Biology subjects

Faro, J.

Publications and source records attributed to Faro, J..

5 recordsLinked to original sources

bioRxiv2026

Emergent catch bonds from slip-like contacts: cooperative force-driven proofreading at receptor--ligand interfaces

Mechanical forces modulate receptor-ligand kinetics, sometimes producing the so-called catch bond: a tensile force strengthens the bond, whereas in most cases engagement time is reduced (a slip bond). Multiple phenomenological models reproduce catch-slip behavior between receptors and ligands, primarily by positing how the reaction's free-energy landscape changes under applied forces. In this paper, we show that distinct microscopic mechanisms often collapse into mathematically equivalent multi-exponential forms. This operational equivalence makes them difficult to distinguish (or even falsify) based solely on bond survival curves. We expose this mathematical equivalence and the validity regimes of these reductions and introduce \emph{c}ooperative \emph{f}orce-driven \emph{p}roof\emph{r}eading (CFPR), a network-level mechanism where pulling geometry enables contact-zone reorganization and additional nucleation of residue binding contributions, generating emergent catch-like responses from individually slip-like residue binding contributions. Our model can incorporate data from molecular dynamics simulations to predict, in advance, whether catch-slip will be present in a system and under what conditions, making it a predictive tool that complements experimental data.

biophysics↗

bioRxiv2025

Leveraging mathematical models to predict and control T-cell activation

T-cell receptor (TCR)-mediated T-cell activation is a key process in adaptive immune responses. The complexity of this process has led to the development of different mathematical models that seek to describe and predict the conditions of antigen-TCR interactions required for TCR triggering and T-cell activation. These models are characterized by describing different sets of sequential molecular interactions and their kinetics, positing the generation of a final product as a necessary and sufficient condition for T-cell activation. Such modeling could provide an effective tool for simulating antigen recognition by T cells and, consequently, aid in the design of effective therapeutic strategies. However, it is necessary to previously assess the predictive capabilities of the proposed models when fitted to experimental data. To achieve this goal, in this work we examine the parameter identifiability and sensitivity of the published models of TCR-based T-cell activation. For each model, we consider different, often experimentally measured, output quantities and show how their availability affects the results. These analyses allow us to determine the ability of each model to correctly describe different experimental situations, and to establish to what extent these models can be applied to reliably predict and control T-cell activation by specific therapeutic targets. Author summaryThe adaptive immune system is a highly specialized defense mechanism present in vertebrates. It produces a response that results from a complex set of interrelated cellular and molecular mechanisms. A key process is antigen-dependent activation of T lymphocytes, for which several mechanisms have been postulated. The corresponding mathematical models allow us to explore competing hypotheses, make quantitative predictions, and eventually aid in the design of strategies to control the immune response. To achieve these goals, it is essential that the models be identifiable and observable, that is, it must be possible to infer their parameters and state from output measurements. Furthermore, their response should exhibit an adequate level of sensitivity to certain key parameters. In this work, we evaluate the degree to which currently existing models possess these qualities. Our findings provide minimum requirements for the design of system identification experiments, allowing us to discard those models that cannot be successfully calibrated with a given experimental setup.

immunology↗

bioRxiv2025

Response metrics of T-cell activation models

T-cell activation models describe the way in which antigens and T-cell receptors (TCRs) interact in order to trigger an immune response. A number of mathematical models of TCR-mediated T-cell activation have been proposed in the last decades. Often, the only measurable quantities available for the identification of these models are Emax, the maximum achievable T cell activation level (over the background level), and EC50, that is, the antigen concentration that results in 50% of Emax. In order to analyse model properties such as identifiability, observability, and sensitivity, it is necessary to know how Emax and/or EC50 relate to the model parameters. However, their expressions are only available for a few of the published models of T-cell activation. Here we present a general method to derive the expressions of Emax and/or EC50, and apply it to those models for which they are yet unknown. Our results expand the range of analyses that can be performed on T-cell activation models, contributing to enable their exploitation to understand and control the immune response.

systems biology↗

bioRxiv2022

The rotational diffusion of B-cell receptor vs antibody paratopes differentially affects their antigen binding kinetics

Since the pioneering works of Berg and Purcell, discriminating between diffusion followed by binding has played a central role in understanding cell signaling. B-cell receptors (BCR) and antibodies (Ab) challenge that simplified view as binding to antigen follows after a chain of diffusion and rotations, including whole molecule rotation, and independent tilts and twists of their Fab arms due to their Y-shaped structure and flexibility. In this paper, we combine analytical calculations with Brownian simulations to derive the first-passage times due to these three rotations positioning the Fab paratopes at a proper distance and orientation required for antigen binding. Applying these estimations and those for 2-dimensional (2D) and 3D translational diffusion of, respectively, BCRs and Abs, we evidence that measuring Ab-Ag effective kinetic binding rates using experimental methods in which the analyte is in solution gives values proportional to the intrinsic binding rates, k+ and k-, only for values of k+ up to 109 s-1, beyond which a plateau of the effective 3D on rate between 108 M-1s-1 and 109 M-1s-1 is attained. Moreover, for BCR-Ag interactions, the effective 2D on and off binding rates can be inferred from the corresponding effective 3D on and off rates only for values of effective 3D on rates lower than 106 M-1s-1. This is highly relevant when one seeks to relate BCR-antigen binding strength and B cell response, particularly during germinal center reactions. Thus, there is an urgent need to revisit our current understanding of the BCR-antigen kinetic rates in germinal centers using state-of-the-art experimental assays for BCR-Ag interactions. Significance StatementIn germinal centers, binding between BCRs and antigen (Ag) tethered on the membrane of follicular dendritic cells occurs via two-dimensional (2D) membrane-to-membrane interactions. In contrast, in in vitro assays antibody (Ab)-antigen interactions occur with one component in solution. Structurally, there are large qualitative and quantitative differences between BCR-Ag 2D and Ab-Ag 3D translational and rotational diffusion processes, with the 2D translational diffusion being about 1000-fold lower than the 3D one. Moreover, the effective binding kinetics of both BCR-Ag and Ab-Ag interactions strongly deviate from the intrinsic molecular on and off rates. Here we expose this mismatch and, performing numerical and analytical calculations, quantify the ranges for which the experimental in-vitro data is informative on the BCR-Ag binding strength.

immunology↗

bioRxiv2021

Affinity selection in germinal centers: Cautionary tales and new opportunities

Our current quantitative knowledge of the kinetics of antibody-mediated immunity is partly based on idealized experiments throughout the last decades. However, new experimental techniques often render contradictory quantitative outcomes that shake previously uncontroversial assumptions. This has been the case in the field of T-cell receptors, where recent techniques for measuring the 2-dimensional rate constants of T-cell receptor-ligand interactions exposed contradictory results to those obtained with techniques measuring 3-dimensional interactions. Recently, we have developed a mathematical framework to rationalize those discrepancies focusing on the proper fine-grained description of the underlying kinetic steps involved in the immune synapse. In this perspective article, we apply this approach to unveil potential blind spots in the case of B-cell receptors (BCR) and to rethink the interactions between B cells and follicular dendritic cells (FDC) during the germinal center (GC) reaction. Also, we elaborate on the concept of "catch bonds" and on the recent observations that B-cell synapses retract and pull antigen generating a "retracting force", and propose some testable predictions that can lead to future research.

immunology↗