MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated "public data libraries", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health

Analysis of cancer progression and morphology using computational modeling and image processing techniques

In the US, an estimated 560,000 people died from cancer in the year 2009 (Jemal et al., 2009). The goal of this dissertation is to improve the fundamental understanding of cancer morphogenesis and so introduce scientific rigor into the diagnostic and prognostic methods used in the clinical setting. To this end, we focus on defining relationships between the history of growth of a cancerous lesion--which is what reliable prognosis depends on – and the morphology observed at an instant in time – which is what clinicians can observe ‐ through a combination of computational modeling and image processing. We achieve this goal by completing three aims. The first aim of the thesis is to examine the architectural progression of ductal carcinoma in situ of the breast (DCIS) using a two‐dimensional computational model. In this work we have found that the distinct architectural subtypes can result from different cellular features or from precancerous growths with similar cellular features but observed at different time points. The second aim of this thesis is to develop a border detection algorithm for skin lesions collected by dermoscopy. This work has resulted in the production of an automated and experimentally validated computational tool for the discrimination of melanoma, benign melanocytic lesions and non‐meanocytic lesions. The third and final aim is to characterize the fully three‐dimensional (3D) morphology of DCIS. In this work, we developed a 3D reconstruction approach to build 3D representations of DCIS. From this research, we have determined for the first time that there are two distinct 3D architectures of cribriform DCIS that are indistinguishable in 2D cross‐sections. Based on this finding, we propose that 3D reconstructions hold additional clinical information that cannot be accessed from analysis of 2D histological samples.Ph.D.Includes bibliographical referencesby Kerri-Ann Norto

Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy

Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics

Additional file 1: of An agent-based model of triple-negative breast cancer: the interplay between chemokine receptor CCR5 expression, cancer stem cells, and hypoxia

This includes a table with the parameters used in the model, as well as the experimental measurements of CCR5 and stem cells in MB231 cells using flow cytometry, migration assays of hypoxic and normoxic MB231 cells. (DOCX 973 kb

Dynamic Changes in Microvascular Flow Conductivity and Perfusion After Myocardial Infarction Shown by Image-Based Modeling

31 p.-5 fig.-4 tab.-6 fig. supl.-1 tab. supl.Background-Microcirculation is a decisive factor in tissue reperfusion inadequacy following myocardial infarction (MI).Nonetheless, experimental assessment of blood flow in microcirculation remains a bottleneck. We sought to model blood flow properties in coronary microcirculation at different time points after MI and to compare them with healthy conditions to obtain insights into alterations in cardiac tissue perfusion.Methods and Results-We developed an image-based modeling framework that permitted feeding a continuum flow model with anatomical data previously obtained from the pig coronary microvasculature to calculate physiologically meaningful permeability tensors. The tensors encompassed the microvascular conductivity and were also used to estimate the arteriole–venule drop in pressure and myocardial blood flow. Our results indicate that the tensors increased in a bimodal pattern at infarcted areas on days 1 and 7 after MI while a nonphysiological decrease in arteriole–venule drop in pressure was observed; contrary, the tensors and the arteriole–venule drop in pressure on day 3 after MI, and in remote areas, were closer to values for healthy tissue. Myocardial blood flow calculated using the condition-dependent arteriole–venule drop in pressure decreased in infarcted areas. Last, we simulated specific modes of vascular remodeling, such as vasodilation, vasoconstriction, or pruning, and quantified their distinct impact on microvascular conductivity.Conclusions-Our study unravels time- and region-dependent alterations of tissue perfusion related to the structural changes occurring in the coronary microvasculature due to MI. It also paves the way for conducting simulations in new therapeutic interventions in MI and for image-based microvascular modeling by applying continuum flow models in other biomedical scenarios.The research leading to these results has received funding from the People Programme (Marie Curie Action) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant Agreement 608027 and from the Spanish Ministerio de Ciencia, Innovación y Universidades (SAF2017-83229-R) to Arroyo. The CNIC (Centro Nacional de Investigaciones Cardiovasculares) is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Popel was supported by NIH grant R01HL101200 from the National Heart, Lung and Blood Institute, NHLBI. Santos acknowledges founding from Ministerio de Ciencia, Innovación y Universidades (TEC2015-66978-R). El-Bouri was funded by a Doctoral Training Partnership studentship, grant reference EP/M50659X/1.Peer reviewe