“…study to build metabolic models for each phase of the cell cycle. We used the DFA approach, a variation of dynamic FBA, to fit the rate of change of metabolites in FBA to experimental measurements from time course metabolomics ( Campit and Chandrasekaran, 2020 ; Chandrasekaran et al., 2017 ). We used this approach to create four different models corresponding to different phases of the cell cycle (G0/G1, G1, G1/S, and G2/M) ( Figure 4 A, STAR Methods ).…”
Summary
Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in
E. coli
,
S. cerevisiae,
and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.
“…study to build metabolic models for each phase of the cell cycle. We used the DFA approach, a variation of dynamic FBA, to fit the rate of change of metabolites in FBA to experimental measurements from time course metabolomics ( Campit and Chandrasekaran, 2020 ; Chandrasekaran et al., 2017 ). We used this approach to create four different models corresponding to different phases of the cell cycle (G0/G1, G1, G1/S, and G2/M) ( Figure 4 A, STAR Methods ).…”
Summary
Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in
E. coli
,
S. cerevisiae,
and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.
“…(MATLAB) >ecoli_GEM=readCbModel(`Ecoli_GEM_Orth_iJO1366.mat`) Note: It is important to constrain the GEM as necessary in order to better simulate the condition of interest. For modeling different metabolic states, experimental time-course metabolomics can be used with the Dynamic Flux Activity (DFA) algorithm ( Chandrasekaran et al., 2017 ; Campit and Chandrasekaran, 2020 ). For simulating other experimental conditions from transcriptomics or proteomics, a list of up- and down-regulated genes/proteins can be used as inputs to determine fluxes ( Shen et al., 2019 ).…”
“…GEMs potentially offer a framework for measuring how metabolism interacts with epigenetics [ 77 , 78 ]. However, a major limitation of using GEMs is the fact that key reactions involved in epigenetic regulation, such as those transporting or synthesizing metabolites such as Acetyl-CoA in the nucleus, are often missing.…”
Section: Modeling Of Metabolic Regulationmentioning
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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