Available via license: CC BY 2.0
Content may be subject to copyright.
RES E AR C H Open Access
Animal models and conserved processes
Ray Greek
1*
and Mark J Rice
2
* Correspondence: DrRayGreek@
gmail.com
1
Americans For Medical Advancement
(www.AFMA-curedisease.org), 2251
Refugio Rd, Goleta, CA 93117, USA
Full list of author information is
available at the end of the article
Abstract
Background: The concept of conserved processes presents unique opportunities for
using nonhuman animal models in biomedical research. However, the concept must
be examined in the context that humans and nonhuman animals are evolved,
complex, adaptive systems. Given that nonhuman animals are examples of living
systems that are differe ntly complex from humans, what does the existence of a
conserved gene or process imply for inter-species extrapolation?
Methods: We surveyed the litera ture including philosophy of science, biological
complexity, conserved processes, evolutionary biology, comparative medicine,
anti-neoplastic agents, inhalational anesthetics, and drug development journals in
order to determine the value of nonhuman animal models when studying conserved
processes.
Results: Evolution through natural selection has employed components and
processes both to produce the same outcomes among species but also to generate
different functions and traits. Ma ny genes and processes are conserved, but new
combinations of these processes or different regulation of the genes involved in
these processes have resulted in unique organisms. Further, there is a hierarchy of
organization in complex living systems. At some levels, the components are simple
systems that can be analyzed by mathematics or the physical sciences, while at
other levels the system cannot be fully analyzed by reducing it to a physical system.
The study of complex living systems must alternate between focusing on the parts
and examining the intact whole organism while taking into account the connections
between the two. Systems biology aims for this holism. We examined the actions of
inhalational anesthetic agents and anti-neoplastic agents in order to address what
the characteristics of complex livin g systems imply for inter-species extrapolation of
traits and responses related to conserved processes.
Conclusion: We conclude that even the presence of conserved processes is
insufficient for inter-species extrapolation when the trait or response being studied is
located at higher levels of organization, is in a different module, or is influenced by
other modules. However, when the examination of the conserved process occurs at
the same level of organization or in the same module, and hence is subject to study
solely by re ductionism, then extrapolation is possible.
Keywords: Anesthesia, Animal models, Cancer, Complexity, Conserved processes,
Systems biology
© 2012 Greek and Rice; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40
http://www.tbiomed.com/content/9/1/40
Background
Marc Kirschner and John Gerhart introduced the concept of facilitated variation and
conserved core processes in their book, The Plausibility of Life [1], in order to explain
how novelty arises in evolution. Motivated by advances in evolutionary and developmental
biology (evo devo), these investigators proposed that conserved processes are ubiquitous in
eukaryotes but pointed out that by using conserved processes differently, for example by
differently regulating the genes that code for the processes, expressing the genes differently,
varying the sequences or combination of genes or transcription factors, novelty can arise.
Mutations in the genes that regulate the conserved processes can accomplish this novelty.
Moreover, by adjusting the regulatory genes, the organism can evolve with fewer mutations
than would be the case if a trait had to arise de novo or from mutations in structural genes.
This has implications for using nonhuman animals (hereafter referred to simply as animals)
as models for humans in biomedical research. One should expect to discover information
regarding conserved processes in humans by studying animal models. We sought to deter-
mine whether limits exist on this method and if so what those limits are.
Methods
We surveyed the relevant literature including philosophy of science, biological complexity,
conserved processes, evolutionary biology, comparative medicine, anti-neoplastic
agents, inhalational anesthetics, and drug development journals in order to determine
the appropriate role for animal models when studying conserved processes. Philosophy
of science is rele vant to our discussion as it includes the premises and assumptions on
which research is then based. A study or method can be methodologically sound but if
the premises are incorrect, then the study loses much if not all of its value. The drug
development literature was searched because the final application of much research is
targeted intervention via drugs hence that literature can inform regarding the success of
a practice or modality. The literature concerning biological complexity and conserved
processes was surveyed as it directly relates to the issue being explored. All of this must
be placed into the context of evolutionary biology in order to better explain the findings.
We chose inhalational anesthetics and anti-neoplastic agents as examples because of the
well-known conserved nature of these agents.
Results
Animal models
The use of models has a long history in science, which led philosopher of science Richard
Braithwaite to warn that: “T he price of employment of models is eternal vigilance” [2]. In
this section, we will explore what animal models are, how they can be used in scientific
investigation, including biomedical research, and discuss classification schemes. In this art-
icle, we will address the use of predictive animal models in light of the concepts of complex
systems, personalized medicine and pharmacogenomics, and evolutionary biology. We will
then explore what this implies when using animal models to study conserved processes.
Models are important for scientific pursuits and can take the form of abstract models,
computational models, heuristic models, mathematical models, physical models such as
scale models, iconic models , and idealized models . Models can also be divided on the
basis of whether they are used to replicate a portion of the item being modeled or are used
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 2 of 33
http://www.tbiomed.com/content/9/1/40
to test hypotheses or interpret aspects of a theory. Examples of historically important
models include Watson and Crick’s physical model of DNA, Pauling’s model of chemical
bonds, Bohr’s solar system model of the atom, and the billiard ball model of gases. More
recent models include the computer model of the brain, mathematical models of disease
spread, and Lorenz’s model of the atmosphere.
Robert Hinde observed that models:
Should be different from the thing being modeled, because if it is not, the modeler
might assume that all properties demonstrated by the model exist in the thing being
modeled;
Are usually less complicated than the thing being modeled;
Are more readily available than the thing being modeled, and;
“pose qu estions, suggest relations, or can be manipulated in ways not possible with
the original” [3].
In light of the importance of models, some philosophers of science assert that the
study of models per se has been neglected by the philosophy of science community.
Frigg and Hartman n [4 ] state: “What fills in the blank in ‘M re present s T if and only
if ____,’ where M is a model and T a target syste m?” Moreover, how one classifies
models and what criteria must be fulfilled in order for M to be considere d a spe cific
type of model has arguably not been adequately addressed by the philosophy of science
community. Yet another problem with the philosophy of models is the relationship between
theory and model [4]. We maintain that this lack of scholarly attention to models has
played a role in what we see as the confusion surrounding the use of animals as models.
Animal models are physical models and can be further classified based on various
features and uses. For example, they can be distinguished by the phylogenetic distance
of the model species from humans. Animal models can also be classified based on fidel-
ity—how well the model resembles humans—as well as based on validity—how well
what you think you are measuring corresponds to what you really are measuring.
Animal models can also be considered based on reliability—the precision and accuracy
of the measurement [5]. Hau explains that animal models can be categ orized as spon-
taneous, induced, transgenic, negative and orphan. Hau states: “The majority of labora-
tory animal models are developed and used to study the cause, nature, and cure of
human disorders” [[6] p3]. This is important as Hau further states that animal models
can be used to predict human responses: “A third important group of animal models is
employed as predictive models. These models are used with the aim of discovering and
quantifying the impact of a treatment, whether this is to cure a disease or to assess
toxicity of a chemical compound. The appropriateness of any laboratory animal model
will eventually be judged by its capacity to explain and predict the observed effects in
the target species” [6]. Others agree that predicting human response is a common use
for animal models [7-12]. For example, Heywood stated: “Animal studies fall into two
main categories: predictive evaluations of new compounds and their incorporation into
schemes designed to help lessen or clarify a recognised hazard” [13].
Animals are utilized for numerous scientific purposes (see ]Table 1) and one of the
authors (Greek) has addressed these various uses in previous publications [14-20]. One
cannot have a meaningful discussion regarding the utility of animal models unless one
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 3 of 33
http://www.tbiomed.com/content/9/1/40
specifies the category under discussi on. For example, areas in which animal models
have been successfully employed include the evaluation of a phenomenon that can be
described by the physicochemical properties of the organism, the study of basic physiologic
functions, and the study of other traits that can be described by the use of conversion
factors based on the body surface area of the organism. In general, animal models can be
successfully employed in categories 3–9 in Table 1. However, animal models have failed to
be predictive modalities for human response to drugs and disease [13-16,18,21-41], depicted
by categories 1 and 2 in Table 1. (The authors have addressed this failure in numerous
publications and, because an exploration for this failure is not the purpose of the article, we
refer the reader to those publications [14-20,23] even though we realize that some view this
position as controversial [7,11,42-44].) This is not to say that a species can never be found
in retrospect that mimics an outcome in humans. Such a species usually can be identified,
however retrospective correlation is obviously not the same as prediction [45-47]. Moreover,
any process or modality claiming to be predictive can be evaluated by use of the binomial
classification table and equations in Table 2 (as illustrated in Table 3 [48]). Such calculations
are commonly used in science [49-53].
Table 1 Categories of animal use in science and research [16]
1. As predictive models for human disease
2. As predictive models to evaluate human exposure safety in the context of pharmacology
and toxicology (e.g., in drug testing)
3. As sources of ‘spare parts’ (e.g., aortic valve replacements for humans)
4. As bioreactors (e.g., as factories for the production of insulin, or monoclonal antibodies, or
the fruits of genetic engineering)
5. As sources of tissue in order to study basic physiological principles
6. For dissection and study in education and medical training
7. As heuristic devices to prompt new biological/biomedical hypotheses
8. For the benefit of other nonhuman animals
9. For the pursuit of scientific knowledge in and of itself
Table 2 Binary classification test
Gold standard
GS+ GS-
Test T+ TP FP
T- FN TN
Sensitivity = TP/(TP + FN)
Specificity = TN/(FP + TN)
Positive Predictive Value = TP/(TP + FP)
Negative Predictive Value = TN/(FN + TN)
T- = Test negative
T + = Test positive
FP = False positive
TP = True positive
FN = False negative
TN = True negative
GS- = Gold standard negative
GS + = Gold standard positive
The binary classification test allows calculations for determining how well a test or practice compares with reality or the
gold standard.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 4 of 33
http://www.tbiomed.com/content/9/1/40
When judging the predictive value of a modality, one is not using the term predict
in the same sense as when describing how hypotheses generate predictions to be
tested. The predictive value of a commonly used modality usually is known, or can be
ascertained, for example the positive and negative predictive value of x-ray computed
tomography (commonly referred to as a CT scan) for diagnosing pneumothorax (a rupture
of, or interference in, the pleural membrane which allows air to enter the pleural space and
thus interferes with breathing) approaches 1.0 (is accurate for diagnosing the condition in
100% of cases).
