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Quantitative Biology

New submissions

[ total of 22 entries: 1-22 ]
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New submissions for Thu, 22 Aug 19

[1]  arXiv:1908.07520 [pdf]
Title: Transcriptomic Causal Networks identified patterns of differential gene regulation in human brain from Schizophrenia cases versus controls
Subjects: Genomics (q-bio.GN)

Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, which work in a coordinated way. However, the vast majority of studies focus on the differential expression of one individual gene at a time. Here, we aim to provide insight into the underlying relationships of the genes expressed in the human brain in cases with schizophrenia (SCZ) and controls. We introduced a novel approach to identify differential gene regulatory patterns and identify a set of essential genes in the brain tissue. Our method integrates genetic, transcriptomic, and Hi-C data and generates a transcriptomic-causal network. Employing this approach for analysis of RNA-seq data from CommonMind Consortium, we identified differential regulatory patterns for SCZ cases and control groups to unveil the mechanisms that control the transcription of the genes in the human brain. Our analysis identified modules with a high number of SCZ-associated genes as well as assessing the relationship of the hubs with their down-stream genes in both, cases and controls. In addition, the results identified essential genes for brain function and suggested new genes putatively related to SCZ.

[2]  arXiv:1908.07570 [pdf, other]
Title: How kinesin waits for ATP affects the nucleotide and load dependence of the stepping kinetics
Subjects: Subcellular Processes (q-bio.SC); Soft Condensed Matter (cond-mat.soft); Biological Physics (physics.bio-ph)

Dimeric molecular motors walk on polar tracks by binding and hydrolyzing one ATP per step. Despite tremendous progress, the waiting state for ATP binding in the well-studied kinesin that walks on microtubule (MT), remains controversial. One experiment suggests that in the waiting state both heads are bound to the MT, while the other shows that ATP binds to the leading head after the partner head detaches. To discriminate between these two scenarios, we developed a theory to calculate accurately several experimentally measurable quantities as a function of ATP concentration and resistive force.
In particular, we predict that measurement of the randomness parameter could discriminate between the two scenarios for the waiting state of kinesin, thereby resolving this standing controversy.

[3]  arXiv:1908.07611 [pdf]
Title: A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during a pathogen challenge
Subjects: Tissues and Organs (q-bio.TO); Quantitative Methods (q-bio.QM)

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory issues like sepsis, one of the ten leading causes of death in the United States and one of the three leading causes of death in the intensive care unit. Understanding the effects of inflammation on the autonomic control system can improve a patient's chance of recovery after an inflammatory event such as surgery. Though the effects of the autonomic response on the inflammatory system are well defined, there remains a gap in understanding the reverse response. Specifically, the impact of the inflammatory response on the autonomic control system remains unknown. In this study, we investigate hypothesized interactions of the inflammatory system with the thermal and cardiovascular regulatory systems in response to an endotoxin challenge using mathematical modeling. We calibrate the model to data from two independent studies: a) of the inflammatory response in healthy young men and b) a comparative study of the inflammatory response between mice and humans. Simulation analysis is used to explore how the model responds to pathological input and treatment, specifically antibiotics, antipyretics, vasopressors, and combination therapy. Our findings show that multimodal treatment that simultaneously targets both the pathogen and the infection symptoms gives the most favorable recovery outcome.

[4]  arXiv:1908.07631 [pdf, other]
Title: Mapping the bacterial ways of life
Comments: 4 figures, 7 supplementary tables
Subjects: Populations and Evolution (q-bio.PE)

The rise in the availability of bacterial genomes defines a need for synthesis: abstracting from individual taxa, to see the larger patterns of bacterial lifestyles across microbial systems. In community ecology, a central organising theory is the niche concept. A niche is a set of capabilities that enables a population's persistence, and defines its impact on the environment. The set of possible niches is the niche space, a conceptual space delineating the ways in which persistence in an ecosystem is possible. Understanding the structure of the niche space is perhaps the central question in ecology. Here we use data analysis to map the space of metabolic networks describing thousands of bacterial genera. The results reveal a niche space with continuous branching geometry, whose branches correspond to adaptations to habitats, hosts, and unique resource use strategies. This provides a new perspective on the functional capabilities of known bacteria and lays an ecological foundation for the study of microbiomes. The variables defined here constitute a new way to classify and systematise bacterial populations in ecological terms. We regard this as an important step in the quest to bring methods and results from ecology to bear on microbial communities.

