A robust mathematical model of adaxial-abaxial patterning

Abstract Biological development results from intricate and dynamic interactions between members of gene regulatory networks. This is exemplified by the production of flat leaf architecture. Leaves flatten by driving growth along the boundary between their adaxial (top) and abaxial (bottom) domains. These domains are generated by interactions between a complex network of transcription factors and small RNAs. Despite its complexity, flat leaf production is robust to genetic and environmental noise. To identify factors contributing to this robustness, we mathematically modeled the determinants and interactions that pattern the adaxial-abaxial axis in leaves of Arabidopsis thaliana. Model parameters were estimated almost exclusively using experimental data. Our model recapitulates observations of adaxial-abaxial patterning and small RNA-target interactions. Positioning of the adaxial-abaxial boundary is stable across a wide range of small RNA source values and is highly robust to noise in the model. The successful application of our one-dimensional spatial model will enable higher-dimension modeling of the complex and mechanistically challenging process of flat leaf production.

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Ultrabithorax Is a Micromanager of Hindwing Identity in Butterflies and Moths

Frontiers in Ecology and Evolution ◽

10.3389/fevo.2021.643661 ◽

2021 ◽

Vol 9 ◽

Author(s):

Amruta Tendolkar ◽

Aaron F. Pomerantz ◽

Christa Heryanto ◽

Paul D. Shirk ◽

Nipam H. Patel ◽

...

Keyword(s):

Regulatory Networks ◽

Targeted Mutagenesis ◽

Junonia Coenia ◽

Wing Venation ◽

Loss Of Function ◽

Color Patterns ◽

Wide Range ◽

Range Of Functions ◽

Gene Regulatory ◽

Pyralid Moth

The forewings and hindwings of butterflies and moths (Lepidoptera) are differentiated from each other, with segment-specific morphologies and color patterns that mediate a wide range of functions in flight, signaling, and protection. The Hox gene Ultrabithorax (Ubx) is a master selector gene that differentiates metathoracic from mesothoracic identities across winged insects, and previous work has shown this role extends to at least some of the color patterns from the butterfly hindwing. Here we used CRISPR targeted mutagenesis to generate Ubx loss-of-function somatic mutations in two nymphalid butterflies (Junonia coenia, Vanessa cardui) and a pyralid moth (Plodia interpunctella). The resulting mosaic clones yielded hindwing-to-forewing transformations, showing Ubx is necessary for specifying many aspects of hindwing-specific identities, including scale morphologies, color patterns, and wing venation and structure. These homeotic phenotypes showed cell-autonomous, sharp transitions between mutant and non-mutant scales, except for clones that encroached into the border ocelli (eyespots) and resulted in composite and non-autonomous effects on eyespot ring determination. In the pyralid moth, homeotic clones converted the folding and depigmented hindwing into rigid and pigmented composites, affected the wing-coupling frenulum, and induced ectopic scent-scales in male androconia. These data confirm Ubx is a master selector of lepidopteran hindwing identity and suggest it acts on many gene regulatory networks involved in wing development and patterning.

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Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500104 ◽

2016 ◽

Vol 14 (03) ◽

pp. 1650010 ◽

Cited By ~ 6

Author(s):

Sudip Mandal ◽

Abhinandan Khan ◽

Goutam Saha ◽

Rajat Kumar Pal

Keyword(s):

Reverse Engineering ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Real Life ◽

Bat Algorithm ◽

Genetic Network ◽

Model Parameters ◽

Mathematical Tool ◽

Gene Regulatory ◽

Artificial Network

The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

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Construction of small RNA-mediated gene regulatory networks in the roots of rice (Oryza sativa)

BMC Genomics ◽

10.1186/1471-2164-14-510 ◽

2013 ◽

Vol 14 (1) ◽

pp. 510 ◽

Cited By ~ 19

Author(s):

