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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.

To simulate the spatiotemporal distribution of chemical compounds, we present BioFVM, an open-source reaction-diffusion equation solver using finite volume methods with motivation for biological applications. With various numerical solvers, we can simulate the interaction of dozens of compounds, including growth substrates, drugs, and signaling compounds in 3-D tissues, with cells by treating them as various source/sink terms. BioFVM has linear computational cost scalings and demonstrates first-order accuracy in time and second-order accuracy in space. Beyond simulating the transport of drugs and growth substrates in tissues, the ability to simulate dozens of compounds should make 3-D simulations of multicellular secretomics feasible.

Multicellular simulations of tumor growth in complex 3-D tissues, where data come from high content in vitro and bioengineered experiments, have gained significant attention by the cancer modeling community in recent years. Agent-based models are often selected for these problems because they can directly model and track cells' states and their interactions with the microenvironment. We describe PhysiCell, a specific agent-based model that includes cell motion, cell cycling, and cell volume changes. The model has been performance tested on systems of 10^5 cells on desktop computers, and is expected to scale to 10^6 or more cells on single super-computer compute nodes. We plan an open source release of the software in early 2016 at PhysiCell.MathCancer.org.

Systems biology takes a holistic approach to biological questions as it applies mathematical modeling to link and understand the interaction of components in complex biological systems. Multiscale modeling is the only method that can fully accomplish this aim. Mutliscale models consider processes at different levels that are coupled within the modeling framework. A first requirement in creating such models is a clear understanding of processes that operate at each level. This research focuses on modeling aspects of biological development as a complex process that occurs at many scales. Two of these scales were considered in this work: cellular differentiation, the process of in which less specialized cells acquired specialized properties of mature cell types, and morphogenesis, the process in which an organism develops its shape and tissue architecture. In development, cellular differentiation typically is required for morphogenesis. Therefore, cellular differentiation is at a lower scale than morphogenesis in the overall process of development. In this work, cellular differentiation and morphogenesis were modeled in a variety of biological contexts, with the ultimate goal of linking these different scales of developmental events into a unified model of development. Three aspects of cellular differentiation were investigated, all united by the theme of how the dynamics of gene regulatory networks (GRNs) control differentiation. Two of the projects of this dissertation studied the effect of noise and robustness in switching between cell types during differentiation, and a third deals with the evaluation of hypothetical GRNs that allow the differentiation of specific cell types. All these projects view cell types as high-dimensional attractors in the GRNs and use random Boolean networks as the modeling framework for studying network dynamics. Morphogenesis was studied using the emergence of three-dimensional structures in biofilms as a relatively simple model. Many strains of bacteria form complex structures during growth as colonies on a solid medium. The morphogenesis of these structures was modeled using an agent-based framework and the outcomes were validated using structures of biofilm colonies reported in the literature.

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.