BME1450 Term Paper
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Systems biology and ecological risk assessment
Tilak Dutta

Abstract—Environmental risk assessment has traditionally
been a difficult process due to the lack of concrete causal
relationships between environmental stressors and their negative
effects in the ecosystem. The application of the systems biology to
ecological risk assessment can help to create links between
specific anthropogenic stressors and their associated negative
effects, facilitating the task of environmental management.
Exploiting systems biology for ecological risk assessment can be
useful for creating such causal links in a number of ways:
previously compiled genomic data can be reexamined with new
ecological risk assessment questions in mind; genomes of
genetically significant organisms can be sequenced; DNA
microarrays can be used to detect minute variations in gene
expression of thousands of genes at one time to see the effects of
stressors; and finally the use of the overall systems biology goal of
developing predictive computational models. Conventional
approaches in ecological risk assessment along with their
limitations such as an inability of detecting effects of low level
exposure, disentangling effects from multiple stressors, and the
need for more immediate indicators rather than retrospective
ones are discussed along with ways of overcoming these problems
using the systems biology approach. Implications for the future in
the field of ecological risk assessment such as the reduced need for
destructive testing, as well as the potential for advancement of the
systems biology field are considered.
Index Terms—ecology, environmental monitoring, genomics,
systems biology
I. INTRODUCTION
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ssues such as industrial waste treatment, sewage discharges,
ozone layer depletion, and global climate change affect our
daily lives and the sustainability of all ecosystems on the
planet. In the past, damage to the environment has largely been
identified retrospectively and in response to acute events such
as major disasters. This damage has been measured only in
terms of human health impacts and visible changes resulting
from the loss of particular populations or communities.
Increasingly, the scientific community is realizing that human
health is directly linked to the health of the environment
around us [1], [2]. This realization is resulting in a greater
emphasis on ecological risk assessment [1].
Knowledge gaps in our understanding of ecological
significance of anthropogenic stressors result in a lack of
agreement for a decision-making framework, which could
establish the roles of science and policy in formulating
environmental management principles [1]-[3]. As Moore et. al.
state, “[a] major challenge in impact and risk assessment…is
to link harmful effects of pollution…in individual sentinel
animals to their ecological consequences” [1]. Traditional
ecological risk assessment faces a number of limitations such
as an inability of detecting effects of low level exposure,
disentangling effects from multiple stressors, and the need for
more immediate indicators rather than retrospective ones.
The sequencing of the genomes (made up of DNA that each
organism uses to store the information it needs to grow and
function) has created new areas of research such as genomics
(the study of the genomes of various organisms), and
proteomics (the study of proteins). Together these fields of
research, along with many others, make up what is referred to
as systems biology. Systems biology is the study of biological
systems by systematically perturbing them while monitoring
the system globally with the goal of characterizing the
behaviour of, and the relationships between, all elements of the
system. The overall process is an iterative approach, which
forms a model of the system and continually refines it by
alternating between hypothesis driven science and discovery
driven science [4], [5]. Due to the magnitude and complexity
of the data collected, information technology support through
what is called bioinformatics plays the important role of
development of system modeling, databases, and tools
designed to mine these data [6], [7].
The application of systems biology to ecological risk
assessment can help to link negative effects observed in
various ecosystems to the anthropogenic stressors that are their
cause and facilitate the task of environmental management by
overcoming many of the problems faced by conventional
methods in ecological risk assessment.
The field of ecological risk assessment can take advantage
of developments of systems biology to make these causal links
in a number of ways: genomic data that has been amassed can
be examined in trying to answer new ecologically significant
questions; genomes of genetically significant organisms can be
sequenced; and DNA microarrays can be used to detect minute
variations in gene expression of thousands of genes at one time
to see the effects of stressors. In addition, the systems biology
paradigm may be applied to ecology in general with the
ultimate goal of designing predictive computational models
that can help to make ecology and ecological risk assessment
more of a hypothesis driven science.
II. LIMITATIONS OF TRADITIONAL ECOLOGY
Classically, the study of ecology has been discovery driven.
