GenSSI: a software toolbox for structural identifiability analysis of biological models

Summary: Mathematical modeling has a key role in systems biology. Model building is often regarded as an iterative loop involving several tasks, among which the estimation of unknown parameters of the model from a certain set of experimental data is of central importance. This problem of parameter estimation has many possible pitfalls, and modelers should be very careful to avoid them. Many of such difficulties arise from a fundamental (yet often overlooked) property: the so-called structural (or a priori) identifiability, which considers the uniqueness of the estimated parameters. Obviously, the structural identifiability of any tentative model should be checked at the beginning of the model building loop. However, checking this property for arbitrary non-linear dynamic models is not an easy task. Here we present a software toolbox, GenSSI (Generating Series for testing Structural Identifiability), which enables non-expert users to carry out such analysis. The toolbox runs under the popular MATLAB environment and is accompanied by detailed documentation and relevant examples

A Two-Dimensional Multi-Species Model for Different Listeria Monocytogenes Biofilm Structures and Its Numerical Simulation

[Abstract] In this work we propose a two-dimensional multi-species model to describe the dynamics of biofilms formed by the pathogenic bacteria Listeria monocytogenes. Different Listeria monocytogenes strains produce biofilms with different structures, namely flat, honeycomb and clustered. Previous works showed that glucose impaired uptake and the appearance of damaged or dead cells are critical mechanisms underlying Listeria monocytogenes biofilm dynamics. Here we explicitly propose an extension of the two-dimensional multi-species model proposed by Alpkist and Klapper to account for those mechanisms. The result is a continuous two-dimensional multi-species model with non-linear detachment and mass action nutrient consumption. Moreover, we also propose a set of efficient numerical methods to solve the coupled model and we have developed their computer implementation from scratch in C/C++. Mainly based on finite differences schemes, these numerical techniques include Crank-Nicolson schemes for time discretization, Gibou’s ghost node techniques and level set methods to cope with the free boundary associated to the determination of the time-dependent biofilm domain. To finish with, we compare our simulation results with the dynamics of real biofilms as observed in the laboratory. More precisely, by using model parameters calibrated to experiments, the numerical results clearly illustrate the performance of the proposed model and the numerical methods to reproduce the real dynamics of flat, clustered and honeycomb structures shown by different Listeria monocytogenes strains.ALN and CV acknowledge the funding by MINECO from Spanish Government (Grant MTM2016-76497-R) and by Xunta de Galicia (Grants GRC2014/044 and ED431C2018/033). ALN acknowledges FPU fellowship (FPU13/02191) from the Spanish Government program MECD-FPU. CV and ALN as members of CITIC also akcnowledge the grant ED431G 2019/01, funded by Consellería de Educación, Universidade e Formación Profesional of Xunta de Galicia through FEDER funds with 80%, from FEDER Galicia 2014-2020 Program and 20% from Secretaría Xeral de Universidades. EBC acknowledges funding from Contrato Programa and grant Ref. IN607B 2017/02 from Xunta de Galicia. All grants include FEDER fundsXunta de Galicia; GRC2014/044Xunta de Galicia; ED431C2018/033Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IN607B 2017/0

DOTcvpSB, a software toolbox for dynamic optimization in systems biology

<p>Abstract</p> <p>Background</p> <p>Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units.</p> <p>Results</p> <p>Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamic optimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. Problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well.</p> <p>Conclusion</p> <p>Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimization problems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use.</p

AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology

3 páginas, 1 figura.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly citedMotivation: Many problems of interest in dynamic modeling and control of biological systems can be posed as non-linear optimization problems subject to algebraic and dynamic constraints. In the context of modeling, this is the case of, e.g. parameter estimation, optimal experimental design and dynamic flux balance analysis. In the context of control, model-based metabolic engineering or drug dose optimization problems can be formulated as (multi-objective) optimal control problems. Finding a solution to those problems is a very challenging task which requires advanced numerical methods. Results: This work presents the AMIGO2 toolbox: the first multiplatform software tool that automatizes the solution of all those problems, offering a suite of state-of-the-art (multi-objective) global optimizers and advanced simulation approachesEU FP7 project NICHE [ITN grant number 289384], Spanish MINECO/ FEDER projects IMPROWINE [grant number AGL2015-67504-C3-2-R] and SYNBIOFACTORY [DPI2014-55276-C5-2-R]Peer reviewe

On the relationship between sloppiness and identifiability

25 páginas, 11 figuras, 2 tablasDynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppinessThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (“CANPATHPRO”) and from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the projects “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R), and “IMPROWINE” (grant number AGL2015-67504-C3-2-R)N

Quality and safety driven optimal operation of deep-fat frying of potato chips

10 páginas, 10 figuras, 1 tablaIncreasing oil temperature and heating duration in deep-fat frying of potato chips can improve textural quality but worsen the chemical safety of acrylamide formation. Optimal design of this complex process is formulated as a non-linear constrained optimization problem where the objective is to compute the oil temperature profile that guarantees the desired final moisture content while minimizing final acrylamide content subject to operating constraints and the process dynamics. The process dynamics uses a multicomponent and multiphase transport model in the potato as a porous medium taken from literature. Results show that five different heating zones offer a good compromise between process duration (shorter the better) and safety in terms of lower acrylamide formation. A short, high temperature zone at the beginning with a progressive decrease in zone temperatures was found to be the optimal design. The multi-zone optimal operating conditions show significant advantages over nominal constant temperature processes, opening new avenues for optimization.The authors acknowledge financial support from EU [CAFE FP7-KBBE-2007-1(212754)], Spanish Ministry of Science and Innovation [SMART-QC AGL2008-05267-C03-01], Xunta de Galicia [IDECOP 08DPI007402PR] and CSIC [PIE201270E075]. A. Arias-Méndez acknowledges financial support from the JAE-CSIC program.Peer reviewe

Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods

Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided

An operational model for the optimal operation of the freeze-drying process

[Abstract] In this work an operational mathematical model for the freeze-drying process is derived. The model describes the state variables related to product quality and stability. Computational issues associated to the presence of a moving front are approached by using the Landau transform. Unknown model parameters are estimated using experimental data. The model is used to design optimal operation policies that reduce the process duration ensuring product quality

Towards predictive models in food engineering: Parameter estimation dos and don'ts

1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants, etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive properties. Unfortunately, it must be admitted that often experiment design and modeling are fully separated tasks: experiments are not designed for the purpose of modeling and models are usually derived without paying especial attention to available experimental data or experimentation capabilities. When, at some point, the parameter estimation problem is put on the table, modelers use available experimental data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually experiment dependent, with the implications this has in model validation. This work takes a new look into the parameter estimation problem in food process modeling. First the common pitfalls in parameter estimation are described. Second we present the theoretical background and the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses, data fitting with global optimization methods and optimal experimental design. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe