Biology subjects

Sole, R.

Publications and source records attributed to Sole, R..

29 records · Page 2Linked to original sources

bioRxiv2019

Tumor neoantigen heterogeneity thresholds provide a time window for combination immunotherapy

Following the advent of immunotherapy as a possible cure for cancer, remarkable insight has been gained on the role of mutational load and neoantigens as key ingredients in T cell recognition of malignancies. However, not all highly mutational tumors react to immune therapies, and even initial success is often followed by eventual relapse. Recent research points out that high heterogeneity in the neoantigen landscape of a tumor might be key in understanding the failure of immune surveillance. In this work we present a mathematical framework able to describe how neoantigen distributions shape the immune response. Modeling T cell reactivity as a function of antigen dominancy and distribution across a tumor indicates the existence of a diversity threshold beyond which T cells fail at controling heterogeneous cancer growth. Furthemore, an analytical estimate for the evolution of average antigen clonality indicates rapid increases in epitope heterogeneity in early malignancy growth. In this scenario, we propose that therapies targeting the tumor prior to immunotherapy can reduce neoantigen heterogeneity, and postulate the existence of a time window, before tumor relapse due to de novo resistance, rendering immunotherapy more effective.\n\nMajor FindingsGenetic heterogeneity affects the immune response to an evolving tumor by shaping the neoantigen landscape of the cancer cells, and highly heterogeneous tumors seem to escape T cell recognition. Mathematical modeling predicts the existence of a well defined neoantigen diversity threshold, beyond which lymphocites are not able to counteract the growth of a population of highly heterogeneous subclones. Furthermore, evolutionary dynamics predict a fast decay of neoantigen clonality, rendering advanced tumors hard to attack at the time of immunotherapy. Within this mathematical framework we propose that targeted therapy forcing a selective pressure for resistance might as well increase neoantigen homogeneity, providing a novel possibility for combination therapy.

cancer biology↗

bioRxiv2019

Aging, computation, and the evolution of neural regeneration processes

Metazoans gather information from their environments and respond in predictable ways. These computational tasks are achieved with neural networks of varying complexity. Their performance must be reliable over an individuals lifetime while dealing with the shorter lifespan of cells and connection failure - thus rendering aging a relevant feature. How do computations degrade over an organisms lifespan? How reliable can they remain throughout? We tackle these questions with a multiobjective optimization approach. We demand that digital organisms equipped with neural networks solve a computational task reliably over an extended lifespan. Neural connections are costly (as an associated metabolism in living beings). They also degrade over time, but can be regenerated at some expense. We investigate the simultaneous minimization of both these costs and the computational error. Pareto optimal tradeoffs emerge with designs displaying a broad range of solutions: from small networks with high regeneration rate, to large, redundant circuits that regenerate slowly. The organisms lifespan and the external damage act as evolutionary pressures. They improve the exploration of the space of solutions and impose tighter optimality constraints. Large damage rates can also constrain the space of possibilities, forcing the commitment of organisms to unique strategies for neural systems maintenance.

evolutionary biology↗

bioRxiv2019

Cellular heterogeneity results from indirect effects under metabolic tradeoffs

The emergence of multicellularity requires the coexistence of diverse cellular populations displaying cooperative relationships. This enables long-term persistence of cellular consortia, particularly under environmental constraints that challenge cell survival. Toxic environments are known to trigger the formation of multicellular consortia capable of dealing with waste while promoting cell diversity as a way to overcome selection barriers. In this context, recent theoretical studies suggest that an environment containing both resources and toxic waste can promote the emergence of complex, spatially distributed proto-organisms exhibiting division of labor and higher-scale features beyond the cell-cell pairwise interactions. Some previous non-spatial models suggest that the presence of a growth inhibitor can trigger the coexistence of competitive species in an antibiotic-resistance context. In this paper we further explore this idea using both mathematical and computational models taking the most fundamental features of the proto-organisms interactions. It is shown that this resource-waste environmental context, in which both species are lethally affected by the toxic waste and metabolic tradeoffs are present, favours the maintenance of diverse populations. A spatial, stochastic extension confirms our basic results. The evolutionary and ecological implications of these results are outlined.

systems biology↗

bioRxiv2019

Genetic instability as a driver for immune surveillance

Genetic instability is known to relate with carcinogenesis by providing tumors with a mechanism for fast adaptation. However, mounting evidence also indicates causal relation between genetic instability and improved cancer prognosis resulting from efficient immune response. Highly unstable tumors seem to accumulate mutational burdens that result in dynamical landscapes of neoantigen production, eventually inducing acute immune recognition. How are tumor instability and enhanced immune response related? An important step towards future developments involving combined therapies would benefit from unraveling this connection. In this paper we present a minimal mathematical model to describe the ecological interactions that couple tumor adaptation and immune recognition while making use of available clinical estimates of relevant parameters. The possible evolutionary trade-offs associated to both cancer replication and T cell response are analysed, indicating that cancer-clearance states become attainable when both mutational load and immune migration are enhanced. Furthermore, the model predicts the presence of well-defined transitions towards tumor control and eradication after increases in genetic instability consistent with available data of tumor control after Mismatch Repair knockout.

