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=== Network Medicine ===
=== Network Medicine ===
Barabási is one of the [https://www.network-medicine.org/history founders] of [[network medicine]], a term he coined in an article entitled "Network Medicine – From Obesity to the "Diseasome", published in [[The New England Journal of Medicine]], in 2007.<ref>{{cite journal |last1=Barabási |first1=Albert-László |title=Network Medicine — From Obesity to the 'Diseasome' |journal=New England Journal of Medicine |date=26 July 2007 |volume=357 |issue=4 |pages=404–407 |doi=10.1056/NEJMe078114 |pmid=17652657 }}</ref> His work introduced the concept of diseasome, or disease network,<ref>{{Cite journal |date=2007 |title=The human disease network |journal=Proceedings of the National Academy of Sciences |volume=104 |issue=21 |pages=8685–8690|doi=10.1073/pnas.0701361104 |pmid=17502601 |pmc=1885563 |bibcode=2007PNAS..104.8685G |doi-access=free |last1=Goh |first1=Kwang-Il |last2=Cusick |first2=Michael E. |last3=Valle |first3=David |last4=Childs |first4=Barton |last5=Vidal |first5=Marc |last6=Barabási |first6=Albert-László }}</ref> showing that diseases are connected through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data, linking the roots of disease comorbidity to molecular networks.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> A key concept of network medicine is Barabási's discovery that genes associated with the same disease are located in the same network neighborhood,<ref>{{cite journal |last1=Menche |first1=J. |last2=Sharma |first2=A. |last3=Kitsak |first3=M. |last4=Ghiassian |first4=S. D. |last5=Vidal |first5=M. |last6=Loscalzo |first6=J. |last7=Barabasi |first7=A.-L. |title=Uncovering disease-disease relationships through the incomplete interactome |journal=Science |date=20 February 2015 |volume=347 |issue=6224 |pages=1257601 |doi=10.1126/science.1257601 |pmid=25700523 |pmc=4435741 }}</ref> which led to the concept of disease module, currently used to aid [[drug discovery]], [[drug design]], and the development of [[biomarker]]s, as he outlined in 2012 in a [[TEDMED]] talk.<ref>{{Citation |title=Do your proteins have their own social network? |url=https://www.youtube.com/watch?v=10oQMHadGos |language=en |access-date=2022-11-01}}</ref> Barabási's work has led to the founding of the [https://www.brighamandwomens.org/research/departments/channing-division-of-network-medicine/overview Channing Division of Network Medicine] at [[Harvard Medical School]] and the [https://www.network-medicine.org/ Network Medicine Institute], representing 33 universities and institutions around the world committed to advancing the field. Barabási's work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,<ref>{{cite journal |last1=Sharma |first1=Amitabh |last2=Menche |first2=Jörg |last3=Huang |first3=C. Chris |last4=Ort |first4=Tatiana |last5=Zhou |first5=Xiaobo |last6=Kitsak |first6=Maksim |last7=Sahni |first7=Nidhi |last8=Thibault |first8=Derek |last9=Voung |first9=Linh |last10=Guo |first10=Feng |last11=Ghiassian |first11=Susan Dina |last12=Gulbahce |first12=Natali |last13=Baribaud |first13=Frédéric |last14=Tocker |first14=Joel |last15=Dobrin |first15=Radu |last16=Barnathan |first16=Elliot |last17=Liu |first17=Hao |last18=Panettieri |first18=Reynold A. |last19=Tantisira |first19=Kelan G. |last20=Qiu |first20=Weiliang |last21=Raby |first21=Benjamin A. |last22=Silverman |first22=Edwin K. |last23=Vidal |first23=Marc |last24=Weiss |first24=Scott T. |last25=Barabási |first25=Albert-László |title=A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma |journal=Human Molecular Genetics |date=June 2015 |volume=24 |issue=11 |pages=3005–3020 |doi=10.1093/hmg/ddv001 |pmc=4447811 |pmid=25586491 }}</ref> predicting a novel mechanism of action for rosmarinic acid,<ref>{{cite journal |last1=do Valle |first1=Italo F. |last2=Roweth |first2=Harvey G. |last3=Malloy |first3=Michael W. |last4=Moco |first4=Sofia |last5=Barron |first5=Denis |last6=Battinelli |first6=Elisabeth |last7=Loscalzo |first7=Joseph |last8=Barabási |first8=Albert-László |title=Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols |journal=Nature Food |date=19 March 2021 |volume=2 |issue=3 |pages=143–155 |doi=10.1038/s43016-021-00243-7 |pmid=37117448 |s2cid=232317723 |hdl=1871.1/f3838307-b0e0-4c44-91e0-0aca528010c1 |url=https://research.vu.nl/en/publications/f3838307-b0e0-4c44-91e0-0aca528010c1 |hdl-access=free }}</ref> and novel therapeutic functions of existing drugs ([[drug repositioning|drug repurposing]]).