scholarly journals KOPTIC: A novel approach for in silico prediction of enzyme kinetics and regulation

2019 ◽  
Author(s):  
Wheaton L. Schroeder ◽  
Rajib Saha
Keyword(s):  
In Silico ◽  
Time Series Data ◽  
Metabolic Model ◽  
Accurate Method ◽  
Growth Stress ◽  
Series Data ◽  
Omics Data ◽  
Novel Approach ◽  
Transcriptional Regulatory Mechanisms ◽  
Genetic Interventions

AbstractKinetic models of metabolism (kMMs) provide not only a more accurate method for designing novel biological systems but also characterization of system regulations; however, the multi-‘omics’ data required is prohibitive to their development and widespread use. Here, we introduce a new approach named Kinetic OPTimization using Integer Conditions (KOPTIC), which can circumvent the ‘omics’ data requirement and semi-automate kMM construction using in silico reaction flux data and metabolite concentration estimates derived from a metabolic network model to return plausible reaction mechanisms, regulations, and kinetic parameters (defined as ‘reactomics’) using an optimization-based approach. As a benchmark for the performance of KOPTIC, a previously published, four-tissue (leaf, root, seed, and stem) metabolic model of Arabidopsis thaliana was used, consisting of major primary carbon metabolism pathways, named p-ath780 (1015 reactions, 901 metabolites, and 780 genes). Data required for KOPTIC was derived from an Arabidopsis’ lifecycle of 61 days. Nine separate regulator restriction sets (allowing multiple solutions) defining KOPTIC runs hypothesized 3577 total regulatory interactions involving metabolic, allosteric, and transcriptional regulatory mechanisms (with nearly 40 verified by existing literature) with a median fit error of 13.44%. Flux rates of most KOPTIC fits were found to be significantly correlated with (93.6% with p < 0.05) and approximately 1:1 (r = 0.775, p ≪ 0.001) to the input time-series data. Thus, KOPTIC can hypothesize maps the regulatory landscape for a specific reaction, out of which the most relevant regulatory interaction(s) can be defined by the desired growth/stress conditions or the desired genetic interventions for use in the creation of kMMs.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals How to predict relapse in leukaemia using time series data: A comparative in silico study

2020 ◽  
Author(s):  
Helene Hoffmann ◽  
Christoph Baldow ◽  
Thomas Zerjatke ◽  
Andrea Gottschalk ◽  
Sebastian Wagner ◽  
...  
Keyword(s):  
Data Quality ◽  
Computational Methods ◽  
In Silico ◽  
Prediction Accuracy ◽  
Time Course ◽  
Time Series Data ◽  
Specific Treatment ◽  
Series Data ◽  
Patient Specific ◽  
Clinical Markers

SummaryRisk stratification and treatment decisions for leukaemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improving the predictions for patient-specific treatment response.We analyzed the potential of different computational methods to accurately predict relapse for chronic and acute myeloid leukaemia, particularly focusing on the influence of data quality and quantity. Technically, we used clinical reference data to generate in-silico patients with varying levels of data quality. Based hereon, we compared the performance of mechanistic models, generalized linear models, and neural networks with respect to their accuracy for relapse prediction. We found that data quality has a higher impact on prediction accuracy than the specific choice of the method. We further show that adapted treatment and measurement schemes can considerably improve prediction accuracy. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukaemia patients.

scholarly journals TimeNexus: A Novel Cytoscape App to Analyze Time-Series Data Using Temporal MultiLayer Networks (tMLNs)

2020 ◽  
Author(s):  
Michaël Pierrelée ◽  
Ana Reynders ◽  
Fabrice Lopez ◽  
Aziz Moqrich ◽  
Laurent Tichit ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Time Series ◽  
Network Structure ◽  
Time Series Data ◽  
Series Data ◽  
Omics Data ◽  
Expression Data ◽  
Temporal Expression ◽  
Multilayer Networks ◽  
Multilayer Network

Abstract Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: i) over the entire set of layers, ii) over two consecutive layers at a time, iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.

scholarly journals Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 99
Author(s):  
Sultan Daud Khan ◽  
Louai Alarabi ◽  
Saleh Basalamah
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Time Scale ◽  
Prediction Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Economic Recession ◽  
Series Data ◽  
Economic Activities ◽  
Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

scholarly journals Introducing the novel Cytoscape app TimeNexus to analyze time-series data using temporal MultiLayer Networks (tMLNs)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michaël Pierrelée ◽  
Ana Reynders ◽  
Fabrice Lopez ◽  
Aziz Moqrich ◽  
Laurent Tichit ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Time Series ◽  
Network Structure ◽  
Time Series Data ◽  
Series Data ◽  
Omics Data ◽  
Expression Data ◽  
Temporal Expression ◽  
Multilayer Networks ◽  
Multilayer Network

AbstractIntegrating -omics data with biological networks such as protein–protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: (i) over the entire set of layers, (ii) over two consecutive layers at a time, (iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.

scholarly journals Analysis of EEG Data Using Complex Geometric Structurization

2021 ◽  
pp. 1-28
Author(s):  
E. A. Kwessi ◽  
L. J. Edwards
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Data ◽  
Health Condition ◽  
Series Data ◽  
Complex Structures ◽  
Embedding Theory ◽  
Novel Approach ◽  
Eeg Data ◽  
Electroencephalogram Eeg

Abstract Electroencephalogram (EEG) is a common tool used to understand brain activities. The data are typically obtained by placing electrodes at the surface of the scalp and recording the oscillations of currents passing through the electrodes. These oscillations can sometimes lead to various interpretations, depending on, for example, the subject's health condition, the experiment carried out, the sensitivity of the tools used, or human manipulations. The data obtained over time can be considered a time series. There is evidence in the literature that epilepsy EEG data may be chaotic. Either way, the Embedding Theory in dynamical systems suggests that time series from a complex system could be used to reconstruct its phase space under proper conditions. In this letter, we propose an analysis of epilepsy EEG time series data based on a novel approach dubbed complex geometric structurization. Complex geometric structurization stems from the construction of strange attractors using Embedding Theory from dynamical systems. The complex geometric structures are themselves obtained using a geometry tool, the α-shapes from shape analysis. Initial analyses show a proof of concept in that these complex structures capture the expected changes brain in lobes under consideration. Further, a deeper analysis suggests that these complex structures can be used as biomarkers for seizure changes.

scholarly journals How to Predict Relapse in Leukaemia Using Time Series Data: A Comparative in silico Study

2020 ◽  
Author(s):  
Helene Hoffmann ◽  
Christoph Baldow ◽  
Thomas Zerjatke ◽  
Andrea Gottschalk ◽  
Sebastian Wagner ◽  
...  
Keyword(s):  
Time Series ◽  
In Silico ◽  
Time Series Data ◽  
Series Data
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