Towards bridging the gap from molecular forces to the movement of organisms

Muscles are responsible for generating the forces required for the movement of multicellular organisms. Microscopically, these forces arise as a consequence of motor proteins (myosin) pulling and sliding along actin filaments. Current knowledge states that the molecular forces between actin and myosin are linear in nature [Huxley and Simmons (1971) Nature (London) 233, 533–538] and that the physiologically observed non-linearities (e.g. Hill's force–velocity relationship) are a consequence of non-linearities in the attachment/detachment ratios. However, this view has been disputed recently [Nielsen (2002) J. Theor. Biol. 219, 99–119], inspired by results from protein pulling experiments showing that proteins often have non-linear entropic force–extension profiles. Irrespective of the case, the present study aims at integrating such basic force-producing properties into large-scale simulations of muscle, which may accommodate macroscopic properties of muscles, e.g. the catch-like effect, the Henneman principle and accurate twitch force and motor unit size distributions. As a test of the underlying principles, a model of the biceps caput breve muscle is presented and compared with experimental data.

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Rheology of crossbridge ensembles

10.1101/2021.12.29.474482 ◽

2021 ◽

Author(s):

Khoi D Nguyen ◽

Madhusudhan Venkadesan

Keyword(s):

Motor Control ◽

Large Scale ◽

Simplified Models ◽

Time Constants ◽

Complex Models ◽

Force Velocity ◽

Large Scale Simulations ◽

Analytical Expressions ◽

Musculoskeletal Simulations

Muscle rheology, or the characterization of a muscle's response to external mechanical perturbations, is crucial to an animal's motor control and locomotive abilities. How the rheology emerges from the ensemble dynamics of microscopic actomyosin crossbridges known to underlie muscle forces is however a longstanding question. Classical descriptions in terms of force-length and force-velocity relationships capture only part of the rheology, namely under steady but not dynamical conditions. Although much is known about the actomyosin machinery, current mathematical models that describe the behavior of a population or an ensemble of crossbridges are plagued by an excess of parameters and computational complexity that limits their usage in large-scale musculoskeletal simulations. In this paper, we examine models of crossbridge dynamics of varying complexity and show that the emergent rheology of an ensemble of crossbridges can be simplified to a few dominant time-constants associated with intrinsic dynamical processes. For Huxley's classical two-state crossbridge model, we derive exact analytical expressions for the emergent ensemble rheology and find that it is characterized by a single time-constant. For more complex models with up to five crossbridge states, we show that at most three time-constants are needed to capture the ensemble rheology. Our results thus yield simplified models comprising of a few time-constants for muscle's bulk rheological response that can be readily used in large-scale simulations without sacrificing the model's interpretability in terms of the underlying actomyosin crossbridge dynamics.

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An HPC Framework for Large Scale Simulations and Visualizations of Oil Spill Trajectories

Coastal Hazards ◽

10.1061/9780784412664.002 ◽

2013 ◽

Cited By ~ 1

Author(s):

Jian Tao ◽

Werner Benger ◽

Kelin Hu ◽

Edwin Mathews ◽

Marcel Ritter ◽

...

Keyword(s):

Oil Spill ◽

Large Scale ◽

Large Scale Simulations

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Large-Scale Simulations of Monolithic and Segmented Projectiles Impacting Spaced Armor

10.21236/ada219374 ◽

1990 ◽

Author(s):

Daniel R. Scheffler ◽

Thomas M. Sherrick

Keyword(s):

Large Scale ◽

Large Scale Simulations

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Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽

10.1093/sleep/zsab103 ◽

2021 ◽

Author(s):

Dorothee Fischer ◽

Elizabeth B Klerman ◽

Andrew J K Phillips

Keyword(s):

Large Scale ◽

Research Question ◽

Group Differences ◽

Regularity Index ◽

Wake Patterns ◽

Health Related ◽

Phase Deviation ◽

Unbiased Estimates ◽

Large Scale Simulations ◽

Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

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Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽

10.3390/a14050154 ◽

2021 ◽

Vol 14 (5) ◽

pp. 154

Author(s):

Marcus Walldén ◽

Masao Okita ◽

Fumihiko Ino ◽

Dimitris Drikakis ◽

Ioannis Kokkinakis

Keyword(s):

Large Scale ◽

Data Driven ◽

Data Sets ◽

Output Constraints ◽

Data Driven Approach ◽

Scientific Simulations ◽

Multiple Metrics ◽

In Transit ◽

Multiple Compression ◽

Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

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Brain stem – from general view to computational model based on switchboard rules of operation

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2019-0059 ◽

2019 ◽

Vol 16 (1) ◽

Author(s):

Włodzisław Duch ◽

Dariusz Mikołajewski

Keyword(s):

Brain Stem ◽

Large Scale ◽

Ionic Channels ◽

General View ◽

The Central Nervous System ◽

Network States ◽

Large Scale Simulations ◽

Influence Of Noise ◽

The Brain ◽

Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

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Genome-wide Analysis of Alternative Pre-mRNA Splicing

Journal of Biological Chemistry ◽

10.1074/jbc.r700033200 ◽

2007 ◽

Vol 283 (3) ◽

pp. 1229-1233 ◽

Cited By ~ 80

Author(s):

Claudia Ben-Dov ◽

Britta Hartmann ◽

Josefin Lundgren ◽

Juan Valcárcel

Keyword(s):

Alternative Splicing ◽

Large Scale ◽

Mrna Splicing ◽

Diagnostic Tools ◽

Primary Transcript ◽

Genome Wide ◽

Human Genes ◽

Multicellular Organisms ◽

Eukaryotic Genomes ◽

Key Questions

Alternative splicing of mRNA precursors allows the synthesis of multiple mRNAs from a single primary transcript, significantly expanding the information content and regulatory possibilities of higher eukaryotic genomes. High-throughput enabling technologies, particularly large-scale sequencing and splicing-sensitive microarrays, are providing unprecedented opportunities to address key questions in this field. The picture emerging from these pioneering studies is that alternative splicing affects most human genes and a significant fraction of the genes in other multicellular organisms, with the potential to greatly influence the evolution of complex genomes. A combinatorial code of regulatory signals and factors can deploy physiologically coherent programs of alternative splicing that are distinct from those regulated at other steps of gene expression. Pre-mRNA splicing and its regulation play important roles in human pathologies, and genome-wide analyses in this area are paving the way for improved diagnostic tools and for the identification of novel and more specific pharmaceutical targets.

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Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

Biodiversity Data Journal ◽

10.3897/bdj.7.e33303 ◽

2019 ◽

Vol 7 ◽

Author(s):

Brian Stucky ◽

James Balhoff ◽

Narayani Barve ◽

Vijay Barve ◽

Laura Brenskelle ◽

...

Keyword(s):

Natural History ◽

Large Scale ◽

Use Cases ◽

Data Systems ◽

Multiple Sources ◽

History Data ◽

Insect Ecology ◽

Many Sources ◽

Multicellular Organisms ◽

Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

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