Dynamic modeling of gene expression data

Holter, Neal S.; Maritan, Amos; Cieplak, Marek; Fedoroff, Nina V.; Banavar, Jayanth R.

doi:10.1073/pnas.98.4.1693

Cited by 271 publications

(202 citation statements)

References 26 publications

(48 reference statements)

Supporting

Mentioning

199

Contrasting

Order By: Relevance

“…In an increasing number of complex systems one can experimentally monitor the simultaneous activity of hundreds of channels, examples including multichannel measurement of neural activity on in vivo cell colonies [16], simultaneous monitoring of thousands of gene expression data sets for model organisms, like E. Coli or S. Cerevisae [17], flow fluctuation in river networks [18], price variations in individual stocks or goods [19] or the activity of different processors in parallel computation [20]. The method introduced here represents a systematic tool for extracting information from multiple channel measurements, offering detailed insights into the mechanisms that govern the dynamics of these systems.…”

Section: And Their Ratiomentioning

confidence: 99%

Separating Internal and External Dynamics of Complex Systems

2004

View full text Add to dashboard Cite

The observable behavior of a complex system reflects the mechanisms governing the internal interactions between the system's components and the effect of external perturbations. Here we show that by capturing the simultaneous activity of several of the system's components we can separate the internal dynamics from the external fluctuations. The method allows us to systematically determine the origin of fluctuations in various real systems, finding that while the Internet and the computer chip have robust internal dynamics, highway and Web traffic are driven by external demand. As multichannel measurements are becoming the norm in most fields, the method could help uncover the collective dynamics of a wide array of complex systems.Decades of research has lead to the development of sophisticated tools to analyze time series generated by various dynamical systems, allowing us to extract short and long range temporal correlations, periodic patterns and stationarity information [1,2,3,4]. We lack, however, systematic methods to extract from multiple datasets information not already provided by a single time series. Indeed, advances in computer aided measurement techniques increasingly offer the possibility to separately but simultaneously record the time dependent activity of a system's many components, such as information flow on thousands of Internet routers or highway traffic on numerous highways. As the time dependent activity of each component (router or highway) captures the system's dynamics from a different angle, these parallel time series offer us increasingly complete information about the system's collective behavior. Yet, we have difficulty answering a simple question: How can we uncover from multiple time series a system's internal dynamics?Multiple time series are typically available for complex systems whose dynamics is determined by the interaction of a large number of components that communicate with each other through some complex network [5]. The dynamics of each component is determined by two factors: (1) interactions between the components, governed by some internal dynamical rules that distribute the activity between the various parts of the system and (2) global variations in the overall activity of the system. For example, the traffic increase on highways during peak hours and surges in the number of Internet users during working hours represent global activity changes that have a strong impact on the local activity of each component (highway or router) as well. Different components are influenced to a different degree by these global changes, making impossible for an observer that has access only to a single component to separate the internal dynamics from the externally imposed fluctuations. Most important, the inevitable fluctuations in the external conditions systematically obscure the mechanisms that govern the system's internal dynamics.Here we propose a method to separate in a systematic manner for each time series the external from the internal contributions, and validate it on model ...

show abstract

Section: And Their Ratiomentioning

confidence: 99%

Separating Internal and External Dynamics of Complex Systems

2004

View full text Add to dashboard Cite

show abstract

“…Recently, several methods for links assessment have been proposed, such as linear Markov model (LMM)-based methods (9,10) or correlation-based methods (11)(12)(13). We choose to define links on the basis of the time correlation properties of gene expression measurements.…”

mentioning

confidence: 99%

Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics

Remondini

O’Connell

Intrator

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.complex systems ͉ time series ͉ gene interaction

show abstract

“…2 and 5). Other methods for describing periodic expression trajectories include singular-value decomposition (17)(18)(19), B splines (20), and partial least squares (21). In general, these approaches do not provide the parsimony and biologically interpretable parameters that regression models offer.…”

Section: Discussionmentioning

confidence: 99%

A random-periods model for expression of cell-cycle genes

Liu

Umbach

Peddada

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

We propose a nonlinear regression model for quantitatively analyzing periodic gene expression in studies of experimentally synchronized cells. Our model accounts for the observed attenuation in cycle amplitude by a simple and biologically plausible mechanism. We represent the expression level for each gene as an average across a large number of cells. For a given cell-cycle gene, we model its expression in each cell in the culture as following the same sinusoidal function except that the period, which in any individual cell must be the same for all cell-cycle genes, varies randomly across cells. We model these random periods by using a lognormal distribution. The variability in period causes the measured amplitude of the cyclic expression trajectory to attenuate over time as cells fall increasingly out of synchrony. Gene-specific parameters include initial amplitude and phase angle. Applying the model to data from Whitfield et al. bootstrap test ͉ gene expression ͉ microarray ͉ nonlinear regression E xperimental protocols that arrest cells in vitro at a particular phase of the cell cycle and then release them in a synchronized way allow detailed study of the cycling process. In conjunction with such experiments, cDNA microarray technology allows investigators to assess the temporal expression patterns of thousands of genes simultaneously. Gene-expression studies in yeast (1-3) and in cultured human cells (4, 5) have revealed that expression levels for cell-cycle genes vary periodically and have amplitudes that attenuate through time (Fig. 1). The observed attenuation is generally attributed to cells in the cultures falling increasingly out of synchrony through time.In cultures of homogeneous cells released from a block on cycling, asynchrony can arise from at least two mechanisms. Cells throughout the culture can differ slightly in the exact timing of their arrest or release, or cells can differ slightly in the duration of their individual cycles. The former mechanism leaves asynchrony constant through time, so only the latter mechanism, where asynchrony increases, produces the characteristic attenuation. Such attenuation may hold biologic interest in itself. The rate of attenuation (i.e., the variation in the duration of the cell cycle across cells) may vary by strain of organism or by cell type (e.g., across tumors with differing metastatic potential). Some influences of cell characteristics on the scheduling of the cell cycle are already known. For instance, cell-size distribution in yeast mutants is related to time spent in the late G 1 phase (6).Few methods proposed for the analysis of cell-cycle expression data address attenuation explicitly. For example, several investigators used a sinusoidal template to identify periodically expressed genes but did not explicitly account for attenuation (2, 5). The basic single-pulse model (SPM) proposed by Zhao et al. (7) addresses asynchrony by assuming that the cell-specific timings with which individual cells in a culture reach a given observation point have a norma...

show abstract

Dynamic modeling of gene expression data

Cited by 271 publications

References 26 publications

Separating Internal and External Dynamics of Complex Systems

Separating Internal and External Dynamics of Complex Systems

Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics

A random-periods model for expression of cell-cycle genes

Contact Info

Product

Resources

About