Resumen de teoria

1. Introducing Systems Biology for
Nursing Science
Sandra A. Founds, CNM, FNP, PhD
Systems biology expands on general systems theory as
the ‘‘omics’’ era rapidly progresses. Although systems
biology has been institutionalized as an interdisciplin-
ary framework in the biosciences, it is not yet apparent
in nursing. This article introduces systems biology for
nursing science by presenting an overview of the the-
ory. This framework for the study of organisms from
molecular to environmental levels includes iterations
of computational modeling, experimentation, and the-
ory building. Synthesis of complex biological pro-
cesses as whole systems rather than isolated parts is
emphasized. Pros and cons of systems biology are dis-
cussed, and relevance of systems biology to nursing is
described. Nursing research involving molecular,
physiological, or biobehavioral questions may be
guided by and contribute to the developing science
of systems biology. Nurse scientists can proactively
incorporate systems biology into their investigations
as a framework for advancing the interdisciplinary
science of human health care. Systems biology has the
potential to advance the research and practice goals of
the National Institute for Nursing Research in the
National Institutes of Health Roadmap initiative.
Keywords: systems biology; interdisciplinary science;
nursing research; nursing science
S
ystems biology is a new science that has
evolved with the Human Genome Project. As
the form and function of human genes are
identified, burgeoning ‘‘omics’’ data creates the need
to manage and synthesize massive amounts of
information for the biosciences (Kitano, 2001, 2002).
Systems biology provides a conceptual framework
in which interdisciplinary collaborators can build and
test knowledge from these molecular data through
information and computational technologies. Leaders
of the National Institutes of Health (NIH) anticipate
that this form of 21st-century science will promote
the betterment of biology and medicine (Zerhouni,
2006).
This useful framework, however, remains to be
incorporated into nursing science. As an expansion
of general systems theory, its roots are familiar to the
discipline. Nurse scientists may find systems biology
relevant for guiding physiological, biobehavioral, and
molecular research. Participation by these scientists
in teams that conduct investigations based on this
interdisciplinary science could advance the National
Institute of Nursing Research’s (NINR) mission. In
this overview of systems biology, we introduce nurses
to key components of the framework and its potential
use for nursing science.
What Is Systems Biology?
Systems biology is an interdisciplinary science derived
from biology, physics, mathematics, computer science,
engineering, and other disciplines. It is the study of
complex systems in which experimental and computa-
tional methods are synthesized to investigate biological
processes in cells, tissues, and organisms (Hood &
Galas, 2003; Kitano, 2001; 2002). The approach is
holistic rather than atomistic (Hood, Rowen, Galas,
& Aitchison, 2008). Studies undertaken at the systems
level are distinguished by the collection of quantitative
and descriptive data whose relationships are modeled
(Figure 1a). Not only are individual elements cata-
loged, such as genes, proteins, and metabolites, but
the interactions among them are measured as dynamic
processes that vary in time and space. Integral to
FromtheDepartmentofHealthPromotionandDevelopment,School
of Nursing, and Magee-Womens Research Institute, University of
Pittsburgh, Pittsburgh, Pennsylvania.
Address correspondence to Sandra A. Founds, 448A Victoria Building
3500 Victoria Street, Pittsburgh, PA 15090; phone: (412) 624-3822;
e-mail: foundss@pitt.edu.
73
Biological Research for
Nursing
Volume 11 Number 1
July 2009 73-80
# 2009 The Author(s)
10.1177/1099800409331893
http://brn.sagepub.com

2. Figure 1. A, The iterative cycle of building systems biology research. Complexity signified by omics data acquisition is quantified
and synthesized into models that can be used to simulate clinical experiments. Data include complete sets (-omes) of parts, e.g., DNA,
RNA, proteins, and so on, and dynamic interactions among them. Iterative feedback of results informs model building. Permission for
use of this figure was granted by Dr. Leroy Hood, president of the Institute for Systems Biology in Seattle, Washington.
B, Information hierarchy in human health. Integrative signaling flows from a gene to the environment: gene$RNA
$protein$cell$tissues$organs$individual$populations$ecosystems. At each level in the hierarchy, information can be added
or altered for any given element (Hood & Galas, 2003). High-throughput molecular-level data are translated into molecular
interactions, then cellular-level responses, then intercellular responses, and finally to organ-level responses. The interconnections
between organ systems need to be elucidated to understand an organism-level system (Gohlke & Portier, 2007). Permission for use of
this figure was granted by Dr. Christopher J. Portier, National Institute of Environmental Health Sciences.
74 Biological Research for Nursing / Vol. 11, No. 1, July 2009

