Title Applications of Membrane Computing in Systems and Synthetic Biology / Pierluigi Frisco, Marian Gheorghe, Mario J. Pérez-Jiménez, Editors

Published Heidelberg : Springer, [2014] ©2014

Table of Contents
1.	Infobiotics Workbench: A P Systems Based Tool for Systems and Synthetic Biology / Marian Gheorghe					1
1.1.	Introduction					1
1.2.	Overview					2
1.2.1.	Mathematical Continuous Models					3
1.2.2.	Stochastic Discrete Models					3
1.2.3.	Executable Modeling Formalisms					4
1.3.	Lattice Population P Systems					8
1.4.	Infobiotics Workbench					14
1.4.1.	Modelling in LPP Systems					16
1.4.2.	Simulation					16
1.4.3.	Model Checking					20
1.4.4.	Optimisation					22
1.5.	Case Study					25
1.5.1.	LPP Model					26
1.5.2.	Simulations					27
1.5.3.	Model Checking					28
1.5.4.	Supplementary Material					32
1.6.	Discussions and Conclusions					33
	References					36
2.	Statistical Model Checking of Membrane Systems with Peripheral Proteins: Quantifying the Role of Estrogen in Cellular Mitosis and DNA Damage / Sean Sedwards					43
2.1.	Membrane Systems with Peripheral Proteins					43
2.1.1.	Formal Language Preliminaries					46
2.1.2.	Membrane Systems with Peripheral and Integral Proteins					47
2.2.	Statistical Model Checking for Membrane Systems with Peripheral Proteins					53
2.2.1.	Temporal Logic as a Query Language					53
2.3.	A Case Study: The Role of Estrogen in Cellular Mitosis and DNA Damage					54
2.4.	Methodology and Results					57
2.5.	Conclusions					61
	References					62
3.	Molecular Diffusion and Compartmentalization in Signal Transduction Pathways: An Application of Membrane Systems to the Study of Bacterial Chemotaxis / Giancarlo Mauri					65
3.1.	Introduction					66
3.2.	A Multivolume Modeling Approach with Membrane Systems					68
3.2.1.	Membrane Systems					68
3.2.2.	τ-DPP					69
3.3.	The Modeling of Bacterial Chemotaxis					73
3.3.1.	Bacterial Chemotaxis					74
3.3.2.	A Mechanistic Model					76
3.3.3.	Multivolume Model: Diffusion in a Signal Transduction Pathway					79
3.4.	Results					82
3.4.1.	Simulations of Υsv					82
3.4.2.	The Interplay Between Stochastic Fluctuations and the Numher of Bacterial Flagella in Υsv					86
3.4.3.	Simulations of ΥMV					90
3.5.	Conclusion					93
	References					94
4.	Membrane System-Based Models for Specifying Dynamical Population Systems / L. Valencia-Cabrera					97
4.1.	Introduction					98
4.2.	Preliminaries					100
4.3.	A P Systems-Based Probabilistic Modelling Framework					100
4.3.1.	PDP Systems					101
4.3.2.	Additional Definitions					103
4.3.3.	Some Properties of PDP Systems Models					104
4.4.	An Inference Engine: The DCBA Algorithm					105
4.5.	Simulation of PDP Systems					110
4.5.1.	P-Lingua, and the pLinguaCore Library					110
4.5.2.	The Visual Environment MeCoSim					112
4.5.3.	Accelerating PDP Systems Simulations					113
4.6.	A Case Study: Pandemics					113
4.6.1.	Design of a PDP Modelling Pandemics					114
4.6.2.	Results					125
4.7.	Conclusions and Perspectives					129
	References					131
5.	Membrane Systems and Tools Combining Dynamical Structures with Reaction Kinetics for Applications in Chronobiology / Peter Dittrich					133
5.1.	Introduction					134
5.2.	The KaiABC Core Oscillator: A Circadian Clock Component with Dynamical Molecular Structures					135
5.2.1.	Biological Background					135
5.2.2.	Membrane Systems ΠCSM for Cell Signalling Modules					136
5.2.3.	Applying ΠCSM to a KaiABC Core Oscillator Model					143
5.2.4.	Simulation Case Study					145
5.3.	Circadian Clocks as Generalised Frequency Control Systems					147
5.3.1.	A Controllable Goodwin-Type Core Oscillator					147
5.3.2.	Chemical Frequency Control by Phase-Locked Loops					149
5.3.3.	Exploring Circadian Clock's Entrainment Behaviour by Simulation Studies					153
5.4.	Cell Signalling and Gene Regulatory Networks: Logic Circuits in Chronobiological Information Processing					157
5.4.1.	The General Principle of Cell Signalling in vivo					158
5.4.2.	Modelling a Bistable Toggle Switch by a Gene Regulatory Network with Two Feedback Loops					160
5.4.3.	In Vivo Implementation of a Bistable Toggle Switch					162
5.5.	Spatial Rule-Based Simulator Software SRSim at a Glance					164
5.6.	Envisioning an Analysis of Membrane System's Static and Dynamical Behaviour by a Constrained-Based Approach					168
5.7.	Conclusions					170
	References					170
6.	Biochemical Networks Discrete Modeling Inspired by Membrane Systems / Mihaela Paun					175
6.1.	Introduction					176
6.1.1.	Modeling with Differential Equations					177
6.1.2.	Stochastic Methods and the Gillespie Algorithm					178
6.1.3.	Improving the Gillespie Algorithm					179
6.1.4.	Our Work					180
6.2.	Membrane Systems as Cell Simulators					180
6.2.1.	Description of the NWT Algorithm					182
6.2.2.	Maintaining the Min-heap					184
6.2.3.	Memory Enhancement					185
6.2.4.	Case 1: Deterministic Memory Enhancement					186
6.2.5.	Case 2: Nondeterministic Memory Enhancement					188
6.3.	Comparing the NWT Algorithm with the ODE and Gillespie's Algorithm					191
6.4.	Modeling FAS-Induced Apoptosis					194
6.4.1.	Apoptotic Signaling Cascades					195
6.4.2.	Fas-Mediated Apoptosis					195
6.4.3.	Results of Discrete Method					197
6.4.4.	Bcl-2's Effects on the Type II Pathway					198
6.4.5.	Modeling the Behavior of the Type I Pathway					200
6.4.6.	Summary for the FAS Simulation					201
6.5.	HIV-1 Effects on the FAS Pathway					203
6.5.1.	A Brief History of HIV					203
6.5.2.	AIDS Pathogenesis					204
6.5.3.	HIV-1 Infection					205
6.5.4.	HIV-1-Related Effects on the Fas Pathway					208
6.5.5.	Modeling Results					208
6.5.6.	Summary for Simulating HIV Latency					213
6.6.	Conclusions and Final Remarks					214
6.6.1.	Extensions on the HIV Model					214
6.6.2.	Calcium's Role in Apoptosis					216
	References					216
7.	MP Modelling for Systems Biology: Two Case Studies / Aliccia Bollig-Fischer					223
7.1.	Introduction					223
7.1.1.	Log--Gain Stoichiometric Stepwise Regression (LGSS)					226
7.2.	The Glucose/Insulin Dynamics in the Intravenous Glucose Tolerance Test (IVGTT)					228
7.2.1.	Mathematical Models of the Intravenous Glucose Tolerance Test					230
7.2.2.	MP Modelling of IVGTT					231
7.3.	MP Modelling of Gene Networks					236
7.3.1.	From Raw Data to MP Models					239
7.4.	Conclusion and Ongoing Research					242
	References					243
8.	Modelling and Analysis of E. coli Respiratory Chain / Simon Coakley					247
8.1.	Introduction					248
8.2.	Background					249
8.2.1.	kP Systems					249
8.2.2.	X-Machines					252
8.3.	General Description of E. coli					252
8.4.	FLAME Simulations of E. coli Respiratory Chain					254
8.5.	A Kernel P System Corresponding to E. coli					257
8.6.	Modelling, Simulation and Verification					258
8.6.1.	Implementation in Event-B for ProB					258
8.6.2.	Implementation in Promela for Spin					260
8.6.3.	Simulation Results					261
8.6.4.	Verification Results					262
8.6.5.	Event-B Versus Promela					264
8.7.	Conclusions					264
	References					265

