Parameter estimation and treatment optimization in a stochastic model for immunotherapy of cancer

Adoptive Cell Transfer therapy of cancer is currently in full development and mathematical modeling is playing a critical role in this area. We study a stochastic model developed by Baar et al. in 2015 for modeling immunotherapy against melanoma skin cancer. First, we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice. A Nonlinear Mixed Effects Model is estimated by the Stochastic Approximation Expectation Maximization algorithm. With the estimated parameters, we head back to the stochastic model and calculate the probability that the T cells all get exhausted during the treatment. We show that for some relevant parameter values, an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability. Then, focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges.Comment: major reorganisation of the paper and the reformulation of many substantial part

Time-, Frequency-, and Wavevector-Resolved X-Ray Diffraction from Single Molecules

Using a quantum electrodynamic framework, we calculate the off-resonant scattering of a broad-band X-ray pulse from a sample initially prepared in an arbitrary superposition of electronic states. The signal consists of single-particle (incoherent) and two-particle (coherent) contributions that carry different particle form factors that involve different material transitions. Single-molecule experiments involving incoherent scattering are more influenced by inelastic processes compared to bulk measurements. The conditions under which the technique directly measures charge densities (and can be considered as diffraction) as opposed to correlation functions of the charge-density are specified. The results are illustrated with time- and wavevector-resolved signals from a single amino acid molecule (cysteine) following an impulsive excitation by a stimulated X-ray Raman process resonant with the sulfur K-edge. Our theory and simulations can guide future experimental studies on the structures of nano-particles and proteins

The potential to narrow uncertainty in projections of stratospheric ozone over the 21st century

Future stratospheric ozone concentrations will be determined both by changes in the concentration of ozone depleting substances (ODSs) and by changes in stratospheric and tropospheric climate, including those caused by changes in anthropogenic greenhouse gases (GHGs). Since future economic development pathways and resultant emissions of GHGs are uncertain, anthropogenic climate change could be a significant source of uncertainty for future projections of stratospheric ozone. In this pilot study, using an "ensemble of opportunity" of chemistry-climate model (CCM) simulations, the contribution of scenario uncertainty from different plausible emissions pathways for ODSs and GHGs to future ozone projections is quantified relative to the contribution from model uncertainty and internal variability of the chemistry-climate system. For both the global, annual mean ozone concentration and for ozone in specific geographical regions, differences between CCMs are the dominant source of uncertainty for the first two-thirds of the 21st century, up-to and after the time when ozone concentrations return to 1980 values. In the last third of the 21st century, dependent upon the set of greenhouse gas scenarios used, scenario uncertainty can be the dominant contributor. This result suggests that investment in chemistry-climate modelling is likely to continue to refine projections of stratospheric ozone and estimates of the return of stratospheric ozone concentrations to pre-1980 levels

Single‐molecule enzymology à la Michaelis–Menten

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102694/1/febs12663.pd

Dépistage du cancer de la prostate analyse décisionnelle

Le cancer le plus répandu et le deuxième plus meurtrier chez les hommes est le cancer de la prostate. Afin d'améliorer les chances de survie des patients, il est nécessaire de faire un dépistage tôt dans la maladie. La stratégie principale de dépistage utilise différents marqueurs qui identifient la maladie chez le patient. Cependant, le choix des marqueurs est très variable. Depuis le début des années 90, moment où une grande évolution s'effectue au niveau des marqueurs, le choix de quels marqueurs sont les plus performants est devenue une tâche fastidieuse. Nous proposons donc une modélisation décisionnelle qui permettra de faire l'évaluation des différentes stratégies et marqueurs existants. Nous avons utilisé la représentation conceptuelle du problème du cancer de la prostate pour faire un modèle en trois phases : dépistage, déterminer le stade de la maladie, traitement. Les données utilisées proviennent d'études systématiques publiées et d'une étude systématique particulière qui vise le dépistage du cancer de la prostate par de nouveaux marqueurs biochimiques. Différentes stratégies alternatives ont été évaluées : l'antigène spécifique de la prostate totale (tASP), ASP complexe (cASP), ASP libre (lASP), le rapport de ASP libre sur ASP totale (l/tASP), le rapport ASP complexe/totale (c/tASP) ainsi que toutes avec/sans touché rectal (TR). Un niveau de sensibilité a été établit à 90% pour tous les tests de dépistage. L'utilité prévisionnelle des stratégies alternatives a été calculée en utilisant la simulation de Monte-Carlo. De plus, nous avons utilisé le test de Student pour comparer les différentes stratégies de dépistage. Finalement, une analyse de sensibilité avec représentation en diagramme de tornade a été appliquée à la survie des patients en ce qui concerne les caractéristiques de la population. Deux logiciels pour la construction du modèle de décision (ReasonEdge et Data 3.5) ont été utilisés. Différentes méthodologies (modélisation décisionnelle et revue systématique) ont été examinées pour l'évaluation du dépistage du cancer de la prostate. Le processus de modélisation a été basé sur la création du modèle conceptuel du problème et le choix d'informations probabilistes basées sur la relation structurale entre les éléments du modèle de décision. Des lignes directrices de représentation ont été utilisées afin d'éviter les problèmes de transparence et d'augmenter la réutilisation du modèle. De plus, le modèle résultant est généralisable car il est possible de lui poser différentes questions. Finalement, les stratégies de dépistage et l'examen des facteurs importants pour les décisions ont été évaluées [i.e. évalués]. L'examen des influences du dépistage sur la détection du stade du cancer aidera l'estimation de l'impact de ce dépistage sur la survie de la population

The Impact of Time Delays on the Robustness of Biological Oscillators and the Effect of Bifurcations on the Inverse Problem

Differential equation models for biological oscillators are often not robust with respect to parameter variations. They are based on chemical reaction kinetics, and solutions typically converge to a fixed point. This behavior is in contrast to real biological oscillators, which work reliably under varying conditions. Moreover, it complicates network inference from time series data. This paper investigates differential equation models for biological oscillators from two perspectives. First, we investigate the effect of time delays on the robustness of these oscillator models. In particular, we provide sufficient conditions for a time delay to cause oscillations by destabilizing a fixed point in two-dimensional systems. Moreover, we show that the inclusion of a time delay also stabilizes oscillating behavior in this way in larger networks. The second part focuses on the inverse problem of estimating model parameters from time series data. Bifurcations are related to nonsmoothness and multiple local minima of the objective function

Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step