Frequency Domain Model Selection for Servo Systems ensuring Practical Identifiability

Physically motivated models of servo control systems with coupled mechanics are required for control design, simulation etc. Often, however, the effort of modelling prohibits, these model-based methods in industrial applications. Therefore, all approaches of automatic modelling / model selection are naturally appealing. In this paper a procedure for model selection in frequency domain is proposed that minimizes the Kullback-Leibler distance between model and measurement while considering only those models that are practically identifiable. It aims at mechanical models of servo systems including multiplemass resonators. Criteria for practical identifiability are derived locally from the sensitivity matrix which is calculated for different formulations of the equation error. In experiments with two industry-like testbeds the methodology proves to reveal the characteristic mechanical properties of the two setups. © 2020 IEE

Multiobjective Identification of a Feedback Synthetic Gene Circuit

© 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Kinetic (i.e., dynamic) semimechanistic models based on the first principles are particularly important in systems and synthetic biology since they can explain and predict the functional behavior that emerges from the time-varying concentrations in cellular components. However, gene circuit models are nonlinear higher order ones and have a large number of parameters. In addition, experimental measurements are often scarce, and enough signal excitability for identification cannot always be achieved. These characteristics render the identification problem ill-posed, so most gene circuit models present incomplete parameter identifiability. Thus, parameter identification of typical biological models still appears as an open problem, where ensemble modeling approaches and multiobjective optimization arise as natural options. We address the problem of identifying the stochastic model of a closed-loop synthetic genetic circuit designed to minimize the gene expression noise. The model results from the feedback interaction between two subsystems. Besides incomplete parameter identifiability, the closed-loop dynamics cannot be directly identified due to the lack of enough input signal excitability. We apply a two-stage approach. First, the open-loop averaged time-course experimental data are used to identify a reduced-order stochastic model of the system direct chain. Then, closed-loop steady-state stochastic distributions are used to identify the remaining parameters in the feedback configuration. In both cases, multiobjective optimization is used to address the parameter identifiability, providing sets of parameters valid for different state-space regions. The methodology gives good identification results, provides clear guidelines on the effect of the parameters under different scenarios, and it is particularly useful for easily combining time-course population averaged and steady-state single-cell distribution experimental data.This work was supported by the European Union and Spanish Government, MINECO/AEI/FEDER under Grant DPI2017-82896-C2-1-R. The work of Y. Boada was supported by the Universitat Politecnica de Valencia under Grant FPI/2013-3242.Boada-Acosta, YF.; Vignoni, A.; Picó, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology. 28(1):208-223. https://doi.org/10.1109/TCST.2018.2885694S20822328

Statistical and Mechanistic Approaches to Study Cell Signaling Dynamics

Cells use complex signaling systems to constantly detect environmental changes, relay extracellular information from the cell membrane to the nucleus, and drive cell responses, such as transcription. The ability of each single cell to dynamically respond to changes in its environment is the basis for healthy, functioning, multicellular beings. Diseases often arise from dysregulated signaling, and our ability to manipulate cell responses, that stems from our growing understanding of signaling processes, is often the basis for disease treatments. Computational approaches can complement experimental studies of cellular systems, allowing us to formalize our growing body of knowledge of cellular biochemistry. Mechanistic modeling provides a natural framework to describe and simulate complex systems with many system components and causal interactions that often lead to non-intuitive emergent behavior, lending itself well to the analysis of signaling systems. Statistical approaches can complement mechanistic modeling by enabling an analysis of complex input-output relationships in the data, providing insight into how cells translate input environmental cues into output responses, even when the underlying mechanisms are only partially understood. In this thesis, we explore both mechanistic and statistical approaches and address several challenges in modeling signaling processes within a cell, and signaling heterogeneity between cells, using the NF-kB pathway as a model system. First, we evaluate methods to efficiently determine numerical values of model parameters, enabling model simulations that are comparable to experimental data. Second, we develop methods to identify reduced submodels that are sufficient for the data, highlighting simple mechanisms that drive emergent behavior. Third, switching gears to study signaling heterogeneity, we use information-theoretic analyses to evaluate the capabilities of the NF-kB pathway to effectively transduce cytokine dosage information in the presence of biochemical noise. Finally, we develop a framework to calibrate mechanistic models to heterogeneous signaling data, enabling simulation-based analyses of single-cell signaling capabilities

