Biology subjects

Masid, M.

Publications and source records attributed to Masid, M..

8 recordsLinked to original sources

bioRxiv2026

Mechanistic reconstruction of receptor-to-transcription factor signaling integrating prior knowledge and omics

Omics profiling is ubiquitous, yet resolving how signal transduction shapes cellular physiology remains challenging. Most analyses interpret omics data by summarizing catalogs of pathways or by fitting networks to observations. Here we present SIGMA, a framework that converts curated prior knowledge into causally interpretable, elementally balanced signal-transduction cascades that connect sources to targets. By enforcing elemental balance, SIGMA links processes across pathway catalogs and reveals crosstalk beyond canonical definitions. It enumerates alternative cascades to expose parallel routing and identify context-dependent essential steps. We introduce balanced cascade enrichment analysis to map omics data onto alternative cascades and rank them by context-specific molecular support. Using SIGMA, we reconstructed signal flow from receptors to transcription factors that regulate metabolism, uncovering mechanisms that control metabolic reprogramming. In CD8+ T cells, SIGMA identified cascades connecting TGF-{beta} receptors to SP1 and indicated weakening of this axis in exhaustion. Overall, this framework enables mechanistic interpretation of signaling across datasets and guides the design of causal perturbations.

Systems Biology↗

bioRxiv2025

Computational Inference of Metabolic Programs: A Case Study Analyzing the Effect of BRCA1 Loss

Metabolic reprogramming is a hallmark of cancer, yet how oncogenic drivers shape tumor metabolism across disease progression remains incompletely understood. In this study, we present iMSEA (in silico Metabolic State and Enrichment Analysis), a computational framework that infers flux-based metabolic states from omics profiles. Applying iMSEA to isogenic BRCA1-mutant and BRCA1-wild-type ovarian cancer cells, we identified a shift toward glycolysis, nucleotide biosynthesis, and redox imbalance, coupled with impaired oxidative phosphorylation. These predictions were validated with metabolomics, Seahorse, and SCENITH assays, demonstrating the accuracy of our approach. Extending the analysis to homologous recombination deficient patient tumors at single-cell resolution, we found that BRCA1-deficient cancers display heightened metabolic activity and site-specific adaptations, including altered central carbon fluxes, mitochondrial function, nucleotide biosynthesis, and lipid metabolism. By linking transcriptional programs to functional metabolic states, iMSEA reveals hidden metabolic liabilities in BRCA1-deficient ovarian cancer and provides a broadly applicable strategy for dissecting metabolic heterogeneity and therapeutic vulnerabilities in cancer.

systems biology↗

bioRxiv2025

Multi-omics-driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer

BRCA1-deficient ovarian cancer cells undergo extensive metabolic reprogramming, yet the network-level dynamics underlying their proliferation and treatment response remain poorly resolved. Here, we construct large-scale multi-omics-driven kinetic model populations of ovarian cancer metabolism to track how tumor cells adapt to changes in nutrient use, energy production, and metabolite dynamics over time. Across BRCA1 wild-type and mutant cells, these models expose distinct metabolic strategies shaped by transcriptional regulation and prioritize 28 enzyme-mediated vulnerabilities, including 24 linked to existing experimental or approved drugs and 4 previously uncharacterized targets in nucleotide and lipid synthesis. They further recapitulate a ceramide-linked metabolic stress signature shared across diverse chemotherapies. Mechanistic analysis traces the effects of BRCA1 loss to transcription-factor-mediated shifts in enzyme activity, outlining regulatory routes for network-level rewiring. Beyond ovarian cancer, this framework offers a generalizable blueprint for predicting metabolic vulnerabilities, drug responses, and adaptive mechanisms across diverse cancer and metabolic disease contexts. By coupling dynamic metabolism to therapeutic prediction, it delivers actionable hypotheses for biomarker discovery, patient stratification, target prioritization, and precision metabolic medicine.

