0000000000064646
AUTHOR
Jens Timmer
showing 8 related works from this author
Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib
2017
IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced …
Covid-19 in Deutschland – Erklärung, Prognose und Einfluss gesundheitspolitischer Maßnahmen
2020
Zusammenfassung Die Autoren erklären den bisherigen Verlauf von Covid-19 in Deutschland durch Regressionsanalysen und epidemiologische Modelle. Sie beschreiben und quantifizieren den Effekt der gesundheitspolitischen Maßnahmen (GPM), die bis zum 19. April in Kraft waren. Sie berechnen den erwarteten Verlauf der Covid-19-Epidemie in Deutschland, wenn es diese Maßnahmen nicht gegeben hätte, und zeigen, dass die GPM einen erheblichen Beitrag zur Reduktion der Infektionszahlen geleistet haben. Die seit 20. April gelockerten GPM sind zwischen den Bundesländern relativ heterogen, was ein Glücksfall für die Wissenschaft ist. Mittels einer Analyse dieser Heterogenität kann aufgedeckt werden, welche…
Individualizing deep dynamic models for psychological resilience data
2020
ABSTRACTDeep learning approaches can uncover complex patterns in data. In particular, variational autoencoders (VAEs) achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project (MARP), which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, an…
Variations in Substitution Rate in Human and Mouse Genomes
2003
We present a method to quantify spatial fluctuations of the substitution rate on different length scales throughout genomes of eukaryotes. The fluctuations on large length scales are found to be predominantly a consequence of a coarse-graining effect of fluctuations on shorter length scales. This is verified for both the mouse and the human genome. We also found that both species show similar standard deviation of fluctuations even though their mean substitution rate differs by a factor of two. Our method furthermore allows to determine time-resolved substitution rate maps from which we can compute auto-correlation functions in order to quantify how fast the spatial fluctuations in substitu…
RPPanalyzer Toolbox: An improved R package for analysis of reverse phase protein array data
2014
Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new t…
Deconstructing and reconstructing resilience: a dynamic network approach
2019
Resilience is still often viewed as a unitary personality construct that, as a kind of anti-nosological entity, protects individuals against stress-related mental problems. However, increasing evidence indicates that the maintenance of mental health in the face of adversity results from complex and dynamic processes of adaptation to stressors that involve the activation of several separable protective factors. Such resilience factors can reside at biological, psychological and social levels and may include stable predispositions (such as genotype or personality traits) and malleable properties, skills, capacities or external circumstances (such as gene expression patterns, emotion regulatio…
Reciprocal regulation of human platelet function by endogenous prostanoids and through multiple prostanoid receptors
2014
Platelets are permanently exposed to a variety of prostanoids formed by blood cells or the vessel wall. The two major prostanoids, prostacyclin and thromboxane act through well established pathways mediated by their respective G-protein coupled receptors inhibiting or promoting platelet aggregation accordingly. Yet the role of other prostanoids and prostanoid receptors for platelet function regulation has not been thoroughly investigated. We aimed at a comprehensive analysis of prostanoid effects on platelets, the receptors and pathways involved and functional consequences. We analyzed cAMP formation and phosphorylation of proteins pivotal to platelet function as well as functional platelet…
Comprehensive estimation of input signals and dynamics in biochemical reaction networks
2012
Abstract Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function. Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedu…