Time of Day Bias for Biological Sampling in Studies of Mammary Cancer
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE249022 | GEO | 2024/01/09
REPOSITORIES: GEO
Similar Datasets
Project description:Time of Day Bias for Biological Sampling in Studies of Mammary Cancer
| PRJNA1047032 | ENA
Project description:Biological processes are optimized by circadian and circannual biological timing systems. In vertebrates, the pineal gland plays an essential role in these systems by converting time into a hormonal signal, melatonin; in all vertebrates, circulating melatonin is elevated at night, independent of lifestyle. We have analyzed the rat pineal transcriptome at mid-day and mid-night to identify genes that exhibit night/day changes in expression. Keywords: Time course (2 points)
2009-01-15 | GSE12342 | GEO
Project description:Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
2024-09-02 | BIOMD0000000486 | BioModels
Project description:Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
2024-09-02 | BIOMD0000000484 | BioModels
Project description:Time-restricted eating is emerging as a promising dietary intervention that prevents cardiometabolic disease; however, the molecular mechanisms remain largely unknown. It is generally thought that time-restricted feeding-as it is known in animal studies-reprograms circadian rhythms in peripheral organs including skeletal muscle. Recent studies reported that peripheral organs entrain to time-restricted feeding in a highly diverse tissue-specific manner, which is indicated by the kinetics of the circadian clock in peripheral organs, transcriptome and metabolome. A discrepancy is found in the circadian coherence between rhythmic transcripts and rhythmic metabolites, suggesting the presence of additional regulation at the proteome level. To explore the landscape of rhythmic proteins in skeletal muscle from time-restricted fed mice, we sampled 50 mouse tibialis anterior muscle tissues from 11-week-old C57BL/6J female mice. These mice had been fed day time-restricted feeding for 3 weeks, during which food was accessible between Zeitgeber time (ZT) 0 h and ZT 12 h. Samples were dissected and snap-frozen in liquid nitrogen every two hours starting from ZT0 of the first day to ZT0 of the third day. This sampling scheme covers two complete day/night cycles and has two biological replicates per time point for a total of 25 time points. Next, we performed mass spectrometry-based parallel accumulation–serial fragmentation combined with data-independent acquisition (diaPASEF) quantitative proteomics to analyze these mouse skeletal muscle tissues. Together, we have generated a dataset that provide insights into circadian rhythms of skeletal muscle under the regulation of time-restricted feeding in mice.
2023-01-10 | PXD039355 |
Project description:F. Schl�gl. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift f�r Physik 253, 2 (1972).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
2024-09-02 | BIOMD0000000485 | BioModels
Project description:Michael Samoilov, Sergey Plyasunov & Adam P. Arkin. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proceedings of the National Academy of Sciences 102, 7 (2005).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
2024-09-02 | BIOMD0000000487 | BioModels
Project description:Biological processes are optimized by circadian and circannual biological timing systems. In vertebrates, the pineal gland plays an essential role in these systems by converting time into a hormonal signal, melatonin; in all vertebrates, circulating melatonin is elevated at night, independent of lifestyle. We have analyzed the rat pineal transcriptome at mid-day and mid-night to identify genes that exhibit night/day changes in expression. We have also used these data to characterize the non-rhythmic features of the transcriptome that set the pineal gland apart from other tissues by comparing them to the median expression in other rat tissues as found in the Genomics Institute of the Novartis Research Foundation (GNF), Entrez Gene Expression Omnibus (GEO) dataset GDS589. Keywords: Time course (2 points)
2009-01-15 | GSE12341 | GEO
Project description:Background. Chronic fatiguing illness remains a poorly understood syndrome of unknown pathogenesis. We attempted to identify biomarkers for chronic fatiguing illness using microarrays to query the transcriptome in peripheral blood leukocytes. Methods. Cases were 44 individuals who were clinically evaluated and found to meet standard international criteria for chronic fatigue syndrome or idiopathic chronic fatigue, and controls were their monozygotic co-twins who were clinically evaluated and never had even one month of impairing fatigue. Biological sampling conditions were standardized and RNA stabilizing media were used. These methodological features provide rigorous control for bias resulting from case-control mismatched ancestry and experimental error. Individual gene expression profiles were assessed using Affymetrix Human Genome U133 Plus 2.0 arrays. Findings. There were no significant differences in gene expression for any transcript. Conclusions. Contrary to our expectations, we were unable to identify a biomarker for chronic fatiguing illness in the transcriptome of peripheral blood leukocytes suggesting that positive findings in prior studies may have resulted from experimental bias.
2009-05-12 | GSE16059 | GEO