New physics-based computation and AI framework to understand the agressive behavior of cancer cells

By | Feature, Research

Cancer is an illness caused by an uncontrolled division of transformed cells, which can originate in almost  any organ of the body.  Cancer is not a single disease, even when it arises in the same site of the body. Tremendous variability exists in progression of disease and response to therapy among different persons with the same general type of cancer, such as breast cancer. Even at the level of a single person, cancer cells show tremendous heterogeneity within a single tumor and among a primary tumor and metastases. This heterogeneity causes drug resistance and fatal disease. The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating greedy individual cancer cells that compete for growth factors and optimal environments. The rare “winners” in this competition survive and metastasize. However, tumors consistently maintain heterogeneous subpopulations of cancer cells, some of which appear less able to grow and spread. This observation prompted Gary and Kathy Luker, cancer cell biologists at the University of Michigan, to hypothesize that cancer cells may actually collaborate under some circumstances to cause disease and not just compete. The idea that single, heterogeneous cancer cells work collectively within a constrained range of variability to drive population-level outputs in tumor progression is a ground-breaking concept that may revolutionize how we approach cancer biology and therapy.

The team is using innovative approaches to extract and merge data streams from models that generate heterogeneous cell behaviors

...cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer.

To understand causes of single-cell heterogeneity in cancer and conditions that motivate cancer cells to collaborate, an interdisciplinary team of scientists at UM formulated an entirely new conceptual approach to this challenging problem. The cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer. Building on large, single-cell data sets unique to the team, they will combine inverse reinforcement learning, an artificial intelligence method typically applied to discover motivations for human behaviors, with computational models inferred on the basis of the physics and chemistry of cell signaling and migration. They have proposed an entirely new conceptual approach combining single cell data, physics-based modeling and artificial intelligence to single-cell heterogeneity and intercellular interactions. By discovering  testable molecular processes underlying “decision-making” by single cells and their “motivations” for acting competitively or collaboratively, this research blazes a new path to understand and treat cancer. Their high-risk, high-reward approach to understand how each cell in a population processes information and translates that to action driving cancer progression, has attracted an award of $1 million dollars by the Keck Foundation. 

The team includes Gary Luker (Radiology, Microbiology and Immunology; Biomedical Engineering), and Kathryn Luker (Radiology), who are leading the experimental studies of cell signaling and migration; Jennifer Linderman (Chemical Engineering; Biomedical Engineering); and Krishna Garikipati (Mechanical Engineering; Mathematics), who are leading the machine learning and modeling side of the project. Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering) are using artificial intelligence approaches to discover decision-making policies and rewards for cancer cells, working with the rest of the investigators to incorporate experimental data and physics/chemistry-based models into their approaches.

The W. M. Keck Foundation was established in 1954 in Los Angeles by William Myron Keck, founder of The Superior Oil Company. One of the nation’s largest philanthropic organizations, the W. M. Keck Foundation supports outstanding science, engineering and medical research. The Foundation also supports undergraduate education and maintains a program within Southern California to support arts and culture, education, health and community service projects. This project incorporates elements from all the W. M. Keck Foundation’s focus research areas to tackle cancer with a novel, physics-based modeling and AI-centered approach.  The idea for this project originated in the 2020 MICDE faculty workshop in AI for Physically based Bio-medicine Workshop. The workshop brought together an interdisciplinary group of faculty members to discuss ways to advance artificial intelligence and machine learning methods for biomedical problems. After seeding the idea, a subset of these researchers were awarded an MICDE catalyst grant and a MIDAS PODS grant. These funds were used to establish the proof of concept and to generate preliminary results. 

Computational science is becoming increasingly indispensable in many areas of biomedical science. While the current proposal focuses on cancer, this innovative computational framework represents a transformative leap with widespread applications in multiple other biomedical, physical, and social sciences. MICDE supports innovative and interdisciplinary projects aiming to advance the current paradigms.

Portraits of Kathryn Luker, Gary Luker, Krishna Garikipati, Jennifer Linderman, Nikola Banovic and Xun Huan

Project’s principal investigators (left to right): Kathryn Luker (Radiology), Gary Luker (Radiology, Microbiology and Immonology, and Biomedical Engineering), Krishna Garikipati (Mechanical Engineering, and Mathematics), Jennifer Linderman (Chemical Engineering, and Mathematics), Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering).

XSEDE: Python Tools for Data Science

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OVERVIEW

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2 .

See https://portal.xsede.org/course-calendar/-/training-user/class/2467/session/4161 for more information and registration

 

Register via the XSEDE Portal:

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

 

XSEDE HPC HPC Summer Boot Camp

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OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center is pleased to present a Hybrid Computing workshop.

This 4 day event will include MPI, OpenMP, GPU programming using OpenACC and accelerators.

This workshop will be remote to desktop only due to the COVID-19 pandemic.  When the registration has filled, there will be no more students added due to our current limits.

The schedule can be found here:  https://www.psc.edu/resources/training/xsede-hpc-workshop-june-8-11-2021-summer-boot-camp/

 

Register via the XSEDE Portal:

https://portal.xsede.org/course-calendar/-/training-user/class/2338/session/4002

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

Questions

Please address any questions to Tom Maiden at tmaiden@psc.edu.

2016-2017 MICDE Research Snapshot

By | Research

2016-2017 has been a year of sustained growth for MICDE’s research portfolio. The number of faculty affiliated with the institute stands at 130, spanning 30 departments and eight schools and colleges. The Center for Scientific Software Infrastructure was established to bring together the U-M community engaged in developing open scientific software. It will focus on establishing best practices for developing, disseminating and documenting scientific software in the public domain. Led by Prof. Emanuel Gull (Physics), the Center aims to provide training and support for researchers that are ready to transform their research codes into well-engineered software. It offers grant support in the form of programmers, consultants, and administrative assistance. It includes a portal to share your code with the research community at large.

