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EPSRC CASE Studentship in Systems Physiology open

Project title:"Using systems biology methods to optimize human nutrition

Proposed start date: 1/10/2013 
Duration of project: 3.5 Years 
Primary supervisor:Dr Lindsay Edwards, King's College London, UK. 

The central goal of this project is to use cutting-edge computational methods to design candidate nutritional strategies for humans that i) support healthy muscle regeneration and growth, ii) support optimal muscle performance and iii) minimize fat deposition

Systems biology uses mathematical and computational methods to describe and explore complex biological networks. An important recent trend in systems biology has been the development and application of constraint-based modelling 1. Briefly, this approach applies progressive physical, chemical and experimental inequalities (constraints) to the fluxes through a biochemical network, leading to sets of feasible solutions (called the solution space) rather than a single set of values. This process can be applied to very large network models and has proved particularly successful in analysing genome and proteome-scale models of metabolism 2,3. Given that these models are built from genomic and proteomic datasets4, the subsequent computation provides a theoretical framework whereby an organism's phenotypic space may be connected to its genotype1. Once built, they can be deployed and analysed to a variety of ends. For example, constraint-based modelling has been used to successfully predict the metabolic signatures of human inherited diseases5-7 and, intriguingly, to design an effective metabolic anti-cancer drug8. We recently showed that a genome-scale model of human cardiac mitochondrial metabolism was able to predict the effects of the fatty acid uptake inhibitor, perhexiline, with great accuracy and detail9. A similar model recapitulated many features of hypoxic exposure (and even allowed us to predict genes under pressure in humans living in the persistent hypoxia of high altitude)10,11. These latter two examples are pertinent here, because they demonstrate that constraint-based modelling can accurately predict physiological responses to alterations in nutrient supply. 

 Although good exercise nutrition is both event- and client-specific, the broader aims of exercise nutrition include i) to support (or even promote) muscle cell growth (or recovery), while minimizing fat deposition (adipogenesis and lipogenesis) and ii) to support muscle performance.A wealth of research has been conducted into human muscle and adipose cell metabolism under a variety of conditions. However, a fully quantitative dissection of the effects of varying the supply of all known nutrients on the metabolism of specific cell types in healthy humans has not been attempted, perhaps because, until recently, viable tools to answer these questions were not available.Genome-scale models, analysed using constraint-based methods, provide the ideal platform to quantitatively analyze the effect of nutrient supply on muscle and adipose cell metabolic function, both individually and together. Indeed, a multi-cell (muscleadipocyte-liver) genome-scale model of human metabolism was recently reported to have recapitulated key features of human metabolic interaction (for example, the Cori cycle)12

We seek an outstanding graduate with a background in systems biology, applied mathematics or computing (or a biologist with excellent mathematics) to undertake a 3.5-year program of research designed to harness the power of genome-scale models and apply them to key questions in exercise nutrition. This work will, to our knowledge, comprise the first fully quantitative examination of the effects of nutrient availability on muscle and adipose cell metabolic function, and their interactions. 

First, we will build and analyze two, related, genome-scale models of muscle cell metabolism. One will be of mouse C2C12 myotubes and will be used in conjunction with cell culture experiments aimed at refining our computational methods and testing the quality of our experimental predictions quickly, easily and cheaply. The other muscle model will be of mature human skeletal muscle and will ultimately be used for predicting the effect of nutrient manipulation of muscle metabolism (and designing strategies for use in humans). These models will be hand-curated (using standard operating procedures 4) and analysed, using constraint-based methods. This will allow us to systematically analyze the effects of manipulating the supply of every metabolite and small molecule that muscles can exchange with their environment.Thus we will be able to computationally determine optimal patterns of nutrient supply, under a range of conditions and with a number of objectives (to include muscle growth, both hypertrophy and hyperplasia, and performance). We will test our predictions (as well as continually validating our model) by culturing human and mouse muscle cells and making a range of measurements (for example, we will use metabolomic profiling to measure actual metabolite uptake and excretion under various conditions). 

Having analysed muscle cell metabolism quantitatively in this way we will carry out a similar process to study adipose cells. We will use existing 'banked' data to build genome-scale models of human and mouse adipocyte metabolism. Once the models have been built and validated, we will analyze the effects of varying nutrient supply on adipocyte growth and lipogenesis. We will validate our model and test our predictions using cultured adipocytes. Finally, we will build a twotissue model by combining the myocyte and adipocyte models (connected by a blood compartment). This two-cell model will allow us to quantitatively analyze interactions between the two cell types, and to design nutrient supply strategies that optimize muscle cell growth and performance while limiting adipocyte growth and lipogenesis (for example, by minimizing lipid 'overspill').

For more details or for an informal discussion please contact Dr Lindsay Edwards: 
Email: lindsay.edwards@kcl.ac.uk 
Tel: +44 (0)20 7848 6978

  1. Lewis NE, et al.(2012) "Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods" Nat Rev Microbiol. 10(4):291-305
  2. Oberhardt MA, et al.(2009) "Applications of genome-scale metabolic reconstructions" Mol Syst Biol. 5:320
  3. Feist AM, et al.(2008) "The growing scope of applications of genomescale metabolic reconstructions using Escherichia coli" Nat Biotechnol. 26(6):659-667
  4. Thiele I, et al.(2010) "A protocol for generating a high-quality genomescale metabolic reconstruction" Nat Protoc. 5(1):93-121
  5. Sigurdsson MI, et al.(2009) "Genome-scale network analysis of imprinted human metabolic genes" Epigenetics. 4(1):43-46
  6. Smith AC, et al.(2011) "A metabolic model of the mitochondrion and its use in modelling diseases of the tricarboxylic acid cycle" BMC Syst Biol. 5:102
  7. Shlomi T, et al.(2009) "Predicting metabolic biomarkers of human inborn errors of metabolism" Mol Syst Biol. 5:263
  8. Frezza C, et al.(2011) "Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase" Nature. 477(7363):225-228
  9. Yin X, et al.(2013) "Effects of perhexiline-induced fuel switch on the cardiac proteome and metabolome" J Mol Cell Cardiol. 55:27-30
  10. Edwards LM, et al.(2013) "The effect of hypoxia on human mitochondrial genetics and metabolism: studies using a genome-scale network reconstruction"- submitted. 
  11. Edwards LM, et al.(2011) "Studying the effects of hypoxia on mitochondrial metabolism in human heart using a genome-wide metabolic network model" Proc Aus Phys Soc. 42:33P
  12. Bordbar A, et al.(2011) "A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology" BMC Syst Biol. 5:180

posted 2013.06.08