Animal models as used in biomedical research, can also be categorized as causal
analogical models (CAMs) or as heuristic or hypothetical analogical models (HAMs)
[54-59]. The use of animal models to predict human response to drugs and disease, in
accordance with categories 1 and 2 in Table 1, would be an example of using animals
as CAMs. Analogical models in general inc lude the hydraulic model of economies and
the computer model of the brain and can be further divided based on various criteria [4].
Causalism or causal determinism dates to Aristotle who stated: “what is called Wisdom is
concerned with the primary causes and principles.” Causalism can be summarized
succinctly, as “everything has a cause.” This notion of causation was the basis for animal
models as can be appreciated by the writings of Claude Bernard [60], considered the father
of animal modeling since the 19
th
century. Bernard’s thoughts on animal models are an
extension of Aristotle via the determinism of Descartes and Newton [61]. Causal deter-
minism and the principle of uniformity led to the concept, still accepted by many animal
modelers today, that the same cause would result in the same effect in qualitatively similar
systems. This line of thinking was in keeping with the creationist thinking of 19
th
century
French physiologists, including Bernard, who rejected Darwin’s Theory of Evolution
[60,62,63]. The notion of causal determinism and the principle of uniformity combined
with the rejection of evolution led to the belief in the interchangeability of parts. There-
fore, if one ascertained the function of the pancreas in a dog, he could directly extrapolate
that knowledge to the function of the pancreas in humans, once scaling for size had been
factored in [14,63,64]. Unfortunately, this linear thinking persists as the baboon heart
transplant to Baby Fae illustrates. The operation was performed by the creationist surgeon
Leonard Bailey of Loma Linda University in 1984 [[65] p162-3].
We acknowledge that the concept of causation is problematic [66]. Russell suggested
it be abandoned in 1913 [67] and it is clearly more useful for linear systems than
complex systems. While an exhaustive explanation and discussion of the controversies
surrounding causation would occupy more space than is available for this article
Table 3 Example of binary classification values
Gold standard (human)
GS+ GS-
Test T+ 22 26
T- 22 30
Sensitivity = 22/(22 + 22) = 0.5
Specificity = 30/(26 + 30) = 0.54
Positive Predictive Value = 22/(22 + 26) = 0.46
Negative Predictive Value = 30/(22 + 30) = 0.58
Binary classification values for cardiovascular toxicity test in monkeys from 25 compounds also tested in humans [48].
Note the values are approximately what would be expected from a coin toss.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 5 of 33
http://www.tbiomed.com/content/9/1/40
(see Bunge [61] for such an analysis) we should note that a more current explanation for
causation is that of a “first order approximation.” Causation is usually discussed in the
context of a chain of causes. Bunge summarizes current thinking: “neodeterminism . . .
asserts in this connection that causation is only one among several interrelated categories
concurring in real processes” [61]. This principle is appreciated even more fully in
complex systems. Current thinking notwithstanding, the use of animal models assumes
the Cartesian concept of causation in that a causal model assumes a deterministic causal
relationship between variables. We will explore this thinking and show that even in the
traditional context there are problems with using animal models to discover “causal”
relationships. These problems are increased exponentially when placed in the context of
complex systems.
Based on the writings of LaFollette and Shank s [[58]p63], we suggest the following in
order for a model to be considered a CAM. X (the model) and Y (the subject being
modeled) share properties {a...e}. In X, these properties are associated with, and
thought relevant to, state S1. S1 has not been observed directly in Y, but Y likely also
has would exhibit S1 under the same conditions as X. This concept is illustrated in
Table 4. LaFollette and Shanks [58] state that, “there should be no causally-relevant
disanalogies between the model and the thing being modeled.” Unfortunately, causally
relevant disanalogies do exist among species and even within a species, which leads to
different states or outcomes, as illustrated in Table 4. We again paraphrase LaFollette
and Shanks [[58] p112] and suggest that two more conditions must be met for a model
to qualify as a CAM: the shared properties {a,...,e} must have a causal relationship with
state S1 and be the only causally relevant properties associated with S1. As Table 4
illustrates, the commonalities between the humans and chimpanzees are insufficient to
qualify chimpanzees as CAMs for human response to HIV infection. (For more on
Table 4 Causal analogical models
X, the model Y, the system
being modeled
Shared properties
between X and Y
Perturbation
to the model
Outcome
in model
Outcome in system
being modeled
Animal system
(for example,
Pan troglodytes)
Human system
a. Genes. >90% of
nucleotide sequences
identical.
Exposure
to HIV.
State S1.
Mild illness
of limited
duration.
AIDS. State S1 is
not shared despite
the presence of
shared, relevant
properties.
b. Immune system.
Many commonalities.
Constructed on
generally the same
plan.
c. White blood cells
present and function
similarly.
d. Receptors on white
blood cells also present
and function similarly.
e. Shared intracellular
components of white
blood cells.
Shared properties a...efor humans and chimpanzee do not result in state S1 also being shared.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 6 of 33
http://www.tbiomed.com/content/9/1/40
animal models of HIV/AIDS see [14,68].) As we will show, animals and humans are
evolved complex systems and as such exhibit the properties of robustness and redundancy;
hence numerous “causes” can result in the same effe ct and the same perturbation can
result in different outcomes. Because of this and other properties of complex systems,
we should expect different species to exhibit different causal relationships.
Correspondingly, Giere, Bickle, and Mauldin [69] note that some question the use of
causal models in the study of humans because humans are complex systems whereas
casual models assume a deterministic system: an outcome in a simple system is fixed by
the variables. The problems of determining causation are further explored by Bunge [61]
in his neodeterminism explanation alluded to above and his analysis is highly relevant to
this discussion. While we will attempt to contrast the traditional deterministic view of
causality in light of complexity science, this article will not do justice the current thinking
on causation and we refer the reader to Bunge [61] for a fuller explanation.
Giere, Bickle, and Mauldin suggest a probabilistic relationship instead of a 100%
causal relatio nship for the model: “C is a positive causal factor (probabilistic) for E in
an individual, I, characterized by residual state, S, if in I the probability of E given C is
greater than the probability of E given Not-C.” Likewise, LaFollette and Shanks raise
the question as to whether animal models can be weak CAMs: “Begin with two systems
S
1
and S
2
.S
1
has causal mechanisms {a,b,c,d,e}, S
2
has mechanisms {a,b,c,x,y}. When
we stimulate sub-system {a,b,c} of S
1
with stimuli s
f
response r
f
regularly occurs. We
can therefore infer that were we to stimulate sub-systems {a,b,c}of S
2
with s
f
r
f
would
probably occur” [[58] p141]. LaFollette and Shanks then explain that this outcome will
be highly probable if and only if {a,b,c} are causally independent of {d,e} and {x,y}.
Again we anticipate problems in using animal models as weak CAMs, even in the
traditional deterministic-causation view, because, as we shall discuss, various properties
of complex systems will likely give rise to difficulties in isolating subsystems, which
would be required for an an imal model to be a weak CAM. These problems have been
referred to as causal/functional asymmetr y and mandates caution in extrapolating data
between species. Kirschner and Gerhart give an example of this :
The case of the octopus and the human camera eye has been looked into, and the
lessons are clear. Underneath the gross anatomical similarities are many differences.
The eye derives from different tissues by different developmental means. Although
both structures use the same pigment (rhodopsin) for photoreception, and both send
electrical signals to the brain, we now know that the intervening circuitry is
completely different [[1] p240-01].
Independent evolution has also produced spindle neurons in species as diverse as
humans and cetaceans. Spindle neurons connect parts of the brain involved in higher
cognition and were thought to only occur in primates but have recently been discovered
in cetaceans, such as humpback whales and fin whales, as well as elephants [70-72].
Convergent evolution, the acquisition of the same trait in different lineages, is also
important when considering the role of animal models.
Evolved complex systems
Reductionism is a method of study that seeks to break a system down into its compo-
nent parts, study each part individually, and then reach a conclusion about the system
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 7 of 33
http://www.tbiomed.com/content/9/1/40
as a whole or at least the role of the individual part. Descartes introduced the concept
and it has proven effective for ascertaining many facts about the material universe.
Conversely, the clockwork universe of Descartes has not held up to scrutiny on all
levels. Quantum mechanics, relativity, chaos, and complexity have revealed the stochastic
nature of the supposedly clockwork, deterministic universe. Regrettably, while physicists
recognized the limitations of reductionism, biologists were uncritically embracing it. Francis
Crick extended reductionism to all aspects of biology when he stated: “T he ultimate aim of
the modern movement in biology is to explain all biology in terms of physics and chemistry ”
[73]. Biological reductionism arguably reached its zenith in the Human Genome Project
(HGP) [74,75] and, ironically, the consequences of the HGP—that humans have a relatively
small number of genes—have, in large part, been responsible for a re-examination of the
role of reductionism in biology. This has been especially true for human pathophysiology
where animals are used as models for humans.
Systems can be categorized as simple or complex. The world of Newton and Descartes
was largely confined to simple systems hence reductionism functioned well for discovery.
At some levels, the components of a complex system can be simple systems and thus are
subject to study by reductionism while at other levels these simple systems combine to
make complex systems thus necessitating study of the intact whole. Maz zocchi
points out that when reductionism takes a component out of its natural environment
it has consequences for extrapolating the results back to the organism a s a whole:
“But this extrapolation is at best debatable and at worst misleading or even hazardous. The
failure of many promising drug candidates in clinical research shows that it is not always
possible to transfer results from mice or even primates to humans” [76].
While evolution is defined as a change in allele frequency over time, complexity
science can be defined as “the study of the behaviour of large collections of simple,
interacting units, endowed with the potential to evolve with time” [77,78]. Living
organisms are complex systems that have highly variable evolutionary histories and as
such are best modeled using nonlinear differential equations. The difficulty with this
approach is that the values for many of the factors are unknown; hence solving the
equation is impossible [49,77].
Animals and humans are examples of living complex adaptive systems and as such
exhibit the followin g properties [79-97]:
1. Complex systems are composed of many components. Some of these components
may be simple systems, but many are complex systems. These components exist on many
scales and interact extensively with each other. A complex system is a “system of systems.”
2. The components can be grouped as modules. For example, the following could be
considered as modules: the cell; the various processes in a cell; gene networks; gene-
gene interactions; gene-protein interactions; protein-protein interactions; organs; and
all the factors that influence the natural history of a disease. Howe ver, failure in one
module does not necessarily spread demise to the system as a whol e as redundancy
and robustness (see #s 5 and 6 below) also exist and the various modules also
communicate with each other.
