A model-based optimization framework for the inference on gene regulatory networks from DNA array data

Vassily Hatzimanikatis, Eleftherios Papoutsakis
2004
Journal paper

Abstract

Identification of the regulatory structures in genetic networks and the formulation of mechanistic models in the form of wiring diagrams is one of the significant objectives of expression profiling using DNA microarray technologies and it requires the development and application of identification frameworks. Authors have developed a novel optimization framework for identifying regulation in a genetic network using the S-system modeling formalism. It has been shown that balance equations on both mRNA and protein species led to a formulation suitable for analyzing DNA-microarray data whereby protein concns. have been eliminated and only mRNA relative concns. are retained. Using this formulation, authors examd. if it is possible to infer a set of possible genetic regulatory networks consistent with obsd. mRNA expression patterns. Two origins of changes in mRNA expression patterns were considered. One derives from changes in the biophys. properties of the system that alter the mol.-interaction kinetics and/or message stability. The second is due to gene knock-outs. Authors reduced the identification problem to an optimization problem (of the so-called mixed-integer non-linear programming class) and developed an algorithmic procedure for solving this optimization problem. Using simulated data generated by our math. model, it has been shown that the method can actually find the regulatory network from which the data were generated. Authors also show that the no. of possible alternate genetic regulatory networks depends on the size of the dataset (i.e. no. of expts.), but this dependence is different for each of the two types of problems considered, and that a unique soln. requires fewer datasets than previously estd. in the literature. This is the first method that also allows the identification of every possible regulatory network that could explain the data, when the no. of expts. does not allow identification of unique regulatory structure. [on SciFinder (R)]

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Vassily Hatzimanikatis, Eleftherios Papoutsakis
2004
Journal paper

Abstract

Official source

https://infoscience.epfl.ch/record/98655?ln=en

About this result

Related concepts (21)

A gene (or genetic) regulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA

A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its environment, is described by its

A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of

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Related publications (37)

Related publications

Kinetic Analysis of Transcriptional and Post-Transcriptional Processes During Circadian Cycles

Laura Symul

Every morning the sun rises, flooding the earth with its light and heat and every night it disappears leaving the Earth in the darkness of the night. Organisms living on this planet have adapted to this solar rhythm and have spread their activities throughout the day. Early in the development of life, an internal molecular clock appeared in primitive organisms and the evolutionary advantage of being able to anticipate, rather than simply passively face, environmental changes led virtually all organisms to have an internal timing system called "circadian clock." In most species, this clock is constituted of a genetic network ticking in every cell of the body. This genetic network organized in interlocked feedback loops generates oscillations with a 24-hour period. A variable number of proteins and RNAs is thus accumulating in a rhythmic fashion. For example, in mouse liver, which was the main biological model I used during my thesis, it is estimated that thousands of genes are expressed periodically. The molecular processes leading to rhythmic gene expression and protein accumulation are highly regulated. Dynamical control is needed to sustain the oscillations and to maintain their timing. During my PhD, I was particularly interested in the regulation of rhythmic gene activation via transcription factor binding in their promoter region. I also focused on gene transcription itself and its dynamics as well as on transcription associated changes in epigenetic marks. Another interest was given to the regulation of transcripts (messenger RNA) after transcription, especially to understand whether their degradation in the cytoplasm was regulated rhythmically. Finally, I contributed to a project aiming at the detection of transcripts whose translation into protein was specifically regulated in a circadian manner. The four projects on which I worked were done in collaborations with experimental biology labs. Our partners performed the experiments and provided the biological material while I was in charge of the data processing and their statistical analysis. For some projects, I also developed different mathematical models for a more accurate and complete understanding of the phenomena studied and to take into account the inherent variability of biological data. Notably in the project on the post-transcriptional regulation of transcripts, the model-based approach we developed allowed estimating the role of this regulation in a way that is less prone to arbitrary choice of criteria or thresholds compared to previous studies addressing this question. To summarize, the results I obtained show that, in human cell lines, the major transcription factor of the clock (the heterodimer CLOCK/BMAL1) binds to DNA at specific binding sites and their number is comparable to the number of binding sites observed in the mouse liver. However the fraction of rhythmic transcript among the genes with CLOCK/BMAL1 binding sites is about 10 times lower than the corresponding fraction observed in mouse liver. The results of the second project showed that, in mouse liver, rhythmic transcription is followed by dynamical changes in epigenetic marks with delays specific to the marks (tri-methylation of lysines 4 and 36 of histone 3 of nucleosomes) and we highlighted the role of post-transcriptional regulation for the rhythmic expression of a subset of mRNAs. In the third project, we further investigated the regulation at the post-transcriptional level and combining measurements of pre-mRNA and mRNA with a mathematical model, we showed that the post-transcriptional regulation plays a limited role in the rhythmic accumulation of transcripts and is involved in the regulation of only 20 % of rhythmic transcripts. Laboratories of Michael Brunner (University of Heidelberg, Germany), Frederic Gachon, Nouria Hernandez (University of Lausanne, Switzerland) and Ueli Schibler (University of Geneva, Switzerland) 3 Finally, helped by the analysis I performed on micro array data from an experiment aiming at comparing the fraction of translated mRNA to the total accumulation of these mRNAs, the team of Frederic Gachon showed that several transcripts implied in the composition of ribosomes are translated specifically during the night, when rodents are active. All of these results and their links with other published studies on circadian rhythms, show that biological tissues have largely incorporated the clock for the regulation of many other functional pathways in order to optimally synchronize their physiological actions with environmental changes due to the alternation of days and nights. In mouse liver, links with the metabolism are particularly strong and disruptions of the clock induce phenotypes reflecting a disturbed metabolism.

