Biology subjects

Narayanan, B.

Publications and source records attributed to Narayanan, B..

7 recordsLinked to original sources

bioRxiv2026

Scalable biophysical constraints for physiologically consistent metabolic states

Systems biology aims to develop predictive models that connect molecular mechanisms to cellular behavior. Genome-scale metabolic models are among the most widely used frameworks for integrating stoichiometric, thermodynamic, and omics-derived information to predict feasible metabolic phenotypes. However, cellular metabolism operates on timescales governed by enzyme kinetics and by the relationship between metabolic fluxes and metabolite pool sizes. In steady-state metabolic models, this relationship can be expressed in terms of metabolite turnover rates, defined as flux-to-pool-size ratios that quantify how rapidly metabolite pools are renewed. As a result, physiologically consistent steady-state solutions should not only satisfy mass-balance and thermodynamic constraints but also exhibit turnover rates consistent with enzyme-mediated cellular dynamics. Current constraint-based approaches can admit many steady-state flux-concentration states that do not account for turnover rates, resulting in phenotypes incompatible with realistic metabolic dynamics, even when multiple types of data are imposed. Here, we present METEOR-K, an optimization framework that links steady-state metabolic fluxes to metabolite concentrations via turnover rate constraints to identify dynamically plausible flux-concentration reference states. Because these constraints reshape the feasible solution space, we also introduce turnover-rate-aware sampling strategies to efficiently explore the resulting feasible region. We applied METEOR-K to models of increasing scope and scale, including a reduced glycolysis pathway, anaerobic E. coli, and near-genome-scale ovarian cancer models. METEOR-K narrowed the admissible steady-state solution space, reduced uncertainty in feasible flux-concentration states, and improved local dynamic behavior. In nonlinear ODE simulations of bioreactor cultivation and drug-response scenarios, METEOR-K-derived states produced intracellular response times compatible with growth-supporting metabolic operation and perturbation recovery. Overall, these results establish metabolite turnover rates as scalable biophysical constraints that improve the physiological consistency of steady-state metabolic modeling. Because turnover rates encode flux-to-pool-size timescale constraints, METEOR-K moves part of physiological-consistency assessment upstream of kinetic parameterization, yielding better-suited flux-concentration reference states for kinetic modeling and dynamic prediction.

systems 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

AAV-mediated Delivery of Plakophilin-2a Arrests Progression of Arrhythmogenic Right Ventricular Cardiomyopathy in Murine Hearts: Preclinical Evidence Supporting Gene Therapy in Humans

BackgroundPathogenic variants in plakophilin-2 (PKP2) cause arrhythmogenic right ventricular cardiomyopathy (ARVC), a disease characterized by life-threatening arrhythmias and progressive cardiomyopathy leading to heart failure. No effective medical therapy is available to prevent and/or arrest the disease. We tested the hypothesis that AAV-mediated delivery of the human PKP2 gene to an adult mammalian heart deficient in PKP2 can arrest disease progression and significantly prolong survival. MethodsExperiments were carried out using a cardiac-specific, tamoxifen (TAM)-activated PKP2 knockout murine model (PKP2-cKO). The potential therapeutic, AAVrh.74-PKP2a (RP-A601), is a recombinant AAVrh.74 gene therapy viral vector encoding the human PKP2 variant A (PKP2a). AAVrh.74-PKP2a was delivered to adult mice by a single tail vein injection either before or after TAM-activated PKP2-cKO. PKP2 expression was confirmed by molecular and histopathologic analyses. Cardiac function and disease progression were monitored by survival analyses, echocardiography and electrocardiography. ResultsConsistent with prior findings, loss of PKP2 expression caused 100% mortality within 50 days after TAM injection. In contrast, AAVrh.74-PKP2a-mediated PKP2a expression resulted in 100% survival for more than 5 months (at study termination). Echocardiographic analysis revealed that AAVrh.74-PKP2a prevented right ventricle dilation, arrested left ventricle functional decline, and mitigated arrhythmia burden. Molecular and histological analysis showed AAVrh.74-PKP2a- mediated transgene mRNA and protein expression and appropriate PKP2 localization at the cardiomyocyte intercalated disc. Importantly, therapeutic benefit was shown in mice receiving AAVrh.74-PKP2a after disease onset. ConclusionThese preclinical data demonstrate the potential for AAVrh.74-PKP2a (RP-A601) as a therapeutic for PKP2-related ARVC in both early and more advanced stages of disease.

