Evolution - Based Gene Regulatory Network of Yeast Cell Cycle

2006 ◽  
Author(s):  
Shinq-Jen Wu ◽  
Cheng-Tao Wu ◽  
Chia-Hsien Chou ◽  
Tsu-Tian Lee
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Yeast Cell Cycle ◽  
Gene Regulatory

scholarly journals Evolution of Regulatory Complexity for Cell-Cycle Control

2021 ◽  
Author(s):  
Sam H. A. von der Dunk ◽  
Berend Snel ◽  
Paulien Hogeweg
Keyword(s):  
Cell Cycle ◽  
Gene Regulation ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Caulobacter Crescentus ◽  
Genome Structure ◽  
Cell Cycle Checkpoint ◽  
Evolutionary Strategies ◽  
Nutrient Levels ◽  
Gene Regulatory

How complexity arises is a fundamental evolutionary question. Complex gene regulation is thought to arise by the interplay between adaptive and non-adaptive forces at multiple organizational levels. Using a computational model, we investigate how complexity arises in cell-cycle regulation. Starting from the well-known Caulobacter crescentus network, we study how cells adapt their cell-cycle behaviour to a gradient of limited nutrient conditions using 10 replicate in silico evolution experiments. We find adaptive expansion of the gene regulatory network: improvement of cell-cycle behaviour allows cells to overcome the inherent cost of complexity. Replicates traverse different evolutionary trajectories leading to distinct eco-evolutionary strategies. In four replicates, cells evolve a generalist strategy to cope with a variety of nutrient levels; in two replicates, different specialist cells evolve for specific nutrient levels; in the remaining four replicates, an intermediate strategy evolves. The generalist and specialist strategies are contingent on the regulatory mechanisms that arise early in evolution, but they are not directly linked to network expansion and overall fitness. This study shows that functionality of cells depends on the combination of gene regulatory network topology and genome structure. For example, the positions of dosage-sensitive genes are exploited to signal to the regulatory network when replication is completed, forming a de novo evolved cell-cycle checkpoint. Complex gene regulation can arise adaptively both from expansion of the regulatory network and from the genomic organization of the elements in this network, demonstrating that to understand complex gene regulation and its evolution, it is necessary to integrate systems that are often studied separately.

scholarly journals The transcription factor Zic4 acts as a transdifferentiation switch

2021 ◽  
Author(s):  
Matthias Christian Vogg ◽  
Jaroslav Ferenc ◽  
Wanda Christa Buzgariu ◽  
Chrystelle Perruchoud ◽  
Panagiotis Papasaikas ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Transcription Factor ◽  
Gene Regulatory Network ◽  
Cell Fate ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Model System ◽  
Cell Identity ◽  
Transcriptional Switch ◽  
Gene Regulatory

The molecular mechanisms that maintain cell identities and prevent transdifferentiation remain mysterious. Interestingly, both dedifferentiation and transdifferentiation are transiently reshuffled during regeneration. Therefore, organisms that regenerate readily offer a fruitful paradigm to investigate the regulation of cell fate stability. Here, we used Hydra as a model system and show that Zic4 silencing is sufficient to induce transdifferentiation of tentacle into foot cells. We identified a Wnt-controlled Gene Regulatory Network that controls a transcriptional switch of cell identity. Furthermore, we show that this switch also controls the re-entry into the cell cycle. Our data indicate that maintenance of cell fate by a Wnt-controlled GRN is a key mechanism during both homeostasis and regeneration.

scholarly journals Boolean factor graph model for biological systems: the yeast cell-cycle network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Error Propagation ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Yeast Cell Cycle ◽  
Computational Framework ◽  
Gene Regulatory

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

Analysis of Hypoxiamir-Gene Regulatory Network Identifies Critical MiRNAs Influencing Cell-Cycle Regulation Under Hypoxic Conditions

MicroRNA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Apoorv Gupta ◽  
Sugadev Ragumani ◽  
Yogendra Kumar Sharma ◽  
Yasmin Ahmad ◽  
Pankaj Khurana
Keyword(s):  
Cell Cycle ◽  
Gene Regulatory Network ◽  
Stress Responses ◽  
Regulatory Network ◽  
Topological Analysis ◽  
Functional Characterization ◽  
Cell Cycle Checkpoint ◽  
Hypoxic Stress ◽  
Pathway Enrichment ◽  
Gene Regulatory

Background: Hypoxia is a pathophysiological condition which arises due to low oxygen concentration in conditions like cardiovascular diseases, inflammation, ascent to higher altitude, malignancies, deep sea diving, prenatal birth, etc. A number of microRNAs (miRNAs), Transcription Factors (TFs) and genes have been studied separately for their role in hypoxic adaptation and controlling cell-cycle progression and apoptosis during this stress. Objective: We hypothesize that miRNAs and TFs may act in conjunction to regulate a multitude of genes and play a crucial and combinatorial role during hypoxia-stress-responses and associated cellcycle control mechanisms. Method: We collected a comprehensive and non-redundant list of human hypoxia-responsive miRNAs (also known as hypoxiamiRs). Their experimentally validated gene-targets were retrieved from various databases and a comprehensive hypoxiamiR-gene regulatory network was built. Results: Functional characterization and pathway enrichment of genes identified phospho-proteins as enriched nodes. The phospho-proteins which were localized both in the nucleus and cytoplasm and could potentially play important role as signaling molecules were selected; and further pathway enrichment revealed that most of them were involved in NFkB signaling. Topological analysis identified several critical hypoxiamiRs and network perturbations confirmed their importance in the network. Feed Forward Loops (FFLs) were identified in the subnetwork of enriched genes, miRNAs and TFs. Statistically significant FFLs consisted of four miRNAs (hsa-miR-182-5p, hsa- miR-146b-5p, hsa-miR-96, hsa-miR-20a) and three TFs (SMAD4, FOXO1, HIF1A) both regulating two genes (NFkB1A and CDKN1A). Conclusion: Detailed BioCarta pathway analysis identified that these miRNAs and TFs together play a critical and combinatorial role in regulating cell-cycle under hypoxia, by controlling mechanisms that activate cell-cycle checkpoint protein, CDKN1A. These modules work synergistically to regulate cell-proliferation, cell-growth, cell-differentiation and apoptosis during hypoxia. A detailed mechanistic molecular model of how these co-regulatory FFLs may regulate the cell-cycle transitions during hypoxic stress conditions is also put forth. These biomolecules may play a crucial and deterministic role in deciding the fate of the cell under hypoxic-stress.

scholarly journals A Dynamic Gene Regulatory Network Model That Recovers the Cyclic Behavior of Arabidopsis thaliana Cell Cycle

2015 ◽  
Vol 11 (9) ◽  
pp. e1004486 ◽  
Author(s):  
Elizabeth Ortiz-Gutiérrez ◽  
Karla García-Cruz ◽  
Eugenio Azpeitia ◽  
Aaron Castillo ◽  
María de la Paz Sánchez ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Arabidopsis Thaliana ◽  
Gene Regulatory Network ◽  
Network Model ◽  
Regulatory Network ◽  
Cyclic Behavior ◽  
Gene Regulatory
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