Biology subjects

Tikader, B.

Publications and source records attributed to Tikader, B..

5 recordsLinked to original sources

bioRxiv2026

Early detection of sudden transitions in Notch signalling

Identifying sudden transitions during phenotypic decision-making of complex biological systems can be crucial for our ability to control a cellular state. Yet, prior determination of these sudden transitions or tipping points remains challenging, as biological systems often exhibit only subtle early changes, which are often masked by inherent noise or rapid transition dynamics. Using Notch signaling as a model, we systematically analyze dynamical transitions in Notch-Delta (ND), Notch-Delta-Jagged (NDJ), and Fringe-mediated NDJ systems for both one and two-cell contexts. In the one-cell ND system, critical slowing down (CSD)-based early warning signals (EWSs) reliably capture transitions between sender (S) and receiver (R) states and remain robust to variation in forcing rate. We further find that flickering is a precursor to transitions in one-cell NDJ system. In contrast, flickering does not occur in the two-cell Notch model due to the presence of a supercritical bifurcation. Our analysis also offers insight into how NICD (Notch Intracellular Domain)-driven and Fringe-mediated asymmetries, along with the strength of external signals, control the emergence of flickering. Overall, this study identifies sudden transitions in Notch signaling under demographic noise and can be extended to other noisy biological systems, with potential applications in drug development and targeted therapeutic interventions.

systems biology↗

bioRxiv2025

Androgen receptor activation stabilizes a hybrid epithelial/mesenchymal phenotype in presence of Notch-Jagged signaling

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of Epithelial-Mesenchymal Transition (EMT) with the Androgen Receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer. Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse out-comes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting prostate cancer aggressiveness.

systems biology↗

bioRxiv2023

Understanding the dynamics of complex in-situ enzyme-catalyzed reactions undergoing mechanical stress

Understanding the diversity in the enzymatic mechanism have utmost importance as it can temporally control the catalytic efficiency. Recent literature suggests that by influencing mechanical properties of hybrid materials, the catalytic efficiency of the enzymatic reactions can be altered significantly. Here, taking a computational and experimental approach, we have dig out the fate of the kinetics of enzyme reaction systems exhibiting relatively complex mechanism than usual Michaelis Menten kinetics involving multiple substrate/enzyme/enzyme-substrate complex. We have developed a numerical recipe improvising stochastic reaction-diffusion approach to explore the role of mechanical stress like compression-decompression cycle (C-D) on modulating the output of enzymatic reaction. The proposed methodology can be used as a theoretical tool to understand how to enhance the catalytic activity and setup appropriate reaction conditions by efficiently using the catalytic cycles. Hence, our methodology will be crucial to identify the most effective strategy to efficiently convert substrate into product in this type of mechanoresponsive materials, which will enable future development of cost-effective biomaterials. In future, the insights gained from this work may find enormous application in drug delivery, tissue engineering, bio-sensing and bio-catalysis.

systems biology↗

bioRxiv2021

Unraveling the intracellular cross-talk governing the balance between TNFα mediated survival and apoptosis signaling

Tumor necrosis factor alpha (TNF), a pleiotropic cytokine, helps maintain a balance between proliferation and apoptosis in normal cells. This balance is often sacrificed in a diseased cell, such as that of a cancer, by preferring survival phenotype over apoptosis. Restoring this balance requires a detailed understanding of the causal intracellular mechanisms that govern TNF stimulated apoptotic response. In this study, we use a systems biology approach to unravel the interplay between the intracellular signaling markers that orchestrate apoptosis levels. Our approach deciphered the synergism between the early intracellular markers phosphorylated JNK (pJNK) and phosphorylated AKT (pAKT) that modulate the activation of Caspase3, an important apoptotic regulator. We demonstrate that this synergism depends critically on the survival pathway signaling mediated by NF{kappa}B which plays a dominant role in controlling the extent of the overall apoptotic response. By systematic inhibition of the signaling markers, we establish that the dynamic cross-talk between the pJNK and pAKT transients directs the apoptosis phenotype via accumulated Caspase3 response. Interestingly, superposition of the semi-quantitative correlation between apoptosis and Caspase3 transient levels on the proposed TNF network model permits quantification of the dynamic apoptotic response under different stimulation conditions. Thus, the predictive model can be leveraged towards arriving at useful insights that can identify potential targeted therapeutic strategies for altering apoptotic response.

systems biology↗

bioRxiv2020

Unraveling the aggregation dynamics of amyloidogenesis: A data-driven mathematical modeling approach

Proteins often get misfolded into highly ordered amyloid aggregates that are found to be associated with various neurodegenerative diseases. However, the dynamical events that lead to such self organization of different types of proteins are still poorly understood due to the experimentally untractable complex molecular mechanism, which governs the process of amyloidogenesis. Herein, we propose a generic and data-driven mathematical modeling approach that enables us to decipher a most probable molecular mechanism responsible for amyloidogensis in a context dependent manner. The preferred model efficiently elucidates various aspects of amyloid forming kinetics as a function of initial protein concentration, and qualitatively predicts the dynamics of amyloidogenesis for point mutated proteins. Importantly, the model analysis reveals that intermediate aggregates formed just after the primary necleation steps plays a vital role in dictating the nature of amyloid forming kinetics. Moreover, our model makes experimentally feasible insightful predictions to formulate better therapeutic measures in future to counter unwanted amyloidogenesis by just fine-tuning the molecular interactions related to amyloidogenesis. Author SummaryIn cells, proteins mostly function by being in a native folded conformation. However, under certain circumstances, some of these proteins undergo high degree of aggregation and eventually misfold to form highly stable amyloid aggregates that are often involved in different neurodegenerative disease progression and pathogenesis. To develop appropriate therapeutic strategies to resist such oligomerazation of protein, it is imperative to unravel the molecular interactions that essentially organize the dynamical events of amyloidogenesis. We have proposed a generic computational modeling framework to elucidate the important molecular events that govern the dynamics of amyloid formation. Our model reconciles diverse set of experimental observations including different mutant phenotyes from a dynamical perspective, and predicts possible ways to control the kinetics of amyloid formation experimentally. In future, these insights will be helpful to design drugs to treat patients with neurodegenerative disorders. Importantly, our model, in general, will be quite useful to figure out the important molecular events that are orchestrating the amyloidogenesis of different amyloid forming proteins.

systems biology↗