scholarly journals The thermodynamic uncertainty relation in biochemical oscillations

2019 ◽  
Vol 16 (154) ◽  
pp. 20190098 ◽  
Author(s):  
Robert Marsland ◽  
Wenping Cui ◽  
Jordan M. Horowitz
Keyword(s):  
Free Energy ◽  
Computational Models ◽  
Uncertainty Relation ◽  
Detailed Balance ◽  
Thermodynamic Force ◽  
Cycle Period ◽  
Oscillatory Phase ◽  
Internal States ◽  
High Level ◽  
Random Fluctuations

Living systems regulate many aspects of their behaviour through periodic oscillations of molecular concentrations, which function as ‘biochemical clocks.’ The chemical reactions that drive these clocks are intrinsically stochastic at the molecular level, so that the duration of a full oscillation cycle is subject to random fluctuations. Their success in carrying out their biological function is thought to depend on the degree to which these fluctuations in the cycle period can be suppressed. Biochemical oscillators also require a constant supply of free energy in order to break detailed balance and maintain their cyclic dynamics. For a given free energy budget, the recently discovered ‘thermodynamic uncertainty relation’ yields the magnitude of period fluctuations in the most precise conceivable free-running clock. In this paper, we show that computational models of real biochemical clocks severely underperform this optimum, with fluctuations several orders of magnitude larger than the theoretical minimum. We argue that this suboptimal performance is due to the small number of internal states per molecule in these models, combined with the high level of thermodynamic force required to maintain the system in the oscillatory phase. We introduce a new model with a tunable number of internal states per molecule and confirm that it approaches the optimal precision as this number increases.

scholarly journals Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo-yong Park ◽  
Seok-Jun Hong ◽  
Sofie L. Valk ◽  
Casey Paquola ◽  
Oualid Benkarim ◽  
...  
Keyword(s):  
Data Exchange ◽  
Computational Models ◽  
Simulation Models ◽  
Autism Diagnostic Observation Schedule ◽  
Supervised Machine Learning ◽  
Global Network ◽  
Imaging Data ◽  
Phenotypic Data ◽  
Severity Scores ◽  
High Level

AbstractThe pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

scholarly journals Tuning in scene-preferring cortex for mid-level visual features gives rise to selectivity across multiple levels of stimulus complexity

2021 ◽  
Author(s):  
Shi Pui Donald Li ◽  
Michael F. Bonner
Keyword(s):  
Real World ◽  
Computational Models ◽  
Visual Stream ◽  
Computational Theory ◽  
Low Level ◽  
Multiple Levels ◽  
High Level ◽  
Hierarchical Encoding ◽  
Natural Statistics ◽  
High Throughput Experiments

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit hierarchical computational models of mid-level tuning to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that hierarchical encoding models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to high-level preferences along dimensions of real-world size, fixedness, and naturalness/animacy as well as low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of mid-level visual representation, and they demonstrate how multifaceted selectivity profiles naturally emerge from the hierarchical computations of visual cortex and the natural statistics of images.

scholarly journals Free Energy Principle in Human Postural Control System: Skin Stretch Feedback Reduces the Entropy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pilwon Hur ◽  
Yi-Tsen Pan ◽  
Christian DeBuys
Keyword(s):  
Free Energy ◽  
Control System ◽  
Postural Control ◽  
Standing Balance ◽  
Postural Control System ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Sensory Deficits ◽  
Internal States ◽  
Skin Stretch

AbstractHuman upright standing involves an integration of multiple sensory inputs such as vision, vestibular and somatosensory systems. It has been known that sensory deficits worsen the standing balance. However, how the modulation of sensory information contributes to postural stabilization still remains an open question for researchers. The purpose of this work was to formulate the human standing postural control system in the framework of the free-energy principle, and to investigate the efficacy of the skin stretch feedback in enhancing the human standing balance. Previously, we have shown that sensory augmentation by skin stretch feedback at the fingertip could modulate the standing balance of the people with simulated sensory deficits. In this study, subjects underwent ten 30-second trials of quiet standing balance with and without skin stretch feedback. Visual and vestibular sensory deficits were simulated by having each subject close their eyes and tilt their head back. We found that sensory augmentation by velocity-based skin stretch feedback at the fingertip reduced the entropy of the standing postural sway of the people with simulated sensory deficits. This result aligns with the framework of the free energy principle which states that a self-organizing biological system at its equilibrium state tries to minimize its free energy either by updating the internal state or by correcting body movement with appropriate actions. The velocity-based skin stretch feedback at the fingertip may increase the signal-to-noise ratio of the sensory signals, which in turn enhances the accuracy of the internal states in the central nervous system. With more accurate internal states, the human postural control system can further adjust the standing posture to minimize the entropy, and thus the free energy.

scholarly journals Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

2020 ◽  
Vol 10 (6) ◽  
pp. 20200007 ◽  
Author(s):  
Shunzhou Wan ◽  
Agastya P. Bhati ◽  
Stefan J. Zasada ◽  
Peter V. Coveney
Keyword(s):  
Free Energy ◽  
Initial Conditions ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Energy Prediction ◽  
Massive Scale ◽  
Chaotic Nature ◽  
High Level

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

scholarly journals Estimates for the Effective Permeability of Intact Granite Obtained from the Eastern and Western Flanks of the Canadian Shield

