Award details

Combinatorial Responses of Fungal Pathogens To Their Human Hosts: an Integrative Systems Biology Approach

ReferenceBB/F005210/2
Principal Investigator / Supervisor Professor Ken Haynes
Co-Investigators /
Co-Supervisors
Professor Mauricio Barahona, Professor Ivana Gudelj, Professor Michael Stumpf
Institution University of Exeter
DepartmentBiosciences
Funding typeResearch
Value (£) 1,242,699
StatusCompleted
TypeResearch Grant
Start date 01/09/2010
End date 30/09/2013
Duration37 months

Abstract

Biological systems function in constantly changing and complex environments, where they are subject to wide ranging combinations of stimuli and perturbations. To fully understand such systems one must experimentally perturb them, measure the resulting dynamic responses, and account for these responses mechanistically. Most researchers examine responses to individual stimuli in isolation. However, in reality most organisms are simultaneously exposed to multiple stimuli. Understanding and predicting the responses of biological systems to such complex combinatorial perturbations is a difficult but essential challenge, which can only be achieved through a well-organised programme that integrates experimental biology with mathematical modelling. We will address this challenge in the context of the major fungal pathogens Candida albicans and Candida glabrata and their responses to the combinatorial stresses they encounter in their human host. The virulence of these pathogens depends upon these stress responses. Combinatorial stress responses are likely to be complex, dynamic and nonlinear. It is impractical to explore all possible permutations experimentally, and therefore we will replace most experiments with a sequence of increasingly sophisticated models, developed in an iterative fashion with carefully chosen experiments. First we will model individual stress responses, then establish how to combine such models in a pairwise fashion, continue by developing models that describe responses to three simultaneous stresses, and finally determine the extent to which modelling can be used to predict combinatorial stress responses in related species. This will yield important insights into the virulence of major fungal pathogens, and will establish new generic approaches for the integration of individual models and the rational design of combinatorial biological experiments. We predict that these tools will be applicable in a wide variety of biological systems.

Summary

Biological systems are constantly subjected to a wide variety of external stimuli and challenges. Many of these change continuously and in order to survive the organism must respond appropriately to each new set of circumstances. It is however difficult for scientists to study such complex perturbations and so typically researchers have focused on one stimulus at a time. Whilst this has yielded major biological insights, to make further progress we need to develop approaches to studying combinations of several simultaneous perturbations. This cannot be done using conventional experimental techniques alone. These need to be supplemented by mathematical and computational modelling methods, which can integrate data from different experiments, reveal hidden patterns, explain apparently contradictory results and suggest new biological hypotheses. This type of interdisciplinary research is called Systems Biology. Recently a number of centres have been established in the UK to champion Integrative Systems Biology. This project involves one of these centres (CISBIC) at Imperial College London, whose focus is on the interaction between pathogens and their hosts. CISBIC will be partnered by Aberdeen University, thereby extending the range of pathogens studied at CISBIC, strengthening the two institutions existing collaborations and helping to integrate the rapidly growing systems biology group at Aberdeen into the UK and European community. We intend to apply Integrative Systems Biology techniques to understanding how pathogenic fungi respond to the combinations of different stresses they encounter when they invade a human host. We shall focus on the major fungal pathogens of humans, Candida albicans and Candida glabrata. They cause frequent oral and vaginal infections (thrush) and cause life-threatening infections of the bloodstream and internal organs in transplant and cancer patients. When such pathogens invade a patient, the immune system normally responds with a variety of counter-measures designed to kill the pathogen. For the microbe these counter-measures are essentially equivalent to environmental stresses, and hence it activates strategies to minimize the damage done by these stresses. The success of the pathogen depends on how well it counteracts these stresses to defeat the host's defences. We will study how these pathogenic Candida species respond to the combinations of stresses they experience in their human host. We will start by investigating each of three stresses in isolation and then use a mixture of experiments and models to explore how these responses differ when two different stresses are applied together. We will then use this information to predict and then validate what happens when the three stresses are encountered simultaneously. Finally, we will evaluate the extent to which our understanding of stress responses in these two pathogenic species can be used to predict the responses of related species for which less experimental information is available. This is an important biological question / this project will provide invaluable information about how biological systems in general respond to combinations of environmental signals as well as increasing our understanding of how pathogenic microbes interact with humans.
Committee Closed Committee - Plant & Microbial Sciences (PMS)
Research TopicsMicrobiology, Systems Biology, Technology and Methods Development
Research PriorityX – Research Priority information not available
Research Initiative Systems Approaches to Biological Research (SABR) [2007]
Funding SchemeX – not Funded via a specific Funding Scheme
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