Inferring genetic regulatory logic from expression data

Bulashevska, Svetlana; Eils, Roland

doi:10.1093/bioinformatics/bti388

Cited by 51 publications

(53 citation statements)

References 17 publications

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“…We compared average values obtaining from modifying several input parameters. Finally, we set the input parameters and we present in Section 4.2.3 the performance of GarNet versus other benchmark methods reported in the literature [3,[9][10][11].…”

Section: Resultsmentioning

confidence: 99%

“…Second, we applied GarNet to a well-known set of genes from a microarray data of yeast cell cycle and we compared our approach against several benchmark methods. For performance analysis we applied as benchmark methods a decision-tree-based method [9], a regression-tree-based method [3], a probabilistic graphical model [10] and combinatorial optimization algorithm [11,12]. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks (precision, accuracy and others), which were measured using YeastNet database as a true network.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, to compare the inference capability of our approach against other methods we selected as benchmark methods: a decision-tree-based method [9], a regression-tree-based method [3] called RegNet, a probabilistic graphical model [10] and combinatorial optimization algorithms named GRNCOP [11] and GRN-COP2 [12].…”

Section: Benchmark Methods and Description Of The True Networkmentioning

confidence: 99%

“…Specifically, a decision-tree-based method [9], a regression-tree-based method [3], a probabilistic graphical model [10] and combinatorial optimization algorithm [12] are contrasted with the best ones inferred by GarNet.…”

Section: Performance Of Garnet Versus Other Benchmark Methodsmentioning

confidence: 99%

“…Finally, the probabilistic model called Bayesian networks extracted dependencies between genes if no subset of the other genes can explain the correlation. In the literature, one of the most significant works in Bayesian methods is [10].…”

Section: Inferring Gene Network: a Reviewmentioning

confidence: 99%

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Discovering gene association networks by multi-objective evolutionary quantitative association rules

Martínez-Ballesteros

Nepomuceno-Chamorro

Riquelme

2014

Journal of Computer and System Sciences

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In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. 1.IntroductionSince late 1990s, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. Microarray technology has revolutionized the biological research because it allows to study thousand of genes or even whole genomes [1].As molecular biology is rapidly evolving into a quantitative science, it increasingly relies on computational algorithms to make sense of high-throughput data. One of the main goals in Microarray analysis is the reconstruction of gene regulatory processes and a key task is the inference of regulatory interactions among genes from gene expression data [2].Our aimisto infer the relationships between genes from an organism in a particular biological process. This relationships can be modeled in several levels of abstraction, these levels range from the detailed gene regulatory processes (where a chain of intracellular reaction activates a regulatory molecule, transcription factors until a protein is synthesized) to the high models of abstraction named gene association networks. In the reconstruction of gene regulatory processes, building gene association networks has been proven to provide useful insights for such task, the reconstruction of gene regulatory processes. A gene association network can be defined as a graph in which nodes represent genes and edges represent the influence between them. Our goal in this work is the inference of gene association networks from Microarray datasets.There are several statistical methods to infer gene association networks from Microarray data. A microarray dataset is a bidimensional data structure where conditions are experiments or sources and the columns are gene expression values. In our problem the conditions will be the instances and the gene expression values will be the attributes or features. These methods range from relatively straightforward correlation-based methods to more sophisticated methods based on

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Benchmark Methods and Description Of The True Networkmentioning

confidence: 99%

Section: Performance Of Garnet Versus Other Benchmark Methodsmentioning

confidence: 99%

Section: Inferring Gene Network: a Reviewmentioning

confidence: 99%

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Discovering gene association networks by multi-objective evolutionary quantitative association rules

Martínez-Ballesteros

Nepomuceno-Chamorro

Riquelme

2014

Journal of Computer and System Sciences

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Reviewing mathematical models of sperm signaling networks

Priego Espinosa,

Espinal‐Enríquez,

Aldana

et al. 2024

Molecular Reproduction Devel

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Dave Garbers’ work significantly contributed to our understanding of sperm's regulated motility, capacitation, and the acrosome reaction. These key sperm functions involve complex multistep signaling pathways engaging numerous finely orchestrated elements. Despite significant progress, many parameters and interactions among these elements remain elusive. Mathematical modeling emerges as a potent tool to study sperm physiology, providing a framework to integrate experimental results and capture functional dynamics considering biochemical, biophysical, and cellular elements. Depending on research objectives, different modeling strategies, broadly categorized into continuous and discrete approaches, reveal valuable insights into cell function. These models allow the exploration of hypotheses regarding molecules, conditions, and pathways, whenever they become challenging to evaluate experimentally. This review presents an overview of current theoretical and experimental efforts to understand sperm motility regulation, capacitation, and the acrosome reaction. We discuss the strengths and weaknesses of different modeling strategies and highlight key findings and unresolved questions. Notable discoveries include the importance of specific ion channels, the role of intracellular molecular heterogeneity in capacitation and the acrosome reaction, and the impact of pH changes on acrosomal exocytosis. Ultimately, this review underscores the crucial importance of mathematical frameworks in advancing our understanding of sperm physiology and guiding future experimental investigations.

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Modelling dynamic processes in yeast

Klipp

2007

Yeast

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Yeast molecular and cell biology has accumulated large amounts of qualitative and quantitative data of diverse cellular processes. The results are often summarized as verbal or graphical descriptions. Moreover, a series of mathematical models has been developed that should help to interpret such data, to integrate them into a coherent picture and to allow for an understanding of the underlying processes. Dynamic modelling of regulatory processes in yeast focuses on central carbon metabolism, on a number of selected signalling pathways and on cell cycle regulation. These models can explain questions of general relevance, such as whether the dynamics of a network can be understood from the combination of in vitro kinetics of its individual reactions. They help to elucidate complicated dynamic features, such as glycolytic oscillations, effects of feedback regulation or the optimal regulation of gene expression. The availability of comprehensive qualitative information, such as protein interactions or pathway composition, and sets of quantitative data make yeast a perfect model organism. Therefore, yeast-related data are often used to develop and examine computational approaches and modelling methods.

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Inferring genetic regulatory logic from expression data

Cited by 51 publications

References 17 publications

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Reviewing mathematical models of sperm signaling networks

Modelling dynamic processes in yeast

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