Network Medicine Focus


A taste of history

Modern medical research can be set to start about five hundred years ago in the northern Italy, in Padova and Florence, where researchers at that time start doing autopsies and open the human body. In fact, during the Renaissance dissection began to be practiced to investigate the causes of death. This practice led to a gradual understanding of differences between normal and abnormal anatomical structures. These findings led to a general criticism to the long-established concepts from Galen and others (disease comes as a consequence of imbalances in one of the four bodily fluids). More recently, during the 19th century, researchers realized that the human body was too complex, and decide to focus their attention on organs. Then, researchers realized that the organs are still too complex to understand and then in the mid-20th century investigators started focusing on the cell: science has followed a reductionist approach: from the human body to the organs, from the organs to the cell and for the last half of the 20th century one of the leading discipline in science was cell biology. But the cell is still too complex. Since Watson and Crick discovered in 1953 the structure of DNA and genes, a new discipline arose in medical science and this was molecular biology. This reductionist strategy has been extremely successful and probably reached its summit in the year 2001 when the structure of the entire human genome was published. Many people thought at that time that such knowledge would lead to the understanding of all diseases. Few years have gone by since the discovery of the human genome and still we have diseases and don’t have a single genetic cure for any of them. This is, again, a reflection of the complexity of the human body. 

A paradigm shift in medicine    

Before the sequencing of the human genome it was believed that knowing the complete sequence of the bases was enough to understand the extraordinary complexity of our organism, but now we wonder if the only sequence - even if it is complete - can really be enough to understand the biological functions which regulate our organism. “Network medicine” is new field which combine principles and approaches from systems biology and network science in trying to understand the causes of human diseases and find and develop new treatments. It represents the marriage of network science and systems biology applied to human biology and human disease. It reflects the fact that human phenotypes and patho-phenotypes are driven by complex interactions among a variety of molecular mediators.

The basic hypothesis of network medicine is:

Diseases arise as a consequence of one or more biological networks in the relevant organ (or organs) that have become disease-perturbed through genetic and/or environmental changes.

An holistic approach focused on “interactions”

These complex interactions are responsible for certain pathways that facilitate the expression of the phenotypes or patho-phenotype and if we really want to understand how to control and treat disease we have to understand these interactions. Network medicine is inherently an holistic approach trying to look at the whole system at once rather than trying to find a single “magic treatment bullet”, which is the principle behind so many reductionist approaches to disease.

Create networks and study changes

There are many different ways to create networks that underlie human biology and disease. One can look at networks of protein-protein interactions, or networks of expressed genes following changes in expression of messenger RNA. Whether one looks one or the other of these kinds of networks, there are biological variants in sequence that exists among many of the molecular mediators of the network. Some of these variants have no functional consequences some others do. 

What makes the difference

Even if you think of the simplest processes that operate in a cell, those processes are driven by the interactions of many different elements. What we often see is that the network that is constructed in one state and the network that is constructed in another state are subtly different and are those differences that really inform us as what processes are driving healthy cells and disease cells or cells that are sensitive to a therapy or cells that are resistant to a therapy. Clinicians and therapists have been trying for a long time to identify individual molecular factors leading to a specific disease. The common idea was that a single "golden bullet", that is a drug capable of hitting a single molecular target, was enough to treat a disease. This naïve approach overlooked the essential complexity of human diseases.

Find and compare 

Molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). 

The metaphors we live by

The latter comparisons can transfer a formalism from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Medicine is evolving into an information science

The availability of large amounts of biomolecular data, the integration of in silico quantitative methodologies, and the "big data" analysis tools typical of "data science", have the potential to move the frontiers of medicine forward in a definitely new way. What is really exciting doing network medicine is the fact the we have access to unprecedented quantity of data. New genomic technologies have really opened the door to creating massive quantity of data on a scale that has really been unprecedented in biomedical research. We are entering in an era on which we’re going to be very rich in the types, quantity and speed of data we can generate: the challenge is to take that data and put into a biological context. Biomedical research is evolving into an information science.  

Promises for the future

Network medicine holds great promises for our understanding of complex phenotypes like diseases. It should give us the ability to define risk for disease before diseases manifest using network-based signaturesthat put a person at risk for a complex disease. It should give us insight into potential pathways within the network that could be the focus of therapeutic intervention to either prevent the disease from becoming manifest or to arrest the disease or to reverse the disease once it is manifest. Understanding these complex interactions among the molecular mediators of disease give us potentially benefit with respect to prevention, diagnosis and therapeutic intervention. It is time for medicine and physicians to change "paradigm" and move the focus towards the development of multi-level models. It a mind-changing challenge: a truly trans-disciplinary, collaborative mind is needed to reach the goal. Will we be able to do this?



