Shmulevich Lab Overview
“If we can understand the network-based reasons for cancer metastasis, we can be in a stronger position to think about how to prioritize drug targets and intervention strategies.”
–Ilya Shmulevich, PhD, Professor
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The Shmulevich Lab develops and applies computational and mathematical approaches for modeling biological systems and analyzing large-scale data sets. High-throughput measurement technologies have made it possible to gather systems-level data on healthy and diseased cells. Powerful and accurate statistical and computational methods are needed to analyze these data and reveal the global principles underlying biological systems. Because this approach to research is at the core of ISB’s mission, the Shmulevich group collaborates extensively with other research groups at the institute.
For more information about the Shmulevich Lab, visit the Shmulevich Lab Website.
At the heart of ISB’s approach to biological research is the use of computers to analyze, model, and predict the behavior of biological systems. The Shmulevich group creates, applies, and disseminates computational tools that are unlocking the potential of the biosciences in the 21st century.
Complex dynamical systems govern virtually all biological processes. To understand the structure and dynamical properties of such systems, the research team headed by Ilya Shmulevich integrates data from a variety of measurements using models and techniques from mathematics, physics, and engineering. The resulting models can predict how the system will behave when it is perturbed, making it possible to intervene in the system to maintain health or achieve other desired aims.
Shmulevich directs a Genome Data Analysis Center at ISB as part of The Cancer Genome Atlas project, which is using genome analyses to accelerate understanding of the molecular basis of cancer. He also directs the Computational Core of the Systems Approach to Immunity and Inflammation consortium, which consists of a large multidisciplinary team of investigators working in the fields of immunology and systems biology.
The group develops tools, including freely available software packages, to support-large-scale biomedical research investigations. These tools can be rapidly adapted to meet the current and future demands of systems biology, such as data management systems designed to support the seamless mining and analysis of biological data.
The group works on the application of image processing and analysis to high-throughput cellular imaging of both single cells and populations of cells. These measurements can reveal the relationship between the molecular networks in cells and their characteristics and behaviors. Shmulevich and his colleagues also have developed statistical approaches to cancer classification, diagnosis and prognosis and have applied these approaches to the study of metastasis, cancer progression and tumor heterogeneity.
Finally, the Shmulevich group does theoretical studies of complex systems. What is the relationship between the structure of biological networks and their behavior? How do biological systems balance robustness with adaptability in an uncertain and variable environment? How do organisms coordinate complex behaviors and memory? All of these questions and many more are gradually yielding to mathematical and statistical modeling, computer simulations, and large-scale biological measurements.
Cancer occurs when biomolecular networks are perturbed by a combination of genetic and environmental factors. The Cancer Genome Atlas is using genomic, transcriptomic, and epigenomic measurements of cancer cells, coupled with computational approaches and rich clinical data, to understand the dysfunctions that underline the onset, progression, and spread of cancer. Such integrated approaches are used to investigate whether different subtypes or stages of a cancer can be distinguished, whether a cancer has the potential to spread, and whether a given therapy will be successful.
Source: https://shmulevich.isbscience.org/research/cancer-studies/ and http://cancergenome.nih.gov.