Abhinav Arneja (BE Doctoral), in collaboration with Prof. Forest White (BE, MIT)

Allison Claas

Analyzing mechanisms of acquired drug resistance

The Epidermal Growth Factor Receptor (EGFR) is a popular target for cancer therapies due to its increased level of expression in many types of cancer. EGFR inhibitors, both monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs), are common targeted treatments. However tumors that initially respond to EGFR inhibitors often develop resistance. We aim to better characterize the mechanism of acquired resistance to EGFR inhibitors with a particular emphasis on alterations in MET signaling and proteolytic activity.

Nancy Guillen (BE Doctoral)

Role of microRNAs in intracellular signaling networks regulating hepatocellular carcinoma cell behavior

Experimental studies of IFNgamma and TRAIL-induced microRNAs in a hepatocellular carcinoma (HCC) cell line to determine the role of these miRNAs in the apoptotic response. The goal is to find correlations between cell phenotypic behavior and changes in expression of specific microRNAS. These microRNAs target proteins involved in signaling pathways that are relevant for the cell death response. Using quantitative methods for obtaining time dependent data on microRNA and cell death measurements, we can develop a predictive mathematical model to describe cell behavior in response to a combination treatment with IFNgamma and TRAIL.

Abby Hill, co-advised by Chris Love

Network analysis of intercellular communication in the immune system

Cell-cell communication by cytokine secretion is necessary both to maintain homeostasis in the immune system and to mount an immune response to infection. Technology developed in the Love Lab to measure the time-dependent cytokine secretion from single cells and small groups of cells will allow us to infer the effects of specific cell-cell interactions during an immune response. My goal is to use this technology along with network inference tools to create a probabilistic model of interactions among multiple types of immune cells. Using samples from healthy patients and from subjects in different disease states, we plan to apply this model to determine sources of variability in the immune response to viral infection and to vaccination.

Aaron Meyer, co-advised with Frank Gertler

Molecular Regulatory Network Dysregulation in Cell Migration and Invasion

Transformation of epithelial cells into an invasive mode is common in diseases such as endometriosis and cancer. Migration and invasion require coordinated control of disparate processes, and the signaling mechanisms involved in activation are incompletely understood. Investigation of cell migration is further complicated by concurrent contributions from signaling molecules as well as matrix signaling and physical interactions. Using 3D models of cell migration, as well as multivariate signaling measurements and analysis, we aim to identify the mechanisms of signaling dysregulation which promote invasion.

Miles Miller, co-advised with Linda Griffith

"Understanding how MMP and ADAM activities interact with cell signaling networks to mediate cell migration."

Cells express and secrete hundreds of extracellular metalloproteinases, including matrix metalloproteinases (MMPs), A Disintegrin and Metalloproteinases (ADAMs), and ADAM-thrombospondin motif’s (ADAMTSs). These proteases mediate diverse cellular processes, including extracellular matrix (ECM) degradation and surface-bound growth factor shedding. Their dysregulation contributes to a variety of pathological behaviors, particularly tumor proliferation, invasion, and metastasis in breast cancer. Metalloproteinase activities are controlled in the context of complex and intertwined feedback loops and signaling networks. We aim to use multivariate protease activity measurements to understand complex networks of extracellular protease activity and how they interact with signaling pathways to affect cell migration and tumor invasion.

Melody Morris (BE doctoral)

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The general aim of my thesis project is to understand how signaling networks dictate a cellular response in both healthy and diseased cells in order to devise targeted strategies for treatment of disease. Specifically, we have developed methods for using continuous logic-based models to aide our understanding of biological systems and facilitate data interpretation. We call our modeling methodology 'constrained fuzzy logic' [cFL] and have used it to train a prior knowledge network to data, which allowed us to see what aspects of the data set agreed or disagreed with prior knowledge. Additionally, we have developed a tool called Querying Quantitative Logic Models (Q2LM) to quickly and easily construct a cFL model from existing knowledge of a biological system and then query the model to determine if a prediction made using intuition alone is consistent with current understanding of the system. We aim for these complementary approaches of training a cFL model to data or constructing it directly from prior knowledge to assist scientists in making self-consistent, informed decisions for further experiments or choice of therapeutic targets.

Justin Pritchard (Biology Doctoral) in collaboration with Michael Hemann.