Xiaoxia Ma ◽

Chaogang Shao ◽

Huizhong Wang ◽

Yongfeng Jin ◽

Yijun Meng

Keyword(s):

Oryza Sativa ◽

Gene Regulatory Networks ◽

Small Rna ◽

Regulatory Networks ◽

Gene Regulatory

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Towards a study of gene regulatory constraints to morphological evolution of the Drosophila ocellar region

10.1101/031948 ◽

2015 ◽

Author(s):

D. Aguilar-Hidalgo ◽

D. Becerra-Alonso ◽

D. García-Morales ◽

F. Casares

Keyword(s):

Bayesian Networks ◽

Morphological Variation ◽

Regulatory Networks ◽

Morphological Evolution ◽

Model Parameters ◽

Regulatory Interactions ◽

Regulatory Constraints ◽

Gene Regulatory ◽

Morphological Variants ◽

And Function

ABSTRACTThe morphology and function of organs depend on coordinated changes in gene expression during development. These changes are controlled by transcription factors, signaling pathways and their regulatory interactions, which are represented by gene regulatory networks (GRNs). Therefore, the structure of an organ GRN restricts the morphological and functional variations that the organ can experience –its potential morphospace. Therefore, two important questions arise when studying any GRN: what is the predicted available morphospace and what are the regulatory linkages that contribute the most to control morphological variation within this space. Here, we explore these questions by analyzing a small “3-node” GRN model that captures the Hh-driven regulatory interactions controlling a simple visual structure: the ocellar region of Drosophila. Analysis of the model predicts that random variation of model parameters results in a specific non-random distribution of morphological variants. Study of a limited sample of Drosophilids and other dipterans finds a correspondence between the predicted phenotypic range and that found in nature. As an alternative to simulations, we apply Bayesian Networks methods in order to identify the set of parameters with the largest contribution to morphological variation. Our results predict the potential morphological space of the ocellar complex, and identify likely candidate processes to be responsible for ocellar morphological evolution using Bayesian networks. We further discuss the assumptions that the approach we have taken entails and their validity.

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Assigning function to natural allelic variation via dynamic modeling of gene network induction

10.1101/140467 ◽

2017 ◽

Author(s):

Magali Richard ◽

Florent Chuffart ◽

Hélène Duplus-Bottin ◽

Fanny Pouyet ◽

Martin Spichty ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Computational Models ◽

Allelic Variation ◽

Model Parameters ◽

Or Efficiency ◽

Response Network ◽

Dna Variants ◽

Gene Regulatory ◽

Regulatory Models

ABSTRACTMore and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be ‘personalized’ according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

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Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

Statistical Modelling ◽

10.1177/1471082x18776577 ◽

2018 ◽

Vol 19 (4) ◽

pp. 444-465

Author(s):

William Chad Young ◽

Ka Yee Yeung ◽

Adrian E Raftery

Keyword(s):

Gene Expression ◽

Time Series ◽

Steady State ◽

Gene Expression Data ◽

Regulatory Networks ◽

Network Inference ◽

Time Series Model ◽

Model Parameters ◽

Expression Data ◽

Gene Regulatory

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

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Input dose differentiation by NF-κB

10.1101/752394 ◽

2019 ◽

Cited By ~ 1

Author(s):

Minjun Son ◽

Andrew Wang ◽

Hsiung-Lin Tu ◽

Marie O Metzig ◽

Parthiv Patel ◽

...

