The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Gregory J. Podgorski - One of the best experts on this subject based on the ideXlab platform.
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Applying attractor dynamics to infer gene regulatory interactions involved in Cellular Differentiation.
BioSystems, 2017Co-Authors: Ahmadreza Ghaffarizadeh, Gregory J. Podgorski, Nicholas S. FlannAbstract:Abstract The dynamics of gene regulatory networks (GRNs) guide Cellular Differentiation. Determining the ways regulatory genes control expression of their targets is essential to understand and control Cellular Differentiation. The way a regulatory gene controls its target can be expressed as a gene regulatory function. Manual derivation of these regulatory functions is slow, error-prone and difficult to update as new information arises. Automating this process is a significant challenge and the subject of intensive effort. This work presents a novel approach to discovering biologically plausible gene regulatory interactions that control Cellular Differentiation. This method integrates known cell type expression data, genetic interactions, and knowledge of the effects of gene knockouts to determine likely GRN regulatory functions. We employ a genetic algorithm to search for candidate GRNs that use a set of transcription factors that control Differentiation within a lineage. Nested canalyzing functions are used to constrain the search space to biologically plausible networks. The method identifies an ensemble of GRNs whose dynamics reproduce the gene expression pattern for each cell type within a particular lineage. The method's effectiveness was tested by inferring consensus GRNs for myeloid and pancreatic cell Differentiation and comparing the predicted gene regulatory interactions to manually derived interactions. We identified many regulatory interactions reported in the literature and also found differences from published reports. These discrepancies suggest areas for biological studies of myeloid and pancreatic Differentiation. We also performed a study that used defined synthetic networks to evaluate the accuracy of the automated search method and found that the search algorithm was able to discover the regulatory interactions in these defined networks with high accuracy. We suggest that the GRN functions derived from the methods described here can be used to fill gaps in knowledge about regulatory interactions and to offer hypotheses for experimental testing of GRNs that control Differentiation and other biological processes.
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Multistable switches and their role in Cellular Differentiation networks
BMC Bioinformatics, 2014Co-Authors: Ahmadreza Ghaffarizadeh, Nicholas S. Flann, Gregory J. PodgorskiAbstract:Abstract Background Cellular Differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types. Results To explore this question a new methodology is developed and applied to study (a) the multistable switch-containing GRN for hematopoiesis and (b) a large set of random boolean networks (RBNs) in which multistable switches were embedded systematically. In this work, each network attractor is taken to represent a distinct cell type. The GRNs were seeded with one or two identical copies of each multistable switch and the effect of these additions on two key aspects of network dynamics was assessed. These properties are the barrier to movement between pairs of attractors (separation) and the degree to which one direction of movement between attractor pairs is favored over another (directionality). Both of these properties are instrumental in shaping the structure of lineage trees. We found that adding one multistable switch of any type had a modest effect on increasing the proportion of well-separated attractor pairs. Adding two identical switches of any type had a much stronger effect in increasing the proportion of well-separated attractors. Similarly, there was an increase in the frequency of directional transitions between attractor pairs when two identical multistable switches were added to GRNs. This effect on directionality was not observed when only one multistable switch was added. Conclusions This work provides evidence that the occurrence of multistable switches in networks that control Cellular Differentiation contributes to the structure of lineage trees and to the stabilization of cell types.
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Multistable switches and their role in Cellular Differentiation networks.
BMC bioinformatics, 2014Co-Authors: Ahmadreza Ghaffarizadeh, Nicholas S. Flann, Gregory J. PodgorskiAbstract:Cellular Differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types. To explore this question a new methodology is developed and applied to study (a) the multistable switch-containing GRN for hematopoiesis and (b) a large set of random boolean networks (RBNs) in which multistable switches were embedded systematically. In this work, each network attractor is taken to represent a distinct cell type. The GRNs were seeded with one or two identical copies of each multistable switch and the effect of these additions on two key aspects of network dynamics was assessed. These properties are the barrier to movement between pairs of attractors (separation) and the degree to which one direction of movement between attractor pairs is favored over another (directionality). Both of these properties are instrumental in shaping the structure of lineage trees. We found that adding one multistable switch of any type had a modest effect on increasing the proportion of well-separated attractor pairs. Adding two identical switches of any type had a much stronger effect in increasing the proportion of well-separated attractors. Similarly, there was an increase in the frequency of directional transitions between attractor pairs when two identical multistable switches were added to GRNs. This effect on directionality was not observed when only one multistable switch was added. This work provides evidence that the occurrence of multistable switches in networks that control Cellular Differentiation contributes to the structure of lineage trees and to the stabilization of cell types.
Ranran Wang - One of the best experts on this subject based on the ideXlab platform.
