Biology subjects

Aguade-Gorgorio, G.

Publications and source records attributed to Aguade-Gorgorio, G..

8 recordsLinked to original sources

bioRxiv2026

Critical Fragility Emerges from Chromosomal Instability in Cancer

Genomic instability is a major driver of tumor evolution, promoting diversification and adaptation while simultaneously increasing the accumulation of deleterious alterations. How tumor populations balance these opposing effects remains poorly understood. Here, we introduce a computational framework that explicitly represents diploid genomes, functional gene classes, point mutations, and chromosome-segregation errors in spatially constrained and well-mixed tumor populations. We identify a viability boundary separating sustained tumor expansion from instability-induced population collapse. Within the viable regime, mutation and selection generate a stable distribution of genomic-instability classes that is accurately captured by an analytical replicator--mutator description. Near the viability boundary, tumor dynamics exhibit prolonged extinction transients and strong sensitivity to stochastic fluctuations, with important differences between solid and liquid architectures. Chromosomal alterations further modify growth by creating transient benefits through increased gene dosage and genetic redundancy, while ultimately increasing genomic fragility. Finally, simulated interventions show that eliminating low-instability subpopulations or increasing the global mutational burden can displace tumors beyond their viability boundary and trigger irreversible collapse. These results identify genome instability as both an evolutionary advantage and an intrinsic vulnerability, providing a quantitative framework for developing therapies that exploit the limits of tumor evolution.

cancer biology↗

bioRxiv2025

Synthetic Horizontal Gene Transfer for Ecosystem Restoration

Restoring endangered ecosystems has become a pressing issue as the effects of global warming continue to harm existing communities. Over the last decade, several studies have suggested the use of synthetic biology as a tool to protect these biodiverse communities by augmenting their functionality. A critical example concerns soil microbiome communities in drylands, where increasing water retention by some of the constituent species could effectively protect the ecosystem from abrupt degradation. However, how to effectively deploy a functional synthetic construct that can scale its impact to the community level remains an open question. Recent experimental research has designed recombinant gene plasmids with the capacity to horizontally transfer across soil microbial communities. Here, we explore the impacts of synthetic horizontal gene transfer in models of ecological consortia. By coupling consumer-resource dynamics with multispecies gene transfer, our work identifies the design conditions that promote biodiversity while regulating gene propagation and the spread of engineered organisms.

synthetic biology↗

bioRxiv2025

Tumor mutational burden shapes success and resistance in cancer immunotherapy

Immunotherapy, particularly immune checkpoint blockade, has transformed cancer treatment, yet durable responses remain limited to a subset of patients and cancer types. Many tumors exhibit innate resistance or acquire resistance through immune evasion or neoantigen editing. A central factor in shaping these outcomes is the tumor mutational burden. However, cancer mutations can enhance or impair both cellular replication and immune recognition, reflecting the non-trivial role of mutational load in immunotherapy success and failure. Here, we present a minimal eco-evolutionary model that captures trade-offs between oncogenic and immunogenic mutations in cancer cell replication. Despite its simplicity, the model reveals a rich phase space, including an evolutionary bistable regime where both immunologically silent and mutationally active tumor strategies can coexist. Notably, the model explains two key eco-evolutionary mechanisms of resistance to immunotherapy: preexisting resistance, driven by the persistence of silent clones within genetically unstable tumors; and immunoediting, where immune pressure selects for reduced antigenicity over time.

