Biology subjects

Jagadeesan, P.

Publications and source records attributed to Jagadeesan, P..

3 recordsLinked to original sources

bioRxiv2024

Translational profiling of stress-induced small proteins uncovers an unexpected connection among distinct signaling systems

Bacterial small proteins ([≤] 50 amino acids) are an emerging class of regulators that modulate the activity of signaling networks that enable bacterial adaptation to stress. The Escherichia coli genome encodes at least 150 small proteins, most of which are functionally uncharacterized. We identified and characterized 17 small proteins induced in E. coli during magnesium (Mg2+) starvation using ribosome profiling, RNA sequencing, and transcriptional reporter assays. Several of these were transcriptionally activated by the PhoQ-PhoP two-component signaling system, which is crucial for Mg2+ homeostasis. Deletion or overexpression of some of these small proteins led to growth defects and changes in cell size under low-Mg2+ conditions, indicating physiological roles in stress adaptation. The small transmembrane protein YoaI, which was transcriptionally induced by the phosphate-responsive PhoR-PhoB signaling pathway, increased in abundance under Mg2+ limitation independently of yoaI transcription or PhoQ-PhoP signaling. YoaI activated a third signaling system, EnvZ-OmpR, which mediates responses to osmotic stress. Overall, this study establishes an initial framework for understanding how small proteins contribute to bacterial stress adaptation by facilitating cross-talk between different signaling systems. Our results suggest these proteins play broader roles in coordinating stress responses, reflecting the interconnected nature of cellular stress networks rather than strictly compartmentalized pathways responding to specific stressors.

microbiology↗

bioRxiv2023

A generalised method for experiment design and model selection in the Bayesian framework

Computational modelling of dynamical systems often involves many free parameters estimated from experimental data. The information gained from an experiment plays a crucial role in the goodness of predictions and parameter estimates. Optimal Experiment Design (OED) is being used to choose an experiment containing maximum information from a set of possible experiments. This work presents a novel Bayesian Optimal Experiment Design principle for generalised parameter distributions. The generalization is archived by extending the {beta}-information gain to the discrete distributions. The {beta}-information gain is based on what is known as the Bhattacharyya coefficient. We show that maximising the {beta}-information gain is equivalent to maximising the angle between the prior and posterior distributions, reducing the posteriors uncertainty. Further, we apply the proposed BOED criteria for two realistic experiment designs in systems biology. Firstly, we use the {beta} information gain to choose the best measurement method for parameter estimation in a Hes1 transcription model. The measurement method selected by the {beta}-information gain results in the minimum mean square error of the parameter estimates. In the second case, we employ the proposed information gained to select an optimal sampling schedule for HIV 1 2 LTR model. The sampling schedule chosen by the presented method reduces both prediction and parameter uncertainty. Finally, we propose a novel method for model selection using {beta} information gain and demonstrate the working of the proposed method in the model selection in compartmental models.

systems biology↗

bioRxiv2022

Sloppiness: fundamental study, new formalism and quantification

Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection. Author summary

systems biology↗