Biology subjects

Frank, A.-S.

Publications and source records attributed to Frank, A.-S..

7 recordsLinked to original sources

bioRxiv2025

Modeling Bistable Dynamics Arising from Macrophage-Tumor Interactions in the Tumor Microenvironment

Macrophages in the tumor microenvironment (TME), known as tumor-associated macrophages (TAMs), originate primarily from circulating monocytes that differentiate under the influence of tumor-derived signals. Within the TME, naive macrophages can adopt either a pro-inflammatory, anti-tumor (M1-like) or anti-inflammatory, pro-tumor (M2-like) phenotype. These pheno-typic shifts significantly affect tumor progression, making TAMs attractive targets for therapeutic intervention aimed at blocking recruitment, promoting anti-tumor polarization, or disrupting tumor-macrophage interactions. In this study, we develop a mathematical model capturing the temporal dynamics of tumor volume alongside populations of naive, M1-like, M2-like, and mixed (M1/M2) phenotype TAMs. The model incorporates the bidirectional influence between tumor development and macrophage polarization. We conduct the bifurcation as well as global sensitivity analyses to identify regions of bistability for tumor dynamics in the parameter space and the impact of sensitive parameters on TME. The model results are then linked to treatment strategies that may effectively induce transitions from high to low tumor burden. HighlightsO_LIWe propose a mathematical model to describe the temporal evolution of the tumor volume alongside different phenotypes of tumor-associated macrophages (TAMs). C_LIO_LIWe investigate the impact of TAMs on tumor growth and decline as well as the influence of the tumor on transition rates between different TAM phenotypes. C_LIO_LIBifurcation and global sensitivity analyses reveal the regions of bistability for tumor dynamics and the impact of sensitive model parameters on the tumor microenvironment. C_LIO_LIModel results are linked to treatment strategies that manipulate the bistable system to transition from high to low tumor volume. C_LI

systems biology↗

bioRxiv2024

Evaluating the Fidelity of Data-Driven Predator-Prey Models: A Dynamical Systems Analysis

In empirical predator-prey systems, understanding the inherent dynamics typically comes from analyzing a structural model fitted to observation data. However, determining an appropriate model structure and its parameters is often complex and highly uncertain. A promising alternative is to learn the model structure directly from time series data of both predator and prey. This study explores the capability of a data-driven algorithm, Sparse Identification of Nonlinear Dynamics (SINDy), to accurately capture the dynamics of a predator-prey system. We apply SINDy to derive a Learned Model (LM) from data generated by a Reference Model (RM), whose predator-prey dynamics are well understood. The study compares the dynamics of the LM to the RM using criteria such as equilibrium points, stability, sensitivity, and bifurcation analysis. Our results demonstrate general consistency between the RM and LM dynamics, though notable differences remain. We discuss the implications of these differences in the broader context of using learned models to uncover the inherent drivers of predator-prey dynamics and ecological implications. 2010 MSC37M05, 37N25, 92B05, 92D25, 92D40, 65L05, 37G15 HighlightsO_LIEvaluates data-driven models ability to replication of predator-prey dynamics using SINDy framework C_LIO_LILearned Model captures core dynamics but shows parameter sensitivity and bifurcation differences C_LIO_LIAnalysis reveals need for extensive data for effective model learning C_LIO_LISuggests combining data-driven methods with biological priors to improve model accuracy C_LI

