Mardinoglu, Adil

Nielsen, Jens B.

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Nielsen, Jens

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Editorial: The Impact of Systems Medicine on Human Health and Disease

Adil Mardinoglu

Jens Nielsen

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EDITORIAL
published: 24 November 2016
doi: 10.3389/fphys.2016.00552
Frontiers in Physiology | www.frontiersin.org 1 November 2016 | Volume 7 | Article 552
Edited and reviewed by:
Raina Robeva,
Sweet Briar College, USA
*Correspondence:
Adil Mardinoglu
adilm@scilifelab.se
Jens Nielsen
nielsenj@chalmers.se
Specialty section:
This article was submitted to
Systems Biology,
a section of the journal
Frontiers in Physiology
Received: 10 October 2016
Accepted: 03 November 2016
Published: 24 November 2016
Citation:
Mardinoglu A and Nielsen J (2016)
Editorial: The Impact of Systems
Medicine on Human Health and
Disease. Front. Physiol. 7:552.
doi: 10.3389/fphys.2016.00552
Editorial: The Impact of Systems
Medicine on Human Health and
Disease
Adil Mardinoglu 1, 2* and Jens Nielsen 1, 2*
1Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden, 2 Science for
Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
Keywords: systems medicine, metabolic networks and pathways, systems biology, networks medicine, biological
networks, metabolic diseases
Editorial on the Research Topic
The Impact of Systems Medicine on Human Health and Disease
Complex disorders including obesity, diabetes, fatty liver diseases, cardiovascular disease (CVD),
and cancer are results from a combination of genetic, environmental, and lifestyle factors. The
prevalence of such disorders has increased dramatically in the last two decades and there is an
urgent need for the development of new prognostic tools for the treatment of such diseases.
However, this requires a deep understanding of the underlying molecular mechanisms involved
in the occurrence of the diseases. With the advances in high throughput technologies, biological
components of cells can be measured with a very high resolution and these data can be used
for investigating whole systems properties using a network-based approach. Systems medicine
provides an integrative platform for studying the interactions between the biological components
of the cell using a holistic approach and generating mechanistic explanations for the emergent
systems properties (Mardinoglu and Nielsen, 2012). This inter-disciplinary field of study allows
for understanding biological processes of cells in health and disease states, gaining new insights
into what drives the appearance of the disease, and finally identifying proteins and metabolites
implicated in human disease. Systems medicine utilizes mathematical approaches to generate
models which can be employed for designing new sets of experiments and formapping the response
of the system to perturbations quantitatively. These models as well as the developed tools can
accelerate the emergence of personalized medicine which can transform the practice of medicine
and offer better targets for drug development with minimum side effects.
Genome Scale Metabolic Models (GEMs) are important tools in systems biology and employed
for simulating the metabolism of cells/tissues in different states (Mardinoglu et al., 2013). GEM
includes all known metabolic reactions and associated protein coding genes in a particular cell
or tissue and the reconstruction of GEMs is costly and time consuming process. Pacheco et al.
benchmarked the algorithms that have been used in the automated reconstruction of context-
specific GEMs. This may allow researchers to identify limitations of each method and help them
to increase the quality of context-specific reconstruction algorithms and as well as the generated
models. Zhang and Hua reviewed the potential applications of GEMs in systems medicine and
industrial biotechnology. The authors described the key concepts and assumptions used in the
application of the GEMs and provided detailed explanation about the recent applications which
may promote the use of GEMs by biologists.
GEMs have been widely used in the discovery of biomarkers and drug targets that can be used in
the development of efficient treatment strategies (Mardinoglu and Nielsen, 2015). These integrative
models can also be used in the stratification of the patients as well as the identification of the subjects
Mardinoglu and Nielsen The impact of Systems Medicine
at risk of developing complex diseases. CVD includes all the
diseases of the heart and circulation and continues to constitute
the leading cause of death globally. Therefore, there is an urgent
need to develop tools and methods for identifying individuals
at risk of developing CVD. Björnson et al. reviewed the current
CVD risk scores and discussed how systems medicine could
improve the identification of risk and maximize personalized
treatment benefit.
Tumor cells alter their metabolism to maintain their growth
and proliferation. Ghaffari et al. reviewed the metabolic
alterations that are known to occur in cancer and underlined the
use of genome-scale metabolic modeling approach in perceiving
a system level perspective of cancer metabolism which can
be used in discovery of biomarkers and drug targets. Özcan
and Çakır have reconstructed glioblastoma multiforme (GBM)
specific GEMs based on gene expression data and used their
context-specific model to predict metabolic flux distributions in
the brain tumor cells. The authors identified the GBM specific
metabolic alterations and provided a comprehensive coverage
of tumor metabolism. Moreover, flux distributions in glycolysis,
glutaminolysis, TCA cycle, and lipid metabolism were predicted
and validated by additional computational analysis and literature
information.
Zhang et al. evaluated the potential value of current major
systems biology-based approaches, including genome wide
association studies, gene regulatory networks, and protein–
protein interaction networks and GEMs. The authors discussed
how integrative analysis of personal multi-omics data may
provide increased understanding of personal genotype–
phenotype relationships. Moreover, the potential benefit of
integrating different type of networks in increased understanding
of the interactome is discussed. Recently, metabolic networks
have been integrated with other biological networks and these
networks have been used in the analysis if omics data obtained
from different clinical conditions (Lee et al., 2016a,b). In the
same context, Brown reviewed the challenges in the generation
of SuperModels where computational models were integrated
with various data types and analytics. The author has also
discussed how these SuperModels may assist in the development
of personalized and precision medicine.
Stable isotope assisted metabolomics techniques have been
used in systemsmedicine for metabolic flux analysis and pathway
discovery. It is essential to understand the pathophysiology of
dyslipoproteinemia in humans since it has major role in the
progression of complex diseases including CVD and diabetes.
The use of tracers labeled with stable isotopes and mathematical
modeling may provide a powerful tool for probing lipid and
lipoprotein kinetics in vivo. Adiels et al. reviewed the recent
kinetic studies and discussed how these studies may improve
our understanding of impaired human lipoprotein metabolism.
Even though, stable isotope labeling analyses have been highly
targeted, in recent years, tools for the global non-targeted
detection, quantification, and computational analysis of isotopic
enrichment have become available. Weindl et al. discussed how
such non-targeted stable isotope labeling analyses can be applied
for systems medicine applications.
Finally, Shmelkov et al. described an approach to improve
in silico identification of a comprehensive ensemble of targets
for any drug weighted by the expression of those receptors
in relevant tissues. Their approach may increase the sensitivity
of target detection and allow for systematic integration
of bioactivity/docking scores between drugs/compounds and
proteins with the expression patterns of those proteins in human
tissues.
The work presented in this Research Topic addresses many of
the recent progress in the applications of the GEMs is medical
applications. The integration of GEMs with other biological
networks and generation of whole cell models may foster the
development of personalized medicine which may increase
benefits and reduce risks for patients.
AUTHOR CONTRIBUTIONS
AM and JN wrote the editorial together and approved its final
version.
ACKNOWLEDGMENTS
This work was supported by grants from the Knut and Alice
Wallenberg Foundation.
REFERENCES
Lee, S., Mardinoglu, A., Lee, D., and Nielsen, J., (2016a). Dysregulated signaling
hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis.
Nucleic Acids Res. 44, 5529–5539. doi: 10.1093/nar/gkw462
Lee, S., Zhang, C., Kilicarslan, M., Piening, B. D., Bjornson, E., Hallström, B.
M., et al., (2016b). Integrated network analysis reveals an association between
plasma mannose levels and insulin resistance. Cell Metab. 24, 172–184. doi: 10.
1016/j.cmet.2016.05.026
Mardinoglu, A., Gatto, F., and Nielsen, J. (2013). Genome-scale modeling of
human metabolism - a systems biology approach. Biotechnol. J. 8, 985–996.
doi: 10.1002/biot.201200275
Mardinoglu, A., andNielsen, J. (2012). Systemsmedicine andmetabolic modelling.
J. Intern. Med. 271, 142–154. doi: 10.1111/j.1365-2796.2011.02493.x
Mardinoglu, A., and Nielsen, J. (2015). New paradigms for metabolic modeling
of human cells. Curr. Opin. Biotechnol. 34, 91–97. doi: 10.1016/j.copbio.2014.
12.013
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Mardinoglu and Nielsen. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is permitted, provided the original
author(s) or licensor are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
Frontiers in Physiology | www.frontiersin.org 2 November 2016 | Volume 7 | Article 552


Nielsen, Jens B

Chalmers Research

http://dx.doi.org/10.3389/fphys.2016.00552

Editorial: The Impact of Systems Medicine on Human Health and Disease

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