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Principles of Systems Biology and Dmitri Belyaev's Co-Selection of Traits

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Principles of Systems Biology and Dmitri Belyaev's Co-Selection of Traits Principles of systems biology and Dmitri Belyaev’s co-selection of traits Hans V. Westerhoff and friends MCISB, MIB, SCEAS, University of Manchester SysBA, Universities of Amsterdam Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity Bioinformatics: From biological data to information Systems Biology: From that information to understanding Systems Biology: From data to understanding: why is this such an issue? • Because the mapping from genome to function is extremely nonlinear • E.g.: – - – - The DNA in all our cells is the same, but: a heart cell is essentially different from a brain cell – - Self organization, bistability: Belousov, Zhabotinsky, Waddington, Ilya Prigogine, Boris Kholodenko Why systems biology? Cause 1 ~all functions are X network functions Multiple causality Cause 2 Cause 3 X Multifactorial X disease Impaired function 2006 Hornberg et al: ‘Cancer: a systems biology disease’. Now: ‘virtually all disease are Systems Biology diseases.’ This causes the ‘missing heritability problem (Baranov; Stepanov)’ Systems Biology= • The Science that • aims to understand • principles governing • how the biological functions • arise from the interactions = from the networking This leads to precision, personalized, 4P medicine, PPP4M And to precision biotechnology Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity Henrik Kacser (Student of Waddell) Henrik Kacser Recessivity of most lack-of-function mutations Lack of dominance: No loss of function in heterozygote Lack of dominance: observation F0 F0’ F1 (knock out) X Function= 100% 0% 95% Flux J Lack of dominance: single molecule explanation fails F0 F0’ F1 AA 00 A0 X This is almost always incorre Function= 100% 100% 50% Flux J ct Cf. talk by Prof Baranov Lack of dominance: single molecule explanation fails F0 F0’ F1 AA 00 A0 X Function= 100% 100% >90%X50% Flux J The systems biology explanation (Henrik Kacser) Dependence of pathway flux on the concentration of an enzyme Nonlinear: Remember the Polly presentation Flux versus enzyme activity 1 0.5 Flux J 0 0 0.5 1 1.5 2 2.5 Enzyme 1 activity Wild type level How to measure whether a component is limiting: take the relative slope: the flux control coefficient extent of dominance 푑퐽 푑푒 푖 J 푟푒푙푎푡푖푣푒 푠푙표푝푒 = Flux Control Coefficient C I = 퐽 푒푖 푠푡푒푎푑푦 푠푡푎푡푒푠 푎푛푑 푎푙푙 표푡ℎ푒푟 푒푗 푐표푛푠푡푎푛푡 Flux versus enzyme activity The relative 1 slope determines 0.5 Flux J the extent of 0 control 0 0.5 1 1.5 2 2.5 Gene dosage ei Example C is not confined to the single first irreversible step in the pathway dJ d ln | J | '%dJ ' C J J i d ln e de '1%de i steadystate i i ei + 1.01 Systems Biology= • The Science • That aims to understand • principles governing • how the biological functions • arise from the interactions Flux Control Summation Principle 퐽 퐽 퐽 퐽 퐶1 + 퐶2 + 퐶3 + ⋯ + 퐶푛 ≡ 1 • J: steady state flux • 1, 2, 3, : number of the enzyme (gene product) • Consequence: %푟푒푑푢푐푡푖표푛 푖푛 푓푢푛푐푡푖표푛 퐶퐽 ≈ =1/n 푖 푓표푟 푎 50% 푟푒푑푢푐푡푖표푛 푖푛 푔푒푛푒 푑표푠푎푔푒 Flux Control Summation Principle and recessivity Henrik Kacser (Student of 퐽 %푟푒푑푢푐푡푖표푛 푖푛 푓푢푛푐푡푖표푛 Waddell) 퐶 ≈ ≅ 1/10, 푖 푓표푟 푎 50% 푟푒푑푢푐푡푖표푛 푖푛 푔푒푛푒 푑표푠푎푔푒 hence 5 % reduction in function for a pathway of 10 genes Lack of dominance: observation F0 F0’ F1 X Function= 100% 0% 95% Flux J Lack of dominance (expectation?) F0 F0’ F1 X Function= 100% 0% 50% Flux J Lack of dominance (network explanation; Kacser) F0 F0’ F1 X Function= 100% 100% 95% Flux J Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity Principles The Novosibirk example of co-selection: Selection for domestication also brings drooping ears Belyaev’s observation By selection for lack of aggressivity Function: Aggressivity Upright ears Domestified Drooping ears Singe molecule explanation fails By selection for lack of aggressivity Function: Aggressivity Upright ears Domesticated Upright ears Network explanation By selection for lack of aggressivity Function: Aggressivity Upright ears Network explanation By selection for lack of aggressivity Function: Aggressivity Upright ears Domestified Drooping ears N.K. Popova’ lecture Domestication Activation of 5-HT systems Catalepsy Hypophyse al-gonadal system 5- HT Decrease of Reduction aggressiven of stress ess response Selection during domestication Domestication Activation of 5-HT systems Catalepsy Hypophyse al-gonadal system 5- HT Decrease of Reduction aggressiven of stress ess response After selection Domestication Activation of 5-HT systems Catalepsy Hypophyse al-gonadal system 5- HT Decrease of Reduction aggressiven of stress ess response Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity M4 @ ISBE.NL team Stefania Astrologo, Ewelina Weglarz-Tomczak, YanFei Zhang Make Me My Model service: Something 4U? Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity The human: A jungle of 25 000 genes and gene products and various nutrition, life style and ambition factors This must be impossible to deal with. Where to start……? We obtained the consensus genome wide metabolic map (Recon2), i.e. all the human network can make from any nutrition food1 food2 This is all components in the context of their whole food3 Data concerning all metabolic genes have hereby been integrated into a predictive format. Predicting how every molecule in our body is made by our body Does this help? Could it lead to cures? 40 Example of map utilization tyrosine metabolism: nature Phenylketonuria (PKU) = IEM Phenylketone urine Nutrition Protein Phe ✗✗ ✗OK Tyr ✗ ✗ ✗ dopa dopamine Nor-epinephrin Example of map utilization tyrosine metabolism: Nurture and brain Protein dopa (Herbeck dopamine presentat ion) Nor-epinephrin Epinephrin=adrenaline Now: after hearing Prof Popova: Mapping serotonin (?) Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity How can we understand clonal heterogeneity (such as in these glucose transporter activities)? Yeast uptake of fluorescently labelled glucose analogue Explanations Phenotypic heterogeneity • Variations in external conditions (extrinsic noise) • Genetic diversity • Epigenetic diversity: • Intrinsic Noise • Bistability Explanations Phenotypic heterogeneity • Variations in external conditions (extrinsic noise) • Genetic diversity • Epigenetic diversity: • Intrinsic Noise • Bistability What is noise? Due to temporarily increased/decreased activities of molecular processes At different moments in different individual cells, Hence noise cell-cell heterogeneity From statistical mechanics: In a flat (bio)chemical network at steady state, noisy molecule numbers should be (approximately) Poisson distributed Probability distribution: characteristics • 흁 = 푥ഥ푖: the average of x 2 2 2 2 • 휎 = (푥푖 − 휇) = 푥푖 − 휇 : Noise: 0.07 • 휎 = 푠푡푎푛푑푎푟푑 푑푒푣푖푎푡푖표푛 = 휎2: the width of the 0.06 distribution 0.05 휎 • Relative noise = Coefficient of variation: 푐푣 = Τ휇 0.04 =standard deviation in number of 흈ퟐ • Fano factor: 푭 = ൗ흁: deviation from trivial noise 0.03 mRNAs=6.3 (width of distribution) 0.02 0.01 =Average number of mRNAs =40 Probability that number of mRNAs = number N mRNAs of that Probability 0 0 50 100 150 200 -0.01 N=Number of mRNAs/cell The equilibrium case: Poisson distributed molecule numbers F=1, i.e. 휎2 = 휇 휎 Relative noise ( ) decreases with Absolute noise (휎) increases 휇 with average number of average number of molecules molecules () () 휎 1 cv= = 휎 = 휇 휇 휇 0.14 100 0.12 80 0.1 =10 60 0.08 m=10 ) m=40 0.06 =40 40 m=100 P(x=n) 0.04 20 =100 P(x=n/ 0.02 0 0 0 0.5 1 1.5 2 0 50 100 150 200 -0.02 -20 n n/ If Poisson, then for ‘normal’ reactions and ‘usual’ molecule numbers: relative noise < 3% ATP protein E. coli: 1 m3, 5 mM ATP: 3 million molecules; 1/3000000=0.1 % Total protein: 3 million; on average of each type; 1000; 3 % relative noise? S. cerevisiae: 40 m3, 5 mM ATP: 120 million molecules; <0.01% Total protein: 100 million; on average of each type; 16 000; <1 % relative noise? Mammalian cell: 2 pL, 5 mM ATP: 6 billion molecules; << 0.01% Total protein: 10 billion; on average of each type; 400 000; <0.2 % relative noise? Therefore: Poisson noise can not explain our (and others’) observations. But what can? And: Is such noise important? We now study this vis-à-vis oestrogen receptor positive breast cancer Therefore these cases are treated with tamoxifen that 70% OF BREAST TUMORS ARE binds to the Estrogen OESTROGEN RECEPTOR TESTOSTERONE POSITIVE(ER+) receptor without activating it Inadvertent activation leads to proliferation AROMATASE AROMATASE INHIBITOR However TAMOXIFEN 40-50% OF ER+ PATIENTS OESTRADIOL DEVELOP RESISTANCE ESTROGEN RECEPTOR Do all cancer cells become resistant, ER ER PROLIFERATION e.g. due to induction of more ER ER INTRATUMOR HETEROGENEITY ER TARGET GENES estrogen receptor? REPRESENTS A MAJOR No,OBSTACLE just a few doTO
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