3. The different components of a complex system are linked to and affect one
another in a synergistic manner. There is positive and negative feedback in a complex
system [93].
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 8 of 33
http://www.tbiomed.com/content/9/1/40
4. A complex system demonstrates hierarchal levels of organization [98,99]. These
levels range from the subatomic to the molecular to the whole individual to collections
of individuals [100]. Emergence (see # 13 below) occurs at each level; therefore, even a
complete understanding of the lower level is insufficient for explaining the upper level.
The various levels interact such that there is both upward causation and downward
causation. In order to understand a particular level, one must alternate between
looking at the components and looking at the whole while taking into account the
connections between each [76,101]. Moreover, the various levels may respond
differently to the same perturbation.
The various levels of organization are important when considering which responses
to specific perturbations can be extrapolated among species. Living complex systems
have numerous properties that can be studied without consideration of the fact that the
whole, intact organism is a complex system. Some systems or components follow only
the laws of physics, or even simple geometry, while others are best described by their
physicochemical properties or just by chemistry. Some properties of complex systems
can be described simply by math formulas. Growth, for example, can be described as
geometrical in some cases and exponential in others. The surface area of a body
increases by the square of the linear dimensions while the volume increa ses by the
cube. This is a consequence of geometry and is important in physiology, in part,
because heat loss is proportional to surface area while heat production is proportio nal
to volume. Haldane stated: “Comparative anatomy is largely the story of the struggle to
increase surface in proportion to volumes ” [102]. For example, chewing increases the
surface area of food, the rate the small bowel absorbs nutrients and other chemicals
depends in part on the surface area of the small bowel, and air sacs in the lungs rely on
surface area for gas exchange, as do capillaries.
Allometry is the study of the relationship of body size to shape. Examples of allometric
laws include Kleiber’slaw:q
0
~ M
¾
where q
0
is metabolic rate and is proportional to M,
body mass, raised to the ¾ power. The rate t, of breathing and heart contractions are
proportional to M, body mass, raised to ¼ power: t ~ M
¼
. Further, many physiological
functions affect or depend on surface area.
Levels of organization can also be described based on whether they are primarily
chemical reactions and hence subject to analysis by chemistry. Reactions or perturba-
tions that involve the denaturation of proteins should affect all systems, be they simple
or complex, similarly because at this level of organization other factors do not come
into play. Exactly what effects sulfuric acid would have in a person over an extended
period of time are irrelevant as it denatures protein more or less immediately. Perhaps
species differences would manifest if small amounts of H
2
SO
4
were infused over long
periods of time, but the immediate effects are the same across species because of the
chemical properties of the acid.
Animals can be successfully used for numerous purposes in science (see 3–9in
Table 1). One of the purposes for which animals can be successfully used is to evaluate
phenomena that can be described by the physicochemical properties of the organism.
The same applies to basic physiologic functions. There are physiological parameters
that can be applied across specie s lines by the use of conversion factors based on the
weight or surface area of the organism. There are also properties of organisms that can
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 9 of 33
http://www.tbiomed.com/content/9/1/40
be anticipated by the physical or chemical properties of the substance acting on the
organism. All of these are instances of successfully treating a complex system as if it
were a simple system. Problems arise however, as the level of organization under study
increases. Allometric scaling based on body surface area (BSA), for example, does not
include differences that manifest at higher levels of organization for example in the
elimination or metabolism of drugs. Different levels of organization can be acted on by
single factors or many factors but perturbations of simple systems, or systems that can
be described as simple on the le vel or organization being affected, should produce
similar result s.
5. Complex systems are robust, meaning they have the capacity to resist change.
This can be illustrated by the fact that knocking out a gene in one strain of mouse
may produce negligible effects while being lethal to another strain. Gene pleiotropy is
an additional example [103].
6. Complex systems exhibit redundancy. For example, living systems exhibit
redundancy of some genes and proteins [103].
7. Complex systems are dynamic. They communicate with, and are acted on by,
their environment.
8. Complex systems exhibit self-organiz ation, which allows adaptation to the
environment [85,104-106]. The intact cell is a prime example of this property.
9. Complex systems are dependent on initial conditions. The well-known example of
the butterfly flapping its wings and causing a weather catastrophe on the other side of
the earth—the butterfly effect—is an example of dependence on initial conditions. An
example in living complex systems would be that very small differences in genetic
makeup between two systems could result in dramatic differences in response to the
same perturbation. For example, monozygotic twins raised in the same environment
may have different predispositions to diseases such as multiple sclerosis and
schizophrenia [107-110]. Additionally, the above-mentioned obser vation that knocking
out a gene results in different outcomes in two stains of mice illustrates the concept
that small differences in initial conditions—genetic makeup—can mean the difference
between life and death [93,103,111,112].
10. The initial conditions of a complex living system are determined, in part, by
evolution. Various spe cies have different evolutionary histories and thus are differently
organized complex systems. Initial conditions can be different, despite the exact same
genes, secondary to modifier genes, differences in regulation or expression of genes,
epigenetics, and mutations among others factors. For example, small epigenetic
changes probably account for the dissimilarities between monozygotic twins in terms
of disease susceptibility [107,108,113-116].
11. Perturbations to complex systems result in effects that are nonlinear [99]. Large
disturbances may result in no change to the system while minor perturbations may
cause havoc [76,105]. Efforts to describe complex systems in term s of linear cause
and effect relationships are prone to failure [117]. Extrapolating among complex
systems is e ven more problematic because of nonlinearity, along with the other
factors described.
12. The whole of a complex system is greater than the sum of the parts; hence, some
processes and or perturbations are not amenable to study by reductionism.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 10 of 33
http://www.tbiomed.com/content/9/1/40
13. Complex systems have emergent properties that cannot be predicted even in light
of full knowledge of the component parts.
Animal models have historically been utilized for the prediction of human responses
to drugs and disease and this use has also been the justification for animal use in
research in general [118,119]. But because various levels of organization and different
modules can be acted on by the same perturbation, in order to evaluate whether an
animal model can be used as a predictive modality, one needs to understand the levels
affected by the perturbation, what rules are being followed at those levels, and whether
the system is simple or complex at the respective levels. Empirical evidence, explained
and placed in context by theory developed from complexity science and evolutionary
biology, suggests animal models cannot predict human responses to drugs and disea se
[14-16,18,57,58,120], despite the presence of shared physicochemical properties and
conserved processes.
Conserved processes
Theodosius Dobzhansky famously stated: “Nothing in biology makes sense except in
the light of evolution.” We want to examine the consequences that various characteristics
of evolved complex systems, such as modules and different levels of organization, have
on processes conserved by evolution in terms of determining the response of whole
organisms to perturbations. Conserved processes and genes are the subject of much
interest today [1,121-133]. Kirschner and Gerhardt state: “all organisms are a mixture of
conserved and nonconserved processes (said otherwise, or changing and unchanging
processes)” [[1] p34-35]. Conserved processes are not reactions to the laws of physics or
the determination of properties of an organism as they relate to chemistry or geometry.
Nevertheless, conservation reaches across phyla and even kingdoms. Kirschner and
Gerhardt have pointed out that processes conserved include those involved in cell function
and organization, development, and metabolism and that these processes are similar in
animals, yeast, and bacteria. They note that novelty has been the result of using the
conser ved processes in different ways rather than inventing complete ly new processes
[[1] p34-35]. This has critical implications for what can be learned from interspecies study.
Housekeeping genes in general perform the same function; make the same proteins ,
in mice, frogs or humans. The role of FOX transcription factors is conserved among
species [134] as is the role of Sarco(endo)pla smic reticulum (SER) Ca
2+
ATPases
(SERCA) pumps [135]. Modules have also been con served. The fin mod ule of the
modern fish for example, arose roughly 400 million years ago and has been conserved
ever since [[1] p65].
Conserved processes include core genes like those in the homeobox that are involved in
the same developmental processes. Because these processes and genes are conserved
among species, we could reasonably expect the same outcome from the same perturbation,
regardless of the species containing these processes. But is this the case? In 1978 Lewis
[136] published his seminal work on the anterior-posterior layout of Drosophila.Thiswas
followed in 1984 by the discovery of the homeobox by McGinnis et al. [137]. The field of
evo devo developed in large part from this work. In the last decade, enormous strides have
been made as a result of research in evo devo and the various genome projects. The results
of such research have revealed an enormous genetic similarity among mammals. At the
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 11 of 33
http://www.tbiomed.com/content/9/1/40
level of the genes centrally involved in development, e.g., the homeobox genes, bilaterians
are virtually identical. The homeobox class of genes [138] are conserved across species
lines, functioning in early cellular organization and anterior-posterior body plan
layout [139]. There are important differences however. For example, there are nine
Hox genes in flies but thirty-nine in mammals. Pertinently, we understand how
modifications ( gene duplications , deletions , changes in the regulatory processes and
so forth) to these conser ved processes have resulted in the evolution of different
body types and indeed different species [138,140-142].
MicroRNA (miRNA) has been found in essentially all species from Caenorhabditis
elegans to humans and plays a large role in gene regulation [143-145]. Apparently , over
50% of miRNAs are conserved across species lines in vertebrates [145]. An important
consideration for drug development, however, is the fact that even though miRNA is
conserved, up to 50% of miRNAs differs among vertebrates. This is important when
considering the use of animals as predictive human models. Furthermore, miRNA
expression levels change when tissues deteriorate from a healthy state to a diseased
state [146-152]. Thus the exact role of miRNA may differ intra-individually depending
on age and disease. Hence, we see both inter-species and intra-individual differences
with respect to this conserved process.
It is well known tha t humans and nonhuman primates respond differently to infec-
tions. For example, untreated humans usually progress to AIDS when infected with
HIV, are susceptible to malaria (except those with sickle cell anemia), have different
reactions to hepatitis B and C than nonhuman primates and, appear more susceptible
to many cancers and Alzheime r’s disease [153-155]. Barreiro et al. [154] studied gene
expression levels in monocytes from humans, chimpanzees, and rhesus macaques and
found that all three species demonstrated “the universal Toll-like receptor response”
when stimulated with lipopolysaccharide (LPS). However they also discovered tha t only
58% of genes identified in the Toll-like receptor response “showed a conserved regula-
tory response to stimulation with LPS,” and only 31% of those genes demonstrated the
same conserved regulatory response when exposed to viruses or bacteria. Barreiro et al.
also discovered that 335 genes in humans are unique among the species in responding
to LPS, with 273 genes responding only in chimpanzees, and 393 only in rhesus maca-
ques [154]. Even in conserved processes, there are going to be significant differences
that influence the outcomes from disease perturbations. Significant differences in the
details of conserved processes (also illustrated by Figure 1 [156]) mean that there are
differences in the initial conditions of the complex system and this has major implica-
tions for inter-species extrapolation.
