A Multiscale Modeling Framework Based on P Systems

Romero–Campero, Francisco J.; Twycross, Jamie; Cao, Hongrui; Blakes, Jonathan; Krasnogor, Natalio

doi:10.1007/978-3-540-95885-7_5

Cited by 11 publications

(12 citation statements)

References 20 publications

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“…temporal logic), and model generation (creating models from time series data). The multiscale P-system framework of Romero-Campero et al [7] is also attractive for its ability to describe extremely abstract hierarchical systems within one formalism; however, the range of possible analyses is smaller.…”

Section: Scientific Backgroundmentioning

confidence: 99%

Process Algebra with Layers: Multi-scale Integration Modelling Applied to Cancer Therapy

Scott

Nicol

Coulter

et al. 2017

Computational Intelligence Methods for Bioinformatics and Biostatistics

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Abstract. We present a novel Process Algebra designed for multi-scale integration modelling: Process Algebra with Layers (PAL). The unique feature of PAL is the modularisation of scale into integrated layers: Object and Population. An Object can represent a molecule, organelle, cell, tissue, organ or any organism. Populations hold specific types of Object, for example, life stages, cell phases and infectious states. The syntax and semantics of this novel language are presented. A PAL model of the multi-scale system of cell growth and damage from cancer treatment is given. This model allows the analysis of different scales of the system. The Object and Population levels give insight into the length of a cell cycle and cell population growth respectively. The PAL model results are compared to wet laboratory survival fractions of cells given different doses of radiation treatment [1]. This comparison shows how PAL can be used to aid in investigations of cancer treatment in systems biology.

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Section: Scientific Backgroundmentioning

confidence: 99%

Process Algebra with Layers: Multi-scale Integration Modelling Applied to Cancer Therapy

Scott

Nicol

Coulter

et al. 2017

Computational Intelligence Methods for Bioinformatics and Biostatistics

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“…IBW permits modelling, simulating, verifying and optimizing biological systems that exhibit molecular interactions. In IBW, system models are described in a rule-based language, called stochastic P (SP) systems [4], which facilitate the specification of multi-cellular systems and molecular interactions that take place in different locations of cells. The formalism also allows the modelling of membranes distributed geometrically.…”

Section: A Software Suit For Synthetic Biologymentioning

confidence: 99%

Design and Analysis of Genetically Constructed Logic Gates

Konur¹,

Gheorghe²

2015

USES Conference Proceedings

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Synthetic biology, comprising many aspects including in vivo, in vitro and in silico techniques, models and methods, programming paradigms and tools, is a rapidly growing field with promising potential in building new synthetically constructed devices and systems. Synthetic biology features unconventional biological systems that do not naturally exist in nature. In this paper, we discuss a software platform, Infobiotics Workbench, developed to perform in silico experiments for synthetic biology systems. We utilise the tool on an unconventional system, a genetic logic gate.

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“…Different simple molecular interactions or more complex gene expressions, compartment translocation, as well as cell division and death are specified using multiset rewriting or communication rules, and compartment division or dissolution rules. In the case of stochastic P systems, constants are associated with rules in order to compute their probabilities and time needed to be applied, respectively, according to the Gillespie algorithm [13]. This approach is based on a Monte Carlo algorithm for stochastic simulation of molecular interactions taking place inside a single volume or across multiple compartments.…”

Section: Stochastic and Non-deterministic P Systems: Basic Concepts Amentioning

confidence: 99%

“…Definition 1. A stochastic P system (SP system) is a model consisting of a tissue P system with a stochastic semantics [13]:…”

Section: Stochastic and Non-deterministic P Systems: Basic Concepts Amentioning

confidence: 99%

Conventional Verification for Unconventional Computing: a Genetic XOR Gate Example

Konur

Gheorghe

Dragomir

et al. 2014

Fundamenta Informaticae

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A Multiscale Modeling Framework Based on P Systems

Cited by 11 publications

References 20 publications

Process Algebra with Layers: Multi-scale Integration Modelling Applied to Cancer Therapy

Process Algebra with Layers: Multi-scale Integration Modelling Applied to Cancer Therapy

Design and Analysis of Genetically Constructed Logic Gates

Conventional Verification for Unconventional Computing: a Genetic XOR Gate Example

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