ecology↗

bioRxiv2024

Discrete Time Modeling of Stock-Recruit Relationships with Life-History Stanzas

The stock-recruit relationship is a foundational concept in fisheries science, bridging the connection between parental populations (stock) and progeny (recruits). Traditional approaches describe this relationship using closedform analytical functions, which represent only a restricted subset of the broader class of possibilities. This paper advocates for a novel approach that integrates discrete time modeling with a life-history cycle framework, incorporating distinct stanzas and developmental processes. By breaking down the life cycle into identifiable stages, we capture the step-wise progression of life history traits and the factors influencing recruitment outcomes. Through numerical simulations, we explore the advantages of this approach, including complexity handling, dynamic behavior modeling, and scenario exploration. Our simulation results show that we are able to generate a broad spectrum of stock-recruit relationships (including the traditional ones), which best reflect variability observed in nature. We demonstrate how this framework allows for the identification of critical stages, and integration of various factors that influence recruitment. This holistic approach enhances our comprehension of the intricate interactions shaping stock-recruit relationships and advances our understanding of sustainable population dynamics. HighlightsO_LIA novel multi-stage life-cycle model is presented. C_LIO_LIModel simulations reveal three different Stock-Recruitment (SR) patterns. C_LIO_LIOur approach contributes to enhanced understanding of SR relationships. C_LI

ecology↗

bioRxiv2023

Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition

BackgroundThe dynamics of many gene regulatory networks (GRNs) is characterized by the occurrence of metastable phenotypes and stochastic phenotype switches. The chemical master equation (CME) is the most accurate description to model such stochastic dynamics, whereby the long-time dynamics of the system is encoded in the spectral properties of the CME operator. Markov State Models (MSMs) provide a general framework for analyzing and visualizing stochastic multistability and state transitions based on these spectral properties. Until now, however, this approach is either limited to low-dimensional systems or requires the use of high-performance computing facilities, thus limiting its usability. ResultsWe present a domain decomposition approach (DDA) that approximates the CME by a stochastic rate matrix on a discretized state space and projects the multistable dynamics to a lower dimensional MSM. To approximate the CME, we decompose the state space via a Voronoi tessellation and estimate transition probabilities by using adaptive sampling strategies. We apply the robust Perron cluster analysis (PCCA+) to construct the final MSM. Measures for uncertainty quantification are incorporated. As a proof of concept, we run the algorithm on a single PC and apply it to two GRN models, one for the genetic toggle switch and one describing macrophage polarization. Our approach correctly identifies the number and location of metastable phenotypes with adequate accuracy and uncertainty bounds. We show that accuracy mainly depends on the total number of Voronoi cells, whereas uncertainty is determined by the number of sampling points. ConclusionsA DDA enables the efficient computation of MSMs with quantified uncertainty. Since the algorithm is trivially parallelizable, it can be applied to larger systems, which will inevitably lead to new insights into cell-regulatory dynamics.

bioinformatics↗

bioRxiv2022

Macrophage phenotype transitions in a stochasticgene-regulatory network model

Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.

systems biology↗

bioRxiv2020

Population Dynamic Regulators In An Empirical Predator-Prey System

This paper investigates stability conditions of an empirical predator-prey system using a model that includes a single delay term,{tau} , in description of the predator dynamics. We derive theoretical conditions on{tau} , in terms of other model parameters, and determine how changes in these conditions define different stability regimes of the system. We derive optimal model parameters by fitting model to empirical data, using unconstrained optimization. The optimization results are combined with those from the theoretical analysis, to make inference about the empirical system stability. Our results show that Hopf bifurcation occurs in the predatory-prey system when{tau} exceeds a theoretically derived value{tau} * > 0. This value represents the critical time for prey availability in advance of the optimal predator growth period. Set into an ecological context, our findings provide mathematical evidence for validity of the match-mismatch hypothesis, for this particular species.

ecology↗

bioRxiv2020

Crowding Effects In An Empirical Predator-Prey System

AO_SCPLOWBSTRACTC_SCPLOWThis paper uses a Lotka-Volterra (predator-prey) modeling framework to investigate the dynamical link between the biomass of an empirical predator, and that of its prey. We use a system of ordinary (ODE) differential equations to describe the system dynamics, and derive theoretical conditions for stability, in terms of system parameters. We derive the empirical system parameters by fitting the ODE system to empirical data, using non-constrained optimization. We present results to show that the predator biomass is regulated by that of the prey. Furthermore, that the system dynamics is subject to Hopf bifurcation, conditioned on independent second-order terms in the ODE system. In ecological terms, the findings translate into evidence for existence of population crowding (density) effects.

ecology↗