Mathematical modelling of epithelium homeostasis

The body and organs of all animals are covered by epithelial tissues, such as the epidermis and the airway epithelium. Epithelial tissues play a key role in protecting the body from environmental aggressors. Failure to maintain a competent epithelium can lead to the onset of many diseases, including Atopic dermatitis (AD) and infection by Streptococcus pneumoniae. Treatment of AD is currently restricted to the relief of symptoms, mainly because the underlying mechanisms remain elusive. Antibiotic resistance threatens the effectiveness of the prevalent treatments for infection. Devising new and effective therapeutic strategies that halt the progression of these diseases requires an understanding of the different disease mechanisms that can cause loss of epithelial homeostasis in different patients. Intricate regulatory networks of several biochemical and cellular interactions maintain epithelium homeostasis in healthy individuals, but can also propagate different disturbances, resulting in a wide spectrum of possible disease phenotypes. In this thesis, we propose mathematical models of these regulatory networks to analyse the mechanisms that lead to the onset and progression of AD and pneumococcal infection from a systems-level perspective. Our mathematical model of AD reproduced, for the first time, the different stages of the disease that have been observed in the clinic. Moreover, we proposed different pathogenic mechanisms, triggered by different genetic and environmental risk factors that are known to predispose to AD. By assessing the effects of common treatments for AD, we suggested effective treatment strategies that can prevent the aggravation of the disease, in a patient-specific way. Our data-driven mathematical model of pneumococcal infection identified four qualitatively different mechanisms by which co-infection can drive the pathogenic process. They can be counteracted by distinctive treatment strategies that only partially involve antibiotics. Our work provides a theoretical framework for the integration and analysis of clinical and experimental data describing epithelial homeostasis.Open Acces

Gene regulatory network underlying the immortalization of epithelial cells

Abstract Background: Tumorigenic transformationofhumanepithelialcellsinvitrohasbeendescribedexperimentallyas thepotentialresultofspontaneousimmortalization.Thisprocessischaracterizedbyaseriesofcell–statetransitions,in whichnormalepithelialcellsacquirefirstasenescentstatewhichislatersurpassedtoattainamesenchymalstem–like phenotypewithapotentiallytumorigenicbehavior.Inthispaperweaimtoprovideasystem–levelmechanistic explanationtotheemergenceofthesecelltypes,andtothetime–orderedtransitionpatternsthatarecommonto neoplasiasofepithelialorigin.Tothisend,wefirstintegratepublishedfunctionalandwell–curatedmoleculardataof thecomponentsandinteractionsthathavebeenfoundtobeinvolvedinsuchcellstatesandtransitionsintoa networkof41molecularcomponents.Wethenreducethisinitialnetworkbyremovingsimplemediators(i.e.,linear pathways),andformalizetheresultingregulatorycoreintologicalrulesthatgovernthedynamicsofeachofthe networkcomponentsasafunctionofthestatesofitsregulators. Results: ComputationaldynamicanalysisshowsthatourproposedGeneRegulatoryNetworkmodelrecoversexactly threeattractors,eachofthemdefinedbyaspecificgeneexpressionprofilethatcorrespondstotheepithelial, senescent,andmesenchymalstem–likecellularphenotypes,respectively.Weshowthatalthoughamesenchymal stem–likestatecanbeattainedevenunderunperturbedphysiologicalconditions,thelikelihoodofconvergingtothis stateisincreasedwhenpro–inflammatoryconditionsaresimulated,providingasystems–levelmechanistic explanationforthecarcinogenicroleofchronicinflammatoryconditionsobservedintheclinic.Wealsofoundthat theregulatorycoreyieldsanepigeneticlandscapethatrestrictstemporalpatternsofprogressionbetweenthesteady states,suchthatrecoveredpatternsresemblethetime–orderedtransitionsobservedduringthespontaneous immortalizationofepithelialcells,bothinvivoandinvitro. Conclusion: Ourstudystronglysuggeststhattheinvitrotumorigenictransformationofepithelialcells,which stronglycorrelateswiththepatternsobservedduringthepathologicalprogressionofepithelialcarcinogenesisinvivo, emergesfromunderlyingregulatorynetworksinvolvedinepithelialtrans–differentiationduringdevelopment. Keywords: Carcinomas,Generegulatorynetworks,Epigeneticlandscape,Booleanmodels,Phenotypicattractor