cancer biology↗

bioRxiv2024

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production

The use of kinetic models of metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization of cell factories. This is primarily due to difficulties in constructing kinetic models capable of capturing the complexities of the fermentation conditions. Building on recent advances in kinetic-model-based strain design, we present the rational metabolic engineering of an S. cerevisiae strain designed to overproduce p-coumaric acid (p-CA), an aromatic amino acid with valuable nutritional and therapeutic applications. To this end, we built nine kinetic models of an already engineered p-CA-producing strain by integrating different types of omics data and imposing physiological constraints pertinent to the strain. These nine models contained 268 mass balances involved in 303 reactions across four compartments and could reproduce the dynamic characteristics of the strain in batch fermentation simulations. We used constraint-based metabolic control analysis to generate combinatorial designs of 3 enzyme manipulations that could increase p-CA yield on glucose while ensuring that the resulting engineering strains did not deviate far from the reference phenotype. Among 39 unique designs, 10 proved robust across the phenotypic uncertainty of the models and could reliably increase p-CA yield in nonlinear simulations. We implemented these top 10 designs in a batch fermentation setting using a promoter-swapping strategy for down-regulations and plasmids for up-regulations. Eight out of the ten designs produced higher p-CA titers than the reference strain, with 19 - 32% increases at the end of fermentation. All eight designs also maintained at least 90% of the reference strains growth rate, indicating the critical role of the phenotypic constraint. The high experimental success of our in-silico predictions lays the foundation for accelerated design-build-test-learn cycles enabled by large-scale kinetic modeling.

systems biology↗

bioRxiv2023

Transcriptional reprogramming by IL-2 variant generates metabolically active stem-like T cells

Interleukin-2 receptor (IL-2R)-mediated intracellular signaling is a key regulator of T-cell fate decisions. While the potent signals generated by IL-2 engagement execute effector differentiation, elevated metabolic activities and rapid cellular expansion, IL-15 binding induces a stemness/memory phenotype and a quiescent metabolic state. Here, we demonstrate that weak but sustained signaling generated by a non-IL-2R-binding variant of IL-2 (IL-2v) drive proliferation/metabolic and stemness transcriptional programs, thereby reprogramming CD8+ T cells into a hybrid metabolically active stem-like state. We further show that IL-2v-induced T cells are capable of superior engraftment, persistence, and tumor control when utilized in adoptive cell therapy. Taken together, our study highlights the ability to fine-tune cytokine engagement of cognate receptors in order to generate therapeutically relevant T-cell states and further reveals the metabolic plasticity of the T-cell memory program.

systems biology↗

bioRxiv2022

Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models

MotivationLarge-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides the means to implement, parametrize and analyze large-scale kinetic models intuitively and efficiently. ResultsWe present a Python package (SKiMpy) bridging this gap by implementing an efficient kinetic modeling toolbox for the semiautomatic generation and analysis of large-scale kinetic models for various biological domains such as signaling, gene expression, and metabolism. Furthermore, we demonstrate how this toolbox is used to parameterize kinetic models around a steady-state reference efficiently. Finally, we show how SKiMpy can imple-ment multispecies bioreactor simulations to assess biotechnological processes. AvailabilityThe software is available as a Python 3 package on GitHub: https://github.com/EPFL-LCSB/SKiMpy, along with adequate documentation. Contactvassily.hatzimanikatis@epfl.ch

bioinformatics↗

bioRxiv2021

Nitrogen partitioning between branched-chain amino acids and urea cycle enzymes sustains renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage. HighlightsO_LIBranched-chain amino acids catabolism is reprogrammed in ccRCC tumors C_LIO_LIBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCC C_LIO_LIAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginine C_LIO_LIASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levels C_LIO_LISilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells C_LI

cancer biology↗

bioRxiv2021

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the current most accurate models of biological systems include metabolism and expression (ME-models), and Expression and Thermodynamics FLux (ETFL) is one such formulation that efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To therefore adapt this ME-model for Saccharomyces cerevisiae, we herein developed yETFL. To do this, we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the predictive ability of yETFL to capture maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the extended ETFL formulation can be applied to ME-model development for a wide range of eukaryotic organisms. The utility of these ME-models can be extended into academic and industrial research.

systems biology↗