - however, maybe
not that important as songbrids and such are embellishments more than ecosystem service
providers
- behaviours are very sensitive to exposure, but is not routinely monitored, collected
- limited number of organisms that are tested in mesocosms
- population studies - can they be used for modeling purposes? the way that the current
population studies are conducted and the type of data they gather cannot be used for pop.
modelling
- density-dependence is often lifestage specific
- is it better to have crude field data (maybe not as informative for a model) OR a detailed model
that cannot be really parameterized or validated?
- sociological aspect of getting a new modeling paradigm - buy in from the industry and agencies
and be convincing that using models in this context is useful
- convincing the industry to develop models - important to stress the cost-effectiveness - can be
done with the use of control theory
- global sensitivity analysis to reduce the number of parameters
- how to approach the modeling of these different scales? do we pick where we want to be and
pick the lower ones or we develop algorithms that connect everything?
- when and why to use lower levels in the modeling framework - it is a question of money, e.g.
cheap bioassays can only be relevant if we extrapolate from the mol. pathways to ind, pop etc.
Take home:
Model selection - use e.g. MLA to pick the model that reproduces the data we have
Challenge: Model evaludation - crude field data not always useful, the type of data we get is
usually not very useful for the real world - Almost impossible to get behavioural data for species
or systems of interest
How to make the modeling cost effective for it to be better accepted in the regulatory and
industry framework - communication btw. model developers