Assistant Professor (Tenure Track) or Associate Professor, Quantitative Models of Human Communication. For this position, we seek a scholar with research interests focused on quantitative model building in communication. Human communication is an essential building block in the emergence of complex social systems. Models aimed at understanding and identifying the fundamental theoretical building blocks of human communication have the potential to inform all the social sciences, which includes areas such as cooperation and coordination, trust and goal manipulations, contagion and diffusion, technology adaption and technological change, organizational communication and team-building, community development, social network evolution, and democratic processes. Applicants are sought from scholars conducting theory-driven and theory-building research through modern modeling tools, such as agent-based models, computer simulations and other numerical solutions, which are informed by analytical approaches, such as game theory, dynamical systems theory, information theory, or statistical mechanics. The applicant must show evidence that developed models are grounded in empirical data from the social sciences. Applicants must be willing to teach undergraduate and graduate courses in model building, as well as additional courses from the Department’s offerings as needed. Applicants’ research program must be consistent with the Department’s affiliation with the Division of Social Sciences. A doctorate degree and publications and research work in the social sciences are required. Persons with Ph.D. pending will be considered only if the degree will be awarded prior to the beginning of instruction on September 19, 2016. Demonstrated research and teaching competence are required. Applicants must have the potential to secure external funding. Applications must be submitted by November 30, 2016 to receive consideration. Position to begin July 1, 2017.
Is Computational Biology increasingly—and steadily—progressing toward addressing the mammoth challenge of actually computing biology? That is, have we reached the stage where we do not support biological research but drive it? This question is vitally important for all—young and established computational biologists. Even though forecasting future research can be risky, we still venture to predict that the future will see considerably more research projects drifting toward this ambitious aspiration. Computational Biology is powerful for abstracting signatures of disease, for predicting it, and for proposing medications. It is effective in figuring out disease mechanisms and forceful in bridging experimental disciplines to obtain testable predictions. However, perhaps its biggest challenges lie in putting together the available broad and disparate information, devising tools to efficiently and effectively carry out these tasks while sifting through noise and recognizing cell specificity, and most importantly coming up with sound, coherent, and testable schemes.
Nussinov R, Papin JA (2016) Computing Biology. PLoS Comput Biol 12(7): e1005050. doi:10.1371/journal.pcbi.1005050
Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.
Mengistu H, Huizinga J, Mouret J-B, Clune J (2016) The Evolutionary Origins of Hierarchy. PLoS Comput Biol 12(6): e1004829. doi:10.1371/journal.pcbi.1004829
As public health agencies strive to harness big data to improve outbreak surveillance, they face the challenge of extracting meaningful information that can be directly used to improve public health, without incurring additional costs. In this article, we address the question: Which nodes in a social network should be selectively monitored to detect and monitor outbreaks as early and accurately as possible? We derive best-case performance scenarios, and show that a practical strategy for data collection–recruiting friends of randomly selected individuals–is expected to perform reasonably well, in terms of the timing and reliability of the epidemiological information collected.
Herrera JL, Srinivasan R, Brownstein JS, Galvani AP, Meyers LA (2016) Disease Surveillance on Complex Social Networks. PLoS Comput Biol 12(7): e1004928. doi:10.1371/journal.pcbi.1004928