Shmulevich Lab Overview

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The Shmulevich Lab develops and applies computational and mathematical approaches to model biological systems and analyze large-scale data sets. High-throughput measurement technologies have made it possible to collect 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 the function and dysfunction of biological systems. The Shmulevich group collaborates extensively with other research groups at ISB because the computational approach to research is at the very core of our mission.

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Research Overview

The Shmulevich group develops and applies computational and mathematical approaches for modeling biological systems and analyzing large-scale data sets.

Research Foci

Cancer Research. Cancer is a complex disease that results from a combination of genetic and environmental perturbations to biomolecular networks that, in healthy tissues, maintain a homeostatic balance between normal cellular functional states. Computational and statistical techniques for analyzing  primary tumors,  metastases, or cell culture systems can help us to understand cellular dysfunction underlying cancer onset, progression, and metastasis as well as to develop novel diagnostic and prognostic tools.

Multiscale Modeling and Digital Twins. A hallmark of living systems is their multiscale nature – their structure and behavior, in time and space, functions on multiple scales of biological organization. Furthermore, these scales are interlinked in that system behaviors on one scale influence and constrain behaviors on another scale. Cancer research requires multiscale modeling. The enormous power and success of the genomic and molecular paradigm of cancer has made it possible to comprehensively measure genomic, transcriptional, proteomic, and epigenomic information in multiple cancers. This underpins our understanding of how molecular systems in cancer cells are disrupted and is a central goal of large-scale cancer genomics projects, such as The Cancer Genome Atlas (TCGA) in which the Shmulevich Lab participated. This requires the integration of vast amounts of information on the molecular and cellular scales into multiscale models that could  be used to develop personalized therapies for cancer patients.

Such personalized dynamic models culminate in so-called “digital twins” for cancer patients. What makes digital twins different from just models is not only their personalization for each patient, but the dynamic updating of the model in light of longitudinal measurements and clinical data obtained from the patient, representing the patient’s disease trajectory over time. Digital twins can be used to forecast the disease progression, response to interventions such as drugs, and adverse events associated with interventions.

Networks. Complex dynamical biomolecular systems govern virtually all biological processes on developmental and physiological time scales. A paramount problem is to understand how structural and dynamical properties of such systems affect their roles in cellular function and dysfunction. The Shmulevich group has developed network inference approaches by integrating the information from multiple types of measurement data using a variety of modeling formalisms. Such models can be used for developing optimal therapeutic strategies intended to control system behavior in disease.

Biological Image Analysis. High-throughput cellular imaging and microfluidic technologies are enabling phenotypic measurements on single-cell and population-wide scales. The extraction of information from such imaging data is necessary for establishing the relationships between the behavior of molecular networks in cells and quantitative phenotypic features of cells and tissues. The Shmulevich group develops image processing and analysis methods that can help detect, count and describe the shapes of subcellular and multicellular structures and track molecules or cells over time.

Computational Biology Tools & Methods. Large-scale high-throughput measurement technologies have allowed system-wide modeling and analysis of cells in health and disease. In order to be able to make reliable inferences, each type of measurement data calls for the development of appropriate statistical and computational methods. The Shmulevich group develops such methods, which are used to understand the underlying mechanisms of disease and develop new treatments.

Complex Systems. The Shmulevich group is studying complex dynamical systems for understanding fundamental principles governing living systems at various scales of organization. Their work is focused on: the relationships between the structure of such systems and their dynamics; the ability to balance robustness with adaptability in an uncertain and variable environment while making decisions in response to information in the environment; the coordination of complex behaviors, computation, and memory; and the emergence of diversity in multicellular systems. The group is exploring information-theoretic approaches to tackle these questions, using mathematical and statistical modeling, computer simulations, and large-scale biological measurements.