Science Gateways and Cybersecurity: Learning from the Past and Preparing for the Future

Science gateways connect communities of scientists and engineers to distributed cyberinfrastructure (CI). Cybersecurity is therefore an important component to help protect the people, machines, and data from malicious activity and accidental mistakes. The Science Gateways Community Institute (SGCI) and Center for Trustworthy Scientific Cyberinfrastructure (CTSC) have partnered to address cybersecurity for gateway development and operation. This paper and presentation will provide an overview of and goals for this partnership

3-D PhysiCell simulation of ductal carcinoma in situ - stochastic necrosis model

<p>This is Video S4 in Ghaffarizadeh et al. (2018). A higher-resolution (1080p) video can be streamed at <a href="https://www.youtube.com/watch?v=-lRot-dfwJk">https://www.youtube.com/watch?v=-lRot-dfwJk</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p> <p>3-D agent-based simulation of ductal carcinoma in situ (DCIS), a type of breast cancer that is constrained to growth in the breast duct lumen by a basement membrane. In this simulation, cells have a pO2-dependent probability of becoming necrotic wherever pO2 < 5 mmHg. This simulation was completed on a single HPC compute node (dual Xeon 6-core CPUs at 3.4 GHz), requiring 3 hours and 39 minutes to run (including data saves once per simulated hour). Simulations without file I/O are significantly faster. Shown here: A simulation of 30 days' growth in a 1 mm length of duct.</p> <p>Shown here: A simulation of 30 days' growth in a 1 mm length of duct.</p> <p><b>Legend:</b></p> <p><u>Dark circles</u>: cell nuclei</p> <p><u>Green cells</u>: Proliferating Ki67+ cells, prior to mitosis</p> <p><u>Magenta cells</u>: Proliferating Ki67+ cells, after mitosis</p> <p><u>Red cells</u>: Apoptotic cells (cleaved Caspase-3 positive)</p> <p><u>Pale blue cells</u>: Quiescent Ki67- cells</p> <p><u>Brown cells</u>: Necrotic cells</p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

PhysiCell demo: heterogeneous tumor growth

<p>This is Video S7 in Ghaffarizadeh et al. (2018). A higher-resolution (4K) video can be streamed at <a href="https://www.youtube.com/watch?v=bPDw6l4zkF0">https://www.youtube.com/watch?v=bPDw6l4zkF0</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p> <p>Early test on competition in a heterogeneous cell population. Cancer cells have a varying expression of a mutant oncoprotein, here shaded from blue (least expression) to yellow (most expression). </p> <p>Cells proliferate at a rate proportional to oxygen availability and oncoprotein expression. Note the gradual population evolution towards yellower cells. This is selection over the course of 30 days of tumor growth, with parameter values very similar to MCF10A. </p> <p>The brown center is a necrotic core.</p> <p><b>Legend:</b></p> <p><u>Blue cells</u>: tumor cells with least oncoprotein</p> <p><u>Yellow cells</u>: tumor cells with most oncoprotein</p> <p><u>Dark dots</u>: cell nuclei</p> <p><u>Brown cells</u>: necrotic tumor cells and debris<br></p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

PhysiCell Demo: Bio-robots

<p>This is Video S5 in Ghaffarizadeh et al. (2018). A higher-resolution (1080p) video can be streamed at <a href="https://www.youtube.com/watch?v=NdjvXI_x8uE">https://www.youtube.com/watch?v=NdjvXI_x8uE</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p> <p>Using PhysiCell to test design rules for bio-robots. </p> <p>Here, we construct three cell types as a bio-robotic cargo delivery system. </p> <p>1) "Director" cells secrete a chemoattractant to attract worker cells when they have cargo</p> <p>2) "Cargo" cells secrete a different chemoattractant to attract worker cells when they don't have cargo. They turn off the chemoattractant once they are found. </p> <p>3) "Worker" cells chemotax towards cargo cells, test for presence of a receptor, and dock if it's present. They haul cargo (chemotactically) towards the "director" cells and release the cargo if the chemoattractant exceeds a threshold level.</p> <p>This simulation took a few hours to design, and ran in about 12 minutes on a desktop workstation, with data saved once per simulated minute. (2,880 save times) Simulations without file I/O are significantly faster.</p> <p><b>Legend:</b></p> <p><u>Green cells</u>: “Director” cells</p> <p><u>Blue cells</u>: “Cargo” cells</p> <p><u>Red cells</u>: “worker” cells</p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

