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| Doron Levy (U. Maryland) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 9:50 |
| Car T Cell Therapy: what we can learn from math? | ||
We model the distinct in vivo dynamics of donor-derived Memory Stem CAR T Cells and standard CAR T cells (Gattinoni et al., Cell 2026) with a novel approach of coupling a system of ODEs to a multi-type branching process. The entire cohort difference reduces to one parameter, the stem self- renewal probability, which sits on opposite sides of an analytically derived phase transition and accounts for both the low-dose efficacy and the observed clonal succession. A profile-likelihood analysis shows the data identify the supercritical-versus-subcritical regime rather than a precise value. Because the transition is a threshold, it suggests a categorical release criterion for manufacturing, and the same parameter accounts for the per-cell amplification that underlies the low-dose efficacy. | ||
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| Angelika Manhart (U. Vienna) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 11:00 |
| Modelling & predicting breast milk dynamics | ||
Breastfeeding creates a feedback loop between milk removal by an infant or breastpump, and the stimulation of milk production. In this talk I will present and discuss a hybrid continuous/discrete mechanistic model that captures this process. The model is fitted to and tested against time-course data collected by wearable breast pumps. Model analysis allows to understand which pumping/feeding strategies lead to sustainable milk production, as well as how to optimize one's pumping/feeding strategy. | ||
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| Marie-José Chaaya (U. Aix-Marseille) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 11:40 |
| A Mathematical Model for PDAC Tumorigenesis, Stiffness and Axons Remodelling | ||
Pancreatic cancer is one of the deadliest cancers, and despite decades of research, treatments remain largely ineffective. To make progress, scientists must look beyond the cancer cells themselves and examine everything surrounding them. This work uses mathematical models as a virtual laboratory to study two hidden forces that shape how pancreatic cancer grows: the nerves that infiltrate the tumor and the progressive hardening of the surrounding tissue. Nerves are not passive bystanders; some slow tumor growth, while others accelerate it. Meanwhile, tissue stiffening acts as armor around the tumor, making it harder for treatments to penetrate and be effective. By building mathematical models of these interactions, this work helps explain why the same treatment can succeed in one patient and fail in another and opens the door to more personalized therapeutic strategies. | ||
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| Morten Andersen (U. Roskilde) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 14:00 |
| Mathematical modeling of blood cancers, chronic inflammation and treatment | ||
Human blood cell production is maintained by hematopoietic stem cells (HSC) which give rise to all types of mature blood cells. Experimental observation of HSC in their physiologic bone-marrow microenvironment is challenging including malignant mutations of HSC. To investigate the significance of the interaction between the HSC, malignant HSC, the stem cell niche and chronic inflammation, we propose a mechanism-based mathematical model that takes into account several standard-of-care treatments as well as novel inflammation reducing treatments. The model was calibrated to individualized patient-data consisting of longitudinal hematologic and molecular measurements from several patient cohorts. We believe that this approach could have direct clinical relevance, offering expert guidance for clinical decision in terms of disease understanding and optimal treatment scheduling. | ||
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| Sandy Anderson (MOFFITT) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 14:40 |
| Mathematical Biomarkers of Adaptive Therapy Outcomes in Prostate Cancer | ||
Adaptive therapy is an evolution-based treatment paradigm has been shown to delay resistance in prostate cancer through treatment breaks that control, rather than minimize, tumor burden. However, patient responses are highly heterogeneous, and there is a significant unmet clinical need for biomarkers to personalize treatment scheduling. We develop and retrospectively validate mathematical biomarkers that predict time to progression (TTP), mean daily dose (MDD), and overall survival (OS) under adaptive therapy from first-cycle prostate-specific antigen (PSA) dynamics.This retrospective modeling and validation study utilized longitudinal clinical trial data from 2 independent cohorts: 45 patients with castrate-sensitive prostate cancer (CSPC) and 13 patients with metastatic castrate-resistant prostate cancer (mCRPC). Patients received either intermittent androgen deprivation therapy (for CSPC) or adaptive abiraterone acetate (for mCRPC). The initial treatment cycle served as the exposure period to extract longitudinal PSA kinetics. Mechanism-based mathematical biomarkers (AT Score, expected TTP, and expected MDD) were derived from first-cycle PSA kinetics. Outcomes included in silico benchmarking experiments and retrospective validation against clinical TTP and OS. Performance was benchmarked against standard phenomenological PSA metrics (e.g., PSA nadir, time to nadir, and doubling time). In the CSPC cohort (N = 40), the AT Score derived from first-cycle data was highly prognostic for prolonged clinical TTP. In the mCRPC cohort (N = 13), the AT Score exhibited a strong rank correlation with clinical TTP and was associated with prolonged TTP. Analysis of long-term survival data in the mCRPC cohort demonstrated that both the AT Score and eTTP were significantly associated with prolonged OS, whereas standard empirical PSA metrics displayed no association with survival. Mechanism-based mathematical biomarkers derived from the initial-cycle PSA dynamics accurately predict patient-specific outcomes and survival, outperforming traditional phenomenological PSA monitoring. We propose these accessible metrics as a mathematically informed decision-support framework to stratify patients into personalized treatment protocols. | ||
