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2017 ; 114
(45
): 11832-11837
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Algorithm for cellular reprogramming
#MMPMID29078370
Ronquist S
; Patterson G
; Muir LA
; Lindsly S
; Chen H
; Brown M
; Wicha MS
; Bloch A
; Brockett R
; Rajapakse I
Proc Natl Acad Sci U S A
2017[Nov]; 114
(45
): 11832-11837
PMID29078370
show ga
The day we understand the time evolution of subcellular events at a level of
detail comparable to physical systems governed by Newton's laws of motion seems
far away. Even so, quantitative approaches to cellular dynamics add to our
understanding of cell biology. With data-guided frameworks we can develop better
predictions about, and methods for, control over specific biological processes
and system-wide cell behavior. Here we describe an approach for optimizing the
use of transcription factors (TFs) in cellular reprogramming, based on a device
commonly used in optimal control. We construct an approximate model for the
natural evolution of a cell-cycle-synchronized population of human fibroblasts,
based on data obtained by sampling the expression of 22,083 genes at several time
points during the cell cycle. To arrive at a model of moderate complexity, we
cluster gene expression based on division of the genome into topologically
associating domains (TADs) and then model the dynamics of TAD expression levels.
Based on this dynamical model and additional data, such as known TF binding sites
and activity, we develop a methodology for identifying the top TF candidates for
a specific cellular reprogramming task. Our data-guided methodology identifies a
number of TFs previously validated for reprogramming and/or natural
differentiation and predicts some potentially useful combinations of TFs. Our
findings highlight the immense potential of dynamical models, mathematics, and
data-guided methodologies for improving strategies for control over biological
processes.
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