Biology subjects

Liu, Y.-Y.

Publications and source records attributed to Liu, Y.-Y..

8 recordsLinked to original sources

bioRxiv2018

Overcome Competitive Exclusion in Ecosystems

Explaining biodiversity in nature is a fundamental problem in ecology. One great challenge is embodied in the so-called competitive exclusion principle1-4: the number of species in steady coexistence cannot exceed the number of resources4-7. In the past five decades, various mechanisms have been proposed to overcome the limit on diversity set by the competitive exclusion principle8-25. Yet, none of the existing mechanisms can generically overcome competitive exclusion at steady state4,26. Here we show that by forming chasing triplets in the predation process among the consumers and resources, the number of coexisting species of consumers can exceed that of resources at steady state, naturally breaking the competitive exclusion principle. Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.

ecology↗

bioRxiv2018

Link Prediction through Deep Learning

Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1-3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7-9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13-17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance. SummaryWe propose a new link prediction method based on deep generative models.

bioinformatics↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

Horizontal Gene Transfer Can Help Maintain the Equilibrium of Microbial Communities

Horizontal gene transfer and species coexistence are two focal points in the study of microbial communities. The evolutionary advantage of horizontal gene transfer has not been well-understood and is constantly being debated. Here we propose a simple population dynamics model based on the frequency-dependent interactions between different genotypes to evaluate the influence of horizontal gene transfer on microbial communities. We find that both structural stability and robustness of the microbial community are strongly affected by the gene transfer rate and direction. An optimal gene flux can stablize the ecosystem, helping it recover from disturbance and maintain the species coexistence.

evolutionary biology↗

bioRxiv2017

On the Stability Landscape of the Human Gut Microbiome: Implications for Microbiome-based Therapies

Understanding how gut microbial species determine their abundances is crucial in developing any microbiome-based therapy. Towards that end, we show that the compositions of our gut microbiota have characteristic and attractive steady states, and hence respond to perturbations in predictable ways. This is achieved by developing a new method to analyze the stability landscape of the human gut microbiome. In order to illustrate the efficacy of our method and its ecological interpretation in terms of asymptotic stability, this novel method is applied to various human cohorts, including large cross-sectional studies, long longitudinal studies with frequent sampling, and perturbation studies via fecal microbiota transplantation, antibiotic and probiotic treatments. These findings will facilitate future ecological modeling efforts in human microbiome research. Moreover, the method allows for the prediction of the compositional shift of the gut microbiome during the fecal microbiota transplantation process. This result holds promise for translational applications, such as, personalized donor selection when performing fecal microbiota transplantations.\n\nOne Sentence SummaryA new method for analyzing the stability landscape of the human gut microbiome and predicting its steady-state composition is developed.

ecology↗

bioRxiv2017

Deciphering Functional Redundancy in the Human Microbiome

Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved1-5---implying an ecological property known as functional redundancy. Such functional redundancy is thought to underlie the stability and resilience of the human microbiome6,7, but its origin is elusive. Here, we investigate the basis for functional redundancy in the human microbiome by analyzing its genomic content network --- a bipartite graph that links microbes to the genes in their genomes. We show that this network exhibits several topological features, such as highly nested structure and fat-tailed gene degree distribution, which favor high functional redundancy. To explain the origins of these topological features, we develop a simple genome evolution model that explicitly considers selection pressure, and the processes of gene gain and loss, and horizontal gene transfer. We find that moderate selection pressure and high horizontal gene transfer rate are necessary to generate genomic content networks with both highly nested structure and fat-tailed gene degree distribution, and consequently favor high functional redundancy. These findings provide insights into the relationships between structure and function in complex microbial communities. This work elucidates the potential ecological and evolutionary processes that create and maintain functional redundancy in the human microbiome and contribute to its resilience.

microbiology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗

bioRxiv2017

Controlling microbial communities: a theoretical framework

Microbes comprise nearly half of all biomass on Earth. Almost every habitat on Earth is teeming with microbes, from hydrothermal vents to the human gastrointestinal tract. Those microbes form complex communities and play critical roles in maintaining the integrity of their environment or the well-being of their hosts. Controlling microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. Yet, our ability to precisely manipulate microbial communities has been fundamentally impeded by the lack of a systematic framework to control them. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. This framework allows identifying minimal sets of \"driver species\" through which we can achieve feasible control of the entire microbial community. We numerically validate our control framework on large microbial communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros.

microbiology↗