Simon Coakley

An integrated systems biology approach to understanding the rules of keratinocyte colony formation

Closely coupled in vitro and in virtuo models have been used to explore the self-organization of normal human keratinocytes (NHK). Although it can be observed experimentally, we lack the tools to explore many biological rules that govern NHK self-organization. An agent-based computational model was developed, based on rules derived from literature, which predicts the dynamic multicellular morphogenesis of NHK and of a keratinocyte cell line (HaCat cells) under varying extracellular Ca++ concentrations. The model enables in virtuo exploration of the relative importance of biological rules and was used to test hypotheses in virtuo which were subsequently examined in vitro. Results indicated that cell–cell and cell–substrate adhesions were critically important to NHK self-organization. In contrast, cell cycle length and the number of divisions that transit-amplifying cells could undergo proved non-critical to the final organization. Two further hypotheses, to explain the growth behaviour of HaCat cells, were explored in virtuo—an inability to differentiate and a differing sensitivity to extracellular calcium. In vitro experimentation provided some support for both hypotheses. For NHKs, the prediction was made that the position of stem cells would influence the pattern of cell migration post-wounding. This was then confirmed experimentally using a scratch wound model.

Exploitation of High Performance Computing in the FLAME Agent-Based Simulation Framework

This paper describes the design of an agent-based modelling framework for high performance computing. Rather than a collection of methods that require parallel programming expertise the framework presented allows modellers to concentrate on the model while the framework handles the efficient execution of simulations. The framework uses a state machine based representation of agents that allows a statically calculated optimal ordering of agent execution and parallel communication routines. Some experiments with the current implementation and the results of using a simple communication dominant model for benchmarking performance are reported. The model with half a million agents is used to show that a parallel efficiency of above 80% is achievable when distributed over 432 processors. Future improvements are discussed including data dependency analysis, vector operations over agents, and dynamic task scheduling.

Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.

Agent-based modeling of oxygen-responsive transcription factors in Escherichia coli.

In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.

Large-Scale Simulations with FLAME

This chapter presents the latest stage of the FLAME development—the high-performance environment FLAME-II and the parallel architecture designed for Graphics Processing Units, FLAMEGPU. The architecture and the performances of these two agent-based software environments are presented, together with illustrative large-scale simulations for systems from biology, economy, psychology and crowd behaviour applications.

Modelling and Analysis of E. coli Respiratory Chain

In this chapter we present some results obtained in the study of the bacterium E. coli related to its behavior at different level of oxygen in the environment. The biological model is expressed in terms of different molecules and their reactions. First, an agent-based model of E. coli is implemented in the FLAME framework for multi-agents and some simulation results are given. Each agent is represented by an X-machine and the model corresponds to communicating X-machines. Then this model is transformed into a kernel P system. This kernel P system is implemented in the Rodin platform and in Spin and some properties are verified using the associated model checkers. Formulated using the LTL formalism, the verified properties refer to the variation of the number of different molecules as a result of the occurring reactions. Our main contribution is a simplified model of E. coli that preserves the main properties of the initial model, and can be formally verified using a model checker.