systems biology↗

bioRxiv2018

Habitat loss-induced tipping points in metapopulations with facilitation

Habitat loss is known to pervade extinction thresholds in metapopulations. Such thresholds result from a loss of stability that can eventually lead to collapse. Several models have been developed to understand the nature of these transitions and how are they affected by the locality of interactions, fluctuations, or external drivers. Most models consider the impact of grazing or aridity as a control parameter that can trigger sudden shifts, once critical values are reached. Others explore instead the role played by habitat loss and fragmentation. Here we consider a minimal model incorporating facilitation along with habitat destruction, with the aim of understanding how local cooperation and habitat loss interact with each other. An explicit mathematical model is derived, along with a spatially explicit simulation model. It is found that a catastrophic shift is expected for increasing levels of habitat loss, but the breakpoint dynamics becomes continuous when dispersal is local. Under these conditions, spatial patchiness is found and the qualitative change from discontinuous to continuous results from a universal behaviour found in a broad class of nonlinear ecological systems (Weissmann and Shnerb, 2014; Martin et al. PNAS (2015) E1828-E1836). Our results suggest that species exhibiting facilitation and displaying short-range dispersal will be markedly more capable of dealing with habitat destruction, also avoiding catastrophic tipping points.

ecology↗

bioRxiv2018

Statistical physics of liquid brains

Liquid neural networks (or \"liquid brains\") are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (\"solid brains\"), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.

systems biology↗

bioRxiv2017

Nonequilibrium entropic bounds for Darwinian replicators

Life evolved on our planet by means of a combination of Darwinian selection and innovations leading to higher levels of complexity. The emergence and selection of replicating entities is a central problem in prebiotic evolution. Theoretical models have shown how populations of different types of replicating entities exclude or coexist with other classes of replicators. Models are typically kinetic, based on standard replicator equations. On the other hand, the presence of thermodynamical constrains for these systems remain an open question. This is largely due to the lack of a general theory of out of statistical methods for systems far from equilibrium. Nonetheless, a first approach to this problem has been put forward in a series of novel developements in non-equilibrium physics, under the rubric of the extended second law of thermodynamics. The work presented here is twofold: firstly, we review this theoretical framework and provide a brief description of the three fundamental replicator types in prebiotic evolution: parabolic, malthusian and hyperbolic. Finally, we employ these previously mentioned techinques to explore how replicators are constrained by thermodynamics.

biophysics↗

bioRxiv2017

Adaptive dynamics of unstable cancer populations: the canonical equation

In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by definition evolving changing instability leads to changes in the underlying landscape. In this paper we explore mathematically a simple version of unstable tumor progression using the formalism of Adaptive Dynamics (AD) where selection and mutation are explicitly coupled. Using a set of basic fitness landscapes, the so called canonical equation for the evolution of genetic instability on a minimal scenario associated to a population of unstable cells is derived. The implications and potential extensions of this model are discussed.

cancer biology↗

bioRxiv2017

Exploiting delayed transitions to sustain semiarid ecosystems after catastrophic shifts

Semiarid ecosystems (including arid, semiarid and dry-subhumid ecosystems) span more than 40% of extant habitats and a similar percentage of human population. As a consequence of global warming, these habitats face future potential shifts towards the desert state characterized by an accelerated loss of diversity and stability leading to collapse. Such possibility has been raised by several mathematical and computational models, along with several early warning signal methods applied to spatial vegetation patterns. Here we show that just after a catastrophic shift has taken place an expected feature is the presence of a ghost, i.e., a delayed extinction associated to the underlying dynamical flows. As a consequence, a system might exhibit for very long times an apparent stationarity hiding in fact an inevitable collapse. Here we explore this problem showing that the ecological ghost is a generic feature of standard models of green-desert transitions including facilitation. If present, the ghost could hide warning signals, since statistical patterns are not be expected to display growing fluctuations over time. We propose and computationally test a novel intervention method based on the restoration of small fractions of desert areas with vegetation as a way to maintain the fragile ecosystem beyond the catastrophic shift caused by a saddle-node bifurcation, taking advantage of the delaying capacity of the ghost just after the bifurcation.

ecology↗

bioRxiv2017

The Paradox Of Constant Oceanic Plastic Debris: Evidence For Evolved Microbial Biodegradation?

Although the presence of vast amounts of plastic in the open ocean has generated great concern due to its potential ecological consequences, recent studies reveal that its measured abundance is much smaller than expected. Regional and global studies indicate that the difference between expected and actual estimates is enormous, suggesting that a large part of the plastic has been degraded by either physical and biotic processes. A paradoxical observation is the lack of a trend in plastic accumulation found in the North Atlantic Subtropical Gyre, despite the rapid increase in plastic production and disposal. In this paper we show, using mathematical and computer models, that this observation could be explained by the nonlinear coupling between plastic (as a resource) and an evolved set of organisms (the consumers) capable of degrading it. The result is derived using two different resource-consumer mathematical approaches as well as a spatially-dependent plastic-microbial model incorporating a minimal hydrodynamical coupling with a two-dimensional fluid. The potential consequences of the evolution of marine plastic garbage and its removal are outlined.

ecology↗

bioRxiv2016

Population dynamics of synthetic Terraformation motifs

Ecosystems are complex systems, currently experiencing several threats associated with global warming, intensive exploitation, and human-driven habitat degradation. Such threats are pushing ecosystems to the brink of collapse. Because of a general presence of multiple stable states, including states involving population extinction, and due to intrinsic nonlinearities associated with feedback loops, collapse can occur in a catastrophic manner. Such catastrophic shifts have been suggested to pervade many of the future transitions affecting ecosystems at many different scales. Many studies have tried to delineate potential warning signals predicting such ongoing shifts but little is known about how such transitions might be effectively prevented. It has been recently suggested that a potential path to prevent or modify the outcome of these transitions would involve designing synthetic organisms and synthetic ecological interactions that could push these endangered systems out of the critical boundaries. Four classes of such ecological engineering designs or Terraformation motifs have been defined in a qualitative way. Here we develop the simplest mathematical models associated with these motifs, defining the expected stability conditions and domains where the motifs shall properly work.

synthetic biology↗