The team

Pic 16Pic 17Pic 18Pic 19
From left: Prof. Ingo Roeder, Dr. Hans. H. Diebner, Dipl.-Math. Matthias Kuhn, Dr. Michael Seifert

Second Funding Period

Accomplishments of the first funding period

RP4 developed, implemented, and analytically analyzed a population based model for the competition of T-cell clones for spMHC resources. Thereby, the TCRs can interact either highly specifically or to some degree unspecifically with molecules out of the diverse spMHC repertoire. The model is able to simulate the experimental finding that clonal diversity prevents from coexistence or outgrowth of malignant T-cell clones, whereas in oligoclonal situations the healthy T-cell system gets destabilized in favor of tumor formation. Based on a newly developed mathematical model, which describes T-cell circulation between different lymphatic tissues, RP4 estimated TCR-specific kinetic parameter values and lymph node dwell times for T cells. This work used data on T-cell counts in a bi-clonal (HA and DO11.10 TCRs) setting provided by RP3. RP4 also estimated parameter values for IL-2 secretion, dependent on different stimulations in the presence of varied expression levels of the TCL1 oncogene. Here a statistical modeling approach was used, namely a logistic growth process to fit data from ELISA experiments provided by RP5. RP4 also started to develop/implement a single cell-based simulation platform. In pilot simulations, RP4 could already explain/confirm the optimal dwell time hypothesis (i.e., a well-balanced relation between affinities and off-rates of the TCR-spMHC interactions) using this model. In addition, first hypotheses on mechanisms and critical kinetic parameters playing crucial roles for the destabilisation of a previously balanced TCR repertoire have been formulated.
Finally, RP4 established a general statistical/biometrical counselling/support service for CONTROL-T, which has intensely been used by RP2, 3 and 5. Additionally, RP4 supported the NGS analysis conducted by RP1. Specifically, RP4 developed, implemented and applied a bioinformatic analysis pipeline for SNP, SV and virus detection, based on NGS data (WES, WGA) and developed a new method for the particular detection of small SVs.


Pic 7 Fig 1: Scheme from Newrzela et al. Leukemia 2012
T cells need survival signals mediated by lymphokines and by TCR-­ligation to self-­peptide/MHC (spMHC) complexes. Competition between T-­cell clones is suggested by the observation that a poly-­clonal, but not a mono-­clonal T-­cell environment is able to suppress the outgrowth of T-­cell leukemia in Rag1 deficient recipient mice (see fig. 1). It has been hypothesized that pre-­leukemic cells gain a competitive advantage for the trophic resources, however, clonal diversity is able to inhibit tumor outgrowth. Although the impact of some kinetic parameters, like the TCR-­spMHC reaction off-­rate (dwell time hypothesis), appears to be relevant here, this mechanism alone is not sufficient to explain all experimental observations. Thus, most of the underlying mechanisms are unknown. To reveal these, we apply systems biological modeling approaches.


Overall objective

The overall objective of this project is to provide an explanatory mathematical modeling basis, to delineate testable mechanistic hypotheses that lead to a quantitative understanding of survival and proliferation of mature T-­cell clones in the homeostatic situation and of unregulated expansion in the malignant (transformed) state.

Aim 1: Establishing an individual cell-­based simulation platform and providing simulation scenarios based on biological requirements

Pic Aim 1 Fig 2: Structure of the multi-level model (aim 1) integrating the levels from aims 2 and 3.

Aim 2: Modeling lymph node heterogeneity and topological structures of leukemo-­genesis

Expected results of this aim are:
  • Quantification of effects of spatial heterogeneity on stable / disturbed homeostasis
  • Estimates of kinetic and circulation parameters (e.g. lymph node dwell times)
  • Identification of local spots of leukemic expansion
The results will be integrated into the multi-level model (aim 1).

Aim 3: Modeling oncogene-­dependent signaling and intracellular processes

Expected results of this aim are:
  • Model predictions of the impact of oncogenes on signaling
  • Estimation of kinetic parameters
  • Predicting related TCR-­dynamics
The findings will be integrated into the multi-level model (aim 1).

