Living systems are manifestations of their underlying complex dynamical networks of molecular interactions. A paramount problem is to understand how functional cellular behavior and interaction with the cell’s environment is mediated by these complex molecular systems. Recent advances in measurement technologies allow us to interrogate biological systems and collect massive amounts of heterogeneous information under a variety of experimental conditions. Integrating this information and constructing predictive models of system behavior are the central goals of systems biology. I will discuss our efforts focused on the inference of models of transcriptional regulatory networks from high-throughput measurement data, integration of multiple sources of evidence from diverse data sources, the development of powerful computational analysis, simulation, and visualization tools, and the use of such models for gaining insight into the nature of cellular behavior in health and disease. Finally, I will make some brief remarks on our investigations of emerging general organizing principles underlying the exquisite ability of living systems to coordinate complex behavior while maintaining a balance between robustness and adaptability.
Dr. Shmulevich joined the ISB faculty in April 2005. His work focuses on the computational, mathematical, and statistical aspects of systems biology, complex systems theory, and applications of signal processing and machine learning to genomics and proteomics. A major emphasis of his work has been on building models that capture the complex dynamical interplay of genes interacting in genetic networks, developing statistically robust and computationally efficient methods for inferring such multivariate relationships between genes from measurement data, and using the inferred models to make predictions about the dynamical behavior of genes.
This has resulted in a class of models called Probabilistic Boolean Networks, which have been used in studies involving melanoma and glioma by Dr. Shmulevich and colleagues at M. D. Anderson Cancer Center, Texas A&M University, Houston, TX, and the Translational Genomics Research Institute, Pheonix, AZ. He is currently leading a five-year effort, with support from NIGMS, to further develop and refine these mathematical and computational methods and models, while closely integrating computational and experimental approaches.
The overarching goal of this research direction is to improve our understanding of how a cell´s behavior is governed by a complex dynamical system of genetic interactions and how these systems fail in disease, such as cancer. In a related project, also supported by NIGMS, Dr. Shmulevich, together with colleagues at the M. D. Anderson Cancer Center and the Institute for Biocomplexity and Informatics at the University of Calgary, are studying the processes of cellular differentiation and homeostasis from a dynamical systems perspective, using human promyelocytic leukemia cells (HL60) as a model system.
Another emphasis is on the development of algorithms and statistical methods for classifying subtypes of cancers on the basis of their transcriptional profiles, especially when such classification is clinically significant in terms of survival or response to therapy, but difficult or impossible to achieve by traditional histopathological assays. An important aspect of this work is the design of robust classification algorithms that have high predictive accuracy and do not suffer from overfitting, especially in the context of small sample sizes often encountered in clinical studies.
Dr. Shmulevich also has been actively involved in developing and improving new and existing high-throughput technologies for measuring gene and protein expression, with an emphasis on measurement accuracy and quality control. These efforts resulted in the development of so-called composite microarrays, robust methods for quantifying protein expression with protein lysate microarrays, and a co-authored book on microarray quality control.
Ilya received his Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA, in 1997. In 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition. In 1998-2000, he worked as a senior researcher at the Institute of Signal Processing in Tampere University of Technology, Tampere, Finland. Prior to joining the ISB, he was an Assistant Professor in the Department of Pathology at The University of Texas M. D. Anderson Cancer Center and also held an Adjunct Professor position in the Department of Statistics in Rice University.