scholarly journals High-throughput Methods for Dissection of Trypanosome Gene Regulatory Networks

2018 ◽  
Vol 19 (2) ◽  
Author(s):  
Esteban D. Erben
Keyword(s):  
High Throughput ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

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.

scholarly journals Cell-free gene-regulatory network engineering with synthetic transcription factors

2019 ◽  
Vol 116 (13) ◽  
pp. 5892-5901 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

Gene-regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein–protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here, we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene-regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene-regulatory networks and for studying the biophysics of transcriptional regulation.

Abstraction Methods for Analysis of Gene Regulatory Networks

2010 ◽  
pp. 352-385
Author(s):  
Hiroyuki Kuwahara ◽  
Chris J. Myers
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Experimental Work ◽  
Stochastic Analysis ◽  
Regulatory Networks ◽  
Model Abstraction ◽  
Wet Lab ◽  
Sheer Size ◽  
Gene Regulatory

With advances in high throughput methods of data collection for gene regulatory networks, we are now in a position to face the challenge of elucidating how these genes coupled with environmental stimuli orchestrate the regulation of cell-level behaviors. Understanding the behavior of such complex systems is likely impossible to achieve with wet-lab experiments alone due to the amount and complexity of the data being collected. Therefore, it is essential to integrate the experimental work with efficient and accurate computational methods for analysis. Unfortunately, such analysis is complicated not only by the sheer size of the models of interest but also by the fact that gene regulatory networks often involve small molecular counts making discrete and stochastic analysis necessary. To address this problem, this chapter presents a model abstraction methodology which systematically performs various model abstractions to reduce the complexity of computational biochemical models resulting in substantial improvements in analysis time with limited loss in accuracy.

scholarly journals Cell-free gene regulatory network engineering with synthetic transcription factors

2018 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

AbstractGene regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein – protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription factor binding energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene regulatory networks and for studying the biophysics of transcriptional regulation.

Methods for Reverse Engineering of Gene Regulatory Networks

2009 ◽  
pp. 497-515
Author(s):  
Hendrik Hache
Keyword(s):  
Experimental Data ◽  
Reverse Engineering ◽  
Computational Methods ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are discussed and compared. Inferring gene networks from different kinds of experimental data are a challenging task that emerged, especially with the development of high throughput technologies. Various computational methods based on diverse principles were introduced to identify new regulations among genes. Mathematical aspects of the models are highlighted, and applications for reverse engineering are mentioned.

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