Nathan Baker

Nathan Baker

Laboratory Fellow

Richland/Kennewick/Pasco, Washington Area

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Nathan Baker's Overview


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Nathan Baker's Summary

Nathan A. Baker, Ph.D. is a Laboratory Fellow and Technical Group Manager in the Applied Statistics and Computational Modeling Group at Pacific Northwest National Laboratory (PNNL). His research is in the area of applied mathematics, signature discovery, computational biophysics, nanotechnology, and informatics. He is Lead for the Signature Discovery Initiative at PNNL and serves as co-PI and program manager for the DOE ASCR Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4).

Dr. Baker is actively involved in the development of new algorithms and software for computational biology and modeling in support of these research projects, including development of the APBS and PDB2PQR biomolecular electrostatics software packages and the NanoParticle Ontology.

He is currently co-chair of the United States-European Union Community of Research for Nanotechnology Databases and Ontology and has served as Lead for the National Cancer Informatics Program Nanotechnology Working Group and Chair for the ASTM E56.01 Subcommittee on Nanotechnology Informatics and Terminology. Dr. Baker has served on numerous review panels for agencies including NIH and NSF and is currently a member of the NIH Macromolecular Structure and Function D study section.

Dr. Baker is the Editor-in-Chief for the Computational Science and Discovery journal, serves on the editorial boards for Biophysical Journal and NPG Scientific Data, is a member of the Faculty of 1000 Biology, and is a Section Editor for Annual Reports in Computational Chemistry. He is the author of over 75 peer-reviewed publications. Dr. Baker is a Fellow of the American Association for the Advancement of Science, has been awarded the Hewlett-Packard Junior Faculty Excellence Award by the American Chemical Society, the National Cancer Institute caBIG® Connecting Collaborators Award, and an Alfred P. Sloan Research Fellowship.

Nathan Baker's Experience

Technical Group Manager

Pacific Northwest National Laboratory

Government Agency; 1001-5000 employees; Research industry

December 2013Present (10 months) Richland/Kennewick/Pasco, Washington Area

As the technical group manager for Applied Statistics and Computational Modeling (ASCM), I manage a group of approximately 50 staff members. The ASCM group includes statistics, mathematics, and operations research experts who work in multi-disciplinary teams and employ powerful tools and techniques, such as mathematical modeling, optimization, statistical analysis, algorithm development, and operational modeling and simulation to solve complex problems and reach mission-focused solutions. Work focuses on applied research and development, and project management or support in the areas of statistics, operations research, machine learning, modeling and simulation, systems engineering, information visualization, cognitive informatics, and data analysis.

Government Agency; 1001-5000 employees; Research industry

June 2012Present (2 years 4 months) Richland/Kennewick/Pasco, Washington Area

Chief Scientist for Signature Science

Pacific Northwest National Laboratory

Government Agency; 1001-5000 employees; Research industry

June 2010June 2012 (2 years 1 month) Richland/Kennewick/Pasco, Washington Area

Associate Professor

Washington University

Educational Institution; 10,001+ employees; Higher Education industry

December 2006June 2010 (3 years 7 months) Greater St. Louis Area

Director, Computational and Molecular Biophysics Graduate Program

Assistant Professor

Washington University

Educational Institution; 10,001+ employees; Higher Education industry

July 2002December 2006 (4 years 6 months) Greater St. Louis Area

Nathan Baker's Education

University of California, San Diego

Postdoctoral researcher, Computational Biology


J. Andrew McCammon group

University of California, San Diego

Ph.D., Physical Chemistry


Thesis advisors: Profs. J. Andrew McCammon (Chemistry & Biochemistry) and Michael J. Holst (Mathematics)

University of Iowa

BS, Chemistry


Honors thesis advisor: Prof. Daniel Quinn (Chemistry)

Nathan Baker's Honors and Awards

  • Undergraduate Fellowship

    Barry M. Goldwater Fund
    • 1995
  • Collegiate Scholar

    University of Iowa
    • 1997
  • Predoctoral Fellowship

    Howard Hughes Medical Institute
    • 1997
  • Predoctoral Fellowship

    Burroughs-Wellcome La Jolla Interfaces in Science Program
    • 1999
  • Research Fellow

