RII Track-2 FEC: Highly Predictive, Explanatory Models to Harness the Life Science Data Revolution

University Of Wyoming, Laramie WY

Investigators

Abstract

Dramatic increases in the scale and availability of data are profoundly reshaping the life sciences. Data acquisition and availability are outpacing our capacity for analysis, including the development of models that represent our knowledge of biological processes. This collaborative project among three universities in Wyoming, Montana, and Nevada will address this pressing need in the life sciences through research and education efforts led by our consortium. Some types of models can fit observed data very well, but lack generality and the ability to extrapolate to novel settings or future time points. Conversely, other types of models can be more general, but provide a poorer fit to individual data sets. In our research we will develop knowledge of these trade-offs and methods that combine advantageous features of different types of models. In each year our consortium will train a diverse cohort of twelve postdoctoral researchers in cutting edge modeling techniques and prepare them for the workforce. The project investigators and postdoctoral researchers at our three institutions will create an integrated, highly collaborative and interdisciplinary consortium of data scientists. We will develop educational tools to aid the dissemination of the methodologies we develop, promoting the efficient use of high dimensional data in the life sciences. Dramatic increases in the scale and availability of data are profoundly reshaping all domains in the life sciences. Data acquisition and availability from DNA sequencers, environmental sensors, parallel global studies, and imagery (among many others), across time and space, are outpacing our capacity for analysis, including the development of models that represent our knowledge of biological processes. We will address this gap in the life sciences through research and education efforts led by our consortium at the University of Wyoming, the University of Montana, and the University of Nevada–Reno. We will compete and further develop computational, statistical, and machine learning methods for multi-dimensional data to develop highly predictive and explanatory models for the life sciences. We will test and refine methods and develop critical tools for harnessing the data revolution. We will apply them to three cross-scale domains within the life sciences and advance our mechanistic understanding of key ecological and evolutionary phenomena. By bringing together a consortium of scientists currently using existing techniques from multiple disciplines, including computer science and applied mathematics, and competing these techniques with simulated and real data, we will evaluate the efficacy of existing methods. Further, we will build on these methods to develop novel hybrid modeling techniques and expanded use of linear and non-linear sparse models to maximize both the predictive accuracy and mechanistic insight of models applied to high dimensional data sets. We will apply these techniques to critical challenges in the life sciences, including mapping phenotypes to genomic data, modeling community dynamics in highly diverse systems, and disentangling the interplay among different temporal scales of drivers in aquatic ecosystems. In each year our consortium will train a diverse cohort of twelve postdoctoral researchers in modeling techniques and prepare them for the workforce. The project investigators and postdoctoral researchers in Wyoming, Montana, and Nevada will create an integrated, highly collaborative and interdisciplinary consortium of data scientists. We will develop educational tools to aid the dissemination of the methodologies we develop, promoting the efficient use of high dimensional data in the life sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.