[5]  arXiv:1908.07662 [pdf, other]
Title: Assessment of protein assembly prediction in CASP13
Subjects: Biomolecules (q-bio.BM)

We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality. Looking at the tertiary structure predictions we observe that ignoring the oligomeric state of the targets hinders modelling success. We also note that some contact prediction groups successfully predicted homomeric interfacial contacts, though it appears that these predictions were not used for assembly modelling. Homology modelling with sizeable human intervention appears to form the basis of the assembly prediction techniques in this round of CASP. Future developments should see more integrated approaches to modelling where multiple subunits are a natural part of the modelling process, which would benefit the structure prediction field as a whole.

[6]  arXiv:1908.07837 [pdf]
Title: Mathematical analysis of a two-strain disease model with amplification
Comments: 22 pages, 11 figures
Subjects: Populations and Evolution (q-bio.PE)

We investigate a two-strain disease model with amplification to simulate the prevalence of drug-susceptible (s) and drug-resistant (m) disease strains. We model the emergence of drug resistance as a consequence of inadequate treatment, i.e. amplification. We perform a dynamical analysis of the resulting system and find that the model contains three equilibrium points: a disease-free equilibrium; a mono-existent disease-endemic equilibrium with respect to the drug-resistant strain; and a co-existent disease-endemic equilibrium where both the drug-susceptible and drug-resistant strains persist. We found two basic reproduction numbers: one associated with the drug-susceptible strain $R_{0s}$; the other with the drug-resistant strain $R_{0m}$,and showed that at least one of the strains can spread in a population if ($R_{0s}$,$R_{0m}$) > 1 (epidemic).Furthermore, we also showed that if $R_{0m}$ > max($R_{0s}$,1), the drug-susceptible strain dies out but the drug-resistant strain persists in the population; however if $R_{0s}$ > max($R_{0m}$,1), then both the drug-susceptible and drug-resistant strains persist in the population. We conducted a local stability analysis of the system equilibrium points using the Routh-Hurwitz conditions and a global stability analysis using appropriate Lyapunov functions. Sensitivity analysis was used to identify the most important model parameters through the partial rank correlation coefficient (PRCC) method. We found that the contact rate of both strains had the largest influence on prevalence. We also investigated the impact of amplification and treatment rates of both strains on the equilibrium prevalence of infection; results suggest that poor quality treatment make coexistence more likely but increase the relative abundance of resistant infections.

[7]  arXiv:1908.07884 [pdf, other]
Title: Evolution of specialization in dynamic fluids
Comments: 20 pages, 5 figures
Subjects: Populations and Evolution (q-bio.PE); Biological Physics (physics.bio-ph)

Previously we found mechanical factors involving diffusion and fluid shear promote evolution of social behavior in microbial populations (Uppal and Vural 2018). Here, we extend this model to study the evolution of specialization using realistic physical simulations of bacteria that secrete two public goods in a dynamic fluid. Through this first principles approach, we find physical factors such as diffusion, flow patterns, and decay rates are as influential as fitness economics in governing the evolution of community structure, to the extent that when mechanical factors are taken into account, (1) Generalist communities can resist becoming specialists, despite the invasion fitness of specialization (2) Generalist and specialists can both resist cheaters despite the invasion fitness of free-riding. (3) Multiple community structures can coexist despite the opposing force of competitive exclusion. Our results emphasize the role of spatial assortment and physical forces on niche partitioning and the evolution of diverse community structures.

[8]  arXiv:1908.07935 [pdf, other]
Title: An Experimental Protocol to Derive and Validate a Quantum Model of Decision-Making
Subjects: Neurons and Cognition (q-bio.NC); Quantum Physics (quant-ph)

This study utilises an experiment famous in quantum physics, the Stern-Gerlach experiment, to inform the structure of an experimental protocol from which a quantum cognitive decision model can be developed. The 'quantumness' of this model is tested by computing a discrete quasi-probabilistic Wigner function. Based on theory from quantum physics, our hypothesis is that the Stern-Gerlach protocol will admit negative values in the Wigner function, thus signalling that the cognitive decision model is quantum. A crowdsourced experiment of two images was used to collect decisions around three questions related to image trustworthiness. The resultant data was used to instantiate the quantum model and compute the Wigner function. Negative values in the Wigner functions of both images were encountered, thus substantiating our hypothesis. Findings also revealed that the quantum cognitive model was a more accurate predictor of decisions when compared to predictions computed using Bayes' rule.