physiology↗

bioRxiv2023

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine practice to gain insights into cellular processes, yet deciphering such massive datasets and determining intracellular metabolic states remains challenging. Kinetic models of metabolism play a critical role in integrating omics data, as they provide explicit connections between metabolite concentrations, metabolic fluxes, and enzyme levels. Nevertheless, the challenges associated with determining the kinetic parameters that underlie cellular physiology pose significant obstacles to the broader acceptance and adoption of these models within the research community. Here, we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration and consolidation of diverse omics data and other relevant information, like extracellular medium composition, physicochemical data, and expertise of domain specialists, we show that the proposed framework accurately characterizes unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations in E. colis metabolic network. Moreover, we show that RENAISSANCE successfully estimates missing kinetic parameters and reconciles them with sparse and noisy experimental data, resulting in a substantial reduction in parameter uncertainty and a notable improvement in the accuracy and reliability of the parameter estimates. The proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.

systems biology↗

bioRxiv2022

Rational strain design with minimal phenotype perturbation

Increased availability of multi-omics data has facilitated the characterization of metabolic phenotypes of cellular organisms. However, devising genetic interventions that drive cellular organisms toward the desired phenotype remains challenging in terms of time, cost, and resources. Kinetic models, in particular, hold great potential for accelerating this task since they can simulate the metabolic responses to environmental and genetic perturbations. Although the challenges in building kinetic models have been well-documented, there exists no consensus on how to use these models for strain design in a computationally tractable manner. A straightforward approach that exhaustively simulates and evaluates putative designs would be impractical, considering the intensive computational requirements when targeting multiple enzymes. We address this issue by introducing a framework to efficiently scout the space of designs while respecting the physiological requirements of the cell. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions in a scalable manner while accounting for the network effects of these perturbations. More importantly, the framework ensures the engineered strains robustness by maintaining its phenotype close to that of the reference strain. We use the framework to improve the production of anthranilate, a precursor for pharmaceutical drugs, in E. coli. The devised strategies include eight previously experimentally validated targets and also novel designs suitable for experimental implementation. As an essential part of the future design-build-test-learn cycles, we anticipate that this novel framework will enable high throughput designs and accelerated turnover in biotechnological processes.

systems biology↗

bioRxiv2022

Polypyrimidine Tract-Binding Protein 1 (PTBP1) regulates CD4 T cell Activation independent of its role in proliferation

Our previous work found that the RNA binding protein polypyrimidine tract-binding protein (PTBP1) is critical for regulating multiple events in T cell activation including changes in proliferation, and expression of activation markers and cytokines. These changes corresponded to the regulation of the ERK1/2 and NF-{kappa}B pathways as well as through changes in steady-state RNA levels. Because proliferation is critical for driving T cell activation, it was unclear whether PTBP1 was required for optimal activation per se or whether changes were secondary to a requirement for initiating/sustaining proliferation. To address this question, the human T cell lymphoma cell line, Jurkat, which recapitulates many of the molecular events of TCR-induced activation, was used to understand how PTBP1 impacts early events in T cell activation with ongoing proliferation. Using two phenotypically distinct Jurkat subclones (D1.1 and B2.7), we first profiled global RNA expression patterns using RNAseq analysis and found marked differences between the two cell lines with the D1.1 line giving a more antigen-experienced phenotype. Reducing PTBP1 by shPTB expression, to 60% WT levels resulted in no significant decrease in proliferation in the two subclones. However, we observed that PTBP1 was required for both optimal expression of activation markers, CD25, CD38, CD69, and CD40L, and signaling through the ERK1/2, P38 and AKT pathways. Importantly, limiting PTBP1 had different effects on the activation signals for each cell line suggesting that the differentiation state of the cell is a critical factor in understanding the role of PTBP1 in T cell activation. This was further reinforced by our finding that PTBP1 regulated distinct groups of genes specific for each line. Together, our findings suggest that PTBP1 regulates specific T cell activation responses independent of its role in proliferation and that the initial phenotype of the T cell plays an essential role in the dependency of the cell on PTBP1 for driving these changes.

immunology↗

bioRxiv2017

Common variants of NRXN1, LRP1B and RORA are associated with increased ventricular volumes in psychosis - GWAS findings from the B-SNIP deep phenotyping study

Schizophrenia, Schizoaffective, and Bipolar Disorders share common illness traits, intermediate phenotypes and a partially overlapping polygenic basis. We performed GWAS on deep phenotyping data, including structural MRI and DTI, clinical, and behavioral scales from 1,115 cases and controls. Significant associations were observed with two cerebrospinal fluid volumes: the temporal horn of left lateral ventricle was associated with NRXN1, and the volume of the cavum septum pellucidum was associated with LRP1B and RORA. Both volumes were associated with illness. Suggestive associations were observed with local gyrification indices, fractional anisotropy and age at onset. The deep phenotyping approach allowed unexpected genetic sharing to be found between phenotypes, including temporal horn of left lateral ventricle and age at onset.

genetics↗