The implications of the various properties of complex systems also become apparent
when scientists study processes such as preimplantation embryonic development
(PED). PED is thought to be highly conserved among species which led Xie et al. [157]
to study gene expression profiles in embryos from humans, mice, and cows. They
found that: “40.2% orthologous gene triple ts exhibited differ ent expression patterns
among these species.” Differences in expression profiles have implications for drug and
disease response.
The Cdc14 gene was discovered in the yeast Saccharomyces cerevisiae and is classified
as a dual-specificity phosphatase. It has since been found in many organisms including
humans. Human Cdc14B fulfills the role, in yeast, of the yeast gene Cdc14. Because the
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 12 of 33
http://www.tbiomed.com/content/9/1/40
yeast gene plays a role in regulating late mitosis, it was assumed the gene would have
the same role in mammals. In actuality, neither Cdc14A nor Cdc14B are necessary for
cell-cycle progression in humans [158]. Thus, we have a conserved gene but not a
conserved function.
Pyrin proteins have been found to be ubiquitous in mammals. Pyrin-only protein 2
(POP2) was found in humans and thought to be important in inflammatory diseases.
Atianand et al. [159] studied mice but did not find POP2. They the n discovered that
POP2 was not in rodents or many other mammals but was present in chimpanzees
(Pan troglodytes ) and rhesus macaques ( Macaca mulatta). Moreover, the chimpanzee
Figure 1 Variation in sialic acid (Sia)-recognizing Ig-superfamily lectins among primates. “Expression
of CD33rSiglecs on human and great ape lymphocytes. (A) Percentage of positive lymphocytes for each
Siglec Ab (staining above negative controls) for 16 chimpanzees, 5 bonobos, and 3 gorillas are shown, as
well as data for 8 humans (the latter were tested on one or more occasions). Examples of flow cytometry
histograms of human (B) and chimpanzee (C) lymphocytes using Abs recognizing Siglec-3, Siglec-5, Siglec-7,
and Siglec-9 (y axis: normalized cell numbers expressed as percent of maximum cell number detected). In later
samples examined, low levels of Siglec-11 staining (<5% positive) were occasionally detected on lymphocytes
in both great apes and humans (data not shown)” [156].
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 13 of 33
http://www.tbiomed.com/content/9/1/40
POP2 was identical to humans POP2 at both the DNA and protein levels but the
macaque POP2 was not.
Conserved processes act, are affected by, or interact at multiple levels of organization.
As Cairns-Smith points out, proteins, catalysts, nucleic acids, membranes, and lipids
are interlocked and all are dependent on the others for their production. Cairns-Smith
summarizes by stating: “Subsystems are highly interlocked . . . The inter-locking is tight
and critical. At the centre everything depends on everything” [[81] p39]. The same per-
turbation may result in different effects or outcomes for different levels of organization
in the same intact system. This further complicates our ability to predict outcomes
between two intact living complex systems. Thus it appears that a perturbation of
complex system S
1
containing conserved process P
1
resulting in outcome O
1
will not
necessarily result in O
1
in the very similar complex system S
2
that also has P
1
.
We will now examine in more detail the response of organisms to inhalational anesthetics
and anti-neoplastic agents in order to illustrate what can and cannot be extrapolated
between spe cies knowing that spe cies are acted on and affe cted by the fundamental
principles of geometry, chemistry, and physics as well as shared conserved processes.
Conserved processes in anesthesia
General anesthesia by means of inhalational anesthetics (IAs) provides us with an excellent
opportunity to examine where the effects of conserved processes can and cannot be extra-
polated between species. We expect to see various effects at different levels of organization
and in different modules. We also anticipate effects on emergent properties. Bec ause
IAs act on the system as a whole, we expect to see effects that cannot be predicted from
reductionism. This has implications for what can be expected in terms of predicting human
response by studying a different species or perhaps even a different individual. Therefore,
both the primary effect of the anesthetic agent as well as the side effects may vary.
Broadly speaking, general anesthesia in humans and animals is define d by amnesia,
controlled insensitivity and consciousness, and immobility. It has been observed that most,
if not all, extant vertebrate species exhibit an anesthetic-like response to a wide variety of
chemicals that seemingly have little in common. This has been termed the universal
response. Multiple mechanisms for the universal response have been postulated and this is
an area of intense current research [160-164]. There seems to be general agreement that
ligand gated ion channel (LGIC) protein receptors are involved as well as possible effects on
the cellular membrane. Regardless of the exact details, the conservation of mechanisms can
be seen in that inhalational anesthetics (IAs) have observable effects on motor or motility
responses in vertebrates and invertebrates [165-168], tactile plants [169] and ciliated protists
[170]. (We note that this is probably an example of an exaptation, specifically a spandrel,
rather than an adaptation [171-173].) Interestingly, effects have even been observed in S.
cerevisiae (Baker’s yeast) [174], suggesting that crucial aspects of the universal response go
beyond metazoans to include Eukaryotes. Moreover, IAs have been shown to have effects
on membrane composition in prokaryote species [163,175] e.g., A. laidlawii [176,177],
Bacillus halodurans [175] and E. coli [178] and the single-celled eukaryote tetrahymena
[179,180] (a ciliated protozoan). The universal response appears to date far back in evolu-
tionary time and strongly suggests that the mechanism has been conserved among species.
However, there are differences in outcomes with respect to IAs. Humphrey et al.
[181] studied genes in Caenorhabditis elegans and Drosophila melanogaster in order to
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 14 of 33
http://www.tbiomed.com/content/9/1/40
assess the function of genes thought involved in the response to IAs. They found that a
gene in C elegans, unc-79, and a gene in Drosophila, narrow abdomen (na), were
related to each other and play a conserved role in response to anesthetics. However,
mutations in each gene produced unique changes in sensitivity to IAs. The sensitivity
to halothane, an IA, was increased but the sensitivity to enflurane, a different IA, was
unchanged or perhaps even lowered. This is perplexing because one would have
expected the two inhalational agents to be affected in a similar fashion by the mutation.
The gene unc-79 appears to be a post-transcriptional regulator of na, thus the genes
operate in the same pathway. Interestingly, both genes are also associated with similar
phenotypes regarding locomotion: “fainting” in C. eleg ans and “hesitant walking” in
Drosophila.
Stimulation of the conserved processes controlling the universal response results in
clinically significant variability among humans, even though the minimum alveolar
concentration (MAC) for IAs for most species is approximately the same. MAC is the
most often used metric to assess IA potency. However, the concept of MAC implies
variability. MAC
50
, simply called MAC in anesthesiology, is the minimum alveolar
concentration necessary to suppress movem ent in response to painful stimuli in 50% of
subjects [182]. MAC is significantly variable among humans depending on a number of
factors including age and sex. Why is this the case?
Sonner et al. reported, “one hundred forty-six statistically significant differences
among the 15 strains [of mice] were found for the three inhaled anesthetics (isoflurane,
desflurane, and halothane)” [164]. They concluded that multiple genes must be
involved in anesthetic potency. Wang et al. developed two strains of mice that mani-
fested different sensitivities to isoflurane [183]. MAC is an example of a phenomenon
controlled by quantitative trait loci [184] , which may explain in part why, while one
can obtain a rough approximation of MAC by studying other species, there will still be
clinically significant differences.
IAs also function at different levels of organization and on modules in addition to the
one involved in the universal response. The side effects of the same chemical that
produce an effect on the conserved receptors or other processes vary greatly from
species to species and in some cases, e ven from person to person. A good example is
the case of isoflurane and coronary steal. In the 1980s, there was heated controversy
regarding the administration of the inhalation anesthetic isoflurane to patient s with
heart disease. The controversy centered on research using canines that indicated that
the drug caused myocardial ischemia during certain situations in patients with coronar y
disease. The phenomenon appeared to result from isoflurane causing dilation of the
normal coronary arteries, and thus blood being shunted away from the occluded coron-
ary arteries; the arteries and tissues that most needed it. This was called coronary steal.
Further, this situation was worsened by a decrease in blood pressure; a condition that
often occurs during general anesthesia with IAs. This supposed danger, based almost
entirely on studies in canines, wa s seized on by many in the anesthesiology community
as dogma [185,186].
This was an interesting reaction from clinicians for two reasons. First, experiments
with other species had failed to demonstrate coronary steal [187,188] and second,
anesthesiologists had not noticed ischemic changes associated with isoflurane despite
much use of the agent. The situation was also trouble some because isoflurane was a
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 15 of 33
http://www.tbiomed.com/content/9/1/40
needed addition to an anesthesiologist’s armamentarium when initially approved for
clinical practice. Six years after its introduction, it was the most frequently used IA, in
part because of the favorable properties of the drug [185]. Further studies continued to
demonstrate vary ing effects intra- and inter-species [189,190]. Ultimately, studies began
to appear that suggested isoflurane was in fact cardio-protective. The mechanism for
this protection was called “preconditioning and involves the opening of adenosine
triphosphate-dependent potassium channels” [191]. Isoflurane went from being contrain-
dicated in patients with coronary artery disease to being the drug of choice in such
patients. Studies from animals, specifically dogs, figured heavily in forming both, mutually
exclusive, conclusions.
Just as with the homeobox, miRNAs, and the response to inflammation, there are
differences among species in how the conserved process known as the universal
response to anesthesia manifests. Clinically, these differences are significant and limit
the amount of information that can be extrapolated between species even when the
underlying process is conserved. Inhalation anesthetics are also a good example of why,
when the level of organization or module being examined changes, extrap olation breaks
down. The same chemical that induces general anesthesia in a dog will probably result
in the same effect in humans but the dose may vary in a clinically significant fashion
and the side effects will most likely vary, as the conse rved process does not dictate the
side effects. Differences in outcomes from perturbations like the ones we have seen
above have been explained by evolution-based species-specific differences, for example
background genes, mutations, expression levels, and modifier genes [192-209].
