Biological Networks Regulating Cell Fate Choice are Minimally Frustrated

AbstractIn biological networks, steady state dynamics of cell-fate regulatory genes often exhibit Mushroom and Isola kind of bifurcations. How these complex bifurcations emerge for these complex networks, and what are the minimal network structures that can generate these bifurcations, remain elusive. Herein, by employing Waddington’s landscape theory and bifurcation analysis, we have shown that both Mushroom and Isola bifurcations can be realized with four minimal network motifs that are constituted by combining positive feedback motifs with different types of incoherent feedback motifs. Our study demonstrates that the intrinsic bi-stable dynamics due to the presence of the positive feedback motif can be fine-tuned by altering the extent of the incoherence of these proposed minimal networks to orchestrate these complex bifurcations. These modeling insights will be useful in identifying and analyzing possible network motifs that may give rise to either Mushroom or Isola bifurcation in other biological systems.

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Role of metabolic spatiotemporal dynamics in regulating biofilm colony expansion

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1706920115 ◽

2018 ◽

Vol 115 (16) ◽

pp. 4288-4293 ◽

Cited By ~ 8

Author(s):

Federico Bocci ◽

Yoko Suzuki ◽

Mingyang Lu ◽

José N. Onuchic

Keyword(s):

Cell Fate ◽

Biological Networks ◽

Cell Communication ◽

Spatiotemporal Dynamics ◽

Cell Populations ◽

Modeling Framework ◽

Oscillatory Dynamics ◽

Oscillatory Dynamic ◽

Cell Cell

Cell fate determination is typically regulated by biological networks, yet increasing evidences suggest that cell−cell communication and environmental stresses play crucial roles in the behavior of a cell population. A recent microfluidic experiment showed that the metabolic codependence of two cell populations generates a collective oscillatory dynamic during the expansion of aBacillus subtilisbiofilm. We develop a modeling framework for the spatiotemporal dynamics of the associated metabolic circuit for cells in a colony. We elucidate the role of metabolite diffusion and the need of two distinct cell populations to observe oscillations. Uniquely, this description captures the onset and thereafter stable oscillatory dynamics during expansion and predicts the existence of damping oscillations under various environmental conditions. This modeling scheme provides insights to understand how cells integrate the information from external signaling and cell−cell communication to determine the optimal survival strategy and/or maximize cell fitness in a multicellular system.

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Faculty Opinions recommendation of Biological networks regulating cell fate choice are minimally frustrated.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.738582354.793583155 ◽

2021 ◽

Author(s):

Masaki Sasai

Keyword(s):

Cell Fate ◽

Biological Networks

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Predicting genetic interactions from Boolean models of biological networks

Integrative Biology ◽

10.1039/c5ib00029g ◽

2015 ◽

Vol 7 (8) ◽

pp. 921-929 ◽

Cited By ~ 15

Author(s):

Laurence Calzone ◽

Emmanuel Barillot ◽

Andrei Zinovyev

Keyword(s):

Cell Fate ◽

Biological Networks ◽

Decision Model ◽

Genetic Interaction ◽

Interaction Network ◽

Genetic Interactions ◽

Cell Fate Decision ◽

Boolean Models ◽

Genetic Interaction Network ◽

Fate Decision

The network representation of the cell fate decision model (Calzoneet al., 2010) is used to generate a genetic interaction network for the apoptosis phenotype. Most genetic interactions are epistatic, single nonmonotonic, and additive (Dreeset al., 2005).

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NETISCE: A Network-Based Tool for Cell Fate Reprogramming

10.1101/2021.12.30.474582 ◽

2022 ◽

Author(s):

Lauren Marazzi ◽

Milan Shah ◽

Shreedula Balakrishnan ◽

Ananya Patil ◽

Paola Vera-Licona

Keyword(s):

Cell Fate ◽

Biological Networks ◽

Cancer Biology ◽

Cellular Reprogramming ◽

Full Model ◽

Computational Tool ◽

Initial State ◽

Large Sets ◽

Model Parameterization ◽

Systems Framework

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. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. 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 that are relevant for the desired reprogramming task.

