Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts

We present 'CongNaMul', a comprehensive dataset designed for various tasks in soybean sprouts image analysis. The CongNaMul dataset is curated to facilitate tasks such as image classification, semantic segmentation, decomposition, and measurement of length and weight. The classification task provides four classes to determine the quality of soybean sprouts: normal, broken, spotted, and broken and spotted, for the development of AI-aided automatic quality inspection technology. For semantic segmentation, images with varying complexity, from single sprout images to images with multiple sprouts, along with human-labelled mask images, are included. The label has 4 different classes: background, head, body, tail. The dataset also provides images and masks for the image decomposition task, including two separate sprout images and their combined form. Lastly, 5 physical features of sprouts (head length, body length, body thickness, tail length, weight) are provided for image-based measurement tasks. This dataset is expected to be a valuable resource for a wide range of research and applications in the advanced analysis of images of soybean sprouts. Also, we hope that this dataset can assist researchers studying classification, semantic segmentation, decomposition, and physical feature measurement in other industrial fields, in evaluating their models. The dataset is available at the authors' repository. (https://bhban.kr/data)Comment: Accepted to International Conference on ICT Convergence 202

Mixed Reality Interface for Digital Twin of Plant Factory

An easier and intuitive interface architecture is necessary for digital twin of plant factory. I suggest an immersive and interactive mixed reality interface for digital twin models of smart farming, for remote work rather than simulation of components. The environment is constructed with UI display and a streaming background scene, which is a real time scene taken from camera device located in the plant factory, processed with deformable neural radiance fields. User can monitor and control the remote plant factory facilities with HMD or 2D display based mixed reality environment. This paper also introduces detailed concept and describes the system architecture to implement suggested mixed reality interface.Comment: 5 pages, 7 figure

Nonlinear Sliding Mode Observer Applied to Microalgae Growth

Modeling biological processes, such as algae growth, is an area of ongoing research. The ability to understand the multitude of parameters that influence this system provides a platform for better understanding the dynamics of microalgae growth. Empirical modeling efforts look to understand sources of driving nutrients that influence harmful algal blooms (HABs). These harmful algal blooms are dense aggregates that have an increasingly negative impact on local economics, marine and freshwater systems, and public health. They result from a high influx of nitrogen and nutrients that drive the algae biomass to exponentially grow. This growth blocks out the sun, potentially releases dangerous toxins, and suffocates marine life, damaging ecosystems, especially in Florida. Modeling microalgae behavior and growth is complex due to its nonlinear behavior and coupled variables. Recently, cultivating oleaginous microalgae for biofuel production has been another region of ongoing research, especially application of observer theory to estimate internal parameters that are not easily measured in algal systems. Linear observer theory has generally been applied to algae growth systems to estimate internal parameters that are beyond hardware sensor capabilities, but they are still severely limited. Nonlinear observer theory application to biological systems is still relatively new. This thesis explores the application of a nonlinear observer based off sliding mode to an algae system. Sliding mode is derived from modern control theory and is based off variable structure control. An algae system is modeled using the widely accepted Droop model for algae growth and a linear and nonlinear sliding mode observer is developed for the system to estimate internal nitrogen within the algae biomass

Real-time Spectroscopic Analysis of Microalgal Adaptation to Changing Environmental Conditions

Increases in anthropogenic pollution are causing many environmental problems; understanding their impact on the environment has become an important issue. Industrialization and the burning of fossil fuels have caused increased levels of carbon dioxide to enter the atmosphere, which is contributing to global warming and ocean acidification. Agricultural runoff has caused levels of inorganic nitrogen and phosphorus to rise, where they have been noted to cause harmful algal blooms. Marine ecosystems have been particularly affected as both of these forms of pollution accumulate in bodies of water. Microalgae are important organisms in these ecosystems because they sequester these pollutants and convert them into biomass. Because the chemical composition of microalgae’s biomass depends on the nutrient availability in their environment, further use of microalgae as an in situ indicator of environmental conditions is investigated. The objective of this thesis is to determine how microalgae samples respond in real time to changes in the nutrient availability of their environment. To accomplish this, a novel sensing technique was developed that allowed spectroscopic analysis of live microalgae samples. FTIR spectroscopy in ATR mode was used to repeatedly monitor cells at two distinct carbon dioxide concentrations, standard and elevated, over a twelve hour time period. The change in absorbance over time was modeled nonlinearly to gain information about how the chemical composition of microalgae adapted to higher levels of carbon dioxide. This innovative hard-modeling of changing spectroscopic time series demonstrated the ability to yield interpretable chemical information about the adaptation of microalgae to an altered environment