Anti-neoplastic drugs acting on mitosis
As discussed, a relationship exists between BSA and many physiological parameters
[210]. For example, Reagan-Shaw, Nihal, and Ahmad state: “BSA correlates well across
several mammalian species with several parameters of biology, including oxygen
utilization, caloric expenditure, basal metabolism, blood volume, circulating plasma
proteins, and renal function” [211]. Dosing algorithms for first-in-man (FIM) trials are
based on the assumption that there is a one-to-one dose scale between humans and
animals when BSA is taken into account [212]. The first study suggesting a relationship
between dose and body surface area was performed by Pinkel in 1958 [210] involving
anti-neoplastic agents, drugs where the effe cts and side effects are largely the same—
cell death. Subsequently, Freireich et al., [213] studied 18 anti-neoplastic drugs in six
animal species and concluded that the maximum tolerated dose (MTD) for humans
was 1/12 of the dose in mice that resulted in the death of 10% of the mice (LD10 ). They
also noted that the MTD was 1/7 of the LD10 in rats. These were also the ratios for
converting from a mg/kg dose to a dose based on BSA. Fifty anti-neoplastic drugs were
then studied using this formula and all were reportedly introduced into human trials
without incident [214,215]. The standard for FIM doses then became the 1/10
th
the
LD10 for mice. Actually Freireich recommended a starting dose of 1/3
rd
the LD10 not
1/10
th
but that changed over time. The 1/3
rd
recommendation was found to be too
large for FIM and wa s changed to 1/10
th
[216]. More studies appeared to confirm the
1/10
th
value [217].
The above makes a prima facie ca se that animal models can pred ict a starting dose
for humans in clinical trials for anti-neopla stics. Further substantiating this is the fact
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 16 of 33
http://www.tbiomed.com/content/9/1/40
that anti-neoplastics are not always metabolized by the liver [218], thus possibly elimin-
ating a complex system from consideration. Cell division by mitosis is arguably the
most conserved process one can find in biology and the traditional drugs for treating
cancer act by interfering with mitosis. (Newer drugs act on targeted pathways as
opposed to the cell cycle.) Anti-neoplastics kill the cells that are dividing most rapidly
—the cancer cells. However, hair cells, cells in the bone marrow, and cells in the gut
also divide at a similar rate such that anti-neoplastics can affect them. Thus, in part,
the effects and side effects of anti-neoplastics are the same—cell death. The problem
with traditional anti-neoplastic s is that they do not discriminate adequately.
Anti-neoplastic drugs are unique in medicine in that: 1) they are nonspecific; 2) long
term toxicities are anticipated and accepted because the patient frequently does not
have any other viable options; 3) the effect s and side effects of the drugs are the same
—cell death; and 4) they act on a universally conserved process—mitosis. This is why
body surface area appears to be so effective for calculating FIM dose. Whereas, when
one is examining effects and side effect s of drugs based on interactions at the level of
organization where complexity is rele vant, for example metabolism [219-229], there are
simply too many other factors to allow for the expectation of one-to-one correlations.
Species-specific differences create perturbations in the complex system thus the differ-
ences among species outweigh the similarities [13-16,18,21-41].
However, in the final analysis even the FIM dose of the anti-neoplastic agents cannot
be reliably ascertained based on BSA. Most anti-neoplastics are effective only at doses
near the maximum tolerated dose and the drugs are given in an escalating fashion
during clinical trials. “Patients treated at the lower end of the dose escalation strategy
are unlikely to receive even a potentially therapeuti c dose since most cytotoxic drugs
are only active at or near the MTD” [217]. Differences among species in dose response
for anti-neoplastics are due in part to differences in pharmacokinetics [217,230-232],
which cannot be accou nted for based on BSA. Brennan et al. state that: “While prope r
determination of drug doses can be complicated within the same species, it can be an
incredible challenge and burden between species” [233]. Brennan et al. continue by
pointing out that metabol ism and clearance differ among species and that “...the liver,
kidneys and hematopoietic system between species may have significant differ ences in
their sensitivity to chemotherapeutic agents. None of these factors are taken into
account with the use of the species-specific dose calculations” [233]. They recommend
area under the curve (AUC) for calculating FIM dose but then concede: “However,
there are numerous examples in which the species -specific conversion dose varies
significantly from the AUC guided dose and/or far exceeds the animal’s maximum
tolerated dose.” They then list examples from pediatrics where the recommended and
actual doses differ significantly [233].
Horstmann et al. [234] reviewed 460 Phase I National Cancer Institute trials involving
11,935 adults that occurred between 1991 and 2002. Approximately 25% of the trials
were FIM trials. Horstmann et al. found that ser ious nonfatal effects occurred in 15%
of the patients undergoing single chemotherapy, with 58 deaths that were probably
treatment-related [234,235]. Concern has also been expressed that animal models have
derailed anti-neoplastics that would have been successful in huma ns [30,235-239].
FIM dose based on animal models is ineffective for predicting dose for other drug
classes as well-TGN1412 being a recent notable example [26,240,241]. An unnamed
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 17 of 33
http://www.tbiomed.com/content/9/1/40
clinician, speaking of toxicity trials for new drugs in general in humans, was quoted in
Science, stating, “If you were to look in [a big company’s] files for testing small-
molecule drugs you’d find hundreds of deaths” [242]. Chapman reinforced this stating:
“. . . but other incidents of harm [besides TGN1412], even death, to part icipants in
Phase I trials, some then known and other unpublicized, had taken place” [235]. It is
also important to note that the 1/3
rd
or 1/10
th
safety factor is fabricated. Perlstein et al.
state: “Due to uncertainty in translating animal model findings to humans, particularly
for unprecedented mechanisms, a wide dose range (1000-fold) is exp ected to cover the
entire exposure–response curve” [243]. Extrapolating from species to species should
not require fudge factors if the process is truly scien ce-based. In Phase I trials, where
FIM or first in human (FIH) occurs, scientists want to characterize the drug’s PK prop-
erties and safety margins [244]. Wexler and Bertelsen summarize the situation when
they state: “Although allometric scaling techniques continue to provide poor predictive
estimates for human pharmacokinetic parameters, FIH starting doses are selected with
substantial safety factors applied to human equivalent dose, often in excess of regulatory
guidelines....Approachesthatcouldenhancethepredictivenatureofacompound’s
disposition and adaptive nature of FIH studies could provide a tremendous benefit for
drug development” [245]. FIM for all classes of drug could be easily accomplished using
microdosing [246-248] with the first dose of one nanogram [249,250] and increasing
subsequent doses to the desired endpoint.
Finally, one must recall that 95% [31,251,252] of anti-neoplastic agents fail in clinical trials.
Oncology drugs fail more frequently in clinical trials than most other categories [253,254]
and a higher percentage of anti-neoplastic drugs fail in Phase III trials than drugs from any
other category [255]. Reasons for the attrition include the fact that most of the effects and
side effects, even of the anti-neoplastic agents, when placed into the context of a complex
system, are not predicted from animal studies. Interfering in mitosis is a universal
phenomenon but the degree and success of that interference varies. The FIM dose estima-
tion is apparently successful because the level of organization in question is very basic and
conserved and because the dose is lowered even further by fudge factors. Picking a starting
dose based on the most toxic substances in nature [249,250] would be more scientific. The
apparent success also breaks down because the types of cancers in humans differ from those
in animals, the genetic background of humans varies from that in animals, and because the
reality of a complex system—the interactions of all the other systems (for example how the
drugs are eventually metabolized and eliminated and how those metabolites interact with
other systems and so on)—eventually appear. These are the problems that cannot be solved
by animal models and are why the attrition rate is 95%. Weinberg stated: “it’s been well
known for more than a decade, maybe two decades, that many of these preclinical human
cancer models have very little predictive power in terms of how actual human beings—actual
human tumors inside patients—willrespond...preclinicalmodelsofhumancancer,inlarge
part, stink . . . hundreds of millions of dollars are being wasted every year by drug companies
using these [animal] models” [236]. Others have also pointed out the inadequacy of animal
models of cancer, including genetically modified animal models [41,214,252,256-261].
Conserved processes in light of systems biology
As the level of organization in a complex system increases, we expect to see an increase
in the number of emergent properties as well as more overall interactions. A gene or
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 18 of 33
http://www.tbiomed.com/content/9/1/40
process that has been conserved will interact with the intact whole organism yielding
new processes and states. Perturbations of these conserved genes or processes will thus
likely result in new states not seen in other organisms that share the conserved
processes; perhaps not even in organisms of the same lineage (clade) or species.
The lack of appreciation for the differences between levels of organization and other
properties of complex systems is apparent in the following from Kardon g [[262] p2],
writing in his textbook of comparative vertebrate anatomy: “For example, by testing a
few vertebrate muscles, we may demonstrate that they produce a force of 15 N (newtons)
per square centimeter of muscle fiber cross section. Rather than testing all vertebrate
muscles, a time-consuming process, we usually assume that other muscles of similar cross
section produce a similar force (other things being equal). The discovery of force production
in some muscles is extrapolated to others. In medicine, the comparative effects of drugs on
rabbits or mice are extrapolated to tentative use in humans.” At the level of organization
where one studies the force generated by muscle fibers, no doubt inter -species extrapolation
is useful, but that is an entirely different level from where drug actions occur.
Indeed the successes from using animal models have been examples of pert urbations
occurring at subsystems that can be described as simple systems and or outcomes or
characteristics that apply on the gross level of examination. For example, the Germ
Theory of Disease applies to humans and animals. The immune system reacts to
foreign entities in a manner that is grossly similar across specie s lines. The details of
immunity are clinically very different, for example HIV infection leads to AIDS in
humans but not chimpanzees [263-265]. Nevertheless, grossly, inflammation, white
blood cells, and antibodies are identifying characteristics of the immune system in the
phylum Chordata. Likewise, while the heart functions to circulate the blood in
mammals, the diseases various mammalian hearts are subject to differ considerably
[266-272]. The fail ures of animal models have occurred when attempting to extrapolate
data from higher levels of organization, levels where complexity is an important com-
ponent in the system or subsystem under consideration. For example, a drug that has
passed animal tests and is in Phase I human clinical trials has only an 8% chance of
making it to market [273]. Over 1,000 drugs have been shown to improve outcomes in
cerebral ischemia in animal models but none, save aspirin and thrombolysis, which
were not animal-based discoveries, have been successful in humans [35,274-277]. The
animal model for polio, monkeys, revealed a pathophysiology that was very different
from that of humans [278-281]. Extracranial-intracranial bypass for inoperable carotid
artery disease was successful in animals but results in net harm for humans [282-285].