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Adapting machine-learning algorithms to design gene circuits

10.1101/213587 ◽

2017 ◽

Author(s):

Tom Hiscock

Keyword(s):

Machine Learning ◽

Cell Fate ◽

Biological Networks ◽

Machine Learning Algorithms ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Gene Circuits ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks

AbstractBiological systems rely on complex networks, such as transcriptional circuits and protein-protein interaction networks, to perform a variety of functions e.g. responding to stimuli, directing cell fate, or patterning an embryo. Mathematical models are often used to ask: given some network, what function does it perform? However, we often want precisely the opposite i.e. given some circuit – either observedin vivo, or desired for some engineering objective – what biological networks could execute this function? Here, we adapt optimization algorithms from machine learning to rapidly screen and design gene circuits capable of performing arbitrary functions. We demonstrate the power of this approach by designing circuits (1) that recapitulate importantin vivophenomena, such as oscillators, and (2) to perform complex tasks for synthetic biology, such as counting noisy biological events. Our method can be readily applied to biological networks of any type and size, and is provided as an open-source and easy-to-use python module, GeneNet.

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Using linkage logic theory to control dynamics of a gene regulatory network of a chordate embryo

Scientific Reports ◽

10.1038/s41598-021-83045-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kenji Kobayashi ◽

Kazuki Maeda ◽

Miki Tokuoka ◽

Atsushi Mochizuki ◽

Yutaka Satou

Keyword(s):

Gene Regulatory Network ◽

Cell Fate ◽

Biological Networks ◽

Regulatory Network ◽

Cell Types ◽

Sufficient Information ◽

Biological Functions ◽

Cellular Environment ◽

Vertex Set ◽

Gene Regulatory

AbstractLinkage logic theory provides a mathematical criterion to control network dynamics by manipulating activities of a subset of network nodes, which are collectively called a feedback vertex set (FVS). Because many biological functions emerge from dynamics of biological networks, this theory provides a promising tool for controlling biological functions. By manipulating the activity of FVS molecules identified in a gene regulatory network (GRN) for fate specification of seven tissues in ascidian embryos, we previously succeeded in reproducing six of the seven cell types. Simultaneously, we discovered that the experimentally reconstituted GRN lacked information sufficient to reproduce muscle cells. Here, we utilized linkage logic theory as a tool to find missing edges in the GRN. Then, we identified a FVS from an updated version of the GRN and confirmed that manipulating the activity of this FVS was sufficient to induce all seven cell types, even in a multi-cellular environment. Thus, linkage logic theory provides tools to find missing edges in experimentally reconstituted networks, to determine whether reconstituted networks contain sufficient information to fulfil expected functions, and to reprogram cell fate.

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Protein Kinase C Activation Drives a Differentiation Program in an Oligodendroglial Precursor Model through the Modulation of Specific Biological Networks

International Journal of Molecular Sciences ◽

10.3390/ijms22105245 ◽

2021 ◽

Vol 22 (10) ◽

pp. 5245

Author(s):

Marina Damato ◽

Tristan Cardon ◽

Maxence Wisztorski ◽

Isabelle Fournier ◽

Damiana Pieragostino ◽

...

Keyword(s):

Protein Kinase C ◽

Protein Kinase ◽

Cell Fate ◽

Biological Networks ◽

Molecular Mechanisms ◽

Cellular Reprogramming ◽

Multiple Networks ◽

Kinase C ◽

Network Modules ◽

Pkc Activation

Protein kinase C (PKC) activation induces cellular reprogramming and differentiation in various cell models. Although many effectors of PKC physiological actions have been elucidated, the molecular mechanisms regulating oligodendrocyte differentiation after PKC activation are still unclear. Here, we applied a liquid chromatography–mass spectrometry (LC–MS/MS) approach to provide a comprehensive analysis of the proteome expression changes in the MO3.13 oligodendroglial cell line after PKC activation. Our findings suggest that multiple networks that communicate and coordinate with each other may finally determine the fate of MO3.13 cells, thus identifying a modular and functional biological structure. In this work, we provide a detailed description of these networks and their participating components and interactions. Such assembly allows perturbing each module, thus describing its physiological significance in the differentiation program. We applied this approach by targeting the Rho-associated protein kinase (ROCK) in PKC-activated cells. Overall, our findings provide a resource for elucidating the PKC-mediated network modules that contribute to a more robust knowledge of the molecular dynamics leading to this cell fate transition.

