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

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.

Ta-Chun Hang, co-advised with Linda Griffith

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.

Miles Miller

"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)

Email

Development of constrained fuzzy logic for modeling signal transduction pathways: an extension of Boolean logic modeling

Determination of a biological signaling network’s response to various stimuli and perturbations is of great interest for the understanding of basic biological mechanisms as well as the development of pharmacological agents for the treatment of disease. Recent work demonstrates that Boolean logic models can be used along with a hypothesized network structure and experimental data to determine an optimal signaling network structure. However, the Boolean technique is limited in that it only allows for species to be fully active or inactive. As biological systems often demonstrate a range of relevant levels, the ability to learn about these features is desirable. Fuzzy logic has been used to represent biological signaling networks (Aldridge et al. (2009) PLoS Comp Bio 5(4)). However, the flexibility of this modeling method necessitated that we first constrain the fuzzy logic framework. This constrained framework allows the logic models obtained by this optimization process to represent a continuous range of node states rather than the discrete states represented by Boolean logic modeling. A combination of mathematical functions are used to represent species’ interactions within signaling networks. This modeling framework will be used in conjunction with the Cell Net Optimizer algorithm developed by Julio Saez-Rodriguez in the Lauffenburger and Sorger labs to determine the constrained fuzzy logic network that best represents data from various signaling pathways.

Kristen Naegle (BE doctoral), in collaboration with Prof. Forest White (BE, MIT)

Development of computational tools and algorithms for exploring MS phosphoproteomics datasets.

Ericka Noonan (BE doctoral), in collaboration with Prof. Leona Samson (BE, MIT)

Email

Analysis of the signaling events downstream of DNA damage, specifically damage produced by alkylating agents, and how these signals control the cellular response of death/survival.

Megan Palmer (BE doctoral), in collaboration with Darrell J. Irvine (MIT Biological Engineering / Materials Science) and Jianzhu Chen (MIT Biology)

Email

The capacity to support a healthy adaptive immune response relies upon the maintenance of sufficient naïve T cell numbers with a wide diversity of target specificity. Naïve T cell survival is dependent both upon T cell receptor (TCR) stimulation by self-peptide/major histocompatibility complex (spMHC) in combination with cytokine signaling by interleukin-7 (IL-7) through the IL-7 receptor (IL-7R). IL-7R and TCR stimuli are thought to act synergistically in their regulation of T cell homeostasis. However, the mechanisms of signaling cross-talk are not known.

To elucidate how TCR and IL-7R signals direct cellular responses, we are developing both in vitro and in vivo systems to enable controlled TCR and IL-7R stimulation. Dynamic signaling activities across common downstream pathways are simultaneously measured alongside phenotypic responses to characterize network behavior governing distinct T cell fates. Computational modeling approaches are then used to evaluate from these portraits of signaling behavior potential mechanisms of signal integration in the TCR-IL7 network.

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.

Tharathorn (Joy) Rimchala in collaboration with Prof. Frank Gertler, MIT

Intrinsic and Extrinsic Regulation of EGF-induced Chemotactic Responses in Invasive Cancer Cells

Carcinoma cells in metastatic tumors are polarized toward and are attracted to nearby blood vessels or lymphatics by chemoattractants (Wyckoff et al, 2004; Dabes et al, 2005). This chemotactic response is essential for tumor cell intravasation and determines their metastatic capability. EGF-induced chemotactic ability of invasive cancer cells appears to be governed by intrinsic cell properties (such as motility and directional persistence) and extrinsic cues (such as cell-ECM and cell-cell interaction). To acquire mechanistic understanding of the role of chemotaxis in intravasation, we investigate the regulation of EGF induced chemotactic responses in highly invasive mouse adenocarcinoma (MTLn3) and human glioblastoma (U87MG) cell lines. Preliminary work in our laboratory shows that Mena overexpression leads to a dose-dependent increase in directional persistence of U87MG cells in 3D. We aim to utilize this system to assess the EGF-induced chemotactic ability of U87MG cells with varying Mena overexpression in 2D Collagen I coated surface and in 3D Collagen I gel. To determine the effect of cell-cell and cell-ECM interactions on chemotactic responses, we will assess the chemotactic ability of U87MG cells in organotypic brain slice culture, in the presence of brain extracellular matrix and neurons.