Most diseases are multifactorial hence it should come as no surprise that conserved
processes play a sm all, although at times important role, in major diseases like heart
disease, cancer and stroke. The field of systems biology wa s formed in part in an
attempt to place the parts of molecular biology and genet ics in the larger context of the
human system; the system that actually responds to drugs and disease. An editorial in
Nature asks: “What is the difference between a live cat and a dead one? One scientific
answer is 'systems biology'. A dead cat is a collection of it s component parts. A live cat
is the emergent behaviour of the system incorporating those parts” [286]. According to
the Department of Systems Biology at Harvard Medical School: “Systems biolog y is the
study of systems of biological components, which may be mole cules, cells, organisms
or entire species. Living systems are dynamic and complex, and their behavior may be
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 19 of 33
http://www.tbiomed.com/content/9/1/40
hard to predict from the properties of individual parts” [287]. Systems biology [288]
takes a top-down approach as opposed to reductionism, which evaluates organisms
from the bottom-up. Systems biology is concerned more with networks tha n individual
component s, although both are studied. It also recognizes the importance of emergent
phenomena. (See Figure 2 [79]). Such top-down approaches are used by the fields com-
monly referred to as “Omics,” for example: interactomics, metabolomics, proteomics,
transcriptomics, and even fractalomics [289].
Nobel laureate Sydney Brenner, in 1998, emphasized that the interactions of components
was important in understanding an organism [290]. Only by studying proteins and
processes in the context of their systems can we expect to understand what happens to the
intact organisms as a result of these processes and genes. Further, evolution uses old
pathways and processes in different ways to create novelty [1,133]. Everything is context
dependent. Noble stresses that in order to predict how drugs will act, one must understand
“how a protein behaves in context” at higher levels of organization [291].
Heng [292], writing in JAMA states that, because of reductionism, biological scientists
have sought individual components in a disease process so they could intervene. A linear
cause and effect relationship was assumed to exist. Heng cites diabetes intervention in an
attempt to control blood glucose and cancer therapies as examples. He points out that
while this has worked well in many cases, very tight control of blood glucose was recently
found to increase the risk of death [293]. Along the same lines, chemotherapies for cancer
have decreased the size of the tumors but at the expense of an increase in frequency of
secondary tumors and a very adversely affected lifestyle. Furthermore, most chemotherapy
does not prolong life or result in a longer, high quality life [294-296]. Instead of focusing on
small modules or components of a system, complexity theory mandates that biomedical
science look at the system as a whole.
Closely related to systems biology are the concepts of personalized medicine and
pharmacogenomics [226,297-305]. It has long been appreciated that humans respond
differently to drugs and have different susceptibilities to disease. Based on studies of
Figure 2 Reductionism versus systems biology.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 20 of 33
http://www.tbiomed.com/content/9/1/40
twins, there appears to be a genetic component to susceptibility to leprosy, poliomyelitis
and hepatitis B, as well as response to opioids [306-309]. Other infectious diseases that
appear to have a genetic component to susceptibility include HIV, Hepatitis C, malaria,
dengue, meningococcal disease, variant Creutzfeldt–Jakob disease and perhaps tuberculosis
among others [310]. Differences in drug and disease response are manifest among ethnic
groups [311-319] and sexes [320-326]. Even monozygotic twins manifest differences in
response to such perturbations [107,108,113-116]. Rashmi R Shah, previous Senior Clinical
Assessor, Medicines and Healthcare products Regulatory Agency, London stated in 2005:
“During the clinical use of a drug at present, a prescribing physician has no means of
predicting the response of an individual patient to a given drug. Invariably, some patients
fail to respond beneficially as expected whereas others experience adverse drug reactions
(ADRs)” [327].
Similarly, Allen Roses, then-w orldwide vice-president of genetics at GlaxoSmithKline
(GSK), said fewer than half of the patients prescribed some of the most expensive drugs
derived any benefit from them: “The vast majority of drugs - more than 90% - only
work in 30 or 50% of the people.” Most drugs had an efficacy rate of 50% or lower
[328]. Because of differences in genes, like SNPs, all children may not currently be
protected by the same vaccine [329,330]. It is estimated that “between 5 and 20 per
cent of people vaccinated against hepatitis B, and between 2 and 10 per cent of those
vaccinated agains t measles, will not be protected if they ever encounter these viruses”
[330]. In the future suc h children may be able to receive a personalized shot. Currently,
numerous drugs have been linked to genetic mutations and alleles. See Table 5 [303]
and Table 6 [331]. The number of personalized medicine products has increased from
13 in 2006 to 72 as of 2012 [332].
When animals were being used as models in the 19
th
century, many of the scientists
who were using them had not accepted evolution and believed that animal parts were
interchangeable with their human counterparts [60,62,63]. Given that we now under-
stand that intra-human variation results in such markedly different responses to drugs
and disease, attem pting to predict human response from animal models, even for
perturbations acting on conserved processes, seems unwarranted. Yet, despite the
implications of personalized medicine [22], some scientists continue to commit the
fallacy described by Burggren and Bemis: “Yet the use of ‘cockroach as insect,’‘frog as
amphibian,’ or ‘the turtle a s repti le’ persists, in spite of clear evidence of the dangers of
this approach. Not surprisingly, this type of comparative physiology has neither
contributed much to evolutionary theories nor drawn upon them to formulate and test
hypotheses in evolutionary physiology” [[333] p206]. Comparative research will yield a
nice comparison of the trait or process among species or phyla. However, one simply
cannot assume that the outcome from a specific perturbation in, say the cockroach, will
be seen in insects in general and this concept becomes even more important when
relying on animal models for medical interventions in humans.
Conclusion
A perturbation of living complex system S
1
containing conserved process P
1
resulting
in outcome O
1
will not result in O
1
in the very similar living complex system S
2
that
also has P
1
often enough to qualify S
1
as a predictive modality for S
2
when the trait or
response being stud ied is located at higher levels of organization , is in a different
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 21 of 33
http://www.tbiomed.com/content/9/1/40
Table 5 Examples of drugs with genetic information in thier labels
Drug Sponsor Indication Gene or
genotype
Effect of
genotype
Clinical directive on
label
Abacavir
(Ziagen)
GlaxoSmithKline HIV-1 HLA-B*5701 Hypersensitivity Black-box warning.
”Prior to initiating
therapy with abacavir,
screening for the HLA-
B*5701 allele is
recommended.”“Your
doctor can determine
with a blood test if
you have this gene
variation.”
Azathioprine
(Imuran)
Prometheus Renal allograft
transplantation,
rheumatoid
TPT*2TPT*3Aand
TPMT*3C
Severe
myeloxicity
“TPT genotyping or
phenotyping can help
identify patients who
are at an increased
risk for developing
Imuran toxicity.”
“Phenotyping and
genotyping methods
are commercially
available.”
Carbamazepine
(Tegretol)
Novartis Epilepsy,
trigeminal
neuralgia
HLA-B*1502 Stevens-
Johnson
syndrome or
toxic epidermal
necrolysis
Black-box warning:
“Patients with
ancestry in genetically
at-risk populations
should be screened
for the presence of
HLA-B*1502 prior to
initiating treatment
with Tegretol. Patients
testing positive for
the allele should not
be treated with
Tegretol.”“For
genetically at-risk
patients, high-
resolution HLA-B*1502
typing is
recommended.”
Cetuximab
(Erbitux)
Imclone Metastatic
colorectal
cancer
KRAS mutations Efficacy “Retrospective subset
analyses of metastatic
or advanced
colerectal cancer trials
have not shown a
treatment benefit for
Erbitux in patients
whose tumors had
KRAS mutations in
codon 12 or 13. Use
of Erbitux is not
recommended for the
treatment of
colorectal cancer with
mutations.”
Clopidogrel
(Plavix)
Bristol-Myer
Squibb
Anticoagulation CYP2C19*2*3 Efficacy “Tests are available to
identify a patient’s
CYP2C19 genotype;
these tests can be
used as an aid in
determining
therapeutic strategy.
Consider alternative
treatment or
treatment strategies
in patienrs identified
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 22 of 33
http://www.tbiomed.com/content/9/1/40
module, or is influenced by other modules. However, when the examination of the
conserved process occurs at the same or lower level of organization or in the same
module, and hence is subject to study solely by reductionism, then extrapolation is
possible. We believe this is a valuable principle.
Our current understanding of evo de vo, evolution in general, complexity science, and
genetics allows us to generalize regarding trans-spe cies extrapolation, even when
conserved processes are involved. Shanks and Greek:
Table 5 Examples of drugs with genetic information in thier labels (Continued)
as CYP2C19 poor
metabolizer.”
Irinotecan
(Camptosar)
Pfizer Metastatic
colorectal
cancer
UGT1A1*28 Diarrhea
neutropenia
“A reduction in the
starting dose by at
least one level of
Camptosar should be
consider for patients
knows to be
homozygous for the
UGT1A1*28 allele. “A
laboratory test is
available to determine
the UGT1A1 status of
patients.”
Pantumumab
(Vectibix)
Amgen Metastatic
colorectal
cancer
KRAS mutations Efficacy “Retrospective subset
analyses of metastatic
colorectal cancer trials
have not shown a
treatment benefit for
Vectibix in patients
whose tumors had
KRAS mutations in
codon 12 or 13. Use
of Vectibix is not
recommended for the
treatment of
colorectal cancer with
these mutations.”
Transtuzumab
(Herceptin)
Genetech HER2-positive
breastcancer
HER2 expression Efficacy “Detection of HER2
protein
overexpression is
necessary for
selection of patients
appropriate for
Herceptin therapy
because these are the
only patients studied
and for whom benefit
has shown.”“Several
FDA-approved
commercial assays are
available to aid in the
selection of breast
cancer and metastatic
cancer patients for
Herptin therapy.”
Wafarin
(Coumadin)
Bristol-Myer
Squibb
Venous
thrombosis
CYP2C9*2*3 and
VKORC1 variants
Bleeding
complications
Includes the following
table: Range of
Expected Therapeutic
Warfarin Doses Based
on CYP2CP and
VKORC1 Genotypes.