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Centrosome Aurora A gradient ensures single polarity axis in C. elegans embryos

Biochemical Society Transactions ◽

10.1042/bst20200298 ◽

2020 ◽

Vol 48 (3) ◽

pp. 1243-1253 ◽

Cited By ~ 1

Author(s):

Sukriti Kapoor ◽

Sachin Kotak

Keyword(s):

Symmetry Breaking ◽

Cell Fate ◽

Cell Cortex ◽

Axis Formation ◽

Aurora A Kinase ◽

Aurora A ◽

C Elegans ◽

Cell Embryo ◽

Polarity Establishment

Cellular asymmetries are vital for generating cell fate diversity during development and in stem cells. In the newly fertilized Caenorhabditis elegans embryo, centrosomes are responsible for polarity establishment, i.e. anterior–posterior body axis formation. The signal for polarity originates from the centrosomes and is transmitted to the cell cortex, where it disassembles the actomyosin network. This event leads to symmetry breaking and the establishment of distinct domains of evolutionarily conserved PAR proteins. However, the identity of an essential component that localizes to the centrosomes and promotes symmetry breaking was unknown. Recent work has uncovered that the loss of Aurora A kinase (AIR-1 in C. elegans and hereafter referred to as Aurora A) in the one-cell embryo disrupts stereotypical actomyosin-based cortical flows that occur at the time of polarity establishment. This misregulation of actomyosin flow dynamics results in the occurrence of two polarity axes. Notably, the role of Aurora A in ensuring a single polarity axis is independent of its well-established function in centrosome maturation. The mechanism by which Aurora A directs symmetry breaking is likely through direct regulation of Rho-dependent contractility. In this mini-review, we will discuss the unconventional role of Aurora A kinase in polarity establishment in C. elegans embryos and propose a refined model of centrosome-dependent symmetry breaking.

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Centromere assembly and non-random sister chromatid segregation in stem cells

Essays in Biochemistry ◽

10.1042/ebc20190066 ◽

2020 ◽

Vol 64 (2) ◽

pp. 223-232 ◽

Cited By ~ 1

Author(s):

Ben L. Carty ◽

Elaine M. Dunleavy

Keyword(s):

Stem Cells ◽

Cell Division ◽

Cell Fate ◽

Sister Chromatid ◽

Asymmetric Cell Division ◽

Protein A ◽

Germline Stem Cells ◽

Sister Chromatids ◽

Centromere Protein A ◽

Oocyte Meiosis

Abstract Asymmetric cell division (ACD) produces daughter cells with separate distinct cell fates and is critical for the development and regulation of multicellular organisms. Epigenetic mechanisms are key players in cell fate determination. Centromeres, epigenetically specified loci defined by the presence of the histone H3-variant, centromere protein A (CENP-A), are essential for chromosome segregation at cell division. ACDs in stem cells and in oocyte meiosis have been proposed to be reliant on centromere integrity for the regulation of the non-random segregation of chromosomes. It has recently been shown that CENP-A is asymmetrically distributed between the centromeres of sister chromatids in male and female Drosophila germline stem cells (GSCs), with more CENP-A on sister chromatids to be segregated to the GSC. This imbalance in centromere strength correlates with the temporal and asymmetric assembly of the mitotic spindle and potentially orientates the cell to allow for biased sister chromatid retention in stem cells. In this essay, we discuss the recent evidence for asymmetric sister centromeres in stem cells. Thereafter, we discuss mechanistic avenues to establish this sister centromere asymmetry and how it ultimately might influence cell fate.

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