3. systems biology are the ‘‘-ome’’ terms, which refer to
wholeness or completeness of the lists of molecules
(Yadav, 2007). The metabolome, for example, repre-
sents all metabolites in a biological organism, giving
an instantaneous view of the physiology of a cell
(Daviss, 2005). The interactome refers to all interac-
tions among human proteins (genome.gov, 2000).
Computational models of these parts and processes
integrate environmental and evolutionary contexts. Suc-
cessive iterations of experiment and theory development
are conducted, incorporating physiological responses to
‘‘perturbations,’’ or stimuli (Figure 1a; Ideker, Galitski,
& Hood, 2001). Eventually properties of the system
emerge from the biology encoded in the information
hierarchy: deoxyribonucleic acid (DNA), ribonucleic
acid (RNA), proteins, interactions, cells, tissues and
organs, and individuals and ecologies (Figure 1b;
Hood et al., 2008). In health research, the growth
of systems-based approaches is most evident at
the molecular level, progressing to cellular-level systems
then to the organ and organism levels (Kitano, 2002),
building networks of interactions among molecules,
cells, tissues, and organs to form a predictive view of
an individual (Figure 1b; Gohlke and Portier, 2007).
Simulations can be developed within a computer
model of a biological system (in silico) before an
experiment is applied to animals or human research
participants. Results are then fed back as input to the
iterative cycle of theory development and model
building (Figure 1a; Hwang et al., 2005; Suresh
Babu, Joo, & Yoo, 2006). When applied to human
health, these systems models contribute to the
understanding of complex phenotypes and treatment
of disease. For example, investigations involving opti-
mal radiation dosing in a lung cancer trial (van
Baardwijk et al., 2008), and nutrition and measure-
ment error effects on glucose control in intensive
care patients (Wilinska, Chassin, & Hovorka, 2006)
as well as many drug development studies have been
conducted with simulated patients through computa-
tional modeling before application in vivo.
Why Consider Systems Biology?
Precursors of systems biology included fields such
as enzyme kinetics, cybernetics, neurophysiology,
and immunology, areas in which integrated systems
and simulation play central roles (Hood et al., 2008;
Kay, 1995). Major components also evolved from
general systems theory (von Bertalanffy, 1951) and
complexity theory (Mesarovic, 1968). Systems biol-
ogy emerged from these various sources as a distinct
discipline in 2000, when the first Institutes of
Systems Biology were established in Seattle and
Tokyo. Departments of Systems Biology opened at
the Massachusetts Institute of Technology (MIT)
and Harvard around that same time. Importantly,
the NIH has funded and continues to solicit
research and training programs to accelerate devel-
opment of systems biology (National Institute of
General Medical Sciences [NIGMS], 2008). In
addition, entire journals dedicated to systems
biology are now available, such as BMC Systems
Biology published by BioMed Central, Molecular
Systems Biology, and EURASIP Journal on Bioinfor-
matics and Systems Biology.
Adopting systems biology into nursing science
would advance the mission of the NINR and would
respond to the NIH Roadmap for Medical Research
initiative. NINR aims to develop nursing science and
practice ‘‘by integrating the biological and behavioral
sciences, employing new technologies for research
questions, improving research methods, and develop-
ing the scientists of the future’’ (NINR, 2006, p. 7).
Nurses who are being trained in and conducting
research with high throughput technologies, geno-
mics, and translational science help nursing science
meet these NINR goals by bringing focus to health
promotion, disease prevention, and holistic care
within interdisciplinary systems-oriented collabora-
tions. Participation in systems biology by nurse
scientists would also respond to the call for interdis-
ciplinary science put forth by the NIH Roadmap,
which states, ‘‘The scale and complexity of today’s
biomedical research problems demand that scientists
move beyond the confines of their individual
disciplines and explore new organizational models for
team science . . . (including) development of meth-
odologies aimed at integrating behavioral and social
science into interdisciplinary research’’ (Office of
Portfolio Analysis and Strategic Initiatives [OPASI],
2008). For instance, diseases with a genetic basis,
such as schizophrenia (Basile, Masellis, Potkin, &
Kennedy, 2002) or obesity (Keitt, Resnick, Simon,
Iskikian, & Marts, 2008), could be researched by
teams of nurse scientists, physician scientists, psy-
chologists, sociologists, nutritionists, and epidemiol-
ogists to develop tailored behavioral interventions
dependent on the individual’s genotype, using diet,
exercise, and counseling therapy.
Systems Biology / Founds 75

