Nathan D. Price
Contact Information:
e-mail:
phone: (217) 244-0596
fax: (217) 333-5052
119 Roger Adams Lab
MC-712, Box C-3
600 S. Mathews Ave.
Urbana, IL 61801
Assistant Professor
B.S., Brigham Young University, 2000
Ph.D., University of California, San Diego, 2005
Postdoctorate, Institute for Systems Biology, 2005-2007
Computational and Systems Biology
Our lab works in the emerging field of systems biology – the intersection of high-throughput experimental biology with large-scale computational modeling to drive biological discovery. Within the
context of a computational model, various data types interact in precisely defined ways such that predictions of cellular behavior can be made under novel conditions or perturbations. Failure modes of computational models point towards gaps in understanding and lead to directed lines of questioning from which understanding of the cell's workings can be increased in a systematic fashion. Constructing and revising computational models based on data constitutes a powerful iterative model building approach that lies at the heart of systems biology.
Complex Biomolecular Networks
We are interested in the structure, dynamics, and information-processing capabilities of complex biomolecular networks. These networks can be interrogated using high-throughput measurement technologies (genomics, transcriptomics, proteomics, metabolomics, CHIP-chip, etc.) and computational network models can be built to explain and even predict the outcome of such experiments. These network models can be of many types, including genome-scale biochemical networks with known stoichiometries, statistical inference models, kinetic models, discrete dynamic networks, probabilistic networks and so forth. Predictive cell-scale models will enable synthetic biology and cellular engineering by allowing for the rationale design of cells as an engineering problem. These engineering designs can have applications to many important challenges including for bioenergy and medicine.
Systems Biology of Cancer
The systems approach to medicine derives from a simple idea – the difference between normal and diseased cells lies in one or more disease-perturbed network. These disease-perturbed networks have altered patterns of expression in genes and proteins, and these altered patterns necessarily result in altered molecular fingerprints that can be measured. Cancer is an important and well-suited disease to study with a systems approach. We are interested in integrating high-throughput data to construct computational models of complex biomolecular processes in cancer, including brain cancer (glioblastoma), ovarian cancer, and gastro-intestinal cancers. For each of these cancers, our lab has ongoing collaborations with clinicians and cancer biologists. The aims of this research are to 1) identify molecular signatures for cancer diagnosis and treatment selection, and 2) generate predictive cancer network models that will link observed molecular signatures to underlying causal perturbations and identify therapeutic targets.
Model-guided Cellular Engineering
We are interested in using genome-scale computational models to guide modification of organisms to accomplish engineering goals. In particular, we will focus on reconstructing genome-scale metabolic and regulatory networks to guide engineering of micro-organisms to convert feedstocks to biofuels, as part of an emerging large-scale effort on campus in bioenergy research. To accomplish this, it is necessary to model how the cell will respond as an integrated system under given environments with novel perturbations so that our engineering objective (i.e. biofuel production) is aligned with the "objective" of the organism in its given environment (i.e. cell growth). Reliable predictive models hold the promise of significantly increasing our ability to rationally design and modify biological systems.
Selected Publications
M. Nykter, N.D. Price, A. Larjo, T. Aho, M. Aldana, S.A. Kauffman, L. Hood,
O. Yli-Harja, and I. Shmulevich, "Critical Boolean networks exhibit
maximal information diversity in structure-dynamics relationships," Physical
Review Letters, 100, 058702 (2008).
M. Nykter, N.D. Price, M. Aldana, S. Ramsey, S.A. Kauffman, L. Hood, O.
Yli-Harja, and I. Shmulevich, "Gene expression dynamics in the macrophage
exhibit criticality," Proceedings of the National Academy of Sciences
USA, 105, 1897-1900 (2008).
N.D.Price, G. Foltz, A. Madan, L. Hood, and Q. Tian, "Systems biology
and cancer stem cells," Journal of Cellular and Molecular Medicine,
12, 97-110 (2008).
N.D. Price and I. Shmulevich, "Biochemical and statistical network
models for systems biology," Current Opinion in Biotechnology, 18 365-370
(2007).
N.D. Price, J. Trent, A.K. El-Naggar, D. Cogdell, E. Taylor, K.K. Hunt,
R.E. Pollock, L. Hood, I. Shmulevich and W. Zhang, "Highly accurate
two-gene classifier for differentiating gastrointestinal stromal tumors and
leiomyosarcomas," Proceedings of the National Academy of Sciences USA, 104, 3414-3419 (2007).
E.J. Gianchandani, J.A. Papin, N.D. Price, A. Joyce, and B.O. Palsson,
"Matrix formalism for representing functional states of transcriptional
regulatory networks," PLoS Computational Biology, 2,
e101 (2006). Cover Article
N.D. Price, I. Thiele and B.O. Palsson, "Candidate states of Helicobacter
pylori's genome-scale metabolic network upon application of “loop
law” thermodynamic constraints," Biophysical Journal, 90,
3919-3928 (2006).
T.E. Allen, N.D. Price, A. Joyce, and B.O. Palsson, "Long-range patterns
in prokaryotic genome sequences indicate significant chromosomal organization," PLoS
Computational Biology, 2, e2 (2006). Cover Article
I. Thiele, T.D. Vo, N.D. Price and B.O. Palsson, " Expanded metabolic
reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in
silico genome-scale
characterization of single- and double-deletion mutants," Journal of
Bacteriology, 187, 5818-5830 (2005).
I. Thiele, N.D. Price, T.D. Vo and B.O. Palsson, "Candidate metabolic network
states in human mitochondria: impact of diabetes, ischemia, and diet," Journal
of Biological Chemistry, 280,
11683-11695, (2005).
N.D. Price, J.L. Reed and B.O. Palsson, "Genome-scale models of microbial
cells: Evaluating the consequences of constraints," Nature Reviews Microbiology,
2, 886-897, (2004).
N.D. Price, J. Schellenberger and B.O. Palsson, "Uniform sampling
of steady-state flux spaces: Means to design experiments and to interpret
enzymopathies," Biophysical
Journal, 87, 2172-2186 (2004).