Systems Biology I-Temporal Systems PDF

Introduction to Systems Biology Kees Weijer I: Temporal Reaction Networks  Systems Biology Biological Systems are controlled by complex regulatory networks and span a wide range of space and time scales Organism...

Introduction to Systems Biology Kees Weijer I: Temporal Reaction Networks  Systems Biology Biological Systems are controlled by complex regulatory networks and span a wide range of space and time scales Organism Organs Tissues Signaling Cells Organelles Molecules System understanding requires quantitative network analysis  Systems Biology Quantitative understanding of organisation and functioning of biological systems Enumerate components (genomics, transcriptomics, proteomics, glycomics, lipidomics, etc) Analyse individual reactions (Biochemistry) Analysis of Temporal Networks (gene regulation, transcription, signalling) Analyse Spatial Networks (spatial interactions controlling formation of subcellular, cellular, super cellular structures, tissues, organs and organisms, morphogenesis) Understanding of spatio-temporal networks that control cellular and super cellular networks Understanding of emergent properties and their roles in morphogenesis (organ models, digital organisms All activities include computation and modelling  Temporal Analysis of Reaction Networks Biochemical reaction Signalling networks Gene regulatory networks Analysis: – Transcription – Positive and negative feedback – Simple network motifs – More complex motives  Enzyme catalysed reactions Law of Mass Action/Michaelis Menten kinetics E = free enzyme ES= enzyme substrate complex E0= total enzyme S= substrate P= Product (1) Kf=forward reaction rate Kr=reverse reaction rate (2) Kcat= rate limiting reaction rate (3) (4) Enzyme acts as catalyst and is conserved in the reaction (5)  Quasi steady state assumption: Concentration of intermediate enzyme substrate complex does not change with time (d[ES]/dt=0) Equation 3 reduces to (6) This together with (5) (7) Initial conditions Using (7) equation (4) can be rewritten as the well known Michaelis-Menten equation  Complex networks underlie critical biological functions Metabolic pathways E coli gene regulatory network Analysis of gene regulatory networks Simple transcription model Transcription networks respond to external signals  Simple Positive Regulation β α Y (Steady State) (Half Time) The degradation rate (α) determines the response time Kinetics of Y accumulation Decay of Y after stopping synthesis  Negative regulation Simple regulation Negative auto regulation A X A X Negative and positive auto regulation can be used to speed up /slow down the promoter response compared to simple regulation Negative autoregulation Simple regulation Positive autoregulation A: In simple regulation, transcription factor Y is activated by a signal Sy. When active, it binds the promoter of gene X to enhance or inhibit its transcription rate. B: In negative autoregulation (NAR), X is a transcription factor that represses its own promoter. C: In positive autoregulation (PAR), X activates its own promoter. D: NAR (green curve) speeds the response time (the time needed to reach halfway to the steady-state concentration) relative to a simple-regulation system (blue curve) that reaches the same steady-state expression. PAR slows the response time (red curve). 20% of E coli transcriptional network, showing different regulatory motifs  Network motif detection Feed forward loop  Al possible three node Networks Two prevalent classes can be distinguished feed forward and feedback loops  8 possible coherent and incoherent 3 node feed-forward loops No change of sign of input along either path into Z. Both stimulation or inhibition Change of sign of input into Z, one stimulatory and one inhibitory input The eight types of feedforward loops (FFLs) are shown. In coherent FFLs, the sign of the direct path from transcription factor X to output Z is the same as the overall sign of the indirect path through transcription factor Y. Incoherent FFLs have opposite signs for the two paths. Occurrence of 3 node feed forward loops in E.coli and yeast Molecular interactions in type 1 Coherent Feed Forward Loop Promoter displays AND logic, Needs binding of both X and Y to be active. Coherent feed forward loop results in delay of “on” response Coherent Feed Forward loop has no effect on “off” response Coherent Feed Forward network can filter out small perturbations (noise filter) Three node Incoherent feed-forward network results in pulse like dynamics Depending on repression strength pulse response of incoherent feed-forward loop can be sharp F=repression coefficient Incoherent feed forward loop speeds up response time compared to simple regulation reaching the same steady state level  Three ways to speed up a transcriptional network response Increasing the degradation rate in case of simple regulation Negative feedback allows stronger promoters to be used speeding up the initial response Incoherent feed forward loops can also make use of stronger promoters to speed the initial response  4 node networks 199 possible four node connected graphs Generalisation of feed forward loop Multi node generalisations  References Uri Alon (2007) Network motifs: theory and experimental approaches Nat. Gen. Rev. 8, 451 Uri Alon (2007) An Introduction to Systems Biology, Chapman& Hall