The Epistemology of Systems Biology Lies in Systems Theory and Designed Experiments, Not In Data Processing and Commonsense Explanation

Edward R. Dougherty, Department of Electrical and Computer Engineering, Texas A&M University and Computational Biology Division, Translational Genomics Research Institute

4:00 p.m., Friday September 19, 2008
Room 124, HR Bright Building

• Talk: | Announcement | Slides | Video Not Available |

Abstract

Systems Biology can only yield scientific knowledge if it adheres to the scientific epistemology grounded on the duality of a mathematical model characterizing relations between quantifiable variables and its validation via a designed experiment. What is particular to Systems Biology, and other systems-based science and engineering disciplines, is the central role played by dynamical systems in the form of multi-dimensional random processes. Indeed, the mathematical ground, that is, the form in which the knowledge of Systems Biology must be constituted, was set down by Norbert Wiener in 1948 when he noted that he and others “had already become aware of the essential unity of the set of problems centering about communication, control, and statistical mechanics, whether in the machine or in living tissue.” Simply put, Systems Biology requires Systems Theory. Nothing has since occurred to raise any impediment to Wiener’s original insight, and much has occurred to validate it. Indeed, upon what besides Systems Theory would one expect Systems Biology to be based? Nevertheless, biological systems areas such as genomics have to a great extent ignored both modern scientific epistemology and Systems Theory, often choosing to engage in a contemporary form of Cartesianism involving ad hoc data processing and a postiori explanation.

Related publications:

• On the Epistemological Crisis in Genomics, Edward R. Dougherty, Current Genomics, 2008, 9:69-79.
• Validation of Computational Methods in Genomics, Edward R. Dougherty, Jianping Hua, and Michael L. Bittner, Current Genomics, 2007, 8:1-19.
• Validation of Inference Procedures for Gene Regulatory Networks, Edward R. Dougherty, Current Genomics, 2007, 8:351-359.

Biography

Edward R. Dougherty is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University in College Station, TX, where he holds the Robert M. Kennedy ‘26 Chair in Electrical Engineering and is Director of the Genomic Signal Processing Laboratory. He is also the Director of the Computational Biology Division of the Translational Genomics Research Institute in Phoenix, AZ, and an Adjunct Professor in the Department of Pathology of the University of Texas M. D. Anderson Cancer Center in Houston, TX. He holds a Ph.D. in mathematics from Rutgers University and an M.S. in Computer Science from Stevens Institute of Technology, and has been awarded the Doctor Honoris Causa by the Tampere University of Technology in Finland. He is a fellow of SPIE, has received the SPIE President’s Award, and served as the editor of the SPIE/IS&T Journal of Electronic Imaging. At Texas A&M he has received the Association of Former Students Distinguished Achievement Award in Research, been named Fellow of the Texas Engineering Experiment Station, and named Halliburton Professor of the Dwight Look College of Engineering. Prof. Dougherty is author of fourteen books, editor of five others, and author of more than two hundred journal papers. He has contributed extensively to the statistical design of nonlinear operators for image processing and the consequent application of pattern recognition theory to nonlinear image processing. His current research in genomic signal processing is aimed at diagnosis and prognosis based on genetic signatures and using gene regulatory networks to develop therapies based on the disruption or mitigation of aberrant gene function contributing to the pathology of a disease.

EcoliWiki and GONUTS: wiki-based systems for community annotation

James C. Hu, Department of Biochemistry and Biophysics, Texas A&M University

4:00 p.m., Friday October 17, 2009
Room 124, HR Bright Building

• Talk: | Announcement | Slides | Video |

Abstract

Wikis are a promising for community annotation. We will demonstrate two wiki tools: 1) EcoliWiki and GONUTS, the Gene Ontology Normal Usage Tracking System.

EcoliWiki is the community annotation component of EcoliHub. EcoliWiki provides articles for genes and gene products for E. coli K-12, its bacteriophages, plasmids, and mobile genetic elements. EcoliWiki also provides information about other online resources, cloning vectors, and methods.

GONUTS is a wiki-based Gene Ontology term browser and annotation interface. GONUTS treats every GO term as a MediaWiki category page, taking advantage of the representation of both categories and ontology terms as directed acyclic graphs. GONUTS has pages displaying the gene associations for several model organism genomes as examples of best practices. GONUTS also allows registered users to create gene pages for any gene with a UniProt accession, to allow community annotation for organisms without their own organism database.

All pages in EcoliWiki and GONUTS provide mechanisms for community users to add or edit either free text or information structured in tables. Software extensions to the underlying MediaWiki softare developed for EcoliWiki and GONUTS have been built to improve the use of wikis for biological curation. These can be applied to other projects, and are being used by GMOD, modENCODE, SO, and the iPlant Collaborative. Our demonstration of EcoliWiki and GONUTS will emphasize the properties of these resources that could be used by other wiki-based projects.

James C. Hu is an Associate Professor of Biochemistry and Biophysics at Texas A&M University, where he is also a member of the Faculty of Genetics, and of the Chemistry/Biology Interface Training Program. He received his B.S. from Stanford University in 1975 and his Ph.D. from the University of Wisconsin in 1987. He was a postdoc at MIT from 1987-1992 and joined the Texas A&M faculty in 1992.

The Research Roadmap Process of the Texas A&M Academic Master Plan

H. Joseph Newton, Dean of Science and Professor of Statistics, Texas A&M University

4:00 p.m., Friday November 21, 2008
Room 124, HR Bright Building

• Talk: | Announcement | Slides Not Available | Video Not Available |

Abstract

Dr. Newton will describe the Research Roadmap Process for the Academic Master Plan. He will answer questions related to the white paper preparation and selection process. (Note: Provost Vitter was originally scheduled to present this seminar. However, he was unable to attend and so Dr. Newton, who is chairing the Research Roadmap committing, presented.)

After the presentation and questions, we'll have a brainstorming session and try to identify potential white paper topics and teams in areas relevant to ABCS. Though 'walk ons' will be welcome, researchers interested in presenting ideas are encouraged to contact the ABCS chair, Nancy Amato, in advance so that we can organize a presentation schedule.

Related Material:

• Provost's Invitation to Faculty and Staff to the Research Roadmap Process for the Academic Master Plan (Sept 11, 2008)
• Texas A&M Academic Master Plan Process (Sept. 10, 2008)

Biography

Professor H. Joseph Newton, who joined the Texas A&M faculty in 1978, has served as Dean of the College of Science and holder of the Richard H. Harrison III/External Advisory & Development Council Endowed Dean's Chair in Science since July 2002. He is also the inaugural holder of the George P. Mitchell `49 Chair in Statistics.

Prior to becoming dean, he spent two years as interim dean, two years as executive associate dean and eight years as head of the Department of Statistics.

Newton is the author of numerous research articles and two books in the areas of time series analysis, computational statistics and technology-mediated instruction. Elected a Fellow of the American Statistical Association in 1995, he currently serves as American co-editor of "Computational Statistics" and as editor of "The Stata Journal."

A native of Syracuse, N.Y., Newton holds a doctorate in statistical sciences and a master of science in statistics from the State University of New York (SUNY) at Buffalo, and a bachelor of science degree in mathematics from Niagara University. .

Robust Powerful Methods for Understanding Gene-Gene and Gene-Environment Interactions

Raymond J. Carroll, Distinguished Professor of Statistics, Nutrition and Toxicology, Texas A&M University

4:00 p.m., Friday February 20, 2009
Room 124, HR Bright Building

• Talk: | Announcement | Slides | Video |

Abstract

We consider population-based case-control studies of gene-environment and gene-gene interactions using prospective logistic regression models. Data sets like this arise when studying pathways based on haplotypes as well as in multistage genome wide association studies (GWAS). In a typical case-control study, logistic regression is used and there is little power for detecting interactions. However, in many cases it is reasonable to assume that, for example, genotype and environment are independent in the population, possibly conditional on factors to account for population stratification. In such as case, we have developed an extremely statistically powerful semiparametric approach for this problem, showing that it leads to much more efficient estimates of gene-environment interaction parameters and the gene main effect than the standard approach: decreases of standard errors for the former are often by factors of 50% and more. The issue of course that arises is the very assumption of independence, because if that assumption is violated, biases result so that one can announce gene-environment interactions or gene effects even though they do not exist. We will describe two simple, computationally fast approaches for gaining robustness without losing statistical power, one based on the idea of Empirical Bayes methodology and the other on penalization. Examples to colorectal adenoma studies of the NAT2 gene and prostate cancer in the VDR pathway are described to illustrate the approaches.

Biography

Dr. Raymond J. Carroll  is Distinguished Professor of Statistics at Texas A&M University. He is a member of the Faculty of Nutrition, a member of the Faculty of Toxicology and he holds a courtesy appointment in the Department of Epidemiology and Biostatistics. He is the Principal Investigator on a National Cancer Institute (NCI)-funded Bioinformatics training program involving the Colleges of Science, Engineering and Agriculture and Life Sciences, this being the first R25T program funded at Texas A&M University. He has been P.I. of a major NCI grant for the development of statistical methodology since 1990, and was recently given a NCI MERIT Award, the first statistician to be so honored by NCI, an award that funds his research until 2015. He is the director of the Texas A&M Center for Statistical Bioinformatics  and the Deputy Director for Research of the Texas A&M Institute for Applied Mathematics and Computational Science.

Dr. Carroll’s work on statistical methodology has found application in a broad variety of fields, including marine biology, laboratory assay methods, econometrics, molecular biology, nutritional epidemiology and radiation epidemiology, among many others. He is one of the leading figures in the analysis of gene-environment interactions. He wrote the authoritative text on modern statistical analysis of data when exposure measurements are subject to uncertainties, the so-called measurement error problem. He has won many honors in the profession, including the 1988 COPSS Presidents’ Award, given annually by the North American statistical societies to the outstanding statistician under the age of 40. He gave the Fisher Lecture at the 2002 Joint Statistical Meetings, an award given by the major statistical societies in honor of a senior statistician whose research has “influenced the theory and practice of statistics”.

Molecular Dynamics Simulation of Structure, Assembly, Thermo-mechanical Properties and Transport in Cyclic Peptide Nanotubes

Tahir Cagin, Artie McFerrrin Department of Chemical Engineering, Texas A&M University

4:00 p.m., Friday April 24, 2009
Room 124, HR Bright Building

• Talk: | Announcement | Slides | Video |

Abstract

Cyclic peptides self assemble into nanotubes and further into two-dimensional crystalline arrangements with interesting properties that might be relevant for many technologically important applications. Using molecular dynamics simulation methods we have simulated structure and stability of these self assembled cyclic peptide nanotubes. We determine the thermal and mechanical stability of these structures as a function of temperature and pressure as well as investigating the transport of water and ions through these structures.

Biography

Dr. Tahir Cagin received his Ph. D. in 1988 in Physics, at Clemson University on Computational Condensed Matter Physics, M.S. in Mathematical Physics in 1983 at Middle East Technical University (METU), Ankara, Turkey, and B. S. in Physics in 1981 again at METU. He held Research Associate position in Chemistry Department of University of Houston; visiting research scientist position at Materials Laboratory of Wright Patterson AFB, Ohio; before joining TAMU Chemical Engineering Department in 2005, he served the Director of Materials Science and Technology of the Materials and Process Simulation Center at Caltech. Professor Cagin, currently serves as the chair of the interdisciplinary materials science and engineering program.

During his scientific career, he developed methods and algorithms for studying properties of materials in condensed phases (both in equilibrium and under steady state non-equilibrium conditions), and applied atomistic level simulation methods to a wide variety of materials ranging from polymers/biopolymers, to metals, alloys, ceramics, minerals, zeolites and functional materials such as carbon nanotubes, ferroelectrics, dendrimers and biopolymers. He has published over 140 research articles, reviews, and book chapters. He is recipient of Feynman Prize in Nanotechnology (Theory) in 1999 for the work on modeling and studies on molecular level machines/motors.

Improving Animal and Human Health by Accelerating Drug and Device Development

Mark Lenox, The Texas A&M Institute for Preclinical Studies (TIPS)

4:00 p.m., Friday November 6, 2009
Room 124 HRBB

• Talk: | Announcement | Slides | Video |

Abstract

The Texas A&M Institute for Preclinical Studies (TIPS) was created as a conduit through which new ideas in healthcare technology could be implemented and FDA approved. FDA certification of new drugs, devices, and therapies requires that all testing be performed according to FDA Good Laboratory Procedures (GLP). Without GLP protocols, even the most promising technologies will not pass FDA certification. Research performed at universities generates a tremendous number of good ideas that never reach the clinic because they are unable to overcome the barriers necessary for FDA approval. Similar barriers exist for small companies. By providing a framework under which testing protocols can be performed under GLP conditions, TIPS dramatically lowers the cost barriers for university researchers as well as small companies who wish to further develop their ideas and get them ready for an FDA application. In this presentation, we will discuss the origins, capabilities, and structure of TIPS and how it can achieve this goal.

Biography

Mark Lenox, PhD leads the Imaging core at the Texas A&M Institute for Preclinical Studies (TIPS). His BS degree is in Systems Engineering from Arizona State, with an MSEE from Texas A&M, and PhD in Computer Science from the University of Tennessee. Dr. Lenox has 20 years of experience in imaging, including 17 years at CTI Molecular Imaging (now part of Siemens Medical), and was part of the engineering team that performed the early development and commercialization of Positron Emission Tomography (PET). He led the High Resolution Research Tomograph program at CTI, and later was director of new product development in the preclinical imaging division. At preclinical he was responsible for the design and implementation of the industry leading Inveon preclinical PET imaging systems. Dr. Lenox’s experience includes all levels of design and implementation of imaging systems hardware and software as well as regulatory approvals.