Arbitrary Nesting of Spatial Computations

International audienceModern programming languages allow the definition and the use of arbitrary nested data structures but this is not generally considered in unconventional programming models. In this paper, we present arbitrary nesting in MGS, a spatial comput- ing language. By considering different classes of neighborhood relationships, MGS can emulate several unconventional computing models from a programming point of view. The use of arbitrary nested spatial structures allows a hierarchical form of coupling between them. We propose an extension of the MGS pattern- matching facilities to handle directly nesting. This makes possible the straightforward emulation of a larger class of unconventional programming models

Rule-based multi-level modeling of cell biological systems

<p>Abstract</p> <p>Background</p> <p>Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.</p> <p>Results</p> <p>Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.</p> <p>Conclusions</p> <p>Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.</p

박테리아의 항생제 내성에 대한 시뮬레이션 모델 연구

학위논문 (석사)-- 서울대학교 보건대학원 : 보건학과(바이오인포매틱스 전공), 2015. 8. 손현석.Antibiotic resistance or antimicrobial resistance(AMR) refers to infections caused by bacteria, fungi, parasites and viruses resistant to antibiotics. Antibiotic resistance has become a major public health threat as it breaks out regardless of geographical conditions or socioeconomic status. Infectious diseases such as HIV/AIDS, Tuberculosis, Malaria, etc., are known to be more widely spread in developing countries compared to other developed countries.Experts from various fields are putting their effort to tackle this problem however is yet to be solved.Various computational approaches including bioinformatics have been conducted such as genome sequencing, constructing databases of resistance genes and antibiotics information, providing tools for analysis and designing simulation models. The purpose of this study is to design and implement simulation models of bacterial growth and antibiotic resistance to find the proper antibiotics against antibiotic resistant bacteria. Simulation models were designed based on Individual based Modeling (IbM). A simulation tool named ARSim was developed in order to conduct experiments using simulation models. We designed models on top ofARSim to observe the growth of bacteria and predict the consequences of adding antibiotics into the bacterial population.Simulations of bacterial growth were conducted by growing K.pneumoniae bacteria on a virtual plate with predefined parameters. By assessing the change in bacterial population as time goes by, the result was nearly identical to the four phases of a bacterial growth curve.The next experiment was predicting the effects of antibiotics when added to two different groups, one group of non-resistant bacteria and another group of both resistant and non-resistant bacteria. We assumed carbapenem class Imipenem and Meropenem as antibiotics and carbapenem resistant bacteria as the bacterial strain. In the first experiment, we predicted that the non-resistant bacterial population steadily grows when 0.05μg/ml of Imipenem is added to the population. On the contrary the population instantly died out when 0.1μg/mlwas added which is greater than the minimum inhibitory concentration of the strain.In the second experiment, we added Imipenem and Meropenem with concentrations of 16μg/ml, 32μg/ml and 64μg/ml each. The results for adding Imipenems were akinto previous lab experiments in literature and results for Meropenemswerevery much alike to Imipenems.We used Individual based Modeling methods to design and implement models of bacteria, antibiotics, enzymes and the environment and conducted simulations of these entities through the ARSim program. Results were shown that properties and interactions among these entities were properly defined,and the models to a certain degree follow the biological principles of bacteria and their mechanisms of antibiotic resistance.Using the computational approaches made in this study, we hope to provide researchers with a better option on finding new ways of fighting antibiotic resistance.ABSTRACT ⅰ TABLE OF CONTENTS ⅳ LIST OF TABLES ⅵ LIST OF FIGURES ⅶ LIST OF ABBREVIATIONS ⅸ CHAPTER I. INTRODUCTION 1.1 Background 1 1.1.1Antibiotics 3 1.1.2Antibiotic resistant bacteria 4 1.1.3Bioinformatics approaches 6 1.1.4Simulation methods 7 1.2Objectives 13 CHAPTER II. MATERIALS AND METHODS 2.1 Modeling the bacterial populationgrowth 16 2.1.1Exponential and logistic models 16 2.1.2Individual-based models 18 2.2 Modeling antibiotic resistance 21 2.2.1Resistance genes 21 2.2.2Horizontal gene transfer 22 2.2.3Antibiotics-bacteria interactions 24 2.3Modeling the environment 28 2.4Modeling the molecular movements 30 2.4.1Data structure of molecules 30 2.4.2Molecular movements 31 2.5Database construction 37 2.6Implementing the simulation program 43 CHAPTER III. RESULTS 3.1 Simulation of the bacterial growth model 52 3.2 Simulation of the antibiotic resistance model 59 3.2.1Simulation without antibiotic resistant bacteria 59 3.2.2Simulation with antibiotic resistant bacteria 60 CHAPTER IV. DISCUSSIONANDCONCLUSION 4.1 Discussion 84 4.2 Conclusion 87 CHAPTERV.SUMMARY 89 BIBLIOGRAPHY 92 ABSTRACT (KOREAN) 99 ACKNOWLEDGEMENT 101Maste

Rule-based programming for integrative biological modeling Application to the modeling of the λ phage genetic switch

International audienceSystems biology aims at integrating processes at various time and spatial scales into a single and coherent formal description to allow computer modeling. In this context, we focus on rule-based modeling and its integration in the domain-specific language MGS. Through the notions of topological collections and transformations, MGS allows the modeling of biological processes at various levels of description. We validate our approach through the description of various models of the genetic switch of the lambda phage, from a very simple biochemical description of the process to an individual-based model on a Delaunay graph topology. This approach is a first step into providing the requirements for the emerging field of spatial systems biology which integrates spatial properties into systems biology