Professor of Computational Biology and Bioinformatics, Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute (Watch Webinar "Integrative Systems Approaches to Network Modeling of Biological Processes" by John Quackenbush,...
Dmitry Alexeev's insight:
well a prominent professor and comprehensive seminar
The ISME Journal: Multidisciplinary Journal of Microbial Ecology is the official Journal of the International Society for Microbial Ecology, publishing high-quality, original research papers, short communications, commentary articles and reviews in the rapidly expanding and diverse discipline of microbial ecology.
By applying the strengths of corporate models for effective teamwork, academic scientists can drive transdisciplinary research and accelerate biomedical translation.
Transdisciplinary (TD) investigations occur when individuals or teams from different disciplines converge to conduct research focused on solving a specific problem that cannot be solved by a single discipline. Working at the intersections of disciplines allows TD research teams to create new science while fostering a higher plane of enquiry—the unearthing of solutions to complex human health and societal problems. Academic medical centers are strategically positioned to drive local, national, and global TD biomedical research. However, academia’s disciplinary-bound culture—organized physically and conceptually around specific content areas—limits TD research and challenges scientists who are working within a university’s traditional department-based organization. In quest of solutions, we explore the characteristics of high-performing corporate teams that might be adapted for use in academic TD research and discuss the premise that collaborations between academic and industry teams present opportunities to harness the unique strengths of each.
Dmitry Alexeev's insight:
impressive how this is inline with what we are doing now in our team and it thought to the basis of interdisplinary research
A new phylogenetics approach and algorithm with which to chart the evolutionary history of organisms is presented. It utilizes mass spectral data produced from the proteolytic digestion of proteins, rather than partial or complete gene or translated gene sequences. The concept and validity of the approach is demonstrated herein using both theoretical and experimental mass data, together with the translated gene sequences of the hemagglutinin protein of the influenza virus. A comparison of the mass trees with conventional sequenced-based phylogenetic trees, using two separate tree comparison algorithms, reveals a high degree of similarity and congruence among the trees. Given that the mass map data can be generated more rapidly than gene sequences, even when next generation parallel sequencing is employed, mass trees offer new opportunities and advantages for phylogenetic analysis.