<ref>{{cite journal |last1=Cheng |first1=Feixiong |last2=Desai |first2=Rishi J. |last3=Handy |first3=Diane E. |last4=Wang |first4=Ruisheng |last5=Schneeweiss |first5=Sebastian |last6=Barabási |first6=Albert-László |last7=Loscalzo |first7=Joseph |title=Network-based approach to prediction and population-based validation of in silico drug repurposing |journal=Nature Communications |date=12 July 2018 |volume=9 |issue=1 |page=2691 |doi=10.1038/s41467-018-05116-5 |pmc=6043492 |pmid=30002366 |bibcode=2018NatCo...9.2691C }}</ref> The products of network medicine have reached the clinic, helping doctors decide if rheumatoid arthritis patients respond to anti-TNF therapy.<ref>{{cite journal |last1=Cohen |first1=Stanley |last2=Wells |first2=Alvin F. |last3=Curtis |first3=Jeffrey R. |last4=Dhar |first4=Rajat |last5=Mellors |first5=Theodore |last6=Zhang |first6=Lixia |last7=Withers |first7=Johanna B. |last8=Jones |first8=Alex |last9=Ghiassian |first9=Susan D. |last10=Wang |first10=Mengran |last11=Connolly-Strong |first11=Erin |last12=Rapisardo |first12=Sarah |last13=Gatalica |first13=Zoran |last14=Pappas |first14=Dimitrios A. |last15=Kremer |first15=Joel M. |last16=Saleh |first16=Alif |last17=Akmaev |first17=Viatcheslav R. |title=A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study |journal=Rheumatology and Therapy |date=September 2021 |volume=8 |issue=3 |pages=1159–1176 |doi=10.1007/s40744-021-00330-y |pmc=8214458 |pmid=34148193 }}</ref><ref>{{Cite journal |last1=Ghiassian |first1=Susan D |last2=Voitalov |first2=Ivan |last3=Withers |first3=Johanna B |last4=Santolini |first4=Marc |last5=Saleh |first5=Alif |last6=Akmaev |first6=Viatcheslav R |date=August 2022 |title=Network-based response module {{sic|comprised |hide=y|of}} gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis |journal=Translational Research |language=en |volume=246 |pages=78–86 |doi=10.1016/j.trsl.2022.03.006|pmid=35306220 |s2cid=247514416 |doi-access=free }}</ref> During COVID  Barabási led a major collaboration involving researchers from [[Harvard University]], [[Boston University]] and The Broad Institute, to predict and experimentally test the efficacy for COVID patients of 6,000 approved drugs.<ref>{{cite journal |last1=Morselli Gysi |first1=Deisy |last2=do Valle |first2=Ítalo |last3=Zitnik |first3=Marinka |last4=Ameli |first4=Asher |last5=Gan |first5=Xiao |last6=Varol |first6=Onur |last7=Ghiassian |first7=Susan Dina |last8=Patten |first8=J. J. |last9=Davey |first9=Robert A. |last10=Loscalzo |first10=Joseph |last11=Barabási |first11=Albert-László |title=Network medicine framework for identifying drug-repurposing opportunities for COVID-19 |journal=Proceedings of the National Academy of Sciences |date=11 May 2021 |volume=118 |issue=19 |doi=10.1073/pnas.2025581118 |pmid=33906951 |pmc=8126852 |arxiv=2004.07229 |bibcode=2021PNAS..11825581M |doi-access=free }}</ref><ref>{{Cite journal |last1=Patten |first1=J.J. |last2=Keiser |first2=Patrick T. |last3=Morselli-Gysi |first3=Deisy |last4=Menichetti |first4=Giulia |last5=Mori |first5=Hiroyuki |last6=Donahue |first6=Callie J. |last7=Gan |first7=Xiao |last8=Valle |first8=Italo do |last9=Geoghegan-Barek |first9=Kathleen |last10=Anantpadma |first10=Manu |last11=Boytz |first11=RuthMabel |last12=Berrigan |first12=Jacob L. |last13=Stubbs |first13=Sarah H. |last14=Ayazika |first14=Tess |last15=O’Leary |first15=Colin |date=September 2022 |title=Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization |journal=iScience |language=en |volume=25 |issue=9 |pages=104925 |doi=10.1016/j.isci.2022.104925 |pmc=9374494 |pmid=35992305|bibcode=2022iSci...25j4925P }}</ref>
