Biology subjects

Adler, F.

Publications and source records attributed to Adler, F..

6 recordsLinked to original sources

bioRxiv2025

Sample-level modeling of single-cell data at scale with tinydenseR

Single-cell studies now routinely encompass hundreds of samples and millions of cells, offering unprecedented opportunities to link sample-level phenotypes with cellular and molecular states. However, current workflows often depend on cell-level inference and rigid clustering, which can distort significance and obscure subtle, continuous variation, in particular for complex experimental designs. Here, we present tinydenseR, a clustering-independent framework that enables robust, scalable, and statistically sensitive detection of differential cell state density, outperforming existing workflows in speed and memory usage. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, a publicly available COVID-19 study, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle cell state heterogeneity, while embedding samples in a quantitative manner. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.

bioinformatics↗

bioRxiv2023

Mathematical Modeling of Field Cancerization through the Lens of Cancer Behavioral Ecology

Field cancerization is a process in which a normal tissue is replaced with pre-cancerous but histologically normal tissue. This transformed field can give rise to malignancy and contribute to tumor relapse. In this paper, we create a mathematical model of field cancerization from the perspective of cancer behavioral ecology. In our model, field cancerization arises from a breakdown in signaling integrity and control, and investigate implications for acute wounding, chronic wounding, aging, and therapeutic interventions. We find that restoration of communication networks can lead to cancer regression in the context of acute injury. Conversely, long term loss of controls, such as through chronic wounding or aging, can promote oncogenesis. These results are paralleled in therapeutic interventions: those that simply target cells in cancerous states may be less effective than those that reestablish signaling integrity. Viewing cancer as a corruption of communication systems rather than as a corruption of individual cells may lead to novel approaches for understanding and treating this disease.

cancer biology↗

bioRxiv2022

Quantifying the Fitness Benefit of Learning in Changing Environments

The costs and benefits of learning for a foraging organism are difficult to quantify, and depend sensitively on the environment. We construct a minimal mathematical model of learning in which a forager learns the quality of different food types through experience. In our model, learning depends on two parameters: rate of memory updating and rate of exploration. Our method returns optimal learning parameters for environments in which the value and variance of food types may change in any fashion. We analyze the effect of five components of environmental change on the optimal memory and exploration parameters. The fitness outcomes from learning foragers are compared to the outcomes from following fixed strategies, explicitly quantifying the fitness benefit (or cost) of learning as a function of environmental change. We find that variance in resource values negatively biases foragers estimates for those values, potentially explaining experimental results showing that animals prefer less variable resources. Learning is beneficial only if memory and exploration are optimized. The benefit of learning is largely determined by the ratio between the overall expected value of taking one resource compared to the overall expected value of taking the other: As these two expectations diverge, the fitness benefit of learning decreases, and can even become negative. In many environments, sub-optimal learning performs as bad or even worse than following a fixed strategy. SignificanceLearning is commonly observed in foraging organisms. However, measuring the fitness benefits (and costs) of learning is difficult, and depends critically on the environment in which an organism lives. We build a minimal model of learning in the context of optimal foraging and optimal diet choice theory, with two learning parameters: , corresponding to the duration of the foragers memory, and{epsilon} , corresponding to how much the forager explores the environment to learn more about it. We identify the optimal ,{epsilon} for different types of environmental change, and quantify the benefits and costs of learning. The benefit of learning is often surprisingly small, and in many environments, learning provides lower fitness than following a fixed strategy.

animal behavior and cognition↗

bioRxiv2021

Reconstructing tumor trajectories during therapy through integration of multiple measurement modalities.

BackgroundAccurately determining changes in tumor size during therapy is essential to evaluating response or progression. However, individual imaging methodologies often poorly reflect pathologic response and long-term treatment efficacy in patients with estrogen receptor positive (ER+) early-stage breast cancer. Mathematical models that measure tumor progression over time by integrating diverse imaging and tumor measurement modalities are not currently used but could increase accuracy in measuring response and provide biological insights into cancer evolution. MethodsFor ER+ breast cancer patients enrolled on a neoadjuvant clinical trial, we reconstructed their tumor size trajectories during therapy by combining all available information on tumor size, including different imaging modalities, physical examinations and pathological assessment data. Tumor trajectories during six months of treatment were generated, using a Gaussian process and the most probable trajectories were evaluated, based on clinical data, using measurement models that account for biases and differences in precision between tumor measurement methods, such as MRI, ultrasound and mammograms. ResultsReconstruction of tumor trajectories during treatment identified five distinct patterns of tumor size changes, including rebound growth not evident from any single modality. These results increase specificity to distinguish innate or acquired resistance compared to using any single measurement alone. The speed of therapeutic response and extent of subsequent rebound tumor growth quantify sensitivity or resistance in this patient population. ConclusionsTumor trajectory reconstruction integrating multiple modalities of tumor measurement accurately describes tumor progression on therapy and reveals various patterns of patient responses. Mathematical models can integrate diverse response assessments and account for biases in tumor measurement, thereby providing insights into the timing and rate at which resistance emerges.

systems biology↗

bioRxiv2018

Dynamics of the p53 response to ionizing and ultraviolet radiation

The tumor suppressor protein p53 compiles information about cellular stressors to make decisions on whether the cell should survive or undergo apoptosis. However, the p53 response depends on the source of damage, displaying a digital oscillatory response after ionizing radiation (IR) damage and a proportional non-oscillatory response following UV damage. We propose a mathematical model that qualitatively replicates this observed behavior. The difference in p53 dynamics in the model results from two mechanisms: IR damage is fully detected minutes after exposure while UV damage is detected over several hours; and the p53-controlled transcriptional response is dominated by inactive p53 following UV damage. In particular, we hypothesize that an unidentified positive feedback loop controlled by inactive p53 is required to maintain the qualitative high p53 response to UV damage. This work proposes an explanation for two distinct responses of p53 to DNA damage and how each response can lead to cell cycle arrest or apoptosis.\n\nAuthor summaryWe propose a mathematical model hypothesizing how the tumor suppressor protein p53 produces two contrasting dynamical responses in response to different types of DNA damage. In particular, we predict the existence of a positive feedback loop controlled by the inactive form of p53, which allows the cell to respond to slowly detected damage. The existence of differing dynamic responses by p53 has implications for our understanding of tumor development and possibly p53-related therapeutic strategies.

cancer biology↗

bioRxiv2017

The relationship between species richness and ecosystem variability is shaped by the mechanism of coexistence

Theory relating species richness to ecosystem variability typically ignores the potential for environmental variability to promote species coexistence. Failure to account for fluctuation-dependent coexistence mechanisms may explain observed deviations from the expected negative diversity-ecosystem variability relationship, and limits our ability to predict the consequences of future increases in environmental variability. We use a consumer-resource model to explore how coexistence via the temporal storage effect and relative nonlinearity affects ecosystem variability. We show that a positive, rather than negative, diversity-ecosystem variability relationship is possible when ecosystem function is sampled across a natural gradient in environmental variability and diversity. We also show how fluctuation-dependent coexistence can buffer ecosystem functioning against increasing environmental variability by promoting species richness and portfolio effects. Our work provides a general explanation for variation in observed diversity-ecosystem variability relationships and highlights the importance of conserving regional species pools to help buffer ecosystems against predicted increases in environmental variability.

ecology↗