NETISCE: a network-based tool for cell fate reprogramming

Abstract

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

Reproducibility and FAIR principles: the case of a segment polarity network model

“…Unfortunately those two publications used their own C programs but did not publish them. It was work in Sethna’s lab ( Gutenkunst et al, 2007 ; Daniels et al, 2008 ) that resulted in an electronic version of the model being created in the SBML format that is still available (see notes to Table 1 ), and which was re-used by Marazzi et al (2022) . However this SBML implementation coded the ODEs directly without representing the reaction network, an important limitation.…”

“…Only two actually reproduced their results ( Ingolia, 2004 ; Ma et al, 2006 ), and another expanded the analysis to diploidy ( Kim and Fernandes, 2009 ). Several authors used the SPN model to illustrate other issues, such as robustness ( Chaves et al, 2009 ; Dayarian et al, 2009 ; Albert et al, 2011 ), “sloppyness” ( Gutenkunst et al, 2007 ; Daniels et al, 2008 ), or new methodologies ( Tegner et al, 2003 ; Zañudo et al, 2017 ; Rozum and Albert, 2018 ; Marazzi et al, 2022 ). Several software applications were used, such as the original Ingeneue ( Meir et al, 2002 ; Kim, 2009 ) and Little b ( Mallavarapu et al, 2009 ), both now unavailable, and bespoke C programs that were never distributed ( Ingolia, 2004 ; Ma et al, 2006 )—all those results are now difficult to reproduce.…”

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

“…We currently applied our approach to study AML tumorigenesis whereas the dataset captures mainly two cellular states. It would be interesting to apply such an approach to systems where one or multiple intermediate states are captured in the data and systems with complex structures of cellular state transitions, such as those during cell fate reprogramming 62 . Additionally, the integration of multiomics datasets, such as microarray gene expression data and ATAC-seq chromatin accessibility data obtained from separate experiments, may benefit from the generation of multimodal datasets, where both datasets are obtained from the same cells.…”

Hepatocyte reprogramming in liver regeneration: Biological mechanisms and applications

“…Indeed, under certain liver injuries, hepatocytes have been observed to undergo reprogramming into hepatic progenitor cells (HPCs), BECs, liver cancer cells (LCCs) or liver cancer progenitor cells (LCPCs) [21,22]. Cell reprogramming refers to the process of converting a cell's current fate to another [23][24][25][26]. And in the context of hepatocyte reprogramming, it involves losing the original identity as hepatocytes and acquiring features of other cell types, such as hepatocyte dedifferentiation to HPCs, transdifferentiation to BECs, or transformation into LCCs or LCPCs.…”