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Quantitative Biology

New submissions

[ total of 19 entries: 1-19 ]
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New submissions for Thu, 23 Nov 17

[1]  arXiv:1711.08032 [pdf, other]
Title: Efficient low-dimensional approximation of continuous attractor networks
Comments: 23 pages, 6 figures, 3 tables. A previous version of this article was published as a thesis chapter of the first author
Subjects: Neurons and Cognition (q-bio.NC)

Continuous "bump" attractors are an established model of cortical working memory for continuous variables and can be implemented using various neuron and network models. Here, we develop a generalizable approach for the approximation of bump states of continuous attractor networks implemented in networks of both rate-based and spiking neurons. The method relies on a low-dimensional parametrization of the spatial shape of firing rates, allowing to apply efficient numerical optimization methods. Using our theory, we can establish a mapping between network structure and attractor properties that allows the prediction of the effects of network parameters on the steady state firing rate profile and the existence of bumps, and vice-versa, to fine-tune a network to produce bumps of a given shape.

[2]  arXiv:1711.08056 [pdf]
Title: Assessing Mortality of Blunt Trauma with Co-morbidity
Comments: 10 pages,2 figures, 37 references
Subjects: Tissues and Organs (q-bio.TO)

Objectives: To obtain a better estimate of the mortality of individuals suffering from blunt force trauma, including co-morbidity. Methodology: The Injury severity Score (ISS) is the default world standard for assessing the severity of multiple injuries. ISS is a mathematical fit to empirical field data. It is demonstrated that ISS is proportional to the Gibbs/Shannon Entropy. A new Entropy measure of morbidity from blunt force trauma including co-morbidity is derived based on the von Neumann Entropy, called the Abbreviated Morbidity Scale (AMS). Results: The ISS trauma measure has been applied to a previously published database, and good correlation has been achieved. Here the existing trauma measure is extended to include the co-morbidity of disease by calculating an Abbreviated Morbidity Score (AMS), which encapsulates the disease co-morbidity in a manner analogous to AIS, and on a consistent Entropy base. Applying Entropy measures to multiple injuries, highlights the role of co-morbidity and that the elderly die at much lower levels of injury than the general population, as a consequence of co-morbidity. These considerations lead to questions regarding current new car assessment protocols, and how well they protect the most vulnerable road users. Keywords: Blunt Force Trauma, Injury Severity Score, Co-morbidity, Entropy.

[3]  arXiv:1711.08064 [pdf]
Title: Effect of sodium chloride (NaCl) on the growth of six Acacia species
Comments: in French
Journal-ref: American Journal of Innovative Research and Applied Sciences 4(4) (2017) 105-113
Subjects: Tissues and Organs (q-bio.TO)

Background: Salinity is one of the major abiotic stresses affecting plant production in arid and semi-arid regions. It causes reduction of cultivable area and combined with other factors, presents a serious threat to food stability in these areas. Context: In front of this problem, the selection of salt tolerant species and varieties remains the best economic approach for exploitation and rehabilitation of salt-affected regions. Objective: The purpose of this study was to assess and compare the seed germination response of six Acacia species under different NaCl concentrations in order to explore opportunities for selection and breeding salt tolerant genotypes. Methods: The salinity effect was examined by measuring some agro-morphological parameters in controlled growth environment using five treatment levels: 0, 100, 200, 300 and 400 mM of NaCl. Results: The analyzed data revealed significant variability in salt response within and between species. All growth parameters were progressively reduced by increased NaCl concentrations. Growth in height, leaf number and total plant dry weight were considered as the most sensitive parameters. However, the growth reduction varied among species in accordance with their tolerance level. It is important to note that all species survived at the highest salinity (400 mM). Whereas A. horrida and A. raddiana were proved to be often the best tolerant, they recorded the lowest reduction percentage at this stage. Conclusion: The genetic variability found in the studied species at seedling stage may be used to select genotypes particularly suitable for rehabilitation and exploitation of lands affected by salinity.

