Information theoretic framework for stochastic sensitivity and specificity analysis in biochemical networks

Biochemical reaction networks involve many chemical species and are inherently stochastic and complex in nature. Reliable and organised functioning of such systems in varied environments requires that their behaviour is robust with respect to certain parameters while sensitive to other variations, and that they exhibit specific responses to various stimuli. There is a continuous need for improved models and methodologies to unravel the complex behaviour of the dynamics of such systems. In this thesis, we apply ideas from information theory to develop novel methods to study properties of biochemical networks. In the first part of the thesis, a framework for the study of parametric sensitivity in stochastic models of biochemical networks using entropies and mutual information is developed. The concept of noise entropy is introduced and its interplay with parametric sensitivity is studied as the system becomes more stochastic. Using the methodology for gene expression models, it is shown that noise can change the sensitivities of the system at var- ious orders of parameter interaction. An approximate and computationally more efficient way of calculating the sensitivities is also developed using unscented transform. Finally, the methodology is applied to a circadian clock model, illustrating the applicability of the approach to more complex systems. In the second part of the thesis, a novel method for specificity quantification in a receptor-ligand binding system is proposed in terms of mutual information estimates be- tween appropriate stimulus and system response. The maximum specificity of 2 × 2 affinity matrices in a parametric setup is theoretically studied. Parameter optimisation methodology and specificity upper bounds are presented for maximum specificity estimates of a given affinity matrix. The quantification framework is then applied to experimental data from T-Cell signalling. Finally, generalisation of the scheme for stochastic systems is discussed.Open Acces

Computational model to explore the endocrine response to trastuzumab action in HER-2/neu positive breast cancer

Breast cancer is a very frequent type of cancer and much attention is paid to therapy with considerable efforts both in the pharmacological and clinical fields.The present work aims to create a non-linear dynamic model of action of the drug Trastuzumab against HER-2 + breast cancer, mainly considering its action of ADCP (antibody-dependent phagocytosis) killing of cancer cells. The model, while also considering the other therapeutic effects induced by Trastuzumab, shows how the action of this monoclonal antibody in the induction of ADCP through the action of macrophages, is strictly connected to the formation of a multi-complex “Trastuzumab -HER-2 - macrophage” that shows a prolonged action over time, responsible for the increase in the Overall Survivor (OS) parameter reported in various. The model shows the correlation between the various therapeutic effects and the killing action of cancer cells through the variation of the dynamic fluctuation of the representative ”c” parameter

Exact Solution for the Heat Transfer of Two Immiscible PTT Fluids Flowing in Concentric Layers through a Pipe

This article investigates the heat transfer flow of two layers of Phan-Thien-Tanner (PTT) fluids though a cylindrical pipe. The flow is assumed to be steady, incompressible, and stable and the fluid layers do not mix with each other. The fluid flow and heat transfer equations are modeled using the linear PTT fluid model. Exact solutions for the velocity, flow rates, temperature profiles, and stress distributions are obtained. It has also been shown that one can recover the Newtonian fluid results from the obtained results by putting the non-Newtonian parameters to zero. These results match with the corresponding results for Newtonian fluids already present in the literature. Graphical analysis of the behavior of the fluid velocities, temperatures, and stresses is also presented at the end. It is also shown that maximum velocity occurs in the inner fluid layer

Optimization of tank engine crank shaft material properties

Metals and coating materials, with best material properties are highly desired in the field of engineering to control the stresses and strains, that play important role in design strategies. The crankshaft, an important part of tank engine, depends on the cranking mechanism in the internal combustion engine. The function of the crankshaft is to convert the sliding motion of the piston into rotary motion. Since most industrial mechanisms and processes make more efficient use of rotary motions rather than displacements, the function of crankshaft becomes very vital. To understand the reason behind the fatigue and heavy load cycles, a mathematical model is simulated. The complex geometry of crankshaft is developed in a numerical solver. The model is simulated relative to several material properties to mimic the resulting fatigue. The numerical data was optimized using the python Bayesian optimization tools, to forecast the threshold values relative to longer duration. In the recent literature, different nano-composites are acquiring at- tention as the coating materials, to tackle with the friction load. The hybrid modeling approach used during this research can help to simulate the stresses, resulting from dif- ferent nanocomposite coatings.Open Access funding provided by the Qatar National Library. The authors would like to acknowledge the partial support from the project: PSF/ILP/P-HIT/Engg(085).Scopu