1. We should use some of the text material from the proposal in the introductory (and perhaps
other) parts of the paper
2. Major intellectual challenges facing organismal biology (of adaptive processes)
Spatio-temporal resolution of analysis of complex organisms
Organisms are highly complex integrated systems. The resolution we need regarding the scales of
temporal and spatial environmental variation within organism and populations may be enormous to
comprehensively answer physiological questions. Is the scope of resolution needed attainable given the
available resources? On the other hand, collecting comprehensive physiological, behavioral,
morphological datasets (phenotypes) needed for achieving high spatio-temporal resolution requires
novel high-throughput approaches ("spatial-temporal phenotyping"). There are obvious limits on
resources needed for characterizing phenotypes with high spatial and temporal resolution and precision
just as there are limits on the diversity of species that can be studied in great depth. How far can we
stretch these limits? G X E: Changes through time and individual experience throughout life history
(development is not confined to just embryos or juveniles) and phenotypic plasticity need to be
accounted for. Critical and sensitive periods during life history need to be identified. Study stem cells in
from an adaptive perspective, study the evolution of learning, social insects castes etc.
[Acute, chronic and long-term responses acclimation and evol. Responses, tissue-/ cellular resolution]
Individual variation in physiological, behavioral, morphological traits
The role of individual variation in physiological, behavioral, morphological traits (phenotypes) needs to
be emphasized and embraced. This will require more resources. The handling of errors, individuality,
and genetic diversity within populations may have to be altered. Technical challenges may be faced
when assessing individual variation and more appropriate statistical tools may be needed. A common
problem with whole organism models is that they often characterize normative states instead of
accounting for inter-individual variation, which should be incorporated in the models.
Emphasis on variation and embracing power of variation to understand capacity for adaption, plasticity,
response to change, resiliency. [need tools and approaches to determine how much of the observed
biological variation is relevant to the problem at hand.]
Finding an optimal combination of field and laboratory studies
To study mechanisms of adaptive processes (tolerance, resilience, homeostasis, allostasis) we need to
integrate field and laboratory settings, consider realistic combinations of multiple environmental
stressors / parameters and be able to isolate the relative contribution of each environmental parameter
to a particular trait.
Problems associated with field studies that often need to be dealt with include access to a sufficient
number of animals, limitations in how environmental data are collected (new sensory nanotechnologies
need to be developed for these studies), and synchronized collection of environmental and biological
data. Capturing/monitoring the totality of an individual’s current environment represents a significant
challenge.
Micro scale sensing systems-a priority.
Mini sensing system- can do birds heart rate during behaviour but we want to do a single cell neuronal
recording or reporting. Optogenetics-develop conserved molecules. Imagers for small animals (there are
rat imagers-adapt for a bird). Can put animal in a scanner and present it with images, smell, olfaction
and record brain images. This may also help us understand parts of the brain that could be used in
‘omics’ analyses. Lab-field interface-take the lab to the animal. Adapt existing technologies to do this. .
Common problems with lab experiments pertain to the representation of meaningful conditions that
mimic at least some important aspect of a natural field setting and map reasonably well onto the real
world. “Plastic shoebox cages”
Let your phenotype lead the way (e.g. a behaviour)
Start with different phenotypes (stickleback story, bird courtship displays, foraging types) or use artificial
or natural selection experiments to select for particular traits on similar genetic backgrounds. Use
candidate gene and candidate pathway approach. Complex phenotypes with multiple traits may mask
much of the essence and make it difficult to get to the core of the adaptation. For instance, every gene
expression array you harbors information requiring to learn a new aspect of biology.
- capacity for largescale, rapid phenotyping of physiological traits and environmental factors is
insufficient
- Lab and field phenotyping approaches need to be better integrated with fine scale environmental data
collection and microsensor development to enable such data collection
Start with different phenotypes (stickleback story, bird courtship displays, foraging types) or use artificial
or natural selection experiments to generate different groups. Use candidate genes and pathways.
Complex phenotypes with multiple traits-how to get to the core of the adaptation.
- 5. STRATEGIC CORE ISSUES
- "PHENOTYPING" technology
- repeated measurements in animals in the natural environements
Common model system or unifying theme?
Identify a theme. For instance plant people have identified nitrogen fixation as a priority and followed it
up with ‘omics etc. Examples for animals: overwintering (hibernation, diapause), migration and
navigation, social behaviour, speciation and climate change etc.