A quantitative approach to combination therapies in cancer. With an emphasis on in-vivo mouse models.

Sarah Schrier

Application of Modeling Techniques to Intercellular Signalling

Immune cells communicate to mount a response, often involving secretion of cytokines. As modeling tools have been developed to study intracellular signaling, similar techniques could be applied to cell-cell communication. My project will aim to understand signaling networks not just internal to immune cells, but also to incorporate intercellular signalling with a focus on immunology and inflammation applications.

Joel Wagner (BE Doctoral)

Utilizing a spectrum of computational approaches to improve strategies for therapeutic discovery

Improved experimental technologies have allowed us to measure many more components of proteomic and genomic networks. Understanding how to explore these data, and how to develop and validate hypotheses from these data, requires the application of potentially multiple computational tools. A spectrum of computational tools can be applied depending on the type of data and the biological questions of interest. Ranging from statistical features, to network inference, to differential equation models, methods can be used in concert to extract as much information as possible from data. Using this spectrum of approaches, we seek to improve our understanding of signaling and transcriptional networks, with an aim of developing improved strategies for therapeutic discovery.

Jennifer Wilson

Computational approaches for functional genomics in cancer

High-throughput RNAi screens offer an opportunity to interrogate cellular signaling on a genome-wide scale. These screens also represent a computational challenge in that the data sets are often large and rich in multiple layers of information. My project focuses on developing new tools for analyzing and interpreting these data sets and then using them to better lymphoma and breast cancer systems. The hope is that these novel tools will enable a better understanding of how networks chance in complex cancer systems.

Research Professionals

Shannon K. Alford (Postdoctoral Fellow)

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The role of Mena invasive isoforms in breast cancer metastasis

Pathological dissemination of malignant breast cancer involves specific alteration to underlying cell migration processes. Recent work from the Lauffenburger and Gertler labs identified Mena, a member of the Ena/VASP family, as being differentially regulated in metastatic carcinoma versus cells within the original primary tumor. The expression of specific invasive Mena isoforms (MenaINV or MenaINV/+) results in hypersensitivity to EGF stimulation in vitro and in vivo. In human mammary tumors, Mena overexpression is highly correlated with disease outcome. However, the intracellular mechanisms regulating EGF sensitivity in the Mena-overexpressing cells are not yet known.

We hypothesize that differential expression Mena isoforms result in quantifiable changes in motility-related signaling network activities involving multiple pathway components, with respect to both spatial and temporal dynamics. Due to the complex multivariate nature of this problem, an integrative systems approach may be especially appropriate to its address. We are combining experiment and computation to develop a data-driven model capable of elucidating important signaling variables in the context of differential Mena isoform overexpression. In the long term, the multivariate model could be used to predict network responses to pharmacologic intervention and may lend important insight to the effects of drugs that target individual, and perhaps more importantly, multiple points within the motility pathway.

Kelly Benedict (Postdoctoral Fellow)

Towards a systems-level understanding immune cell-cell interactions in HIV

In 2009, the World Health Organization reported 33.6 million people living with HIV/AIDS worldwide, with millions of new cases diagnosed every year. The disease continues to spread as the HIV virus is highly mutagenic and primarily targets CD4+ helper T cells of the immune system, making traditional vaccine development methods ineffective.

New insight for a future HIV vaccine may come from the study HIV elite controllers, the <5% of patients who become infected with HIV but are able to maintain extremely low viral loads and a sustained CD4+ T cell count without therapy. Though many genetic and functional differences have been associated with immune cells from these patients, no one characteristic seems to explain control of HIV, suggesting it may be the result of a complex system of interactions.

In collaboration with the Ragon Institute of Harvard, MGH and MIT and the Irvine and Love labs at MIT, we use single and multi-cell cytokine secretion measurements and various computational modeling techniques in an effort to get new, systems-level perspective on immune cell-cell interactions in HIV and other immune diseases. Results should help illuminate how complex functional and genetic properties of immune cells from elite controllers combine to result in control of HIV and may identify potential new strategies for vaccine development based on systems-level properties of immune cell networks.

