Signal flow control of complex signaling networks

Lee, Daewon; Cho, Kwang-Hyun

doi:10.1038/s41598-019-50790-0

Cited by 7 publications

(10 citation statements)

References 70 publications

(67 reference statements)

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“…Last, we have modified the original SFA algorithm 47 to apply FVS control-based perturbations. Unlike the SFA control method introduced by Lee and Cho 74 , where edge modifications (removals or additions) were implemented to perform perturbations, our method of perturbations to a node’s state maintains the FVS of the network. In addition, we have implemented permanent overrides on FVS control nodes rather than the original form of SFA perturbations that are transiently applied by changing only the initial states of nodes.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, b is the logarithm of the initial state of node i and α is a hyperparameter used to weigh the influence of the network structure and initial node state on the Signal Flow. By default, in our pipeline, the hyperparameter α is set to 0.9 to provide greater weight to the network topology rather than the initial activity based on the parameter settings used in previous control studies using SFA 74 .…”

Section: Methodsmentioning

confidence: 99%

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NETISCE: a network-based tool for cell fate reprogramming

et al. 2022

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The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

NETISCE: a network-based tool for cell fate reprogramming

et al. 2022

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“…"NM" indicates a non-locally-monotonic model. [25] 1953 669 >1000 DNF 23.62 5.93 DNF 32 SN-5 [26] 2746 829 >1000 DNF 28.94 DNF DNF 33 turei-2016 [30] 4691 1257 ??? DNF DNF DNF DNF mbpn can handle only locally-monotonic models and Trappist can handle general models, it is difficult to further compare between them.…”

Section: Selected Modelsmentioning

confidence: 99%

Minimal Trap Spaces of Logical Models are Maximal Siphons of Their Petri Net Encoding

Trinh

Benhamou

Hiraishi

et al. 2022

Lecture Notes in Computer Science

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Boolean modelling of gene regulation but also of post-transcriptomic systems has proven over the years that it can bring powerful analyses and corresponding insight to the many cases where precise biological data is not sufficiently available to build a detailed quantitative model. This is even more true for very large models where such data is frequently missing and led to a constant increase in size of logical models à la Thomas. Besides simulation, the analysis of such models is mostly based on attractor computation, since those correspond roughly to observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field allowing to consider medium-sized models that used to be out of reach. However, with the continuing increase in model-size, the state-of-the-art computation of minimal trap spaces based on prime-implicants shows its limits as there can be a huge number of implicants.In this article we present an alternative method to compute minimal trap spaces, and hence complex attractors, of a Boolean model. It replaces the need for prime-implicants by a completely different technique, namely the enumeration of maximal siphons in the Petri net encoding of the original model. After some technical preliminaries, we expose the concrete need for such a method and detail its implementation using Answer Set Programming. We then demonstrate its efficiency and compare it to implicant-based methods on some large Boolean models from the literature.

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“…Several studies have employed ordinary differential equations 22 , 23 or Boolean logical models 24 – 26 , which require specific parameter fitting or logical rule inference using accumulated experimental data. Recently, a signal flow analysis 27 , 28 method was developed, in which the influence of signals of perturbed nodes on the activity changes of the other nodes can be estimated based on the network structure and mode of action of each edge.…”

Section: Introductionmentioning

confidence: 99%

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Lee

Jeon

Kim

et al. 2021

Sci Rep

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Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

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Signal flow control of complex signaling networks

Cited by 7 publications

References 70 publications

NETISCE: a network-based tool for cell fate reprogramming

NETISCE: a network-based tool for cell fate reprogramming

Minimal Trap Spaces of Logical Models are Maximal Siphons of Their Petri Net Encoding

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

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