Biology subjects

Traulsen, A.

Publications and source records attributed to Traulsen, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗

bioRxiv2025

Population structure amplifies mutation load

The interplay of mutation and natural selection is key to evolution: Mutations introduce genetic variation, whereas selection favours higher-fitness individuals. The balance between the two is quantified by the mutation load--the reduction in mean population fitness due to deleterious mutations. In large well-mixed populations, selection dominates, and the mutation load is proportional to the mutation probability per reproduction event. However, the role of spatial structure in shaping mutation-selection dynamics is poorly understood. Here, we use evolutionary graph theory to show that heterogeneous spatial structures generically amplify the effect of mutation relative to selection. In contrast to well-mixed populations, the mutation load in heterogeneous structures can remain substantial even when the mutation rate is very small, signaling a breakdown of selection. For example, in star-shaped populations the mutation rate is effectively amplified by a factor proportional to the population size. When mutation is coupled to reproduction, this leads to the proliferation of less-fit types over fitter ones, a scenario of "survival of the weakest". The reduction of natural selection may help explain, for example, tumour heterogeneity in spatially structured tissues. It also suggests that heterogeneous social networks can promote the persistence of unpopular opinions, thereby fostering diversity. By showing how spatial structure modulates the effect of selection, our work points to potentially new ways of steering evolution toward desired outcomes.

evolutionary biology↗

bioRxiv2025

Higher-order equivalence of Lotka-Volterra and replicator dynamics

The Lotka-Volterra equations are foundational in ecology, modelling logistic growth in isolated populations and complex dynamics in interacting species. Hofbauer and Sigmund established that these equations are equivalent to the replicator dynamics of evolutionary games, so dynamic patterns in ecology are mirrored in evolutionary games and vice versa. Both fields have since turned to non-linearities and higher-order interactions, where it is unclear whether this equivalence still holds. Here, we demonstrate the general equivalence and illustrate it in classical non-linear models from theoretical ecology. Non-linearities in either field leave this foundational connection intact. Yet directly modelling ecological dynamics with evolutionary games, or the reverse, risks misinterpretation, since species in Lotka-Volterra dynamics cannot be equated with strategies in evolutionary games. Our study reveals the tight interplay between ecology and evolutionary game theory, and the robustness of their mathematical connection even in complex scenarios, alongside its caveats.

evolutionary biology↗

bioRxiv2024

Quantification of CAR T cell performance against acute myeloid leukemia using Bayesian inference

Chimeric Antigen Receptor (CAR) T-cell therapy has transformed cancer immunotherapy by genetically engineering T-cells to target tumor antigens. Acute myeloid leukemia (AML) presents unique challenges due to resistance mechanisms, especially in patients with TP53 loss mutations. The complex dynamics of CAR T-cell expansion remain poorly understood. The field lacks validated quantitative frameworks to systematically evaluate different CAR T-cell target constructs, such as CD33, CD123, and CD371, against resistant AML variants. We address this gap by combining mathematical modeling with in vitro assay data and Bayesian inference. We select, train, and validate a two-compartment deterministic mathematical model that describes the nonlinear dynamics of target AML and CAR T cells, accounting for expansion, killing, and exhaustion. Using Bayesian inference, we train and select the best-performing functional form for CAR T expansion and then validate it on unseen data. Our framework selects a CAR T-cell expansion model that accounts for handling time and T-cell self-interference, highlighting that expansion is a dynamic process in which target-cell handling time and T-cell crowding negatively affect T-cell expansion. Analysis of posterior parameter distributions reveals target-antigen-specific responses against TP53-deficient AML. For instance, CD33-targeting CARs have reduced attack rates against TP53-deficient cells, while CD123- and CD371-targeting CARs show moderately increased attack rates; however, the former exhibit higher death rates, and the latter have increased handling times, impeding efficacy. This target-dependent form of resistance challenges the assumption of uniform performance and reveals a unifying nonlinear expansion model for integrated, yet antigen-specific, preclinical predictions of efficacy. Short SummaryWe identified the target antigen-specific expansion properties and the efficacy of CAR T cells against TP53 wild-type and deficient AML by integrating experimental data from cytolytic assays, mechanistic mathematical modeling, and Bayesian inference.

cancer biology↗

bioRxiv2024

Deficient cell-cell cohesion is linked with lobular localization in simplified models of lobular carcinoma in situ (LCIS)

