A Hybrid Approach to Modeling Biological Systems

This paper investigates a hybrid approach to modeling molecular interactions in biology. P systems, π-calculus, and Petri nets models, and two tools, Daikon, used in software reverse-engineering, and PRISM, a probabilistic model checker, are investigated for their expressiveness and complementary roles in describing and analyzing biological systems. A simple case study illustrates this approach

Large Scale Dynamical Model of Macrophage/HIV Interactions

Properties emerge from the dynamics of large-scale molecular networks that are not discernible at the individual gene or protein level. Mathematical models - such as probabilistic Boolean networks - of molecular systems offer a deeper insight into how these emergent properties arise. Here, we introduce a non-linear, deterministic Boolean model of protein, gene, and chemical interactions in human macrophage cells during HIV infection. Our model is composed of 713 nodes with 1583 interactions between nodes and is responsive to 38 different inputs including signaling molecules, bacteria, viruses, and HIV viral particles. Additionally, the model accurately simulates the dynamics of over 50 different known phenomena, including molecular events associated with viral infection, endocytosis, transport, replication, budding, and cellular release. Statistical analyses of the model reveal network components with significant potential to influence molecular systems in both normal and infected macrophages, many of which have been confirmed in cell and animal models of HIV infection. We designed a Probabilistic Confidence Interval analysis for Boolean models (PCIB), demonstrating that our model emulates approximately 82% of a mass spectrometry dataset, collected from 7 macrophage samples infected with HIV across 67 proteins known to be central to the HIV infection process. The reproducibility of our model will facilitate guided hypothesis creation for future in vitro and in vivo studies. Additionally, the model allows for protein signaling interactions in human macrophages during HIV infection to be studied from a non-reductionist point of view

Modelling drug coatings: A parallel cellular automata model of ethylcellulose-coated microspheres

Pharmaceutical companies today face a growing demand for more complex drug designs. In the past few decades, a number of probabilistic models have been developed, with the aim of improving insight on microscopic features of these complex designs. Of particular interest are models of controlled release systems, which can provide tools to study targeted dose delivery. Controlled release is achieved by using polymers with different dissolution characteristics. We present here an approach for parallelising a large-scale model of a drug delivery system based on Monte Carlo methods, as a framework for Cellular Automata mobility. The model simulates drug release in the gastro-intestinal tract, from coated ethylcellulose microspheres. The objective is high performance simulation of coated drugs for targeted delivery. The overall aim is to understand the importance of various molecular effects with respect to system evolution over time. Important underlying mechanisms of the process, such as erosion and diffusion, are described

Modeling Cellular Signaling Systems: An Abstraction-Refinement Approach

International audienceThe molecular mechanisms of cell communication with the environment involve many concurrent processes governing dynamically the cell function. This concurrent behavior makes traditional methods, such as differential equations, unsatisfactory as a modeling strategy since they do not scale well when a more detailed view of the system is required. Concurrent Constraint Programming (CCP) is a declarative model of concurrency closely related to logic for specifying reactive systems, i.e., systems that continuously react with the environment. Agents in CCP interact by telling and asking information represented as constraints (e.g., x > 42). In this paper we describe a modeling strategy for cellular signaling systems based on a temporal and probabilistic extension of CCP. Starting from an abstract model, we build refinements adding further details coming from experimentation or abstract assumptions. The advantages of our approach are: due to the notion of partial information as constraints in CCP, the model can be straightforwardly extended when more information is available; qualitative and quantitative information can be represented by means of probabilistic constructs of the language; finally, the model is a runnable specification and can be executed, thus allowing for the simulation of the system. We outline the use of this methodology to model the interaction of G-protein-coupled receptors with their respective G-proteins that activates signaling pathways inside the cell. We also present simulation results obtained from an implementation of the framewor

Defect Characterization and Yield Analysis of Array-Based Nanoarchitecture

With molecular-scale materials and fabrication techniques recently developed, high-density computing systems in nanometer domain emerge. An array-based nanoarchitecture has been recently proposed based on nanowires such as carbon nanotubes (CNTs), silicon nanowires (SiNWs). High-density nanoarray-based systems consisting of nanometer-scale elements are likely to have many imperfections; thus, defect-tolerance is considered as one of the most significant challenges. In this paper, we propose a probabilistic yield model for the array-based nanoarchitecture. The proposed yield model can be used 1) to accurately estimate the raw and net array densities, and 2) to design and optimize more defect and fault-tolerant systems based on the array-based nanoarchitecture

The study of probability model for compound similarity searching

Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model

Modeling Yield of Carbon-Nanotube/Silicon-Nanowire FET-Based Nanoarray Architecture with H-Hot Addressing Scheme

With molecular-scale materials, devices and fabrication techniques recently being developed, high-density computing systems in the nanometer domain emerge. An array-based nanoarchitecture has been recently proposed based on nanowires such as carbon nanotubes (CNTs) and silicon nanowires (SiNWs). High-density nanoarray-based systems consisting of nanometer-scale elements are likely to have many imperfections; thus, defect-tolerance is considered one of the most significant challenges. In this paper we propose a probabilistic yield model for the array-based nanoarchitecture. The proposed yield model can be used (1) to accurately estimate the raw and net array densities, and (2) to design and optimize more defect and fault-tolerant systems based on the array-based nanoarchitecture. As a case study, the proposed yield model is applied to the defect-tolerant addressing scheme called h-hot addressing and simulation results are discussed