CRFIEKF: An Efficient Parameter Estimation Method for Biochemical Pathway Modeling

Bakshi, Abhisek; Dasgupta, Abhijit; Chakrabarti, Amlan; De, Rajat K.

doi:10.2139/ssrn.4071343

Cited by 1 publication

(3 citation statements)

References 39 publications

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“…1 PID controller Figure 2. The pipeline of the methodology: a) The formulation of the state space model of the signaling pathway (Figure 1); b) Estimation of kinetic parameters involved in the pathway 8 and incorporation of necessary perturbation to meet the state of CS in the pathway; c) Application of PID controller in identifying possible drug target; d) Deep learning model 12 for predicting drug-target interaction among FDA approved drugs and target protein(s); e) Molecular docking evaluating the potentiality of the possible drug(s); f) Finding the best probable drugs combination to combat CS.…”

Section: State Space Modellingmentioning

confidence: 99%

“…Numerous kinetic parameters, encompassing binding rates and feedback constants, were derived for the state-space model embodying the specific interest pathway's inherent behavior. It was accomplished by leveraging the latest technique 8 . The approach entailed treating these kinetic parameters as the system's extended state, maintaining their constancy throughout time.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…We employ a control theory-based approach to model these pathways 6,7 , employing an ordinary differential equation (ODE)-based state-space model (Figure 1) that emulates baseline behavior before the viral assault. Accurate parameter estimation is crucial, and we employ the Kalman filter-based method for approximating these values, acknowledging the intricate relationships within the investigated pathways 8 . Once the model convincingly mirrors normal pathway dynamics as corroborated by existing literature 9 , we introduce calibrated perturbations (e.g., elevated ACE2 expressions) inspired by prior research 10 to induce a coronavirus-triggered cytokine storm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

Bakshi,

Gangopadhyay,

Basak

et al. 2023

Preprint

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The recent global COVID-19 outbreak, attributed by the World Health Organization to the rapid spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), underscores the need for an extensive exploration of virological intricacies, fundamental pathophysiology, and immune responses. This investigation is vital to unearth potential therapeutic avenues and preventive strategies. Our study delves into the intricate interaction between SARS-CoV-2 and the immune system, coupled with exploring therapeutic interventions to counteract dysfunctional immune responses like the ‘cytokine storm’ (CS), a driver of disease progression. Understanding these immunological dimensions informs the design of precise multiepitopetargeted peptide vaccines using advanced immunoinformatics and equips us with tools to confront the cytokine storm. Employing a control theory-based approach, we scrutinize the perturbed behavior of key proteins associated with cytokine storm during COVID-19 infection. Our findings support ACE2 activation as a potential drug target for CS control and confirm AT1R inhibition as an alternative strategy. Leveraging deep learning, we identify potential drugs to individually target ACE2 and AT1R, with Lomefloxacin and Fostamatinib emerging as standout options due to their close interaction with ACE2. Their stability within the protein-drug complex suggests superior efficacy among many drugs from our deep-learning analysis. Moreover, there is a significant scope for optimization in fine-tuning protein-drug interactions. Strong binding alone may not be the sole determining factor for potential drugs; precise adjustments are essential. The application of advanced computational power offers novel solutions, circumventing time-consuming lab work. In scenarios necessitating both ACE2 and AT1R targeting, optimal drug combinations can be derived from our analysis of drug-drug interactions, as detailed in the manuscript.

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Section: State Space Modellingmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

Bakshi,

Gangopadhyay,

Basak

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

CRFIEKF: An Efficient Parameter Estimation Method for Biochemical Pathway Modeling

Cited by 1 publication

References 39 publications

In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

In Silico Therapeutic Intervention on Cytokine Storm in COVID-19

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