CG020 Genomika BÍ7201 Základy genomiky 10. Systémová biologie 10. Systems biology Kamil Růžička Funkční genomika a proteomika rostlin, Mendelovo centrum genomiky a proteomiky rostlin, Středoevropský technologický institut (CEITEC), Masarykova univerzita, Brno kamil.ruzicka@ceitec.muni.cz, www.ceitec.muni.cz Přehled ■ What is systems biology ■ System theory ■ Omics ■ Reductionism vs. holism ■ Networks ■ Modular concept ■ Regulation of gene expression - example task for systems biology ■ Gene regulation X->Y ■ Transcriptional network of E. coli ■ Negative autoregulatory networks ■ Robustness of negative autoragulatory networks ■ (Positive autoregulatory networks) What is systems biology • fashionable catchword? • a real new (philosophical) concept? • new discipline in biology? • just biology? What is systems biology fashionable catchword? a real new (philosophical) concept? new discipline in biology? just biology? http://www.ceitec.eu/programs/genomics-and-proteomics-of-plant-systems/ Systems theory The behavior of a system depends on: • Properties of the components of the system • The interactions between the components Systems theory • The behavior of a system depends on: • Properties of the components of the system • The interactions between the components Forget about reductionism, think holistically. 6Xoq [hol'-os] - greek, all, the whole, entire, complete Systems biology meeting of old and new • Systems theory and theoretical biology are old • Experimental and computational possibilities are new Ludwig von Bertalanffy (1901-1972) GENERAL SYSTEM THEORY Gathered here are Ludwig von Bertalanffy's writings on general system theory, selected and edited to show the evolution o1 systems theory and to present its applications to problem solving An attempt to formulate common laws 1hat apply to virtually every scientilic Held, this conceptual approach has had a profound impact on such widely diverse disciplines as biology, economics, psychology, and demography. A German-Canadian biologist and philosopher Ludwig von Bertalanffy (1901-1972) was the creator and chief exponent of general system theory. He is the authpr of ten boohs including Robots Men. and Minds and Modern Theories of Development, both which have been published in several languages. Ai&o available Irom Georgs Brazilian Inc. The Systems Vmvof'tis World lseNO-8076-0630-7 pb.ST.tt The fletei/ance ol General Systems Theory ISBN O-S07&-0E59-6. hh. tt » Hierarchy Theory l SEN 0-W&-06M-X, 1*57.95 GEORGE BRAZILLER, INC. 171 Madison Avenue New York, NY 10016 ■ ■ ■ m ISBN GENERAl SYSTEM THEORY ■ ■ ■ ■ ■ ■ I I I I I I I I I L immun in I ■ ■ ■ ■ Ludwig von Bertalanffy Copyrighted material; sample page 3 of 22 4 Attninn in General System Thwry ................... Approach*! ami Airni in Syticrm Sfkn« MefbOfU in GenrTal SjHUH Research AdtjOMft ol General Swteta Theory Cl" Jhe Orgjfliun CvfliiJcrcil ai Phyutal Syitcnj,?-1 The Oifcaiiiuu ji Qj«ii 3ynum QtBtlii Cham riTi.ii,. l.: Open Chcmieil Sjsumi _______................._.. EqulmBaltiy.....................________________™.-----------,— Biological Applications............................................, 6 The Model of Open SyUon________..................................... I Living Machine arid In Limitation* Some Chjrjtcemiira of Open. Syileras Open Systems in Biology......................-------..... Open Systems nrJ Cybernetics .......... UBtehnd BtoMmm ........... .............. i'JSgidc Aipctts ol Sfiltrm Theory [n Biology^"""} —rtp*n tyufnt jDfj SieHv Si-1'"------- Feeilbact and Horn eo« jus »______..................... AHomeljy and ihc Surface Rule...................... .... Theory of Animal Growth Summary ■■ ".......___________________...........—...........- 1 8 The System Concept in the Science* of Man I'lif Orfprtiiirik R*k-olurJon The Ifmajrc- of Man in Contemporary Tltonihi Syitcm-Th cored til Rc^jtitntaiic-n v iLciin in the "— '■-1 ■ I Science*.............................-- A Syslem-ThroTeticaL t.■<:■■ tpl of History The Future in Syjtern Theoretical A>p«t 9 General Syilera Theory in Psychology and Piy^chiairy The quandary of Modern Piydinlogy Syitem Concepts ir> Psytlsopatlidogy <-..;;. |.r, 10 The Relativity ol Cil-cgiiiks The Whorfian Hypothesis: Omics-revolution shifts paradigm to large systems High Throughput Data o • i» • O a 90 , i» J 0 J ■ • 1-N Cellular Complexity 1-iX - Integrative bioinformatics - (Network) modeling Two roots of systems biology Biology root Molecular hiology grows rapidly (1960s & 70s) High-throughput sequencing (1980s) Feed back regulation in metabolism (1957) Lac Operon (1961) Analog simulation (early 1960s) 111 at the genome-scale (1990s) biology became data rich_ Genome-scale analysis; bioin forma tics grows (1990s) Systems biology requirement* Genome-scale Broad fundamentals Teamwork: multisitc infrastructure New curricula Avoid incremental thinking Systems biology Large-scale simulators of metabolic networks (1970s) Genome scale models and Data poor in silico M analysis (laic 1990s) biology, models of viruses, red blood cell (19S0s & early 1990s) Systems root Palsson 2007 Associated disciplines o Genomics o Epigenomics o Transcriptomics o Translatomics / Proteomics o Interactomics o Metabolomics o Fluxomics o NeuroElectroDynamics o Phenomics o Biomics Associated disciplines o Genomics o Epigenomics o Transcriptomics o Translatomics / Proteomics o Interactomics o Metabolomics o Fluxomics o NeuroElectroDynamics o Phenomics Jozef Mravec s term: o Biomics multidimensional biology How I understand systems biology Genetics - you have one or few RNA processing genes where you show their importance in protoxylem development Functional genomics - you find in e.g protoxylem expression profiles numerous RNA processing genes and demonstrate which are important for protoxylem developments Systems biology - based on obtained large scale data you propose model how genes (and/or other components) collectively regulate protoxylem development How I understand systems biology o Good biology - you explain why just some genes regulate protoxylem development (sorry for aphorisms) Reconstructed genome-scale networks Species Escherichia coii Saccharomyces cerevisiae Bacillus subtilis 020 Lactobacillus plantarum 543 Human I 3673 Arabidopsis Reference 1260 Feist AM. etal. (2007), Mol. Syst. Biol. 708 Förster J. etal. (2003), Genome Res. 844 OhYK. etal. (2007), J. Biol. Chem. 721 Teusink B. etal., (2006), J. Bio. Chem. 1865 Duarte NC. etal., (2007), PNAS Arabidopsis Interactome Mapping Consortium (2011), Science Reconstructed genome-scale networks Species Reference Escherichia coli 2077 1260 1175 708 Feist AM. etal. (2007), Mol. Syst. Biol. Saccharomyces cerevisiae Förster J. etal. (2003), Genome Res. Bacillus subtilis i020 844 Lactobacillus plantarum 543 721 OhYK. etal. (2007), J. Biol. Chem. Teusink B. etal., (2006), J. Bio. Chem. Human 3673 1865 Duarte NC. etal., (2007), PNAS Arabidopsis Arabidopsis Interactome Mapping Consortium (2011), Science Complexity of cellular networks in E. coli Interactions Components Information processing Structure Stress Other functions Sometimes the things are different than we just think Reconstruction of networks from -omics for systems analysis • Gene expression networks: based on transcriptional profiling and clustering of genes • Protein-protein interaction networks (Y2H, TAP etc). • Metabolic networks: network of interacting metabolites through biochemical reactions. Reconstruction of networks from -omics for systems analysis • Gene expression networks: based on transcriptional profiling and clustering of genes • Protein-protein interaction networks (Y2H, TAP etc). • Metabolic networks: network of interacting metabolites through biochemical reactions. How to simplify. Modularity concept. Lets e.g. assume that transcription and translation is one module. E. coli - o ■ ■ ■ ■ * Binding of a small molecule (a signal) to a transcription factor, causing a change in iraiiseriplion factor activity Binding of active transcription factor to its DNA site Transcription + translation of the gene Tiniescate for 50% change in concentration of the translated protein (stable proteins) -1 msec -1 sec -5 min ~1 h (one cell generation) Generation time 20 min Description of