Michael T. Beste (Postdoctoral Associate), in collaboration with Prof. Linda Griffith (MIT Biological Engineering)

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Inflammatory Networks in Endometriosis

Endometriosis is a hormone dependent inflammatory disorder characterized by the development of endometrial-like tissue outside the uterus, closely associate with chronic pelvic pain and infertility. Our work aims to identify key effectors of the endometriotic inflammatory response by profiling cytokine signatures across healthy, diseased, and treated patient populations. A major goal is the inference of immune cell networks and the development of predictive models to describe changes in intercellular communication that contribute to disease progression.

David C. Clarke (Postdoctoral Associate)

Systems Analysis of the Hepatic Acute Phase Response

I am interested in studying mammalian cell signal transduction using quantitative approaches. Within this theme, my postdoctoral research involves the following projects: 1) quantitative experimental and computational analysis of the hepatic acute phase response and 2) mixed-effects modeling techniques for statistical analysis of multiplexed cell signaling data.

Ta-Chun Hang (Postdoctoral Associate)

Primary liver sinusoidal endothelial cells are notoriously difficult to culture in vitro. Following 48-72 hours in culture, massive death occurs among the sinusoidal endothelial cells. My work is focused on trying to parse out the contributions of certain cues on the signaling mechanisms for prolonging their cell survival and phenotype. Concomitantly, I am also interested in trying to parse out signaling pathways activated in angiogenesis within the context of inflammation. Overlaps of both areas of interest occur as a result of inflammation and disease, as sinusoidal endothelial phenotype and survival decreases when inflammation occurs in the liver.

Douglas S. Jones (Postdoctoral Associate) in collaboration with Dr. Peter Sorger (Harvard Medical School, Systems Biology)

Experimental and computational analysis of signaling dysregulation in rheumatoid arthritis

We are implementing an integrated experimental and computational framework to dissect the intricacies of cellular dysregulation in rheumatoid arthritis. Through this work we will map signaling pathways that mediate cellular response in healthy and diseased cells. We will also determine the systems-level effects of standard clinical therapeutics on these cells, with the ultimate goal of predicting and testing new drug targets with potential to complement existing treatment modalities.

Brian Joughin (Koch Institute Research Scientist)

Mechanistic interpretation of phosphoproteomic data.

I am interested in ways to use publically accessible information to aid in the mechanistic interpretation of phosphoproteomic (e.g., mass spec) data. More generally, I am available for collaboration with researchers with the Integrative Cancer Biology Program at MIT who would like help turning their data into models.

Sarah Kolitz (Postdoctoral Associate)

Connecting the inner signaling state of the cell to its overall phenotype in response to environmental cues is key for understanding how cells make decisions. Typically, measurements of signaling state (e.g., levels or activities of signaling proteins) are made in bulk, averaging out differences that may exist between the signaling states of individual cells. In order to produce an unblurred picture of cell signaling states, we are working in collaboration with the Han lab to make measurements of kinase activities from a few or single cells using a microfluidic preconcentration device. I will also be investigating the role of communication between cell types in inflammatory processes related to disease.

Tharathorn (Joy) Rimchala (Postdoctoral Associate)

Cell Decision Model of Signaling in Endothelial Angiogenesis

Angiogenic sprouting, the formation of new branch from an existing blood vessel are governed by intracellular signaling that directs coordinated proliferation, migration and apoptosis of population of endothelial cells. Most existing quantitative models of endothelial angiogenesis minimally accounts for the role of intracellular singaling that drive endothelial cell decision.

My project involves developing a multi-state cell decision model that relates the effect of intracellular signaling to changes in cell decision in response to angiogenic and angiostatic cytokines including VEGF and Angiogepoietins. In collaboration with the EFRI team, I also work on developing corresponding methods to estimate parameters from cellular response data. Using human dermal microvascular cell line(hMVEC) as a model system, I measure cell proliferation, migration survival and apoptosis using flow cytometry and single cell tracking.

Juliesta E. Sylvester (Postdoctoral Associate)

Engineering cells and their microenvironment to control metastasis

The metastatic process results in the spread of cancer from the primary tumor site to a distant organ. Our goal is to better understand how communication between multiple cell types facilitates cell movement and to what extent particular genes control the resulting signaling network. Our focus is on the design and fabrication of a function-based screening assay using pooled shRNA libraries. These libraries suppress the expression of genes that influence the movement of breast cancer cells in a complex environment. The effects of these genetic screens are understood in the context of pathway regulation and network information flow using our bioinformatic analysis pipeline.

Administrative Assistant

JoAnn Sorrento

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Aran Parillo

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