Dynamical compensation and structural identifiability: analysis, implications, and reconciliation

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. Here we show that, according to its original definition, dynamical compensation is equivalent to lack of structural identifiability. This is relevant if model parameters need to be estimated, which is often the case in biological modelling. This realization prompts us to warn that care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability

Seismic Waveguide of Metamaterials

We have developed a new method of an earthquake-resistant design to support conventional aseismic designs using acoustic metamaterials. We suggest a simple and practical method to reduce the amplitude of a seismic wave exponentially. Our device is an attenuator of a seismic wave. Constructing a cylindrical shell-type waveguide that creates a stop-band for the seismic wave, we convert the wave into an evanescent wave for some frequency range without touching the building we want to protect.Comment: 4 pages, 4 figure

Pearled papules over tattoo: Molluscum cotagiosum

No Abstract

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

[Background] Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges.[Methods] We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph.[Results] Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub ( https://github.com/gabora/visid ).[Conclusions] Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (“CANPATHPRO”), from the EU FP7 project "NICHE" (ITN Grant number 289384), and from the Spanish MINECO project "SYNBIOFACTORY" (grant number DPI2014-55276-C5-2-R).Peer reviewe

PREMER: Parallel reverse engineering of biological networks with information theory

A common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms which are crucial for discarding spurious interactions and determining causal relationships  to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://sites.google.com/site/premertoolbox/) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial software.AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C fellowship ED481B2014/133-0. KB was supported by the German Federal Ministry of Research and Education (BMBF, OncoPath consortium). JRB acknowledges funding from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the project “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 686282 (CanPathPro). We thank David R. Penas and David Henriques for assistance with the implementation

AutoRepar: a method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGMechanistic dynamic models of biological systems allow for a quantitative and systematic interpretation of data and the generation of testable hypotheses. However, these models are often over-parameterized, leading to nonidentifiability and nonobservability, that is, the impossibility of inferring their parameters and state variables. The lack of structural identifiability and observability (SIO) compromises a model's ability to make predictions and provide insight. Here we present a methodology, AutoRepar, that corrects SIO deficiencies of nonlinear ODE models automatically, yielding reparameterized models that are structurally identifiable and observable. The reparameterization preserves the mechanistic meaning of selected variables, and has the exact same dynamics and input-output mapping as the original model. We implement AutoRepar as an extension of the STRIKE-GOLDD software toolbox for SIO analysis, applying it to several models from the literature to demonstrate its ability to repair their structural deficiencies. AutoRepar increases the applicability of mechanistic models, enabling them to provide reliable information about their parameters and dynamics.Consejo Superior de Investigaciones Científicas https://doi.org/10.13039/501100003339 | Ref. PIE 202070E062MCIN/AEI/10.13039/501100011033 | Ref. RYC-2019-027537-IMCIN/AEI/10.13039/501100011033 | Ref. PID2020-113992RA-I00MCIN/AEI/ 10.13039/501100011033 | Ref. PID2020-117271RB-C2MCIN/AEI/ 10.13039/501100011033 | Ref. DPI2017-82896-C2-2-RXunta de Galicia | Ref. ED431F 2021/00

The Genealogy Project: The Founding of a Podcast

When thinking about a new journal, my first thought about this was tohave a multimedia aspect to the journal that would include a series ofongoing podcasts that Daniel Chapman and I would do collaboratively.This turned into The Genealogy Project. Since we began this project about a year and half ago, Daniel and I have interviewed many scholars across generations. As conversations unfolded, I found that many of us have had inter-connected life histories and backgrounds. As I began thinking about a podcast in curriculum studies I thought that it might be a way to archive the work being done by my generation. I wanted to make sure that our work did not disappear from the archives. But, too, I wanted to show that my generation is also linked backwards to previous generations. As Derrida teaches, the archive is more about the to-come. The Genealogy Project Podcast is about archiving the future of a field. What we are able to do in the field today is due to the work that was done by scholars who came before us and mentored us. As my generation mentors future generations to-come, the field will go its own way and take on new life. I would liketo showcase scholars from all generations to join in the conversationswe are having about the field

Improving dynamic predictions with ensembles of observable models

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGMotivation: Dynamic mechanistic modelling in systems biology has been hampered by the complexity and variability associated with the underlying interactions, and by uncertain and sparse experimental measurements. Ensemble modelling, a concept initially developed in statistical mechanics, has been introduced in biological applications with the aim of mitigating those issues. Ensemble modelling uses a collection of different models compatible with the observed data to describe the phenomena of interest. However, since systems biology models often suffer from lack of identifiability and observability, ensembles of models are particularly unreliable when predicting non-observable states. Results: We present a strategy to assess and improve the reliability of a class of model ensembles. In particular, we consider kinetic models described using ordinary differential equations (ODEs) with a fixed structure. Our approach builds an ensemble with a selection of the parameter vectors found when performing parameter estimation with a global optimization metaheuristic. This technique enforces diversity during the sampling of parameter space and it can quantify the uncertainty in the predictions of state trajectories. We couple this strategy with structural identifiability and observability analysis, and when these tests detect possible prediction issues we obtain model reparameterizations that surmount them. The end result is an ensemble of models with the ability to predict the internal dynamics of a biological process. We demonstrate our approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway. Availability: The code that implements the methodology and reproduces the results is available at https://doi.org/10.5281/zenodo.6782638. Supplementary information: Supplementary data are available at Bioinformatics online.MCIN/AEI/ 10.13039/501100011033 | Ref. PID2020-117271RBC22MCIN/AEI/ 10.13039/501100011033 | Ref. PID2020-113992RA-I00MCIN/AEI/ 10.13039/501100011033 | Ref. RYC-2019-027537-IXunta de Galicia | Ref. ED431F 2021/00