Biology subjects

Friedman, S. H.

Publications and source records attributed to Friedman, S. H..

6 recordsLinked to original sources

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗

bioRxiv2018

Open source tools and standardized data in cancer systems biology

To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.

systems biology↗

bioRxiv2017

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

cell biology↗

bioRxiv2016

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.

systems biology↗

bioRxiv2016

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.

systems biology↗

bioRxiv2016

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

systems biology↗