Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa.
Ontology highlight
ABSTRACT:
SUBMITTER: Johnston IG
PROVIDER: S-EPMC7306607 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach.ResultsIn this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network) to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network). Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data.ConclusionWe focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory pathway in Arabidopsis thaliana and metabolic shift pathway from fermentation to respiration in yeast Saccharomyces cerevisiae, are reconstructed using microarray data to evaluate the performance of our proposed method. In the circadian regulatory pathway, we identified mainly the interactions between the biological clock and the photoperiodic genes consistent with the known regulatory mechanisms. We also discovered the now less-known regulations between crytochrome and phytochrome. In the metabolic shift pathway, the casual relationship of enzymatic genes could be detected properly.
| S-EPMC555938 | biostudies-literature
Project description:A dynamic treatment regime (DTR) comprises a sequence of decision rules, one per stage of intervention, that recommends how to individualize treatment to patients based on evolving treatment and covariate history. These regimes are useful for managing chronic disorders, and fit into the larger paradigm of personalized medicine. The Value of a DTR is the expected outcome when the DTR is used to assign treatments to a population of interest.The Value of a data-driven DTR, estimated using data from a Sequential Multiple Assignment Randomized Trial, is both a data-dependent parameter and a non-smooth function of the underlying generative distribution. These features introduce additional variability that is not accounted for by standard methods for conducting statistical inference, for example, the bootstrap or normal approximations, if applied without adjustment. Our purpose is to provide a feasible method for constructing valid confidence intervals (CIs) for this quantity of practical interest.We propose a conceptually simple and computationally feasible method for constructing valid CIs for the Value of an estimated DTR based on subsampling. The method is self-tuning by virtue of an approach called the double bootstrap. We demonstrate the proposed method using a series of simulated experiments.The proposed method offers considerable improvement in terms of coverage rates of the CIs over the standard bootstrap approach.In this article, we have restricted our attention to Q-learning for estimating the optimal DTR. However, other methods can be employed for this purpose; to keep the discussion focused, we have not explored these alternatives.Subsampling-based CIs provide much better performance compared to standard bootstrap for the Value of an estimated DTR.
| S-EPMC4265005 | biostudies-other
Project description:One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.
| S-EPMC6531928 | biostudies-literature
Project description:Unraveling how chemistry can give rise to biology is one of the greatest challenges of contemporary science. Achieving life-like properties in chemical systems is therefore a popular topic of research. Synthetic chemical systems are usually deterministic: the outcome is determined by the experimental conditions. In contrast, many phenomena that occur in nature are not deterministic but caused by random fluctuations (stochastic). Here, we report on how, from a mixture of two synthetic molecules, two different self-replicators emerge in a stochastic fashion. Under the same experimental conditions, the two self-replicators are formed in various ratios over several repeats of the experiment. We show that this variation is caused by a stochastic nucleation process and that this stochasticity is more pronounced close to a phase boundary. While stochastic nucleation processes are common in crystal growth and chiral symmetry breaking, it is unprecedented for systems of synthetic self-replicators.
| S-EPMC9011346 | biostudies-literature
Project description:Feedforward GABAergic inhibition sets the dendritic integration window, thereby controlling timing and output in cortical circuits. However, the manner in which feedforward inhibitory circuits emerge is unclear, despite this being a critical step for neocortical development and function. We found that sensory experience drove plasticity of the feedforward inhibitory circuit in mouse layer 4 somatosensory barrel cortex in the second postnatal week via two distinct mechanisms. First, sensory experience selectively strengthened thalamocortical-to-feedforward interneuron inputs via a presynaptic mechanism but did not regulate other inhibitory circuit components. Second, experience drove a postsynaptic mechanism in which a downregulation of a prominent thalamocortical NMDA excitatory postsynaptic potential in stellate cells regulated the final expression of functional feedforward inhibitory input. Thus, experience is required for specific, coordinated changes at thalamocortical synapses onto both inhibitory and excitatory neurons, producing a circuit plasticity that results in maturation of functional feedforward inhibition in layer 4.