MICDE’s two established centers, the Center for Network and Storage-Enabled Collaborative Computational Science (CNSECCS) and the Center for Data-Driven Computational Physics (DaCoP), each held their first symposium, showcasing their first year of research activities. This included evidence of the growing reach of OSiRIS, the open framework for storage, computation and collaboration against big scientific data, and the first results from ConFlux, U-M’s groundbreaking computing cluster for data-driven computational physics. These results have been presented at several conferences, and are appearing in the leading computational journals.

Vorticity field at a late time in the evolution of an elliptic vortex patch computed by a Lagrangian particle method with remeshing and treecode-accelerated evaluation of the Biot-Savart integral. (source: Ling Xu)

MICDE also funded its first round of Catalyst Grants, that are supporting four innovative computational science research projects. Research funded by the Catalyst Grants is breaking new ground, while helping define the future of computational science. This research consists of:

  • studies of the neuronal dynamics of learning and memory formation;
  • new algorithms for the complex, nonlinear dynamics of power grids;
  • novel integral equations methods using recent advances in numerical analysis;  
  • and probabilistic computational frameworks for rare but often catastrophic events.

The past academic year MICDE hosted 14 external speakers with backgrounds and research concentrations that span the breadth of computational science of today and the future. The series culminated in MICDE’s annual symposium: “The New Era of Data-Enabled Computational Science,” which featured talks by worldwide leaders in computational science, including U-M faculty. The symposium included a student poster competition with over 50 entries.

Dr. Ann Almgren from the Lawrence Livermore National Lab speaking about Next Generation AMR, part of the 2016-2017 MICDE Seminar Series

MICDE faculty are committed to growing the already strong U-M community of computational scientists. Over the past year, as before, we have organized a number of workshops to foster collaboration and put together interdisciplinary teams in response to funding calls from federal agencies and foundations.   MICDE offers faculty teams institutional support and direct links to our excellent educational programs as well as cyberinfrastructure, all of which strengthen faculty proposals. With the backing of our parent unit, Advanced Research Computing (ARC), and its technical and consulting services (ARC-Technology Services, and Consulting for Statistics, Computing and Analytics Research), this effort has raised over $22M in external funding over the past 2 years. This includes support from federal agencies (NSF, NIH, and DOD), as well as from industry.  We also work with the academic units at U-M to identify compelling new areas for recruiting the type of faculty members who will drive computational science in the future.

 

Info sessions on graduate studies in computational and data sciences — Sept. 21 and 25

By | Educational, Events, General Interest, News, Research

Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Times / Locations:

2017 MICDE Annual Symposium

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Please join us for the Michigan Institute for Computational Discovery and Engineering 2017 Symposium. The event features eminent scientists from around the world and the U-M campus. The symposium this year focuses on the “New Era of Data-Enabled Computational Science.”

Speakers:

  • Frederica Darema — Director, Air Force Office of Scientific Research
  • George Karniadakis —  Professor of Applied Mathematics, Brown University
  • Tinsley Oden Director of the Institute for Computational Engineering and Sciences, V.P. for Research, University of Texas at Austin
  • Karen Willcox — Professor of Aerospace and Aeronautics, Massachusetts Institute of Technology, co-Director of MIT Center for Computational Engineering
  • Jacqueline H. Chen — Distinguished Member of Technical Staff at the Combustion Research Facility, Sandia National Laboratories
  • Laura Balzano — Assistant Professor, Electrical Engineering and Computer Science, U-M
  • Emanuel Gull — Assistant Professor, Physics

The symposium features a poster competition and more. For more information and to register go to http://micde.umich.edu/symposium17/

Past Symposia

2016 MICDE Annual Symposium

Research Computing Symposium Fall 2014 

 

Designing optimal shunts for newborns with heart defects using computational modeling

By | General Interest, Happenings, News, Research

shuntFor babies born with hypoplastic left heart syndrome, several open-heart surgeries are required. During Stage I, a Norwood procedure is performed to construct an appropriate circulation to both the systemic and the pulmonary arteries. The pulmonary arteries receive flow from the systemic circulation, often by using a Blalock-Taussig (BT) shunt between the innominate artery and the right pulmonary artery. This procedure causes significantly disturbed flow in the pulmonary arteries.

A group of researchers led by U-M Drs. Ronald Grifka and Alberto Figueroa used computational hemodynamic simulations to demonstrate its capacity for examining the properties of the flow through and near the BT shunt. Initially, the researchers constructed a computational model which produces blood flow and pressure measurements matching the clinical magnetic resonance imaging (MRI) and catheterization data. Achieving this required us to determine the level of BT shunt occlusion; because the occlusion is below the MRI resolution, this information is difficult to recover without the aid of computational simulations. The researchers determined that the shunt had undergone an effective diameter reduction of 22% since the time of surgery. Using the resulting geometric model, they showed that we can computationally reproduce the clinical data. The researchers then replaced the BT shunt by with a hypothetical alternative shunt design with a flare at the distal end. Investigation of the impact of the shunt design revealed that the flare can increase pulmonary pressure by as much as 7%, and flow by as much as 9% in the main pulmonary branches, which may be beneficial to the pulmonary circulation.

Read more in Frontiers in Pediatrics.

Graduate Studies in Computational & Data Sciences Info Session — Jan 9 & 11

By | Educational, Events, General Interest, News

Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources

There will be two sessions in January 2017: