Molecular Systems Biology

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.

Cells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computational traditions describe this behaviour: mechanistic differential-equation models and representation-learning methods. Mechanistic models encode pathway topology and kinetics but cannot easily represent variation arising outside the modelled pathway. Representation learning, instead, maps genome-wide measurements onto low-dimensional manifolds but offers no mechanistic account of how the resulting cell states execute their functions. Reconciling these views, explaining signalling heterogeneity in a manner that is at once data-driven and mechanistically interpretable, has remained difficult. Here we introduce deep mechanistic models (DMMs), which couple semi-supervised representation learning to an ordinary-differential-equation model of EGFR/MAPK signalling, trained end-to-end so that the learnt representation and mechanistic parametrisation inform each other. Applying DMMs to multiplexed signalling data from 63 breast cancer cell lines, we show that the models generalise to held-out cell lines and attribute most heterogeneity to pathway-extrinsic factors, namely baseline ERBB2 activation and a Ca{superscript 2}/p38 signalling axis, rather than to variation in core MAPK components. Where the models fail, the discrepancies pinpoint rare signalling-altering mutations and recurrent programmes, including a putative AMPK- BRAF MEK-inhibitor-resistance axis and a cytoskeletal programme. We further find that mechanistic integration of EGFR receptor levels reshapes the learnt representation, rendering a molecular and a systems-level account of the same data equivalent in predictive power. DMMs thus offer a general framework for fusing mechanism with learning, naturally extendable to further modalities such as imaging, and simultaneously turn model failure into a systematic route to discover and evaluate candidate biology.

Bacterial growth laws quantitatively connect intracellular resource allocation to growth rate, enabling accurate predictions of physiology and antibiotic responses. Yet these laws have been rigorously tested for only a handful of perturbations. Here, we show that the growth law linking ribosome levels to growth rate under translation-inhibiting antibiotics is not universal, but rather depends on the antibiotic's mechanism of action. Quantitative proteomics across finely resolved one- and two-dimensional antibiotic gradients showed that inhibitors of translocation elongation or peptide bond formation elicit the canonical rise in ribosome levels, consistent with the growth law. By contrast, antibiotics disrupting translation initiation or fidelity produced distinct responses without ribosome upregulation. The transcription inhibitor rifampicin even reduced ribosome abundance. Combining antibiotics with divergent ribosome responses revealed a generalized growth law, in which the individual responses to perturbations superimpose. Embedding this law in a mathematical model explains distinct drug interaction patterns observed between rifampicin and different translation inhibitors. A low-dimensional structure pervades the entire proteome, enabling prediction of responses to drug pairs based on single-drug measurements. Together, these findings broaden the scope of bacterial growth laws and provide new principles for predicting responses to antibiotic combinations.

High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inherently dynamic and methods for reconstructing continuous proteome changes remain limited. Here, we introduce proteome velocity, a framework for inferring continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data using flow matching, in which a neural network learns velocity fields over proteome space. Proteome velocity estimates how rapidly and in which direction protein abundances change along a biological progression, such as disease. In mouse sepsis, covariate-conditioned velocity models resolved tissue- and pathogen-specific proteome trajectories and identified inflammatory proteins with distinct temporal activation patterns across infection routes and organ systems. In clinical COVID-19 plasma proteomes, inferred trajectories separated into distinct velocity programs associated with disease severity. These results show how generative trajectory models can transform cross-sectional proteomics data into interpretable, protein-resolved representations of molecular progression.

Metabolic diseases such as type 2 diabetes (T2D) arise through complex interactions between physiological, molecular, and environmental processes. Clinical traits including age, sex, adiposity, and glycaemic status are strongly associated with disease risk and progression, yet most molecular studies examine these factors independently and assume relatively static molecular regulation. Consequently, how physiological state dynamically reshapes molecular organisation across omics layers remains poorly understood. Here, we integrated transcriptomic, proteomic, metabolomic, and genetic data from 3,027 individuals in the IMI DIRECT cohort to characterise the joint molecular effects of age, sex, body mass index (BMI), and glycated haemoglobin (HbA1c). We identified widespread associations between these traits and molecular phenotypes. However, interaction analyses revealed a more complex context-dependent regulation, showing that the molecular effect of one trait frequently depends on the state of another, with sex-specific effects of age being more prominent. We also investigated relationships between different types of molecular phenotypes and how these relationships are modulated by metabolic disease relevant traits, demonstrating that cross-omic molecular coordination is itself dynamically remodelled by physiological and metabolic state. Probabilistic causal inference identified a directionally structured network of age-associated molecules, revealing pathways through which age effects propagate across omics layers, showcased in the example of the mTOR signalling pathway. Integration of this directed network with genetic colocalisation analyses also identified a sub-network relevant for T2D. Collectively, our findings demonstrate that metabolic disease relevant traits not only independently influence molecular phenotype abundance but also jointly reshape the directional organisation of cross-omic molecular networks. These results support a model in which metabolic disease susceptibility emerges through dynamic rewiring of interconnected molecular systems and provide a framework for context-dependent biomarker discovery, disease stratification, and precision metabolic medicine.