What is a network?

A network is a collection of point (nodes) that are joined in pairs by lines (edges). A graphical approach to visualizing and analyzing relationships between variables of interests.

What is network medicine?

The study of cellular, disease, and social networks which aims to quantify the complex interlinked factors contributing to individual diseases

What is systems patho-biology?

The science of integrating genetic, genomic, biochemical, cellular, physiological, and clinical data to create a network that can be used to model predictively a biological event.


Selected references

General and reviews

  2. Barabási A.-L., Gulbahce N., Loscalzo J. Network medicine: a network-based approach to human disease. Nature Review Genetics 12 56-68 (2011) link
  3. Loscalzo J., A.L. Barabasi and E.K. Silverman, Network Medicine, Harvard University Press (2017) link
  4. M. Gustafsson et al., Modules, networks and system medicine for understanding disease and aiding diagnosis, Genome Medicine (2014) 6:82 link
  5. G. Fiscon, F. Conte, L. Farina and P. Paci, Network-based approaches to explore complex biological systems towards network medicine, Genes 9:437 (2018) link
  6. P. McGillivray et al., Network analysis as a grand unifier in biomedical data science, Annu. Rev. Biomed. Data Sci. (2018) 1:153-80 link


Methods and applications

  1. Zhou X., Menche J., Barabási A.-L., & Sharma A. Human symptoms–disease network. Nature Communications 5 (2014) link
  2. Barabási A.-L. Network Medicine — From Obesity to the “Diseasome.” New England Journal of Medicine 357 404-407 (2007) link
  3. Menche J., Sharma A., Kitsak M., Ghiassian S., Vidal M., Loscalzo J., & Barabasi A.-L. (2015): Uncovering disease-disease relationships through the human interactome. Science 347(6224):841. Doi: 10.1126/science.1257601 link
  4. S. Pai and G.D. Bader, Patient similarity networks for precision medicine, J. Mol. Biol (2018), 2924-2938 link
  5. E. Guney, J. Menche, M. Vidal and A.L. Barabasi, Network-based in silico drug efficacy screening, Nature Communications (2016) 7:10331 link
  6. J.P Pinto et al, Targeting molecular networks for drug research, Frontiers in Genetics (2014) 5:160 link
  7.  C. Bracken, H.S. Scott and G.J. Goodall, A network biology perspective of microRNA function and dysfunction in cancer, Nature Reviews Genetics (2016) link
  8. F. Cheng, .... , A.L. Barabasi, Joseph Loscalzo, Network based approach to prediction and population-based validation of in silico drug repurposing, Nature Communications (2018) 9:2691 link
  9. F. Barrenas et al, Highly interconnected genes in disease specific networks are enriched for disease associated polymorphisms, Genome Biology (2012) 13:R46 link
  10. K. Faust and J. Raes, Microbial interactions: from network to models, Nature (2012) 10:338 link
  11. H. Chuang, E. Lee, Y. Liu, D. Lee, and T. Ideker, Network-based classification of breast cancer metastasis, Mol Syst Biol. 2007; 3: 140 link


SWIM methodology and applications

  1. M.C. Palumbo, S. Zenoni, M. Fasoli, M. Massonet,  L. Farina, F. Castiglione, M. Pezzotti and P. Paci, Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development, Plant Cell (2014), 26(12):4617-35 link
  2. P. Paci, T. Colombo, G. Fiscon, A. Gurtner, G. Pavesi  and L. Farina, SWIM: a computational tool to unveiling crucial nodes in complex biological networks, Scientific Reports – Nature 7:44797 (2017) link
  3. G. Fiscon, F. Conte, V. Licursi, S. Nasi, P. Paci: Computational identification of specific genes for glioblastoma stem-like cells identity, Scientific Report, 8, 2018 link
  4. G. Fiscon, F. Conte, P. Paci: SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines, BMC Bioinformatics, 19, 2018 link
  5. Falcone R., Conte F., Fiscon G., Pecce V., Sponziello M., Durante C., Farina L., Filetti S., Paci P., Verrienti A. BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response. Endocrine. (2019) 8 link


Author: Lorenzo Farina, Computer, Control and Management Engineering "Antonio Ruberti" Department, Sapienza University of Rome



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