Laura Sontag-Kleiman (CSB doctoral), in collaboration with Peter Sorger (MIT Biological Engineering and Harvard Medical School Systems Biology)

Computational and experimental analysis of ErbB receptor signaling and oncogenic K-Ras mutations.

Joel Wagner (BE Doctoral)

Using Bayesian networks for the rational selection of targets to modify cell phenotype

Our ability to measure protein levels and modifications in a high-throughput manner has outpaced our ability to assign kinetic rates to each of those proteins and their interactions with other species inside the cell. Differential equation models can describe the well-studied portions of the cellular network for which kinetic parameters can be reasonably estimated. However, improved experimental technologies have allowed more data to be collected for many more network components; consequently, modeling strategies beyond differential equations are now needed to describe the newly expanded but poorly understood networks.

In an effort to better capture the complexity of cellular networks by including many more measured species from high-throughput technologies—while still operating in a mathematically rigorous, quantitative framework—we propose the use of Bayesian networks. Bayesian networks are one method used for network inference, the process of determining the relationships among species that comprise the cellular network. Focusing on the epidermal growth factor and insulin signaling networks, we propose extensive use of the parameters of Bayesian networks (the conditional probability tables describing parent-child relationships), in addition to the structure of inferred Bayesian networks, to aid in therapeutic design.

Shan Wu (BE Doctoral)

Investigating the cue-signal-response relationship of mesenchymal stem cell migration

Mesenchymal stem cells (MSCs) and related connective tissue progenitors (CTPs) hold great potential for tissue engineering applications because of their ability to pluripotently differentiate, even in vitro, into a variety of cells crucial to injury repair. In order to effectively use MSCs, we must understand and control their responses of proliferation, differentiation, and migration. Previous work in our lab suggests that PMMA-g-PEO base polymers tethered with epidermal growth factor (tEGF) may be designed to control MSC responses, but MSC migration in general remains a largely unstudied field. This project aims to quantify MSC migration on a variety of polymer substrates, as well as investigate EGF-induced signaling in these same conditions to ultimately computationally correlate the growth-factor-stimulated migration cue-signal-response in MSCs.

Research Professionals

Shannon K. Alford (Postdoctoral Associate)

Email

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.

Neda Bagheri (BE Postdoctoral Associate)

Email
Personal web page

Development of mechanisitic, data-driven, quantitative models to optimize oncolytic adenovirus cancer therapy.

Replication-selective adenoviruses, such as ONYX-015, replicate in cells containing certain mutations, motivating their use in targeted gene therapy. Although ONYX-015 is designed to preferentially target cancer cells, experimental data suggests that the adenovirus receptor is down-regulated in highly malignant cells, hindering ONYX-015’s ability to infect. Although pharmaceutical intervention into the Raf-MEK-ERK pathway has shown to counter this effect, it also causes cell cycle arrest thereby stunting viral replication and virus-induced cancer cell death. Through the data-driven modeling of cancer cells subject to drug treatment and ONYX-015 infection, we aim to characterize and predict system dynamics, providing a means to optimize the efficacy of oncolytic adenovirus cancer treatment. Preliminary studies have supported a population based (cellular level) deterministic model that highlights sub-cellular virus-host dynamics as components necessary for accurate and predictive simulations. Thus, we aim to refine the model to include relevant sub-cellular events that better characterizes both the mechanistic and dynamic behavior of adenovirus cancer treatment. Pending successful validation of model predictions, our goal is to elucidate optimization strategies that could offer practical and effective means for minimizing cancer growth.

Research Collaborators: Dr. Marisa Shiina and Professor W. Michael Korn at the Division of Gastroenterology and Medical Hematology/Oncology, UCSF Helen Diller Family Comprehensive Cancer Center, Department of Medicine, San Francisco, CA 94115-1705.

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

Email

Dysregulation of apoptosis and inflammatory responsiveness in endometriosis

In healthy human endometrium, cyclic changes in local hormone levels regulate maturation from a proliferative to quiescent cell phenotype. However, endometrial epithelia and stroma in 6-10% of women exhibit reduced hormonal sensitivity, allowing tissue fragments to survive and proliferate following autotransplantation into the peritoneal cavity. Aberrant growth of endometriotic lesions is further aggravated by a chronic inflammatory response that stimulates proteolytic invasion but fails to induce programmed cell death.