Barabási is one of the [https://www.network-medicine.org/history founders] of [[network medicine]], a term he coined in an article entitled "Network Medicine – From Obesity to the "Diseasome", published in [[The New England Journal of Medicine]], in 2007.<ref>{{cite journal |last1=Barabási |first1=Albert-László |title=Network Medicine — From Obesity to the 'Diseasome' |journal=New England Journal of Medicine |date=26 July 2007 |volume=357 |issue=4 |pages=404–407 |doi=10.1056/NEJMe078114 |pmid=17652657 }}</ref> His work introduced the concept of diseasome, or disease network,<ref>{{Cite journal |date=2007 |title=The human disease network |journal=Proceedings of the National Academy of Sciences |volume=104 |issue=21 |pages=8685–8690|doi=10.1073/pnas.0701361104 |pmid=17502601 |pmc=1885563 |bibcode=2007PNAS..104.8685G |doi-access=free |last1=Goh |first1=Kwang-Il |last2=Cusick |first2=Michael E. |last3=Valle |first3=David |last4=Childs |first4=Barton |last5=Vidal |first5=Marc |last6=Barabási |first6=Albert-László }}</ref> showing that diseases are connected through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data, linking the roots of disease comorbidity to molecular networks.<ref>{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Natali |last3=Loscalzo |first3=Joseph |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |date=January 2011 |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918|pmid=21164525 |pmc=3140052 }}</ref> A key concept of network medicine is Barabási's discovery that genes associated with the same disease are located in the same network neighborhood,<ref>{{cite journal |last1=Menche |first1=J. |last2=Sharma |first2=A. |last3=Kitsak |first3=M. |last4=Ghiassian |first4=S. D. |last5=Vidal |first5=M. |last6=Loscalzo |first6=J. |last7=Barabasi |first7=A.-L. |title=Uncovering disease-disease relationships through the incomplete interactome |journal=Science |date=20 February 2015 |volume=347 |issue=6224 |pages=1257601 |doi=10.1126/science.1257601 |pmid=25700523 |pmc=4435741 }}</ref> which led to the concept of disease module, currently used to aid [[drug discovery]], [[drug design]], and the development of [[biomarker]]s, as he outlined in 2012 in a [[TEDMED]] talk.<ref>{{Citation |title=Do your proteins have their own social network? | date=May 31, 2012 |url=https://www.youtube.com/watch?v=10oQMHadGos |language=en |access-date=2022-11-01}}</ref> Barabási's work has led to the founding of the [https://www.brighamandwomens.org/research/departments/channing-division-of-network-medicine/overview Channing Division of Network Medicine] at [[Harvard Medical School]] and the [https://www.network-medicine.org/ Network Medicine Institute], representing 33 universities and institutions around the world committed to advancing the field. Barabási's work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,<ref>{{cite journal |last1=Sharma |first1=Amitabh |last2=Menche |first2=Jörg |last3=Huang |first3=C. Chris |last4=Ort |first4=Tatiana |last5=Zhou |first5=Xiaobo |last6=Kitsak |first6=Maksim |last7=Sahni |first7=Nidhi |last8=Thibault |first8=Derek |last9=Voung |first9=Linh |last10=Guo |first10=Feng |last11=Ghiassian |first11=Susan Dina |last12=Gulbahce |first12=Natali |last13=Baribaud |first13=Frédéric |last14=Tocker |first14=Joel |last15=Dobrin |first15=Radu |last16=Barnathan |first16=Elliot |last17=Liu |first17=Hao |last18=Panettieri |first18=Reynold A. |last19=Tantisira |first19=Kelan G. |last20=Qiu |first20=Weiliang |last21=Raby |first21=Benjamin A. |last22=Silverman |first22=Edwin K. |last23=Vidal |first23=Marc |last24=Weiss |first24=Scott T. |last25=Barabási |first25=Albert-László |title=A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma |journal=Human Molecular Genetics |date=June 2015 |volume=24 |issue=11 |pages=3005–3020 |doi=10.1093/hmg/ddv001 |pmc=4447811 |pmid=25586491 }}</ref> predicting a novel mechanism of action for rosmarinic acid,<ref>{{cite journal |last1=do Valle |first1=Italo F. |last2=Roweth |first2=Harvey G. |last3=Malloy |first3=Michael W. |last4=Moco |first4=Sofia |last5=Barron |first5=Denis |last6=Battinelli |first6=Elisabeth |last7=Loscalzo |first7=Joseph |last8=Barabási |first8=Albert-László |title=Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols |journal=Nature Food |date=19 March 2021 |volume=2 |issue=3 |pages=143–155 |doi=10.1038/s43016-021-00243-7 |pmid=37117448 |s2cid=232317723 |hdl=1871.1/f3838307-b0e0-4c44-91e0-0aca528010c1 |url=https://research.vu.nl/en/publications/f3838307-b0e0-4c44-91e0-0aca528010c1 |hdl-access=free }}</ref> and novel therapeutic functions of existing drugs ([[drug repositioning|drug repurposing]]).