EPFL2013

Development and application of experimental tools to elucidate the gene regulatory network underlying adipogenesis

Carine Delattre-Gubelmann

Excess fat mass in obese individuals, which is characterized by an increase of white adipocyte cell size and number, significantly raises the risk of developing metabolic syndrome symptoms as well as cancer. A detailed understanding of the transcriptional mechanisms mediating white adipocyte differentiation (i.e., adipogenesis) is required to counteract the growing obesity epidemics. In this thesis, we have implemented two combinatory approaches to detect new protein-DNA interactions in the mouse, one of which targets the adipogenic gene regulatory network (GRN) that underlies the white adipocyte differentiation process. As a first approach to shed light on GRNs, we have developed and validated a powerful approach that combines a high-throughput yeast one-hybrid-based (Y1H) system to detect transcription factors (TFs)-DNA element interactions, together with a novel microfluidics-based technique, enabling the identification and characterization of individual binding sites for detected TFs within the respective regulatory sequences at unprecedented throughput and resolution. Using well-described regulatory elements as well as an uncharacterized enhancer which can play also a role in adipogenesis, we show that our approach in a first phase uncovers many known as well as novel TF-DNA interactions, of which a large proportion were independently validated. In addition, we further demonstrate how our approach can be used in a second phase to characterize detected interactions by enabling the generation of a relative DNA occupancy landscape over the length of the respective DNA element. Furthermore, we provide evidence that this system enables the detection of novel interactions that may have in vivo relevance. Given the strict requirements for direct and well-characterized protein-DNA interaction data to generate and model gene regulatory networks, the presented two-tiered approach should be of great value to enhance our understanding of the regulatory principles underlying mouse gene expression. To identify transcription factors involved in white adipocyte differentiation, we overexpressed TFs in preadipocytes and quantified their effect on differentiation. To this end, we have created a mouse open-reading frame resource consisting of more than 750 fully sequence-verified TF clones. We transferred these TF clones to a Tet-On lentiviral vector and used them to infect white preadipocytes. Based on microscopy-assisted quantification of lipids after induction of differentiation, we identified putatively novel TF candidates that strongly enhanced differentiation based on the increased lipid accumulation, indicating a potential role in adipogenesis. Gene expression and ChIP-seq experiments have been performed for the three top candidates (ZEB1, ZFP30 and ZFP277) to further study their function within the adipogenic regulatory network. To assess whether the effect of these TFs on adipogenesis could be reproduced in vivo, murine adipose stromal-vascular fraction containing adipocyte precursor cells were implanted into mice fed a high-fat diet, after each of the three TF candidates were knockdown or overexpressed in these cells. This experiment and the RNA-seq analyses are currently being processed while this thesis is being written. Furthermore, due to the absence of quantitative real-time PCR (qPCR) primer designing tools that would allow for gene-specific expression profiling in a high-throughput fashion, we design a user-friendly platform: GETPrime. This platform uniquely combines and automates several features critical to optimal qPCR primer design for all annotated Homo sapiens, Mus musculus, Caenorhabditis elegans, Drosophila melanogaster and Danio rerio genes in the Ensembl database. GETPrime primers have been extensively validated experimentally, demonstrating their high quality as well as high transcript specificity in complex samples.

EPFL2013

Background: Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks. Results: We use an S-system based model for the transcription and translation process. We propose an optimizationbased regulatory network inference approach that uses time-varying data from DNA microarray analysis. Currently, this seems to be the only model-based method that can be used for the analysis of time-course "relative" expressions (expression ratios). We perform an analysis of the dynamic behavior of the system when the number of experimental samples available is varied, when there are different levels of noise in the data and when there are genes that are not considered by the experimenter. Our studies show that the principal factor affecting the ability of a method to infer interactions correctly is the similarity in the time profiles of some or all the genes. The less similar the profiles are to each other the easier it is to infer the interactions. We propose a heuristic method for resolving networks and show that it displays reasonable performance on a synthetic network. Finally, we validate our approach using real experimental data for a chosen subset of genes involved in the sporulation cascade of Bacillus anthracis. We show that the method captures most of the important known interactions between the chosen genes. Conclusion: The performance of any inference method for regulatory interactions between genes depends on the noise in the data, the existence of unknown genes affecting the network genes, and the similarity in the time profiles of some or all genes. Though subject to these issues, the inference method proposed in this paper would be useful because of its ability to infer important interactions, the fact that it can be used with time-course DNA microarray data and because it is based on a non-linear model of the process that explicitly accounts for the regulatory role of proteins.

2007

Kinetic Analysis of Transcriptional and Post-Transcriptional Processes During Circadian Cycles

Laura Symul

EPFL2013

Development and application of experimental tools to elucidate the gene regulatory network underlying adipogenesis

Carine Delattre-Gubelmann

EPFL2013

2007