scholarly journals Biophysically motivated regulatory network inference: progress and prospects

2016 ◽  
Author(s):  
Tarmo Äijö ◽  
Richard Bonneau
Keyword(s):  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Network Models ◽  
Cell Types ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Regulatory Factor ◽  
Regulatory Structure ◽  
Dynamic Genome

AbstractVia a confluence of genomic technology and computational developments the possibility of network inference methods that automatically learn large comprehensive models of cellular regulation is closer than ever. This perspective will focus on enumerating the elements of computational strategies that, when coupled to appropriate experimental designs, can lead to accurate large-scale models of chromatin-state and transcriptional regulatory structure and dynamics. We highlight four research questions that require further investigation in order to make progress in network inference: using overall constraints on network structure like sparsity, use of informative priors and data integration to constrain individual model parameters, estimation of latent regulatory factor activity under varying cell conditions, and new methods for learning and modeling regulatory factor interactions. We conclude that methods combining advances in these four categories of required effort with new genomic technologies will result in biophysically motivated dynamic genome-wide regulatory network models for several of the best studied organisms and cell types.

scholarly journals Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

2017 ◽  
Vol 114 (23) ◽  
pp. 5822-5829 ◽  
Author(s):  
Fiona K. Hamey ◽  
Sonia Nestorowa ◽  
Sarah J. Kinston ◽  
David G. Kent ◽  
Nicola K. Wilson ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Network Models ◽  
Cell Types ◽  
Mature Cell ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.

Large scale gene regulatory network inference with a multi-level strategy

2016 ◽  
Vol 12 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Large Scale ◽  
Network Inference ◽  
Biological Processes ◽  
Molecular Processes ◽  
Gene Regulatory Network Inference ◽  
Cell Functions ◽  
Multi Level ◽  
Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

scholarly journals A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382090911
Author(s):  
Qi-en He ◽  
Yi-fan Tong ◽  
Zhou Ye ◽  
Li-xia Gao ◽  
Yi-zhi Zhang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Treatment Options ◽  
Genomic Data ◽  
Full Potential ◽  
Gene Regulatory Network Inference ◽  
Signature Genes

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

scholarly journals A data-informed mean-field approach to mapping of cortical parameter landscapes

2021 ◽  
Vol 17 (12) ◽  
pp. e1009718
Author(s):  
Zhuo-Cheng Xiao ◽  
Kevin K. Lin ◽  
Lai-Sang Young
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Mean Field ◽  
Network Models ◽  
Complete Characterization ◽  
Model Parameters ◽  
Cortical Model ◽  
Experimental Values ◽  
The Many ◽  
Data Informed

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a “biologically plausible” region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

scholarly journals A data-informed mean-field approach to mapping of cortical parameter landscapes

2021 ◽  
Author(s):  
Zhuo-Cheng Xiao ◽  
Kevin K Lin ◽  
Lai-Sang Young
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Mean Field ◽  
Network Models ◽  
Complete Characterization ◽  
Model Parameters ◽  
Cortical Model ◽  
Experimental Values ◽  
The Many ◽  
Data Informed

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

scholarly journals A novel probabilistic generator for large-scale gene association networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259193
Author(s):  
Tyler Grimes ◽  
Somnath Datta
Keyword(s):  
Gold Standard ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Large Scale ◽  
Network Inference ◽  
Real Data ◽  
R Package ◽  
Reference Network ◽  
Gene Association ◽  
Inference Methods

Motivation Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. Results We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. Availability This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

scholarly journals Geometry of gene regulatory dynamics

2021 ◽  
Vol 118 (38) ◽  
pp. e2109729118 ◽  
Author(s):  
David A. Rand ◽  
Archishman Raju ◽  
Meritxell Sáez ◽  
Francis Corson ◽  
Eric D. Siggia
Keyword(s):  
Gene Networks ◽  
Dynamic Models ◽  
Network Models ◽  
Time Lapse ◽  
Cell Types ◽  
Model Parameters ◽  
Formal Representation ◽  
Riemann Metric ◽  
Low Dimensional ◽  
Two Parameters

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all three-way cellular decisions realizable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs. model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form (i.e., as topographic elevation). Turing systems appear nonpotential, yet in suitable variables the dynamics are low dimensional and potential. A time-independent embedding recovers the original variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.

scholarly journals Corynebacterium glutamicum regulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

2021 ◽  
Author(s):  
Juan Miguel Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio Augusto Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.

scholarly journals Corynebacterium glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small RNA and Protein–Protein Interactions

2021 ◽  
Vol 9 (7) ◽  
pp. 1395
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by nondirected experiments, and protein–protein interactions with a direct effect on gene transcription; sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNA-mediated regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C.glutamicum regulatory network characterization.

scholarly journals Corynebacterium Glutamicum Regulation beyond Transcription: Organizing Principles and Reconstruction of an Extended Regulatory Network Incorporating Regulations Mediated by Small Rna and Protein-Protein Interactions

2020 ◽  
Author(s):  
Juan M. Escorcia-Rodríguez ◽  
Andreas Tauch ◽  
Julio A. Freyre-González
Keyword(s):  
Corynebacterium Glutamicum ◽  
Protein Interactions ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Network Models ◽  
Global Scale ◽  
System Level ◽  
Model Organisms ◽  
Protein Protein Interactions ◽  
Regulatory Structure

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory interactions, which study at the global scale has been challenged by the lack of data integration. Here, we update the manually-curated C. glutamicum transcriptional regulatory network, now including protein-protein interactions having a direct effect on gene transcription. The network model with regulations supported by any experimental evidence increased by 557 interactions regarding the previous (2018) version. 73 interactions supported by directed experiments were also included in a second model. We included 545 sRNA-mediated regulations in a third model with a total of 5164 interactions. We deposited the three network models in Abasy Atlas v2.4. We study the C. glutamicum regulatory structure by comparing it against the networks for more than 40 species, finding it to contrast in several global structural properties. We analyze the system-level components of the networks, finding that the inclusion of the sRNAs regulations changes their proportions, transferring part of the basal machinery to the modular class and increasing the number of modules while decreasing their size. Finally, we use strong networks of three model organisms to provide insights in future directions of the C. glutamicum network characterization.

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