Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes

Law, Richard C.; Lakhani, Aliya; O’Keeffe, Samantha; Erşan, Sevcan; Park, Junyoung O.

doi:10.1016/j.copbio.2022.102701

Cited by 12 publications

(5 citation statements)

References 58 publications

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“…Advances in in vivo MFA using stable isotope tracers have facilitated assessment of differences in metabolic pathway utilizawww.e-enm.org 625 tion in tissues and quantification of systemic turnover rates of circulating metabolites and their contribution to multiple organs [91][92][93][94]. To bridge the gap between omics data and metabolic phenotypes in endocrinological disorders, interdisciplinary approaches are crucial [95]. Integrating omics data with clinical observations, longitudinal studies, and functional assays can provide a more holistic understanding of the disease mechanisms and their metabolic consequences.…”

Section: Discussionmentioning

confidence: 99%

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Lakhani,

Kang,

Kang

et al. 2023

Endocrinol Metab

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Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing’s syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing’s syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

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Section: Discussionmentioning

confidence: 99%

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Lakhani,

Kang,

Kang

et al. 2023

Endocrinol Metab

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“…Thus, generating time series of measurements may become prohibitive because of associated costs. Flows may in some cases be easier to measure than internal concentrations, especially flows across the cell membrane, and several methods allow for real-time measurements of flows in vivo [90]. Although the practicality of obtaining experimental data is outside the scope of this paper, we will discuss some examples of how flow measurements can be used to provide time series measurements.…”

Section: Discussionmentioning

confidence: 99%

Structural identifiability of biomolecular controller motifs with and without flow measurements as model output

Haus,

Drengstig,

Thorsen

2023

PLoS Comput Biol

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Controller motifs are simple biomolecular reaction networks with negative feedback. They can explain how regulatory function is achieved and are often used as building blocks in mathematical models of biological systems. In this paper we perform an extensive investigation into structural identifiability of controller motifs, specifically the so–called basic and antithetic controller motifs. Structural identifiability analysis is a useful tool in the creation and evaluation of mathematical models: it can be used to ensure that model parameters can be determined uniquely and to examine which measurements are necessary for this purpose. This is especially useful for biological models where parameter estimation can be difficult due to limited availability of measureable outputs. Our aim with this work is to investigate how structural identifiability is affected by controller motif complexity and choice of measurements. To increase the number of potential outputs we propose two methods for including flow measurements and show how this affects structural identifiability in combination with, or in the absence of, concentration measurements. In our investigation, we analyze 128 different controller motif structures using a combination of flow and/or concentration measurements, giving a total of 3648 instances. Among all instances, 34% of the measurement combinations provided structural identifiability. Our main findings for the controller motifs include: i) a single measurement is insufficient for structural identifiability, ii) measurements related to different chemical species are necessary for structural identifiability. Applying these findings result in a reduced subset of 1568 instances, where 80% are structurally identifiable, and more complex/interconnected motifs appear easier to structurally identify. The model structures we have investigated are commonly used in models of biological systems, and our results demonstrate how different model structures and measurement combinations affect structural identifiability of controller motifs.

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“…Here, we review a selection of the methods available for measuring the metabolic fluxes referred to in Equation 13 and the associated metabolite concentrations. Technologies for flux and concentration measurements have matured considerably over the past two decades; modern metabolomicsand spectroscopy-based methods have enabled measurements with previously inaccessible spatial and temporal resolution (43). These new technologies have produced a wealth of flux and metabolite concentration data in living cells, but it remains a challenge to integrate these data to infer the energetic cost of specific cellular processes.…”

Section: Methods and Technologies For Measuring Fluxes And Concentrat...mentioning

confidence: 99%

Dissecting Flux Balances to Measure Energetic Costs in Cell Biology: Techniques and Challenges

Arunachalam

Ireland

Yang

et al. 2023

Annu. Rev. Condens. Matter Phys.

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Life is a nonequilibrium phenomenon: Metabolism provides a continuous supply of energy that drives nearly all cellular processes. However, very little is known about how much energy different cellular processes use, i.e., their energetic costs. The most direct experimental measurements of these costs involve modulating the activity of cellular processes and determining the resulting changes in energetic fluxes. In this review, we present a flux balance framework to aid in the design and interpretation of such experiments and discuss the challenges associated with measuring the relevant metabolic fluxes. We then describe selected techniques that enable measurement of these fluxes. Finally, we review prior experimental and theoretical work that has employed techniques from biochemistry and nonequilibrium physics to determine the energetic costs of cellular processes. Expected final online publication date for the Annual Review of Condensed Matter Physics, Volume 14 is March 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes

Cited by 12 publications

References 58 publications

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Structural identifiability of biomolecular controller motifs with and without flow measurements as model output

Dissecting Flux Balances to Measure Energetic Costs in Cell Biology: Techniques and Challenges

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