*All drug labels were accessed through Drugs @FDA at www.accessdata.fda.gov/scripts/cder/drugsatfda. HIV-1 denotes
human immunodeficiency virus type 1, TPMT thiopurine methyltransferase, UGT1A1 UDP glucuronosyltransferanse 1
family polypeptide A1, and VKORC1 vitamins K epoxide reductase complex subunit 1
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 23 of 33
http://www.tbiomed.com/content/9/1/40
Living complex systems belonging to different species, largely as a result of the
operation of evolutionary mechanisms over long periods of time, manifest different
responses to the same stimuli due to: (1) differences with respe ct to genes
present; (2) differences with respe ct to mutations in the same gene (where one
species has an ortholog of a gene found in another); (3) differences with
respect to proteins and protein activity; (4) differences with respect to gene
regulation; (5) diffe rences in gene expression; (6) differe nces in protein-protein
interactions; (7) differences in genetic networks; (8) differences with respect to
organismal organization (humans and rats may be intact systems, but may be
differently intact); (9) differences in environmental exposures; and last but not
least; (10) differences with respe ct to evolutionary histories. These are some of
the important reasons why members of one spe cies often respond differently to
Table 6 The most significant genetic predictors of drug response
Organ or system involved Associated gene/allele Drug/drug response phenotype
Blood
Red blood cells G6PD Primaquine and others
Neutrophils TMPT*2 Azathioprine/6MP-induced neutropenia
UGT1A1*28 Irintotecan-induced neutropenia
Plates CYP2C19*2 Stent thrombusis
Coagulation CY2C9*2, *3, VKORC1 Warfarin dose-requirement
Brain and peripheral nervous system
CNS depression CYP2D6*N Codeine-related sedation and respiratory depression
Anaesthesia Butyrylcholinesterase Prolonged apnoea
Peripheral nerves NAT-2 Isoniazid-induced peripheral neuropathy
Drug hypersesitivity HLA-B*5701 Abacavir hypersensitivity
HLA-B*1502 Carbamazepine-induced Steve Johnson syndrome
(in some Asian groups )
HLA-A*3101 Carbamazepine-induced hypersensitivity in Causians
and Japanese
HLA-B*5801 Allopurinol-induced serious cutaneous reactions
Drug-induced liver injury HLA-B*5701 Flucloxacillin
HLA-DR81*1501-DQ81*0602 Co-amoxiclav
HLA-DR81*1501-DQ81*0602 Lumiracoxib
HLA-BR81*07-DOA1*02 Ximelagatran
HLA-DQA1*0201 Lapatinib
Infection
HIV-1 infection CCRS Maraviroc efficacy
Hepatitis C infection IL288 Interferon-alpha efficacy
Malignancy
Breast cancer CYP2D^ Response to tamoxifen
Chronic myeloid leukaemia BCR-ABL Imatinib and other tyrosine kinase inhibitors
Colon cancer KRAS Cetuximab efficacy
GI stromal tumours c-kit Imatinib efficacy
Lung cancer EGFR Gefinib efficacy
EML4-ALK Crizotinib efficacy
Malignant melanoma BRAF V600E Vemurafenib efficacy
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 24 of 33
http://www.tbiomed.com/content/9/1/40
drugs and toxins , and experienc e different diseases. Immense empirical e vidence
supports this position ([14] p358).
The failures of animal mod els as a predictive modality for human response to disease
and drugs, even when such perturbations are acting on conserved processes, can be
explained in the context of evolved complex systems. One does not need to study every
such perturbation in every spe cies in order to conclude that the animal model will not
be a predictive modality for humans when perturbations occur at higher levels of
organization or involve different modules or affect the system as a whole. This is not to
deny that animal models, as characterized by 3–9 in Table 1, have contributed and will
continue to contribute to scientific advancements.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
The authors contributed equally to this paper.
Authors’ information
Ray Greek, MD has been on faculty in the Department of Anesthesiology at the University of Wisconsin-Madison and
at Thomas Jefferson University in Philadelphia. He is currently president of the not-for-profit Americans For Medical
Advancement (www.AFMA-curedisease.org).
Mark Rice, MD is currently on faculty at the University of Florida (UF). He is chief of the liver transplant division at UF
Department of Anesthesiology, has seven US patents, and reviews for several major journals.
Acknowledgements
None.
Author details
1
Americans For Medical Advancement (www.AFMA-curedisease.org), 2251 Refugio Rd, Goleta, CA 93117, USA.
2
Department of
Anesthesiology, University of Florida College of Medicine, PO Box 100254, Gainesville, FL 32610-0254, USA.
Received: 30 July 2012 Accepted: 31 August 2012 Published: 10 September 2012
References
1. Kirschner MW, Gerhart JC: The Plausibility of Life . New Haven: Yale University Press; 2006.
2. Braithwaite RB: Scientific explanation: a study of the function of theory, probability and law in science. Cambridge:
Cambridge University Press; 1953.
3. Hinde R: Animal-Human Comparisons.InThe Oxford Companion to the Mind. Edited by Gregory RL. Oxford:
Oxford University Press; 1987:25–27.
4. Frigg R, Hartmann S: Scientific Models.InThe Philosophy of Science: An Encyclopedia Volume 2 N-Z. Edited by
Sarkar S, Pfeifer J. New York: Routledge; 2012:740–749.
5. Shapiro K: Animal Model Research. The Apples and Oranges Quandry. ATLA 2004, 32:405–409.
6. Hau J: In Animal Models, Handbook of Laboratory Animal Science Second Edition Animal Models, Volume II. 2nd
edition. Edited by Hau J, van Hoosier GK Jr. Boca Rotan: CRC Press; 2003:1–9.
7. Gad S: Preface.InAnimal Models in Toxicology. Edited by Gad S. Boca Rotan: CRC Press; 2007:1–18.
8. Longer Tests on Lab Animals Urged for Potential Carcinogens. http://www.cspinet.org/new/200811172.html.
9. Huff J, Jacobson MF, Davis DL: The limits of two-year bioassay exposure regimens for identifying chemical
carcinogens. Environ Health Perspect 2008, 116:1439–1442.
10. Devoy A, Bunton-Stasyshyn RKA, Tybulewicz VLJ, Smith AJH, Fisher EMC: Genomically humanized mice:
technologies and promises. Nat Rev Genet 2012, 13:14–20.
11. Vassar R: Alzheimer's therapy: a BACE in the hand? Nat Med 2011, 17:932–933.
12. THS CEO criticized for links to animal testing. http://m.torontosun.com/2011/09/23/ths-ceo-criticized-for-links-to-
animal-testing?noimage.
13. Heywood R: Clinical Toxicity--Could it have been predicted? Post-marketing experience.InAnimal Toxicity
Studies: Their Relevance for Man. Edited by Lumley CE, Walker S. Lancaster: Quay; 1990:57–67.
14. Shanks N, Greek R: Animal Models in Light of Evolution. Boca Raton: Brown Walker; 2009.
15. Greek R, Greek J: Is the use of sentient animals in basic research justifiable? Philos Ethics Humanit Med 2010, 5:14.
16. Greek R, Shanks N: FAQs About the Use of Animals in Science: A handbook for the scientifically perplexed. Lanham:
University Press of America; 2009.
17. Shanks N, Greek R: Experimental use of nonhuman primates is not a simple problem. Nat Med
2008, 14:807–808.
18. Shanks N, Greek R, Greek J: Are animal models predictive for humans? Philos Ethics Humanit Med 2009, 4:2.
19. Shanks N, Greek R, Nobis N, Greek J: Animals and Medicine: Do Animal Experiments Predict Human Response?
Skeptic 2007, 13:44–51.
20. Greek R: Letter. Dogs, Genes and Drugs. Am Sci 2008, 96:4.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 25 of 33
http://www.tbiomed.com/content/9/1/40
21. Greek R, Hansen LA, Menache A: An analysis of the Bateson Review of research using nonhuman primates.
Medicolegal Bioethics 2011, 1:3–22.
22. Greek R, Menache A, Rice MJ: Animal models in an age of personalized medicine. Personalized Med 2012, 9:47–64.
23. Greek R, Shanks N, Rice MJ: The History and Implications of Testing Thalidomide on Animals. The Journal of
Philosophy, Science & Law 2011, 11. http://www6.miami.edu/ethics/jpsl/archives/all/TestingThalidomide.html.
24. Collins FS: Reengineering Translational Science: The Time Is Right. Sci Transl Med 2011, 3:90cm17.
25. Cook N, Jodrell DI, Tuveson DA: Predictive in vivo animal models and translation to clinical trials. Drug Discov
Today 2012, 17:253–260.
26. Dixit R, Boelsterli U: Healthy animals and animal models of human disease(s) in safety assessment of human
pharmaceuticals, including therapeutic antibodies. Drug Discov Today 2007, 12:336–342.
27. Drake DR III, Singh I, Nguyen MN, Kachurin A, Wittman V, Parkhill R, Kachurina O, Moser JM, Burdin N, Moreau M,
et al : In Vitro Biomimetic Model of the Human Immune System for Predictive Vaccine Assessments. Disruptive
Sci Technol 2012, 1:28–40.
28. FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficient. http://www.fda.gov/
NewsEvents/Newsroom/PressAnnouncements/2006/ucm108576.htm.
29. Fletcher AP: Drug safety tests and subsequent clinical experience. J R Soc Med 1978, 71:693–696.
30. Horrobin DF: Modern biomedical research: an internally self-consistent universe with little contact with
medical reality? Nat Rev Drug Discov 2003, 2:151–154.
31. Kola I, Landis J: Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711–715.
32. Lumley C: Clinical toxicity: could it have been predicted? Premarketing experience.InAnimal Toxicity Studies:
Their Relevance for Man. Edited by Lumley C, Walker S.: Quay; 1990:49–56.
33. M.E: This Issue.InModels that better mimic human cancer, Nat Biotechnol, Volume 28.; 2010:vii.
34. Markou A, Chiamulera C, Geyer MA, Tricklebank M, Steckler T:
Removing obstacles in neuroscience drug
discovery: the future path for animal models. Neuropsychopharmacol Offic Publ Am Coll Neuropsychopharmacol
2009, 34:74–89.
35. O'Collins VE, Macleod MR, Donnan GA, Horky LL, van der Worp BH, Howells DW: 1,026 experimental treatments
in acute stroke. Ann Neurol 2006, 59:467–477.
36. Sharp PA, Langer R: Promoting Convergence in Biomedical Science. Science 2011, 333:527.
37. Sietsema WK: The absolute oral bioavailability of selected drugs. Int J Clin Pharmacol Ther Toxicol 1989, 27:179–211.
38. Suter K: What can be learned from case studies? The company approach.InAnimal Toxicity Studies: Their
Relevance for Man. Edited by Lumley C, Walker S. Lancaster: Quay; 1990:71–78.
39. Wall RJ, Shani M: Are animal models as good as we think? Theriogenology 2008, 69:2–9.
40. Weaver JL, Staten D, Swann J, Armstrong G, Bates M, Hastings KL: Detection of systemic hypersensitivity to
drugs using standard guinea pig assays. Toxicology 2003, 193:203–217.