Areas of inquiry that are historically ecological include species
BME1450 Term Paper
interactions, the organization of communities and ecosystem
functioning [8]. Risk to the environment has been done
retrospectively in response to major disasters. Impacts have
been measured based on immediate visible changes resulting
from the total loss of populations or communities, decline in
numbers of a sensitive indicator species [2], or on measures of
whole-organism responses such as growth, and reproduction of
indicator species [7]. Tests of substances are done on
laboratory animals to generate data about toxicological
responses seen in test organisms. The problem is that long
term and chronic exposure to environmental stress will seldom
result in rapid and catastrophic change. Rather, the impact will
be much less pronounced, and frequently difficult to
disentangle from the effects of natural environmental change.
Another limitation is the inability to distinguish between
effects of different stressors, as well as the issue that by the
time significant population declines have been recorded, much
damage has already occurred [1], [9].
Despite these drawbacks, data gathered through these
methods is currently used to provide the foundation for risk
assessments used by industry and regulatory agencies in
deciding what actions may be necessary to protect the health of
exposed populations [2]. However, the limitations make it
clear that there is not enough information to make suitable
links between stressors and their effects. Fully understanding
the adaptive functions of an organism requires an account of
how genotype, phenotype and environment interact [8].
III. LINKING CAUSE AND EFFECT
The key to finding causal relationships between
environmental stressors and their associated negative effects is
to develop ways of detecting “distress signals” [1] at the
molecular and cellular levels of organization. Ideally, we
would want to link to high level effects in the organism to
indicate overall health status of the organism [1], [2], [9].
However, restrictions imposed by our current knowledge level
mean that within the foreseeable future it is only at the lower
levels that we will have the reasonable expectation of
developing a basis of mechanistic understanding. This
knowledge of how the environment can affect organism
function will help to link causality with predictability of
response. Our inability for higher-level prediction is in part
due to our ability to make certain generalizations about
biological organization and function at the molecular and
cellular level, which rapidly disappear within the complexity
of biological and ecological interactions as we increase in
system complexity [1]. In principle, distress signals at the
molecular, cellular and physiological levels of organization
should be capable of providing early warnings indicating
reduced performance, impending disease and damage to health
[1], [8], [9].
A. Low Level Exposure
Of particular importance is the need to find distress signals
for detection of exposure to stressors in the field at very low
but chronic levels. Also important, is defining the level of
exposure that will cause an effect [9].
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Systems biology provides us with a solution to this problem.
Gene expression in the form of mRNA or proteins has been
shown to be an early indicator of physiological problems that
are not yet phenotypically visible. Changes in gene expression
occur well in advance of reproductive or morphological
problems and can be detected after small environmental
changes [9].
This ability to see such effects on the molecular level may
even challenge current definitions of threshold toxicological
effects. Risk assessors have traditionally grouped chemicals in
two categories: carcinogens, for which there is no threshold;
and noncarcinogens, which are thought to have a threshold.
However, at the molecular level, we may find the thresholds
do not exist and that actually all effects of stressors are
continuous [1].
B. Effects of Multiple Stressors
Another problem is that there may be multiple potential
stressors in any environment and effects can be a result of
chronic low-level exposure to multiple stressors. It is difficult
to separate the effects from each stressor and to know whether
these factors act synergistically within an organism. Current
monitoring techniques, such as measuring chemical
concentrations in tissues are limited in that they are not able to
distinguish among different pollutants or stressors [1], [9].
Within an organism, different forms of the same protein are
produced to deal with compounds that are of the same
structural group but not identical. Genomics takes advantage
of this by providing not just a single compound as an indicator
but a profile of hundreds of proteins or genes that have been
up-regulated or down-regulated in response to a stimulus,
providing a unique and specific profile of the organism’s
response [9].
C. Current Monitoring Versus Retrospective Measurement
Measurements are often retrospective, in that a change such
as a reduction in the size of a population is recorded, and only
afterward is there an attempt to find the cause. An early
indicator that provides real-time information on the status of
species and communities within the environment is needed [1],
[9].
Ultimately, the creation of a database of stressors and their
respective gene and protein expression profiles should make it
possible to use this information outside the laboratory as a tool
to more immediately identify problems within a population.
Genomic technologies will be a direct means to identify the
types of stressors to which an organism is exposed and to
differentiate among the effects of various environmental
factors that affect a population. The early changes in gene
expression that occur in response to environmental stimuli, and
the specificity of these responses, provide data about the
immediate response of a species before a population decline.
The development of tests specific to a particular population,
which examines changes in the gene expression of native
species, could provide an indication of exactly what an
organism may have been exposed to and a way of monitoring
the effects of various compounds at low doses before these
effects are visible in the natural population [9].
