Short-range interactions govern cellular dynamics in microbial multi-genotype systems; Rapid microbial interaction network inference in microfluidic droplets

2019 ◽  
Author(s):  
Connor Rosen
Keyword(s):  
Short Range ◽  
Network Inference ◽  
Interaction Network ◽  
Microbial Interaction ◽  
Cellular Dynamics ◽  
Microfluidic Droplets

scholarly journals Microbial Interaction Network Inference in Microfluidic Droplets

Cell Systems ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 229-242.e4 ◽  
Author(s):  
Ryan H. Hsu ◽  
Ryan L. Clark ◽  
Jin Wen Tan ◽  
John C. Ahn ◽  
Sonali Gupta ◽  
...  
Keyword(s):  
Network Inference ◽  
Interaction Network ◽  
Microbial Interaction ◽  
Microfluidic Droplets

scholarly journals Rapid microbial interaction network inference in microfluidic droplets

2019 ◽  
Author(s):  
Ryan H. Hsu ◽  
Ryan L. Clark ◽  
Jin Wen Tan ◽  
Philip A. Romero ◽  
Ophelia S. Venturelli
Keyword(s):  
Molecular Mechanisms ◽  
Community Dynamics ◽  
Interaction Network ◽  
Microbial Interactions ◽  
Microbial Consortia ◽  
Cellular Interaction ◽  
Cell Detection ◽  
Microbial Interaction ◽  
Absolute Abundance ◽  
Microfluidic Droplets

ABSTRACTMicrobial interactions are major drivers of microbial community dynamics and functions. However, microbial interactions are challenging to decipher due to limitations in parallel culturing of sub-communities across many environments and accurate absolute abundance quantification of constituent members of the consortium. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop), a high-throughput method to rapidly infer microbial interactions in microbial consortia in microfluidic droplets. Fluorescence microscopy coupled to automated computational droplet and cell detection was used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per experiment. We show that MINI-Drop can accurately infer pairwise as well as higher-order interactions using a microbial interaction toolbox of defined microbial interactions mediated by distinct molecular mechanisms. MINI-Drop was used to investigate how the molecular composition of the environment alters the interaction network of a three-member consortium. To provide insight into the variation in community states across droplets, we developed a probabilistic model of cell growth modified by microbial interactions. In sum, we demonstrate a robust and generalizable method to probe cellular interaction networks by random encapsulation of sub-communities into microfluidic droplets.

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

2013 ◽  
pp. 188-202
Author(s):  
Tsuyoshi Kato ◽  
Kinya Okada ◽  
Hisashi Kashima ◽  
Masashi Sugiyama
Keyword(s):  
Metabolic Network ◽  
Transfer Learning ◽  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Network Inference ◽  
Interaction Network ◽  
Statistical Test ◽  
Learning Approach ◽  
Selective Integration

The authors’ algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.

scholarly journals Identification of microbial interaction network: zero-inflated latent Ising model based approach

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiota ◽  
Relative Abundance ◽  
Interaction Network ◽  
Interaction Networks ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Conditional Correlation ◽  
Zero Inflation ◽  
Step Algorithm

Abstract Background Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. Conclusions Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

scholarly journals Phytobeneficial bacteria improve saline stress tolerance in Vicia faba and modulate microbial interaction network

2020 ◽  
Vol 729 ◽  
pp. 139020 ◽  
Author(s):  
Loubna Benidire ◽  
Fatima El Khalloufi ◽  
Khalid Oufdou ◽  
Mohamed Barakat ◽  
Joris Tulumello ◽  
...  
Keyword(s):  
Stress Tolerance ◽  
Vicia Faba ◽  
Interaction Network ◽  
Saline Stress ◽  
Microbial Interaction

scholarly journals Dynamic interaction network inference from longitudinal microbiome data

2018 ◽  
Author(s):  
Jose Lugo-Martinez ◽  
Daniel Ruiz-Perez ◽  
Giri Narasimhan ◽  
Ziv Bar-Joseph
Keyword(s):  
Network Inference ◽  
Dynamic Models ◽  
Dynamic Bayesian Network ◽  
Interaction Network ◽  
Predictive Performance ◽  
Dynamic Bayesian Networks ◽  
Data Sets ◽  
Computational Pipeline ◽  
Novel Interactions ◽  
Microbiome Data

AbstractBackgroundSeveral studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies researchers collect longitudinal data with the goal of understanding not just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.ResultsHere we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions.ConclusionsWe propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.

scholarly journals Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nisar Wani ◽  
Debmalya Barh ◽  
Khalid Raza
Keyword(s):  
Network Analysis ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Expression Profiles ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Expression Data ◽  
Cancer Data

Abstract Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

scholarly journals Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data

2021 ◽  
Author(s):  
Jie Zhou ◽  
Jiang Gui ◽  
Weston D Viles ◽  
Anne G Hoen
Keyword(s):  
16S Rrna ◽  
Permutation Test ◽  
Sequence Data ◽  
Interaction Network ◽  
Microbial Interactions ◽  
Microbial Interaction ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Data Set ◽  
Graphical Lasso

Though being vital for human health, microbial interactions with their host and with each other are still largely obscure for researchers. To deepen the understanding, the analyses based on longitudinal data are a better choice than the cross-sectional data since the information provided by the former is usually more stable. To this end, in this paper, we first propose an EM-type algorithm to identify microbial interaction network for the irregularly spaced longitudinal measurements. Correlation functions are employed to account for the correlation across the temporal measurements for a given subject. The algorithms take advantage of the efficiency of the popular graphical lasso algorithm and can be implemented straightforwardly. Simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms such as graphical lasso or neighborhood method when the correlation between measurements grows larger. In second part of the paper, based on a 16S rRNA sequence data set of gut microbiome, module-preserving permutation test is proposed to test the independence of the estimated network and the phylogeny of the microbe species. The results demonstrate evidences of strong association between the interaction network and the phylogenetic tree which indicates that the taxa closer in their genomes tend to have more/stronger interactions in their functions. The proposed algorithms can be implemented through R package lglasso at \url{https://github.com/jiezhou-2/lglasso

Drug-target Interaction Prediction by Metapath2vec Node Embedding in Heterogeneous Network of Interactions

2020 ◽  
Vol 29 (01) ◽  
pp. 2050001
Author(s):  
Mina Samizadeh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Drug Development ◽  
Heterogeneous Network ◽  
Drug Target ◽  
Network Inference ◽  
Interaction Network ◽  
Ensemble Methods ◽  
New Drugs ◽  
Molecular Entity ◽  
Heterogeneous Interaction ◽  
Scale Down

Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods is an important area of drug research, which brings about a broad prospect for fast and low-risk drug development. By accurate prediction of drugs and targets interactions scientists can scale-down huge experimental space and reduce the costs and help to faster drug development as well as predicting the side effects and potential function of new drugs. Many approaches have been taken by researchers to solve DTI problem and enhance the accuracy of methods. State-of-the-art approaches are based on various techniques, such as deep learning methods-like stacked auto-encoder-, matrix factorization, network inference, and ensemble methods. In this work, we have taken a new approach based on node embedding in a heterogeneous interaction network to obtain the representation of each node in the interaction network and then use a binary classifier such as logistic regression to solve this prominent problem in the pharmaceutical industry. Most introduced network-based methods use a homogeneous network of interactions as their input data whereas in the real word problem there exist other informative networks to help to enhance the prediction and by considering the homogeneous networks we lose some precious network information. Hence, in this work, we have tried to work on the heterogeneous network and have improved the accuracy of methods in comparison to baseline methods.

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