Joshua Epstein - US grants

Cytokines, cytokine receptors and matrix interactions control and regulate the development of the hematopoietic system, normal as well as malignant. IL-6 has been recognized to have a major role in the development of multiple myeloma, although its mode of action is unclear. Preliminary studies indicate co-existence of autocrine and paracrine IL- 6 stimulation and lead us to believe that IL-6 gene expression by myeloma cells is inducible rather than constitutive. Identifying the cytokines which induce IL-6 gene expression by myeloma cells and determining the cells which produce them is one of the goals of this project. Sensitive techniques such as PCR and ELISA will be employed and new techniques developed for single cell analysis. Although myeloma cells produce IL-6 and display IL-6 receptors, their low in vivo proliferative activity and the inability of the cytokine to induce sustained proliferation in vitro suggest that the target cell for IL-6 may be a myeloma precursor cell. To address this hypothesis, methods for purifying the pre-myeloma cells from myeloma patient's blood will be developed. High resolution flow sorting on the basis of phenotypic characteristics reported to be associated with pre-myeloma cells will be used to obtain these cells in high purity. The effects of cytokines on the proliferation and differentiation of these cells into the recognizable monoclonal myeloma cells will be studied using a variety of culture conditions, including co-cultures with cytokine secreting cells. Sophisticated cell cycle analysis techniques like BUdR/IUdR labeling and expression of the Ki-67 antigen will be used to determine proliferation. Morphologic, genetic and phenotypic analysis with the aid of image analysis will help determine differentiation. Examination of the effects of dexamethasone and interferon which are effective in the treatment of myeloma will reveal if they have regulatory effects on the production of cytokines and if resistance to these drugs is a tumor and/or an accessory cell phenomenon. These data, generated with help from the Cores, will elucidate the role of cytokines in the development of multiple myeloma.

The research will investigate the computing challenges posed by extending large-scale multi-agent computational models to achieve both greater scale and more complex and realistic behaviors by individual agents. Individual agents in these models are represented by complex data structures that represent their internal states and behavioral repertoires. In agent populations of even modest size, the number of interaction histories (which determines the state of a model) is huge, so building realistic models poses significant computational hurdles. The primary aim of this research is to explore the quantity-complexity frontier: point at which there are tradeoffs between models with large numbers of simple agents and models with greater numbers of more complex agents. This project aims to develop innovative computational techniques to extend both the scale and the complexity of models by two to three orders of magnitude. Such advances are crucial to understanding whether phase changes occur when scale and/or complexity is significantly increased in multi-agent or social systems. It is also critical to future real-world applications of multi-agent systems modeling to problems such as understanding development and migration patterns (e.g. China), which pose problems of scale, and understanding peer-group effects on crime rates (which pose problems of modeling the complexity of individual agent behaviors).

This award supports the use of agent-based models to study the emergence of multi-agent institutions in society and to test these models against data. The computational approach taken here departs from perfectly rational models in favor of boundedly rational models and non-equilibrium dynamics. The models are inherently concerned with social interactions and the ways in which institutions (and social norms and conformity effects) emerge out of those interactions. Five models will be developed including: the Long House Valley Anasazi from 800 AD to their disappearance in 1300 AD; crime rates; retirement decision-making; the distribution of firm sizes; and institutions of governance. These models will advance scientific knowledge about multi-agent systems, simulation and anthropology as well as contribute to policy-making about criminal, retirement and organizational behaviors.

DESCRIPTION: (Applicant's Description) The Cell Analysis Core is designed to provide support to the clinical and basic projects by centralizing common procedures. These include sample acquisition, characterization, distribution and storage, cell sorting, analytical flow cytometry, histology and immunohistochemistry, and fluorescence in situ hybridization (FISH). By centralizing sample acquisition, we will be able to maintain records of expected data for each sample used, thus, increasing the efficiency of data collection. A priority list for sample distribution will be established and updated periodically to accommodate requirements of the different projects in terms of cell numbers, sample type, patient characteristics, and other relevant criteria. This mechanism will greatly enhance the efficiency of sample utilization by the different projects. A centralized histology, immunohistochemistry, and FISH service will avoid the need to establish the procedures in each investigator?s laboratory, providing for uniformity of procedures and efficient use of materials. The flow cytometry and cell sorting service offered will provide state-of-the-art flow cytometry and cell sorting support to the projects in this program application. The different functions of the core will be supervised by able individuals with ample experienced in the various procedures.