[4]  arXiv:1711.08078 [pdf, other]
Title: HTMoL: full-stack solution for remote access, visualization, and analysis of Molecular Dynamics trajectory data
Subjects: Biomolecules (q-bio.BM); Emerging Technologies (cs.ET)

The field of structural bioinformatics has seen significant advances with the use of Molecular Dynamics (MD) simulations of biological systems. The MD methodology has allowed to explain and discover molecular mechanisms in a wide range of natural processes. There is an impending need to readily share the ever-increasing amount of MD data, which has been hindered by the lack of specialized tools in the past. To solve this problem, we present HTMoL, a state-of-the-art plug-in-free hardware-accelerated web application specially designed to efficiently transfer and visualize raw MD trajectory files on a web browser. Now, individual research labs can publish MD data on the Internet, or use HTMoL to profoundly improve scientific reports by including supplemental MD data in a journal publication. HTMoL can also be used as a visualization interface to access MD trajectories generated on a high-performance computer center directly.
Availability: HTMoL is available free of charge for academic use. All major browsers are supported. A complete online documentation including instructions for download, installation, configuration, and examples is available at the HTMoL website this http URL Supplementary data are available online. Corresponding author: mauricio.carrillo@cinvestav.mx

[5]  arXiv:1711.08079 [pdf, other]
Title: Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes
Comments: To appear in the book "Gene Regulatory Networks: Methods and Protocols"
Subjects: Quantitative Methods (q-bio.QM)

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and thus gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

[6]  arXiv:1711.08145 [pdf, other]
Title: Species notions that combine phylogenetic trees and phenotypic partitions
Comments: 19 pages, 5 figures
Subjects: Populations and Evolution (q-bio.PE)

A recent paper (Manceau and Lambert, 2016) developed a novel approach for describing two well-defined notions of 'species' based on a phylogenetic tree and a phenotypic partition. In this paper, we explore some further combinatorial properties of this approach and describe an extension that allows an arbitrary number of phenotypic partitions to be combined with a phylogenetic tree for these two species notions.

[7]  arXiv:1711.08198 [pdf, other]
Title: A multiobjective deep learning approach for predictive classification in Neuroblastoma
Comments: NIPS ML4H workshop 2017
Subjects: Quantitative Methods (q-bio.QM); Learning (cs.LG)

Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical courses that may vary from spontaneous regression to fatal progression; an accurate patient's risk estimation at diagnosis is essential to design appropriate tumor treatment strategies. Neuroblastoma is a paradigm disease where different diagnostic and prognostic endpoints should be predicted from common molecular and clinical information, with increasing complexity, as shown in the FDA MAQC-II study. Here we introduce the novel multiobjective deep learning architecture CDRP (Concatenated Diagnostic Relapse Prognostic) composed by 8 layers to obtain a combined diagnostic and prognostic prediction from high-throughput transcriptomics data. Two distinct loss functions are optimized for the Event Free Survival (EFS) and Overall Survival (OS) prognosis, respectively. We use the High-Risk (HR) diagnostic information as an additional input generated by an autoencoder embedding. The latter is used as network regulariser, based on a clinical algorithm commonly adopted for stratifying patients from cancer stage, age at insurgence of disease, and MYCN, the specific molecular marker. The architecture was applied to Illumina HiSeq2000 RNA-Seq for 498 neuroblastoma patients (176 at high risk) from the Sequencing Quality Control (SEQC) study, obtaining state-of-art on the diagnostic endpoint and improving prediction of prognosis over the HR cohort.

[8]  arXiv:1711.08309 [pdf, other]
Title: Subthreshold signal encoding in coupled FitzHugh-Nagumo neurons
Subjects: Neurons and Cognition (q-bio.NC)