Recognize power of model systems: interdisciplinary teams should discuss and then choose the best
model system (ideally some component field-based) to solve the problem, and that allows use of
intellectual, cyber, remote sensing, tacking, engineering and other tools etc. to address the problem
under study and that can be used by wider organismal biology community that might not work on the
model system. This will allow leverage of the large-scale problem solving approaches for the subset of
projects that can be supported in a large scale to indirectly help advance other problems/species.
Physiological model systems need to be better enabled
Changing the culture of physiology
Emerging models for physiology (controversy – do we need to push particular model
systems or rather focus on common questions/ themes/ problems):
For different physiology processes, environmental drivers?
Connections to developmental, morphological models
Comparative perspective
Leveraging existing models
The role of omics for the study of organism x environment interactions
High-throughput omics approaches (horizontal platforms) should be applied to (vertical) problems that
make sense in the spirit of comparative biology. Conceptually, the many-to-one and one-to-many
problem (multiple phenotypes from one genotype; many genes give the same phenotype) needs to be
addressed. Network theory needs to be applied to organismal responses, incorporating different actions
of conserved networks, and combinatorial design of signaling networks taking into account threshold
effects. We must take care that the technology does not drive the research questions but the other way
around. Omics approaches focusing on complex organism x environment interactions will be highly
interdisciplinary by nature. It may be very difficult to address a problem in this area using omics
approaches unless you create a cluster group of individuals, which work together to target a single
theme (biological problem/ phenomenon). A cluster group could be comprised of geneticist,
physiologist, computational, neuro, behaviour, omics, etc. But the research climate has to support this.
Mechanisms for collaboration need to benefit all.
New experimental designs are needed to better infer the causality of organismal traits (behavior,
physiology). Different types of networks (gene x environment interaction, neural circuits, epigenetics)
need to be mapped onto each other. Genomes of more than just one individual are needed for studies
of adaptation. Know-how on knock downs, shifting landscapes, machines as models is needed.
Computational approach. Huge data sets have been generated but context can be lost-where when and
why-shared protocols needed. Recently there is more opportunities for sequencing the genome or
transcriptome for a defined problem and the cost is becoming less and less! Sample across space and
time. Shared data good for bench marking purposes and sometimes comparison across species can work
(foraging flies and bees) but often not.
‘Omics’ analyses are correlational. When do you need to show cause and effect? Depends on the
question you are asking. How can causation be demonstrated?
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Getting core parties together (Glue grant)
– Organismal biologists
– Technologists (‘-omics’)
– Systems biologists (analysis)
Can’t sell a one-behavior paradigm for all species
Comparative approach: need many genomes, transcriptomes
Complex experimental designs needed
– >1 environment
– Individual variation
– Better statistical tools
• Using the environment to interrogate genes and networks
Future directions:
• Dominance of classical Darwinian evolution
• Noninvasive RNA reporting
• Optigenomics (brainbow)
• Interrogating multiple genes, networks
• Synthesizing multiple sensory inputs
– (chemical, visual, etc)
Using the environment to interrogate genes and networks
Future directions:
• Dominance of classical Darwinian evolution
• Noninvasive RNA reporting
• Optigenomics (brainbow)
• Interrogating multiple genes, networks
A theoretical framework / theory for organismal biology
Such a framework is currently missing. Can mathematical models that generate testable hypothesis
provide a general framework for organismal adaptation biology? It seems that a unifying (even though
simplified) framework would enable community building and more efficient integration of the major
elements of O x E interactions (i.e. the environment  development, sensory systems, physiology,
morphology, behavior  fitness  evolution). Evolutionary adaptation based on natural selection) is a
major driver of evolution (although neutral genetic drift may also play a role) and it is therefore critical
to learn how organisms adapt to environmental change, how very complex neural circuits and cellular
signaling networks evolve to coordinate organismal responses, and how these networks behave and are
modified under intense selection pressure.
A theory of organismal scale biology would support a better understanding of organism x environmental
processes. One model is hierarchy theory aimed at partitioning out what phenomenon belongs at what
level. The objective of such a theory of organismal scale biology would include:
- being able to address general (over-arching) questions
- combining models that address different specific aspects of those questions
- some metric of fitness is always a key
- a submodel that relates phenotypes to fitness integrated across the lifecyle
- mapping from environment to fitness/phenotype
- phenotype as a function of environment
- can we fill in the black boxes?