3-D PhysiCell simulation of a hanging drop spheroid - stochastic necrosis model

<p>This is Video S2 in Ghaffarizadeh et al. (2018). A higher-resolution (1080p) video can be streamed at <a href="https://www.youtube.com/watch?v=xrOqqJ_Exd4">https://www.youtube.com/watch?v=xrOqqJ_Exd4</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><b><br></b></p><p>3-D agent-based simulation of a hanging drop spheroid experiment. In this simulation, cells have a pO2-dependent probability of becoming necrotic wherever pO2 < 5 mmHg. This simulation was completed on a single HPC compute node (dual Xeon 6-core CPUs at 3.4 GHz), requiring 73 hours and 25 minutes to run (including data saves once per simulated hour). It took 60 hours and 56 minutes to simulate 17 days to the first ~800k cells. Simulations without file I/O are significantly faster.<br></p> <p><b>Legend:</b></p> <p><u>Dark circles</u>: cell nuclei</p> <p><u>Green cells</u>: Proliferating Ki67+ cells, prior to mitosis</p> <p><u>Magenta cells</u>: Proliferating Ki67+ cells, after mitosis</p> <p><u>Red cells</u>: Apoptotic cells (cleaved Caspase-3 positive)</p> <p><u>Pale blue cells</u>: Quiescent Ki67- cells</p> <p><u>Brown cells</u>: Necrotic cells</p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

PhysiCell demo: immune cells attacking a heterogeneous tumor

<p>This is Video S8 in Ghaffarizadeh et al. (2018). A higher-resolution (4K) video can be streamed at <a href="https://www.youtube.com/watch?v=nJ2urSm4ilU">https://www.youtube.com/watch?v=nJ2urSm4ilU</a> </p><p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p><p>This is a PhysiCell demo of immune cells (red) attacking a 3-D heterogeneous tumor, using a basic biophysical model of an adaptive immune response to a tumor. In the simulation:</p><p>1) Cancer cells each have an individual expression of a mutant oncoprotein, which drives proliferation. Yellow cells divide faster (lots of oncoprotein) than blue ones (very little oncoprotein).</p><p>2) If the tumor gets too big, it outstrips the nutrient supply and a necrotic core (dead center) forms.</p><p>3) As a simple model, tumor cells release an immunostimulatory factor that diffuses outward. Cells are assumed to have immunogenicity proportional to the mutant oncoprotein (e.g., by altering MHC with mutant peptides).</p><p>4) At 14 days, we introduce 7500 immune cells (red) which perform a biased random walk towards the immunostimulatory factor.</p><p>5) Whenever an immune cell touches another cell, it:</p><p>.. a) adheres to the cell</p><p>.. b) checks for immunogenicity</p><p>.. c) induces apoptosis in the tumor cell at a rate proportional to immunogenicity)</p><p>.. d) detaches either after a random time or after inducing apoptosis in the tumor cell</p><p>6) Immune cells break away from newly apoptotic cells (cyan) and continue to seek more targets.</p><p>The simulation took about 2 days on a quad-core desktop (i7-4770k), including time spent on saving simulation data once every 3 simulated minutes. Simulations with less frequent output are substantially faster.</p><p><b>Legend:</b></p><p><u>Blue cells</u>: tumor cells with oncoprotein < 0.5</p><p><u>Yellow cells</u>: tumor cells with oncoprotein > 1.5</p><p><u>In between</u>: tumor cells with 0.5 < oncoprotein < 1.5</p><p>(yellow is greater)</p><p><u>Dark dots</u>: cell nuclei</p><p><u>Cyan cells</u>: apoptotic tumor cells</p><p><u>Brown cells</u>: necrotic tumor cells and debris</p><p><u>Red cells</u>: attacking immune cells.</p><p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://PhysiCell.MathCancer.org">http://PhysiCell.MathCancer.org</a>.</p><p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p><p><b>Software source: </b>PhysiCell is available as open source at <a href="http://PhysiCell.MathCancer.org">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>. </p

