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.

Designing regulatory DNA with tunable and context-specific activity is a major goal in biotechnology and medicine. Deep generative models offer a promising route for sequence design, yet it remains unclear whether synthetic sequences faithfully recapitulate the motif organization and functional specificity of natural regulatory DNA. Here we present DNA Discrete Diffusion (D3), a generative model that designs regulatory DNA through an iterative nucleotide-substitution process. Across computational benchmarks, D3 improves regulatory sequence generation relative to matched diffusion baselines, producing sequences that more closely match target activity, activity distributions, and sequence composition. In K562 lentiMPRA experiments, D3-designed sequences retained measurable regulatory activity and more closely recapitulated the activity distribution of genomic regulatory sequences than matched diffusion baselines. D3 performs robustly with limited training data and generates sequences informative enough to improve predictive models when labeled data are scarce. When trained without activity labels on task-specific regulatory sequence sets, D3 learns frozen sequence representations that are predictive of enhancer activity and compare favorably to several off-the-shelf genomic language model embeddings. Analysis of the sampling process identifies reproducible phases of sequence exploration, compositional refinement, and motif-associated convergence, providing an interpretable view of how D3 constructs regulatory sequences. These results establish D3 as a practical framework for designing synthetic regulatory DNA and studying sequence features associated with context-specific regulatory activity.

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective result in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. Here, we present single-cell optimization objective and trade-off inference (SCOOTI), which infers metabolic objectives and trade-offs in biological systems by integrating bulk and single-cell omics data, using metabolic modeling and machine learning. We validated SCOOTI by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells, during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in one-cell zygote, two-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation. A record of this paper's transparent peer review process is included in the supplemental information.

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.

Single-cell optimization objective and trade-off inference (SCOOTI) is a computational framework that integrates bulk and single-cell omics data with genome-scale metabolic modeling to infer metabolic objectives and trade-offs in biological systems. Here, we present a protocol for installing and running SCOOTI, using transcriptomics, proteomics, and metabolomics data to constrain metabolic models. We describe steps to interpret metabolic priorities across different cell states or conditions through clustering, dimensionality reduction, and trade-off analysis.

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective results in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. To define metabolic objectives and tradeoffs in biological systems mathematically, we integrated bulk and single-cell omics data with a novel framework to infer cell objectives using metabolic modeling and machine learning. We validated this framework by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells and during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in 1-cell zygote, 2-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation.