Primer on Medical Genomics Part XIV: Introduction to Systems Biology—A New Approach to Understanding Disease and Treatment
Open accessAbstract
The advent of the "-omics revolution" has forced us to reevaluate our ability to acquire, measure, and handle large data sets. Omic platforms such as expression arrays and mass spectrometry, with their exquisite selectivity, sensitivity, and specificity, are unrivaled technologies for detection, quantitation, and identification of DNA, messenger RNA, proteins, and metabolites derived from complex body tissue and fluids. More recently, attempts have been made to capture the utility of these platform technologies and combine them under the umbrella of systems biology, also referred to as pathway, network, or integrative biology. Applied systems biology is the integrated analysis of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. It necessitates the use of a variety of analytic platforms as well as biostatistics, bioinformatics, data integration, computational biology, modeling, and knowledge assembly protocols. Such sophisticated analyses may provide new insight into the understanding of disease processes and mechanisms of action of pharmaceutical agents. Ultimately, this requires a perspective on how complex systems behave and are modulated. In this regard, systems biology, more appropriately considered as a process containing a series of modules, aims to provide tools and capabilities to carry out such tasks. We describe the essentials required to carry out systems biology experiments, the method in which integrated data in the form of a systems biology correlation network affords new insight into understanding disease, and the vista of developing more efficient biomarkers and therapeutic agents.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most major changes in physiological states have multigenic or multifactorial origins, and most diseases are therefore to be seen as systems or network diseases.
“It is not news that most major changes in physiological states have multigenic or multifactorial origins (e.g. [2092,2093]). This means, as an inevitable consequence, that we need to recognize that their observation requires a systems approach, and that most diseases are therefore in fact to be seen as systems or network diseases [631,2094-2102].”
From Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases (2009) · cites this paper as [2096]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 28 · Cited by 71
Research works printed but not linked: 8
- Haberman A. Potential of systems biology and pathways studies. G enet Eng New s. 2002;22:26-32.
- Abbott A. And now for tbe protcome. Nature.200l;409:747.
- Ideker T, Thorsson V, Ranish JA, et al. Integ¡ated geaomic and proteomic analysos of a systematically pertubed melabolic net- work. Science. 2001 ;292:929 -934.
- KifanoH. Foundatìorc of Systems Biolog, Carnbidge, Mass: MIT Press; 200 I .
- Chakabarti R, Rajagopalan R Diabetes and insulin ræistance associ¿ted disorde¡s: disease and the therapy. Cun Scì.2N2;83: 1533-1538.
- Clish CB, Davidov E, Ore.sic M, et al. Integra.tive biological analy- sis of the APOE*3-Loiden transgenic mouse. OMICS.20M;8:3-13.
- Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS. Discovering fi¡¡ctional relationships between RNA expression and chemothera- peutic suscçtibility using relevance netwo¡ks. Proc Natl Acad Sci U S A. 2000;97 : l2lE2-12186.
- Naylor S. Biomarke¡s: current perspectives and ft¡ture prospects lúilonalf. Expert Rev Mol Diagn. 2003;3:525-529. 9 10. ll. 12. 13. 14. 15. 16. 17, 18. r9. 20 'r1
- Authors
- Morel, N. M., Holland, J. M., van der Greef, J., Marple, E. W., Clish, C., Loscalzo, J., & Naylor, S.
- Affiliations
- Boston University (US)
- Corresponding author
- Naylor, Stephen
- Venue
- Mayo Clinic Proceedings
- Publisher
- Elsevier BV
- Published
- 2004-05
- Volume
- 79
- Issue
- 5
- Pages
- 651-658
- ISSN
- 0025-6196 (ISSN-L)
- OpenAlex
- W2092797897
- Cited by
- 71
- References
- 28
- Citations by year
- 2025: 1 · 2021: 1 · 2019: 1 · 2018: 2 · 2016: 1 · 2015: 5 · 2014: 2 · 2013: 3 · 2012: 7
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- green
- resolver.tno.nl
- Locations
- Mayo Clinic Proceedings (publishedVersion)
- PubMed (publishedVersion)
- TNO Repository (submittedVersion)
- Repository hosted by TU Delft Library (TU Delft) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- systems biology, multi-omics integration, correlation networks, disease mechanisms, drug mechanisms of action, biomarker discovery, drug target identification, bioinformatics, computational modeling, mass spectrometry, gene expression profiling
- MeSH terms
- Animals, Genetics, Medical, Humans, Molecular Biology, Systems Theory, Genomics, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review