Protein phosphorylation regulates nearly every cellular process, yet most of the hundreds of thousands of human phosphosites remain functionally uncharacterized. Rather than prioritising phosphosites by conservation or structural features, here we use the abundance of an interaction partner as a readout of whether a phosphosite affects that interaction. This idea exploits the fact that subunits of stable complexes are often degraded when unbound. Here, we apply a nested linear regression model to pan-cancer data from 1,006 tumours, while controlling for transcriptional and other covariates. We identified 6,160 associations between 3,038 phosphosites and the abundance of interacting proteins, including several known interaction-regulating sites. Mapping these onto AlphaFold-predicted complexes placed 239 sites at interaction interfaces, while another 402 were linked to compartment-specific localisation, indicating that phosphorylation can also tune interactions by relocating proteins between compartments. Affinity-purification mass spectrometry of NKAP and NUF2 phosphosite mutants experimentally supported some of these predictions. Together, this framework reveals a widespread coupling between phosphorylation and interaction-dependent protein abundance and provides a prioritized, structure-informed resource for characterizing the human phosphoproteome

Biologically inspired neural networks (BINNs) embed pathway, ontology, or protein-interaction structure directly into neural networks, promising interpretable disease prediction where hidden nodes map to named biological entities. Yet BINNs have been hard to train at biobank scale, and the reliability of their interpretations remains largely untested. Here we present a fast BINN implementation trained on UK Biobank genotype and plasma proteomics data from about 500,000 individuals across six common diseases. BINNs achieve competitive predictive performance, but we uncover two major limits to their interpretability. First, attribution scores are strongly biased by graph topology, because node degree and layer position influence the scores. Normalization reduces this bias but can weaken enrichment for known disease genes. Second, BINNs show substantial predictive multiplicity, that is, independently trained models with identical architecture and data reach similarly accurate solutions while prioritizing different genes and pathways. Although this multiplicity makes single-model explanations unstable, the range of interpretations can itself reveal disease biology. Across 100 replicate BINNs for type 2 diabetes, we find distinct solution clusters prioritizing either inflammatory or hepatic-metabolic pathways, mirroring known disease heterogeneity. Thus, analyzing the space of BINN explanations can turn multiplicity into a tool for studying complex disease mechanisms.

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

Huntington disease (HD) is caused by a polyglutamine expanded huntingtin protein that exerts progressive cellular toxicity. However, the temporal sequence of pathogenic, particularly early and reversible versus late and irreversible events remain incompletely defined, despite their distinct therapeutic implications. To delineate this trajectory, we profiled the proteome of a huntingtin expressing cell model at early (72 h) and late (144 h) stages. Rather than a linear progression, pathogenicity unfolded in two discrete phases. At the early stage, cells exhibited a broad activation of RNA processing, splicing, and protein synthesis machinery, consistent with an adaptive response aimed at preserving gene expression fidelity under stress. By the late stage, this compensatory program had collapsed, giving rise to a dominant failure in mitochondrial energy metabolism. Notably, 85% of proteins altered at both time points reversed direction of change between stages, indicating that mutant huntingtin reprograms cellular function wholesale rather than amplifying a fixed set of perturbations. Detailed analysis of mitochondrial respiratory complexes revealed that terminal ATP generating components (cytochrome c oxidase and ATP synthase) were severely affected, whereas upstream electron transport elements were retained or upregulated. Leveraging this proteomic map, we applied an AI assisted, direction aware drug repurposing strategy. Of 1,712 differentially expressed proteins, 498 were druggable, and 89 mapped to approved agents with mechanisms concordant with the required correction. These included Complex I targeted agents (metformin, ME 344) and mitochondria directed therapeutics (SS 31, MitoQ), several of which have previously been evaluated in HD. Collectively, these findings define a biphasic course of huntingtin toxicity and highlight an early therapeutic window in which intervention is most likely to be applied, prior to irreversible deterioration of mitochondrial respiratory function.

