# MOMI Data Management

> **NIH NIH U19** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $470,796

## Abstract

Data Management and Analysis Core: Summary
 While previously regarded as a state of immunosuppression, emerging immunological studies conversely
suggest that immune system shifts throughout pregnancy from inflammatory to anti-inflammatory, shifting to
balance implantation and growth of the fetal allograft. Instead, OMIC level investigation has begun to point to an
immunological clock that appears throughout pregnancy that may drive this balance between fetal-protection
and maternal immunity- however the specific mechanisms that contribute to this biology and whether the same
changes occur simultaneously throughout the immune system is incompletely understood. Thus, here we aim to
develop an OMIC level data – integrating measures across the system and using vaccines as a mechanism to
perturb the system in vivo. These datasets will be captured across gestation for the first time, building the
foundational data to understand the immunological switches that occur throughout pregnancy to improve
maternal health, develop novel strategies to treat infertility, to guide diseases requiring improved tolerance, as
well as to improve neonatal health. In addition to assisting Project investigators with application of traditional
systems biology mathematical tools, such as differential expression, enrichment, and clustering analysis, the
Data Management and Analysis Core (DMAC) will develop and employ a spectrum of computational
approaches arising from the realms of engineering and computer science, including machine learning
techniques. We will emphasize modeling frameworks in which multiple features are used concomitantly for
explanation or prediction of responses, as multi-variate correlates of protection. Moreover, these frameworks
can examine how these multiple variables interact, offering potential advances in biological insights concerning
mechanism. Both supervised and unsupervised classes of algorithms will be utilized, permitting two different
perspectives on identifying correlates. The efforts of this Core will be intimately integrated into each of the
experimental Projects.

## Key facts

- **NIH application ID:** 10420111
- **Project number:** 1U19AI167899-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** DOUGLAS A LAUFFENBURGER
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $470,796
- **Award type:** 1
- **Project period:** 2022-04-19 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10420111

## Citation

> US National Institutes of Health, RePORTER application 10420111, MOMI Data Management (1U19AI167899-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10420111. Licensed CC0.

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