One model fits all: combining inference and simulation of gene regulatory networks

Ventre, Elias; Herbach, Ulysse; Espinasse, Thibault; Galand, Benoît; Gandrillon, Olivier

doi:10.1101/2022.06.19.496754

Cited by 5 publications

(7 citation statements)

References 69 publications

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“…UMAP representation is also a natural way of comparing the output of a model and an experimental single-cell dataset (see e.g. [58]). In our case, the GRN we introduced was able to correctly generate a time-dependent evolution of single cells at the molecular level, and we could observe the expected time-dependent trajectory in the UMAP space, as well as the branching resulting from the decision making process through the toggle switch as observed experimentally [54].…”

Section: Discussionmentioning

confidence: 99%

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In silicomodelling of CD8 T cell immune response links genetic regulation to population dynamics

Nguyen,

Martin,

Arpin

et al. 2024

Preprint

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The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process (PDMP) formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell-cell contacts, influencing the GRN behavior.Results show the ability of the model to correctly capture both population behaviour and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing that a higher degradation rate for the protein controlling cell death induces a later peak in the response.

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Section: Discussionmentioning

confidence: 99%

“…The next step in our approach will consist in changing the principle-based GRN to a more realistic one. We are currently in the process of using CARDAMOM [58] to infer a GRN from the time-stamped…”

Section: Discussionmentioning

confidence: 99%

In silicomodelling of CD8 T cell immune response links genetic regulation to population dynamics

Nguyen,

Martin,

Arpin

et al. 2024

Preprint

Self Cite

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“…Most of these methods assume Gaussian noise for gene expression, even though transcription occurs in bursts [ 140 , 141 ], a phenomenon that can only be captured on a single cell level. These dynamics can be modeled as a Markov process including transcriptional bursting and degradation [ 142 ]. Theoretically these mechanistic models could be great tools for hypothesis generation, but more work is needed to prove their practical usefulness.…”

Section: Single Cell Sequencing For Cell Type Specific Regulationmentioning

confidence: 99%

Computational approaches to understand transcription regulation in development

Sande¹,

Frölich²,

Heeringen

2023

Biochemical Society Transactions

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Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

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“…The overwhelming majority of network inference methods aim to reconstruct a single static network [5, 8] that describes the set of possible interactions occurring within an observed population. Given the biological importance of cellular heterogeneity, a natural expectation is that variations in transcriptional state may correspond to variations in (cell state dependent) regulatory interactions which cannot be represented as static networks [7], and indeed has been observed empirically in previous studies [10, 11].…”

Section: Introductionmentioning

confidence: 99%

Learning cell-specific networks from dynamics and geometry of single cells

Zhang

Stumpf

2023

Preprint

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Cellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes, we propose locaTE, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data, locaTE provides a powerful, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.

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One model fits all: combining inference and simulation of gene regulatory networks

Cited by 5 publications

References 69 publications

In silicomodelling of CD8 T cell immune response links genetic regulation to population dynamics

In silicomodelling of CD8 T cell immune response links genetic regulation to population dynamics

Computational approaches to understand transcription regulation in development

Learning cell-specific networks from dynamics and geometry of single cells

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