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| Chiara Villa (CNRS) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 15:50 |
| Predicting the efficacy of CAR-based immunotherapy: an interdisciplinary approach | ||
Adoptive cell therapy, also known as cellular immunotherapy, aims at enhancing the cancer-fighting capabilities of the patient’s own immune cells. A promising approach is to genetically engineer immune cells to express a synthetic receptor, namely a chimeric antigen receptor (CAR), capable of targeting specific antigens (e.g. the MET receptor) expressed by cancer cells. Despite the potential of CAR-based immunotherapy to achieve durable clinical responses, a key obstacle to its efficacy is posed by antigen expression heterogeneity both within the same tumour and across patients. In this talk I will show how mathematical modelling, integrated with experimental data, can help predict the efficacy of CAR-based immunotherapy against antigen expression-heterogeneous tumours. | ||
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| Heiko Enderling (U. Texas) | Skylounge, 12th floor Fak.Math. Univ. Wien | Wed, 22. Jul 26, 16:30 |
| Digital Twins in (Radiation) Oncology | ||
To give the right treatment at the right time to the right patient is the mantra of personalized medicine. A framework that could facilitate personalized therapies is the so-called digital twin. I present the latest developments in mathematical and computational modelling in radiation oncology to develop digital twins. To personalize cancer radiation therapy, we must give the right dose and dose fractionation, at the right time, dynamically adapted, to best harness the radiobiological effects of radiation as well as synergy with the patient’s immune system. I present different simple approaches to build predictive pipelines and how to integrate those into clinical decision making towards the concept of real-time adaptive personalized radiation treatments. I will discuss past, present, and future clinical trials of such model-guided treatments. | ||
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| Maria-José Cáceres (U. Granada) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Thu, 23. Jul 26, 9:30 |
| Long-Time Dynamics of Neuronal Population Models: Effects of Synaptic Delay and Connectivity | ||
In this talk, we study the long-time dynamics of two mesoscopic models for neuronal populations, with a particular focus on the role of synaptic delay and network connectivity. At this scale, the system is described by a probability density representing the distribution of neuronal states, such as the membrane potential or the time elapsed since the last discharge. More precisely, we analyze the behavior of the NNLIF (Nonlinear Noisy Leaky Integrate-and-Fire) model, formulated as a nonlinear Fokker--Planck type equation, and the age-structured model based on the time-since-last-discharge description. While entropy dissipation methods provide a natural analytical framework, they encounter significant limitations, especially in regimes of strong connectivity. We present alternative approaches based on two main ideas: first, the analysis of simplified discrete models, which provide fast and intuitive insight into the nonlinear dynamics; and second, a linearization around equilibrium combined with a reformulation of the problem as a Volterra-type integral equation. These techniques allow us to better understand the influence of delay and connectivity on the long-time behavior of the system. This talk is based on joint works with José A. Cañizo, Alejandro Ramos-Lora, and Nicolás Torres. | ||
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| Louis Fostier (INRIA) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Thu, 23. Jul 26, 10:45 |
| PINNs for Structured Population Dynamics Inference | ||
Population dynamics are often observed as distributions of individuals with respect to structuring variables such as size or age. The associated inverse problem consists of inferring the underlying vital rates (recruitment/birth, growth, and death) which are typically complex, nonparametric functions due to limited prior knowledge. Physics- Informed Neural Networks (PINNs) provide a natural framework for such problems by combining neural networks as universal function approximators with mechanistic constraints encoded in differential equations. In this talk, we showcase the ability of PINNs to solve inverse problems in the context of size-structured population dynamics governed by coupled PDE-ODE models, in two biological contexts: oocyte dynamics in fish ovaries and adipocyte dynamics in adipose tissue. | ||
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| Luis Gómez-Nava (U. Paris-Saclay) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Thu, 23. Jul 26, 11:30 |
| Modelling Intermittent Biological Systems Across Scales: From Bacteria to Sheep | ||
