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Principal Investigator: Adam L MacLean
Organization: UNIVERSITY OF SOUTHERN CALIFORNIA
Fiscal Year: 2024
Award: $412,500
Funding agency: National Institute of General Medical Sciences
Project Summary
The goal of this research program is to provide tools for the discovery of transcriptional networks that control
cell fate decisions. Cell fate decisions driven by cell state transitions underlie essential cell processes from
development to cellular reprogramming. There is an opportunity to make use of publicly available genomic data
to develop predictive computational models of cell state transition dynamics. The methods proposed will offer
means to gain insight into cell fate decision-making and how it is transcriptionally regulated, given specific cell
fate decision points and suitable data. Examples of such decision points include control of epidermal
regeneration, or the maintenance of balance among myeloid cell fates during hematopoiesis. In order to bridge
the gap between genomics and cell dynamics, statistical and computational modeling challenges must be
overcome. Two key challenges form the basis of this research program: 1) developing statistical methods to
infer regulatory networks while accounting for the levels of variability between single cells, and 2) developing
computational models to couple gene regulatory dynamics within cells and cell-cell communication between
cells. To address the first challenge, we will develop machine learning models to predict gene expression
dynamics from time-series data. These models will be able to classify genes by their temporal patterns, and
the results will inform gene network inference. We will then develop methods for network inference that
integrate muti-modal data (single-cell RNA and ATAC sequencing) as well as cell-cell signaling information to
learn networks that control specific cell state transitions. To address the second challenge, we will develop
differential equation-based multiscale models of the gene regulatory network dynamics coupled with the cell-
external signaling dynamics. This will allow us to capture both molecular and cellular dynamics in high
resolution, and thus identify which parameters exert key control over the system. We will use Bayesian
methods for parameter inference to fit models to data and perform model selection, adapting methods where
needed for multiscale model inference. Models will be rigorously evaluated through their application to specific
systems, including cell differentiation (e.g. myeloid fate decisions during hematopoiesis) and development (e.g.
nephron progenitor cell fate decisions). In each of these organ systems, models predictions will be tested
experimentally via collaborations. Following iterative testing, open-source, validated methods will be made
widely available for the study of the dynamic processes of cell fate decision-making.
Terms: <ATAC sequencing><ATAC-seq><ATACseq><Address><Assay for Transposase-Accessible Chromatin using sequencing><Bayesian Method><Bayesian Methodology><Bayesian Statistical Method><Bayesian approaches><Bayesian classification method><Bayesian classification procedure><Bayesian posterior distribution><Body System><Cancers><Cell Body><Cell Communication><Cell Communication and Signaling><Cell Differentiation><Cell Differentiation process><Cell Fate Control><Cell Fate Regulation><Cell Function><Cell Interaction><Cell Physiology><Cell Process><Cell Reprogramming><Cell Signaling><Cell model><Cell to Cell Communication and Signaling><Cell-Cell Signaling><Cell-to-Cell Interaction><Cells><Cellular Function><Cellular Physiology><Cellular Process><Cellular model><Classification><Collaborations><Computer Models><Computerized Models><Computing Methodologies><Coupled><Data><Decision Making><Development><Differential Algebraic Equation><Differential Equation><Equilibrium><Gene Expression><Gene Transcription><Genes><Genetic Transcription><Genomics><Goals><Health><Hematopoiesis><Hematopoietic Cellular Control Mechanisms><Intracellular Communication and Signaling><Life><Maintenance><Malignant Neoplasms><Malignant Tumor><Methods><Modeling><Molecular><Myelogenous><Myeloid><Myeloid Cells><Organ><Organ System><Pattern><Probabilistic Models><Probability Models><RNA Expression><Research><Resolution><Series><Signal Transduction><Signal Transduction Systems><Signaling><Skin tissue regeneration><Statistical Methods><Statistical Models><Subcellular Process><System><Systematics><Testing><Time><Transcription><assay for transposase accessible chromatin followed by sequencing><assay for transposase accessible chromatin seq><assay for transposase accessible chromatin sequencing><assay for transposase-accessible chromatin with sequencing><balance><balance function><biological signal transduction><blood cell formation><cellular differentiation><cellular reprogramming><computational methodology><computational methods><computational modeling><computational models><computer based method><computer based models><computer based prediction><computer methods><computerized modeling><computing method><data modalities><dermis regeneration><developmental><developmental disease><developmental disorder><epidermal regeneration><gene network><gene regulatory network><genomic data><genomic data-set><genomic dataset><insight><intercellular communication><learning network><machine learning based model><machine learning model><malignancy><multi-scale computational modeling><multi-scale mathematical modeling><multi-scale modeling><multiscale computational modeling><multiscale mathematical modeling><multiscale modeling><neoplasm/cancer><nephron progenitor><open source><predictive modeling><progenitor cell fate><progenitor fate><programs><regenerate skin><resolutions><scRNA-seq><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell transcriptomic profiling><single-cell RNA sequencing><skin regeneration><statistic methods><statistical linear mixed models><statistical linear models><stem and progenitor cell fate><stem cell fate><tool>