DESCRIPTION: (Applicant's Description) Multiple myeloma remains an incurable disease, in spite of the considerable progress made in increasing the rate of complete remissions achieved by patients. Relapses are caused by the myeloma cells with self-renewal capacity. These cells are able to survive even the most aggressive treatment. Interactions of the myeloma cells with their microenvironment through adhesion molecules and cytokines provides the myeloma cells with a sanctuary, protecting the cells from spontaneous and drug-induced apoptosis. The long-term goals of this project are to elucidate the biology of myeloma at the molecular and cellular level as a guide to better clinical management of the disease. The central hypothesis this project addresses is: Myeloma and stroma interactions result in a supportive microenvironment that allows the myeloma plasma cells to achieve a state of inexhaustible proliferation and disease subsistence, and to eventually develop resistance to drug therapy. By targeting the myeloma plasma cells directly and by interfering with elements of the supportive and protective micro-environment, we can develop effective therapies against myeloma. Our specific aims are: (1) To control the growth of myeloma and its manifestations by changing the bone marrow environment. We will determine whether normal bone marrow stroma can support sustained proliferation of myeloma cells. We will identify elements of the supportive micro-environment required for myeloma cell growth by disrupting the functions of the osteoclasts and vascular endothelial cells and determine the effects on the survival and the growth of the myeloma cells and on the cytokine milieu. These studies will reveal the importance of osteoclasts and vascular endothelial cells, in supporting myeloma. (2) To prevent emergence of drug resistance in myeloma. We will determine if the bone marrow stromal environment facilitates emergence of drug resistance, and investigate if the molecular mechanisms of in vivo drug resistance are the same as have been reported for in vitro resistance. These studies will shed light on the development of drug resistance in vivo, and will point to strategies that will increase treatment efficacy. (3) To determine the role of pre-plasmacytic cells in the blood and bone marrow of myeloma patients in sustaining the disease process. We will determine the ability of purified myeloma plasma cells to achieve a state of inexhaustible proliferation and production of myeloma manifestations in the SCID-hu host, and whether the preplasmacytic cells in the bone marrow and blood of patients are able and required to produce sustained symptomatic myeloma in the SCID-hu host. These findings will determine if therapy needs to consider preplasmacytic cells as an important component of the myeloma disease process. By its conclusion, work under this project will have determined the significance of reported anomalies in the myeloma bone marrow microenvironment to the disease process, elucidated the role of osteoclasts and vascular endothelial cells in supporting myeloma cells, determined whether disrupting osteoclast function and interfering with neo-angiogenesis inhibits growth of myeloma cells and increases the efficacy of treatment, demonstrated the ability of myeloma plasma cells to maintain the disease process, and determined whether preplasmacytic clonal cells have a continuous active role in sustaining the disease process.

The Cell Analysis Core is designed to provide support to the clinical and basic projects by centralizing common procedures. These include sample acquisition, characterization, and distribution and banking; cell sorting; analytical flow cytometry; and histology and immunohistochemistry. By centralizing sample acquisition and storage we will be able to track samples and maintain records of expected data for each sample used, thus increasing the efficiency of data collection. A priority list for sample distribution will be established by the Core Oversight Committee and updated periodically to accommodate requirements of the different projects in terms of cell numbers, sample type, patient characteristics, and other ielevant criteria. This mechanism will greatly enhance the efficiency of sample utilization by the different projects. A centralized histology and immunohistochemistry service will avoid the need to establish the procedures in each investigator's laboratory, providing for uniformity of procedures and efficient use of materials. The flow cytometry and cell sorting service offered will provide state-of-the-art flow cytometry and cell sorting support to the projects in this program application. Able individuals experienced in the various procedures will supervise the different functions of the core.

