Biomedical subjects

Daniel E Zak

Publications and source records attributed to Daniel E Zak.

5 recordsLinked to original sources

PubMed2006

Systems analysis of circadian time-dependent neuronal epidermal growth factor receptor signaling.

BACKGROUND: Identifying the gene regulatory networks governing physiological signal integration remains an important challenge in circadian biology. Epidermal growth factor receptor (EGFR) has been implicated in circadian function and is expressed in the suprachiasmatic nuclei (SCN), the core circadian pacemaker. The transcription networks downstream of EGFR in the SCN are unknown but, by analogy to other SCN inputs, we expect the response to EGFR activation to depend on circadian timing. RESULTS: We have undertaken a systems-level analysis of EGFR circadian time-dependent signaling in the SCN. We collected gene-expression profiles to study how the SCN response to EGFR activation depends on circadian timing. Mixed-model analysis of variance (ANOVA) was employed to identify genes with circadian time-dependent EGFR regulation. The expression data were integrated with transcription-factor binding predictions through gene group enrichment analyses to generate robust hypotheses about transcription-factors responsible for the circadian phase-dependent EGFR responses. CONCLUSION: The analysis results suggest that the transcriptional response to EGFR signaling in the SCN may be partly mediated by established transcription-factors regulated via EGFR transcription-factors (AP1, Ets1, C/EBP), transcription-factors involved in circadian clock entrainment (CREB), and by core clock transcription-factors (Ror alpha). Quantitative real-time PCR measurements of several transcription-factor expression levels support a model in which circadian time-dependent EGFR responses are partly achieved by circadian regulation of upstream signaling components. Our study suggests an important role for EGFR signaling in SCN function and provides an example for gaining physiological insights through systems-level analysis.

Animals↗

PubMed2005

Modeling the VPAC2-activated cAMP/PKA signaling pathway: from receptor to circadian clock gene induction.

Increasing evidence suggests an important role for VPAC2-activated signal transduction pathways in maintaining a synchronized biological clock in the suprachiasmatic nucleus (SCN). Activation of the VPAC2 signaling pathway induces per1 gene expression in the SCN and phase-shifts the circadian clock. Mice without the VPAC2 receptor lack an overt, coherent circadian rhythm in clock gene expression, SCN neuron firing rate, and locomotor behavior. Using a systems approach, we have developed a kinetic model integrating VPAC2 signaling mediated by the cyclic AMP (cAMP)/protein kinase A (PKA) pathway and leading to induced circadian clock gene expression. We fit the model to experimental data from the literature for cAMP accumulation, PKA activation, cAMP-response element binding protein phosphorylation, and per1 induction. By linking the VPAC2 model to a published circadian clock model, we also simulated clock phase shifts induced by vasoactive intestinal polypeptide (VIP) and matched experimental data for the VIP response. The simulated phase response curve resembled the hamster response to a related neuropeptide, GRP1-27, and light. Simulations using pulses of VIP revealed that the system response is extraordinarily robust to input signal duration, a result with physiologically relevant consequences. Lastly, simulations using varied receptor levels matched literature experimental data from animals overexpressing VPAC2 receptors.

Animals↗

PubMed2003

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗

PubMed2003

Continuous-time identification of gene expression models.

One objective of systems biology is to create predictive, quantitative models of the transcriptional regulation networks that govern numerous cellular processes. Gene expression measurements, as provided by microarrays, are commonly used in studies that attempt to infer the regulation underlying these processes. At present, most gene expression models that have been derived from microarray data are based in discrete-time, which have limited applicability to common biological data sets, and may impede the integration of gene expression models with other models of biological processes that are formulated as ordinary differential equations (ODEs). To overcome these difficulties, a continuous-time approach for process identification to identify gene expression models based in ODEs was developed. The approach utilizes the modulating functions method of parameter identification. The method was applied to three simulated systems: (1) a linear gene expression model, (2) an autoregulatory gene expression model, and (3) simulated microarray data from a nonlinear transcriptional network. In general, the approach was well suited for identifying models of gene expression dynamics, capable of accurately identifying parameters for small numbers of data samples in the presence of modest experimental noise. Additionally, numerous insights about gene expression modeling were revealed by the case studies.

Data Interpretation, Statistical↗

PubMed2003

Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

Computational Biology↗