Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.

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Executable cell biology.

https://www.inf.ed.ac.uk/publications/online/1231.pdf

Bio-PEPA: A framework for the modelling and analysis of biological systems

We introduce the continuous π-calculus, a process algebra for modelling behaviour and variation in molecular systems. Key features of the language are: its expressive succinctness; support for diverse interaction between agents via a flexible network of molecular affinities; and operational semantics for a continuous space of processes. This compositional semantics also gives a modular way to generate conventional differential equations for system behaviour over time. We illustrate these features with a model of an existing biological system, a simple oscillatory pathway in cyanobacteria. We then discuss future research directions, in particular routes to applying the calculus in the study of evolutionary properties of biochemical pathways.

/pdf/the-continuous-p-calculus-a-process-algebra-for-biochemical-4c6layja6j.pdf

The Continuous π-Calculus: A Process Algebra for Biochemical Modelling

Stochastic process algebra models have been successfully used in the area of performance modelling for the last twenty years, and more recently have been adopted for modelling biochemical processes in systems biology. Most research on these modelling formalisms has been on quantitative analysis, particularly the derivation of quantified dynamic information about the system modelled in the face of the state space explosion problem. In this paper we instead consider qualitative analysis, looking at how recent developments to tackle state space explosion in quantified analysis can be also harnessed to establish properties such as freedom from deadlock in an efficient manner.

Structural analysis for stochastic process algebra models

The exact performance analysis of large-scale software systems with discrete-state approaches is difficult because of the well-known problem of state-space explosion. This paper considers this problem with regard to the stochastic process algebra PEPA, presenting a deterministic approximation to the underlying Markov chain model based on ordinary differential equations. The accuracy of the approximation is assessed by means of a substantial case study of a distributed multithreaded application.

/pdf/scalable-differential-analysis-of-process-algebra-models-25rnwtwwrb.pdf

Scalable Differential Analysis of Process Algebra Models

The PEPA Eclipse Plug-in supports the creation and analysis of performance models, from small-scale Markov models to large-scale simulation studies and differential equation systems. Whichever form of analysis is used, models are expressed in a single highlevel language for quantitative modelling, Performance Evaluation Process Algebra (PEPA).

/pdf/the-pepa-eclipse-plugin-4crhh3f6z1.pdf

The PEPA eclipse plugin

Starting from a biochemical signalling pathway model expressed in a process algebra enriched with quantitative information we automatically derive both continuous-space and discrete-state representations suitable for numerical evaluation. We compare results obtained using implicit numerical differentiation formulae to those obtained using approximate stochastic simulation thereby exposing a flaw in the use of the differentiation procedure producing misleading results.

/pdf/stronger-computational-modelling-of-signalling-pathways-1dzyy75uji.pdf

Stronger computational modelling of signalling pathways using both continuous and discrete-state methods

Bio-PEPA is a timed process algebra designedspecifically for the description of biological phenomena and theiranalysis through quantitative methods such asstochastic simulation and probabilistic model-checking.Two software tools are available for modelling with Bio-PEPA, theBio-PEPA Workbench and the Bio-PEPA Eclipse Plugin. The Bio-PEPAWorkbench is the research prototype tool which allows us to try outnew language features and new types of analysis through rapidprototyping. The Bio-PEPA Eclipse Plugin is a polished modellingenvironment which targets end-users who wish to do Bio-PEPA modellingsupported by a comprehensive integrated development environment. Bothmodelling tools allow the user to analyse their model both in thediscrete stochastic regime and in the sure continuous regime whilemaintaining only a single source in the Bio-PEPA language.

/pdf/the-bio-pepa-tool-suite-4ectrt2ubx.pdf

The Bio-PEPA Tool Suite

/pdf/quantitative-evaluation-of-systems-2009-qest-09-sixth-293axo6b2g.pdf

Quantitative Evaluation of Systems, 2009. QEST '09. Sixth International Conference on the

This paper surveys the design of software tools for the Bio-PEPA process algebra. Bio-PEPA is a high-level language for modelling biological systems such as metabolic pathways and other biochemical reaction networks. Through providing tools for this modelling language we hope to allow easier use of a range of simulators and model-checkers thereby freeing the modeller from the responsibility of developing a custom simulator for the problem of interest. Further, by providing mappings to a range of different analysis tools the Bio-PEPA language allows modellers to compare analysis results which have been computed using independent numerical analysers, which enhances the reliability and robustness of the results computed.

/pdf/design-and-development-of-software-tools-for-bio-pepa-3pjdoqi9q0.pdf

Adam Duguid

Papers

The PEPA eclipse plugin

Stronger computational modelling of signalling pathways using both continuous and discrete-state methods

The Bio-PEPA Tool Suite

Quantitative Evaluation of Systems, 2009. QEST '09. Sixth International Conference on the

Design and development of software tools for Bio-PEPA