gene regulation Transcription factor X regulates gene Y: X -> Y (X -» transcription -»translation -» F) Description of gene regulation X -> Y Rate of production: R [units .time-1] Rate of degradation: a [time1] Description of gene regulation X -> Y Rate of production: fl [units .time-1] Rate of degradation: a [time-1] a~ adil+ adeg Description of gene regulation X -> Y Rate of production: fl [units time-1] Rate of degradation: a [time1] O — ( ) — o +o cells grow protein is degraded Description of gene regulation X ^ Y Rate of production: (I [units.time-1] Rate of degradation: a [time-1] Change of concentration: dY 1. Steady state - ustálený stav dY dY ■3-= 0 dt I Y i Y --1st — a t 28 2. Production of Y stops dY (3 = 0 I The decay is exponential, 2. Production of Y stops: Measure of Y decay - response time (7\/2). Yt = Yste~c 1 Yt-2Y« I 7l/2" "V (log => In [.CZ]) 2. Production of Y stops: Measure of Y decay - response time (7\/2). Yt = Yste~c 1 Yt-2Y« I T - l0S : Large a —► rapid degradation Stable proteins (most of E. coli proteins) log 2 ^1/2 — 1 a a= adll+ ade. T - cell generation Stable proteins log 2 ^1/2 — 1 a a= adll+ ade. T - cell generation Jog 2 1/2 T^ /9 —--— t a Hi/ Response time is one generation. 3. Production of Y starts from zero dY dt ~ fi-aY t 34 3. Production of Y starts from zero 3. Production of Y starts from zero ...—^_ ._______ ft 08 dY i " , = p aY 0.4 at 02 1 ■ \ 0 A i ■ 1 1 - - ... Yst - ~ a (magic) < > 0.5 ^ 1 l.S 1 2.5 1 3.5 4 4.3 ! \ (/I,,, \ K =-(l-e"at) \ \ * Y grows almost linearly initially 3. Production of Y starts from zero Response time: Yt = Yst(l-e~m) u 1 > 0.4 log 2 ^1/2- „ 0-5 1 1 S 2 3.5 .1 1 4.5 5 The same response time as in case 2. Response time does not depend on production rate! 3. Production of Y starts from zero Response time: Yt = Yst(l-e~m) u 1 > 0.4 log 2 ^1/2- „ 0-5 1 ! S 2 3.5 .1 1 4.5 5 Not many degradation mechanisms in E. coli (energy consuming). Perhaps in plants? Networks node thread (CZ: uzel) (CZ: hrana) Transcriptional network of E. coli 420 nodes, 520 edges How may self-edges? (CZ: samohrana?) Likelihood of the self-edge Assumptions from random network (400 nodes (N), 500 edges (E)). How many self-edges? Autoregulation is a network motif 420 nodes, 520 edges. 40 self-edges! Autoregulation is a network motif protein X 0 protein X 0 pX PX negative regulation positive regulation E. coli:40 autoregulatory loops: 36 negative, 4 positive Negative autoregulatory loop is best described by Hill's function dY dY nr -T = P-aY =p(Y)-aY Negative autoregulatory loops Hill's function max 'max Y Negative autoregulatory loops Hill's function max maximum production rate Y concentration of Y needed for 50 % repression 'max steepness (Hill's function) Negative autoregulatory loops Hill's function Y max v maximum (initial) production rate 000 = 'max 1 + Negative autoregulatory loops (i synthesis rate - stochastic noise (stochastický ruch) IJ(Y) 0 3! J ax 03 HIS 0.1 ax * •acs 15 20 25 t(min) Y & may vary by 10 - 30 % (other parameters stable) Negative autoregulatory loops Hill's coeficcient varies between 1 - 4, the higher the steeper important factor: multimerization p(Y) = max 1 + ft) n Negative autoregulatory loops K - repression coefficient • depends on chemical bonds between Y and its binding sites • a point mutation can increase K -10 times CO -> . NNNNN _ ATG Y concentration of Y needed for 50 % repression /5(F) = max Positive autoregulatory loops Hill's function b(y) ____R ■'■'max s1/2 n fY\n k y Back to simple regulation /removal (aY) production (ß) rate of change (ß - aY) 1 St Example: if ß=0 (no production) 53 production > removal removal > production production (B) Y, St Y /iremoval (aY) production (ft) rate of i y ■ change (ft - aY) Y, Y 1 St the longer the distance, the faster the change Therefore more difficult to come to Yst with time Y St t Autoregulation vs. simple production y/\removal (aY) production (&) /I Yt Y 1 St Comparison assume that these values are the same 1Yst 2. a Comparison Lets assume that these values are the same 1Yst 2. a Lets put it in one graph. Autoregulation vs. simple production