Ecological modelling to describe the role of light on microbial interactions in Ulva spp. with implications in aquaculture

Dissertação de mestrado em BioinformaticsCom a população mundial a aumentar e as quotas de pesca a estagnar, são necessárias novas formas de produzir alimentos de origem marinha sem comprometer o ambiente. Uma destas formas é através da Aquacultura Multi-Trófica Integrada num Sistema de Recirculação de Água (IMTA-RAS). Um dos maiores problemas das produções intensivas é o dos agentes patogénicos ou oportunistas que podem causar uma taxa de mortalidade dos peixes da ordem dos 75% ou mesmo superior. Num sistema do tipo IMTA-RAS, são necessárias pelo menos duas espécies, uma alimentada (como os peixes) e outra extrativa (como, por exemplo, as algas) capaz de remover os nutrientes orgânicos e inorgânicos da água. Neste trabalho, consideramos Ulva ohnoi, uma espécie que tem atraído grande atenção devido à sua facilidade de cultivo, produtividade, elevado teor proteico, e outros nutrientes essenciais. As comunidades bacterianas associadas a Ulva spp. desempenham um papel funcional importante tanto na morfogénese como na reprodução de algas. Uma espécie bacteriana específica encontrada na superfície de Ulva, Phaeobacter gallaeciensis, pode produzir um antibiótico natural atuando contra agentes patogénicos oportunistas como o agente patogénico dos peixes, Vibrio anguillarum. No entanto, a retenção de Phaeobacter gallaeciensis na superfície das algas é afetada pelas condições de funcionamento do sistema IMTA. Mais especificamente, na intensidade da luz. Aqui propusemos a formulação de um modelo ecológico para ter em conta esses efeitos e descrever como a intensidade da luz afeta as interações entre espécies: alga - microbioma bacteriano - Phaeobacter. Para este propósito, primeiro foi realizada uma experiência para obter dados de crescimento das espécies com diferentes intensidades de luz. Os dados foram então utilizados para identificar iterativamente um modelo Lotka-Volterra. Foi realizada uma análise de identificabilidade, o que levou a um modelo reduzido. Posteriormente, utilizando uma abordagem de estimativa de parâmetros multi-experimental, foram estimados os tipos de interações e a sua dependência da intensidade luminosa. Os resultados finais revelam que a taxa de crescimento das algas depende da intensidade da luz. No entanto, intensidades mais elevadas podem ser prejudiciais. Além disso, as melhores condições de crescimento das algas parecem ser as piores para a retenção de Phaeobacter. Embora estes resultados necessitem de mais validação experimental, concluímos que a intensidade da luz deve ser seleccionada para obter bons compromissos entre o crescimento das algas e a produção de antibióticos naturais.With the world’s population increasing and fishing quotas stagnating, new ways to produce marine food without compromising the environment are needed. One of these ways is through Integrated Multi Trophic Aquaculture in a water Recirculation System (IMTA-RAS). One of the greatest problems of intensive productions is the opportunistic pathogens that can cause a mortality rate of fish on the order of 75% or even higher. In an IMTA-RAS type of system, at least two species are needed, one fed (like fish) and other extractive (like, for example, algae) capable of removing organic and inorganic nutrients from the water. In this work, we considered Ulva ohnoi, a species that has attracted important attention due to its ease of cultivation, productivity, high protein content, and other essential nutrients. The bacterial communities associated with Ulva spp. play an important functional role in both morphogenesis and algae reproduction. A particular bacterial species found on the surface of Ulva, Phaeobacter gallaeciensis, can produce a natural antibiotic acting against opportunistic pathogens as fish pathogen, Vibrio anguillarum. However, the retention of Phaeobacter gallaeciensis on the surface of the algae is affected by the operating conditions of the IMTA system. More specifically, on light intensity. Here we proposed the formulation of an ecological model to account for those effects and describe how light intensity affects the interactions between species: algae - bacterial microbiome - Phaeobacter. For this purpose, an experiment was first performed to obtain species growth data at different light intensities. The data were then used to iteratively identify a Lotka-Volterra model. Identifiability analysis was performed, which led to a reduced model. Afterwards, using a multi-experiment parameter estimation approach, the types of interactions and their dependence on the light intensity were estimated. The final results reveal that the algae growth rate depends on the light intensity. However, higher intensities can be detrimental. In addition, the best algae growth conditions appear to be the worst for retention of Phaeobacter. Although these results need further experimental validation, we concluded that light intensity must be selected to obtain good compromises between algae growth and the production of natural antibiotics