Lobular carcinoma in situ (LCIS) is a precursor of invasive lobular carcinoma of the breast. LCIS cells lack cell-cell cohesion due to the loss of E-cadherin. LCIS cells grow in mammary lobules rather than in ducts. The etiology of this pattern, especially its dependence on cellular cohesion, is incompletely understood. We simulated passive intra-glandular scattering of carcinoma in situ (CIS) cells in an ultra-simplified hollow mold tissue replica (HMTR) and a discrete-time mathematical model featuring particles of variable sizes representing single cells (LCIS-like particles) or groups of cohesive carcinoma cells (DCIS-like particles). The HMTR features structures reminiscent of a mammary duct with associated lobules. The discrete mathematical model characterizes spatial redistribution over time and includes transition probabilities between ductal or lobular localizations. Redistribution of particles converged toward an equilibrium depending on particle size. Strikingly, equilibrium proportions depended on particle properties, which we also confirm in a continuous-time mathematical model that considers controlling lobular properties such as crowding. Particles of increasing size, representing CIS cells with proficient cohesion, showed increasingly higher equilibrium ductal proportions. Our investigations represent two conceptual abstractions implying a link between loss of cell-cell cohesion and lobular localization of LCIS, which provide a much-needed logical foundation for studying the connections between collective cell behavior and cancer development in breast tissues. In light of the findings from our simplified modeling approach, we discuss multiple avenues for near-future research that can address and evaluate the redistribution hypothesis mathematically and empirically.

cancer biology↗

bioRxiv2024

Integration of immune cell-target cell conjugate dynamics changes the time scale of immune control of cancer

The human immune system can recognize, attack, and eliminate cancer cells, but cancers can escape this immune surveillance. The dynamics of these cancer control mechanisms by cells of the adaptive immune system can be captured by variants of ecological predator-prey models. These dynamical systems can describe the interaction of cancer cells and, e.g., effector T cells to form tumor cell-immune cell conjugates, cancer cell killing, immune cell activation, and T cell exhaustion. Target (tumor) cell-T cell conjugation is integral to the adaptive immune systems cancer control or immunotherapy dynamics. However, it is incompletely understood whether conjugate dynamics should be explicitly included in mathematical models of cancer-immune interactions. Here, we analyze the dynamics of a cancer-effector T cell system regarding the impact of explicitly modeling the conjugate compartment to elucidate the role of cellular conjugate dynamics. We formulate a deterministic modeling framework to compare possible equilibria and their stability, such as tumor extinction, tumor-immune coexistence (tumor control), or tumor escape. We also formulate the stochastic analog of this system to analyze the impact of demographic fluctuations that arise when cell populations are small. We find that explicit consideration of a conjugate compartment can change long-term steady-state, critically change the time to reach an equilibrium, alter the probability of tumor escape, and lead to very different extinction time distributions. Thus, we demonstrate the importance of the conjugate compartment in defining tumor-effector interactions. Accounting for transitionary compartments of cellular interactions may better capture the dynamics of tumor control and progression.

immunology↗

bioRxiv2024

Graph-structured populations elucidate the role of deleterious mutations in long-term evolution

Birth-death models have long been employed to understand the interplay of genetic drift and natural selection. While well-mixed populations remain unaffected by the choice of replacement rules, the evolutionary outcomes in spatially structured populations are strongly impacted by this choice. Moving parent individuals to vacant sites gives rise to new update rules, leading to new fixation categories for spatial graphs. We discover a new category of graphs, amplifiers of fixation, where a structure has a higher probability of fixation for mutants than the well-mixed population, regardless of their fitness value. Under death-Birth updating with parents moving to vacant sites, the star graph is an amplifier of fixation. For very large population sizes, the probability to fix deleterious mutants on the star graph converges to a non-zero value, in contrast to the result from well-mixed populations where the probability goes to zero. Additionally, most random graphs are amplifiers of fixation for death-Birth updating, with parent individuals replacing dead individuals. Conversely, most random graphs are suppressors of fixation- graphs with lower fixation probability for mutants regardless of their fitnesses- for Birth-death updating with offspring replacing dead individuals. When subjected to long-term evolution, amplifiers of fixation, despite being more efficient at fixing beneficial mutants, attain lower fitness than the well-mixed population, whereas suppressors attain higher fitness despite their inferior ability to fix beneficial mutants. These surprising findings can be explained by their deleterious mutant regime. Therefore, the deleterious mutant regime can be as crucial as the beneficial mutant regime for adaptive evolution.