<ref>{{cite journal |last1=Cheng |first1=Feixiong |last2=Desai |first2=Rishi J. |last3=Handy |first3=Diane E. |last4=Wang |first4=Ruisheng |last5=Schneeweiss |first5=Sebastian |last6=Barabási |first6=Albert-László |last7=Loscalzo |first7=Joseph |title=Network-based approach to prediction and population-based validation of in silico drug repurposing |journal=Nature Communications |date=12 July 2018 |volume=9 |issue=1 |page=2691 |doi=10.1038/s41467-018-05116-5 |pmc=6043492 |pmid=30002366 |bibcode=2018NatCo...9.2691C }}</ref> The products of network medicine have reached the clinic, helping doctors decide if rheumatoid arthritis patients respond to anti-TNF therapy.<ref>{{cite journal |last1=Cohen |first1=Stanley |last2=Wells |first2=Alvin F. |last3=Curtis |first3=Jeffrey R. |last4=Dhar |first4=Rajat |last5=Mellors |first5=Theodore |last6=Zhang |first6=Lixia |last7=Withers |first7=Johanna B. |last8=Jones |first8=Alex |last9=Ghiassian |first9=Susan D. |last10=Wang |first10=Mengran |last11=Connolly-Strong |first11=Erin |last12=Rapisardo |first12=Sarah |last13=Gatalica |first13=Zoran |last14=Pappas |first14=Dimitrios A. |last15=Kremer |first15=Joel M. |last16=Saleh |first16=Alif |last17=Akmaev |first17=Viatcheslav R. |title=A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study |journal=Rheumatology and Therapy |date=September 2021 |volume=8 |issue=3 |pages=1159–1176 |doi=10.1007/s40744-021-00330-y |pmc=8214458 |pmid=34148193 }}</ref><ref>{{Cite journal |last1=Ghiassian |first1=Susan D |last2=Voitalov |first2=Ivan |last3=Withers |first3=Johanna B |last4=Santolini |first4=Marc |last5=Saleh |first5=Alif |last6=Akmaev |first6=Viatcheslav R |date=August 2022 |title=Network-based response module {{sic|comprised |hide=y|of}} gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis |journal=Translational Research |language=en |volume=246 |pages=78–86 |doi=10.1016/j.trsl.2022.03.006|pmid=35306220 |s2cid=247514416 |doi-access=free }}</ref> During COVID  Barabási led a major collaboration involving researchers from [[Harvard University]], [[Boston University]] and The Broad Institute, to predict and experimentally test the efficacy for COVID patients of 6,000 approved drugs.<ref>{{cite journal |last1=Morselli Gysi |first1=Deisy |last2=do Valle |first2=Ítalo |last3=Zitnik |first3=Marinka |last4=Ameli |first4=Asher |last5=Gan |first5=Xiao |last6=Varol |first6=Onur |last7=Ghiassian |first7=Susan Dina |last8=Patten |first8=J. J. |last9=Davey |first9=Robert A. |last10=Loscalzo |first10=Joseph |last11=Barabási |first11=Albert-László |title=Network medicine framework for identifying drug-repurposing opportunities for COVID-19 |journal=Proceedings of the National Academy of Sciences |date=11 May 2021 |volume=118 |issue=19 |doi=10.1073/pnas.2025581118 |pmid=33906951 |pmc=8126852 |arxiv=2004.07229 |bibcode=2021PNAS..11825581M |doi-access=free }}</ref><ref>{{Cite journal |last1=Patten |first1=J.J. |last2=Keiser |first2=Patrick T. |last3=Morselli-Gysi |first3=Deisy |last4=Menichetti |first4=Giulia |last5=Mori |first5=Hiroyuki |last6=Donahue |first6=Callie J. |last7=Gan |first7=Xiao |last8=Valle |first8=Italo do |last9=Geoghegan-Barek |first9=Kathleen |last10=Anantpadma |first10=Manu |last11=Boytz |first11=RuthMabel |last12=Berrigan |first12=Jacob L. |last13=Stubbs |first13=Sarah H. |last14=Ayazika |first14=Tess |last15=O’Leary |first15=Colin |date=September 2022 |title=Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization |journal=iScience |language=en |volume=25 |issue=9 |pages=104925 |doi=10.1016/j.isci.2022.104925 |pmc=9374494 |pmid=35992305|bibcode=2022iSci...25j4925P }}</ref>


