Download Applicazioni biotecnologiche in systems biology

Applicazioni biotecnologiche in systems biology Lezione #8 Dr. Marco Fondi AA 2012/2013 Network biology (metabolic networks) Lezione #8 Dr. Marco Fondi AA 2012/2013 A key aim of post-­‐genomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell Protein-­‐protein interaction (PPI) Gene regulation Metabolic reaction(s) L’analisi dei grafi permette di descrivere con precisione le interazioni che caratterizzano molti sistemi biologici. Misure sui network: • • • • • Degree Degree distribution Shortest path and mean path length Clustering coefficient Betweenness centrality Topological characteristics, such as node degree (connectivity) and bridging centralities are also informative in identifying critical network components. In scale-­‐
free networks, most nodes possess few connections, whereas a small number of nodes, termed hubs, are highly connected. This makes networks functionally robust, with random node loss more likely to eliminate a low connection node than a hub, resulting in a low probability of disrupting network function. On the other hand, identification of hubs and their targeted inhibition can be used to assess hub criticality to overall network integrity and function. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-­‐free networks, which include the World-­‐Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealisNcally high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to aOacks (that is, to the selecNon and removal of a few nodes that play a vital role in maintaining the network's connecNvity). Such error tolerance and aOack vulnerability are generic properNes of communicaNon networks. Error and aOack tolerance of complex networks Réka Albert, Hawoong Jeong & Albert-­‐László Barabási Nature, 2000 Mycobacterium tubercolosis PPI network Virus-­‐Host PPI network Strategia comune: gli HUB sono i migliori target per il drug desing. Gli effetti di una loro perturbazione influenzano il comportamento dell’intera rete. MA: .. analysis of several genomes indicates a significant trend toward evolutionary conservation of proteins with high degree and betweeness centrality.10 We therefore argue that drug targeting with currently available centrality metric models is likely to prove suboptimal because of the lack of specificity/selectivity of effects and the high risk of side effects W-­‐C Hwang, A Zhang and M Ramanathan 2008 Metabolism Metabolism is the totality of all the chemical reactions that operate in a living organism. Catabolic reactions Breakdown and produce energy Anabolic reactions Use energy and build up essential cell components 15 Prodotti derivati dal metabolismo microbico Saccharomyces cerevisiae ...all living organisms are essentially chemical systems, predicated by a set of chemical reactions taking place in an aque-­‐ ous solution within membrane-­‐bounded compartments... Steuera and Junkerb 2009
Deciphering the architecture underlying these interconnected physicochemical processes remains one of the greatest challenges of our time. Steuera and Junkerb 2009
Why Study Metabolism?  It s the essence of life..  Tremendous importance in Medicine: In born errors of metabolism cause acute symptoms and even death on early age  Metabolic diseases (obesity, diabetics) are major sources of morbidity and mortality  Metabolic enzymes and their regulators gradually becoming viable drug targets   Bioengineering:   Efficient production of biological products The best understood cellular network 20 Metabolites and Biochemical Reactions • Metabolite: an organic substance, e.g. glucose, oxygen • Biochemical reaction: the process in which two or more molecules (reactants) interact, usually with the help of an enzyme, and produce a product Glucose + ATP Glucokinase Glucose-­‐6-­‐Phosphate + ADP • Most of the reactions are catalyzed by enzymes (proteins) 21 -­‐omics technologies unprecedented new opportunities to study the mechanisms and interactions that govern metabolic processes. mathematical and computationalmethods to Aim: constructing a (working) computational representation of cellular metabolic processes -­‐ optimized biosynthesis of compounds -­‐ disease pathways -­‐ metabolic engineering -­‐ optimized growth of organism -­‐ essential genes -­‐ well grounded wet lab experiments Come si costruiscono i network (modelli) metabolici? Come