evolutionary biology↗

bioRxiv2024

Reconciling ecology and evolutionary game theory or When not to think cooperation

Evolutionary game theory (EGT)--overwhelmingly employed today for the study of cooperation in a variety of systems, from microbes to cancer and from insect to human societies--started with the seminal 1973 paper by John Maynard Smith and George Price [1], in which they probed the logic of limited war in animal conflict. If fighting was essential to get access to mates and territory, then why did fights rarely lead to serious injury? Maynard Smith and Price developed game theory to show that limited war can be selected at the individual level. Owing to the explanatory potential of this first paper, and enabled by the elegant and powerful machinery of the soon-to-be-developed replicator dynamics [2, 3], EGT took off at an accelerated pace and began to shape expectations across systems and scales. But, even as it expanded its reach from animals to microbes [4-8] and from microbes to cancer [9-11], the field did not revisit a fundamental assumption of that first paper, which subsequently got weaved into the very fabric of the framework--that individual differences in reproduction are determined only by payoff from the game (i.e. in isolation, all individuals, regardless of strategy, were assumed to have identical intrinsic growth rates). Here, we argue that this original assumption substantially limits the scope of EGT. But, because it is not explicitly presented as a caveat, predictions of EGT have been empirically tested broadly across real systems, where the intrinsic growth rates are generally not equal. That has, unsurprisingly, led to puzzling findings and contentious debates [7, 12-15]. Flagging the high potential for confusion to arise from applications of EGT to empirical systems that it is not designed to study and suggesting a way forward constitute our main motivation for this work. In the process, we reestablish a dialog with ecology that can be fruitful both ways, e.g., by providing a so-far-elusive explanation for how diverse ecological communities can assemble evolutionarily.

evolutionary biology↗

bioRxiv2024

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.

cancer biology↗

bioRxiv2023

Infection dynamics in the urinary tract - The importance of non-planktonic phases during early infection and resistance evolution

Treatment of urinary tract infections (UTIs) and the prevention of their recurrence is a pressing global health problem. During the infection establishment, pathogenic bacteria may attach to and invade into the bladder tissue, and thus access non-planktonic phases besides the bladder lumen. Planktonic, attached, and intracellular bacteria face different selection pressures from physiological responses (frequent micturition and immune responses), or antibiotic treatment. Acknowledging this heterogeneity of selection pressures is essential to increase treatment efficacy, reduce resistance evolution and develop new treatments. Here, we present a mathematical model capturing the initial infection phase to unravel the effects of this heterogeneity on the ecological and evolutionary dynamics of UTIs. We explicitly represent planktonic bacteria in the bladder lumen, bacteria attached to epithelial cells of the bladder wall, and bacteria that have invaded these epithelial cells. We find that access to non-planktonic compartments increases the risk of infection establishment substantially. With antibiotic treatment, the trajectories of resistance evolution are shaped by the accessibility of the intracellular compartment and antibiotic permeation into the epithelial cells. Further, we evaluate conditions where probiotic pretreatment can reduce the pathogen load during an infection and increase the efficacy of accompanying antibiotic therapies. We find that faster-growing probiotic strains achieve a stronger increase in antibiotic treatment efficacy, although too strong antibiotic treatment can diminish this effect. Our study shows that accounting for the selection heterogeneity is crucial to understand the eco-evolutionary dynamics of UTIs, and that mathematical modeling can help to steer this complex system into a healthy state. Competing InterestsThe authors declare no competing interests.