[9]  arXiv:1908.07957 [pdf, other]
Title: DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis
Subjects: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Image and Video Processing (eess.IV)

Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings. Although many sophisticated methods have been proposed, the cell extraction from calcium imaging data can still be prohibitively laborious and require manual annotation and correction. We present DISCo, a novel approach for the cell segmentation in Calcium Imaging Analysis (CIA) that combines the advantages of Deep learning with a state-of-the-art Instance Segmentation algorithm and uses temporal information from the recordings in a computationally efficient way by computing Correlations between pixels.

[10]  arXiv:1908.07960 [pdf]
Title: Toward a trait-based species pool hypothesis
Subjects: Populations and Evolution (q-bio.PE)

The species pool hypothesis highlights the effects of historical processes and past adaptation on contemporary patterns of species diversity. This hypothesis has been contentious because it is difficult to test. Here we argue that a trait-based approach enables an effective test of this hypothesis where increasing mismatch between community mean trait values (the optimal trait strategy) and the mean of the species pool (representing historical legacy) decreases species diversity. We confirm this prediction using a simulation model and demonstrate its utility using experimental communities of annual plants. Using this case study, we show that our hypothesis is more easily falsifiable than the classical species pool hypothesis and could be applied as a null hypothesis to any system. We also discuss the implications of our framework for the relationship between species diversity and functional diversity and propose additional testable predictions for classical hypotheses invoking past adaptations as drivers of current diversity patterns.

[11]  arXiv:1908.08024 [pdf, other]
Title: Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware
Comments: 17 pages, 7 figures, published in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Subjects: Neurons and Cognition (q-bio.NC); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)

Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks. With the growing size and complexity of these networks, hardware implementation becomes challenging because scaling up the size of a single array (crossbar) of fully connected neurons is no longer feasible due to strict energy budget. Modern neromorphic hardware integrates small-sized crossbars with time-multiplexed interconnects. Partitioning SNNs becomes essential in order to map them on neuromorphic hardware with the major aim to reduce the global communication latency and energy overhead. To achieve this goal, we propose our instantiation of particle swarm optimization, which partitions SNNs into local synapses (mapped on crossbars) and global synapses (mapped on time-multiplexed interconnects), with the objective of reducing spike communication on the interconnect. This improves latency, power consumption as well as application performance by reducing inter-spike interval distortion and spike disorders. Our framework is implemented in Python, interfacing CARLsim, a GPU-accelerated application-level spiking neural network simulator with an extended version of Noxim, for simulating time-multiplexed interconnects. Experiments are conducted with realistic and synthetic SNN-based applications with different computation models, topologies and spike coding schemes. Using power numbers from in-house neuromorphic chips, we demonstrate significant reductions in energy consumption and spike latency over PACMAN, the widely-used partitioning technique for SNNs on SpiNNaker.

Cross-lists for Thu, 22 Aug 19

[12]  arXiv:1908.07847 (cross-list from cs.LG) [pdf]
Title: CUDA optimized Neural Network predicts blood glucose control from quantified joint mobility and anthropometrics
Comments: 5 pages, 6 figures, conference paper: International Conference on Information System and Data Mining, published at this https URL
Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)

Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++. This optimized neural network was tasked with predicting the level of glycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase in the prevalence of Diabetes Mellitus has resulted in an urgent need for early detection and accurate diagnosis. However, due to the invasiveness and limitations of conventional tests, alternate means are being considered. Limited Joint Mobility (LJM) has been reported as an indicator for poor glycemic control. LJM of the fingers is quantified and its link to HbA1c is investigated along with other potential non-invasive markers of HbA1c. We collected readings of 33 potential markers from 120 participants at a clinic in south Trinidad. Our neural network achieved 95.65% accuracy on the training and 86.67% accuracy on the testing set for male participants and 97.73% and 66.67% accuracy on the training and testing sets for female participants. Using 960 CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network was trained 50 times faster on both subsets, than its corresponding CPU implementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.