| S-EPMC2950257 | biostudies-literature
Project description:MotivationInferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein-protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.ResultsWe developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.Availability and implementationCode, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3624811 | biostudies-literature
Project description:Mediator is an evolutionary conserved coregulator complex required for transcription of almost all RNA polymerase II-dependent genes. The Schizosaccharomyces pombe Mediator consists of two dissociable components-a core complex organized into a head and middle domain as well as the Cdk8 regulatory subcomplex. In this work we describe a functional characterization of the S. pombe Mediator. We report the identification of the S. pombe Med20 head subunit and the isolation of ts alleles of the core head subunit encoding med17+. Biochemical analysis of med8(ts), med17(ts), Deltamed18, Deltamed20 and Deltamed27 alleles revealed a stepwise head domain molecular architecture. Phenotypical analysis of Cdk8 and head module alleles including expression profiling classified the Mediator mutant alleles into one of two groups. Cdk8 module mutants flocculate due to overexpression of adhesive cell-surface proteins. Head domain-associated mutants display a hyphal growth phenotype due to defective expression of factors required for cell separation regulated by transcription factor Ace2. Comparison with Saccharomyces cerevisiae Mediator expression data reveals that these functionally distinct modules are conserved between S. pombe and S. cerevisiae.
| S-EPMC2377428 | biostudies-literature
Project description:Colonial tunicates are marine organisms that possess multiple brains simultaneously during their colonial phase. While the cyclical processes of neurogenesis and neurodegeneration characterizing their life cycle have been documented previously, the cellular and molecular changes associated with such processes and their relationship with variation in brain morphology and individual (zooid) behavior throughout adult life remains unknown. Here, we introduce Botryllus schlosseri as an invertebrate model for neurogenesis, neural degeneration, and evolutionary neuroscience. Our analysis reveals that during the weekly colony budding (i.e., asexual reproduction), prior to programmed cell death and removal by phagocytes, decreases in the number of neurons in the adult brain are associated with reduced behavioral response and significant change in the expression of 73 mammalian homologous genes associated with neurodegenerative disease. Similarly, when comparing young colonies (1 to 2 y of age) to those reared in a laboratory for ∼20 y, we found that older colonies contained significantly fewer neurons and exhibited reduced behavioral response alongside changes in the expression of 148 such genes (35 of which were differentially expressed across both timescales). The existence of two distinct yet apparently related neurodegenerative pathways represents a novel platform to study the gene products governing the relationship between aging, neural regeneration and degeneration, and loss of nervous system function. Indeed, as a member of an evolutionary clade considered to be a sister group of vertebrates, this organism may be a fundamental resource in understanding how evolution has shaped these processes across phylogeny and obtaining mechanistic insight.
| S-EPMC9303981 | biostudies-literature
Project description:UNLABELLED:The purpose of this study was to take advantage of the nematode Caenorhabditis elegans to perform a whole-animal chemical screen to identify potential immune activators that may confer protection against bacterial infections. We identified 45 marketed drugs, out of 1,120 studied compounds, that are capable of activating a conserved p38/PMK-1 mitogen-activated protein kinase pathway required for innate immunity. One of these drugs, the last-resort antibiotic colistin, protected against infections by the Gram-negative pathogens Yersinia pestis and Pseudomonas aeruginosa but not by the Gram-positive pathogens Enterococcus faecalis and Staphylococcus aureus. Protection was independent of the antibacterial activity of colistin, since the drug was administered prophylactically prior to the infections and it was also effective against antibiotic-resistant bacteria. Immune activation by colistin is mediated not only by the p38/PMK-1 pathway but also by the conserved FOXO transcription factor DAF-16 and the transcription factor SKN-1. Furthermore, p38/PMK-1 was found to be required in the intestine for immune activation by colistin. Enhanced p38/PMK-1-mediated immune responses by colistin did not reduce the bacterial burden, indicating that the pathway plays a role in the development of host tolerance to infections by Gram-negative bacteria. IMPORTANCE:The innate immune system represents the front line of our defenses against invading microorganisms. Given the ever-increasing resistance to antibiotics developed by bacterial pathogens, the possibility of boosting immune defenses represents an interesting, complementary approach to conventional antibiotic treatments. Here we report that the antibiotic colistin can protect against infections by a mechanism that is independent of its microbicidal activity. Prophylactic treatment with colistin activates a conserved p38/PMK-1 pathway in the intestine that helps the host better tolerate a bacterial infection. Since p38/PMK-1-mediated immune responses appear to be conserved from plants to mammals, colistin may also activate immunity in higher organisms, including humans. Antibiotics with immunomodulatory properties have the potential of improving the long-term outcome of patients with chronic infectious diseases.
| S-EPMC4145682 | biostudies-literature
Project description:Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model's ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda's diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.
| S-EPMC9182318 | biostudies-literature