Despite intensive research, the mechanisms underlying how neurons encode external inputs remain poorly understood. Recent work has focused on the response of a single neuron to a weak, subthreshold periodic signal. By simulating the FitzHugh-Nagumo stochastic model and then using a symbolic method to analyze the firing activity of the neuron, preferred and infrequent spike patterns (defined by the relative timing of the spikes) were detected, whose probabilities encode information about the signal. As not individual neurons in isolation but neuronal populations are responsible for the emergence of complex behaviors, a relevant question is whether this coding mechanism is robust when the neuron is not isolated. We study how a second neuron, which does not perceive the subthreshold signal, affects the detection and the encoding of the signal, done by the first neuron. Through simulations of two coupled FitzHugh-Nagumo neurons we show that the coding mechanism is indeed robust, as the neuron that perceives the signal fires a spike train that has symbolic patterns whose probabilities depend on the features of the signal. Moreover, we show that the second neuron facilitates the detection of the signal, by lowering the firing threshold of the first neuron. This in turn decreases the internal noise level need to fire the spikes that encode the signal. We also show that the probabilities of the symbolic patterns achieve maximum or minimum values when the period of the external signal is close to (or is half of) the mean firing period of the neuron.

Cross-lists for Thu, 23 Nov 17

[9]  arXiv:1711.08063 (cross-list from stat.ML) [pdf]
Title: Clonal analysis of newborn hippocampal dentate granule cell proliferation and development in temporal lobe epilepsy
Comments: 44 pages, 6 figures
Journal-ref: eNeuro. 2015;2(6):ENEURO.0087-15.2015. doi:10.1523/ENEURO.0087-15.2015
Subjects: Machine Learning (stat.ML); Neurons and Cognition (q-bio.NC)

Hippocampal dentate granule cells are among the few neuronal cell types generated throughout adult life in mammals. In the normal brain, new granule cells are generated from progenitors in the subgranular zone and integrate in a typical fashion. During the development of epilepsy, granule cell integration is profoundly altered. The new cells migrate to ectopic locations and develop misoriented basal dendrites. Although it has been established that these abnormal cells are newly generated, it is not known whether they arise ubiquitously throughout the progenitor cell pool or are derived from a smaller number of bad actor progenitors. To explore this question, we conducted a clonal analysis study in mice expressing the Brainbow fluorescent protein reporter construct in dentate granule cell progenitors. Mice were examined 2 months after pilocarpine-induced status epilepticus, a treatment that leads to the development of epilepsy. Brain sections were rendered translucent so that entire hippocampi could be reconstructed and all fluorescently labeled cells identified. Our findings reveal that a small number of progenitors produce the majority of ectopic cells following status epilepticus, indicating that either the affected progenitors or their local microenvironments have become pathological. By contrast, granule cells with basal dendrites were equally distributed among clonal groups. This indicates that these progenitors can produce normal cells and suggests that global factors sporadically disrupt the dendritic development of some new cells. Together, these findings strongly predict that distinct mechanisms regulate different aspects

[10]  arXiv:1711.08095 (cross-list from cs.LG) [pdf, ps, other]
Title: SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis
Comments: 8 pages
Subjects: Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)

Motivation: How do we integratively analyze large-scale multi-platform genomic data that are high dimensional and sparse? Furthermore, how can we incorporate prior knowledge, such as the association between genes, in the analysis systematically? Method: To solve this problem, we propose a Scalable Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts parallel stochastic gradient descent approach on the proposed parallelizable network constrained optimization function. SNeCT decomposition is applied to tensor constructed from large scale multi-platform multi-cohort cancer data, PanCan12, constrained on a network built from PathwayCommons database. Results: The decomposed factor matrices are applied to stratify cancers, to search for top-k similar patients, and to illustrate how the matrices can be used for personalized interpretation. In the stratification test, combined twelve-cohort data is clustered to form thirteen subclasses. The thirteen subclasses have a high correlation to tissue of origin in addition to other interesting observations, such as clear separation of OV cancers to two groups, and high clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the top-k search, a new patient's genomic profile is generated and searched against existing patients based on the factor matrices. The similarity of the top-k patient to the query is high for 23 clinical features, including estrogen/progesterone receptor statuses of BRCA patients with average precision value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also provide an illustration of how the factor matrices can be used for interpretable personalized analysis of each patient.