Aim 4: Provision of bioinformatical and statistical services within CONTROL-­T

First Funding Period


Achieving a quantitative, systemic understanding of T-cell organization requires the integration of information from different descriptive levels. In our project we foster this integration by applying mathematical modeling techniques to consistently explain processes that potentially lead to malignant transformations of T cells. Specifically, we establish a mathematical framework, in which the cellular interactions of multiple T-cell receptor-specific clones will be coupled to the underlying, cell intrinsic signaling and transcriptional networks. Based on the modeling work, we will be able to derive experimentally testable predictions, addressing particular regulatory mechanisms, but also proposing potential therapeutic strategies to treat T-cell related malignancies. The modeling approach is a central component to link the experimental efforts within CONTROL-T and to achieve a quantitative, scale-bridging understanding of proliferation and survival of mature T-cell clones in the normal homeostatic and in the malignant situation. Beyond the mathematical modeling, our group also supports the bioinformatical and statistical analysis of next generation sequencing data to identify genetic lesions that are potentially related to the malignant transformation of T cells.



Our overall objective is a better quantitative understanding of proliferation and survival of mature T-cell clones. Complementary to the homeostatic situation we will specifically focus on the malignant expansion of T-cell clones. Therefore, we will adapt and verify different modeling approaches on the basis of experimental data generated within CONTROL-T. Ultimately, we aim for a multi-scale mathematical model, integrating intracellular and intercellular processes.

To achieve this objective, we formulated the following specific aims:

Aim 1

Consolidation and adaptation of our previously established model of T-cell homeostasis (see preliminary work) to new quantitative, experimental data, such as cell kinetic measurements, the distribution of T-cell receptor (TCR) affinities among T-cells or cytokine effects.

Aim 2

Extending the modeling framework to couple cellular interactions of multiple TCR-specific T-cell clones with underlying signaling and transcriptional networks.

Aim 3

Applying the modeling framework developed in aim 2, we will study the molecular consequences of certain oncogenic transformations (e.g. driven by ALK, TCL1, or Myc). Specifically, we will investigate the correspondence between the intra-cellular regulation and the observed escape from homeostasis on the cellular and tissue level.

Aim 4

Last but not least, we will provide bioinformatical and statistical services within CONTROL-T to support the exploration, processing, analysis, and functional integration of high-throughput data, such as next-generation sequencing (NGS) data.

Preliminary Data

The group of Ingo Roeder has been working in the field of mathematical modeling of different biological systems, with a focus on stem cell organization and the treatment of (chronic myeloid) leukemia, for more than ten years (see also These activities comprise the theoretical analysis of clonal competition phenomena related to gene therapeutic applications of hematopoietic stem cells.

More recently, T cells dynamics have become another research focus. In this context, we developed a new mathematical model of T-cell homeostasis. The major entities in this model are T-cell clones, defined by their T-cell receptor (TCR) and T-cell niches. The biological (molecular) counterpart of our conceptual T-cell niche is a particular profile of self-peptides presented by MHC molecules on antigen presenting cells. The T-cell niches provide resources that T cells need for survival and proliferation. These resources are limited, therefore, inducing a resource competition of the clones.

The model is able to reproduce the experimentally observed pattern of stable polyclonality in an unperturbed T-cell system. In order to systematically evaluate an oncogenic situation, we divided the T-cell population into a „healthy“ and a „pre-leukemic“ sub-population, each with its own specific set of model parameter values. For a description of the technical details we refer to our corresponding (open access) publication (Gerdes et al., 2013). In the following, we will briefly summarize the key results of our modeling approach:

The central aim was to answer the question, whether competition of T-cell clones for TCR-related niche resources is a potential mechanism to control the outgrowth of transformed, pre-leukemic T cells. Specifically, we wanted to use our mathematical model to consistently explain the experimental observation that T cells transduced with oncogenes are forming a leukemia in the monoclonal situation, whereas a polyclonal system of T cells is able to control the outgrowth of a leukemia (Fig.1, Newrzela et al. Leukemia 2012).

Using a systematic screen of model parameters (which identified a confined region of consistent parameters), we could show that niche competition is indeed a candidate mechanism that can explain the described phenomenology. Specifically, our model results suggest that decreased TCR avidities (i.e. a down-regulation of TCR-mediated control mechanisms and/or impairment of intracellular TCR signaling) together with a (partial) independence from niche-mediated growth signals in pre-leukemic cells is a potential mechanistic explanation for the observed protective effect of a polyclonal system.

Figure 3 shows a selection of simulation results for the monoclonal and the polyclonal situation. For more details we again refer to our corresponding (open access) publication (Gerdes et al., 2013).

Pic 15 Fig. 3 from Gerdes et al., Front. Immunol. 2013