27,570 research outputs found

    Trajectory-based differential expression analysis for single-cell sequencing data

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    Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data. Downstream of trajectory inference for cell lineages based on scRNA-seq data, differential expression analysis yields insight into biological processes. Here, Van den Berge et al. develop tradeSeq, a framework for the inference of within and between-lineage differential expression, based on negative binomial generalized additive models

    Behavioral Aspects of Organizational Learning and Adaptation

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    In this paper, I seek to understand the behavioral basis of higher organizational learning and adaption as a teleological dynamic equilibrium process to decipher the underlying psycho-physiological aspects of individual cognitive learning related to organizational adaption. Dynamics of cognitive learning has some differential paths within the neural circuitry which follows certain patterns that leads to individual as well as organized evolution in course of a learning process. I undertake a comparative analysis of human cognitive and behavioral changes and the active mechanisms underlying animal behavior and learning processes to understand the differential patterns of these adaptive changes in these two species. Cognitive behavioral learning processes have certain economic perspectives which help an individual to attain efficiency in workplace adaptation and in learning which however, the individual when being part of an alliance, ember positive influence on the society or organization as a whole. Comparatively, in primates, I review some empirical evidences drawn from chronological studies about cognitive behavioral learning process and adaptation as well as the presence of the capacity of making attributions about mental states, which exists in rudimentary form in chimpanzees and apes. Following this, I apply the outcomes of the findings on different aspects of human cognitive and adaptive behavioral learning-induced evolutionary changes and how human beings are able to exploit the presence of these additive advantages under cluster settings.Animal behavior, cognitive economics, motivational energy, neural adaptation, neuroscience, Organizational learning, organizational adaptation, teleological process

    Critical Transitions In a Model of a Genetic Regulatory System

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    We consider a model for substrate-depletion oscillations in genetic systems, based on a stochastic differential equation with a slowly evolving external signal. We show the existence of critical transitions in the system. We apply two methods to numerically test the synthetic time series generated by the system for early indicators of critical transitions: a detrended fluctuation analysis method, and a novel method based on topological data analysis (persistence diagrams).Comment: 19 pages, 8 figure

    Reduction of dynamical biochemical reaction networks in computational biology

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    Biochemical networks are used in computational biology, to model the static and dynamical details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multi-scaleness is another property of these networks, that can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler networks, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state and quasi-equilibrium approximations, and provide practical recipes for model reduction of linear and nonlinear networks. We also discuss the application of model reduction to backward pruning machine learning techniques

    Isolating intrinsic noise sources in a stochastic genetic switch

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    The stochastic mutual repressor model is analysed using perturbation methods. This simple model of a gene circuit consists of two genes and three promotor states. Either of the two protein products can dimerize, forming a repressor molecule that binds to the promotor of the other gene. When the repressor is bound to a promotor, the corresponding gene is not transcribed and no protein is produced. Either one of the promotors can be repressed at any given time or both can be unrepressed, leaving three possible promotor states. This model is analysed in its bistable regime in which the deterministic limit exhibits two stable fixed points and an unstable saddle, and the case of small noise is considered. On small time scales, the stochastic process fluctuates near one of the stable fixed points, and on large time scales, a metastable transition can occur, where fluctuations drive the system past the unstable saddle to the other stable fixed point. To explore how different intrinsic noise sources affect these transitions, fluctuations in protein production and degradation are eliminated, leaving fluctuations in the promotor state as the only source of noise in the system. Perturbation methods are then used to compute the stability landscape and the distribution of transition times, or first exit time density. To understand how protein noise affects the system, small magnitude fluctuations are added back into the process, and the stability landscape is compared to that of the process without protein noise. It is found that significant differences in the random process emerge in the presence of protein noise
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