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Systems Biology: Genome-scale metabolic reconstruction of biological networks Juan Nogales Enrique Department of Environmental Biology Centro de Investigaciones.

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Presentation on theme: "Systems Biology: Genome-scale metabolic reconstruction of biological networks Juan Nogales Enrique Department of Environmental Biology Centro de Investigaciones."— Presentation transcript:

1 Systems Biology: Genome-scale metabolic reconstruction of biological networks
Juan Nogales Enrique Department of Environmental Biology Centro de Investigaciones Biológicas (CSIC), Madrid, Spain FluxA (VA) FluxB (VB) FluxC (VC)

2 Vs Reduccionismo Vs Aproximación Holística (Biología de Sistemas)
Velocidad de vuelo Resistencia al aire Vs The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge Systems biology...is about putting together rather than taking apart, integration rather than reduction. Science 316 (5824): 550–551 The music of life: Biology beyond the genome

3

4 What is a Network? A map of interactions or relationships
A collection of nodes and links (edges)

5 Why Networks instead of single components?
Focus on the organization of the system Simple representation Allows the visualization of complex systems Allows the use of Networks as a tools Underlying difusion models (evolution) The structure/topology of the system determine its function

6 Computational Representation of Networks

7 Biological Networks A map of interactions or relationships
A collection of nodes and links (edges) Metabolic components Regulatory components Signaling components Protein-Protein interactions Gene interactions […]

8 Regulatory Networks Metabolic Networks Signaling Networks
Silva-Rocha et al, 2011 Metabolic Networks Signaling Networks Nogales’ repository

9 Step 1. Modeling Metabolism
Metabolism is the set of life-sustaining chemical transformations within the cells of biological systems. Catabolism Anabolism

10 Levels of Metabolism Elemental level Intermediate level System level
Glucose ATP ADP Pi G6P F6P F16di P G3P DHAP 3PG 2PG PEP Elemental level HEX1 PGI PFK TPI GADP PGK ENO Intermediate level Bordbar A et al. Mol Syst Biol 2014;10:737 System level (Genome-scale)

11 Outlook Genome-scale metabolic reconstruction and analysis (GEMREs)
Applications of GEMREs (Nest Class)

12 System Vs Components Metabolism is the set of life-sustaining chemical transformations within the cells of biological systems. Biological system: is a group of biological components that work together to perform a certain task (phenotype). Phenotype: Is the composite of an organism’s observable charachteristics or traits which result from the expresion of an organism’s genotype as well as the influence of environmental factors and the interactions between the two.

13 System Vs Components FluxC (VC) FluxB (VB) FluxA (VA)
Phenotype from unknown components and/or interactions Phenotype from emergent properties FluxA (VA) FluxB (VB) FluxC (VC) Full phenotype space Known phenotype space Velocidad de vuelo Resistencia al aire

14 How systems metabolism can be studied?
P. putida KT2440 Seiscientos Ferrari

15 Metabolic models Topological Models Kinetics Models Constraints Models

16 Metabolic Flux as phenotype
The flux of the metabolites through each reaction (J) is the rate of the forward reaction (Vf), less that of the reverse reaction (Vr). PLoS Comput Biol 6(7): e Vf A B J=Vf-Vr Vr

17 What is a GEnome-scale Metabolic Reconstruction?
“A GEMRE is a stoichiometric representation of the metabolic capabilities of a given organism at genome-scale, which can be further translated to a mathematical format allowing the computation of the phenotype from its genotype” Stoichiometric Representation Gene RNA Protein Reaction 2pg pep h2o Metabolites Genotype Phenotype Glucose ATP ADP Pi G6P F6P F16di P G3P DHAP 3PG 2PG PEP Biochemical Representation HEX1 PGI PFK TPI GADP PGK ENO Mathematical Representation Reed JL et al, Nature Reviews Genetics: 2006

