Theoretical studies on the lineage specification of hematopoietic stem cells

Hämatopoetische Stammzellen besitzen die Fähigkeit, die dauerhafte Erhaltung ihrer eigenen Population im Knochenmark zu gewährleisten und gleichzeitig zur Neubildung der verschiedenen Zelltypen des peripheren Blutes beizutragen. Die Sequenz von Entscheidungsprozessen, die den Übergang einer undifferenzierten Stammzelle in eine funktionale ausgereifte Zelle beschreibt, wird als Linienspezifikation bezeichnet. Obwohl viele Details zu den molekularen Mechanismen dieser Entscheidungsprozesse mittlerweile erforscht sind, bestehen noch immer große Unklarheiten, wie die komplexen phänotypischen Veränderungen hervorgerufen und reguliert werden. Im Rahmen dieser Dissertation wird ein geeignetes mathematisches Modell der Linienspezifikation hämatopoetischer Stammzellen entwickelt, welches dann in ein bestehendes Modell der hämatopoetischen Stammzellorganisation auf Gewebsebene integriert wird. Zur Verifizierung des theoretischen Modells werden Simulationsergebnisse mit verschiedenen experimentellen Daten verglichen. Der zweite Teil dieser Arbeit konzentriert sich auf die Beschreibung und Analyse der Entwick- lungsprozesse von Einzelzellen, die aus diesem integrierten Modell hervorgehen. Aufbauend auf den entsprechenden Modellsimulationen wird dazu eine topologische Charakterisierung der resultierenden zellulären Genealogien etabliert, welche durch verschiedener Maße für deren Quantifizierung ergänzt wird. Das vorgestellte mathematische Modell stellt eine neuartige Verknüpfung der intrazellulären Linienspezifikation mit der Beschreibung der hämatopoetischen Stammzellorganisation auf Populationsebene her. Dadurch wird das Stammzellm- odell von Röder und Löffler um die wichtige Dimension der Linienspezifikation ergänzt und damit in seinem Anwendungsbereich deutlich ausgedehnt. Durch die Analyse von Einzelzellverläufen trägt das Modell zu einem grundlegenden Verständnis der inhärenten Heterogenität hämatopoetischer Stammzellen bei

Impact of network structure on the capacity of wireless multihop ad hoc communication

As a representative of a complex technological system, so-called wireless multihop ad hoc communication networks are discussed. They represent an infrastructure-less generalization of todays wireless cellular phone networks. Lacking a central control authority, the ad hoc nodes have to coordinate themselves such that the overall network performs in an optimal way. A performance indicator is the end-to-end throughput capacity. Various models, generating differing ad hoc network structure via differing transmission power assignments, are constructed and characterized. They serve as input for a generic data traffic simulation as well as some semi-analytic estimations. The latter reveal that due to the most-critical-node effect the end-to-end throughput capacity sensitively depends on the underlying network structure, resulting in differing scaling laws with respect to network size.Comment: 30 pages, to be published in Physica

A track of the clones: new developments in cellular barcoding.

International experts from multiple disciplines gathered at Homerton College in Cambridge, UK from September 12-14, 2018 to consider recent advances and emerging opportunities in the clonal tracking of hematopoiesis in one of a series of StemCellMathLab workshops. The group included 35 participants with experience in the fields of theoretical and experimental aspects of clonal tracking, and ranged from doctoral students to senior professors. Data from a variety of model systems and from clinical gene therapy trials were discussed, along with strategies for data analysis and sharing and challenges arising due to underlying assumptions in data interpretation and communication. Recognizing the power of this technology underpinned a group consensus of a need for improved mechanisms for sharing data and analytical protocols to maintain reproducibility and rigor in its application to complex tissues.We thank the BBSRC (BB/R021465/1), the German Stem Cell Network, the Wellcome-MRC Cambridge Stem Cell Institute and the Labex CelTisPhyBio (No. ANR-10-LBX-0038) for funds to support this workshop and the conference staff at Homerton Colleg

Modelling of immune response in chronic myeloid leukemia patients suggests potential for treatment reduction prior to cessation

Introduction: Discontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials. Methods: Here, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success. Results: Our systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI. Discussion: Our findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients

Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data

Single cell tracking, based on the computerised analysis of time-lapse movies, is a sophisticated experimental technique to quantify single cell dynamics in time and space. Although the resulting cellular genealogies comprehensively describe the divisional history of each cell, there are many open questions regarding the statistical analysis of this type of data. In particular, it is unclear, how tracking uncertainties or spatial information of cellular development can correctly be incorporated into the analysis. Here we propose a generalised description of single cell tracking data by spatiotemporal networks that accounts for ambiguities in cell assignment as well as for spatial relations between cells. We present a way to measure correlations among cell states by analysing the mutual information in state space considering causal (time-respecting) paths and illustrate our approach by a corresponding example. We conclude that a comprehensive spatiotemporal description of single cell tracking data is ultimately necessary to fully exploit the information obtained by time-lapse imaging. Index Terms — cell tracking, lineage trees, temporal networks, information theory, stem cells 1

Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions

Motivation: Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time. Results: We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes. Availability and implementation: The Matlab source code is available at http://treschgroup.de/Genealogies.html Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Quantitative prediction of long-term molecular response in TKI-treated CML – Lessons from an imatinib versus dasatinib comparison

Longitudinal monitoring of BCR-ABL transcript levels in peripheral blood of CML patients treated with tyrosine kinase inhibitors (TKI) revealed a typical biphasic response. Although second generation TKIs like dasatinib proved more efficient in achieving molecular remission compared to first generation TKI imatinib, it is unclear how individual responses differ between the drugs and whether mechanisms of drug action can be deduced from the dynamic data. We use time courses from the DASISION trial to address statistical differences in the dynamic response between first line imatinib vs. dasatinib treatment cohorts and we analyze differences between the cohorts by fitting an established mathematical model of functional CML treatment to individual time courses. On average, dasatinib-treated patients show a steeper initial response, while the long-term response only marginally differed between the treatments. Supplementing each patient time course with a corresponding confidence region, we illustrate the consequences of the uncertainty estimate for the underlying mechanisms of CML remission. Our model suggests that the observed BCR-ABL dynamics may result from different, underlying stem cell dynamics. These results illustrate that the perception and description of CML treatment response as a dynamic process on the level of individual patients is a prerequisite for reliable patient-specific response predictions and treatment optimizations

Impact of observational incompleteness on the structural properties of protein interaction networks

The observed structure of protein interaction networks is corrupted by many false positive/negative links. This observational incompleteness is abstracted as random link removal and a specific, experimentally motivated (spoke) link rearrangement. Their impact on the structural properties of gene-duplication-and-mutation network models is studied. For the degree distribution a curve collapse is found, showing no sensitive dependence on the link removal/rearrangement strengths and disallowing a quantitative extraction of model parameters. The spoke link rearrangement process moves other structural observables, like degree correlations, cluster coefficient and motif frequencies, closer to their counterparts extracted from the yeast data. This underlines the importance to take a precise modeling of the observational incompleteness into account when network structure models are to be quantitatively compared to data.Comment: 17 pages, 7 figures, accepted by Physica