3-D PhysiCell simulation of ductal carcinoma in situ - deterministic necrosis model

<p>This is Video S3 in Ghaffarizadeh et al. (2018). A higher-resolution (1080p) video can be streamed at <a href="https://www.youtube.com/watch?v=ntVKOr9poro">https://www.youtube.com/watch?v=ntVKOr9poro</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a> </p><p><br></p> <p>3-D agent-based simulation of ductal carcinoma in situ (DCIS), a type of breast cancer that is constrained to growth in the breast duct lumen by a basement membrane. In this simulation, cells immediately become necrotic wherever pO2 < 5 mmHg. This simulation was completed on a single HPC compute node (dual Xeon 6-core CPUs at 3.4 GHz), requiring 4 hours and 24 minutes to run (including data saves once per simulated hour). Simulations without file I/O are significantly faster.</p><p>Shown here: A simulation of 30 days' growth in a 1 mm length of duct.</p> <p><b>Legend:</b></p> <p><u>Dark circles</u>: cell nuclei</p> <p><u>Green cells</u>: Proliferating Ki67+ cells, prior to mitosis</p> <p><u>Magenta cells</u>: Proliferating Ki67+ cells, after mitosis</p> <p><u>Red cells</u>: Apoptotic cells (cleaved Caspase-3 positive)</p> <p><u>Pale blue cells</u>: Quiescent Ki67- cells</p> <p><u>Brown cells</u>: Necrotic cells</p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

3-D PhysiCell simulation of a hanging drop spheroid - deterministic necrosis model

<p>This is Video S1 in Ghaffarizadeh et al. (2018). A higher-resolution (1080p) video can be streamed at <a href="https://www.youtube.com/watch?v=WMhYW9D4SqM">https://www.youtube.com/watch?v=WMhYW9D4SqM</a> </p> <p><b>Paper:</b> <a href="https://doi.org/10.1371/journal.pcbi.1005991">https://doi.org/10.1371/journal.pcbi.1005991</a><br></p> <p>3-D agent-based simulation of a hanging drop spheroid experiment. In this simulation, cells immediately become necrotic wherever pO2 < 5 mmHg. This simulation was completed on a single HPC compute node (dual Xeon 6-core CPUs at 3.4 GHz), requiring 82 hours and 23 minutes to run (including data saves once per simulated hour). It took 66 hours and 56 minutes to simulate 17 days to the first ~1 million cells. Simulations without file I/O are significantly faster.</p> <p>The tumor maintains its expected spherical symmetry until the end of the simulation, when the tumor nears the computational boundary and experiences higher oxygenation that drive non-spherical growth.</p> <p><b>Legend:</b></p> <p><u>Dark circles</u>: cell nuclei</p> <p><u>Green cells</u>: Proliferating Ki67+ cells, prior to mitosis</p> <p><u>Magenta cells</u>: Proliferating Ki67+ cells, after mitosis</p> <p><u>Red cells</u>: Apoptotic cells (cleaved Caspase-3 positive)</p> <p><u>Pale blue cells</u>: Quiescent Ki67- cells</p> <p><u>Brown cells</u>: Necrotic cells</p> <p>This work is based on PhysiCell, an open source 3-D modeling package for multicellular biology at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>.</p> <p><b>Method: </b>Demonstration of PhysiCell, an agent-based, lattice-free model. Cell velocities determined by balance of adhesive, repulsive, and motile forces. Each cell has a phenotypic state governed by stochastic processes derived from nonhomogeneous Poisson processes.</p> <p><b>Software source: </b>PhysiCell is available as open source at <a href="http://physicell.mathcancer.org/">http://PhysiCell.MathCancer.org</a>, <a href="http://physicell.sf.net/">http://PhysiCell.sf.net</a>, and <a href="https://github.com/mathcancer/physicell/releases">https://github.com/mathcancer/physicell/releases</a>.</p

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow [Presented at SuperComputing SC17, Computational Approaches for Cancer Workshop]

These are my slides on combining PhysiCell with EMEWS for high-throughput evaluation of biological hypotheses. <div><br></div><div>It was presented at the Computational Approaches for Cancer Workshop on November 17, 2017 at the SuperComputing SC17 Meeting. </div