Ranked prediction of p53 targets using hidden variable dynamic modeling

Barenco, Martino; Daniela, Tomescu; Brewer, Daniel; Callard, Robin E.; Stark, Jaroslav; Hubank, Michael

doi:10.1186/gb-2006-7-3-r25

Cited by 101 publications

(59 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This information cannot readily be obtained from comparing the expression data from just two conditions, normal and γ-irradiation [34] for instance (Figure 4-C). Recent single cell measurements with high temporal resolution observed p53 pulses with fixed amplitude and duration, suggesting an on/off rapid switching in the p53 dynamics [35-37].…”

Section: Resultsmentioning

confidence: 99%

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

Liu

Chan

2011

BMC Genomics

View full text Add to dashboard Cite

BackgroundAn important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.ResultsWe propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.ConclusionsOur mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

show abstract

Section: Resultsmentioning

confidence: 99%

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

Liu

Chan

2011

BMC Genomics

View full text Add to dashboard Cite

show abstract

“…Our approach has some intrinsic limitations, as a number of genes requiring basal Trp63 expression may not be significantly affected by TRP63 overexpression. Recently, another approach has been proposed for direct target identification, but it requires prior information on a subset of target genes, it does not take into account the effect of other genes, and was not extensively validated (Barenco et al 2006). An integrative approach to infer a transcriptional network using both ChIP-chip and steady-state expression data in wild-type and knockout cells has been applied to investigate a DNA damage response network in yeast (Workman et al 2006).…”

Section: Discussionmentioning

confidence: 99%

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

Gatta¹,

Bansal²,

Ambesi-Impiombato³

et al. 2008

Genome Res.

View full text Add to dashboard Cite

Genome-wide identification of bona-fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists of measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel "reverse-engineering" algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of TRP63, a crucial regulator of skin development. TSNI-predicted TRP63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify TRP63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that TRP63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by TRP63 through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription-factor binding sites.

show abstract

“…Because they are extensively studied, considerable large-scale functional screening data exist for these examples. But while a growing number of studies report detailed and time-resolved analyses of regulatory and signalling processes [4,5], mapping these temporally changing networks systematically remains a major and increasingly pressing challenge.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference of the time-varying structure of gene-regulation networks

et al. 2010

View full text Add to dashboard Cite

BackgroundBiological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.MethodsTo overcome these limitations, we propose here a new statistical modelling framework, the ARTIVA formalism (Auto Regressive TIme VArying models), and an associated inferential procedure that allows us to learn temporally varying gene-regulation networks from biological time-course expression data. ARTIVA simultaneously infers the topology of a regulatory network and how it changes over time. It allows us to recover the chronology of regulatory associations for individual genes involved in a specific biological process (development, stress response, etc.).ResultsWe demonstrate that the ARTIVA approach generates detailed insights into the function and dynamics of complex biological systems and exploits efficiently time-course data in systems biology. In particular, two biological scenarios are analyzed: the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning.ConclusionsARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.

show abstract

Ranked prediction of p53 targets using hidden variable dynamic modeling

Cited by 101 publications

References 45 publications

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

Statistical inference of the time-varying structure of gene-regulation networks

Contact Info

Product

Resources

About