    Alfred P. Sloan Foundation
    • 2004
  • Hewlett-Packard Junior Faculty Excellence Award

    American Chemical Society
    • 2007
  • Fellow

    American Association for the Advancement of Science
    • 2012

Nathan Baker's Skills & Expertise

  1. Computational Biology
  2. Bioinformatics
  3. Biophysics
  4. Applied Mathematics
  5. Nanotechnology
  6. Molecular Modeling
  7. Science
  8. Protein Chemistry
  9. Genomics
  10. Physical Chemistry
  11. Molecular Biology
  12. Computational Chemistry
  13. Scientific Computing
  14. Structural Biology
  15. Molecular Dynamics
  16. Chemistry
  17. Algorithms
  18. Informatics
  19. Nanoparticles
  20. Nanomaterials
  21. Physics
  22. Machine Learning
  23. Numerical Analysis
  24. Mathematical Modeling
  25. Materials Science
  26. Parallel Computing
  27. High Performance Computing
  28. Cancer
  29. Project Portfolio Management

View All (29) Skills View Fewer Skills

Nathan Baker's Projects

  • Signature Discovery Initiative

    • October 2010 to September 2016

    The Signature Discovery Initiative is a 6-year internal investment by Pacific Northwest National Laboratory to transform the process by which signatures are discovered and used. In its most general form, a signature is a unique or distinguishing measurement, pattern, or collection of data that identifies a phenomenon (object, action, or behavior) of interest. The discovery of signatures is an important aspect of a wide range of disciplines from basic science to national security for the rapid and efficient detection and/or prediction of phenomena. Current practice in signature discovery is typically accomplished by asking domain experts to characterize and/or model individual phenomena to identify what might compose a useful signature. What is lacking is an approach that can be applied across a broad spectrum of domains and information sources to efficiently and robustly construct candidate signatures, validate their reliability, measure their quality, and overcome the challenge of detection — all in the face of dynamic conditions, measurement obfuscation, and noisy data environments. Our research has focused on the identification of common elements of signature discovery across application domains and the synthesis of those elements into a systematic process for more robust and efficient signature development. In this way, a systematic signature discovery process lays the groundwork for leveraging knowledge obtained from signatures to a particular domain or problem area, and, more generally, to problems outside that domain.

  • Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4)

    • August 2012 to July 2017

    The Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4) focuses on developing rigorous mathematical foundations for understanding and controlling fundamental mechanisms in mesoscale processes to enable scalable synthesis of complex materials, through the design of efficient modeling methods and corresponding scalable algorithms.

    This project is funded by DOE ASCR.

  • APBS

    • 1999 to February 2018

    APBS is a software package for modeling biomolecular solvation through solution of the Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions between molecular solutes in salty, aqueous media. Continuum electrostatics plays an important role in several areas of biomolecular simulation, including: simulation of diffusional processes to determine ligand-protein and protein-protein binding kinetics, implicit solvent molecular dynamics of biomolecules, solvation and binding energy calculations to determine ligand-protein and protein-protein equilibrium binding constants and aid in rational drug design, and biomolecular titration studies. APBS was designed to efficiently evaluate electrostatic properties for such simulations for a wide range of length scales to enable the investigation of molecules with tens to millions of atoms. We also provide implicit solvent models of nonpolar solvation which accurately account for both repulsive and attractive solute-solvent interactions.

    This work was supported by NIH grants R01 GM069702 and P41 GM103426, NPACI, and XSEDE/TeraGrid.


    • 2002 to February 2018

    PDB2PQR is a Python software package that automates many of the common tasks of preparing structures for continuum electrostatics calculations, providing a platform-independent utility for converting protein files in PDB format to PQR format. These tasks include: adding a limited number of missing heavy atoms to biomolecular structures, determining side-chain pKas, placing missing hydrogens, optimizing the protein for favorable hydrogen bonding, assigning charge and radius parameters from a variety of force fields.

    This work was supported by NIH grants R01 GM069702 and P41 GM103426.

  • Nanoparticle Ontology

    • 2006 to Present

    Data generated from cancer nanotechnology research are so diverse and large in volume that it is difficult to share and efficiently use them without informatics tools. In particular, ontologies that provide a unifying knowledge framework for annotating the data are required to facilitate the semantic integration, knowledge-based searching, unambiguous interpretation, mining and inferencing of the data using informatics methods. Here, we discuss the design and development of NanoParticle Ontology (NPO), which is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO is developed to represent the knowledge underlying the description, preparation, and characterization of nanomaterials in cancer nanotechnology research.

    This development was supported by the NIH through grants U54 CA119342 and U54 HG004028. Currently, the NCI caBIG Nanotechnology Working Group supports the development and use of the NanoParticle Ontology

  • ISA-TAB-Nano

    • 2010 to Present

    High-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials. We have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats (Investigation, Study, and Assay files) have been adapted from the established ISA-TAB specification; while the Material file format was developed de novo to more readily describe the complexity of nanomaterials and associated small molecules. The ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption.

    This work was supported by NIH grants U01 NS073457-01, U54 CA11934205-CCNE, U54 HG004028, and the National Cancer Informatics Program Nanotechnology Working Group.

  • National Cancer Informatics Program Nanotechnology Working Group

    • 2008 to September 2013

    The National Cancer Institute (NCI) National Cancer Informatics Program (NCIP) Nanotechnology Working Group was established in 2008 for researchers with a specific interest in informatics and computational approaches to nanotechnology, with a particular emphasis on nanomedicine. The goal of this working group is to demonstrate the scientific potential of federating nanotechnology databases through pilot projects aimed at integrated semantic search and retrieval of nanomedicine and nanotoxicology datasets that are applicable across nanoscience. The NCIP Nanotechnology Working Group (NCIP Nano WG) comprises over 20 active participants from academia, government and industry with diverse interests.

  • US-EU Nanotechnology Databases & Ontology Community of Research

    • 2012 to Present

    Interconnected, freely communicating and agreed information systems are urgently needed for collating (a) nanoscale material descriptions; (b) their intrinsic and context-dependent properties and their effects, including environmental and health-related; and (c) their interactions with biological entities. The goal of the US-EU Nanotechnology Databases and Ontology Community of Research (CoR) is to enable the sharing, searching, and analysis of nanoscale material characterization data across a wide range of active and archived experimental sources and to give advice on how to structure these data to enable their widest possible use. Achievement of this goal will deliver important new capabilities to (1) allow integration of pertinent risk assessment data among labs to (2) provide situational awareness of data coverage across nanomaterial categories, and to (3) enable predictive computational models for bridging physical properties and biological outcomes with exposure, dispersal and fate. In order to realize this goal, the Community of Research will initially focus on the following three areas of investigation: (1) Identification of the data elements necessary to establish common data-sharing model(s) for this domain; (2) Specification of requirements for sharing data between research groups and repositories in human- and machine-interpretable forms; (3)
    Definition of concepts necessary to support the above activities and representation of those concepts in an ontological framework. This will include characterizers for the material itself as well as descriptors for its interaction with the environment and elements to characterize intermediate effects in adverse outcome pathways. Through these activities the CoR will provide tools for improving the overall quality of experimental data being generated in the research communities in the EU and US.

  • Mechanism of oxysterol activation of membrane cholesterol

    • January 2011 to November 2015

    Supported by NIH grant R01 HL067773.

  • DNA-DNA interactions with atomic detail

    • September 2012 to July 2016

    This work is supported by NIH grant R01 GM099450.

  • Allosteric Regulation on the Nickel-dependent NikR Repressor

    • February 2006 to January 2009

    This work was supported by NSF grant MCB-0520877.

  • The Siteman Cancer Center Nanotechnology Excellence at Washington University

    • September 2005 to August 2010

    This project was supported by NIH grant U54 CA11934205-CCNE.

  • Cancer Nanotechnology Knowledgebase for Nanoparticle Analysis and Design

    • November 2008 to August 2012

    This project was supported by NIH grant U54 HG004028.

  • National Biomedical Computation Resource

    • June 2004 to April 2013

    This project was supported by NIH grant P41 RR0860516.

  • Geometric flow approach to implicit solvation modeling

    • August 2009 to July 2013

    This project was supported by NIH grant R01 GM090208.

  • Characterization/bioinformatics-modeling of nanoparticle-complement interactions

    • September 2010 to August 2013

    This project was supported by NIH grant U01 NS073457-01.

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