Nathan Price Group

Dr. Nathan D. Price

PhD, Bioengineering
University of California, San Diego

"Systems biology is beneficially transforming critical aspects of human wellbeing, including for health, energy, and the environment. The science of systems biology - and the opportunity to drive it forward with such brilliant and engaging colleagues here at ISB and around the world - excites me every day.

–Nathan Price, PhD, Associate Director

Nathan Price Group

The Price group is interested in advancing the science of systems biology to address important challenges in human health, the environment, and energy. In particular, the lab uses high-throughput experimental methods to interrogate complex biological systems as a means of re-engineering predictive network models. These models are then used as a basis for in silico designs in the case of microbes and for disease diagnosis and target identification in the case of medicine. 

For more information about the Price Lab, visit the Price Lab website.

Research Overview

The Price Lab uses high-throughput experimental technologies coupled with large-scale computational modeling to drive biological and medical discovery on a variety of fronts. In particular, our efforts focus in the following two areas:

1) systems approaches to enable personalized, predictive, personalized, and participatory (P4) medicine, and

2) in silico network models to guide strain design of microbes, particularly for biofuel or commodity chemical production. 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. Our lab has particular expertise in modeling integrated metabolic and transcriptional regulatory networks, as well as in large-scale analysis of large datasets for diagnostic discovery. We also run experiments for both the microbial side of the lab, as well as for human cell lines to investigate network perturbations and responses in glioblastoma (brain cancer).

Research Focus

Systems Medicine: We pursue systems approaches to advance personalized, predictive, preventive, and participatory (P4) medicine. The future of medicine – like so many aspects of our lives – will be utterly transformed by the era of ‘big data.’ The envisioned future of medicine will be driven by exponential rises in capabilities and exponential declines in costs of key technologies for 1) high-throughput biological data generation, and 2) computing power. When integrated together using the kinds of systems approaches we are pioneering at ISB, these technologies will provide an explosion of usable information about each individual patient – far beyond what is available today. While these new possibilities open up tremendous potential, there are very significant challenges to making this vision a reality. My lab is deeply interested in building tools to help address these challenges. Our efforts focus on such areas as: 1) identifying molecular fingerprints of disease, in particular towards screening diagnostics that can differentiate the major diseases of organ-systems simultaneously; 2) building integrated regulatory-metabolic models for human cells and for model organisms that can provide mechanistic underpinnings for interpreting large-scale datasets relevant to health and disease; 3) working within our P4 Medicine Institute to do pilot projects with hospitals related to wellness and data-intensive systems medicine.

In silico network models to guide strain design of microbes: The other primary focus of the Price Lab is on building predictive models that encompass diverse biological functions (e.g., metabolism, transcription) and span different scales of space and time. Importantly, given the exponential rise in sequencing and other high-throughput experimental capabilities, automated generation and validation of genome-scale biochemical reactions networks that can link genotype and phenotype are critical. My lab is actively developing novel approaches to harness high-throughput biological data by contextualizing and quantifying these data via mechanistic networks with defined physical and functional detail – an important step towards grounding statistical observations in high-throughput studies to the realities of the underlying biochemistry. Once reconstructed and validated, these models become highly useful to guide rational strain modifications through genetic manipulation, ultimately enabling synthetic designs. To help increase the practicality of rapidly generating computable genome-scale models, my group has recently developed the first semi-automated method for integrating a genome-scale transcriptional regulatory network based on statistical learning, with a biochemically detailed metabolic network (PNAS, 2010). To enhance the synergistic co-creation of metabolic and transcriptional regulatory models, my lab is also developing an automated regulatory-metabolic reconstruction suite that will build maximum likelihood network models based on highly disparate data stemming not only from evolutionary comparative genomics, but also from comparisons to emergent computed phenotypes from perturbations to both metabolic and regulatory circuits.  My lab is also developing computational approaches based on single cell measurements, having recently demonstrated how single cell variation in protein copy numbers can greatly distort measured enzyme kinetics away from the true values, even when averaged across a population of cells (Physical Review Letters, 2010).

We are also interested in using genome-scale computational models to guide modification of organisms to accomplish engineering goals. In particular, we focus on reconstructing genome-scale metabolic and regulatory networks to guide engineering of microorganisms to convert feedstocks to biofuels or commodity chemicals. 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.