Learning restricted Boolean network model by time-series data

Ouyang, Hongjia; Fang, Jie; Shen, Liangzhong; Dougherty, Edward R.; Liu, Wenbin

doi:10.1186/s13637-014-0010-5

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…However, our approach requires all qualitative observations to be reproduced exactly, and noise of sufficient magnitude (causing a component to be observed in the incorrect state) could impact our results. Similar robustness issues have been considered as part of other approaches [33, 34]. On the other hand, noise that is inherent to a biological mechanism could be incorporated and studied in our framework as non-determinism, using asynchronous updates or by introducing additional components with unspecified initial states.…”

Section: Discussionmentioning

confidence: 93%

A method to identify and analyze biological programs through automated reasoning

et al. 2016

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Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function.

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Section: Discussionmentioning

confidence: 93%

A method to identify and analyze biological programs through automated reasoning

et al. 2016

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“…Note that the function f (·) in (1) is a special case of Boolean threshold functions, which can be used to represent many Boolean functions [2]. Note also that (1) is related to another class of BNs called restricted BNs [36].…”

Section: A Motivating Examplesmentioning

confidence: 99%

“…In [30], an estimation procedure for BNps from temporal data sequence was proposed based on a transition counting matrix and the optimal selection of input nodes. The authors studied the estimation problem of restricted BNs in [20], [36], but did not present a rigorous performance analysis. Additionally, [3], [32] investigated the inferring of Boolean threshold functions for probabilistic BNs, but data samples were assumed to be independent instead of taken from a time series.…”

Section: B Related Workmentioning

confidence: 99%

Network Weight Estimation for Binary-Valued Observation Models

Fang

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper studies the problem of recursively estimating the weighted adjacency matrix of a network out of a temporal sequence of binary-valued observations. The observation sequence is generated from nonlinear networked dynamics in which agents exchange and display binary outputs. Sufficient conditions are given to ensure stability of the observation sequence and identifiability of the system parameters. It is shown that stability and identifiability can be guaranteed under the assumption of independent standard Gaussian disturbances. Via a maximum likelihood approach, the estimation problem is transformed into an optimization problem, and it is verified that its solution is the true parameter vector under the independent standard Gaussian assumption. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Finally, numerical simulations are conducted to illustrate the results and to show that the proposed algorithm is insensitive to small unmodeled factors.

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“…In Higa et al ( 2011 ) the data is considered as given constraint and the set of systems fulfilling the constraints is searched. This approach was then further improved by reducing the sensitivity to noise in Ouyang et al ( 2014 ). An example of recent research is the identification of Boolean models for transient dynamics after perturbations from time course data with answer set programming (Ostrowski et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge

Leifeld

Zhang

2018

Front. Physiol.

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Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge.Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response.Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

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Learning restricted Boolean network model by time-series data

Cited by 12 publications

References 22 publications

A method to identify and analyze biological programs through automated reasoning

A method to identify and analyze biological programs through automated reasoning

Network Weight Estimation for Binary-Valued Observation Models

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge

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