The use of Sensitivity Analysis for the design of Functional Structural Plant Models

International audienceDeveloped recently, Functional Structural Models of Plant Growth (FSPM) aim at describing plant structural development (organogenesis and geometry), functional growth (biomass accumulation and allocation) and the complex interactions between both. They serve as a framework to integrate complex biological and biophysical processes in interaction with the environment, at different scales. The resulting complexity of such models regarding the dimensionalities of the parameter space and state space often makes them difficult to parameterize. There is usually no systematic model identification from experimental data and such models still remain ill-adapted for applicative purposes. The objective of this study is to explore how global sensitivity analysis can help for the parameterization of FSPM, by quantifying the driving forces during plant growth and the relative importance of the described biophysical processes regarding the outputs of interest. The tests are performed on the GreenLab model. Its particularity is that both structural development and functional growth are described mathematically as a dynamical system (Cournède et al., 2006). Its parameterization relies on parameter estimation from experimental data. Sensitivity analysis may help to optimize the trade-off between experimental cost and accuracy. This is crucial to develop a predictive capacity that scales from genotype to phenotype for FSPM

Sensitivity analysis of GreenLab model for maize

International audienceAs one necessary step for a good modeling, this study was aimed at analyzing the sensitivity of GreenLab model for maize. When instantaneous value of biomass generation is considered as the output, the system tends to be linear, the level is above 94% in SRC(Standardized Regression coefficients)study. Conversion efficiency and characteristic surface are proved to be the most sensitive factors. In Sobol's measure, we excluded the two most sensitive factors in the analysis, then the system linearity tends to be weaker and we got the detailed sensitivity indexes for the other uncertain parameters, by which we get the clearer driven force of maize growth in different stages

Sensitivity analysis: A discipline coming of age

Sensitivity analysis (SA) as a ‘formal’ and ‘standard’ component of scientific development and policy support is relatively young. Many researchers and practitioners from a wide range of disciplines have contributed to SA over the last three decades, and the SAMO (sensitivity analysis of model output) conferences, since 1995, have been the primary driver of breeding a community culture in this heterogeneous population. Now, SA is evolving into a mature and independent field of science, indeed a discipline with emerging applications extending well into new areas such as data science and machine learning. At this growth stage, the present editorial leads a special issue consisting of one Position Paper on “The future of sensitivity analysis” and 11 research papers on “Sensitivity analysis for environmental modelling” published in Environmental Modelling & Software in 2020–21.publishedVersio

Assessment of Non-Linearity in Functional-Structural Plant Models

International audienceGlobal sensitivity analysis (SA) has known an increasing interest to assess the relative importance of parameters in ecological models [Cariboni et al., 2007] or crop models [Makowski et al., 2006]. Such methods have an important role to play in functional-structural plant growth modeling. The complexity of the underlying biological processes, especially the interaction between functioning and structure [Vos et al., 2009], usually makes parameterization a key step in modeling, and the analysis of model sensitivity to parameters provides useful information in this process. A side result of global SA is that it provides an indicator of the degree of non-linearity of the model by computing the level of interaction between parameters and how this interaction contributes to the variance of the output. Plants are known as complex systems with a strong level of interactions and compensations, and the aim of FSPMs is to describe and understand this complexity. As such, non-linearity is expected to play a key role in the study, since it reveals the interactions between parameters [Cariboni et al., 2007] [Saltelli, 2002]. The knowledge of the intrinsic non-linearity of the model and of its dynamic evolution throughout plant growth is very useful to study model behavior and properties, to underline the occurrence of particular biological phenomena or to improve the statistical analysis when confronting models to experimental data (e.g. statistical properties of estimators or numerical methods to compute the propagation of errors [Julier et al., 2000]). The objective of this paper is thus to explore the level of linearity of 3 FSPMs with different levels of complexity, and infer in each case what information can be drawn from this analysis. We first introduce the basic principles of Standard Regression Coefficients (SRC) method which is used for the analysis and gives a short overview of the different models addressed. We then analyze the results of the linearity study, particularly stressing on the emergence of non-linearity. We end by discussing the interest and potential extensions of this work

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

International audienceMathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics. Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application. Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques

Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices

Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question ‘How uncertain is the prediction?’ Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods

Game and Balance Multicast Architecture Algorithms for Sensor Grid

We propose a scheme to attain shorter multicast delay and higher efficiency in the data transfer of sensor grid. Our scheme, in one cluster, seeks the central node, calculates the space and the data weight vectors. Then we try to find a new vector composed by linear combination of the two old ones. We use the equal correlation coefficient between the new and old vectors to find the point of game and balance of the space and data factorsbuild a binary simple equation, seek linear parameters, and generate a least weight path tree. We handled the issue from a quantitative way instead of a qualitative way. Based on this idea, we considered the scheme from both the space and data factor, then we built the mathematic model, set up game and balance relationship and finally resolved the linear indexes, according to which we improved the transmission efficiency of sensor grid. Extended simulation results indicate that our scheme attains less average multicast delay and number of links used compared with other well-known existing schemes

A Subset of CXCR5+CD8+ T Cells in the Germinal Centers From Human Tonsils and Lymph Nodes Help B Cells Produce Immunoglobulins

Recent studies indicated that CXCR5+CD8+ T cells in lymph nodes could eradicate virus-infected target cells. However, in the current study we found that a subset of CXCR5+CD8+ T cells in the germinal centers from human tonsils or lymph nodes are predominately memory cells that express CD45RO and CD27. The involvement of CXCR5+CD8+ T cells in humoral immune responses is suggested by their localization in B cell follicles and by the concomitant expression of costimulatory molecules, including CD40L and ICOS after activation. In addition, CXCR5+CD8+ memory T cells produced significantly higher levels of IL-21, IFN-γ, and IL-4 at mRNA and protein levels compared to CXCR5−CD8+ memory T cells, but IL-21-expressing CXCR5+CD8+ T cells did not express Granzyme B and perforin. When cocultured with sorted B cells, sorted CXCR5+CD8+ T cells promoted the production of antibodies compared to sorted CXCR5−CD8+ T cells. However, fixed CD8+ T cells failed to help B cells and the neutralyzing antibodies against IL-21 or CD40L inhibited the promoting effects of sorted CXCR5+CD8+ T cells on B cells for the production of antibodies. Finally, we found that in the germinal centers of lymph nodes from HIV-infected patients contained more CXCR5+CD8+ T cells compared to normal lymph nodes. Due to their versatile functional capacities, CXCR5+CD8+ T cells are promising candidate cells for immune therapies, particularly when CD4+ T cell help are limited