Metapopulations are models of ecological systems, describing the interactions and the behavior of populations that live in fragmented habitats. In this paper, we present a model of metapopulations ...based on the multivolume simulation algorithm tau-DPP, a stochastic class of membrane systems, that we utilize to investigate the influence that different habitat topologies can have on the local and global dynamics of metapopulations. In particular, we focus our analysis on the migration rate of individuals among adjacent patches, and on their capability of colonizing the empty patches in the habitat. We compare the simulation results obtained for each habitat topology, and conclude the paper with some proposals for other research issues concerning metapopulations.
Abstract
Motivation
The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here ...we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy.
Results
We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments.
Availability and implementation
The source code of FUMOSO is available under the GPL 2.0 license on GitHub at the following URL: https://github.com/aresio/FUMOSO
Supplementary information
Supplementary data are available at Bioinformatics online.
The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and ...experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. ProCell is a software that exploits flow cytometry data to model and simulate the kinetics of fluorescence loss that is due to stochastic events of cell division. Since the rate of cell division is not known, ProCell embeds a calibration process that might require thousands of stochastic simulations to properly infer the parameterization of cell proliferation models. To mitigate the high computational costs, in this paper we introduce a parallel implementation of ProCell's simulation algorithm, named cuProCell, which leverages Graphics Processing Units (GPUs). Dynamic Parallelism was used to efficiently manage the cell duplication events, in a radically different way with respect to common computing architectures. We present the advantages of cuProCell for the analysis of different models of cell proliferation in Acute Myeloid Leukemia (AML), using data collected from the spleen of human xenografts in mice. We show that, by exploiting GPUs, our method is able to not only automatically infer the models' parameterization, but it is also <inline-formula><tex-math notation="LaTeX">237\times</tex-math></inline-formula> faster than the sequential implementation. This study highlights the presence of a relevant percentage of quiescent and potentially chemoresistant cells in AML in vivo , and suggests that maintaining a dynamic equilibrium among the different proliferating cell populations might play an important role in disease progression.
The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning the tumbling and running motions that are due to clockwise and counterclockwise rotations of their ...flagella. The pathway is tightly regulated by feedback mechanisms governed by the phosphorylation and methylation of several proteins. In this paper, we present a detailed mechanistic model for chemotaxis, that considers all of its transmembrane and cytoplasmic components, and their mutual interactions. Stochastic simulations of the dynamics of a pivotal protein, CheYp, are performed by means of tau leaping algorithm. This approach is then used to investigate the interplay between the stochastic fluctuations of CheYp amount and the number of cellular flagella. Our results suggest that the combination of these factors might represent a relevant component for chemotaxis. Moreover, we study the pathway under various conditions, such as different methylation levels and ligand amounts, in order to test its adaptation response. Some issues for future work are finally discussed.
•Cardiac ischemic scar can be accurately assessed on dark blood late enhancement imaging.•The developed model accurately segments ischemic scar (IoU: 0.85 on test dataset).•The developed model ...reduces computational time 5-fold compared to manual segmentation.•The developed system automatically generates a quantitative clinical report.
Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients’ clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.
DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.
The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction.
Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.
Abstract
Introduction
Identifying the optimal Inversion Time (TI) is pivotal to null the myocardium and obtain high quality cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging. ...Setting the optimal TI can be challenging in some diseases and for less experienced operators. We propose an Artificial Intelligence (AI) tool to automatically predict the optimal TI in CMR-LGE imaging.
Methods
The AI tool, named THAITI, consists of a Random Forest regression model whose hyperparametrs were optimized by means of evolutionary computation. The model considers as input parameters patient-specific TI determinants, such as age, gender, weight, height, kidney function, heart rate, contrast dose, and time from injection to image acquisition. THAITI was trained on 155 patients (2588 CMR-LGE images) with mixed cardiac conditions who underwent CMR (1.5T Siemens AvantoFit; Gadovist; averaged, motion-corrected, free-breathing true-FISP IR). Clinical testing was performed on 55 matched patients, randomized to experimental (THAITI-set TI) vs control (experienced operator-set TI) group. A user interface was developed for clinical testing. Image quality was assessed blindly by 2 independent experienced operators.
Results
THAITI Mean Squared Error (MSE) in the validation set was 4.7 and percentage of mispredicted TI of 4.5%. During clinical testing, LGE quality did not differ between the experimental vs control group: quality was “optimal” or “good” in 96% vs 93%, “poor” in 4% vs 7%. The average number of LGE images acquired and LGE imaging duration were similar (experimental vs control group: 17 ± 3 vs 16 ± 3 LGE images per patient; 12:14 vs 12:20 mm:ss, respectively).
Conclusion
THAITI efficiently predicts optimal TI for CMR-LGE imaging. Further development is needed to increase generalizability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to improve LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top panel: THAITI interface. Bottom panel: examples of experimental group LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ±SD
GPU-powered model analysis with PySB/cupSODA Harris, Leonard A; Nobile, Marco S; Pino, James C ...
Bioinformatics (Oxford, England),
11/2017, Letnik:
33, Številka:
21
Journal Article
Recenzirano
Odprti dostop
A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter ...spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.
The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org.
paolo.cazzaniga@unibg.it or c.lopez@vanderbilt.edu.
Supplementary data are available at Bioinformatics online.
In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU ...computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a
86
×
speedup on GPUs with respect to equivalent executions of LSODA on the CPU.
Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. ...Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.
PDF