Such a theory or model could be used in many disciplines concerned with organismal responses to the
environment, for instance:
- Development:
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body size and proportions, interactions in growth,and development of organs at an
organismal level.
how does it resist and respond to environmental change
robustness
e.g could connect higher level theories of body size (e.g. termperature-size rule) with
development.
e.g. common framework for comparative studies.
- Eco/Evolutionary physiology:
 explain degree of covariance between early and adult traits.
 Bridge the conceptual gap between cell/developmental process and higher levels of
organization.
 An old example of this was the effort to build physiological models of humans.
-Behavior
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Top-down models of learning.
Models of how ability to learn is influenced by environment and early experience.
Challenges
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What sources of information do we need – do we need the omics? The model/theory could
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help identify key pieces of data and provide a framework for data analysis.
Data should move beyond omics and include dynamic high level phenotyping.
Researchers at working at lower levels of biological organization may not see the utility of
the theory. How do you convince reductionist biologists to care about integrative theories?
Integrative research is, by its very nature, not cutting edge? – requires disciplinary
advances that it can integrate. It is derivative. This is a problem for funding bodies that are
concerned only with cutting edge research.
Tools for analyzing omics data may be limiting.
Solutions
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A workshop on ‘A theoretical framework for Organismal Biology of Adaptation’
Invite developmental, physiological and behavioral modelers: ‘what is a corresponding
useful model in your area of biology’
Identify common themes and approaches.
Outcome
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Development of a framework for generating hypothesis and analyzing and interpreting data
from multiple levels of biological organization.
A common language for integration.
Technical
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o New ‘omics’ hard to transfer from model to nonmodel
Epigenetics
o New theory linking evolution and epigenetics needed
o Mapping epigenetics to the genome
Behavior genes can be complex
o Pleiotropic, complex, background effects, sites of action
Databasing
o Lack of resources for phenotypes
o Lack of culture in the community
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Cyberinfrastructure
Community-driven major, important questions in organism X environment interactions whose solution
requires interdisciplinary input, and likely development of new intellectual/software/hardware to reach
the goal/solve the problem.
CyberInfrastructure for organismal biology / physiology needs to be developed
Data ‘standards’ or formats for physiological traits that allowcross system comparisons
Analysis tools and workflows: customizing existing tools and new development
CI collaboration and training
Data management systems
A related problem concerns divergent terminology (e.g. the use of the term adaptation) and extremely
divergent / non-standardized data formats.
Publication is no longer adequate/sufficient way to share results supplementary material is non-uniform
and not reviewed.
How much data is too much data? Just putting out all the raw data may make the problem worse.
Bad data can impede progress?
How do we integrate across scales of datasets to do model selection, and go beyond model selection.
How do we enable innovation in this area; language in systems biology context is limiting.
One piece of innovation that infrastructure can:
Create a marketplace of ideas through well-defined standards that allow the sharing of alternate
algorithms, workflows, etc Some keys to this: Data standards so you can plug in replacements, easy
replacement of components, statistics about how these are used.
What are the classes of things we have to automate? for instance, in image processing, what is the
unused information content in an image? In a sequence image, what base is called might encode most
of the useful data. But in a high resolution image of an organ, much untapped information might still be
there.
Combining organism response with environmental data; need data on both, and need ways to correlate
them, infrastructure, and timing NEON hopes to address this problem.
1. Can we frame the specializations of our animals in a common
currency or language?
- A GOOD POSITIVE EXAMPLE: Circadian biology
- eg., "all animals face challenges vs opportunities"
- 2. Technology development for phenotyping live animals.
- 3. Incorporating variation (including sex/gender) in our thinking
- A CLUSTER GRANT that brings in bioinfo, evol, mechansistic
biologists...
- MULTIDISCIPLINARY TRAINING
- students need to be trained in omics
- need for statistics
- leveraging an appreciation of "variation"
- DATA STORAGE -- common platforms for lots of data
Phenotype data may be the harder of the 2 kinds to share (vs. genotype) quality is harder to quantify.
Infrastructure to share is one (necessary) thing. We can and should continue to invest in repositories.
How you use it effectively is harder. Need for better model description and sharing. Need for better
analysis/workflow description and sharing.
Broader impacts of organismal adaptation biology
Consider broader significance of these large-scale interdisciplinary problems that intrinsically focus on
basic science of animal/environment issues. These could be impacts of environmental/climate change or
disturbance, urbanization, and human societal impacts such as public/global health issues (e.g., vector
biology), resource management (e.g. fisheries), conservation, ecosystem services, human-animal
interface.