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perturbation of brd4 protein function by brd4 nut protein abrogates Cellular Differentiation in nut midline carcinoma
Journal of Biological Chemistry, 2011Co-Authors: Jason Diaz, Jing Jiao, Ranran WangAbstract:NUT midline carcinoma (NMC) belongs to a class of highly lethal and poorly differentiated epithelial cancers arising mainly in human midline organs. NMC is caused by the chromosome translocation-mediated fusion of the NUT (nuclear protein in testis) gene on chromosome 15 to a few other genes, most frequently the BRD4 gene on chromosome 19. The mechanism by which the BRD4-NUT fusion product blocks NMC Cellular Differentiation and contributes to oncogenesis remains elusive. In this study, we show that BRD4-NUT and BRD4 colocalize in discrete nuclear foci that are hyperacetylated but transcriptionally inactive. BRD4-NUT recruits histone acetyltransferases to induce histone hyperacetylation in these chromatin foci, which provide docking sites for accumulation of additional BRD4 and associated P-TEFB (positive transcription elongation factor b) complexes in the transcriptionally inactive BRD4-NUT foci. These molecular events lead to repression of a BRD4·P-TEFB downstream target gene c-fos, a component of activator protein 1 (AP-1), that directly regulates epithelial Differentiation. Knockdown of BRD4-NUT in NMC cells disperses the transcriptionally inactive chromatin foci and releases the transcriptional activators to stimulate c-fos expression, leading to restoration of Cellular Differentiation. Our study provides a novel mechanism by which the BRD4-NUT oncogene perturbs BRD4 functions to block Cellular Differentiation and to contribute to the oncogenic progression in the highly aggressive NMC.
Chengna Yuan - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Optimization Algorithm for Super High Dimensional Numerical Function Inspired by Cellular Differentiation
2020Co-Authors: Yanjiang Wang, Chengna Yuan, Hui Li, Yujuan QiAbstract:Inspired by the Cellular Differentiation behaviors, a new biomimetic optimization algorithm, Cellular Differentiation optimization algorithm (CDOA), is proposed. First, a certain number of cells are randomly distributed in the search space in which each cell represents a solution. Then, several Cellular Differentiation behaviors such as division, growth, migration, adhesion and apoptosis are exhibited for finding the optimal solution according to the activity value of a cell. The proposed algorithm is applied to several benchmark complex functions optimization with 20-1000 dimensions. Experimental results show that the proposed Cellular Differentiation optimization algorithm can converge to the optimum of complex numerical functions with super high dimensions rapidly in spite of its simple procedure and effortless implementation. Copyright © 2013 IFSA.
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ICMLA (1) - Cellular Differentiation Algorithm for High Dimensional Numerical Function Optimization
2012 11th International Conference on Machine Learning and Applications, 2012Co-Authors: Yanjiang Wang, Chengna YuanAbstract:Inspired by the Cellular Differentiation mechanism of organisms, combined with the theory of artificial life and swarm intelligence, a new biomimetic optimization algorithm, Cellular Differentiation optimization algorithm (CDOA), is proposed in this paper. A certain number of cells are randomly distributed in the search space to find the optimal solution by activating their differential behaviors such as division, growth, migration, adhesion and apoptosis. Experimental results on several benchmark complex functions with high dimensions show that the proposed Cellular Differentiation optimization algorithm can rapidly converge at high quality solutions and outperform some of the state-of-art in high-dimension numerical function optimization.
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Cellular Differentiation Algorithm for High Dimensional Numerical Function Optimization
2012 11th International Conference on Machine Learning and Applications, 2012Co-Authors: Yanjiang Wang, Chengna YuanAbstract:Inspired by the Cellular Differentiation mechanism of organisms, combined with the theory of artificial life and swarm intelligence, a new biomimetic optimization algorithm, Cellular Differentiation optimization algorithm (CDOA), is proposed in this paper. A certain number of cells are randomly distributed in the search space to find the optimal solution by activating their differential behaviors such as division, growth, migration, adhesion and apoptosis. Experimental results on several benchmark complex functions with high dimensions show that the proposed Cellular Differentiation optimization algorithm can rapidly converge at high quality solutions and outperform some of the state-of-art in high-dimension numerical function optimization.
Patrick J Kennedy - One of the best experts on this subject based on the ideXlab platform.
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bistable switches control memory and plasticity in Cellular Differentiation
Proceedings of the National Academy of Sciences of the United States of America, 2009Co-Authors: Lei Wang, Brandon L Walker, Stephen Iannaccone, Devang Bhatt, Patrick J KennedyAbstract:Development of stem and progenitor cells into specialized tissues in multiCellular organisms involves a series of cell fate decisions. Cellular Differentiation in higher organisms is generally considered irreversible, and the idea of developmental plasticity in postnatal tissues is controversial. Here, we show that inhibition of mitogen-activated protein kinase (MAPK) in a human bone marrow stromal cell-derived myogenic subclone suppresses their myogenic ability and converts them into satellite cell-like precursors that respond to osteogenic stimulation. Clonal analysis of the induced osteogenic response reveals ultrasensitivity and an “all-or-none” behavior, hallmarks of a bistable switch mechanism with stochastic noise. The response demonstrates Cellular memory, which is contingent on the accumulation of an intraCellular factor and can be erased by factor dilution through cell divisions or inhibition of protein synthesis. The effect of MAPK inhibition also exhibits memory and appears to be controlled by another bistable switch further upstream that determines cell fate. Once the memory associated with osteogenic Differentiation is erased, the cells regain their myogenic ability. These results support a model of cell fate decision in which a network of bistable switches controls inducible production of lineage-specific Differentiation factors. A competitive balance between these factors determines cell fate. Our work underscores the dynamic nature of Cellular Differentiation and explains mechanistically the dual properties of stability and plasticity associated with the process.
