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Deep Curation via an Integrated Whole-Cell Computational Model

Award Number: R01LM013229
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/01/2020
PERIOD OF PERFORMANCE END DATE: 02/28/2025

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Deep Curation via an Integrated Whole-Cell Computational Model - Research Summary/Abstract The generation of biological data is rapidly presenting us with one of the most demanding data analysis challenges the world has ever faced - not only in terms of storage and accessibility, but perhaps more critically in terms of its extensive heterogeneity and variability. In this proposal, we present a new approach to these challenges, which we call “Deep Curation”: a large-scale, integrated modeling approach to simultaneously cross-evaluate millions of heterogeneous data against themselves. The word “deep” reflects the multiple layers of curation we perform, including layers not only for data, but also for parameters derived from these data, the mathematical equations, the unified model, and the simulation output. Thus, the deeply-curated model is an invaluable tool for processing, curating and analyzing data automatically. Our proposed efforts in Deep Curation are based on a computer model of Escherichia coli that accounts for the function of roughly 40% of the well-annotated genes, and is based on an extensive set of diverse measurements compiled from thousands of reports (currently in 2nd round of review at Science). The goal of this proposal is to expand this model to enable Deep Curation of data related to growth on >100 currently-unincorporated environments. We can then assess the cross-consistency of the data sets simultaneously, as a unified whole, identifying critical areas in which datasets are not cross-consistent and therefore further experimental investigation is needed. The Significance of this proposal is that Deep Curation represents a first-in-kind quantum leap forward in our ability to exploit massively heterogeneous, variable and complex biological datasets; that it automates and accelerates transformative biomedical discovery; that we will create a bi-directional pipeline between EcoCyc, the most comprehensive database on any organism, and the most complex biological model in existence; and that whole-cell modeling is a rapidly-growing field with transformative potential as it advances towards more complex cells and groups of cells. The Innovation associated with this proposal is that Deep Curation is a brand-new and highly innovative approach that is not currently available to any other lab in the world; that the proposed work will produce a dramatically expanded whole-cell model of previously-unseen complexity; as well as novel and highly innovative modeling technology; that we include explicit curation of knowledge regarding mechanism in addition to data; and that the automated communication between the EcoCyc database and the E. coli model will dramatically expand the capacity, scope and visibility of both in a synergistic way. Our Specific Aims are: Aim 1 (Curation), build the Data and Parameter layers related to E. coli growth on diverse environments; Aim 2 (Modeling), implement the Equation, Model and Simulation layers; Aim 3 (Deep Curation), use the integrated model to cross-evaluate the unified data set at the whole-organism scale; and Aim 4 (Distribution), make the model available to the broader community via GitHub (software tools), EcoCyc (data and parameters), and Google Cloud (simulations and interactive visualizations).


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2023	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	000	4	6/5/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $371,729 )
2023	2023	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	000	4	1/31/2023	NON-COMPETING CONTINUATION	$371,729
														Subtotal = $371,729

Issue Date FY: 2022 ( Subtotal = $371,063 )
2022	2022	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	000	3	1/28/2022	NON-COMPETING CONTINUATION	$371,063
														Subtotal = $371,063

Issue Date FY: 2021 ( Subtotal = $370,347 )
2021	2021	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	001	2	4/27/2021	NON-COMPETING CONTINUATION	$18,518
2021	2021	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	000	2	2/22/2021	NON-COMPETING CONTINUATION	$351,829
														Subtotal = $370,347

Issue Date FY: 2020 ( Subtotal = $381,923 )
2020	2020	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Medical Library Assistance	000	1	4/30/2020	NEW	$381,923
														Subtotal = $381,923

Grand Total All Awards = $1,495,062

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Deep Curation via an Integrated Whole-Cell Computational Model

Award Number: R01LM013229

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/01/2020

PERIOD OF PERFORMANCE END DATE: 02/28/2025

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