Notices by arXiv Quantitative Biology (arxivbio@qoto.org)

arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Friday, 13-Dec-2019 03:25:06 UTC arXiv Quantitative Biology

Adaptive Markov State Model estimation using short reseeding trajectories. (arXiv:1912.05724v1 [q-bio.BM]) http://arxiv.org/abs/1912.05724
In conversation Friday, 13-Dec-2019 03:25:06 UTC from qoto.org permalink
Attachments
1. Adaptive Markov State Model estimation using short reseeding trajectories
  
  In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow timescales. A promising approach to enhanced sampling of MSMs is to use so-called "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape, and for mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations, and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Monday, 09-Dec-2019 03:25:03 UTC arXiv Quantitative Biology

Dynamic metabolic resource allocation based on the maximum entropy principle. (arXiv:1906.03919v3 [q-bio.MN] UPDATED) http://arxiv.org/abs/1906.03919
In conversation Monday, 09-Dec-2019 03:25:03 UTC from qoto.org permalink
Attachments
1. Dynamic metabolic resource allocation based on the maximum entropy principle
  
  This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. This concept both generalises and unifies prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Wednesday, 04-Dec-2019 03:25:07 UTC arXiv Quantitative Biology

A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents. (arXiv:1912.01359v1 [eess.IV]) http://arxiv.org/abs/1912.01359
In conversation Wednesday, 04-Dec-2019 03:25:07 UTC from qoto.org permalink
Attachments
1. A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents
  
  Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle human data. Nonetheless, these tools are not equally useful to handle the data derived from animal studies, especially from rodents. This represents a major problem to the field because rodent studies generate larger datasets from larger populations, which implies that preprocessing these images manually to remove the skull becomes a bottleneck in the data analysis pipeline. In this study, we address this problem by implementing a neural network based method that uses a U-Net architecture to segment the brain area into a mask and removing the skull and other tissues from the image. We demonstrate several strategies to speed up the process of generating the training dataset using watershedding and several strategies for data augmentation that allowed to train faster the U-Net to perform the segmentation. Finally, we deployed the trained network freely available.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Wednesday, 13-Nov-2019 03:25:06 UTC arXiv Quantitative Biology

The 3D genome shapes the regulatory code of developmental genes. (arXiv:1911.04779v1 [q-bio.GN]) http://arxiv.org/abs/1911.04779
In conversation Wednesday, 13-Nov-2019 03:25:06 UTC from qoto.org permalink
Attachments
1. The 3D genome shapes the regulatory code of developmental genes
  
  We revisit the notion of gene regulatory code in embryonic development in the light of recent findings about genome spatial organisation. By analogy with the genetic code, we posit that the concept of code can only be used if the corresponding adaptor can clearly be identified. An adaptor is here defined as an intermediary physical entity mediating the correspondence between codewords and objects in a gratuitous and evolvable way. In the context of the gene regulatory code, the encoded objects are the gene expression levels, while the concentrations of specific transcription factors in the cell nucleus provide the codewords. The notion of code is meaningful in the absence of direct physicochemical relationships between the objects and the codewords, when the mediation by an adaptor is required. We propose that a plausible adaptor for this code is the gene domain, that is, the genome segment delimited by topological insulators and comprising the gene and its enhancer regulatory sequences. We review recent evidences, based on genome-wide chromosome conformation capture experiments, showing that preferential contact domains found in metazoan genomes are the physical traces of gene domains. Accordingly, genome 3D folding plays a direct role in shaping the developmental gene regulatory code.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Wednesday, 13-Nov-2019 03:25:04 UTC arXiv Quantitative Biology

Communication in Plants: Comparison of Multiple Action Potential and Mechanosensitive Signals with Experiments. (arXiv:1911.04895v1 [q-bio.MN]) http://arxiv.org/abs/1911.04895
In conversation Wednesday, 13-Nov-2019 03:25:04 UTC from qoto.org permalink
Attachments
1. Communication in Plants: Comparison of Multiple Action Potential and Mechanosensitive Signals with Experiments
  