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Description 1 online resource Series Emergence, complexity and computation, 2194-7287 ; volume 7 Emergence, complexity and computation ; 7. Contents From the Contents: A P Systems based Tool for Systems and Synthetic Biology -- Statistical Model Checking of Membrane Systems with Peripheral Proteins: Quantifying the Role of Estrogen in Cellular Mitosis and DNA Damage -- Molecular Diffusion and Compartmentalization in Signal Transduction Pathways: An Application of Membrane Systems to the Study of Bacterial Chemotaxis Summary Membrane Computing was introduced as a computational paradigm in Natural Computing. The models introduced, called Membrane (or P) Systems, provide a coherent platform to describe and study living cells as computational systems. Membrane Systems have been investigated for their computational aspects and employed to model problems in other fields, like: Computer Science, Linguistics, Biology, Economy, Computer Graphics, Robotics, etc. Their inherent parallelism, heterogeneity and intrinsic versatility allow them to model a broad range of processes and phenomena, being also an efficient means to solve and analyze problems in a novel way. Membrane Computing has been used to model biological systems, becoming with time a thorough modeling paradigm comparable, in its modeling and predicting capabilities, to more established models in this area. This book is the result of the need to collect, in an organic way, different facets of this paradigm. The chapters of this book, together with the web pages accompanying them, present different applications of Membrane Systems to Biology. Deterministic, non-deterministic and stochastic systems paired with different algorithms and methodologies show the full potential of this framework. The book is addressed to researchers interested in applications of discrete biological models and the interplay between Membrane Systems and other approaches to analyze complex systems Bibliography Includes bibliographical references Notes English Print version record Subject Membranes (Biology) -- Mechanical properties. Synthetic biology. SCIENCE -- Life Sciences -- Anatomy & Physiology. Ingénierie. Membranes (Biology) -- Mechanical properties Synthetic biology Form Electronic book Author Frisco, Pierluigi, 1970- editor. Gheorghe, Marian, 1953- editor. Pérez-Jiménez, Mario J., editor. ISBN 9783319031910 3319031910

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