Introduction to Computational Neuroscience
Introduction to Computational Neuroscience PHYS 597A
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Date Created: 11/01/15
Understanding the dynamics and function of cellular networks Cells are complex systems functionally diverse elements diverse interactions that form networks signal transduction gene regulatory metabolic have a function that needs to be performed sense and respond to the environment maintain homeostasis replicate need certain dynamical features sensitive to some changes insensitiveadaptable to others robust to unwanted perturbations evolvable shaped by evolution What is the relationship between the topological features of intracellular interaction networks and the dynamic behavior of cells Signaling gene regulation and protein interactions are intertwined transcriptional activation translation proteinprotein interac 39on post trans modi cation transcriptional repression I I 34 signal transduction Mapping of cellular interaction networks Experimental advances allow the construction of genomewide cellular interaction networks Protein networks Uetz et al 2000 Ito et al 2001 S cerevisiae Giot et al 2003 Drosophila melanogaster Li et al 2004 C elegans Human interactome Metabolic networks KEGG WIT Ecocyc Transcriptional regulatory networks ShenOrr et al 2002 E coli Guelzim et al 2002 Lee et al 2002 S cerevisiae Davidson et al 2002 sea urchin Signal transduction networks Ma ayan et al 2005 mammalian hippocampal neuron Graph analysis uncovered common architectural features of cellular networks Connected short path length heterogeneous scalefree conserved interaction motifs node degree number of edges indicating regulation byof multiple components degree distribution fraction of nodes With a given degree 0 1071 kg 1N 4 NE 1N ik Metabolites 19 7 our H r 7 our 6 7 gm Plquotkk39 10a 7 r ZZ moi K 11mm 75 7 39 7 39 V V mE A fulgl dus E To U r C Velegn nn H Jeong etaquot Nature 1039 10 10 101 103100 10 102 m3 10 10 102 103 407 651 2000 k k k quot regulators per gene S cerewsiae g mm 5 1 genes Per TF tmnscriptional network E 339 s E g m gm Lee et al Science 298 799 2002 Z Egmgwwgsmuh mmmummm me mmwwmm S cerevisiae protein network C Elegans protein network 39 39 L n a Ll et al Scrence 303 540 2004 D melanogaster protein network A Dllgt 107 o lto coredala I 3333135 92 marinersWong 10 10 102 2004 g quot x k g m 39 t Biological networks are highly heterogeneous g quot5 Many nodes have only a few edges but highly g 39 64 interactive hub nodes are also possible g A Z O 39 This suggests robustness to random m 39 95 mutations but vulnerability to mutations in highlyconnected components 7 39 R Albert A L Barabasi Rev Mod Phys 74 g g 1 2 0 5390 50 250 47 2002 Giot et al Science 302 1727 2003 Abundant regulatory motifs Amaregulatlon MumComponent Loop Feedfurward Loop 2 2 i I l V Positive and negative feedbaCk 100pS Positive and negative feedfOIWard loops blfans M scaffolds A Shen Orr et al Nature Genetics 2002 Lee et al Science 298 799 2002 Ma ayan et al Science 309 1078 2005 Feedfonivard loop convergent direct and indirect regulation noise filter Single input module one TF regulates several genes temporal program Bifans combinatorial regulation Scaffold protein complexes Positive and negative motifs Balance homeostasis More positive longterm info storage Importance of a dynamical understanding Only subsets of the genomewide interaction networks are active in a given external condition Han et all 2004 dynamical modularity of protein interaction networks Luscombe et al 2004 endogeneus and exogeneus transcriptional subnetworks see next two slides for a recap Q How can we incorporate the information on the presenceabsence or abundance of the molecules represented as nodes Not all interactions are simultaneously active Calculate the correlation between the 5 quot77 Genome expression timecourse of genes encoding 4 the first neighbors of hub proteins 3 g 3 Two peaks two different types of hubs Equot 5m M 0 05 m Party hubs are lnSlde connected modules g Sporulation that Interact Simultaneously Date hubs g 5 quot9 connect different modules 1 5 a f l x I f K V V R i ll 0 o5 o 05 10 i that H 1 Average PCC 1 77 Fanyihub V same time dllterenl lime and space andor space Han cl 3 Nature 443 88 2004 and space Conditiondependent transcription sub ne gdygymgrks Static Emgmu a fancier quot337mm may cynzamga Suzi l39cmmkr uda uerwmm H has 3 Magya Lia s timers w Iny39znrrwcn toriw QR Tr uxjialm Lon Liam imam S srl pan k qliu I p D q 5 1rgtr wowcm DH 5w my rmI39m vaneavian ram395 nclcr a V V quot A Iday 1 mm vmlm 6 h 039 39 m x quot u i h v i r Eh I33 mug ulreszs NSFr154 Endogenous Exogenous Compex TF combination Simple TF combination It 39JL E Few targets per TF Many targets per TF z u rum 39Long path length Short path length PM nter connected TF Few Inter connected TF Many FFL Singe input motifs Luscombe et al Nature 431 308 2004 Toward network dynamics Network topology needs to be complemented by a description of network dynamics states of the nodes and changes in the state First step pseudodynamics propagation of reactions in chemical interaction space starting from a source signal This can only be done in directed networks In effect we use topological analysis as a proxy for dynamic information on signal propagation Q What topological properties should be studied and what dynamic properties do they reflect Complete dynamical description is only feasible on smaller networks modules Signal transduction in bacterial chemotaxis NFkB signaling module the yeast cell cycle Drosophila embryonic segmentation Pseudodynamic signal propagation Act dist 2 Act dist 4 O nh dist 2 Pseuldodynamic effects of knockouts Forward and reverse dynamic modeling Dynamic modeling of interaction network Input components interactions states of components Hypotheses interactions kinetics rates parameters Output behavior of components in time Validation capture known behavior Explore study cases that are not accessible experimentally change parameters change assumptions Reverse problem Network inference from dynamic information lnput components states of components in time Hypotheses regulatory framework Output proposed regulatory network Validation capture known interactions We will study network inference later in the course Types of dynamic models Continuous similar to chemical kinetics differential equations Discrete assume a small set of qualitative states eg active or inactive basal intermediate high the changes in state are given by discrete logical rules Deterministic no randomness is involved in the development of future states of the system Stochastic nondeterministic in that the next state of is not fully determined by the previous state can take into account the fluctuations in mRNAprotein numbers and external noise