Community building
Many challenges require many specialists –need cluster groups. Every time you do a gene expression
array you have to learn a new field of biology  necessaty for collaboration / clusters.
Hire at universities this way.
Problems should involve inter/multi-disciplinary approach, include relevant fields of organismal biology
as well as other disciplines such as engineering, math, ecology/evolution, biochem/molecular/omics,
even biomedical community if appropriate to problem. Pursue novel strategies and tool development
e.g., sensory systems, remote tracking and data collection, emphasis on non-invasive/field-based studies
when possible (can combine lab & field).
Use a problem solving approach to insure interdisciplinarity in research approach, tool development,
computational/bioinformatics capacity and student training.
Genome to organism; genome could be the filing cabinet for information; (no environmental
interactions are included).
Whole organism data may not linearly build just from genomes (biophysical processes, etc). How do you
need to integrate this data, and when do you need to integrate this data?
Just gene-by-gene interaction is taking a huge amount of the resources of NIH and still isn't solved (for
one organism); gene-by-environment could be much harder. Disconnect but what is being said in
terms of what people want, and the interactions across scale that are required.
To make reasonable progress, we're going to have to be careful about what to include and what to
exclude (because we are a long way from including it all).
Network is a real, but hidden, growing, cost. The age of moving datasets to your lab is likely ending.
Co-locate computation and data resources so you can do analysis where the data are.
How do you support innovation *and* production use.
Sustaining investment in software remains a huge issue. NIH has a software maintenance program
which is worth looking at. How we will fund application development, and sustaining the software
(support, etc). How do we prioritize to know what is worth sustaining?
Community building tools
Synthesis centers: catalysis meeting  RCNs
Physiological model systems
Data methods and best practices for physiological traits
LTORs: long-term organismal research
Long-term, place-based
Consistent environmental data
Science-driven CI development
Centers for collaborative research teams
Whether innovation comes collectively or from individuals, sharing is critical, because without this, there
is no way to build on the work of others.
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Getting core parties together (Glue grant)
– Organismal biologists
– Technologists (‘-omics’)
– Systems biologists (analysis)
Can’t sell a one-behavior paradigm for all species
3. Education and Training
How do we go about encouraging interdisciplinary views? (see many other reports, such as Bio2010).
Can't increase course numbers, but encourage more quantitative stuff throughout existing biology
courses.
Statistics is going to be more important. Coding is too. Can integrate this in existing courses as well.
Needs some repetition throughout the curriculum. At least basics of algorithms.
Where to move from basic concepts to building real software? Does the graduate level require a deeper
understanding of algorithms and software? How do we get people to understand what their software
is actually doing?
Is there room at the undergrad level for new majors that marry math, programming, stats with the
biology (With interdisciplinary research integrated every year)?
TRAINING and COMMUNICATION:
- the importance of the comparative approach to explain natural
organismal variation
- engaging horizontal platform guys with vertical biology
(i.e., there's not a single ideal phentoype)
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Taxonomic vs. conceptual routes to science
A ‘Woods Hole’ course for Ecological Genomics
Bridging the computational divide
– Mentoring graduate students
Choosing a particular system or species for particular questions
Teach biology from an organismal perspective. Center on the organism from and function rather than
breaking organsism down into levels of organization.
Problem, Mismatch between the speed of science and the speed of granting-especially in the speed of
advancement in ‘omoics’ and nanotech.
Suggestions:
SMALL GRANTS Provide a small grant competition to a particular researcher for a specific project of this
type, short writeup.
Transdisciplinary expertise is required including computational expertise hence CLUSTER GROUP GRANT
FUNDING
TRAINING: Train a new cohort of individuals in transdisciplinary research. Have dual supervision in
different areas of expertise. Project has to be transdisciplinary. Several month placements in different
labs.
CULTURE CHANGE:
Reward for collaboration-federal gov’t
Non-tenured professors encouraged to collaborate and be part of a cluster group.
Hire people into cluster groups (not Noah’s ark-one of each).
Nurture and help people who work at the edges of disciplines.
Train in multiple labs/areas need more computation
- 2) "rodent-centric" biology (a training and communication
challenge)
- 2. how to promote awareness and respect for organismal diversity:
"Animals Are Cool"
- LOSS of ORGANIMSAL in Intor Biology
- "ANIMALS ARE COOL"