# Systems Biology of Shape and Size Regulation

> **NIH NIH R35** · UNIVERSITY OF MARYLAND BALTIMORE COUNTY · 2021 · $378,160

## Abstract

Abstract
The molecular regulation of body shape and size during development and regeneration involves
numerous pathways precisely integrated together with the biophysical properties of cellular and
tissue dynamics, a complex process poorly understood at the level of whole animals. The overall
goal of this project is to gain a mechanistic understanding of the genetic regulation and
coordination of large-scale tissue growth by developing and applying a novel integrated systems
biology approach. Combining in vivo experiments and their morphological formalization with
machine learning of mathematical biophysical models, we will discern the molecular mechanisms
that control growth, shape, and size regulation. We will leverage the robustness of the planarian
worm to address the molecular and physical mechanisms regulating their extraordinary
homeostatic and regenerative capacity to grow, degrow, and regenerate their whole-body shapes
and organs from almost any amputation and across one order of magnitude in sizes.
This work will develop novel computational systems biology methods and integrate them with
whole-body gene expression imaging and surgical and genetic manipulations assays to elucidate
the molecular regulators of body shape and size. Morphological, genetic, and surgical data will
be formalized with novel mathematical ontologies, which will serve as input to new machine
learning methods able to infer mechanistic gene regulatory networks. The regulatory networks
will be quantitatively modeled with a novel mathematical continuous approach for whole-body
biophysical simulation, including tissue growth, adhesion molecules, and gene regulation. This
computational framework combining machine learning with biophysical modeling will be able to
discover the mechanisms of growth and shape regulation from large formalized experimental
datasets. Novel genetic interactions will be discovered by the machine learning methodology,
which predictions in terms of morphological and gene expression outcomes resulting from genetic
and surgical manipulations will be validated at the bench via RNAi and in situ hybridization assays.
Integrating machine learning, biophysical mathematical modeling, ontological formalizations, and
in vivo surgical and molecular assays represents a comprehensive systems biology approach for
elucidating the regulation of shape and size. This work will provide a mechanistic understanding
of the diverse genetic pathways that regulate tissue growth dynamics and how they interact
precisely between them and with tissue biophysics to create and maintain whole-body scale
targeted shapes and sizes. This work will pave the way for new applications and novel therapies
in human developmental, regenerative, and cancer medicine.

## Key facts

- **NIH application ID:** 10249318
- **Project number:** 5R35GM137953-02
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE COUNTY
- **Principal Investigator:** Daniel Lobo
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $378,160
- **Award type:** 5
- **Project period:** 2020-09-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249318

## Citation

> US National Institutes of Health, RePORTER application 10249318, Systems Biology of Shape and Size Regulation (5R35GM137953-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10249318. Licensed CC0.

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