Identification of genetic network dynamics with unate structure

Porreca, Riccardo; Cinquemani, Eugenio; Lygeros, John; Ferrari-Trecate, Giancarlo

doi:10.1093/bioinformatics/btq120

Cited by 28 publications

(66 citation statements)

References 32 publications

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“…This first stage is very similar to our approach, in the sense that it separates the issues of network topology and the functional/parametric representation of the dynamics. However, our approach differs from the one in [17] in that, (i) we propose a discrete-time model structure that is directly derived from the time series data, (ii) we prove that the conditions that we use to reject some network topologies are both necessary and sufficient (see Theorem 4), while in [17], no such result is derived, (iii) we present some theoretical analysis on irrefutable models and data.…”

Section: Introductionmentioning

confidence: 94%

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Genetic regulatory network identification using multivariate monotone functions

Cooper

Belta

Julius

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-We present a method for identification of gene regulatory network topology using a time series of gene expression data. The underlying assumption is that the regulatory effects of a set of regulators to a gene can be described by a multivariate function. The multivariate function is constrained to be continuous, nonnegative and monotonic in each variable. We present necessary and sufficient conditions for the validity of the regulation hypothesis. Checking these conditions can be expressed as a Linear Programming feasibility problem. This paper builds on our previous work, where the regulation is described by a summation of multiple regulator functions, one function for each gene in the regulator set. Our procedure is two phased; the first identifies the correct set of regulators, the second uses the data and the regulator set to generate an appropriate regulator function. This paper focuses on the identification of the correct regulator set. As demonstration, we run our main algorithm on some experimental data from a synthetic gene network in yeast. We are able to show that the correct set of regulators is picked by the algorithm.

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Section: Introductionmentioning

confidence: 94%

“…A recent work by Porreca et al, published earlier in [17], proposed a two-staged process in identifying a continuoustime differential equation model for GRNs. In the first stage, network topologies that are inconsistent with the data are rejected.…”

Section: Introductionmentioning

confidence: 99%

Genetic regulatory network identification using multivariate monotone functions

Cooper

Belta

Julius

2011

IEEE Conference on Decision and Control and European Control Conference

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“…An approach that preserves the form of Boolean-like kinetic models, accounts for sigmoidal activation functions and avoids parsing all possible model structures was proposed in [1] (see also [16]). The method relies on the use of unate functions [17], a class of Boolean gene activation rules that appears to be a comprehensive description of the observable interactions among genes [18].…”

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confidence: 99%

“…In this paper we investigate the applicability of the identification algorithm [1] to the common situation where only protein concentration measurements are available over time. Based on the standard biological assumption that protein degradation rates are known, we introduce a preliminary step where the missing information is reconstructed from the available data.…”

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confidence: 99%

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Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

Porreca

Cinquemani

Lygeros

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [1] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae. Abstract-We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [1] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae.

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Invalidation of the structure of genetic network dynamics: a geometric approach

Porreca

Cinquemani

Lygeros

et al. 2012

Intl J Robust & Nonlinear

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International audienceThis work concerns the identification of the structure of a genetic network model from measurements of gene product concentrations and synthesis rates. In earlier work, we developed a data preprocessing algorithm that is able to reject many hypotheses on the network structure by testing certain monotonicity properties for a wide family of network models. Here, we develop a geometric interpretation of the method. Then, for a relevant subclass of genetic network models, we extend our approach to the combined testing of monotonicity and convexity-like properties associated with the network structures. The theoretical aspects and practical performance of the enhanced methods are illustrated by way of numerical results

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Identification of genetic network dynamics with unate structure

Cited by 28 publications

References 32 publications

Genetic regulatory network identification using multivariate monotone functions

Genetic regulatory network identification using multivariate monotone functions

Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

Invalidation of the structure of genetic network dynamics: a geometric approach

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