41. Zielinska E: Building a better mouse. Scientist 2010, 24:34–38.
42. Ringach DL: The use of nonhuman animals in biomedical research. Am J Med Sci 2011, 342:305–313.
43. Rudczynski AB: Letter to the editor. New Haven Register; 2011. Available at http://www.nhregister.com/articles/2011/
03/25/opinion/doc4d8bb9186a82b265857273.txt.
44. Fomchenko EI, Holland EC: Mouse models of brain tumors and their applications in preclinical trials.
Clin Cancer Res 2006, 12:5288–5297.
45. Litchfield JT Jr: Predictability of Conventional Animal Toxicity Tests. Ann N Y Acad Sci 1965, 123:268–272.
46. Lasagna L: Regulatory agencies, drugs, and the pregnant patient.InDrug use in pregnancy. Edited by Stern L.
Sydney: ADIS Health. Science Press; 1984.
47. Lin JH: Species similarities and differences in pharmacokinetics. Drug Metab Dispos
1995, 23:1008–1021.
48. Dixon RL: Toxicology of environmental agents: a blend of applied and basic research. Environ Health Perspect
1972, 2:103–116.
49. Zhang S, Wang Y-M, Sun C-D, Lu Y, Wu L-Q: Clinical value of serum CA19-9 levels in evaluating resectability of
pancreatic carcinoma. World J Gastroenterol 2008, 14:3750–3753.
50. Sasson C, Hegg AJ, Macy M, Park A, Kellermann A, McNally B: Prehospital Termination of Resuscitation in Cases
of Refractory Out-of-Hospital Cardiac Arrest. JAMA 2008, 300:1432–1438.
51. Salekin RT, Rogers R, Ustad KL, Sewell KW: Psychopathy and recidivism among female inmates. Law Hum Behav
1998, 22:109–128.
52. Mayanja BN, Baisley K, Nalweyiso N, Kibengo FM, Mugisha JO, Paal LV, Maher D, Kaleebu P: Using verbal autopsy
to assess the prevalence of HIV infection among deaths in the ART period in rural Uganda: a prospective
cohort study, 2006–2008. Population Health Metrics 2011, 9:36. doi:10.1186/1478-7954-9-36.
53. Santos G, Souza A, Virtuoso J, Tavares G, Mazo G: Predictive values at risk of falling in physically active and no
active elderly with Berg Balance Scale. Rev Bras Fisioter 2011, 15:95–101.
54. Committee on Models for Biomedical Research Board on Basic Biology: Committee on Models for Biomedical
Research. Board on Basic Biology. Commission on Life Science. National Research Council. Models for Biomedical
Research: A New Perspective. Washington, DC: National Academy Press; 1985.
55. Tkacs NC, Thompson HJ: From bedside to bench and back again: research issues in animal models of human
disease. Biol Res Nurs 2006, 8:78–88.
56. Overmier JB, Carroll ME: Basic Issues in the Use of Animals in Health Research.InAnimal Research and Human
Health. Edited by Carroll ME, Overmier JB. Washington DC: American Psychological Association; 2001:5.
57. LaFollette H, Shanks N: Two Models of Models in Biomedical Research. Phil Q 1995, 45:141–160.
58. LaFollette H, Shanks N: Brute Science: Dilemmas of animal experimentation. London and New York: Routledge; 1996.
59. Schaffner KF: Theories, Models, and Equations in Systems Biology.InSystems Biology: Philosophical Foundations.
Edited by Boogerd F, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV. Netherlands: Elsevier; 2007:145–162.
60. Bernard C: An Introduction to the Study of Experimental Medicine. New York: Dover; 1957.
61. Bunge M:
Causality And Modern Science
. 3rd edition. New York: Dover; 1979.
Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 26 of 33
http://www.tbiomed.com/content/9/1/40
62. Elliot P: Vivisection and the Emergence of Experimental Medicine in Nineteenth Century France.InVivisection
in Historical Perspective. Edited by Rupke N. New York: Croom Helm; 1987:48–77.
63. LaFollette H, Shanks N: Animal Experimentation: The Legacy of Claude Bernard. Int Stud Philos Sci 1994, 8:195–210.
64. Klaassen CD, Eaton DL: Principles of Toxicology.InCasarett and Doull's Toxicology. 4th edition. Edited by Amdur
MO, Doull J, Klaassen C. New York: McGraw-Hill; 1993.
65. Milner R: Darwin's Universe: Evolution from A to Z. Berkeley: University of California Press; 2009.
66. Wagner A: Causality in Complex Systems. Biol Philos 1999, 14:83–101.
67. Russell B: On the Notion of Cause. Proceedings of the Aristotelian Society. New Ser 1913, 13:1–26.
68. Greek R: Animal Models and the Development of an HIV Vaccine. J AIDS Clin Res 2012, S8:001.
69. Giere RN, Bickle J, Mauldin RF: Understanding Scientific Reasonoing. 5th edition. Toronto: Thomson Wadsworth; 2006.
70. Holden C: Random Samples. Well-Wired Whales. Science 2006, 314:1363.
71. Hof PR, Van der Gucht E: Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae
(Cetacea, Mysticeti, Balaenopteridae). Anat Rec (Hoboken) 2007, 290:1–31.
72. Hakeem AY, Sherwood CC, Bonar CJ, Butti C, Hof PR, Allman JM: Von Economo neurons in the elephant brain.
Anat Rec (Hoboken) 2009, 292:242–248.
73. Crick F: Of Molecules and Man. Seattle: University of Washington Press; 1966.
74. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et al: The
sequence of the human genome. Science 2001, 291:1304–1351.
75. McPherson JD, Marra M, Hillier L, Waterston RH, Chinwalla A, Wallis J, Sekhon M, Wylie K, Mardis ER, Wilson RK, et
al: A physical map of the human genome. Nature 2001, 409:934–941.
76. Mazzocchi F:
Complexity in biology. Exceeding the limits of reductionism and determinism using complexity
theory. EMBO Rep 2008, 9:10–14.
77. Coveney PV, Fowler PW: Modelling biological complexity: a physical scientist's perspective. J R Soc Interface
2005, 2: 267–280.
78. Coveney PV, Highfield RR: Frontiers of complexity. London: Faber and Faber; 1996.
79. Ahn AC, Tewari M, Poon CS, Phillips RS: The limits of reductionism in medicine: could systems biology offer an
alternative? PLoS Med 2006, 3:e208.
80. Alm E, Arkin AP: Biological networks. Curr Opin Struct Biol 2003, 13:193–202.
81. Cairns-Smith AG: Seven Clues to the Origin of Life: A Scientific Detective Story. Cambridge: Cambridge University
Press; 1986.
82. Csete ME, Doyle JC: Reverse engineering of biological complexity. Science 2002, 295:1664–1669.
83. Goodwin B: How the Leopard Changed Its Spots: The Evolution of Complexity. Princeton: Princeton University Press; 2001.
84. Jura J, Wegrzyn P, Koj A: Regulatory mechanisms of gene expression: complexity with elements of
deterministic chaos. Acta Biochim Pol 2006, 53:1–10.
85. Kauffman SA: he Origins of Order: Self-Organization and Selection in Evolution.: Oxford University Press; 1993.
86. Kitano H: Computational systems biology. Nature 2002, 420:206–210.
87. Kitano H: Systems biology: a brief overview. Science 2002, 295:1662–1664.
88. Definitions, Measures, and Models of Robustness in Gene Regulatory Network. Report of research work for CSSS05.
http://www.santafe.edu/education/csss/csss05/papers/monte_et_al._cssssf05.pdf.
89. Morowitz HJ: The Emergence of Everything: How the World Became Complex. Oxford: Oxford University Press; 2002.
90. Novikoff AB: The Concept of Integrative Levels and Biology. Science 1945, 101:209–215.
91. Ottino JM: Engineering complex systems. Nature 2004, 427:399.
92. Sole R, Goodwin B: Signs of Life: How Complexity Pervades Biology
, Basic Books.; 2002.
93. Van Regenmortel M: Reductionism and complexity in molecular biology. Scientists now have the tools to
unravel biological complexity and overcome the limitations of reductionism. EMBO Rep 2004, 5:1016–1020.
94. van Regenmortel M: Biological complexity emerges from the ashes of genetic reductionism. J Mol Recognit
2004, 17:145–148.
95. Van Regenmortel MH, Hull DL: Promises and Limits of Reductionism in the Biomedical Sciences (Catalysts for Fine
Chemical Synthesis). West Sussex: Wiley; 2002.
96. Vicsek T: The bigger picture. Nature 2002, 418:131.
97. Woodger JH: Biological Principles. New York: Humanities Press; 1967.
98. Kola I: The state of innovation in drug development. Clin Pharmacol Ther 2008, 83:227–230.
99. de Haan J: How emergence arises. Ecol Complex 2006, 3:293–301.
100. Southern J, Pitt-Francis J, Whiteley J, Stokeley D, Kobashi H, Nobes R, Kadooka Y, Gavaghan D: Multi-scale
computational modelling in biology and physiology. Prog Biophys Mol Biol 2008, 96:60–89.
101. Morin E: Introduction á la Pensée Complexe. Paris: ESF; 1990.
102. Haldane JBS: On Being the Right Size. New York: Harper's; 1926.
103. Morange M: The misunderstood gene. Cambridge: Harvard University Press; 2001.
104. Kauffman S: Theoretical Biology.InEpigenetic and Evolutionary Order from Complex Systems. Edited by Goodwin B,
Saunders P. Edinburgh: Edinburgh University Press; 1990.
105. Coffey DS: Self-organization, complexity and chaos: the new biology for medicine. Nat Med 1998, 4:882–885.
106. Misteli T: The concept of self-organization in cellular architecture. J Cell Biol 2001, 155:181–185.
107. Bruder CE, Piotrowski A, Gijsbers AA, Andersson R, Erickson S, de Stahl TD, Menzel U, Sandgren J, von Tell D,
Poplawski A, et al: Phenotypically concordant and discordant monozygotic twins display different DNA copy-
number-variation profiles. Am J Hum Genet 2008, 82:763–771.
108. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, Benitez J,
et al
: Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci USA 2005,
102:10604–10609.
109. Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, Martin-Subero JI, Rodriguez-Ubreva J, Berdasco M, Fraga MF,
O'Hanlon TP, Rider LG, et al: Changes in the pattern of DNA methylation associate with twin discordance in
systemic lupus erythematosus. Genome Res 2010, 20:170