Neuronal differentiation requires coordinated regulation across chromatin organization, gene expression, protein abundance, and post-translational modifications. Using the LUHMES human dopaminergic neuronal differentiation model, we integrated proteome and phosphoproteome profiling with previously generated enhancer-promoter interaction maps from NET-CAGE and HiCap and transcriptomic analysis across three consecutive differentiation stages. Differentiation was accompanied by increased abundance and phosphorylation of proteins involved in axon guidance, cytoskeletal organization, and synaptic signaling, alongside repression of the cell cycle, DNA replication, and chromatin-associated programs. Phosphoproteome analysis further revealed extensive remodeling of signaling networks associated with neuronal maturation. Enhancer-promoter interaction analysis revealed substantially greater rewiring at enhancers than promoters and identified master and relay transcription factors regulated across multiple molecular layers. siRNA-mediated knockdown showed that transcription factors MYT1, ISL2, and NHLH2 are crucial for proper neuronal maturation, whereas LCOR acts as a negative regulator of differentiation. Integrative network reconstruction further nominated MEOX2 as a candidate enhancer-associated regulator of late dopaminergic maturation. Together, these findings provide a multi-layered view of regulatory networks governing human neuronal differentiation.

Spatial subcellular proteomics provides key insights into subcellular organization but is frequently constrained by missing data values and an inability to robustly classify dual-localized proteins. To address these analytical bottlenecks, we introduce Choragraph, a deep-learning framework that uses an ensemble of deep neural networks incorporating whole-proteome cross-attention and Bayesian variational inference (BVI). Choragraph has two concurrent aims: it provides context-dependent reconstruction of missing proteomic values and predicts proteins subcellular compartment in a manner natively aware of multi-localisation. We applied Choragraph to a comprehensive Arabidopsis thaliana hyperLOPIT dataset containing 84 fractions across eight replicate LOPIT experiments. By successfully reconstructing profiles with up to 35% missing values, Choragraph incorporated over 2,000 low-abundance proteins that traditional methods would exclude. The models confidently classified 87% to 94% of singly-localised data-sufficient proteins across 14 subcellular compartments with a macro F1 score of 0.917, outperforming conventional classifiers. Crucially, Choragraph also identified over 1,000 dual-localized proteins, mapping continuous trafficking trails along the secretory pathway, highlighting functional zonation and membrane contact sites. To ensure accessibility, all data, predictions of subcellular localisation, and interactive 2D UMAP visualizations are available via an installation-free web application at choragraph.org. This framework provides a high-resolution, user-friendly resource that advances research capacity to explore subcellular location and dynamics

Formalin-fixed paraffin-embedded (FFPE) tissue is the dominant clinical pathology resource yet whether it faithfully preserves organ signalling biology and supports directional regulatory analysis remains unquantified. We generated a phosphoproteome map from eight healthy rat organs, separating preservation effects from biological variation. Using mass spectrometry, we quantified 54,710 phosphosites on 5,994 proteins across receptors, kinase cascades and nuclear regulators. Organ-specific phosphosite signatures matched known physiological and proliferative states. Paired antagonistic phosphosites converted into "activating-minus-inhibitory" indices that quantified net tissue-specific pathway activity, while a "kinase-by-organ activity" matrix resolved functional hierarchies. Joint analysis with an external fresh-frozen phosphoproteome dataset yielded 58,631 phosphosites total, recovering 86% of the 28,888 sites detected in the frozen dataset. Organ identity explained over 92% of the total variance after batch correction, versus under 0.5% for preservation method. Per-organ phosphosite intensities agreed closely between preservation modes except in brain. This establishes that archived pathology tissue supports biologically faithful phosphoproteome analysis at organ, pathway, and site resolution, providing a framework for retrospective signalling studies in clinical archives. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/741173v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@7ec1forg.highwire.dtl.DTLVardef@1f22bcorg.highwire.dtl.DTLVardef@217f5forg.highwire.dtl.DTLVardef@1316b61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes - suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.