4. How Does Systems Biology Differ from
General Systems Theory?
Systems biology expands on general systems theory.
Von Bertalanffy (1950, 1951) challenged the reduc-
tionistic paradigm within which scientists explained
observable phenomena by reducing them to elemen-
tary units to be investigated independently of each
other. His theoretical framework, general systems
theory, recognized the importance of ‘‘wholeness,’’
that is, the whole being greater than the sum of the
parts (von Bertalanffy, 1951). From this perspective,
scientists could better understand phenomena
through analysis of dynamic interactions among parts
in systems rather than through examination of
isolated parts. The main domains in general systems
theory, which are homologous in all systems, include
input, throughput, output, and feedback to the sys-
tem (Figure 2). These components interact through
dynamic properties that can be quantified mathema-
tically (von Bertalanffy, 1950).
General systems theory evolved into systems biology
as the omics era rapidly progressed to greater complex-
ity. Technology, computation, and more complex
dynamics are key components that systems biology
has added to general systems theory (Rigoutsos,
2006). High-throughput technologies are auto-
mated processes that quickly identify thousands
to millions of molecules related to function and
phenotype in a cell or tissue. Genomics, transcrip-
tomics, and proteomics are examples of molecular
biological fields that use high-throughput technol-
ogies such as microarray analysis, polymerase
chain reaction, and mass spectroscopy. Massive
volumes of digitized information result from
high-throughput analyses. Genes, RNA, amino
acids, or proteins comprise a parts list, and compu-
tational and modeling computer programs have
become essential for generating, organizing, and
analyzing these datapoints and the interactions
among input, throughput, output, and feedback
(Figure 1a).
Pros and Cons of Systems Biology
Controversy surrounded the early development of
systems biology insofar as it represents a paradigm
shift from traditional reductionistic scientific
method (Kitano, 2001). Systems biology incorporates
Figure 2. Expansion of general systems theory to nursing in the omics era. The domains comprising general systems theory translate
to components of a nursing metaparadigm. Nursing interventions become input, or perturbation, to the system’s biology cycle when
the person is conceptualized as an organism comprised of integrated multilevel informational networks. Health outcome becomes
feedback that can be quantified and cycled back as input to system model building. Permission for adaptation of general systems
theory diagram was granted by Dr. David L. Sturges, associate professor in the Department of Management, Marketing, and
International Business at the University of Texas—Pan American.
76 Biological Research for Nursing / Vol. 11, No. 1, July 2009

