Ritwik Kumar Layek

Dynamics of cancer progression and suppression: A novel evolutionary game theory based approach

In this paper, a novel mathematical approach is proposed for the dynamics of progression and suppression of cancer. We define mutant cell density, ρμ (μ × ρ), as a primary factor in cancer dynamics, and use logistic growth model and replicator equation for defining the dynamics of total cell density (ρ) and mutant fraction (μ), respectively. Furthermore, in the proposed model, we introduce an analytical expression for a control parameter D (drug), to suppress the proliferation of mutants with extra fitness level σ. Lastly, we present a comparison of the proposed model with some existing models of tumour growth.

A GPU based Real-Time CUDA implementation for obtaining Visual Saliency

We present a GPU-based implementation of the saliency model proposed by Achanta et al. [1] to perform real-time and detailed saliency map generation. We map all the components of the algorithm to GPU-based kernels and data structures. The parallel version of the algorithm is able to accurately simulate the desired results in a very low time. We describe the streaming pipeline and address many issues in terms of obtaining high throughput on multi-core GPUs. We highlight the parallel performance of the algorithm on three different generations of GPUs. On a high-end NVIDIA Tesla K20m, we observe up to 600x order of magnitude performance improvement as compared to a single-threaded CPU-based algorithm, and about 300x order of magnitude improvement over a CPU-based OpenCV implementation.

Shape recognition based on shape-signature identification and condensibility: Application to underwater imagery

In this paper, a shape recognition method is proposed for a few common geometrical shapes including straight line, circle, ellipse, triangle, quadrilateral, pentagon and hexagon. In the present work, two indices namely Unique Shape Signature (USS) and Condensibility (C) are employed for shape recognition of an object. Using the USS index, all the above mentioned non-circular shapes are neatly recognized, whereas, the C index recognized the circular objects. An added advantage of the proposed method is that it can further differentiate triangles, quadrilaterals and both symmetric and non-symmetric shapes of pentagon and hexagon using distance variance (V ar(dsi)) parameter calculated from USS. Applying the proposed method on above mentioned shapes, an overall recognition rate of 98.80% is achieved on several simulated and real objects of different shapes. Proposed method has also been compared with two existing methods, presents better result. Performance of the proposed method is illustrated by applying it on underwater images and it is observed to perform satisfactory on all the images under test.

Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data

The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN.

Psychovisual saliency in color images

Visual attention is an indispensable component of complex vision tasks. When looking at a complex scene, our ocular perception is confronted with a large amount of data that needs to be broken down for processing by our psychovisual system. Selective visual attention provides a mechanism for serializing the visual data, allowing for sequential processing of the content of the scene. A Bottom-Up computational model is described that simulates the psycho-visual model of saliency based on features of intensity and color. The method gives sequential priorities to objects which other computational models cannot account for. The results demonstrate a fast execution time, full resolution maps and high detection accuracy. The model is applicable on both natural and artificial images.

Predicting Escherichia coli's chemotactic drift under exponential gradient

Bacterial species are known to show chemotaxis, i.e., the directed motions in the presence of certain chemicals, whereas the motion is random in the absence of those chemicals. The bacteria modulate their run time to induce chemotactic drift towards the attractant chemicals and away from the repellent chemicals. However, the existing theoretical knowledge does not exhibit a proper match with experimental validation, and hence there is a need for developing alternate models and validating experimentally. In this paper a more robust theoretical model is proposed to investigate chemotactic drift of peritrichous Escherichia coli under an exponential nutrient gradient. An exponential gradient is used to understand the steady state behavior of drift because of the logarithmic functionality of the chemosensory receptors. Our theoretical estimations are validated through the experimentation and simulation results. Thus, the developed model successfully delineates the run time, run trajectory, and drift velocity as measured from the experiments.

A boolean approach to bacterial chemotaxis

Bacterium such as Escherichia coli (E. coli) show biased Brownian motion in different chemical concentration gradients. This chemical sensitive motility or chemotaxis has gained considerable interest among scientists for some remarkable features such as chemo-sensory dynamic range, adaptation, diffusion and drift. A Boolean model of the whole chemotaxis process has been developed in this manuscript. The response of the circuit is in accordance with the experimental results available in the literature, providing indirect validation of the model. This simple Boolean network (BN) can be easily integrated into the paradigm of modular whole cell modelling. Another crucial application is in designing bio-inspired micro-robots to detect certain spatio-temporal chemical signatures.Bacterium such as Escherichia coli (E. coli) show biased Brownian motion in different chemical concentration gradients. This chemical sensitive motility or chemotaxis has gained considerable interest among scientists for some remarkable features such as chemo-sensory dynamic range, adaptation, diffusion and drift. A Boolean model of the whole chemotaxis process has been developed in this manuscript. The response of the circuit is in accordance with the experimental results available in the literature, providing indirect validation of the model. This simple Boolean network (BN) can be easily integrated into the paradigm of modular whole cell modelling. Another crucial application is in designing bio-inspired micro-robots to detect certain spatio-temporal chemical signatures.

Inference of asynchronous Boolean network from biological pathways.

Gene regulation is a complex process with multiple levels of interactions. In order to describe this complex dynamical system with tractable parameterization, the choice of the dynamical system model is of paramount importance. The right abstraction of the modeling scheme can reduce the complexity in the inference and intervention design, both computationally and experimentally. This article proposes an asynchronous Boolean network framework to capture the transcriptional regulation as well as the protein-protein interactions in a genetic regulatory system. The inference of asynchronous Boolean network from biological pathways information and experimental evidence are explained using an algorithm. The suitability of this paradigm for the variability of several reaction rates is also discussed. This methodology and model selection open up new research challenges in understanding gene-protein interactive system in a coherent way and can be beneficial for designing effective therapeutic intervention strategy.