=== Human Dynamics ===
=== Human Dynamics ===

Revision as of 06:17, 28 June 2024

Albert-László Barabási
Barabási at the World Economic Forum Annual Meeting of the New Champions in 2012
Born
Barabási Albert László

(1967-03-30) March 30, 1967 (age 57)
Citizenship
Romanian
Hungarian
American
Alma materUniversity of Bucharest (BS)
Eötvös Loránd University (MS)
Boston University (PhD)
Known forResearch of network science
The concept of scale-free networks
Proposal of Barabási–Albert model
Founder of Network Medicine
Introducing Network controllability
Awards
Scientific career
FieldsPhysics, Network Science, Network Medicine
ThesisGrowth and roughening of non-equilibrium interfaces (1994)
Doctoral advisorH. Eugene Stanley
Doctoral students
Websitebarabasilab.com

Albert-László Barabási (born March 30, 1967) is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

He is a distinguished university professor and Robert Gray Professor of Network Science at Northeastern University, and holds appointments at the department of medicine, Harvard Medical School and the department of network and data science[1] at Central European University. He is the former Emil T. Hofmann Professor of Physics at the University of Notre Dame and former associate member of the Center of Cancer Systems Biology (CCSB) at the Dana–Farber Cancer Institute, Harvard University.

He discovered in 1999 the concept of scale-free networks and proposed the Barabási–Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the World Wide Web or online communities. He is the founding president of the Network Science Society,[2] which sponsors the flagship NetSci Conference series held since 2006.

Birth and education

Barabási was born to an ethnic Hungarian family in Cârța, Harghita County, Romania. His father, László Barabási, was a historian, museum director and writer, while his mother, Katalin Keresztes, taught literature, and later became director of a children's theater.[3] He attended a high school specializing in science and mathematics; in the tenth grade, he won a local physics olympiad. Between 1986 and 1989, he studied physics and engineering at the University of Bucharest; during that time, he began doing research on chaos theory, publishing three papers.[3]

In 1989, Barabási emigrated to Hungary, together with his father. In 1991, he received a master's degree at Eötvös Loránd University in Budapest, under Tamás Vicsek, before enrolling in the Physics program at Boston University, where he earned a PhD in 1994. His thesis, written under the direction of H. Eugene Stanley,[4] was published by Cambridge University Press under the title Fractal Concepts in Surface Growth.[5][6]

Academic career

After a one-year postdoc at the IBM Thomas J. Watson Research Center, Barabási joined the faculty at the University of Notre Dame in 1995. In 2000, at the age of 32, he was named the Emil T. Hofman Professor of Physics, becoming the youngest endowed professor. In 2004 he founded the Center for Complex Network Research.

In 2005–06 he was a visiting professor at Harvard University. In fall 2007, Barabási left Notre Dame to become the distinguished professor and director of the Center for Network Science at Northeastern University and to take up an appointment in the department of medicine at Harvard Medical School.

As of 2008, Barabási holds Hungarian, Romanian and U.S. citizenship.[7][8][9]

Research and achievements

Barabási has had fundamental contributors to the development of network science, complex systems and network medicine.

