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1. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

   https://doi.org/10.1093/bioinformatics/btad186  

   Kaste, Joshua A; Shachar-Hill, Yair (May 2023, Bioinformatics)

   Birol, Inanc (Ed.)

   Abstract Motivation The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Results Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Availability and implementation Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights. 

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2. Model validation and selection in metabolic flux analysis and flux balance analysis

   https://doi.org/10.1002/btpr.3413  

   Kaste, Joshua_A_M; Shachar‐Hill, Yair (November 2023, Biotechnology Progress)

   Abstract 13C‐Metabolic Flux Analysis (13C‐MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint‐based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state‐of‐the‐art in constraint‐based metabolic model validation and model selection. Applications and limitations of the χ²‐test of goodness‐of‐fit, the most widely used quantitative validation and selection approach in 13C‐MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C‐MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint‐based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology. 

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3. Metabolomics for early detection of stress in freshwater alga Poterioochromonas malhamensis exposed to silver nanoparticles

   https://doi.org/10.1038/s41598-020-77521-0  

   Liu, Wei; Majumdar, Sanghamitra; Li, Weiwei; Keller, Arturo_A; Slaveykova, Vera_I (November 2020, Scientific Reports)

   Abstract Silver nanoparticles (AgNPs) are one of the most used engineered nanomaterials. Despite progress in assessing their environmental implications, knowledge gaps exist concerning the metabolic perturbations induced by AgNPs on phytoplankton, essential organisms in global biogeochemical cycles and food-web dynamics. We combine targeted metabolomics, biouptake and physiological response studies to elucidate metabolic perturbations in algaPoterioochromonas malhamensisinduced by AgNPs and dissolved Ag. We show time-dependent perturbation of the metabolism of amino acids, nucleotides, fatty acids, tricarboxylic acids, photosynthesis and photorespiration by both Ag-treatments. The results suggest that dissolved Ag ions released by AgNPs are the major toxicity driver; however, AgNPs internalized in food vacuoles contributed to the perturbation of amino acid metabolism, TCA cycle and oxidative stress. The metabolic perturbations corroborate the observed physiological responses. We highlight the potential of metabolomics as a tool for understanding the molecular basis for these metabolic and physiological changes, and for early detection of stress. 

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4. Dynamic Flux Balance Analysis Models in SBML

   Watanabe, L.; König, M.; and Myers, C. (January 2018, BioRXiv)

   Motivation: Systems biology models are typically simulated using a single formalism such as ordinary differential equations (ODE) or stochastic methods. However, more complex models require the coupling of multiple formalisms since different biological concepts are better described using different methods, e.g., stationary metabolism is often modeled using flux-balance analysis (FBA) whereas dynamic changes of model components are better described via ODEs. The coupling of FBA and ODE frameworks results in dynamic FBA models. A major challenge is how to describe such hybrid models coupling multiple frameworks in a standardized way, so that they can be exchanged between tools and simulated consistently and in a reproducible manner. Results: This paper presents a scheme and implementation for encoding dynamic FBA models in the Systems Biology Markup Language (SBML), thereby allowing to exchange multi-framework computational models between software tools. The paper shows the feasibility of the approach using various example models and demonstrates that different tools are able to simulate the hybrid models and agree on the results. As part of this work, two independent implementations of a multi-framework simulation method for dynamic FBA have been developed supporting such models: iBioSim and sbmlutils. Availability: All materials and models are available from https://github.com/matthiaskoenig/dfba. The tools used in this project are freely available: iBioSim at http://www.async.ece.utah.edu/ibiosim and sbmlutils at https://github.com/matthiaskoenig/sbmlutils/. 

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5. A Metric to Quantify Subjective Information in Biological Gradient Sensing

   Barker, Tyler S.; Thomas, Peter J.; Pierobon, Massimiliano (December 2023, GLOBECOM 2023 - 2023 IEEE Global Communications Conference)

   Information theory has been successfully applied to biology with interesting results and applications, ranging from scientific discovery, to system modeling, and engineering. Novel concepts such as semantic and useful information have been proposed to address the peculiarity of biological systems in contrast to Shannon’s classical theory. In this paper, the concept of subjective information, previously observed as an emergent property in a simulated biological system with determinate char- acteristics, is further explored through the proposal of a novel metric for its quantification. This measure is based on a biological system’s ability to dynamically sense and react to environmental signals to achieve a goal. The novel metric is validated through the simulation of a computational model that enables its correlation with different strategies for information acquisition from the environment and processing. The obtained results indicate that the proposed measure of subjective information is reliable in quantifying the effectiveness of a biological system’s strategy in using information from the environment for its growth and survival. 

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