evolutionary biology↗

bioRxiv2023

Dynamic cell differentiation in multicellularity with specialized cell types

The specialization of cells is a hallmark of complex multicellularity. Cell differentiation enables the emergence of specialized cell types that carry out separate functions previously executed by a multifunctional ancestor cell. One view is that initial cell differentiation occurred randomly, especially for genetically identical cells, exposed to the same life history environment. How such a change in differentiation probabilities can affect the evolution of differentiation patterns is still unclear. We develop a theoretical model to investigate the effect of stage-dependent cell differentiation - cells change their developmental trajectories during a single round of development via cell divisions - on the evolution of optimal differentiation patterns. We found that irreversible differentiation - a cell type gradually losing its differentiation capability to produce other cell types - is more favored under stage-dependent than stage-independent cell differentiation in relatively small organisms with limited differentiation probability variations. Furthermore, we discovered that irreversible differentiation of germ cells, which is the gradual loss of germ cells ability to differentiate, is a prominent pattern among irreversible differentiation patterns under stage-dependent cell differentiation. In addition, large variations in differentiation probabilities prohibit irreversible differentiation from being the optimal differentiation pattern. Author summaryThe differentiation of cells into different branches is a characteristic feature of multicellular organisms. To understand its origin, the mechanism of division of labour was proposed, where cells are specialized at distinct tasks. In previous models, a cell type is usually assumed to produce another cell type with a fixed probability which is referred to as stage-independent differentiation. However, it has been argued that cell differentiation is a dynamic process in which cells possess changing differentiation capabilities during the different stages of an organisms development. Stage-dependent differentiation exhibits more diverse patterns of development than differentiation with fixed probabilities, thus it can lead to novel targets of selection. How does stage-dependent differentiation impact the evolution of optimal differentiation patterns compared with stage-independent one? To address this question, we built a stage-dependent cell differentiation model and classified differentiation patterns based on the cells differentiation capability in their last cell division. We investigate how stage-dependent differentiation probabilities impact the evolution of the optimal differentiation pattern, which acts on the fitness of an organism. As we take the growth rate as a proxy of an organisms fitness, we seek the "optimal strategy" that leads to the fastest growth. Our numerical results show that irreversible differentiation which gradually loses its differentiation capability, is favored over stage-independent differentiation in small organisms. Meanwhile, irreversible differentiation wont be optimal when there are no constraints on the changes of stage-dependent differentiation probabilities between successive cell divisions.

evolutionary biology↗

bioRxiv2023

Inferring interactions from microbiome data

Parameter inference of high-dimensional data is challenging and microbiome time series data is no exception. Methods aimed at predicting from point estimates exist, but often even fail to recover the true parameters from simulated data. Computational methods to robustly infer and quantify the uncertainty in model parameters are needed. Here, we propose a computational workflow addressing such challenges - allowing us to compare mechanistic models and identify the values and the certainty of inferred parameters. This approach allows us to infer which kind of interactions occur in the microbial community. In contrast to point-estimate inference, the distribution for the parameters, our outcome, reflects their uncertainty. To achieve this, we consider as many equations for the statistical moments of the microbiome as parameters. Our inference workflow, which builds upon a mechanistic foundation of microscopic processes, can take into account that commonly metagenomic datasets only provide information on relative abundances and hosts ensembles. With our framework, we move from qualitative prediction to quantifying the likelihood of certain interaction types in microbiomes.

ecology↗

bioRxiv2023

Gut-associated functions are favored during microbiome assembly across C. elegans life

The microbiome expresses a variety of functions that influence host biology. The range of functions depends on composition of the microbiome, which itself can change during the lifetime of the host as a consequence of neutral assembly processes, host-mediated selection, and/or environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants as well as the resulting effects on host-associated functions are not always well understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across the lifetime of individual hosts under controlled experimental conditions. By applying the neutral and null models, we demonstrate that bacterial community composition initially shows strongly declining levels of stochasticity, which, however, increase during late worm life, suggesting the action of random assembly processes in aged hosts following first colonization of C. elegans. The adult microbiome is enriched in strains of the genera Ochrobactrum and Enterobacter in comparison to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modelling, we further found that the lifetime assembly dynamics lead to an increase in gut-associated functions in the host-associated microbiome, possibly indicating that the initially colonizing bacteria are beneficial for the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on the stochastic models and inference of functions, yielding new insights into the processes determining host-associated microbiome composition and function. ImportanceThe microbiome plays a crucial role in host biology, with its functions depending on microbiome composition that can change during a hosts lifetime. To date, the dynamics of microbiome assembly and the resulting functions are not well understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modelling both, the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time series data, obtained for the microbiome of the nematode Caenorhabditis elegans. The results revealed significant differences in host-associated and environmental microbiomes. Stochastic processes only played a minor role, and the host showed an increase in beneficial bacteria and an enrichment of gut-associated functions, possibly indicating that the host actively shapes composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.

evolutionary biology↗

bioRxiv2023

Self-loops in Evolutionary Graph Theory: Friends or Foes?