Minerals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 667
Author(s):  
A. P. S. Selvadurai ◽  
A. Blain-Coallier ◽  
P. A. Selvadurai
Keyword(s):  
Effective Permeability ◽  
Computational Models ◽  
Granitic Rock ◽  
Geometric Mean ◽  
Surface Region ◽  
Canadian Shield ◽  
Surface Permeability ◽  
Experimental Values ◽  
The One ◽  
High Level

Granitic rock from the western part of the Canadian Shield is considered as a potential host rock for the siting of a deep geological repository for the storage of heat-emitting high-level nuclear fuel waste. The research program focused on the use of surface permeability measurements conducted at 54 locations on a 300 mm cuboid of granite, obtained from the Lac du Bonnet region in Manitoba, to obtain an estimate for the effective permeability of the cuboid. Companion experiments are conducted on a 280 mm cuboid of granite obtained from Stanstead, Quebec, located in the eastern part of the Canadian Shield. The surface permeabilities for the cuboids of granite are developed from theoretical relationships applicable to experimental situations where steady flow is initiated at a sealed annular surface region with a pressurized central domain. The experimental values for the surface permeability are used with a kriging procedure to estimate the permeability variations within the cuboidal region. The spatial variations of permeability are implemented in computational models of the cuboidal regions to determine the one-dimensional permeabilities in three orthogonal directions. The effective permeability of the granite cuboids is estimated by appeal to the geometric mean. The research provides a non-destructive methodology for estimating the effective permeability of large specimens of rock and the experiments performed give estimates for the effective permeability of the two types of granitic rock obtained from the western and eastern flanks of the Canadian Shield.

scholarly journals DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 898 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Kil To Chong
Keyword(s):  
Computational Models ◽  
Short Term Memory ◽  
Binary Sequence ◽  
The Neural Network ◽  
Proposed Model ◽  
Time Parameters ◽  
Long Short Term Memory ◽  
High Level ◽  
User Friendly ◽  
Fold Cross Validation

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

scholarly journals The origin of parity changes in the solar cycle

2019 ◽  
Vol 489 (3) ◽  
pp. 4329-4337 ◽  
Author(s):  
Soumitra Hazra ◽  
Dibyendu Nandy
Keyword(s):  
Solar Cycle ◽  
Hemispheric Asymmetry ◽  
Sunspot Cycle ◽  
Dynamo Model ◽  
Sunspot Activity ◽  
Cycle Period ◽  
Causal Pathways ◽  
Random Fluctuations ◽  
Simulated Time

ABSTRACT Although sunspots have been systematically observed on the Sun’s surface over the last four centuries, their magnetic properties have been revealed and documented only since the early 1900s. Sunspots typically appear in pairs of opposite magnetic polarities which have a systematic orientation. This polarity orientation is opposite across the equator – a trend that has persisted over the last century. Taken together with the configuration of the global poloidal field of the Sun – this phenomena is consistent with the dipolar parity state of an underlying magnetohydrodynamic dynamo. Although transient hemispheric asymmetry in sunspot emergence is observed, a global parity shift has never been observed. We simulate hemispheric asymmetry through introduction of random fluctuations in a computational dynamo model of the solar cycle and demonstrate that changes in parity are indeed possible in long-term simulations covering thousands of years. Quadrupolar modes are found to exist over significant fraction of the simulated time. In particular, we find that a parity shift in the underlying nature of the sunspot cycle is more likely to occur when sunspot activity dominates in any one hemisphere for a time which is significantly longer than the cycle period. We establish causal pathways connecting hemispheric asymmetry to parity flips mediated via a decoupling of the dynamo cycle period across the two solar hemispheres. Our findings indicate that the solar cycle may have resided in quadrupolar parity states in the past, and provides a possible pathway for predicting parity flips in the future.

scholarly journals Rational models of conditioning

2009 ◽  
Vol 32 (2) ◽  
pp. 204-205 ◽  
Author(s):  
Nick Chater
Keyword(s):  
Reinforcement Learning ◽  
Computational Models ◽  
Rational Models ◽  
Nonhuman Animals ◽  
High Level

AbstractMitchell et al. argue that conditioning phenomena may be better explained by high-level, rational processes, rather than by non-cognitive associative mechanisms. This commentary argues that this viewpoint is compatible with neuroscientific data, may extend to nonhuman animals, and casts computational models of reinforcement learning in a new light.

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

2016 ◽  
Vol 20 (5) ◽  
pp. 743-754 ◽  
Author(s):  
Shunsuke Tatsumi ◽  
◽  
Masanori Hariyama ◽  
Norikazu Ikoma ◽  
Keyword(s):  
Particle Filter ◽  
Fpga Implementation ◽  
Design Tool ◽  
Computational Time ◽  
Major Drawback ◽  
Large Power ◽  
Internal States ◽  
Field Programmable ◽  
Power Delay Product ◽  
High Level

Particle filter is one promising method to estimate the internal states in dynamical systems, and can be used for various applications such as visual tracking and mobile-robot localization. The major drawback of particle filter is its large computational amount, which causes long computational-time and large power-consumption. In order to solve this problem, this paper proposes an Field-Programmable Gate Array (FPGA) platform for particle filter. The platform is designed using the OpenCL-based design tool that allows users to develop using a high-level programming language based on C and to change designs easily for various applications. The implementation results demonstrate the proposed FPGA implementation is 106 times faster than the CPU one, and the power-delay product of the FPGA implementation is 1.1% of the CPU one. Moreover, implementations for three different systems are shown to demonstrate flexibility of the proposed platform.

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