Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices

Kamalabad, Mahdi Shafiee; Heberle, Alexander Martin; Thedieck, Kathrin; Grzegorczyk, Marco

doi:10.1093/bioinformatics/bty917

Cited by 11 publications

(10 citation statements)

References 30 publications

(44 reference statements)

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“…In recent works alternative model refinements have been proposed [ 7 , 8 ]. These models distinguish coupled from uncoupled network edges rather than distinguishing coupled from uncoupled time segments.…”

Section: Introductionmentioning

confidence: 99%

“…These models distinguish coupled from uncoupled network edges rather than distinguishing coupled from uncoupled time segments. The partially non-homogeneous model from Shafiee Kamalabad et al [ 7 ] builds on the idea that only some network parameters (i.e some edges) might be subject to changes, while other network parameters (i.e. edges) might stay constant.…”

Section: Introductionmentioning

confidence: 99%

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A new Bayesian piecewise linear regression model for dynamic network reconstruction

Kamalabad

Grzegorczyk

2021

BMC Bioinformatics

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Background Linear regression models are important tools for learning regulatory networks from gene expression time series. A conventional assumption for non-homogeneous regulatory processes on a short time scale is that the network structure stays constant across time, while the network parameters are time-dependent. The objective is then to learn the network structure along with changepoints that divide the time series into time segments. An uncoupled model learns the parameters separately for each segment, while a coupled model enforces the parameters of any segment to stay similar to those of the previous segment. In this paper, we propose a new consensus model that infers for each individual time segment whether it is coupled to (or uncoupled from) the previous segment. Results The results show that the new consensus model is superior to the uncoupled and the coupled model, as well as superior to a recently proposed generalized coupled model. Conclusions The newly proposed model has the uncoupled and the coupled model as limiting cases, and it is able to infer the best trade-off between them from the data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new Bayesian piecewise linear regression model for dynamic network reconstruction

Kamalabad

Grzegorczyk

2021

BMC Bioinformatics

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“…The tools and methodologies in theoretical biology are as diverse as in the experimental life sciences and they are constantly developing according to the specific biological problems that are being investigated. For instance, theoreticians develop new ways to deal with noisy data [ 7 , 8 ] or non-equidistant dynamic measurements [ 9–11 ]. Likewise, experimentalists develop new methods to satisfy the demand for higher quantitative accuracy [ 12–14 ] enabling in turn new modeling approaches [ 12 , 15–17 ] relying e.g.…”

Section: Introductionmentioning

confidence: 99%

Finding new edges: systems approaches to MTOR signaling

Heberle

Rehbein

Peiris

et al. 2021

Biochemical Society Transactions

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Cells have evolved highly intertwined kinase networks to finely tune cellular homeostasis to the environment. The network converging on the mechanistic target of rapamycin (MTOR) kinase constitutes a central hub that integrates metabolic signals and adapts cellular metabolism and functions to nutritional changes and stress. Feedforward and feedback loops, crosstalks and a plethora of modulators finely balance MTOR-driven anabolic and catabolic processes. This complexity renders it difficult — if not impossible — to intuitively decipher signaling dynamics and network topology. Over the last two decades, systems approaches have emerged as powerful tools to simulate signaling network dynamics and responses. In this review, we discuss the contribution of systems studies to the discovery of novel edges and modulators in the MTOR network in healthy cells and in disease.

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“…In our recent work ( Shafiee Kamalabad et al , 2019 ), we have proposed a partially non-homogeneous DBN for learning networks from a collection of datasets that have been measured under different experimental conditions. The model assumes the data segmentation to be known (one segment per condition), and then treats the segments as interchangeable units.…”

Section: Introductionmentioning

confidence: 99%

Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters

Kamalabad

Grzegorczyk

2019

Bioinformatics

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Motivation Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning networks with time-varying interaction parameters. A multiple changepoint process is used to divide the data into disjoint segments and the network interaction parameters are assumed to be segment-specific. The objective is to infer the network structure along with the segmentation and the segment-specific parameters from the data. The conventional (uncoupled) NH-DBNs do not allow for information exchange among segments, and the interaction parameters have to be learned separately for each segment. More advanced coupled NH-DBN models allow the interaction parameters to vary but enforce them to stay similar over time. As the enforced similarity of the network parameters can have counter-productive effects, we propose a new consensus NH-DBN model that combines features of the uncoupled and the coupled NH-DBN. The new model infers for each individual edge whether its interaction parameter stays similar over time (and should be coupled) or if it changes from segment to segment (and should stay uncoupled). Results Our new model yields higher network reconstruction accuracies than state-of-the-art models for synthetic and yeast network data. For gene expression data from A. thaliana our new model infers a plausible network topology and yields hypotheses about the light-dependencies of the gene interactions. Availability Data are available from earlier publications. Matlab code is available at Bioinformatics online. Supplementary information A supplementary paper is available at Bioinformatics online.

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Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices

Cited by 11 publications

References 30 publications

A new Bayesian piecewise linear regression model for dynamic network reconstruction

A new Bayesian piecewise linear regression model for dynamic network reconstruction

Finding new edges: systems approaches to MTOR signaling

Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters

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