18 integration and contextualization
Multiple input points for omics data integration and contextualization Pput_2878 Pput_2879 Pput_2880 Pput_2881 TodC1 TodC2 TodB TodA TOLDOX tol[c] + o2[c] + nadh[c] + h[c] toldh[c] + nad[c] DNA RNA Peptide Enzyme Reaction Metabolites Gen-Protein-Reaction association (GPR) Genomics Transcriptomics Proteomics Fluxomics Metabolomics

19 Defining the allowed solution space
x = d[mi]/dt=0 […] S x v = Applying Constraints d[m1]/dt=0 Steady State n reactions m metabolites mi Rxns Boundering ai < v1 < bi -1000 < v1 < 1000 0 < v1 < 1000 0 < v1 < Vmax v fluxes max cv(Z) subject to Sv = 0 Optimization of objective function

20 FBA at work: The assumption of Steady state
v1 v2 v3 v4 b1 b2 b3 A -1 1 B C d[A]/dt = d[B]/dt d[C]/dt v1 v2 v3 v4 b1 b2 b3 X S v = d[mi]/dt=0 Steady state assumption

21 FBA at work: Linear programing formulation
v1 v2 v3 v4 b1 b2 b3 A -1 1 B C d[A]/dt = d[B]/dt d[C]/dt v1 v2 v3 v4 b1 b2 b3 X d[A]/dt= -v1-v2+v3+b1 = 0 d[B]/dt= v1+v4 – b2 = 0 d[C]/dt= v2-v3-v4 –b3 = 0 v1= -v2+v3+b1 v4 =-v1+b2 v2=v3+v4 +b3 Linear programing system

22 FBA at work: Optimization of the objective function
v1= -v2+v3+b1 v4 =-v1+b2 v2=v3+v4 +b3 Optimization of objective function max cv(Z) subject to Sv = 0

23 Properties of the solution space
FluxC (VC) FluxA (VA) FluxB (VB)

24 Metabolic reconstruction process

25 Metabolic reconstruction process
Thiele & Palsson (2010). Nature Protocols 5,

26 Draft reconstruction Organism Genome Draft of metabolic network
Manual: Automatic:

27 Manual curation

28 Manual curation

29 Manual curation

30 BIOMASS OBJECTIVE FUNCTION
Abbreviation mmol/gDW.h 5mthf 0.05 leu-L 0.428 accoa lys-L 0.326 ala-L 0.488 met-L 0.146 amp 0.001 Nad arg-L 0.281 Nadh asn-L 0.229 Nadp asp-L nadph 0.0004 atp pe120 0.0005 clpn120 pe160 clpn160 pe161 clpn161 pe180 clpn180 pe181 clpn181 peptido_kt 0.028 coa pg120 cpe160 pg160 cpe180 pg180 cpg160 phe-L 0.176 cpg180 pro-L 0.21 ctp 0.126 ptrc 0.035 cys-L 0.087 ser-L 0.205 datp 0.0247 sheme dctp 0.0254 succoa dgtp thr-L 0.241 dttp trp-L 0.054 fad tyr-L 0.131 gln-L 0.25 udpg 0.003 glu-L utp 0.136 gly 0.582 val-L 0.402 gtp 0.203 adp h2o h hemeO pi his-L 0.09 ppi 0.7302 ile-L 0.276 BIOMASS OBJECTIVE FUNCTION Byproducts Nutrients Growth Growth = Biomass + Mantenance

31 Conversion to matemathical model
Stoichiometric Representation Glucose ATP ADP Pi G6P F6P F16di P G3P DHAP 3PG 2PG PEP Biochemical Representation HEX1 PGI PFK TPI GADP PGK ENO Mathematical Representation Reed JL et al, Nature Reviews Genetics: 2006

32 Metabolic model evaluation

33 COnstraints-Based Reconstruction and Analysis Methods
Lewis NE, et al. 2013

34 Applications

35 Cutting edge applications of metabolic models in the post-genome era: From basic biology to biotechnology Juan Nogales Enrique Departament of Environmental Biology Centro de Investigaciones Biológicas (CSIC), Madrid, Spain FluxA (VA) FluxB (VB) FluxC (VC)

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