Combinatorial Responses of Fungal Pathogens To Their Human Hosts: an Integrative Systems Biology Approach
Lead Research Organisation:
Imperial College London
Department Name: Life Sciences - Molecular Biosciences
Abstract
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.
Technical Summary
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.
Publications
Stark J
(2008)
Systems Biology: a noisy coming of age.
in Current opinion in biotechnology
Ingram PJ
(2008)
Nonidentifiability of the source of intrinsic noise in gene expression from single-burst data.
in PLoS computational biology
Kirk PD
(2009)
Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data.
in Bioinformatics (Oxford, England)
Brown AJ
(2009)
Nitrosative and oxidative stress responses in fungal pathogenicity.
in Current opinion in microbiology
Toni T
(2009)
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.
in Journal of the Royal Society, Interface
Toni T
(2009)
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.
in Journal of the Royal Society, Interface
Secrier M
(2009)
The ABC of reverse engineering biological signalling systems.
in Molecular bioSystems
Huvet M
(2009)
Model-based evolutionary analysis: the natural history of phage-shock stress response.
in Biochemical Society transactions
Kelly WP
(2010)
Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins.
in BMC bioinformatics
Harmston N
(2010)
What the papers say: text mining for genomics and systems biology.
in Human genomics
Stumpf MP
(2010)
Incomplete and noisy network data as a percolation process.
in Journal of the Royal Society, Interface
Harmston N
(2010)
What the papers say: text mining for genomics and systems biology.
in Human genomics
Strelkowa N
(2010)
Switchable genetic oscillator operating in quasi-stable mode.
in Journal of the Royal Society, Interface
Toni T
(2010)
Simulation-based model selection for dynamical systems in systems and population biology.
in Bioinformatics (Oxford, England)
Lèbre S
(2010)
Statistical inference of the time-varying structure of gene-regulation networks.
in BMC systems biology
Stead DA
(2010)
Impact of the transcriptional regulator, Ace2, on the Candida glabrata secretome.
in Proteomics
Joly N
(2010)
Managing membrane stress: the phage shock protein (Psp) response, from molecular mechanisms to physiology.
in FEMS microbiology reviews
Delvenne JC
(2010)
Stability of graph communities across time scales.
in Proceedings of the National Academy of Sciences of the United States of America
Toni T
(2010)
Parameter inference and model selection in signaling pathway models.
in Methods in molecular biology (Clifton, N.J.)
Grima R
(2010)
Crowding-induced anisotropic transport modulates reaction kinetics in nanoscale porous media.
in The journal of physical chemistry. B
Liepe J
(2010)
ABC-SysBio-approximate Bayesian computation in Python with GPU support
in Bioinformatics
Thorne TW
(2011)
Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae.
in Fungal genetics and biology : FG & B
Zhou Y
(2011)
GPU accelerated biochemical network simulation
in Bioinformatics
Delmotte A
(2011)
Protein multi-scale organization through graph partitioning and robustness analysis: application to the myosin-myosin light chain interaction.
in Physical biology
Barnes CP
(2011)
Bayesian design of synthetic biological systems.
in Proceedings of the National Academy of Sciences of the United States of America
Description | Cellular information processing and decision making: from noise to robust phenotypes |
Amount | £785,000 (GBP) |
Organisation | Human Frontier Science Program (HFSP) |
Sector | Charity/Non Profit |
Country | France |
Start | 01/2011 |
End | 01/2015 |
Description | Cellular information processing and decision making: from noise to robust phenotypes |
Amount | £785,000 (GBP) |
Organisation | Human Frontier Science Program (HFSP) |
Sector | Charity/Non Profit |
Country | France |
Start | 01/2011 |
End | 01/2015 |
Description | Transmission and coevolutionary dynamics drive the evolution of generalist and specialist viruses |
Amount | £259,966 (GBP) |
Funding ID | BB/J010340/1 |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 11/2011 |
End | 10/2014 |
Description | Collaboration with Dr Quinn, Newcastle University |
Organisation | Newcastle University |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Our collaboration with Dr Quinn was used to study oxidative stress responses in Candida spec. |
Start Year | 2010 |
Description | Collaboration with Prof Segal, University at Buffalo |
Organisation | University at Buffalo |
Country | United States |
Sector | Academic/University |
PI Contribution | A collaboration was established with Prof Segal to study stress in Candida spec. in transgenic mice. |
Start Year | 2012 |
Description | Collaboration with Prof White, University of Missouri-Kansas City |
Organisation | University of Missouri-Kansas City |
Country | United States |
Sector | Academic/University |
PI Contribution | A collaboration was established with Prof Theodore White to model the epidemiological shift from C. albicans to C. glabrata. |
Start Year | 2012 |
Description | Collaboration with Syngenta |
Organisation | Syngenta International AG |
Country | Switzerland |
Sector | Private |
PI Contribution | A collaboration was established with Syngenta to determine the impact of osmotic stress upon Candida spec. metabolomes. |
Start Year | 2008 |