Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli‐Harja, Olli

doi:10.1016/j.sigpro.2005.06.008

Cited by 108 publications

(60 citation statements)

References 42 publications

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“…In particular, we note that two of the most popular gene regulatory network models, PBNs and Dynamic Bayesian Networks (DBNs) can be modeled as Markov chains (Lähdesmäkia et al, 2006). In this article, we consider the Human melanoma gene regulatory network, which is one of the most studied gene regulatory networks in the literature (Datta et al, 2007;Pal et al, 2006;Qian and Dougherty, 2008).…”

Section: Optimal Intervention In the Human Melanoma Gene Regulatory Nmentioning

confidence: 99%

Inverse perturbation for optimal intervention in gene regulatory networks

2010

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Motivation: Analysis and intervention in the dynamics of gene regulatory networks is at the heart of emerging efforts in the development of modern treatment of numerous ailments including cancer. The ultimate goal is to develop methods to intervene in the function of living organisms in order to drive cells away from a malignant state into a benign form. A serious limitation of much of the previous work in cancer network analysis is the use of external control, which requires intervention at each time step, for an indefinite time interval. This is in sharp contrast to the proposed approach, which relies on the solution of an inverse perturbation problem to introduce a one-time intervention in the structure of regulatory networks. This isolated intervention transforms the steadystate distribution of the dynamic system to the desired steady-state distribution. Results: We formulate the optimal intervention problem in gene regulatory networks as a minimal perturbation of the network in order to force it to converge to a desired steady-state distribution of gene regulation. We cast optimal intervention in gene regulation as a convex optimization problem, thus providing a globally optimal solution which can be efficiently computed using standard toolboxes for convex optimization. The criteria adopted for optimality is chosen to minimize potential adverse effects as a consequence of the intervention strategy. We consider a perturbation that minimizes (i) the overall energy of change between the original and controlled networks and (ii) the time needed to reach the desired steady-state distribution of gene regulation. Furthermore, we show that there is an inherent trade-off between minimizing the energy of the perturbation and the convergence rate to the desired distribution. We apply the proposed control to the human melanoma gene regulatory network. Availability: The MATLAB code for optimal intervention in gene regulatory networks can be found online: http://syen.ualr.edu/ nxbouaynaya/Bioinformatics2010.html.

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Section: Optimal Intervention In the Human Melanoma Gene Regulatory Nmentioning

confidence: 99%

Inverse perturbation for optimal intervention in gene regulatory networks

2010

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“…In addition, Ching et al also developed an expected error bound for their approximation. Given the close relationship between DBNs and PBNs (Lähdesmäki et al 2006), this method can be directly used in the context of DBNs as well. Assessment of possible advantages of the approximate method and error bound of Ching et al (2007) in our application will be left for future studies.…”

Section: Discussionmentioning

confidence: 99%

“…DBNs and their non-temporal versions, i.e., static Bayesian networks (BN), have successfully been used in different modeling problems, such as in speech recognition, target tracking and identification, genetics, probabilistic expert systems, and medical diagnostic systems (see, e.g., Cowell et al 1999, and the references therein). Recently, BNs and DBNs have also been intensively studied in the context of modeling genomic regulation, see, e.g., (Hartemink et al 2001(Hartemink et al , 2002Husmeier 2003;Imoto et al 2003;Friedman 2004;Pournara and Wernisch 2004;Sachs et al 2005;Bernard and Hartemink 2005;Werhli et al 2006;Lähdesmäki et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Lähdesmäki

Shmulevich

2008

Mach Learn

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Dynamic Bayesian networks (DBN) are a class of graphical models that has become a standard tool for modeling various stochastic time-varying phenomena. In many applications, the primary goal is to infer the network structure from measurement data. Several efficient learning methods have been introduced for the inference of DBNs from time series measurements. Sometimes, however, it is either impossible or impractical to collect time series data, in which case, a common practice is to model the non-time series observations using static Bayesian networks (BN). Such an approach is obviously sub-optimal if the goal is to gain insight into the underlying dynamical model. Here, we introduce Bayesian methods for the inference of DBNs from steady state measurements. We also consider learning the structure of DBNs from a combination of time series and steady state measurements. We introduce two different methods: one that is based on an approximation and another one that provides exact computation. Simulation results demonstrate that dynamic network structures can be learned to an extent from steady state measurements alone and that inference from a combination of steady state and time series data has the potential to improve learning performance relative to the inference from time series data alone. Keywords

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“…The PBN formalism has been used to construct networks in the context of several cancer studies, including glioma [42], melanoma [41], and leukemia [40]. PBNs, which are stochastic rule-based models, bear a close relationship to dynamic Bayesian networks [43] -a popular model class for representing the dynamics of gene expression.…”

Section: Statistical Influence Networkmentioning

confidence: 99%

Biochemical and statistical network models for systems biology

Price

Shmulevich

2007

Current Opinion in Biotechnology

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IntroductionThe creation of models of the integrated functions of genes and proteins in cells is of fundamental and immediate importance to the emerging field of computational systems biology. Some of the most successful attempts at cell-scale modeling to date have been based on piecing together networks that represent hundreds of experimentally-determined biochemical interactions, while others have been very successful at inferring statistical networks from large amounts of high-throughput data. These networks (metabolic, regulatory, or signaling) can be analyzed, and predictions about cellular behavior made and tested. Many types of models have been built and applied to study cellular behavior and in this review we focus on two broad types: biochemical network models and statistical inference models. Through iterative model prediction, experimentation, and network refinement, the molecular circuitry and functions of biological networks can be elucidated. The construction of genomescale models that integrate the myriad components that produce cellular behavior is a fundamental goal of systems biology today. Biochemical Reaction NetworksBiochemical reaction networks represent the underlying chemistry of the system, and thus at a minimum represent stoichiometric relationships between inter-converted biomolecules. The stoichiometry of biochemical reaction networks can now be reconstructed at the genome-scale, and at smaller scale with sufficient detail to generate kinetic models. These biochemical reaction networks represent many years of accumulated experimental data and can be interrogated in silico to determine their functional states. Genome-scale models based on biochemical networks provide a comprehensive, yet concise, description of cellular functions.For metabolism the reconstruction of the biochemical reaction network is a well-established procedure [1][2][3][4][5][6][7], while methods for the reconstruction of the associated regulatory [8,9] and signaling networks [10][11][12] with stoichiometric detail are being developed. The typically used formalism is to reconstruct the stoichiometric matrix, where each row represents a molecular compound and each column represents a reaction. For metabolism, these networks are often focused on just the metabolites, where the existence of a protein that catalyzes this reaction is used to allow that reaction to be present in the network. It is also possible (and truer to the realities in the system) to represent the proteins themselves as compounds in the network, which enables the integration of proteomics, metabolomics, and flux data (Figure 1). For regulatory and signaling networks, the inclusion of the proteins as compounds is essential. This process * To whom correspondence should be addressed: ishmulevich@sytemsbiology.org. + Present Address: Department of Chemical and Biomolecular Engineering University of Illinois, Urbana-Champaign Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our c...

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Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

Cited by 108 publications

References 42 publications

Inverse perturbation for optimal intervention in gene regulatory networks

Inverse perturbation for optimal intervention in gene regulatory networks

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Biochemical and statistical network models for systems biology

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