Modelling Biological Systems From Molecular Interactions to Population Dynamics

Biological systems are examples of complex systems, which consist of several interacting components. Understanding the behaviour of such systems requires a multidisciplinary approach that encompasses biology, mathematics, computer science, physiscs and chemistry. New research areas are emerging as the result of this multidisciplinarity, such as bioinformatics, systems biology and computational biology. Computer science plays an important role in the newly emerging research areas by offerring techniques, algorithms, languages and software to solve research problems efficiently. On the other hand, the efforts to solve these research problems stimulate the development of new and better computer science techniques, algorithms, languages and software. This thesis describes our approach in modelling biological systems as a way to better understand their complex behaviours. Our approach is based on the Calculi of Looping Sequences, a class of formalisms originally developed to model biological systems involving cells and their membrane-based structures. We choose Stochastic CLS and Spatial CLS, two variants of the calculi that support quantitative analysis of the model, and define an approach that support simulation, statistical model-checking and visualisation as analysis techniques. Moreover, we found out that this class of formalisms can be easily extended to model population dynamics of animals, a kind of biological systems that does not involve membrane-based structures

Modelling Cell Cycle using Different Levels of Representation

Understanding the behaviour of biological systems requires a complex setting of in vitro and in vivo experiments, which attracts high costs in terms of time and resources. The use of mathematical models allows researchers to perform computerised simulations of biological systems, which are called in silico experiments, to attain important insights and predictions about the system behaviour with a considerably lower cost. Computer visualisation is an important part of this approach, since it provides a realistic representation of the system behaviour. We define a formal methodology to model biological systems using different levels of representation: a purely formal representation, which we call molecular level, models the biochemical dynamics of the system; visualisation-oriented representations, which we call visual levels, provide views of the biological system at a higher level of organisation and are equipped with the necessary spatial information to generate the appropriate visualisation. We choose Spatial CLS, a formal language belonging to the class of Calculi of Looping Sequences, as the formalism for modelling all representation levels. We illustrate our approach using the budding yeast cell cycle as a case study

Modelling the Dynamics of an Aedes albopictus Population

We present a methodology for modelling population dynamics with formal means of computer science. This allows unambiguous description of systems and application of analysis tools such as simulators and model checkers. In particular, the dynamics of a population of Aedes albopictus (a species of mosquito) and its modelling with the Stochastic Calculus of Looping Sequences (Stochastic CLS) are considered. The use of Stochastic CLS to model population dynamics requires an extension which allows environmental events (such as changes in the temperature and rainfalls) to be taken into account. A simulator for the constructed model is developed via translation into the specification language Maude, and used to compare the dynamics obtained from the model with real data.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Translating Stochastic CLS into Maude

AbstractThis paper describes preliminary results on the application of statistical model-checking to systems described with Stochastic CLS. Stochastic CLS is a formalism based on term rewriting that allows biomolecular systems to be described by taking into account their structure and by allowing very general events to be modelled. Statistical model-checking is an analysis technique that permits properties of a system to be studied on the results of a number of stochastic simulations. We choose Real-Time Maude as a tool that supports the modelling and analysis of systems with real-time properties. We adapt Gillespie's algorithm for simulating chemical systems into our approach. The resulting method is applied to analyse some simple examples and a model of the lactose operon regulation in E.coli

Asynchronous island model genetic algorithm for university course timetabling

University course timetabling problem (UCTP) is similar to general timetabling problems with some additional unique parts. UCTP involves assigning lecture events to timeslots and rooms subject to a variety of hard and soft constraints. Telkom University has almost similar problem with its course timetabling. The current solution with Informed Genetic Algorithm for Telkom University UCTP still has the time consuming problem. Island Model informed Genetic Algorithm was used in this research to solve this problem. The idea of this research is making distributed model exchanges an island’s local best Individu with another island. Island model GA could create university course timetabling in reasonable time. This distributed model could run faster rather than single machine model decreasing constraint violations to reach optimum fitness. It could have less constraint violations because it could escape from stagnant local optimum easier. Island model GA could even produce great accuracy for Telkom University dataset (99.74%) and acceptable accuracy at 96.80% for Purdue dataset for student level timetabling.</p

A Hybrid SVD-Based Image Watermarking Scheme Utilizing Both U and V Orthogonal Vectors for Robustness and Imperceptibility

SVD-based watermarking algorithm is one of the most preferable algorithms for copyright protection due to its singular values (SVs) that have outstanding stability and represents intrinsic algebraic image properties. Hence, there is a good trade-off between robustness and imperceptibility. However, most SVD-based algorithms have been tested against conventional attacks, such as image manipulations, that do not fully exploit adversary&#x2019;s knowledge. These algorithms are vulnerable to false-positive problem, where an adversary&#x2019;s watermark can be detected in the watermarked image although it was never inserted. The underlying problem is due to the strong influence of $U$ and $V$ orthogonal vectors of SVD on an image. In order to solve false-positive problem, $UV$ can be used to embed the watermark together with SVs. However, this solution is not ideal as $U$ and $V$ hold important structural information of an image and is hypersensitivity to even a little change in $UV$ vectors. Therefore, this research has the objectives to analyse the robustness of existing SVD-based watermarking algorithms that are using orthogonal vectors and then propose a new robust algorithm that is able to solve false-positive problem and sensitivity issued caused by scaling factor. Hence, a new transform domain image watermarking scheme that utilized both $U$ and $V$ orthogonal vectors is proposed with the usage of Human Visual System (HVS), Discrete Wavelet Transform (DWT) and SVD. Experimental results showed that the proposed scheme is more robust against majority types of image processing and geometrical attacks compared to existing schemes while achieving good quality watermarked image level. The significance of new algorithm comes at the right time during the Covid-19 epidemic as organizations involving in business and financial services can be assured of the integrity of its downloadable/streamable/shareable digital files, which are copyrighted through the unique SVD and robustness features of the algorithm ensuring piracy prevention of their content