[13]  arXiv:1908.07974 (cross-list from math.DS) [pdf, other]
Title: Deterministic epidemic models for ebola infection with time-dependent controls
Subjects: Dynamical Systems (math.DS); Optimization and Control (math.OC); Populations and Evolution (q-bio.PE)

In this paper, we have studied epidemiological models for Ebola infection using nonlinear ordinary differential equations and optimal control theory. We considered optimal control analysis of SIR and SEIR models for the deadly Ebola virus infection using vaccination, treatment and educational campaign as time-dependent controls functions. We have applied indirect methods to study existing deterministic optimal control epidemic models for Ebola virus disease. These methods in optimal control are based on Hamiltonian function and the Pontryagin maximum principle to construct adjoint equations and optimality systems. The forward-backward sweep numerical scheme with fourth-order Runge-Kutta method is used to solve the optimality system for the various optimal control strategies. From our simulation results, we observed that, SIR model with optimal control strategies shows a significant decrease in the proportions of infected and susceptible individuals and a rapid increase in the recovered individuals compared to SIR model without optimal control. A similar effect was observed in the SEIR model with control strategies. Following the numerical solutions, we can conclude that, effective educational campaigns and vaccination of susceptible individuals as were as effective treatments of infected individuals can help reduce the disease transmission.

[14]  arXiv:1908.08010 (cross-list from cs.NE) [pdf, other]
Title: Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches
Comments: 13 pages, conference paper, containing 2 figures
Subjects: Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM)

De novo peptide sequencing algorithms have been widely used in proteomics to analyse tandem mass spectra (MS/MS) and assign them to peptides, but quality-control methods to evaluate the confidence of de novo peptide sequencing are lagging behind. A fundamental part of a quality-control method is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). Here, we propose a genetic programming (GP) based method, called GP-PSM, to learn a PSM scoring function for improving the rate of confident peptide identification from MS/MS data. The GP method learns from thousands of MS/MS spectra. Important characteristics about goodness of the matches are extracted from the learning set and incorporated into the GP scoring functions. We compare GP-PSM with two methods including Support Vector Regression (SVR) and Random Forest (RF). The GP method along with RF and SVR, each is used for post-processing the results of peptide identification by PEAKS, a commonly used de novo sequencing method. The results show that GP-PSM outperforms RF and SVR and discriminates accurately between correct and incorrect PSMs. It correctly assigns peptides to 10% more spectra on an evaluation dataset containing 120 MS/MS spectra and decreases the false positive rate (FPR) of peptide identification.

Replacements for Thu, 22 Aug 19

[15]  arXiv:1811.08718 (replaced) [pdf, other]
Title: Close spatial arrangement of mutants favors and disfavors fixation
Authors: Yunming Xiao, Bin Wu
Comments: 23 pages, 8 figures
Subjects: Populations and Evolution (q-bio.PE); Computational Engineering, Finance, and Science (cs.CE)
[16]  arXiv:1812.00598 (replaced) [pdf]
Title: A Static Distributed-parameter Circuit Model Explains Electrical Stimulation on the Neuromuscular System
Comments: This manuscript is not completed yet
Subjects: Neurons and Cognition (q-bio.NC)
[17]  arXiv:1812.07716 (replaced) [pdf]
Title: Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Levenberg-Marquardt Algorithm
Journal-ref: Archives of Clinical and Biomedical Research 2018, 2(6):188-197
Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
[18]  arXiv:1812.10384 (replaced) [pdf]
Title: Identification of Cancer -- Mesothelioma Disease Using Logistic Regression and Association Rule
Journal-ref: American Journal of Engineering and Applied Sciences 2018, 11(4):1310.1319
Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
[19]  arXiv:1905.09888 (replaced) [pdf]
Title: Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
Subjects: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
[20]  arXiv:1907.06305 (replaced) [pdf, other]
Title: Caching in or falling back at the Sevilleta
Comments: 13 pages, 6 figures, 2 tables, Appendices
Subjects: Populations and Evolution (q-bio.PE)
[21]  arXiv:1908.06746 (replaced) [pdf]
Title: Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
Subjects: Other Quantitative Biology (q-bio.OT); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
[22]  arXiv:1908.07209 (replaced) [pdf, other]
Title: DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
Comments: Updates to this version 1. It is found that the model architecture described in Section 2.3 corresponds to an older version of our model. Updates are therefore made to this section to reflect the latest model architecture. Figure 5 is also updated accordingly. 2. Section 2.5 is added to improve the clarity of the manuscript. 3. Other minor updates to language and formatting
Subjects: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
[ total of 22 entries: 1-22 ]
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