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control. OBJECTIVE: We aimed to develop a proof of principle mechanistic machine learning model that predicts the patient-specific evolution of AD severity scores on a daily basis. METHODS: We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward-chaining setting. RESULTS: Our model was able to predict future severity scores at the individual level and improved chance-level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient-specific parameters such as the short-term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step-up treatment. CONCLUSIONS: Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level and could inform the design of personalized treatment strategies that can be tested in future studies. Our model-based approach can be applied to other diseases with apparent unpredictability and large variation in symptoms and treatment responses such as asthma

RESTful Web Service USAge for Online Exit-survey at Syiah Kuala University as Data Verification Method

Many applications are developed and deployed in Syiah Kuala University main server. These applications and information system are built as tools to help the University\u27 daily activities. Most of these applications have its own database. As a result, data is inconsistent, and the worst is redundant data cannot be avoided. The idea behind of this research is to build one centralized data that can be used as baseline to other applications. Since the main data of Syiah Kuala University are located behind the proxy which is no internet direct access allowed to the data. The proposed method to answer this problem is touse web service as a gateway for data transfer. This technique keeps the database from direct external access but the data itself can be seen without knowing where the real data is. This method has been used for Online Exit-Survey to proof that the system can verify the students\u27 data. Some student cannot be identified because their data were empty, the other because the data in centralized database server were only prepared for undergraduate students, so that the post graduate and professional students cannot be verified. For undergraduate students this online exit-survey works fine without error on verification phas

Resistance to Water Diffusion in the Stratum Corneum Is Depth-Dependent

<div><p>The stratum corneum (SC) provides a permeability barrier that limits the inflow and outflow of water. The permeability barrier is continuously and dynamically formed, maintained, and degraded along the depth, from the bottom to the top, of the SC. Naturally, its functioning and structure also change dynamically in a depth-dependent manner. While transepidermal water loss is typically used to assess the function of the SC barrier, it fails to provide any information about the dynamic mechanisms that are responsible for the depth-dependent characteristics of the permeability barrier. This paper aims to quantitatively characterize the depth-dependency of the permeability barrier using <i>in vivo</i> non-invasive measurement data for understanding the underlying mechanisms for barrier formation, maintenance, and degradation. As a framework to combine existing experimental data, we propose a mathematical model of the SC, consisting of multiple compartments, to explicitly address and investigate the depth-dependency of the SC permeability barrier. Using this mathematical model, we derive a measure of the water permeability barrier, i.e. resistance to water diffusion in the SC, from the measurement data on transepidermal water loss and water concentration profiles measured non-invasively by Raman spectroscopy. The derived resistance profiles effectively characterize the depth-dependency of the permeability barrier, with three distinct regions corresponding to formation, maintenance, and degradation of the barrier. Quantitative characterization of the obtained resistance profiles allows us to compare and evaluate the permeability barrier of skin with different morphology and physiology (infants vs adults, different skin sites, before and after application of oils) and elucidates differences in underlying mechanisms of processing barriers. The resistance profiles were further used to predict the spatial-temporal effects of skin treatments by <i>in silico</i> experiments, in terms of spatial-temporal dynamics of percutaneous water penetration.</p></div

Results of <i>in silico</i> absorption-desorption experiment with water applied topically for 10 seconds (time = 10–20 s).

<p>(<b>a</b>) Temporal dynamics of total water content (change from steady state) for adults; simulation results (solid line) and experimental data (diamonds) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117292#pone.0117292.ref017" target="_blank">17</a>]. (<b>b</b>) Spatial-temporal dynamics of water concentration before and 30 minutes after topical application of petrolatum. (<b>c</b>) Spatial-temporal increase in water concentration after application of petrolatum. Negative values (time = 10–20 s) and positive values (time = 20–50 s) respectively indicate slower absorption and desorption after application of petrolatum.</p

Resistance profiles (means) before and 30 minutes after topical application of different oils.

<p>(<b>a</b>) almond oil (<i>n</i> = 92), (<b>b</b>) jojoba oil (<i>n</i> = 98), (<b>c</b>) paraffin oil (<i>n</i> = 99), and (<b>d</b>) petrolatum (<i>n</i> = 86).</p