DESCRIPTION (provided by applicant): Significant advances in the treatment of myeloma have resulted in a high rate of complete remissions;however, all patients eventually relapse and succumb to the disease. The bone marrow microenvironment, particularly the cells involved in bone formation and destruction, are intimately involved in the disease process as regulators of myeloma growth and tumor manifestations. The overall goal of our research is to develop a new paradigm of myeloma therapy, whereby control of bone disease helps to control myeloma development and progression. Working toward this goal, we propose experiments to elucidate the role of osteoblasts, the bone-forming marrow cells, in the disease process and to develop approaches to control myeloma by controlling the associated lytic bone disease. Our experiments will address two specific aims to investigate in vitro and in vivo the consequences of interactions between myeloma cells and osteoblasts, at both the physiological and molecular levels. We will examine in vivo in the SCID-hu model whether increasing the number and activity of osteoblasts results in bone formation and impacts myeloma growth (Specific Aim 1). These experiments address our hypothesis that increasing osteoblast numbers and activity will increase bone formation and will control growth and survival of myeloma cells. We will attempt to increase osteoblast number and activity by injecting osteoblast progenitor cells and parathyroid hormone, individually and in combination. We will also elucidate in vitro the nature and consequences of interactions between myeloma cells and bone marrow-derived MSC on osteoblast differentiation and survival and on myeloma cell survival (Specific Aim 2). These experiments are designed to pursue our hypothesis that, in patients with lytic bone disease, myeloma cells disrupt the mesenchymal stem cell to osteoblast differentiation process, and the resultant elimination of osteoblasts facilitates myeloma cell survival and disease progression. Preliminary results suggest that the effects of intercellular interactions will be heterogeneous, and we propose experiments to examine potential sources for this variety. We will employ state-of-the-art proteomics technologies to further investigate these interactions at the molecular level. Our newly developed in vivo and in vitro models of myeloma cell/osteoblast interactions and myeloma disease progression, combined with advanced proteomics technologies, are powerful tools for deciphering critical aspects of myeloma biology, identifying targets for effective therapeutic interventions, and developing molecular tools for evaluating treatment efficacy. The results of the proposed study will lead us to the next stage in which we will design treatment protocols aimed at improving myeloma-related bone disease and test treatment efficacy in preventing myeloma relapses and disease progression.

The project aims to bridge the gap between social sciences, public health and computer science in order to address an important problem in epidemiology. The research focus is on the development of high fidelity computational models for understanding the aggregate effects for interactions among individual behavior, social networks and public health policies. The results will support local and federal officials in their planning and response to the spread of infectious diseases, e.g. pandemic avian influenza. The computational agent-based models will yield practical methods to support officials in decision making both before an outbreak, by identifying critical normative individuals in urban societies, and during an outbreak, by identifying the potential cascading effects of individual and group behavior within social networks. The results will be integrated into a functioning high-fidelity agent based simulation and modeling capability. The project has the following components: (1). Computational agent-based models of individual behavior and its interaction with social networks and public policy; (2) Theoretical investigation using game theory and discrete dynamical systems of the dynamic co-evolution of social networks, individual behavior and public policies; and (3) Illustrative realistic case studies that demonstrate the results of our research to aid in planning for and responding to large scale infectious disease outbreaks.