High expression of heterologous proteins in microbial cell factories frequently triggers a severe burden due to reallocation of finite cellular proteome. Conventional constraint-based models struggle to predict these resource shifts ab initio without relying on condition-specific omics data. To bridge this gap, we developed the Hybrid Transcription-Translation (HyTT) framework, combining multivariate adaptive regression splines (MARS) with enzyme-constrained metabolic models by enforcing an 80S ribosome integrity constraint. Cast as a mixed-integer linear programming problem, HyTT mathematically couples macroscopic spatial boundaries with microscopic, sequence-derived translational costs based on a bisection search. Validation against steady-state chemostat quantitative proteomics data demonstrated the superior capability of HyTT over contenders in predicting system-wide resource (re)allocation in Saccharomyces cerevisiae. Operating ab initio, the framework doubled the predictive accuracy of protein abundances (Pearson r=0.501) compared to conventional models, successfully segregating the minimal essential proteome from the cellular reserve pool. Crucially, HyTT autonomously captures complex stress responses vital for metabolic engineering. Upon simulating a 15% recombinant protein burden, the framework accurately predicted systemic growth retardation, decrease of ribosomal portion of the proteome, and surge of ethanol production, in line with the Crabtree effect. System-level analysis uncovered that cells adapt to restricted proteomic capacity through non-uniform metabolic rerouting, downregulating respiratory complexes in favor of high-turnover glycolytic enzymes, and relying on ribosomal paralog switching to minimize sequence-specific assembly costs. Ultimately, HyTT provides a computationally agile, sequence-driven platform for decoding dynamic resource reallocation, offering a powerful predictive tool to navigate metabolic trade-offs and guide rational strain design without requiring condition-specific multi-omics inputs.

The balance between how much human tumors recapitulate fetal tissue programs versus lose adult tissue identity remains unresolved. I used audited vibe coding, a human-mediated, cross-model critique-and-refinement workflow, to re-analyze a public pan-cancer proteomic atlas. A primary large language model wrote and executed the analysis under scientific direction, while a separate model family audited the code, outputs and claims; findings were returned for correction across seven versioned releases. Among 229 tumor-adjacent pairs in seven organs, tumor-minus-adjacent proteomic change partially aligned with reverse fetal-to-adult maturation (organ-balanced cosine, 0.240; 95% interval, 0.138 to 0.335), with positive alignment in 189 of 229 patients (82.5%). The organ-balanced projection coefficient was 0.195 (95% interval, 0.069 to 0.244), indicating movement along only part of the developmental distance. Although reverse maturation overlapped adult-identity loss, a positive developmental component remained after identity loss entered first (0.203; 95% interval, 0.129 to 0.239). Suppression of adult-high proteins contributed to more positive alignment than reactivation of fetal-high proteins. The vibe coding audits identified substantive defects. A common-mask correction reduced the matched-organ advantage from 0.074 to 0.059; a missing-value correction barely changed aggregate geometry but replaced 5 of the top 40 liver contributors; and coupled resampling repaired uncertainty accounting without changing patient scores. As with any single report, the "vibe reanalysis" biological results are candidate discoveries pending independent replication. The workflow is a single feasibility case, not a reliability benchmark, and shows how conversationally generated analysis can be made more inspectable when model-written code is treated as untrusted, versioned and subject to separate-model critique and executable checks.

The limited treatment options available for the estimated 38% of adults worldwide affected by Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) are largely due to an incomplete understanding of the complex molecular networks underlying disease pathogenesis. To dissect the genetic architecture and proteomic regulation underlying MASLD, we generated a genetically diverse mouse cohort through a four-way cross of founder strains with varying susceptibility to liver disease, producing 444 F2 mice with a spectrum of phenotypes and genotypes. Utilizing deep proteomic profiling of the livers of this population, we identified quantitative trait loci (QTL) for 2,652 proteins, spanning over 4,000 unique genomic loci, and distinguished cis- and trans-acting regulatory mechanisms. Integrating proteomic, genomic, and phenotypic data reveals key regulatory loci and candidate proteins influencing disease progression, creating a mineable proteogenomic resource for MASLD research. We utilize this resource to identify the E3 ubiquitin ligase Ubr1 as a candidate central regulator connecting proteostasis and lipid metabolism, with genetic polymorphisms that may alter its abundance and predispose to metabolic dysfunction. This study demonstrates how high-throughput, deep proteome profiling integrated with QTL mapping can reveal complex gene-protein networks governing MASLD susceptibility and progression, offering novel insights for biomarker discovery and therapeutic targeting.