Keyword(s):

Regulatory Networks ◽

Short Term Memory ◽

Single Cells ◽

Absolute Difference ◽

Transcriptional Responses ◽

Wide Range ◽

Negative Feedbacks ◽

Gene Regulatory ◽

Complex Signaling ◽

Simple Network

AbstractCells receive a wide range of dynamic signaling inputs during immune regulation, but how gene regulatory networks measure and interpret such dynamic inputs is not understood. Here, we used microfluidic live-cell analysis and mathematical modeling to study how NF-κB pathway in single-cells responds to time-varying immune inputs such as increasing, decreasing or fluctuating cytokine signals. Surprisingly, we found that NF-κB acts as a differentiator, responding strictly to the absolute difference in cytokine concentration, and not to the concentration itself. Our analyses revealed that negative feedbacks by the regulatory proteins A20 and IκBα enable dose differentiation by providing short-term memory of prior cytokine level and continuously resetting kinase cycling and receptor levels. Investigation of NF-κB target gene expression showed that cells create unique transcriptional responses under different dynamic cytokine profiles. Our results demonstrate how cells use simple network motifs and transcription factor dynamics to efficiently extract information from complex signaling environments.

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Neuron subtype-specific effector gene expression in the Motor Ganglion of Ciona

10.1101/641233 ◽

2019 ◽

Cited By ~ 1

Author(s):

Susanne Gibboney ◽

Kwantae Kim ◽

Christopher J. Johnson ◽

Jameson Orvis ◽

Paula Martínez-Feduchi ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Gene Expression Analysis ◽

Transcriptome Profiling ◽

Matrix Proteins ◽

Wide Range ◽

Specific Effector ◽

The Central Nervous System ◽

Differential Gene ◽

Gene Regulatory

AbstractThe central nervous system of the Ciona larva contains only 177 neurons. The precise regulation of neuron subtype-specific morphogenesis and differentiation observed in during the formation of this minimal connectome offers a unique opportunity to dissect gene regulatory networks underlying chordate neurodevelopment. Here we compare the transcriptomes of two very distinct neuron types in the hindbrain/spinal cord homolog of Ciona, the Motor Ganglion (MG): the Descending decussating neuron (ddN, proposed homolog of Mauthner Cells in vertebrates) and the MG Interneuron 2 (MGIN2). Both types are invariantly represented by a single bilaterally symmetric left/right pair of cells in every larva. Supernumerary ddNs and MGIN2s were generated in synchronized embryos and isolated by fluorescence-activated cell sorting for transcriptome profiling. Differential gene expression analysis revealed ddN- and MGIN2-specific enrichment of a wide range of genes, including many encoding potential “effectors” of subtype-specific morphological and functional traits. More specifically, we identified the upregulation of centrosome-associated, microtubule-stabilizing/bundling proteins and extracellular matrix proteins and axon guidance cues as part of a single intrinsic regulatory program that might underlie the unique polarization of the ddNs, the only descending MG neurons that cross the midline.

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Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions

10.1101/264671 ◽

2018 ◽

Cited By ~ 2

Author(s):

P. Tsakanikas ◽

D. Manatakis ◽

E. S. Manolakos

Keyword(s):

Machine Learning ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Most Probable Number ◽

State Transitions ◽

Gene Expressions ◽

Dynamic Interactions ◽

Probable Number ◽

Cell State ◽

Gene Regulatory

ABSTRACTDeciphering the dynamic gene regulatory mechanisms driving cells to make fate decisions remains elusive. We present a novel unsupervised machine learning methodology that can be used to analyze a dataset of heterogeneous single-cell gene expressions profiles, determine the most probable number of states (major cellular phenotypes) represented and extract the corresponding cell sub-populations. Most importantly, for any transition of interest from a source to a destination state, our methodology can zoom in, identify the cells most specific for studying the dynamics of this transition, order them along a trajectory of biological progression in posterior probabilities space, determine the "key-player" genes governing the transition dynamics, partition the trajectory into consecutive phases (transition "micro-states"), and finally reconstruct causal gene regulatory networks for each phase. Application of the end-to-end methodology provides new insights on key-player genes and their dynamic interactions during the important HSC-to-LMPP cell state transition involved in hematopoiesis. Moreover, it allows us to reconstruct a probabilistic representation of the “epigenetic landscape” of transitions and identify correctly the major ones in the hematopoiesis hierarchy of states.

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