BME1450 Term Paper
IV. ECOLOGICAL GENOME DATABASES
As mentioned previously, arriving at solutions to the
problem of making risk assessments less retrospective can be
facilitated by the development of genomic databases. Some
such databases already exist. The Rat Genome Database, and
the Daphnia Genomics Consortium are examples of databases
in which all the relevant genomic data from the model
organism has been compiled. Daphnia is of particular
importance because it is a model organism that also happens to
be ecologically interesting [6]. The information contained in
these databases can be probed for answers to new ecologically
relevant questions.
The problem of working only with model organisms is that
very few have been studied to any depth [8]. As Snape et. al.
point out, “[c]learly there is a current lack of DNA sequence
resources for the ecotoxicologist to utilize” [7]. Also, most
model species were selected on the basis of particular genetic
and developmental features (e.g. clonal propagation, self
fertilization and short generation times), for ease of growth in
the laboratory [8], or due to the focus on species that are
models for human health, agricultural species, or disease
organisms, rather than for their ecological importance. As a
result, many ecologically important pathways, processes and
structures are not currently represented in the set model
organisms available today [8], [6].
The obvious solution to this problem is that more model
organisms need to be sequenced. However, significant thought
should be given to the selection of new model species. This
new range of model species should attempt to represent
diverse ecological and physiological attributes. For example,
there exist three pathways of plant photosynthesis: C3, C4 and
CAM. Arabidopsis and rice, the plant species for which
genomic sequences are available, both use the C3 pathway. It
would be advantageous to choose new model species that use
C4 and CAM pathways [8]. Snape et. al. suggest that the most
popular species used in ecotoxicology should be fully
sequenced. For invertebrates they suggest Chironomus
riparius, Daphnia manga and Mytilus edulis. They state that
the situation for fish is encouraging due to the announcement
of plans to fully sequence the zebrafish genome in the near
future [7]. Also encouraging is the predicted drop in time and
expense of genomic research. It is predicted that within the
next decade, projects the size of the Human Genome Project
will cost less than $10,000 and will take only a few hours to
complete [9].
V. SYSTEMS BIOLOGY TOOLS APPLIED TO ECOLOGY
Of course, having a fully sequenced genome is only the
beginning. The next step is to begin assigning functions to
these identified genes [7]. The functions of thousands of genes
in animals, microbes and flowering plants that have been
identified still remain a mystery [8]. One way to gain
information about gene function of newly sequenced
organisms is to make use of existing model organisms that are
well characterized to isolate homologous genes from the new
species [9], [8]. As Klaper and Thomas state, “[c]onserved
pathways among phyla provide a basis for using well-studied
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model species to study species of concern” [9]. It is
encouraging to note that “[b]etween 48% and 60% of genes in
Arabidopsis have counterparts in the other eukaryotic [nonplant] genomes sequenced to date, suggesting at least some
highly conserved gene functions…” [8].
However, many ecological processes arise from complex,
interacting systems that may not be explained by examining
gene function alone. Identifying which ecological processes
key genes control is the challenge ecological risk assessors are
faced with. In the process of “[h]unting for ecological genes”
[8], ecologists attempt to attain a mechanistic understanding of
the phenotype of the organism and its response to ecological
signals [8].
A. DNA Microarrays
One of the tools ecologists have at their disposal to attempt
to develop such in depth mechanistic understanding is the
DNA microarray or DNA chip. Microarrays allow the
expression of hundreds or thousands of genes to be analyzed at
one time and determine which are expressed in a particular cell
type. Although most cells in the body contain the same genes,
not all of the genes are used in each cell. Some genes are
turned on, or “expressed” when needed [2], [8], [9]. Cells
change their pattern of gene expression in response to their
environment, such as exposure to environmental chemicals [2].
Information from the development of gene or protein
expression signatures for a limited range of organisms could
be used to identify the mechanisms of action of a single
chemical or complex environmental mixtures [7]. Microarrays
will be useful in gaining a better understanding of how
particular chemicals interact with genes to produce a particular
kind of toxic response.
Microarrays also make it possible to proceed with
investigations without the need of complete genomic models
for the species of interest. As Klaper and Thomas point out,
“[m]icroarray experiments measuring mRNA production can
be accomplished with or without the complete genomic
sequence of the organism” [9].
