Congratulations to CASCI alumnus Dr. Ahmed Abdeen Hamed who was recognized by FastCompany magazine, among the most creative people in the world, in 2016, for his research publication entitled: Twitter K-H networks in action: Advancing biomedical literature for drug search.Dr. Hamed completed his Computer Science MS degree at Indiana University in May 2005 and joined our Complex Networks & Systems track of the PhD in Informatics in the Fall of 2008. For personal reasons, he finished his PhD at the University of Vermont, but started his research in biomedical text mining with the CASCI group.
Network science has allowed us to understand the organization of complex systems across disciplines. However, there is a need to understand how to control them; for example, to identify strategies to revert a diseased cell to a healthy state in cancer treatment. Recent work in the field—based on linear control theory—suggests that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics. Such graph-based approaches have been used, for instance, to suggest that biological systems are harder to control and have appreciably different control profiles than social or technological systems. The methodology has also been increasingly used in many applications from financial to biochemical networks.
In work published today in Nature Scientific Reports, CNetS graduate student Alexander Gates and Professor Luis Rocha demonstrate that such graph-based methods fail to characterize controllability when dynamics are introduced. The study computed the control profiles of large ensembles of multivariate systems as well as existing Systems Biology models of biochemical regulation in various organisms.
Recent CASCI Complex Systems & Networks Phd program graduate Artemy Kolchinsky, is now a postdoc at the Santa Fe Institute. While at SFI, Kolchinsky is working with “David Wolpert on several projects related to optimal use of information and prediction. One is the problem of modeling and analyzing complicated dynamical systems that require large amounts of time and computational power to simulate. […] Another project investigates connections
between information processing and statistical physics. […] The two are [also] beginning to work on understanding why different social groups develop different organizations, whether the group is a prehistoric tribe or a business firm.” More details on the SFI update newsletter.
Congratulations to Luis Rocha, who has been awarded a Fulbright Scholarship devoted to developing Complex Systems methodologies for the Life Sciences. The 12-month sabbatical is to be pursued under the Fulbright program for educational exchanges between the United States and Portugal. It will focus on studying collective behavior and control in biochemical and social networks. The broader goal is to advance our ability to predict and control the dynamics of complex networks in three domain areas: biochemical, neurodyamic, and social systems. The scholarship will also be used to facilitate the design of a new doctoral program on complex networks and systems for the life sciences.
Update: On March 21st, 2016 the paper described below (PMC4720984) was highlighted by Russ Altman from Stanford University in his yearly review as one of 30 important papers of the year in translational bioinformatics.
Using complex networks analysis and social media mining, CNETS researchers from the CASCI team have found that Instagram, a growing social media platform among teens, can be used “to uncover drug-drug interactions (DDI) and adverse drug reactions (ADR).” The work shows that this popular social media service is “a very powerful source of data with great promise in the public-health domain”. The study, “Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines,” supported by an R01 grant from the National Institutes of Health as well as a gift from Persistent Inc., was recently published and presented at the Pacific Symposium on Biocomputing (PSB 2016), in Hawaii. (PubMed, arXiv). The results are based on almost 7.000 user timelines associated with depression drugs which combined have 5+ million posts.
Modularity in complex systems can be observed in networks and across dynamical states, time scales, and in response to different kinds of perturbations. In a paper published in Physical Review E (Rapid Communication), Kolchinsky, Gates & Rocha propose a principled alternative to detecting communities in static and dynamical networks. The method demonstrates that standard modularity measures on static networks can be seen as a special case of measuring the spread of perturbations in dynamical systems. Thus, the new method offers a powerful tool for exploring the modular organization of complex dynamical systems.
Congratulations to Artemy Kolchinsky a brand new PhD in 2015 in the Complex Systems track of the Informatics PhD Program. Artemy’s PhD Dissertation is entittled “Measuring Scales: Integration and Modularity in Complex Systems“.
Read new papers from CASCI on developing the mathematical toolbox available to deal with computing distances on weighted graphs, applying distance closures for computational fact checking, and computing multi-scale integration in brain networks:
T. Simas and L.M. Rocha .”Distance Closures on Complex Networks”. Network Science, doi:10.1017/nws.2015.11.
G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini . “Computational fact checking from knowledge networks.” PLoS One. In Press. arXiv:1501.03471.
A. Kolchinsky, M. P. Van Den Heuvel, A. Griffa, P. Hagmann, L.M. Rocha, O. Sporns, J. Goni . “Multi-scale Integration and Predictability in Resting State Brain Activity”. Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.
Prof. Luis Rocha from CNETS at IU Bloomington, Prof. Lang Li from IUPUI Medical School, and Prof. Hagit Shatkay from the University of Delaware have been awarded a four-year, $1.7M grant from NIH/NLM to study the large-scale extraction of drug-Interaction from medical text. Drug-drug interaction (DDI) leads to adverse drug reactions, emergency room visits and hospitalization, thus posing a major challenge to public health. To circumvent risk to patients, and to expedite biomedical research, both clinicians and biologists must have access to all available knowledge about potential DDI, and understand both causes and consequences of such interactions. However, mere identification of interactions does not directly support such understanding, as evidence for DDI varies broadly, from reports of molecular interactions in basic-science journals, to clinical descriptions of adverse-effects in a myriad of medical publications. This project will develop tools that focus directly on large-scale identification and gathering of various types of reliable experimental evidence of DDI from diverse sources. The successful completion of the project will provide clinicians and biologists with substantiated knowledge about drug interactions and with informatics tools to obtain such information on a large-scale, laying the basis for preventing adverse drug reactions and for exploring alternative treatments.