Evolutionary dynamics in spatially structured populations has been studied for a long time. More recently, the focus has been to construct structures that speeds up evolution - so called amplifiers of selection. It has been shown that for a structure to amplify selection, self-loops are necessary when mutants appear predominately in nodes that change often. As a result, for low mutation rates, self-looped amplifiers attain higher steady-state average fitness in the mutation-selection than well-mixed populations. But, what happens when the mutation rate increases such that fixation probabilities alone no longer describe the dynamics? We show that self-loops effects are detrimental outside the low mutation rate regime. In the intermediate and high mutation rate regime, amplifiers of selection attain lower steady-state average fitness than the complete graph and the suppressor of selection. We also provide an estimate of mutation rate beyond which the mutation-selection dynamics on a graph deviates from the weak mutation rate approximation. This involves computing how the average fixation time scales with the population size for several graphs.

evolutionary biology↗

bioRxiv2023

Bacterial c-di-GMP plays a key role in the evolution of host-association

Most microbes evolve faster than their hosts and should therefore drive evolution of host-microbe interactions1-3. However, relatively little is known about the characteristics that define the adaptive path of microbes to host-association. In this study we have identified microbial traits that mediate adaptation to hosts by experimentally evolving the bacterium Pseudomonas lurida with the nematode Caenorhabditis elegans. We repeatedly observed the evolution of beneficial host-specialist bacteria with improved persistence in the nematode, achieved by mutations that uniformly upregulate the universal second messenger c-di-GMP. We subsequently upregulated c-di-GMP in different Pseudomonas species, consistently causing increased host-association. Comparison of Pseudomonad genomes from various environments revealed that c-di-GMP underlies adaptation to a variety of hosts, from plants to humans, suggesting that it is fundamental for establishing host-association.

evolutionary biology↗

bioRxiv2022

Understanding and leveraging phenotypic plasticity during metastasis formation

Cancer metastasis is the process of detrimental systemic spread and the primary cause of cancer-related fatalities. Successful metastasis formation requires tumor cells to be proliferative and invasive; however, cells cannot be effective at both tasks simultaneously. Tumor cells compensate for this trade-off by changing their phenotype during metastasis formation through phenotypic plasticity. Given the changing selection pressures and competitive interactions that tumor cells face, it is poorly understood how plasticity shapes the process of metastasis formation. Here, we develop an ecology-inspired mathematical model with phenotypic plasticity and resource competition between phenotypes to address this knowledge gap. We find that phenotypically plastic tumor cell populations attain a stable phenotype equilibrium that maintains tumor cell heterogeneity. Considering treatment types inspired by chemo- and immunotherapy, we highlight that plasticity can protect tumors against interventions. Turning this strength into a weakness, we corroborate current clinical practices to use plasticity as a target for adjuvant therapy. We present a parsimonious view of tumor plasticity-driven metastasis that is quantitative and experimentally testable, and thus potentially improving the mechanistic understanding of metastasis and its treatment consequences.

cancer biology↗

bioRxiv2022

Categorising update mechanisms for graph-structured metapopulations

The structure of a population strongly influences its evolutionary dynamics. In various settings ranging from biology to social systems, individuals tend to interact more often with those present in their proximity and rarely with those far away. A common approach to model the structure of a population is Evolutionary Graph Theory. In this framework, each graph node is occupied by a reproducing individual. The links connect these individuals to their neighbours. The offspring can be placed on neighbouring nodes, replacing the neighbours - or the progeny of its neighbours can replace a node during the course of ongoing evolutionary dynamics. Extending this theory by replacing single individuals with subpopulations at nodes yields a graph-structured metapopulation. The dynamics between the different local subpopulations is set by an update mechanism. There are many such update mechanisms. Here, we classify update mechanisms for structured metapopulations, which allows to find commonalities between past work and illustrate directions for further research and current gaps of investigation.

evolutionary biology↗

bioRxiv2022

Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations

When cancers or bacterial infections establish, small populations of cells have to free themselves from homoeostatic regulations that prevent their expansion. Trait evolution allows these populations to evade this regulation, escape stochastic extinction and climb up the fitness landscape. In this study, we analyse this complex process and investigate the fate of a cell population that underlies the basic processes of birth, death and mutation. We find that the shape of the fitness landscape dictates a circular adaptation trajectory in the trait space spanned by birth and death rates. We show that successful adaptation is less likely for parental populations with higher turnover (higher birth and death rates). Including density- or trait-affecting treatment we find that these treatment types change the adaptation dynamics in agreement with a geometrical analysis of fitness gradients. Treatment strategies that simultaneously target birth and death rates are most effective, but also increase evolvability. By mapping physiological adaptation pathways and molecular drug mechanisms to traits and treatments with clear eco-evolutionary consequences, we can achieve a much better understanding of the adaptation dynamics and the eco-evolutionary mechanisms at play in the dynamics of cancer and bacterial infections.

evolutionary biology↗