Inferring Boolean functions via higher-order correlations

Maucher, Markus; Kracht, David; Schober, Steffen; Bossert, Martin; Kestler, Hans A.

doi:10.1007/s00180-012-0385-2

Cited by 10 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various types of algorithms to extract knowledge about the dependencies of the regulatory factors from time-series data. These algorithms are based on correlation [ 52 ] and Fourier transformation [ 53 ]. An algorithm to infer Boolean functions from binarized time-series data was given by Lähdismäki et al [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Schwab

Siegle

Kühlwein

et al. 2017

Biology

View full text Add to dashboard Cite

Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF-κB signaling is a crucial pathway in the process of aging. The NF-κB system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes—for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF-κB signaling in aged organisms in comparison to young ones.

show abstract

Section: Methodsmentioning

confidence: 99%

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Schwab

Siegle

Kühlwein

et al. 2017

Biology

View full text Add to dashboard Cite

show abstract

“…Boolean networks are one kind of dynamic models based on two-valued logic. Boolean networks can be modeled manually by extraction of Boolean functions from literature resources or inferred automatically from time-series data (Lähdesmäki et al, 2003 ; Maucher et al, 2011 , 2012 ; Hopfensitz et al, 2012 ). Simulation of Boolean networks allows for studying various dynamic network properties of the investigated systems.…”

Section: Introductionmentioning

confidence: 99%

Automatic Screening for Perturbations in Boolean Networks

Schwab

Kestler

2018

Front. Physiol.

Self Cite

View full text Add to dashboard Cite

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior—so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

show abstract

“…Boolean networks (BNs) [4] as models have received much attention in reconstructing GRNs from gene expression data sets [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Many algorithms are computationally expensive, with their complexities on the order of O(N · n k+1 ), as summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Maucher et al [16] proposed an efficient method with a time complexity of O(N · n 2 ). However, this method, as well as its improvement in [17], is only applicable to BNs containing only monotonic Boolean functions, and its specificity is unsatisfactory when the sample size is small. In our work [21], we introduced the Discrete Function Learning (DFL) algorithm with the expected complexity of O(k · (N + log n) · n 2 ) for reconstructing qualitative models of GRNs from gene expression datasets.…”

Section: Introductionmentioning

confidence: 99%

Improved Time Complexities for Learning Boolean Networks

Zheng

Kwoh

2013

Entropy

View full text Add to dashboard Cite

Existing algorithms for learning Boolean networks (BNs) have time complexities of at least O(N · n 0.7(k+1) ), where n is the number of variables, N is the number of samples and k is the number of inputs in Boolean functions. Some recent studies propose more efficient methods with O(N · n 2 ) time complexities. However, these methods can only be used to learn monotonic BNs, and their performances are not satisfactory when the sample size is small. In this paper, we mathematically prove that OR/AND BNs, where the variables are related with logical OR/AND operations, can be found with the time complexity of O(k·(N + logn)·n 2 ), if there are enough noiseless training samples randomly generated from a uniform distribution. We also demonstrate that our method can successfully learn most BNs, whose variables are not related with exclusive OR and Boolean equality operations, with the same order of time complexity for learning OR/AND BNs, indicating our method has good efficiency for learning general BNs other than monotonic BNs. When the datasets are noisy, our method can still successfully identify most BNs with the same efficiency. When compared with two existing methods with the same settings, our method achieves a better comprehensive performance than both of them, especially for small training sample sizes. More importantly, our method can be used to learn all BNs. However, of the two methods that are compared, one can only be used to learn monotonic BNs, and the other one has a much worse time complexity than our method. In conclusion, our results demonstrate that Boolean networks can be learned with improved time complexities.Entropy 2013, 15 3763

show abstract

Inferring Boolean functions via higher-order correlations

Cited by 10 publications

References 18 publications

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Automatic Screening for Perturbations in Boolean Networks

Improved Time Complexities for Learning Boolean Networks

Contact Info

Product

Resources

About