Many biological systems exhibit intermittent dynamics across a wide range of scales, where individuals alternate between distinct behavioral states over time, such as motion and rest, or directed and undirected movement. Examples include microswimmers such as bacteria, microalgae, and amoebae, as well as animal groups such as schools of fish and flocks of sheep. In this talk, I will present one unifying model to describe such systems. First, I will discuss the modeling of intermittent biased motion in microswimmers subjected to an external signal c(x). I will outline the minimal ingredients required for a model to reproduce directed motion of particles toward (or away from) the source of the signal. In the second part, I will focus on intermittent collective dynamics in animal groups, particularly small groups of sheep and large schools of fish. In both systems, transitions can occur between a collective stationary state and a collective moving state, despite the very different biological contexts involved. I will show how both scenarios can be described within a common theoretical framework based on active particles with discrete internal states. Finally, I will discuss the strengths and limitations of this approach, and explain why it provides a particularly effective description of these biological systems. | ||
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| Charles Elbar (CNRS) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Thu, 23. Jul 26, 14:00 |
| Deterministic Particle Approximation of a Fourth-Order PDE | ||
Many partial differential equations describe how a macroscopic density of particles/cells evolves in time. So, it is a natural question to ask whether there’s a microscopic (deterministic) model, which focuses on the interaction between each particles, that leads to a given macroscopic PDE. For second-order aggregation–diffusion equations, this connection has been proved, starting with the porous medium equation in 2001. In this work, we extend that framework to a fourth-order equation, that has applications in cell–cell adhesion in biological systems. This is a joint work with Alejandro Fernandez- Jimenez. | ||
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| Marie-José Chaaya (U. Aix-Marseille) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Thu, 23. Jul 26, 15:15 |
| A Mathematical Model for PDAC Tumorigenesis, Stiffness and Axons Remodelling – part 2: Theorems & Proofs | ||
Pancreatic cancer is one of the deadliest cancers, and despite decades of research, treatments remain largely ineffective. To make progress, scientists must look beyond the cancer cells themselves and examine everything surrounding them. This work uses mathematical models as a virtual laboratory to study two hidden forces that shape how pancreatic cancer grows: the nerves that infiltrate the tumor and the progressive hardening of the surrounding tissue. Nerves are not passive bystanders; some slow tumor growth, while others accelerate it. Meanwhile, tissue stiffening acts as armor around the tumor, making it harder for treatments to penetrate and be effective. By building mathematical models of these interactions, this work helps explain why the same treatment can succeed in one patient and fail in another and opens the door to more personalized therapeutic strategies. | ||
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| Susanne Solem (Norwegian U. of Life Sciences) | WPI Seminar room, 8th floor Fak.Math. U. Wien, 1090 Wien | Fri, 24. Jul 26, 9:30 |
| Navigational-Enabling Settings of a PDE Modelling Noisy Grid Cell Activity | ||
Grid cells, with their striking hexagonal firing patterns, are neurons which play a key role in the internal navigational system of mammals. This talk will concern a nonlocal Fokker- -Planck-type PDE which emerged in a pursuit to better understand the effects of noise on grid cell activity. When this model produces hexagonal neuronal network activity which persists when translated in accordance with the mammal's movement in physical space, the model is in a setting which enables the mammal's ability to orientate itself. But under which conditions could such activity emerge in the model? | ||
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| Nathanaël Boutillon (U. Vienna) | WPI Seminar room, 8th floor Fak.Math. U. Wien | Fri, 24. Jul 26, 10:45 |
| Impact of Diffusion Mechanisms on Persistence and Spreading | ||
I will talk about a model for a population that is structured in space and that diffuses heterogeneously. The model is based on a KPP equation with a “q-diffusion”, which is a framework that unifies various standard linear diffusion regimes: Fickian diffusion (q = 0), Stratonovich diffusion (q = 1/2), Fokker-Planck diffusion (q = 1). I will explore how the ability of persistence and how the asymptotic spreading speed depend on the parameter q and on the phase shift between the growth rate r(x) and the diffusion coefficient D(x). Those results demonstrate that persistence and spreading properties generally depend on q: for example, appropriate configurations of r(x) and D(x) can be constructed such that q-diffusion either enhances or diminishes the ability of persistence and the spreading speed with respect to the traditional Fickian diffusion. This work underscores the importance of carefully selecting diffusion models in ecological and epidemiological contexts, highlighting their potential implications for persistence, spreading, and control strategies. Joint work with Y.-J. Kim and L. Roques. | ||
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| Alejandro Barea Moreno (U. Vienna) | WPI Seminar room, 8th floor Fak.Math. U. Wien, 1090 Wien | Fri, 24. Jul 26, 11:30 |
| A Pseudo-3D Modelling Approach to Understand Heterogeneity in Collective Cell Migration | ||
Collective cell migration is a fundamental process driving essential biological phenomena, including tissue morphogenesis, wound healing, and cancer metastasis. A key driver of these coordinated movements is cellular heterogeneity—be it in size, adhesive properties, signaling, etc. 2D models fail to capture the full interaction between cells, and 3D models can be computationally prohibitive. Based on cellular sub-element modeling, we present a pseudo-3D model to understand the movement of cells in tight groups. | ||
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