Dr. Lee’s grant proposal entitled “Deciphering dynamic signals in control of cell fate decisions” has been selected to receive an R35 Outstanding Investigator ‘MIRA’ award from the NIGMS.
It is an emerging principle that dynamic properties of molecules within a signaling network, such as sub-cellular changes in protein localization or abundance, provide a temporal codes that mediate cellular responses to stimuli. How dynamic molecular signals relay, and process, information is a critical gap in our understanding of how healthy and diseased cells make decisions. Here we propose a hybrid experimental and computational framework to decipher dynamic signals induced by inflammatory factors in single cells, and understand how cells interpret these signals to decide between inflammation, proliferation, or cell death.
Congratulations to Dr. Jeremy M. Berg for his appointment as Editor-in-Chief of Science!
Berg, Associate Senior Vice Chancellor for Science Strategy and Planning in the Health Sciences, who also holds positions as Pittsburgh Foundation Professor and Director of the Institute for Personalized Medicine, Professor of Computational and Systems Biology, and Professor of Chemistry at the University of Pittsburgh, will start his appointment on July 1, 2016. He will be the 20th Editor-in-Chief since the journal’s start in 1880 and will serve a 5-year term.
“I am thrilled and humbled by the opportunity to work with the team at Science and AAAS,” said Berg.
Berg will continue to hold his roles at the University. Dr. Arthur S. Levine said, “Dr. Berg is one of the nation’s leading scientists, with many landmark achievements in biomedical research, a broad and deep sense of all of the sciences, and a profound interest in science policy and the dynamics of the scientific community. I am proud indeed that Dr. Berg has been given this rare recognition, and especially proud that he is, and will remain, a member of our faculty.”
The Xing Lab’s paper, “Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design“, published in PNAS this past Monday, has received publicity from several national and international outlets.
The paper explains the decade-long question related to the maturation of olfactory sensory neurons.
“We are amazed that nature has solved the seemingly daunting engineering process of olfactory receptor expression in such a simple way,” lead researcher Dr. Jianhua Xing said.
Using existing experimental research, Dr. Xing and his collaborators created a computational model to see how olfactory receptor expression can be both uniform across a single neuron, yet very diverse across the entire population of neurons. The computational model suggests that nature solves a daunting engineering problem of olfactory receptor selection through simple physics of cooperativity. The research showed a “three-layer regulation mechanism” of olfactory receptor gene expression that utilized the principle of cooperativity, where elements of a system act non-independently.
Read more about the research in…
Phys.org – Pitt press release
The Daily Mail
Xiao-Jun Tian, Hang Zhang, Jens Sannerud, Jianhua Xing (2016) Achieving diverse and monoallelic olfactory receptor selection through dual-objective optimization design Proceedings of the National Academy of Sciences of the United States of America
We are happy to announce that Dr. Anne-Ruxandra Carvunis has accepted our offer and will be joining the Department in October!
Anne has her Ph.D. in Bioinformatics from the Université Joseph Fourier, Grenoble, France.
She was a Postdoctoral research fellow in Dr. Marc Vidal’s group at the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer Institute, Harvard Medical School. She worked on modeling de novo gene birth in S. cerevisiae through transitory “proto-genes” and also computational analyses of human protein interaction network maps.
She is currently a Postdoctoral research fellow in Dr. Trey Ideker’s group at the University of California San Diego, where she works on modeling the evolution of molecular networks using bioinformatics and yeast genetics.
We look forward to welcoming her to our department! Please join us in congratulating her.
A new seminar on causal discovery is to be offered by Carnegie Mellon University in Fall, 2015 (Course #80-516 for undergraduate level; Course #80-816 for graduate level; Section A for both levels). The instructor will be Kun Zhang, a new faculty member in CMU’s Philosophy Department. Pitt students can register now through the Pitt cross-registration process.
Causal connections are usually more interesting or helpful than purely associational information. This course is mainly concerned with systematic approaches to discovering causal connections from data in various scenarios and the question why causation plays an important role in science, i.e., how it is helpful in understanding, decision making, and prediction in complex environments.
We will study the difference between causal and non-causal systems and make an attempt to characterize a causal system. Apart from identification of causal effects, we will explore two causality-related areas. One is causal discovery, i.e., going beyond the observational data to the underlying causal information. It is well known that “correlation does not imply causality,” but we will make this statement more precise by asking what information in the data and what assumptions enable us to discover causal information from purely observed data. This will cover constraint-based causal discovery, causal discovery based on structural equation models, causal discovery from time series, difficulties in practical causal discovery, causality in neuroscience, causality in biology, and causality in economics and finance. More importantly, we will have the opportunity to solve problems in various fields from a causal perspective: participants may bring any causal problems they are interested in, and we will work together to find potential solutions. The other is how to properly make use of causal information. This includes counterfactual reasoning, improving machine learning in light of causal knowledge, and forecasting in nonstationary unseen environments.
Overall, this course aims to provide fundamentals of causal discovery and inference, review emerging methods for causal discovery, report their applications, find practical causal problems in various fields, and work out potential solutions.