scholarly journals Systems Biology Approach to Model the Life Cycle ofTrypanosoma cruzi

2015 ◽  
Author(s):  
Alejandra Carrea ◽  
Luis Diambra
Keyword(s):  
Life Cycle ◽  
Systems Biology ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Environmental Cues ◽  
Cell Fates ◽  
Gene Regulatory ◽  
Cell Phenotypes ◽  
Optimisation Procedure

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of theTrypanosoma cruzi's life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite's life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.

scholarly journals Dynamics of a hepatocyte-cholangiocyte decision-making gene regulatory network during liver development and regeneration

2021 ◽  
Author(s):  
Sarthak Sahoo ◽  
Ashutosh Mishra ◽  
Anna Mae Diehl ◽  
Mohit Kumar Jolly
Keyword(s):  
Decision Making ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Cellular Reprogramming ◽  
Liver Development ◽  
Cell Fates ◽  
Dynamic Simulations ◽  
Gene Regulatory

AbstractLiver is one of the few organs with immense regenerative potential even at adulthood in mammals. It is composed of primarily two cell types: hepatocytes and cholangiocytes, that can trans-differentiate to one another either directly or through intermediate progenitor states, contributing to remarkable regenerative potential of the liver. However, the dynamical features of decision-making between these cell-fates during liver development and regeneration remains elusive. Here, we identify a core gene regulatory network comprising c/EBPα, TGFBR2 and SOX9 that underlies liver development and injury-induced reprogramming. Dynamic simulations for this network reveal its multistable nature, enabling three distinct cell states – hepatocytes, cholangiocytes and liver progenitor cells (hepatoblasts/oval cells) – and stochastic switching among them. Predicted expression signature for these three states are validated through multiple bulk and single-cell transcriptomic datasets collected across developmental stages and injury-induced liver repair. This network can also explain the experimentally observed spatial organisation of phenotypes in liver parenchyma and predict strategies for efficient cellular reprogramming among these cell-fates. Our analysis elucidates how the emergent multistable dynamics of underlying gene regulatory networks drive diverse cell-state decisions in liver development and regeneration.

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

scholarly journals Characterization of eyes, photoreceptors and opsins in developmental stages of the chaetognath Spadella cephaloptera

2019 ◽  
Author(s):  
Tim Wollesen ◽  
Sonia Victoria Rodríguez Monje ◽  
Adam Phillip Oel ◽  
Detlev Arendt
Keyword(s):  
Regulatory Networks ◽  
Developmental Stages ◽  
Photoreceptor Cells ◽  
Phylogenetic Position ◽  
Developmental Studies ◽  
Head Region ◽  
Platynereis Dumerilii ◽  
Spadella Cephaloptera ◽  
Gene Regulatory

AbstractThe phylogenetic position of chaetognaths has been debated for decades, however recently they have been grouped into the Gnathifera, sister taxon to the Lophotrochozoa. Chaetognaths possess photoreceptor cells that are anatomically unique and arranged remarkably different in the eyes of the various species. Studies investigating eye development and underlying gene regulatory networks are so far missing.In order to gain insights into the development and the molecular toolkit of chaetognath photoreceptors and eyes a new transcriptome of the epibenthic species Spadella cephaloptera was searched for opsins. Our screen revealed single-copies of xenopsin and peropsin and gene expression analyses demonstrated that only xenopsin is expressed in photoreceptor cells of the developing lateral eyes. Adults likewise exhibit two xenopsin+ photoreceptor cells in each of their lateral eyes. Beyond that, a single cryptochrome gene was uncovered and found co-expressed with xenopsin in some photoreceptor cells of the lateral developing eye. In addition, it is co-expressed with peropsin in the cerebral ganglia, a condition reminiscent of a non-visual photoreceptive zone in the apical nervous system of the annelid Platynereis dumerilii that performs circadian entrainment and melatonin release. Cryptochrome expression was also detected in cells of the corona ciliata, a circular organ in the posterior dorsal head region that has been attributed several functions arguing for an involvement of this organ in circadian entrainment. Our study demonstrates the importance to investigate representatives of the Gnathifera, a clade that has been neglected with respect to developmental studies and that might contribute to unravel the evolution of spiralian and bilaterian body plans.

scholarly journals Causal Concepts Guiding Model Specification in Systems Biology

Disputatio ◽  
2017 ◽  
Vol 9 (47) ◽  
pp. 499-527
Author(s):  
Dana Matthiessen
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Causal Structure ◽  
Model Specification ◽  
Biology I ◽  
Adequate Representation ◽  
Gene Regulatory ◽  
High Throughput Experiments

Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

COMPLEX NETWORKS EVOLUTIONARY DYNAMICS USING GENETIC ALGORITHMS

2012 ◽  
Vol 22 (07) ◽  
pp. 1250156 ◽  
Author(s):  
DANIEL AGUILAR-HIDALGO ◽  
ANTONIO CÓRDOBA ZURITA ◽  
Ma CARMEN LEMOS FERNÁNDEZ
Keyword(s):  
Genetic Algorithm ◽  
Systems Biology ◽  
Complex Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Evolutionary Dynamics ◽  
Network Dynamics ◽  
Activity Level ◽  
Transformation Rules ◽  
Gene Regulatory

Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building representative networks of gene expression and their time evolution, i.e. the structure as the network dynamics, for certain development processes. The complexity of this kind of problems makes imperative to analyze the problem in the field of network theory and the evolutionary dynamics of complex systems.All this has led us to investigate, in a first step, the evolutionary dynamics in generic networks. Thus, the results can be used in experimental researches in the field of Systems Biology. This research aims to decode the transformation rules governing the evolutionary dynamics in a network. To do this, a genetic algorithm has been implemented in which, starting from initial and ending network states, it is possible to determine the transformation dynamics between these states by using simple acting rules. The network description is the following: (a) The network node values in the initial and ending states can be active or inactive; (b) The network links can act as activators or repressors; (c) A set of rules is established in order to transform the initial state into the ending one; (d) Due to the low connectivity, frequently observed, in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The "chromosomes" of the genetic algorithm include two parts, one related to the node links and another related to the transformation rules.The implemented rules are based on certain genetic interactions behavior. The rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics.The implemented algorithm is able to find appropriate dynamics in complex networks evolution among different states for several cases.

MOLECULAR BIOCIRCUITS

2009 ◽  
Vol 23 (06) ◽  
pp. 773-789 ◽  
Author(s):  
OVIDIU LIPAN
Keyword(s):  
Systems Biology ◽  
Master Equation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Stochastic Approach ◽  
Genetic Networks ◽  
Equation Approach ◽  
Master Equation Approach ◽  
Gene Regulatory

Systems biology aims to describe gene regulatory networks at both experimental and theoretical levels. Mathematical formalisms used at present to describe the behavior of genetic networks range from stochastic to deterministic. The stochastic approach is further subdivided and moves from Langevin to the Master equation. This review presents the Master equation approach.

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