Hans H. Diebner

A robust, locally interpretable algorithm for Lyapunov exponents

Abstract An enhanced version of the well known Wolf algorithm for the estimation of the Lyapunov characteristic exponents (LCEs) is proposed. It permits interpretation of the local behavior of non-linear flows. The new variant allows for reliable calculation of the non-uniformity-factors (NUFs). The NUFs can be interpreted as standard deviations of the LCEs. Since the latter can also be estimated by the Wolf algorithm, however, without local information on the flow, the new version ensures local interpretability and therefore allows the calculation of the NUFs. The local contributions to the LCEs which we call “local LCEs” can at least be calculated up to three dimensions. Application of the modified method to a hyperchaotic flow in four dimensions shows that an extension to many dimensions is possible and promises new insight into so far not fully understood high-dimensional non-linear systems.

Reversible Control of Chemical Reaction Systems

Abstract Starting from an algorithm for continuous chaos control, a reversible control method based on mutual diffusive coupling of chemical reactors is developed. With sufficient coupling strength, the proposed mutual coupling leads to control even if both reactors are of similar size. The controlling and controlled reactor exchange their roles at a certain size ratio. Insufficient coupling can lead to a more complex dynamics than that of the uncoupled reactors. This method for control via reversible coupling of chemical reactors should be implementable on a purely microscopic level.

A realtime adaptive system for dynamics recognition

Abstract We propose and investigate an adaptive system for the recognition of the dynamics of an external time series. The system consists of a pool of internal dynamical elements, each of which represents a specific dynamical type. Each of the elements is forced by the external time series to the latter dynamics. We use the absolute value of the control signal, i.e., the strength of forcing, as a criterion for which of the elements out of the pool fits best to the dynamics of the time series. By means of an averaging process the system is able to create a new dynamical element as a “mirror system” of the external one. This adaptation can be performed continuously and impressively quickly even if the dynamical type of the external signal undergoes sudden qualitative changes.

A simulating cognitive system with adaptive capability

Dedicated to the memory of Michael Conrad, this paper builds on his seminal ideas expressed in his famous book Adaptability, as well as in his later works. We investigate a recently published adaptive system for the instantaneous recognition of dynamics with respect to its adaptability to the Lorenz system. The system consists of a pool of internal dynamical elements. These elements are defined through a set of parameter values that encode for a specific dynamics behavior. If the system is now faced with an unknown external dynamics-unknown with respect to the parameter-it is capable not only to recognize the dynamics but also to adapt to the correct dynamics, which in turn leads to a simulation capability. The system impressively quickly follows the sudden qualitative changes of the external dynamics. The adaptation works even quicker when the correct dynamics are already represented within the internal pool. This leads to the idea of memorizing the represented dynamics within the pool, whereby the elements that correspond to rarely externally presented dynamics can be given free for the adaptation and memorization of more frequently presented dynamics.

An evolutionary stability perspective on oncogenesis control in mature T-cell populations.

Here we present a mathematical model for the dynamics of oncogenesis control in mature T-cell populations within the blood and lymphatic system. T-cell homeostasis is maintained by clonal competition for trophic niches (survival signals stimulated through interactions with self-antigens bound to major histocompatibility molecules), where a clone is defined as the set of T cells carrying the same antigen specific T-cell receptor (TCR). We analytically derive fitness functions of healthy and leukemic clone variants, respectively, that capture the dependency of the stability of the healthy T-cell pool against leukemic invaders on clonal diversity and kinetic parameters. Similar to the stability of ecosystems with high biodiversity, leukemic mutants are suppressed within polyclonal T-cell populations, i.e., in the presence of a huge number of different TCRs. To the contrary, for a low clonal diversity the leukemic clone variants are able to invade the healthy T-cell pool. The model, therefore, describes the experimentally observed phenomenon that preleukemic clone variants prevail in quasi-monoclonal experimental settings (in mice), whereas in polyclonal settings the healthy TCR variants are able to suppress the outgrowth of tumours. Between the two extremal situations of mono- and polyclonality there exists a range of coexistence of healthy and oncogenic clone variants with moderate fitness (stability) each. A variation of cell cycle times considerably changes the dynamics within this coexistence region. Faster proliferating variants increase their chance to dominate. Finally, a simplified niche variation scheme illustrates a possible mechanism to increase clonal T-cell diversity given a small niche diversity.

Space-Discretized Verlet-Algorithm from a Variational Principle

Abstract A form of the Verlet-algorithm for the integration of Newton’s equations of motion is derived from Hamiltons principle in discretized space and time. It allows the computation of exactly time-reversible trajectories on a digital computer, offers the possibility of systematically investigating the effects of space discretization, and provides a criterion as to when a trajectory ceases to be physical.

