In recent years there has been a rapid growth in the understanding of the basic cellular processes of individual bacterial cells through advances in genomics and proteomics research. However, this has introduced a demand to understand how the interactions between the individual system components contribute to the overall population dynamics, which is of great relevance in both ecological and clinical studies. A useful theoretical approach for relating information at the individual cellular/molecular level with emergent population characteristics is the agent-based (or individual-based) modelling approach.

Micro-Gen is an 'agent-based' model of bacterial growth and interactions in simulated laboratory culture conditions (see ERCIM News, No. 73, p 39-40). It uses information about each individual bacterial cell (the 'agent') to build up a picture of the population dynamics taking place in the colony as a whole. By concentrating on the characteristics of the cells and allowing the behaviour of the colony to emerge as a product of the random interactions between the independent cells, insights can be made into the mechanisms that lead to, for example, the development and spread of antibiotic resistance in bacteria.

Fig. 1: 3D Structure of a bacterial beta-lactamase enzyme, involved in antibiotic resistance.

Fig. 2: Micro-Gen Screenshot.

The simulated culture environment of Micro-Gen is represented by a discrete, two-dimensional grid containing diffusible elements such as nutrients, enzymes and antibiotics that the bacterial cells can interact with. The individual bacteria of a colony are represented by software agents, which store the physical traits such as energy state or antibiotic damage and the behavioural rules of the bacteria. The model is adaptable to represent a wide variety of different gram-positive or gram-negative bacterial species. Figure 2 shows a screenshot from Micro-Gen of bacterial colonies (yellow circles) growing on nutrient agar medium (blue, lighter shade represents higher nutrient concentration).

The model represents a good tool for informing antibiotic treatment strategies since it can be used to investigate the key parameters affecting antibiotic resistance in bacteria and relate this to key clinical indicators such as the MIC of a drug. There is a significant logistical burden associated with growing bacterial cultures and testing novel candidate drug compounds in the lab. However, the ability to simulate a wide variety of different conditions and parameters in a simulated environment could be used to inform rational drug design strategies.