5. iterative cycles of holism and reductionism, coordi-
nates voluminous high-throughput data, and exam-
ines global effects of perturbations. These methods
may reveal new emergent properties that arise from
the systemic view through synthesis of the processes
occurring in a biological system. More work is
needed, however, to augment conceptual under-
standing by defining the organizing principles for
interactions among various levels in a complex
system (Mesarovic, Sreenath, & Keene, 2004).
Challenges persist in the efforts to advance systems
biology (Rigoutsos, 2006). What can be known about
the whole is limited when all parts are not yet known,
resulting in incomplete and sometimes flawed
databases. Modeling and simulation are expensive
because they require extensive interdepartmental
communication and collaboration among various
scientists and disciplines (An, Hunt, Clermont,
Neugebauer, & Vodovotz, 2007). Supplies, equip-
ment, and reagents are expensive for high throughput
research. Integration of the science of systems biology
into nursing may require individuals to train outside
their primary discipline to be better equipped to com-
municate with team members in other disciplines on
joint projects. Nursing will need departmental and
institutional support for studies conducted in this new
paradigm and will need team members who can broker
resources from those at higher organizational levels.
Time and funding will be critical barriers to overcome
to reach the long-range goal of in silico clinical trials to
minimize risk to human research participants.
Application in Nursing Science
One metaparadigm in nursing science incorporates
relationships among the domains of environment-
person-health-nursing (Yura & Torres, 1975). If the
person may be analogized as embodying the internal
environment, the person’s surroundings as the
external environment is the person’s surroundings
and health as a human process, then nursing may
comprise input in general systems theory (Figure 2).
Any nursing intervention, such as asepsis, nutrition,
exercise, medication, psychosocial or behavioral modi-
fications, temperature, or clean air, could affect output
as health outcomes, which feed back to affect input.
In the systems biology framework, the nurse rea-
lizes that an intervention represents input to dynamic
processes in multiple levels of the person, from the
whole being to organ systems, to tissues at the
intercellular and intracellular levels, to the molecular
components including DNA products (Figure 1b).
Nurse scientists with interdisciplinary team members
could determine the effects of health care interven-
tions on the molecular through higher organismal
levels in human health. Models could be developed
within which to design experiments and feedback of
results to revise the model. For example, perhaps
a nurse researcher and her team develop a model of
the heart-myocardium-cytokines-mitochondria-release
of free radicals to simulate interventions. Different
therapeutics, dependent on the pathway affected by
the free radicals, could be tested in silico then in
clinical trials.
An approach for conducting systems biology stud-
ies could include the following steps: (a) clearly
define a discrete set of inputs and outputs, (b) define
relevant parts of the system, (c) move the system
through perturbation, then quantify, (d) relate
changes in the system state to output using mathe-
matical tools, and (e) modify the original model
(Figure 1a; Wiley, 2006). Expertise in these methods
is needed at all levels of the systems hierarchy, with
an eye toward the long-term goal of developing a
mechanistic explanation of cellular processes
(Rigoutsos, 2006).
For example, a research team comprising a nurse,
physician, and epidemiologist predict higher inflamma-
tory T-helper 1 cytokines and lower anti-inflammatory
T-helper 2 cytokines in women with preeclampsia com-
pared to those with unaffected pregnancies. For this
system, inflammation is input and pregnancy outcome
is output (Step 1). Ten molecules comprise the relevant
parts (Step 2) assayed in maternal blood. Potential rela-
tionships among the pro- and anti-inflammatory mar-
kers are examined using a bioinformatics tool, which
assesses the relationship among the molecules via a
database of peer-reviewed research (Figure 3). Pertur-
bations (Step 3) in the cytokine networks of pregnant
women include infection, smoking, and labor. The
correlations are modeled by a computational analyst
(Step 4). If an immunologist and geneticist are added
to the team, they can conduct studies of the cytokine
proteins and genes in T-helper cells in the diseased
versus the normal state, further characterizing the
relevant parts (Step 2) and expanding the model
(Step 4). If the nurse wants to test a perturbation such
as a nutritional supplement, the model can be used to
inform the hypothesis. After testing, the model will be
revised based on results of the experiment (Step 5).
Patience and time both are necessary for systems
biology modeling (Rigoutsos, 2006). Researchers
Systems Biology / Founds 77

6. must define the parts and relationships before compu-
tational modeling begins (Figure 3). It is recommended
that researchers use commercial bioinformatics tools
for omics pathway analysis before proposing aims
involving systems biology to avoid overly ambitious
projects.
Figure 3. Cytokines networked in bioinformatics pathway analysis software (Ingenuity Pathways Analysis, Ingenuity Systems 5.5).
Genes that produce cytokines are filled squares connected in this network through known (solid lines) and putative (dashed lines)
relationships identified in human and animal scientific literature. The relationships, or ‘‘edges,’’ are strengthened or modified by
further experimental findings fed back into the network model.
Abbreviations: interleukin (IL); tumor necrosis factor (TNF).
78 Biological Research for Nursing / Vol. 11, No. 1, July 2009

7. Conclusions
The magnitude of the challenge of integrating biomo-
lecular networks, now visible through the human
genome, compels collaboration among scientists
across disciplines. Nurse scientists who incorporate
long-range goals and specific aims for systems biology
into their investigations would unite the NINR mis-
sion with the NIH Roadmap initiatives (NINR,
2006; OPASI, 2008). Awareness of the domains of
systems biology on the part of these scientists and
their framing of research questions to address com-
plexity, synthesis, modeling, and dynamics will
develop the evolving systems biology framework,
thereby advancing the interdisciplinary science of
human health care. Nurse scientists are urged to con-
sider ways in which systems biology might relate to
their programs of research for eventual translation
to clinical practice.
Acknowledgments
The author acknowledges support for this work
from the NIH/NINR Summer Genetics Institute
2005, NINR T322-T32-NR007100-06 Research for
Vulnerable Women, Children and Families and the
University of Pittsburgh School of Nursing.
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