Abstract: Models are central in important areas of public health, including pandemic flu, chronic disease, and disaster preparedness. However, behavioral factors are virtually ignored in current modeling, a crucial defect. Behavior can fundamentally shape the spread of infectious diseases (such as influenza). People may flout, or not know, government containment directives. Distrustful communities may refuse vaccine. Rather than self-isolate, exposed individuals may flee in fear, accelerating the spatial spread of disease. Epidemic modeling to date has virtually ignored these behavioral adaptations and their consequences. Behavior also shapes chronic disease outcomes-yet no behavioral mechanism is currently offered to explain, for example, the striking historical dynamics of obesity. Theoretical and empirical work could identify such mechanisms, including peer effects. Behavior would also shape health outcomes in a disaster. In an urban toxic plume release, the natural impulse to flee could amplify congestion, undermining evacuation, and increasing exposure. Are there simple decentralized rules or tailored messages that could, instead, generate efficient evacuation? Behavior under stress may be based on poor information, unwarranted fear, or entrenched social norms. These possibilities should condition risk communication and the design of containment/preventive strategies across the spectrum of public health threats. At present, they do not. A central goal of this research, then, is to model boundedly rational endogenous behavioral adaptations and their feedback effects on spatio-temporal disease dynamics. The Project covers both the epidemiology of behaviors (such as panic, non-compliance, distrust) and the role of behavioral factors in the progress of infectious and chronic diseases. The core analytical technique will be agent-based modeling by multi- disciplinary research teams. The PI is a recognized pioneer in this innovative field, and has directed successful multi-disciplinary agent modeling projects on Smallpox, Archaeology, Economics, and Civil Violence, as recounted in his book, Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton, 2006).

The Cell Analysis and Specimen Banking Core is designed to support the clinical and basic science projects by centralizing common procedures (Aim 1) and centralizing tissue and specimen banking procedures (Aim 2). These include sample acquisition, processing, characterization, banking, and distribution; cell sorting; and analytical flow cytometry. The Myeloma Institute for Research and Therapy (MIRT) annually treats nearly 5,000 patients from across the US and the world. From this extensive patient base, Core B banks thousands of patient specimens, many of which are serially collected throughout the course of a patient's disease and treatment. Centralized sample acquisition and storage, together with enhanced database capabilities, is a tremendous resource for program project investigators, as well as a highly efficient operation. It will allow us to track samples and maintain records of expected data for each sample used, thus increasing the efficiency of data collection for all projects. These activities of the core will be enhanced by a constantly updated integrated database. A priority list for sample distribution will be established by the Core Oversight Committee, composed of the program PI and Project Leaders, and will be updated periodically to accommodate requirements of the different projects, such as cell numbers, sample type, and patient characteristics. This mechanism will greatly increase the efficiency of sample utilization by the different projects. A centralized sample processing service will avoid the need to establish the procedures in each investigator's laboratory, providing for uniform procedures and efficient use of materials. The flow cytometry and cell-sorting services offered will provide state-of-the-art support to the projects in this program application, which will be augmented by the expertise of core personnel.

DESCRIPTION (provided by applicant): Data indicate that multiple myeloma is a major health problem. There are approximately 54,000 myeloma patients in the USA, 20,000 new patients will be diagnosed with myeloma in 2007, and nearly 11,000 patients succumb to their disease yearly. The cure rate and 10-year survival remains low indicating the need for new therapies. We have shown that chemo-resistant myeloma can be killed by killer-cell immunoglobulin- like receptor (KIR) ligand (-L) mismatched NK cells from a haplo-identical donor in vitro and in a clinical trial. However, we could find a KIR-L mismatched donor for only 30% of patients. We propose to use a 3-pronged approach to: a) apply NK cell therapy in the autologous setting making therapy possible for all patients and b) enhance the clinical efficacy of NK cells. We hypothesize that we can overcome the inhibition of autologous NK cells induced by HLA-class I on myeloma cells by activating and expanding the NK cells and by modulating the interaction between NK cell effectors and myeloma targets. In Specific Aim 1 we will determine if NK cell dose and potency can be reliably increased by expanding and activating NK cells from myeloma patients. Expansion of NK cells is important if we are to overcome the myeloma burden. Without expansion, there will be too few NK cells to eradicate all myeloma. `Supercharging'of the NK cells will overcome any inhibitory signals delivered by autologous myeloma. We will stimulate the NK cells with K562 cells transfected with membrane-bound IL15 and the co-stimulatory molecule 4-1BB-L. In Specific Aim 2 we will evaluate whether the action of activated autologous NK cells can be enhanced by flagging myeloma cells with a humanized antibody to CS1. CS1 is a CD2 receptor family molecule expressed by myeloma but not by normal tissues, therefore conferring myeloma-specific killing. This antibody will effectively `flag'myeloma cells for ADCC-mediated killing by NK cells. In Specific Aim 3 we will ascertain if the action of activated autologous NK cells can be increased by down regulating inhibitory ligands on myeloma by proteasome inhibition. NK cells do not normally kill autologous myeloma due to the interaction between HLA class I on myeloma cells with inhibitory receptors on NK cells. We have demonstrated that we can down regulate HLA-class I on myeloma in vitro and in vivo after treatment with the proteasome inhibitor bortezomib and that this translates into killing of myeloma by autologous NK cells. Hence, we will evaluate the existence of potential synergistic or additive effects when combining bortezomib with activated NK effectors. This clinical approach can be applied to patients with standard-risk myeloma to obtain even better growth control and more durable remissions once we have demonstrated the efficacy of enhanced NK cell therapy in patients with high-risk or relapsing myeloma. In addition, this research could lead to more efficacious treatment for other NK cell sensitive malignancies. PUBLIC HEALTH RELEVANCE: Multiple myeloma is a form of bone marrow cancer that is currently incurable. There are approximately 54,000 myeloma patients in the USA. This grant proposal describes 3 new ways in which the patient's own immune cells can be used to destroy the cancer. Such treatment may also be useful to treat other cancers.