Metabolism is the Tie: The Bertalanffy-type Cancer Growth Model as Common Denominator of Various Modelling Approaches

Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Abstract 370: T-PLL cells resemble memory-type T-cells with aberrant effector functions implicating a leukemogenic cooperation of TCL1A and TCR signaling

The pathogenesis of the rare and aggressive T-cell prolymphocytic leukemia (T-PLL) is poorly understood, which particularly applies to a mechanistic concept around its hallmark oncogene TCL1A. Existing data implicate TCL1A as a catalytic enhancer of the oncogenic kinase AKT, a central node in a T-cell’s antigen receptor (TCR) signaling cascade, which mediates proliferation and differentiation. The levels and role of TCR activation in T-PLL’s pathogenesis are not known. To first clarify which physiological T-cell subset T-PLL cells most resemble, we performed comprehensive global gene expression profiling and immunophenotyping of primary T-PLL (n=79) in comparison to healthy-donor derived T-cell populations. Principle component analyses and gene signature alignments revealed a high similarity of T-PLL cells to (central) memory T-lymphocytes over naive T-cells. Surface markers revealed a spectrum of memory-type differentiation (n=69/79; 87%) with predominant central-memory stages (n=35/79; 44%). The usually TCR and/or CD28-coreceptor positive T-PLL cells revealed no restrictions to genetic or surface TCR-clonotypes. The abnormally high basal activation levels (surface CD25, CD38, CD69) correlated in their degree with inferior clinical outcomes (med. survival 20.8 vs 58.3 mo.; p=0.0012). In parallel, T-PLL cells lost expression of negative-regulatory TCR-co-receptors (e.g. CTLA-4, LAG3). Fittingly, TCR engagement of primary T-PLL cells revealed a trend to hyperactive intracellular responses and interleukin(IL)-2 release alongside a prominent Th1-cytokine program. T-PLL cells also showed a robust resistance to stimulation-induced cell death and agonistic CD95 ligation. TCR-derived signals (phospho-kinase induction, IL-2 release) were enhanced in vitro by the modulated presence of TCL1A with kinetics indicative of a sensitizer relationship, mainly in the CD3 axis as opposed to the CD28 branch. A mouse model with TCL1A-initiated protracted development of T-PLL (Lckpr-TCL1Atg) revealed congruent findings with the aberrant T-cell phenotype of human T-PLL. TCL1A expressing T-cells of this model, that were further equipped with monoclonal epitope-defined TCRs against ovalbumine or a chimeric-antigen-receptor (CAR) against carcinoembryonic antigen, gained a pre-leukemic growth advantage in scenarios of pulsed or continuous low-level receptor stimulation. Overall, we establish that T-PLL cells resemble antigen-experienced memory T-cells. Retention of functional effector responses to TCR stimulation and loss of restricting activation regulators underlie a highly activated phenotype and a marked resistance to death-inducing signals. TCL1A proactively enhances TCR responses and we postulate that this leukemogenic cooperation drives accumulation of memory-type cells that utilize amplified, hence permissive, low-level cognate antigen input. Note: This abstract was not presented at the meeting. Citation Format: Alexandra Schrader, Kathrin Warner, Sebastian Oberbeck, Giuliano Crispatzu, Petra Mayer, Sabine Putzer, Hans Diebner, Stephan Stilgenbauer, Georg Hopfinger, Jan Durig, Torsten Haferlach, Mark Lanasa, Ingo Roeder, Michael Hallek, Dimitrios Mougiakakos, Michael von Bergwelt-Baildon, Monika Bruggemann, Sebastian Newrzela, Hinrich Abken, Marco Herling. T-PLL cells resemble memory-type T-cells with aberrant effector functions implicating a leukemogenic cooperation of TCL1A and TCR signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 370. doi:10.1158/1538-7445.AM2017-370

Time Reversibility and the Logical Structure of the Universe

Abstract Both Fredkins findings on the reversibility of logical operations and the novel capability to integrate Newtons equation of motion in an exactly reversible manner enables one to perform a gedankenexperiment in the form of a molecular dynamics simulation of the universe. This leads to a new validation of the reversible structure of the universe. Alternatively, the role of information needs to be upgraded in accordance with a recent proposal by Stonier.

Deterministic Continuous Molecular-Dynamics-Simulation of a Chemical Oscillator

Abstract A macroscopic chemical oscillator involving 3 autocatalytic second-order reactions is simulated microscopically. A deterministic Newtonian simulation involving 1024 particles with a smooth 1/r potential in two dimensions is presented.