in such case, always Bmax>li ^ removal {aY) ^max Y production (ft) Yt Y 1 St Autoregulation vs. simple production ^max y removal {aY) r - / \ production (ft) Y, Y 1 1 St the distance always more far => the reactions are faster Response time was confirmed indeed faster (-5 times) Y 0 0.21 0.5 Alon 2007 negative autoregulation simple regulation 1 1.5 2 Cell generations 2.5 time Cases of sharp curve Cases of sharp curve Cases of sharp curve production (ft) ^ max Fluctuations in synthesis or removal don't change much. Cases of sharp curve production (ft) , removal (aY) ^max 15 *'' s -'' Yst=K Y Yst depends only on K - on protein-DNA binding properties. Conclusions Negative autoregulation o speeds up response time o is robust (for a, ft) => basically on/off o bypasses stochastic noise production 67 Conclusions The model explains why negative autoregulation is a common network motif in E. coli. 68 Conclusions The model explains why negative autoregulation is network motif. We will not avoid mathematics in biology. 69 Positive autoregulation leads to slower response A , ^^0* —" — Negative 0.8- ^*--**" ^^-"""""^ autoregulation / Simple 0,6- / / i regulation X 0.4 - / /\ / \ -Positive 0.2-0 \\ / ! / ! autoregulation f 1 / 1 1 /1 A i U f c 1 1 1 1 1 1 1 1 1 1 1 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Cell generations Positive autoregulation leads to higher variation Strong variation: - => differentiation of cells into 2 populations (development) - => memory for maintaining gene expression (development) - helps with maintaining mixed phenotype for better response to changing environment Literature Source literature hťtp://www.voutube.com/watch?v=Z BHVFPOLk and further - excellent talks about systems biology from Uri Alon {Weizman Institute) ■ Rosenfeld N, Negative autoregulation speeds the response times of transcription networks. J Mol Biol. 2002 Nov 8;323(5)785-93. - experimental testing of the data Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet. 2007 Jun;8(6):450-61. Review about the same. ■ Alon, U. (2006). An introduction to Systems Biology: Design Principles of Biological Circuits (Chapman and Hall/CRC). ■ Palsson, B.0. (2011). Systems Biology: Simulation of Dynamic Network States (Cambridge University Press). Most common textbook about systems biology. For enthusiasts ■ Zimmer (2009). Microcosm- E Coli & the New Science of Life (Vintage) (popular scientific book about E. coli as model organism and what you probably didn't know) Albert-László Barabási (2005) V pavučině síti. (Paseka) (znamenitá kniha o matematice sítí, dynamicky se rozvíjejícím oboru od předního světového vědce) ■ PA052 Úvod do systémové biologie, Přednášky. Fakulta Informatiky MU ■ http://sybila.fi.muni.cz/cz/index - obor na fakultě informatiky. Reductionism vs. holism Components view Component Function s + E—»x — E 1 Time-dependent concentration Compute flux for function Systems view Needed homeostasis r Reaction network Steady state flux map Calculate k Calculate C Stochastic noise (stochastický ruch) Cell Generation interní ruch - transkripce, translace, post-transkripční jevy pozice DNA v chromozómu Flux balance analysis (FBA) 1 Identifying optimal solutions . An optimal . solution V Constraints set bounds on solution space, but where in this space does the "real" solution lie? FBA: optimize for that flux distribution that maximizes an objective function (e.g. biomass flux) - subject to S.v=0 and a^v^pj Thus, it is assumed that organisms are evolved for maximal growth -> efficiency! Flux balance analysis (FBA) 1 Identifying optimal solutions . An optimal . solution V Constraints set bounds on solution space, but where in this space does the "real" solution lie? FBA: optimize for that flux distribution that maximizes an objective function (e.g. biomass flux) - subject to S.v=0 and a^v^ Thus, it is assumed that organisms are evolved for maximal growth -> efficiency! PA052 Úvod do systémové biologie Metagenomics 12 Cell Generation interní ruch - transkripce, translace, post-transkripční jevy, pozice DNA v chromozómu externí ruch - fluktuace koncentrací regulačních faktorů 81