si costruiscono i network (modelli) metabolici? Un esempio di ricostruzione metabolica Malate transport via proton symport (3 H) (3) H+[e] + (S)-­‐Malate[e] <=> (3) H+ + (S)-­‐Malate Succintate transport via proton symport (3 H) Succinate[e] + (3) H+[e] <=> Succinate + (3) H+ Fumarate transport via proton symport (3 H) (3) H+[e] + Fumarate[e] <=> (3) H+ + Fumarate succinate:fumarate antiporter Succinate + Fumarate[e] <=> Succinate[e] + Fumarate D-­‐galactonate transport via proton symport, reversible H+[e] + D-­‐Galactonate[e] <=> H+ + D-­‐Galactonate Octadecanoate transport via proton symport H+[e] + Octadecanoic acid[e] <=> H+ + Octadecanoic acid Hexadecanoate transport via proton symport H+[e] + Hexadecanoic acid[e] <=> H+ + Hexadecanoic acid Tetradecanoate transport via proton symport H+[e] + Tetradecanoic acid[e] <=> H+ + Tetradecanoic acid Glyceraldehyde facilitated diffusion D-­‐Glyceraldehyde[e] <=> D-­‐Glyceraldehyde Glycerol-­‐3-­‐phosphate : phosphate antiporter Orthophosphate + sn-­‐Glycerol 3-­‐phosphate[e] <=> Orthophosphate[e] + sn-­‐Glycerol 3-­‐phosphate L-­‐idonate transport via proton symport, reversible H+[e] + L-­‐Idonate[e] <=> H+ + L-­‐Idonate Potassium ABC transporter H2O + ATP + Potassium[e] => ADP + Orthophosphate + H+ + Potassium Lactose transport via proton symport H+[e] + Lactose[e] <=> H+ + Lactose L-­‐alanine transport via ABC system H2O + ATP + L-­‐Alanine[e] => ADP + Orthophosphate + L-­‐Alanine + H+ D-­‐lactate transport via proton symport H+[e] + (R)-­‐Lactate[e] <=> H+ + (R)-­‐Lactate maltopentaose transport via ABC system H2O + ATP + Maltopentaose[e] => ADP + Orthophosphate + H+ + Maltopentaose maltotetraose transport via ABC system H2O + ATP + Maltotetraose[e] => ADP + Orthophosphate + H+ + Maltotetraose maltohexaose transport via ABC system H2O + ATP + Maltohexaose[e] => ADP + Orthophosphate + H+ + Maltohexaose Fructose transport via PEP:Pyr PTS (f6p generating) Phosphoenolpyruvate + D-­‐Fructose[e] <=> Pyruvate + D-­‐Fructose 6-­‐phosphate Indole transport via proton symport, reversible H+[e] + Indole[e] <=> H+ + Indole nitrate transport in via nitrite antiport Nitrite + Nitrate[e] <=> Nitrite[e] + Nitrate 1 gene
1 proteina
Genome sequencing Network construcNon Metabolic modeling Gene annotaNon Gene funcNon Phenotypes predicNon Genome sequencing Network construcNon Metabolic modeling Gene annotaNon Gene funcNon Phenotypes predicNon KAAS -­‐ KEGG Automatic Annotation Server for ortholog assignment and pathway mapping KAAS -­‐ KEGG Automatic Annotation Server for ortholog assignment and pathway mapping A metabolic map A metabolic map A metabolic map A metabolic map Purine metabolism – Reference pathway Purine metabolism – Escherichia coli MetaCyc is a database of non-­‐redundant, experimentally elucidated metabolic pathways and enzymes. A unique property of MetaCyc is that it is curated from the scientific experimental literature according to an extensive process, such that: • More than 2,391 different organisms are represented • The majority of pathways occur in microorganisms and plants • More than 2,000 metabolic pathways are stored, with more than 10,924 enzymatic reactions and more than 35,063 associated literature citations • MetaCyc stores all enzyme-­‐catalyzed reactions that have been assigned EC numbers by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-­‐IUBMB) • MetaCyc also stores thousands of additional enzyme-­‐catalyzed reactions that have not yet been assigned an EC number La classificazione EC è un sistema di categorizzazione degli enzimi attraverso un cosiddetto numero EC (Enzyme Commission) . . . EC 3.4.11.4: idrolasi che agiscono solo sull'amminoacido N-­‐terminale di un tripeptide (il quarto è un numero d'ordine e segna l'ordine di scoperta degli enzimi) Esempio: biosintesi dell’aminoacido leucina Esempio: biosintesi dell’aminoacido leucina Pù dettagli: KEGG contiene più composti di MetaCyc mentre MetaCyc contiene pù reazioni e pathway di KEGG. Il numero di reazioni incluse al’interno di pathway dei duedata base è abbastanza simile. Metabolic models (~100) Complete microbial genomes(~4000) As such, there remains a middle ground between the fully automated and fully manual approaches, where the draft reconstruction and curation process stands to benefit from dedicated software support. Draft metabolic model Complete metabolic model