Pulmonary arterial hypertension (PAH) represents a heterogeneous group of disorders that involves complex molecular dysregulations, which are not fully captured by single-omics analyses. We apply our network-based multi-omics analysis framework, DrDimont, to transcriptomic, proteomic, phosphoproteomic, and kinase screening data from lung microvascular endothelial cells of PAH patients and controls. Thereby, we extend the functionality of DrDimont to incorporate kinase-kinase interactions during the construction of condition-specific multi-omics networks. Kinase interactions are inferred from phosphorylations of screened substrates that are weighted by kinase-substrate predictions. Differential interaction scores from the network-based analysis between PAH and control uncover alterations centered on kinases, in particular top hits relating to MAPK signaling, such as MAPK13, MAP2K, or upstream IRAK1, and other MAPK/MAP2K family members. Further highly differential nodes were ACADSB, GPX7, DSE (for proteins), and AIM1, LY96, CHSY3 (for mRNAs). When prioritizing drug candidates by mapping drug targets onto the differential network, we find high scores for the drug tacrolimus (FK506) and several anti-neoplastic MAPK inhibitors (e.g., selumetinib, trametinib), as well as agents acting on general proliferation via (mitochondrial) DNA transcription (e.g., epirubicin, topotecan). Integrating kinase activity screens into our explainable multi-omics network-based analyses reveals kinase-centered alterations and therapeutic hypotheses in PAH that complement single-layer classical differential expression analyses.

Viral infection can coactivate interferon (IFN)--JAK/STAT1 signalling and the p53--Mdm2 stress-response pathway, two modules that jointly shape antiviral defence and cell-fate decisions. Here, we focus on viral infection contexts capable of inducing genotoxic stress associated with DNA double-strand breaks, thereby triggering oscillatory or sustained p53--Mdm2 dynamics. Whether p53 acts merely as a parallel stress pathway, or actively reshapes how an activated JAK/STAT1 response is temporally decoded and functionally routed, remains unclear. We develop a coupled ordinary [ndash]differential-equation model linking an IFN{gamma}centred JAK/STAT1 core, a p53--Mdm2 module, downstream antiviral and apoptotic effectors, and a coarse-grained viral-burden layer, with p53 regulation placed downstream of STAT1 activation. We find that p53 does not simply increase nuclear STAT1 availability; it redistributes the response towards DNA-bound STAT1 persistence, transcriptional memory and STAT1-driven feedback, producing a persistence--recovery trade-off in which prior p53 stress prolongs the transcriptionally active STAT1 state but delays re-inducibility after repeated IFN stimulation. When IFN and p53-associated stress are both driven by viral burden, p53 is not a uniform amplifier of host defence: p53 preactivation strengthens the upstream memory layer, but downstream effectors buffer rather than mirror this priming. The model further separates antiviral-state engagement from realised viral control: strong effector activation does not guarantee suppression of poorly sensitive viral classes, whereas sensitive viral classes can be cleared before apoptosis. The origin of the stimulus also matters: exogenous IFN or p53 stimulation allows us to assess the host's intrinsic response capacity, whereas virus-induced IFN and p53 stress remain coupled to viral persistence. Persistent viral burden thus emerges as the dynamical link between IFN induction, p53 stress-memory, antiviral maintenance, viral control and the choice between JAK/STAT--IRF1-associated, p53-autonomous or dual apoptotic routing.

Plasma proteomics assays aim to capture biologically interpretable circulating protein signals. Here we systematically assess Olink Explore targets with exceptionally low variance explainability by integrating genetic associations, cross-platform concordance and tissue and peptide atlases. We identify a subset of protein targets likely lacking robust plasma signals, highlighting assay- and tissue-specific limitations with implications for panel design, statistical power and interpretation of large-scale proteomics studies.

Cancer cells rapidly rewire their metabolism, from efficient energy production toward anabolic processes, to sustain uncontrolled growth. Decoding such metabolic shifts is essential for uncovering novel therapeutic targets. To map systems-level metabolic changes across cancer types, we built context-specific genome-scale metabolic models for eight tissues (lung, thyroid, stomach, prostate, liver, kidney, colon, and breast) using gene expression data from The Cancer Genome Atlas (TCGA). Applying constraint-based modelling, we then identified differentially regulated pathways through flux enrichment analysis, revealing tissue-specific rewiring: branched chain amino acid metabolism was suppressed in breast cancer; sphingolipid metabolism was downregulated in colon, kidney, and thyroid but upregulated in breast. We further propose a model-driven pipeline to identify and characterise metabolic vulnerabilities. We first identify synthetic lethal reactions in normal tissues and their corresponding single lethal counterparts in cancers, thereby enabling the identification of metabolic "collateral lethal" reaction pairs for each cancer. Model-predicted collateral lethal gene pairs, including CMPK1-AK in colon, ALDOA-PGD in prostate, and SLC25A26-UQCRB in liver models, were supported through computational validation using DepMap data on gene essentiality. Subsequently, we show how to interpret metabolic rewiring in cancer tissues while accounting for any collateral lethal pairs. In summary, our results establish a systemic framework for decoding metabolic rewiring and synthetic lethal vulnerabilities in cancer.