Scale-Free Networks

He is best known for the discovery of the scale-free networks. He discovered the scale-free nature of the WWW in 1999[10] and the same year, in a Science paper with Réka Albert, he proposed the Barabási–Albert model, predicting that growth and preferential attachment are jointly responsible for the emergence of the scale-free property in real networks. According to the review of one of Barabási's books, preferential attachment can be described as follows:

Barabási has found that the websites that form the network (of the WWW) have certain mathematical properties. The conditions for these properties to occur are threefold. The first is that the network has to be expanding, growing. This precondition of growth is very important as the idea of emergence comes with it. It is constantly evolving and adapting. That condition exists markedly with the world wide web. The second is the condition of preferential attachment, that is, nodes (websites) will wish to link themselves to hubs (websites) with the most connections. The third condition is what is termed competitive fitness which in network terms means its rate of attraction.[11]

He subsequently showed that the scale-free property emerges in metabolic networks[12] and protein–protein interaction[13] networks. Science celebrated the ten-year anniversary of Barabási’s 1999 discovery of scale-free networks, one of the most cited Science papers of all times, by devoting a special issue to Complex Systems and Networks in 2009.[14][15]

Network Robustness

In a 2001 paper with Réka Albert and Hawoong Jeong he demonstrated the Achilles' heel property of scale-free networks, showing that networks are robust to random failures but fragile to attacks.[16] Specifically, networks can easily survive the random failure of a very large number of nodes, showing a remarkable robustness to failures. At the same time, networks quickly collapse under attack, achieved by removing the biggest hubs. The breakdown threshold of a network was linked[17] to the second moment of the degree distribution, whose convergence to zero for large networks explain why heterogenous networks can survive the failure of a large fraction of their nodes.

Network Medicine

Barabási is one of the founders of network medicine, a term he coined in an article entitled "Network Medicine – From Obesity to the "Diseasome", published in The New England Journal of Medicine, in 2007.[18] His work introduced the concept of diseasome, or disease network,[19] showing that diseases are connected through shared genes, capturing their common genetic roots. He subsequently pioneered the use of large patient data, linking the roots of disease comorbidity to molecular networks.[20] A key concept of network medicine is Barabási's discovery that genes associated with the same disease are located in the same network neighborhood,[21] which led to the concept of disease module, currently used to aid drug discovery, drug design, and the development of biomarkers, as he outlined in 2012 in a TEDMED talk.[22] Barabási's work has led to the founding of the Channing Division of Network Medicine at Harvard Medical School and the Network Medicine Institute, representing 33 universities and institutions around the world committed to advancing the field. Barabási's work in network medicine has led to multiple experimentally falsifiable predictions, helping identify experimentally validated novel pathways in asthma,[23] predicting a novel mechanism of action for rosmarinic acid,[24] and novel therapeutic functions of existing drugs (drug repurposing).[25] The products of network medicine have reached the clinic, helping doctors decide if rheumatoid arthritis patients respond to anti-TNF therapy.[26][27] During COVID  Barabási led a major collaboration involving researchers from Harvard University, Boston University and The Broad Institute, to predict and experimentally test the efficacy for COVID patients of 6,000 approved drugs.[28][29]

Human Dynamics

Barabási in 2005 discovered the fat tailed nature of the inter event times in human activity patterns. The pattern indicated that human activity is bursty - short periods of intensive activity are followed by long periods that lack detectable activity. Bursty patterns have been subsequently discovered in a wide range of processes, from web browsing to email communications and gene expression patterns. He proposed the Barabási model[30] of human dynamics, to explain the phenomena, demonstrating that a queuing model can explain the bursty nature of human activity, a topic is covered by his book Bursts: The Hidden Pattern Behind Everything We Do.[31]

Human Mobility

Barabási laid foundational work in understanding individual human mobility patterns through a series of influential papers. In his 2008 Nature publication,[32] Barabási utilized anonymized mobile phone data to analyze human mobility, discovering that human movement exhibits a high degree of regularity in time and space, with individuals showing consistent travel distances and a tendency to return to frequently visited locations. In a subsequent 2010 Science paper,[33] he explored the predictability of human dynamics by analyzing mobile phone user trajectories. Contrary to expectations, he found a 93% predictability of in human movements across all users. He introduced two principles governing human trajectories, leading to the development of the widely used model for individual mobility.[34] Using this modeling framework, a decade before the COVID-19 pandemic, Barabási predicted the spreading patterns of a virus transmitted through direct contact.[35]

Network Control

His work on network controllability and observability asked the fundamental question of how large networks control themselves. To answer this, he was the first to bring the tools of control theory to network science. He proposed a method to identify the nodes through which one can control a complex network, by mapping the control problem, widely studied in physics and engineering since Maxwell, into graph matching, merging statistical mechanics and control theory.[36] He used network control to predict the function of individual neurons in the Caenorhabditis elegans connectome, discoverin new neurons involved in locomotion, and offering direct experimental confirmation of network control principles.[37]

Awards

Barabási was the recipient of the 2023 Julius Edgar Lilienfeld Prize, the top prize of the American Physical Society,[38] "for pioneering work on the statistical physics of networks that transformed the study of complex systems, and for lasting contributions in communicating the significance of this rapidly developing field to a broad range of audiences." In 2021 he received the EPS Statistical and Nonlinear Physics Prize, awarded by the European Physical Society for "his pioneering contributions to the development of complex network science, in particular for his seminal work on scale-free networks, the preferential attachment model, error and attack tolerance in complex networks, controllability of complex networks, the physics of social ties, communities, and human mobility patterns, genetic, metabolic, and biochemical networks, as well as applications in network biology and network medicine."

Barabási has been elected to the US National Academy of Sciences (2024),[39] Austrian Academy of Sciences (2024), Hungarian Academy of Sciences (2004), Academia Europaea (2007), [40] European Academy of Sciences and Art (2018) and Romanian Academy of Sciences[41] (2018) and the Massachusetts Academy of Sciences (2013). He is a Fellow of the American Physical Society in 2003,[42] Fellow of American Association for the Advancement of Science in 2011, Fellow of the Network Science Society in 2021. He was awarded a Doctor Honoris Causa by Obuda University (2023) in Hungary, the Technical University of Madrid[43] (2011), Utrecht University[44] (2018) and West University of Timișoara (2020).[45]

He received The Bolyai Prize from the Hungarian Academy of Sciences (2019), the Senior Scientific Award of the Complex Systems Society (2017) for "setting the basis of what is now modern Network Science",[46] the Lagrange Prize (2011) C&C Prize (2008) Japan "for stimulating innovative research on networks and discovering that the scale-free property is a common feature of various real-world complex networks"[47] and the Cozzarelli Prize, National Academies of Sciences (USA),[48] John von Neumann Medal (2006) awarded by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology[49] and the FEBS Anniversary Prize for Systems Biology (2005).

In 2021 Barabási was ranked 2nd in the world in the field of Engineering and Technology.[50]

Selected publications

  • Barabási, Albert-László, The Formula: The Universal Laws of Success, November 6, 2018; ISBN 0-316-50549-8 (hardcover)
  • Barabási, Albert-László (2018). Network science. Cambridge University Press. ISBN 978-1107076266.
  • Barabási, Albert-László, Bursts: The Hidden Pattern Behind Everything We Do, April 29, 2010; ISBN 0-525-95160-1 (hardcover)
  • Barabási, Albert-László, Linked: The New Science of Networks, 2002. ISBN 0-452-28439-2 (pbk)
  • Barabási, Albert-László and Réka Albert, "Emergence of scaling in random networks", Science, 286:509–512, October 15, 1999
  • Barabási, Albert-László and Zoltán Oltvai, "Network Biology", Nature Reviews Genetics 5, 101–113 (2004)
  • Barabási, Albert-László, Mark Newman and Duncan J. Watts, The Structure and Dynamics of Networks, 2006; ISBN 0-691-11357-2
  • Barabási, Albert-László, Natali Gulbahce, and Joseph Loscalzo, "Network Medicine", Nature Reviews Genetics 12, 56–68 (2011)
  • Réka Albert, Hawoong Jeong & Barabási, Albert-László (1999). "The Diameter of the WWW". Nature. 401 (6749): 130–31. arXiv:cond-mat/9907038. Bibcode:1999Natur.401..130A. doi:10.1038/43601. S2CID 4419938.
  • Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Controllability of complex networks", Nature 473, 167–173 (2011)
  • Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, "Observability of complex systems", Proceedings of the National Academy of Sciences 110, 1–6 (2013)
  • Baruch Barzel and A.-L. Barabási, "Universality in Network Dynamics", Nature Physics 9, 673–681 (2013)
  • Baruch Barzel and A.-L. Barabási, "Network link prediction by global silencing of indirect correlations", Nature Biotechnology 31, 720–725 (2013)
  • B. Barzel Y.-Y. Liu and A.-L. Barabási, "Constructing minimal models for complex system dynamics", Nature Communications 6, 7186 (2015)

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