[11]  arXiv:1711.08260 (cross-list from physics.bio-ph) [pdf, other]
Title: Genetic noise mechanism for power-law switching in bacterial flagellar motors
Comments: 6 pages, 4 figures
Subjects: Biological Physics (physics.bio-ph); Molecular Networks (q-bio.MN); Subcellular Processes (q-bio.SC)

Switching of the direction of flagella rotations is the key control mechanism governing the chemotactic activity of E. coli and many other bacteria. Power-law distributions of switching times are most peculiar because their emergence cannot be deduced from simple thermodynamic arguments. Recently it was suggested that by adding finite-time correlations into Gaussian fluctuations regulating the energy height of barrier between the two rotation states, one can generate a power-law switching statistics. By using a simple model of a regulatory pathway, we demonstrate that the required amount of correlated `noise' can be produced by finite number fluctuations of reacting protein molecules, a condition common to the intracellular chemistry. The corresponding power-law exponent appears as a tunable characteristic controlled by parameters of the regulatory pathway network such as equilibrium number of molecules, sensitivities, and the characteristic relaxation time.

[12]  arXiv:1711.08291 (cross-list from math.OC) [pdf, other]
Title: Variance reduction for antithetic integral control of stochastic reaction networks
Comments: 41 pages, 33 figures, 2 tables
Subjects: Optimization and Control (math.OC); Systems and Control (cs.SY); Molecular Networks (q-bio.MN)

The antithetic integral feedback motif recently introduced in Briat, Gupta & Khammash (Cell Systems, 2017) is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. However, it was observed that it also leads to a higher variance in the controlled network than that obtained when using a constitutive (i.e. open-loop) control strategy. This was interpreted as the cost of the adaptation property and may be viewed as a performance deterioration for the overall controlled network. To decrease this variance and improve the performance, we propose to combine the antithetic integral feedback motif with a negative feedback strategy. Both theoretical and numerical results are obtained. The theoretical ones are based on a tailored moment closure method allowing one to obtain approximate expressions for the stationary variance for the controlled network and predict that the variance can indeed be decreased by increasing the strength of the negative feedback. Numerical results verify the accuracy of this approximation and show that the controlled species variance can indeed be decreased, sometimes below its constitutive level. Three molecular networks are considered in order to verify the wide applicability of two types of negative feedback strategies. The main conclusion is that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.

[13]  arXiv:1711.08359 (cross-list from stat.ML) [pdf, ps, other]
Title: Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
Comments: Presented at NIPS 2017 Workshop on Machine Learning for Health
Subjects: Machine Learning (stat.ML); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.

Replacements for Thu, 23 Nov 17

[14]  arXiv:1505.05895 (replaced) [pdf]
Title: A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies
Comments: 14 pages, 9 figures, 8 tables. Minor revision, additin of a new example and new title. Software: this https URL
Subjects: Quantitative Methods (q-bio.QM); Populations and Evolution (q-bio.PE)
[15]  arXiv:1612.04532 (replaced) [pdf, ps, other]
Title: Influence of cell-cell interactions on the population growth rate in a tumor
Authors: Yong Chen
Comments: 5 pages, 2 figures
Journal-ref: Commun. Theor. Phys. 68, 798 (2017)
Subjects: Populations and Evolution (q-bio.PE); Biological Physics (physics.bio-ph); Tissues and Organs (q-bio.TO)
[16]  arXiv:1703.04554 (replaced) [pdf, ps, other]
Title: Multi-Patch and Multi-Group Epidemic Models: A New Framework
Comments: 29 pages, 10 figures
Journal-ref: Journal of Mathematical Biology, 2017
Subjects: Populations and Evolution (q-bio.PE)
[17]  arXiv:1710.10428 (replaced) [pdf, other]
Title: Generalized Phase Representation of Integrate-and-Fire Models
Authors: Dongsung Huh
Subjects: Dynamical Systems (math.DS); Neurons and Cognition (q-bio.NC)
[18]  arXiv:1711.06967 (replaced) [pdf]
Title: Flow state and the underlying neural dynamics
Subjects: Neurons and Cognition (q-bio.NC)
[19]  arXiv:1711.07205 (replaced) [pdf, other]
Title: Decoding of neural data using cohomological learning
Comments: 12 pages
Subjects: Neurons and Cognition (q-bio.NC); Algebraic Topology (math.AT)
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