David C. Clarke (Postdoctoral Associate)

Engineering TRAIL sensitivity in hepatocellular carcinoma cells

Hepatocellular carcinoma is the second most lethal type of cancer, in part due to a lack of suitable treatments. The cytokine TRAIL has shown promise in specifically killing cancer cells, including HCC cells, by activating the extrinsic apoptosis signaling network. However, TRAIL treatment only kills a moderate fraction of HCC cells in a given sample, possibly due to variability in the quantitative properties of the extrinsic apoptosis signaling network. Therefore, my project involves attempting to engineer the extrinsic apoptosis signaling network into a TRAIL-susceptible state, while accounting for this variability, in order to develop improved treatment strategies for HCC.

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.

Shelly Peyton (BE postdoctoral)

Email

Mesenchymal Stem Cell Migration in 3-D Synthetic ECM Analogs.
Several recent studies highlight the possibility of using multipotent adult bone marrow-derived mesenchymal stem cells (MSCs) for tissue engineering applications, and certain soluble factors (such as EGF) have been identified and characterized for their ability to promote MSC migration; however, much less is known about the role of the 3-D scaffold, designed to mimic the native ECM, in regulating the motility of these stem cells.  I am currently investigating how the physical and chemical properties of a 3-D synthetic ECM analog can regulate the migration and EGF-triggered signaling of adult MSCs.

Julio Saez-Rodriguez (BE postdoctoral), in collaboration with Peter Sorger (Harvard Medical School, Systems Biology)

Email
Personal web page

I am interested in applying engineering methods to understand signal transfer and decision processing in mammalian cells in health and disease. In particular, I am developing computational approaches to use high-throughput proteomics data to understand signaling networks. As a first challenge, we found that it is not trivial to link in an efficient manner high-throughput data to mathematical models: the data has to be stored in a structure manner with additional information (metadata), processed (normalized, etc.), visualized and then exported for analysis. To facilitate these steps, we have developed an open-source MATLAB toolbox called DataRail.

With the data conveniently processed, sophisticated insight can be obtained with detailed, mechanistic models but, due to the large number of unknown parameter values, modeling very large networks is an arduous task. Therefore, we are using a simplified description based on Boolean logic that encapsulates the topology and causality of the network without dealing with kinetic parameters. Using data from different cell-types we are able to determine cell-specific models, which can help to identify targets for drug discovery that influence selectively cancerous cells. These methods are embedded in CellNetOptimizer (CNO), an open-source MATLAB toolbox that uses CellNetAnalyzer as simulation engine and works in concert with DataRail. We are applying this method to different data sets. As a proof of principle, application of this method to primary and transformed liver cells allowed us to uncover significant differences in the rewiring of their signaling networks.

Mark Fleury (BE postdoctoral)

The interleukin family of cytokines plays an important role in the survival and differentiation of immune cells. Interleukin-7 (IL-7) is especially important in the maintenance of T-cells and is currently being investigated for use as an adjuvant to both assist compromised immune systems as well as to increase the efficacy of vaccines. While IL-7 is unique in its ability to sustain T-cell lymphopoeisis, it shares pro-survival traits with other IL family members such as IL-2 and IL-15. All three of these cytokine receptors share the same common gamma chain which is thought to be responsible for its signaling and could explain some of the shared effects. Despite their similarities, they also have important differences in terms of their physiological effect, and the details behind the differences in signaling is currently unclear. My goal is to determine how these IL family cytokines signal, which signaling characteristics they share, and which characteristics are unique for each cytokine. We attempt to determine these signaling pathways using high-throughput biochemical assays to collect intracellular protein phosphorylation profiles that are created through the use the various cytokine stimuli combined with selective pathway inhibitors. These phosphorylation profiles can then be analyzed through the use of regression modeling to build a clearer picture of the intracellular pathways invoked by the IL family of cytokines.

Administrative Assistant

JoAnn Sorrento

Email

Aran Parillo

Email