B. Doing more with less
One advantage of using cellular reactions for environmental
assessment of toxic risk is that isolated cells can be exposed to
chemical or complex mixtures of toxins in vitro, which
facilitates rapid testing. In addition, at a time when the public
is becoming aware and alarmed at animal suffering in the name
of science, an in vitro approach requires the sacrifice of very
few animals. Once disaggregated, a tissue biopsy or sample of
body fluids or eggs can provide sufficient cells to undertake
many exposure experiments in the knowledge that genetic
heterogeneity has been removed as a confounding factor;
which is in sharp contrast to traditional in vivo exposure
studies where it is necessary to treat different animals with a
given compound [1].
Another method to reduce the number of animals sacrificed
for testing is the use of molecular markers from blood or tissue
from an ecological sample. This type of diagnostic could aid in
monitoring threatened or endangered species, replacing more
destructive sampling techniques. Samples of blood and tissue
contain many markers that are currently used in medical
BME1450 Term Paper
research [9]. Plasma proteins, for example have been used as a
basis for human disease diagnostics, and the number of known
protein markers is increasing exponentially because of
advances in proteomics.
One potential drawback to these techniques may be the
variability caused by other environmental conditions.
Organisms are exposed to greater variation in environmental
factors in their natural habitat than in the laboratory, and this
variation can have an effect on gene expression. Therefore,
researchers will need to identify genomic responses that are
consistent between the laboratory and the natural environment.
Another potential difficulty is that some environmental
factors affecting populations are of short duration, and the
changes in gene expression patterns in response to these
factors may last for only a short period of time. Protein
profiles may be the best approach here, as they provide a
longer-lasting signal than mRNA in response to change [9].
VI. APPLYING THE PROCESS OF SYSTEMS BIOLOGY
Up to this point in this paper, the application of systems
biology to ecological risk assessment has been discussed in
terms of the use of tools and data that carry over from work
focused in other areas of scientific research. However, the
systems biology approach may also apply in a much broader
sense.
The systems biology approach, which attempts to
characterize the behaviour of, and the relationships between,
all elements of a given biological system [4], lends itself to the
study of ecology particularly well due to the inherent “systemlevel” understanding desired. As Moore et. al. state, “[t]rying
to understand the behaviour of a complex adaptive (or
dynamic) system, such as an organism, population, ecosystem
or biogeochemical cycle, by a reductionist approach often
irretrievably destroys the inherent nature of the problem being
addressed” [1]. Pursuing a whole system approach with the
development of computational simulation models of cells,
organs and animals in tandem with empirical data will
facilitate the process of linking the impact of pollutants
through the various levels of biological organization up to
ecosystem health [1].
VII. THE FUTURE
We are a long way away from the “canary on a chip”[9] as
Kalper and Thomas put it, referring to the miner’s canary,
who’s death served as an indicator of toxic gases in mine
shafts. Currently, computational models are in their infancy.
However, the advantage of simulation is that it encourages an
understanding of how processes are linked and how systems
properties emerge in space and time [9], and this is precisely
the ultimate goal of ecological risk assessment. Some believe
that, “…genomic methods will eventually allow understanding
of life at the molecular, cellular, organism, species, and
ecosystem level” [2].
The possibility of one day having predictive computational
models is very exciting. Along with providing answers to all
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the problems discussed in this paper so far, such predictive
models will be able to predict the harmful effects of chemical
substances that may not yet exist in the environment. Rapid
developments in nanotechnology raise questions of potential
environmental impacts of nanoparticles particularly because
biological systems did not evolve in the presence of such
substances [1]. This type of information can help us, as a
society, to make preemptive, rather than reactionary decisions
about the use of potentially hazardous substances.
Combining systems biology and ecology research can help
build links between the causes and effects of environmental
pollutants. There is much that can be done with the data and
tools that we have today, but to better realize the full potential,
significant further study and development of the field is
required [8].
Some say that the application of systems biology tools to
ecology is directly beneficial to systems biology as well.
Ecologists can provide a unique perspective for systems
biology researchers. By participating in systems biology
research, ecologists will have a leading role in helping
understand the ecological basis for interactions among cells
and molecules. As Martin Feder said loosely paraphrasing
John F. Kennedy, “[a]sk not what genomics can do for
ecology, but also what ecology can do for genomics” [6].
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