People tend to change and adapt their behavior during epidemics. Historically, behavioral adaptation has played a central role in the progression of epidemics. In some cases, a change in behavior may suppress an epidemic (e.g., through fear-driven flight or self-isolation) while in other cases, a change may intensify the spread of a disease (e.g., through vaccine refusal or premature cessation of distancing). People?s future behavior during the current COVID-19 epidemic will determine how well we cope with two central threats. First is the immediate threat of successive epidemic waves due to the premature lifting of social distancing guidelines, and the abandonment of social distancing by a large percentage of the population. The second threat is that the disease will rebound even after a vaccine is available. This has occurred before (e.g., measles) and could occur with COVID-19 if a sufficient fraction of the population refuses vaccine out of fear. This research will develop a new model to predict human behavior using publicly available social media data to address a known weakness in most current models. The new model, source code, data, parameters, assumptions, and methods will all be completely open and publicly available, ensuring the replicability of all results. This project will also deliver an interactive version of the model for use by policymakers, government agencies, and educators. Other broader impacts are training opportunities for a graduate student.

Most current models that are used to forecast the course of epidemics may provide incomplete results because they do not take into account changes in peoples? behavior (human behavioral adaptation). In the proposed research, human behavior will be included within a new model using data taken from social media platforms such as Twitter and Facebook. The data will allow calibration of the model and replicate the entire New York State epidemic to date. Machine Learning approaches will be applied to determine optimal messages and interventions over a wide range of scenarios and control strategies. This development requires a unique interdisciplinary team spanning social science, infectious disease modeling, biostatistics, social media data mining, and Machine Learning.

This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project studies how agent-based models (ABMs) of social contexts can improve the design and regulation of accountable software systems. Agent-based modeling is a social scientific research method that involves bottom-up modeling of complex systems and computationally determining their emergent properties by running simulations. The investigators use ABMs to model elements of the social and regulatory environment in which a software system operates. The project’s novelties are due to its interdisciplinary synthesis, applying ABMs from social sciences to software specification and automated testing, as done in computer science, to guide the crafting and enforcement of technology regulations, a legal concern. The project's impacts are informing public policy and teaching as well as providing an open-source software toolkit for the automated testing of software systems.

Software, regulation, and society interact with unpredictable and sometimes undesirable dynamic feedback effects. This project explores how ABMs and scientific simulation can address the gap between legal requirements and software design by helping regulators, domain experts, software designers, and other stakeholders assess the potential societal implications of particular software and regulatory systems. The investigators use ABMs for three tasks: (1) creating software specifications using models of regulations and the social environment in which software operates, (2) testing software systems for compliance using simulations of their social impact, and (3) designing regulations that reflect these new tools. The project develops these general methods for improving the design of accountable software systems and advancing the understanding of the legal context of software design through the exploration of two specific domains: the effects of online advertising systems on housing segregation and the tradeoffs between privacy and accuracy in contact tracing for infectious-disease control.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.