Ahmadreza Ghaffarizadeh - One of the best experts on this subject based on the ideXlab platform.
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Applying attractor dynamics to infer gene regulatory interactions involved in Cellular Differentiation.
BioSystems, 2017Co-Authors: Ahmadreza Ghaffarizadeh, Gregory J. Podgorski, Nicholas S. FlannAbstract:Abstract The dynamics of gene regulatory networks (GRNs) guide Cellular Differentiation. Determining the ways regulatory genes control expression of their targets is essential to understand and control Cellular Differentiation. The way a regulatory gene controls its target can be expressed as a gene regulatory function. Manual derivation of these regulatory functions is slow, error-prone and difficult to update as new information arises. Automating this process is a significant challenge and the subject of intensive effort. This work presents a novel approach to discovering biologically plausible gene regulatory interactions that control Cellular Differentiation. This method integrates known cell type expression data, genetic interactions, and knowledge of the effects of gene knockouts to determine likely GRN regulatory functions. We employ a genetic algorithm to search for candidate GRNs that use a set of transcription factors that control Differentiation within a lineage. Nested canalyzing functions are used to constrain the search space to biologically plausible networks. The method identifies an ensemble of GRNs whose dynamics reproduce the gene expression pattern for each cell type within a particular lineage. The method's effectiveness was tested by inferring consensus GRNs for myeloid and pancreatic cell Differentiation and comparing the predicted gene regulatory interactions to manually derived interactions. We identified many regulatory interactions reported in the literature and also found differences from published reports. These discrepancies suggest areas for biological studies of myeloid and pancreatic Differentiation. We also performed a study that used defined synthetic networks to evaluate the accuracy of the automated search method and found that the search algorithm was able to discover the regulatory interactions in these defined networks with high accuracy. We suggest that the GRN functions derived from the methods described here can be used to fill gaps in knowledge about regulatory interactions and to offer hypotheses for experimental testing of GRNs that control Differentiation and other biological processes.
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Multistable switches and their role in Cellular Differentiation networks
BMC Bioinformatics, 2014Co-Authors: Ahmadreza Ghaffarizadeh, Nicholas S. Flann, Gregory J. PodgorskiAbstract:Abstract Background Cellular Differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types. Results To explore this question a new methodology is developed and applied to study (a) the multistable switch-containing GRN for hematopoiesis and (b) a large set of random boolean networks (RBNs) in which multistable switches were embedded systematically. In this work, each network attractor is taken to represent a distinct cell type. The GRNs were seeded with one or two identical copies of each multistable switch and the effect of these additions on two key aspects of network dynamics was assessed. These properties are the barrier to movement between pairs of attractors (separation) and the degree to which one direction of movement between attractor pairs is favored over another (directionality). Both of these properties are instrumental in shaping the structure of lineage trees. We found that adding one multistable switch of any type had a modest effect on increasing the proportion of well-separated attractor pairs. Adding two identical switches of any type had a much stronger effect in increasing the proportion of well-separated attractors. Similarly, there was an increase in the frequency of directional transitions between attractor pairs when two identical multistable switches were added to GRNs. This effect on directionality was not observed when only one multistable switch was added. Conclusions This work provides evidence that the occurrence of multistable switches in networks that control Cellular Differentiation contributes to the structure of lineage trees and to the stabilization of cell types.
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Multistable switches and their role in Cellular Differentiation networks.
BMC bioinformatics, 2014Co-Authors: Ahmadreza Ghaffarizadeh, Nicholas S. Flann, Gregory J. PodgorskiAbstract:Cellular Differentiation during development is controlled by gene regulatory networks (GRNs). This complex process is always subject to gene expression noise. There is evidence suggesting that commonly seen patterns in GRNs, referred to as biological multistable switches, play an important role in creating the structure of lineage trees by providing stability to cell types. To explore this question a new methodology is developed and applied to study (a) the multistable switch-containing GRN for hematopoiesis and (b) a large set of random boolean networks (RBNs) in which multistable switches were embedded systematically. In this work, each network attractor is taken to represent a distinct cell type. The GRNs were seeded with one or two identical copies of each multistable switch and the effect of these additions on two key aspects of network dynamics was assessed. These properties are the barrier to movement between pairs of attractors (separation) and the degree to which one direction of movement between attractor pairs is favored over another (directionality). Both of these properties are instrumental in shaping the structure of lineage trees. We found that adding one multistable switch of any type had a modest effect on increasing the proportion of well-separated attractor pairs. Adding two identical switches of any type had a much stronger effect in increasing the proportion of well-separated attractors. Similarly, there was an increase in the frequency of directional transitions between attractor pairs when two identical multistable switches were added to GRNs. This effect on directionality was not observed when only one multistable switch was added. This work provides evidence that the occurrence of multistable switches in networks that control Cellular Differentiation contributes to the structure of lineage trees and to the stabilization of cell types.