Zuckerman awarded R01 grant

ZuckermanDr. Daniel Zuckerman (PI), in collaboration with Dr. Lillian Chong (PI) and Dr. James Faeder (Co-I), was awarded an NIH R01 grant for $1.3 million over 4 years.

“High-Performance Weighted Ensemble Software for Simulation of Complex Bio-Events”

In response to a call from NIH, the aims are to provide open-source software to enhance the power of simulations at any scale (e.g. molecular, cellular) for a potentially large user base. Thus, the primary impact will be to facilitate key segments of the burgeoning field of computational biomedical research.  Additionally, research to be performed directly by the investigators is designed to yield insights into cancer and neurological processes with potential to enhance drug design efforts.

There is a “silicon ceiling” that ultimately limits many, if not most, types of dynamical biological simulations. That is, even the world’s most powerful computers cannot generate sufficiently long simulations, whether for atomistic models of proteins or for realistic models of cell behavior.  In many cases, the key events may occur beyond simulation timescales – such as protein folding, conformational transitions of proteins, assembly of protein complexes, or transitions of cell behavior from healthy to pathological states.  We therefore propose a response to PA-14-156, “Extended Development, Hardening and Dissemination of Technologies in Biomedical Computing, Informatics and Big Data Science (RO1),”in which we will continue to enhance the “WESTPA” software package.  WESTPA is a tool for controlling other software tools: it orchestrates up to thousands of trajectories run natively by other software at any scale (e.g., Gromacs, Amber, BioNetGen, MCell) using a “weighted ensemble” strategy.  Not only does WESTPA parallelize the use of dynamics engines – but because of the statistical process by which trajectories are added and removed, WESTPA can obtain estimates of key kinetic as well as equilibrium observables in significantly less computing time than would be required in ordinary parallelization.  The aims of the proposal are to improve the ease of use and interoperability of WESTPA; to improve its performance and reliability; to demonstrate the effectiveness of WESTPA through a series of “showcase” examples from molecular to cellular scale using a variety of dynamics engines; and to improve instructional materials based on the showcase examples.

New Seminar on Causal Discovery to be offered by CMU in Fall, 2015

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.

Click here for the announcement and syllabus


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.