  Both action potentials and mechanosensitive signalling are an important communication mechanisms in plants. Considering an information theoretic framework, this paper explores the effective range of multiple action potentials for a long chain of cells (i.e., up to 100) in different configurations, and introduces the study of multiple mechanosensitive activation signals (generated due to a mechanical stimulus) in plants. For both these signals, we find that the mutual information per cell and information propagation speed tends to increase up to a certain number of receiver cells. However, as the number of cells increase beyond 10 to 12, the mutual information per cell starts to decrease. To validate our model and results, we include an experimental verification of the theoretical model, using a PhytlSigns biosignal amplifier, allowing us to measure the magnitude of the voltage associated with the multiple AP and mechanosensitive activation signals induced by different stimulus in plants. Experimental data is used to calculate the mutual information and information propagation speed, which is compared with corresponding numerical results. Since these signals are used for a variety of important tasks within the plant, understanding them may lead to new bioengineering methods for plants.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Monday, 11-Nov-2019 03:25:06 UTC arXiv Quantitative Biology

A Numerical Study of the Time of Extinction in a Class of Systems of Spiking Neurons. (arXiv:1911.02609v1 [cs.NE]) http://arxiv.org/abs/1911.02609
In conversation Monday, 11-Nov-2019 03:25:06 UTC from qoto.org permalink
Attachments
1. A Numerical Study of the Time of Extinction in a Class of Systems of Spiking Neurons
  
  In this paper we present a numerical study of a mathematical model of spiking neurons introduced by Ferrari et al. in an article entitled Phase transition forinfinite systems of spiking neurons. In this model we have a countable number of neurons linked together in a network, each of them having a membrane potential taking value in the integers, and each of them spiking over time at a rate which depends on the membrane potential through some rate function $ϕ$. Beside being affected by a spike each neuron can also be affected by leaking. At each of these leak times, which occurs for a given neuron at a fixed rate $γ$, the membrane potential of the neuron concerned is spontaneously reset to $0$. A wide variety of versions of this model can be considered by choosing different graph structures for the network and different activation functions. It was rigorously shown that when the graph structure of the network is the one-dimensional lattice with a hard threshold for the activation function, this model presents a phase transition with respect to $γ$, and that it also presents a metastable behavior. By the latter we mean that in the sub-critical regime the re-normalized time of extinction converges to an exponential random variable of mean 1. It has also been proven that in the super-critical regime the renormalized time of extinction converges in probability to 1. Here, we investigate numerically a richer class of graph structures and activation functions. Namely we investigate the case of the two dimensional and the three dimensional lattices, as well as the case of a linear function and a sigmoid function for the activation function. We present numerical evidence that the result of metastability in the sub-critical regime holds for these graphs and activation functions as well as the convergence in probability to $1$ in the super-critical regime.
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Monday, 09-Sep-2019 03:25:07 UTC arXiv Quantitative Biology

Additive function approximation in the brain. (arXiv:1909.02603v1 [cs.NE]) http://arxiv.org/abs/1909.02603
In conversation Monday, 09-Sep-2019 03:25:07 UTC from qoto.org permalink
Attachments
1. Additive function approximation in the brain
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Friday, 06-Sep-2019 03:25:07 UTC arXiv Quantitative Biology

Towards a general model for psychopathology. (arXiv:1909.02199v1 [q-bio.NC]) http://arxiv.org/abs/1909.02199
In conversation Friday, 06-Sep-2019 03:25:07 UTC from qoto.org permalink
Attachments
1. Towards a general model for psychopathology
arXiv Quantitative Biology (arxivbio@qoto.org)'s status on Wednesday, 04-Sep-2019 03:25:06 UTC arXiv Quantitative Biology

Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks. (arXiv:1909.00038v1 [math.PR]) http://arxiv.org/abs/1909.00038
In conversation Wednesday, 04-Sep-2019 03:25:06 UTC from qoto.org permalink
Attachments
1. Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks

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Notices by arXiv Quantitative Biology (arxivbio@qoto.org)

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