cancer biology↗

bioRxiv2025

Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies

Recent biological research has highlighted the relevance of myeloid-cell populations in glioma growth, with a particular role played by tumor-associated macrophages (TAMs), which comprise resident microglia and monocyte-derived macrophages. Additionally, radiation therapy, the most common treatment for gliomas, significantly alters the tumor microenvironment, affecting TAMs and contributing to tumor recurrence. Promising preclinical studies have identified and developed drugs targeting TAMs. The development and combined deployment of these therapies require in silico techniques that enable us to optimize their outcomes. To do so, we need mathematical models of glioma growth and therapy response that explicitly incorporate TAMs--an often overlooked component in existing models. Here, we present a dynamical model of glioma growth driven by tumor-immune interactions. The model was parametrized using published data from mice experiments, including responses to ionizing radiation. We used this model to investigate glioma progression under radiotherapy combined with three treatments targeting distinct aspects of TAM biology. Simulations revealed that anti-CD47 enhanced the otherwise weak phagocytic activity, extending the upper tail of the survival curve. -CD49d, which limits monocyte trafficking after irradiation, offered consistent survival benefits across digital twins of mice. Finally, CSF-1R inhibitors, which block the primary growth factor regulating TAM function, resulted in the largest overall survival improvement in silico. Our results aligned well with experimental evidence, suggesting that the model may help inform the optimization of myeloid cell-targeted immunotherapies, including their timing, dosage, and combination with radiation therapy, with potential relevance for improving glioma treatment strategies.

cancer biology↗

bioRxiv2025

Emergent coexistence and the limits of reductionism in ecological communities

Understanding if pairwise interactions explain the species composition of communities is a central goal in ecology. This question has been challenged by the observation of emergent coexistence, where microbial communities contain species that cannot coexist in pairs, suggesting the presence of non-pairwise mechanisms. Instead, we show that emergent coexistence arises naturally in species-rich models with pairwise interactions. Strikingly, this phenomenon does not require additional mechanisms like intransitive or higher-order interactions; rather, coexistence arises from dense networks of indirect effects. As diversity increases, we show that indirect effects become so intricate that pairwise interactions no longer predict community composition, revealing a fundamental limit to reductionist explanations of coexistence. Like chaos emerging from simple rules, our findings provide theoretical foundations to understand how unexpected species coexistence can emerge from pairwise interactions.

ecology↗

bioRxiv2024

Unpacking sublinear growth: diversity, stability and coexistence

How can many species coexist in natural ecosystems remains a fundamental question in ecology. Theory suggests that competition for space and resources should maintain the number of coexisting species far below the staggering diversity commonly found in nature. A recent model finds that, when sublinear growth rates of species are coupled with competition, species diversity can stabilize community dynamics. This, in turn, is suggested to explain the coexistence of many species in natural ecosystems. In this brief note we clarify why the sublinear growth (SG) model does not solve the long standing paradox of species coexistence. This is because in the SG model coexistence emerges from an unrealistic property, in which species per-capita growth rate diverges at low abundance, preventing species from ever going extinct. When infinite growth at low abundance is reconciled with more realistic assumptions, the SG model recovers the expected paradox: increasing diversity leads to competitive exclusion and species extinctions.

ecology↗

bioRxiv2024

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

cancer biology↗

bioRxiv2023

A taxonomy of multiple stable states in complex ecological communities

Many natural and man-made systems, from financial markets to ecosystems or the human brain, are built from multiple interconnected units. This complex high-dimensionality hinders our capacity to understand and predict the dynamics, functioning and fragility of these systems. One fragility scenario, particularly relevant to ecological communities of interacting species, concerns so-called regime shifts: abrupt and unexpected transitions from healthy, species-rich communities towards states of degraded ecosystem function and services. The accepted explanation for these shifts is that they arise as abrupt transitions between alternative stable states: multiple stable configurations of a system under the same internal and external conditions. These alternative states are well-understood in low-dimensional systems, but how they upscale with system complexity remains a debated question. In the present work we investigate the emergence of multiple stable states in a number of complex system models. We find that high-dimensional models with random interactions can unfold at least four different regimes of multistability, each emerging under a specific interaction scheme. More importantly, each multistability regime leaves a distinct and quantifiable fingerprint, providing a framework to analyze experimental evidence of abrupt shifts. By bridging previous results and studying multistability regimes, their fingerprints and their correlation with empirical evidence in ecology, our study helps define a common ground to understand and classify multiple stable states in complex systems.

ecology↗