Parameter Estimation of Complex Systems from Sparse and Noisy Data

Mathematical modeling is a key component of various disciplines in science and engineering. A mathematical model which represents important behavior of a real system can be used as a substitute for the real process for many analysis and synthesis tasks. The performance of model based techniques, e.g. system analysis, computer simulation, controller design, sensor development, state filtering, product monitoring, and process optimization, is highly dependent on the quality of the model used. Therefore, it is very important to be able to develop an accurate model from available experimental data. Parameter estimation is usually formulated as an optimization problem where the parameter estimate is computed by minimizing the discrepancy between the model prediction and the experimental data. If a simple model and a large amount of data are available then the estimation problem is frequently well-posed and a small error in data fitting automatically results in an accurate model. However, this is not always the case. If the model is complex and only sparse and noisy data are available, then the estimation problem is often ill-conditioned and good data fitting does not ensure accurate model predictions. Many challenges that can often be neglected for estimation involving simple models need to be carefully considered for estimation problems involving complex models. To obtain a reliable and accurate estimate from sparse and noisy data, a set of techniques is developed by addressing the challenges encountered in estimation of complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental design for collecting information-rich data by setting optimal experimental conditions; (3) regularization of estimation problem which solves the ill-conditioned large-scale optimization problem by reducing the number of parameters; (4) nonlinear estimation and filtering which fits the data by various estimation and filtering algorithms; (5) model verification by applying statistical hypothesis test to the prediction error. The developed methods are applied to different types of models ranging from models found in the process industries to biochemical networks, some of which are described by ordinary differential equations with dozens of state variables and more than a hundred parameters

Modeling and Optimization of Dynamical Systems in Epidemiology using Sparse Grid Interpolation

Infectious diseases pose a perpetual threat across the globe, devastating communities, and straining public health resources to their limit. The ease and speed of modern communications and transportation networks means policy makers are often playing catch-up to nascent epidemics, formulating critical, yet hasty, responses with insufficient, possibly inaccurate, information. In light of these difficulties, it is crucial to first understand the causes of a disease, then to predict its course, and finally to develop ways of controlling it. Mathematical modeling provides a methodical, in silico solution to all of these challenges, as we explore in this work. We accomplish these tasks with the aid of a surrogate modeling technique known as sparse grid interpolation, which approximates dynamical systems using a compact polynomial representation. Our contributions to the disease modeling community are encapsulated in the following endeavors. We first explore transmission and recovery mechanisms for disease eradication, identifying a relationship between the reproductive potential of a disease and the maximum allowable disease burden. We then conduct a comparative computational study to improve simulation fits to existing case data by exploiting the approximation properties of sparse grid interpolants both on the global and local levels. Finally, we solve a joint optimization problem of periodically selecting field sensors and deploying public health interventions to progressively enhance the understanding of a metapopulation-based infectious disease system using a robust model predictive control scheme