The Centre for Computational Science

IBM Blue Gene Supercomputer

Image courtesy of VLSCI.

The Centre for Computational Science (CCS) promotes computational modelling in solving applied scientific problems of all varieties. It encompasses simulation methods ranging from classical dynamical systems models through discrete-event simulation to recently developed techniques in stochastic optimisation, individual-based modelling (agent-based modelling) and Bayesian networks. In their application these techniques can benefit from improved data analysis, visualisation and high-performance computational techniques, and so the Centre also supports research in these areas. Computational Science is about delivering computational power in effectively targeted simulations to solve specific problems arising from all the applied sciences.

The application sciences involved in successful problem-solving simulations in recent years include not just the physical and natural sciences such as astrophysics, meteorology and genetics, but also the social sciences and economics. Beyond those, computer simulation has been effectively applied in architectural design, environmental modelling, business management and ethics. The Centre takes a broad view of "Science" and so of "Computational Science".

CCS Activities The Centre's activities can be located along three dimensions:

  1. Scientific Disciplines. These are the domains from which problems are taken which computer simulation and modelling may help to solve, ranging over all the empirical sciences.
  2. Computational Methods. These are simulation and modelling technologies which can be applied to empirical scientific problem solving. These include, without being limited to: numerical ODE and PDE simulation, discrete-event simulation, cellular automata, stochastic search, constraint programming, swarm optimisation, Bayesian network modelling, causal modelling, individual- (agent-) based modelling (IBM, ABM), evolutionary algorithms.
  3. Computational Technologies. These technologies deliver the computing power needed to run the simulations and the analytical tools needed to interpret the results. High-performance computing technology, scientific workbenches, data mining tools and platforms, simulation verification and validation methodologies, visualisation software and equipment all contribute to this.

As a whole, the Centre aims to promote Monash as a leader in advanced computer simulation methods in academic research and in their application throughout the community. It builds upon existing strengths, with a view to developing new strengths through recruitment and collaboration.

Raison d'etre

Computational science has existed since the first general purpose digital computer was activated, the ENIAC at the University of Pennsylvania in 1946. That is, John von Neumann used it for stochastic simulations to assist in the design of the hydrogen bomb, ahead of its intended use for generating ballistic tables. While computer science developed as a discipline, winning independence from physics, engineering and mathematics and establishing its own degree programs first at Cambridge and later world-wide, scientists across the other disciplines have continued to avail themselves of the ever-improving computational resources to support their experimental and theoretical work. As the problems tackled have grown in size, and the experimental and analytical efforts have grown even more, the scale of the teams devoted to solving them has expanded, with disciplinary scientists and computer scientists combining in interdisciplinary teams to conduct simulation science. Computational Science is an inherently interdisciplinary activity, and the Centre aims to be a focal point for collaborative cross-disciplinary and cross-organisational research.

Main collaborations

Collaborator Members
Australian Centre of Excellence for Risk Analysis (ACERA), University of Melbourne Ann Nicholson
Bureau of Meteorology
Defence Science and Technology Organisation (DSTO) Kevin Korb
Future University (Hakodate, JP) Bernd Meyer
NICTA Bernd Meyer
NICTA Optimisation Research Group
NICTA Control and Signal Processing Madhu Chetty
School of Public Health, University of Melbourne Kevin Korb
Universidad de Castilla la Mancha Ann Nicholson
Universidad de Granada Ann Nicholson
University of Melbourne Bernd Meyer
University of Warwick

Centre projects

  • Algae as Solar Bio-Fuel

    Bio-fuel production and carbon sequestration have been getting increasing attention in the recent years due to the dwindling fossil fuel reserves and global warming, respectively. Cyanobacteria (commonly known as blue-green algae) are oxygen evolving photosynthetic prokaryotes. Due to their naturally occurring biosynthetic machinery, they play a key role in the harvesting of solar energy and for naturally sequestering a large part of carbon dioxide from the earth’s atmosphere. An emerging idea is to sequester CO2 at source by using cyanobacterial ponds. To understand the underlying regulatory processes that channel the carbon toward various products, the research focusses on a specific cyanobacteria strain, i.e. Cyanothece sp. ATCC 51142 for obtaining a system level view in tuning its biosynthetic machinery. With the aid of circadian clock, Cyanothece can efficiently perform both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen. Studies involve e.g. reconstructing a genome scale gene regulatory network, modeling circadian clocks etc. The project is being carried out in collaboration withIIT Bombay and Medicine faculty of Monash. It has received a combined ~0.6M funding under Australia-India Strategic Research Fund.

  • Modelling Infectious Diseases

    Infectious diseases pose continuing and emerging challenges, with new strains of resistant diseases and novel varieties finding new means of transmission. Policy makers require models which can deliver improved predictions for assessing the value and consequences of public health interventions. This project develops mathematical models, agent-based simulations and Bayesian network models to advance our understanding of the epidemiology of infectious diseases.

  • Self-organized Collective Decision Making in Dynamic Environments

    Self–organisation is a fundamental mechanism used in nature to achieve flexible and adaptive behaviour in unpredictable environments.

    A paradigmatic example of self-organised social groups is an ant colony, whose strikingly organised and seemingly purposeful behaviour at the group level is coordinated without any central "master plan" or leader.

    Complex behaviour at the colony level emerges from simple interactions between myriads of individuals that only process local information. Similar forms of self-organised collective behaviour are found on all levels of size and complexity, from bacteria colonies via schools of fish, flocks of birds and herds of cattle to the social behaviour of human groups.

    Our work is investigates specifically which organisational principles allow such behaviour to adapt efficiently to changing environments.

    This work relies on stochastic modelling techniques and high performance simulation methods developed in a sister project.

    Image © Alex Wild 2013

  • Pollination in a new climate

    Agent-based simulations enable time- and cost-effective evaluations of complex interactions between different real-world entities. Recent investigations on pollinator-plant interactions show that the learnt flower preferences of important pollinators like bees is dependent upon both flower temperature, and regional ambient temperatures. This shows that local and global changes in climatic conditions may directly influence how certain plants are pollinated. This project is producing computer simulations to reveal how climate change may directly influence flower evolution in the future, and how the management of environmentally and economically important plants can be modelled to inform reliable decision making about this important resource.

    Image © Carlo Kopp 2013

 

PhD projects

Project title Supervisors
Computational and mathematical modelling of self organization in biological systems.
Computational Models for Identifying Driver Mutations in Cancer
Large scale modeling and inference of gene regulatory networks
  • Madhu Chetty
  • Rob Evans (University of Melbourne)
  • Terry Caelli (NICTA)
Orthogonal Layout of Biological Pathways in Garuda
Scaling Behaviour of Networks and Communication in Self-Organized Biological Systems
Scientific visualization and analysis of simulated epidemiological data.
Simulating bee foraging: how behavioural diversity in bees interacts with environmental conditions.
Using co-evolution to predict protein-protein interactions Geoff Webb