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ABSTRACT:
SUBMITTER: Triantafyllidis CP
PROVIDER: S-EPMC7924498 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems.
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Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism. Ribosome profiling and mRNA libraries were constructed in triplicate from in vitro PCF and in duplicate from in vitro T. brucei Lister427, to understand global differntial gene transcription.
2016-03-16 | E-GEOD-79208 | biostudies-arrayexpress
Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism.
2016-03-16 | GSE79208 | GEO
Project description:To understand the theoretical effects of pulse width (PW) programming in spinal cord stimulation (SCS), we implemented a mathematical model of electrical fields and neural activation in SCS to gain insight into the effects of PW programming. The computational model was composed of a finite element model for structure and electrical properties, coupled with a nonlinear double-cable axon model to predict nerve excitation for different myelinated fiber sizes. Mathematical modeling suggested that mediolateral lead position may affect chronaxie and rheobase values, as well as predict greater activation of medial dorsal column fibers with increased PW. These modeling results were validated by a companion clinical study. Thus, variable PW programming in SCS appears to have theoretical value, demonstrated by the ability to increase and even 'steer' spatial selectivity of dorsal column fiber recruitment. It is concluded that the computational SCS model is a valuable tool to understand basic mechanisms of nerve fiber excitation modulated by stimulation parameters such as PW and electric fields.
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Project description:UnlabelledICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses speckle tracking (endocardial border detection) on ultrasound (MRI) imaging data in the form of DICOM. Standard American Heart Association segment-based strain analysis can be performed using a browser-based interface. The speckle tracking, border detection and model fitting methods are implemented in C++ using open-source tools. They are wrapped as web services and orchestrated via a JBOSS-based application server.Availability and implementationThe source code for ICMA is freely available under MPL 1.1 or GPL 2.0 or LGPL 2.1 license at https://github.com/ABI-Software-Laboratory/ICMA and a standalone virtual machine at http://goo.gl/M4lJKH for download.Contactr.jagir@auckland.ac.nzSupplementary informationSupplementary materials are available at Bioinformatics online.
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Project description:Inefficient vascularization hinders the optimal transport of cell nutrients, oxygen, and drugs to cancer cells in solid tumors. Gradients of these substances maintain a heterogeneous cell-scale microenvironment through which drugs and their carriers must travel, significantly limiting optimal drug exposure. In this study, we integrate intravital microscopy with a mathematical model of cancer to evaluate the behavior of nanoparticle-based drug delivery systems designed to circumvent biophysical barriers. We simulate the effect of doxorubicin delivered via porous 1000 x 400 nm plateloid silicon particles to a solid tumor characterized by a realistic vasculature, and vary the parameters to determine how much drug per particle and how many particles need to be released within the vasculature in order to achieve remission of the tumor. We envision that this work will contribute to the development of quantitative measures of nanoparticle design and drug loading in order to optimize cancer treatment via nanotherapeutics.
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Project description:Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate's volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.
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Project description:Using a dataset of 150 patients treated with intermittent androgen suppression (IAS) through a fixed treatment schedule, we retrospectively designed a personalized treatment schedule mathematically for each patient. We estimated 100 sets of parameter values for each patient by randomly resampling each patient's time points to take into account the uncertainty for observations of prostate specific antigen (PSA). Then, we identified 3 types and classified patients accordingly: in type (i), the relapse, namely the divergence of PSA, can be prevented by IAS; in type (ii), the relapse can be delayed by IAS later than by continuous androgen suppression (CAS); in type (iii) IAS was not beneficial and therefore CAS would have been more appropriate in the long run. Moreover, we obtained a treatment schedule of hormone therapy by minimizing the PSA of 3 years later in the worst case scenario among the 100 parameter sets by searching exhaustively all over the possible treatment schedules. If the most frequent type among 100 sets was type (i), the maximal PSA tended to be kept less than 100 ng/ml longer in IAS than in CAS, while there was no statistical difference for the other cases. Thus, mathematically personalized IAS should be studied prospectively.
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Project description:Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.
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Project description:Persistent bacteremia caused by Staphylococcus aureus (SA), especially methicillin-resistant SA (MRSA), is a significant cause of morbidity and mortality. Despite susceptibility phenotypes in vitro, persistent MRSA strains fail to clear with appropriate anti-MRSA therapy during bacteremia in vivo. Thus, identifying the factors that cause such MRSA persistence is of direct and urgent clinical relevance. To address the dynamics of MRSA persistence in the face of host immunity and typical antibiotic regimens, we developed a mathematical model based on the overarching assumption that phenotypic heterogeneity is a hallmark of MRSA persistence. First, we applied an ensemble modeling approach and obtained parameter sets that satisfied the condition of a minimum inoculum dose to establish infection. Second, by simulating with the selected parameter sets under vancomycin therapy which follows clinical practices, we distinguished the models resulting in resolving or persistent bacteremia, based on the total SA exceeding a detection limit after five days of treatment. Third, to find key determinants that discriminate resolving and persistent bacteremia, we applied a machine learning approach and found that the immune clearance rate of persister cells is a key feature. But, fourth, when relapsing bacteremia was considered, the growth rate of persister cells was also found to be a key feature. Finally, we explored pharmacological strategies for persistent and relapsing bacteremia and found that a persister killer, but not a persister formation inhibitor, could provide for an effective cure the persistent bacteremia. Thus, to develop better clinical solutions for MRSA persistence and relapse, our modeling results indicate that we need to better understand the pathogen-host interactions of persister MRSAs in vivo.
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