Efficient Boolean Modeling of Gene Regulatory Networks via Random Forest Based Feature Selection and Best-Fit Extension

Gao, Shuhua; Chen, Xiang; Sun, Changkai; Qin, Kairong; Lee, Tong Heng

doi:10.1109/icca.2018.8444221

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…In synchronous updating all rules are applied at the same time and thus all species are updated simultaneously. While synchronous updating is commonly used, there is significant debate as to its biological interpretation [31, 32, 33, 34]. Asynchronous updating has been proposed as an alternative [35, 36, 37, 38, 39].…”

Section: Resultsmentioning

confidence: 99%

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Prugger

Einkemmer

López

2022

Preprint

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Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation.

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Section: Resultsmentioning

confidence: 99%

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Prugger

Einkemmer

López

2022

Preprint

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“…Recently, an effort has been made to utilise feature selection approaches to reduce the size of Boolean network inference search space [22] , [31] . Barman and Kwon [22] introduced a Mutual Information-based Boolean Network Inference method MIBNI, which first identifies a set of initial regulatory genes that can best characterise the target variable.…”

Section: Introductionmentioning

confidence: 99%

“…NNBNI (Neural Network-based Boolean Network Inference) [33] combines mutual information feature selection, genetic algorithms as a global search technique, and a neural network to represent a regulatory rule. Similarly, RFBFE (Random Forest Best-Fit Extension) [31] employs random forest-based feature selection and Best-Fit Extension to infer large Boolean networks. Shi et al introduced ATEN [24] an AND/OR Tree ENsemble algorithm for inferring accurate Boolean network topology and dynamics.…”

Section: Introductionmentioning

confidence: 99%

Review and assessment of Boolean approaches for inference of gene regulatory networks

Pušnik

Mraz

Zimic

et al. 2022

Heliyon

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“…The choice of updating scheme chosen for model evolution, however, can significantly affect the interpretation of model dynamics. For example, synchronous updating schemes may yield network dynamics with no clear biological interpretation [46][47][48][49]. By contrast, sequential node updating schemes, such as General Asynchronous, can provide a mechanism with better biochemical correlation [22,[50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised logic-based mechanism inference for network-driven biological processes

et al. 2021

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Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.

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Efficient Boolean Modeling of Gene Regulatory Networks via Random Forest Based Feature Selection and Best-Fit Extension

Cited by 15 publications

References 12 publications

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Review and assessment of Boolean approaches for inference of gene regulatory networks

Unsupervised logic-based mechanism inference for network-driven biological processes

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