Models of cellular regulation

• A genetic switch• Lambda lysogeny/lysis

– Three operator sites controlling two promoters PRM and PR

– Cro and CI dimers bind to the operator sites, generating two antagonistic feedback loops

– Cro dimer represses expression of CI, while CI represses Cro; bind to operators with different affinities and in opposite order

– Concentration dependent logic

How do cells obtain signals from noise?

• Uneven distribution of biomolecules among cells

• Stochastic gene expression has been observed in both eukaryotic and prokaryotic cells

• How do cells focus a signal for specific gene expression?

A paradigm shift

• Reductionism Integration• System properties are determined by

concentration of each component and reaction rates – even with steady state assumptions still a complex issue

• Model systems– Metabolism – Signal transduction

Genomics, proteomics, structural genomics, etc.

• Looking to reveal networks inherent to cell physiology– Looking at models– Turning stochastic processes into deterministic

events

Biological signaling occurs at multiple levels

• Intracellular signaling complexity results from:– Interactions between pathways– Compartmentalization– Signal channeling

Compartments

• Many signaling components are membrane-bound, and there is a distinct dearth in our understanding of membrane biochemistry.

• Still, it has been readily identified that cells use compartments to derive specific microenvironments, which can offer distinct responses to the same signals

• Look at compartments as wires or appliances

Reaction channeling

• Central tenet of metabolism

• Compartments communicate via transporters

• Consider transporters as switches (?) controlling the flow of signals down gradients

• An intersection between cell biology and biochemistry

Fatty acids are activated and transported into the mitochondria

Transduction by carnitine is the major regulatory point of fatty

acid oxidation

Molecular scaffolds

• Once considered the function of rRNA

• Term used for a new class of signaling proteins that do not have information transfer capability of their own but interact with multiple signaling proteins in a pathway

• “The scaffold provides an assembly line along which a series of enzymes process their substrates in a well-defined sequence and with an efficiency and specificity that are orders of magnitude higher than would be possible.”

Approaches to the complexity issue

• Development of signaling databases (ie. BIND)

• Systematic cataloging of proteins, lipids, sugars, and other signaling molecules together with genomic data of model systems

An example in modeling – metabolic phenomics

• “It is now clear that we need to develop creative approaches and technologies to use all of this information [genomics and proteomics] to explore and determine genome function. We must essentially take on the view of a gene that we began with over 50 years ago, wherein the focus was on the functional attributes of a gene within the context of the whole organism.”

Surprise!

• Even when multiple knockouts are generated, a surprising number of mutants result in no effect on growth.

• Flexibility in metabolic genotype – rerouting of metabolites

• Clear example given by PK knockout in E. coli

Yet, some metabolic modeling and engineering successes

• Prediction and correlation of defined growth media

• Glucose transporter confers heterotrophic growth upon a photosynthetic algae

• Check out PLAS

Integrated circuits

• How do metabolic pathways communicate?

• How do signal transduction pathways illicit appropriate responses?

• Etc.

Start with a simple model

• Michaelis-Menten

• Modeling interactions between adenosine receptor with adenylate cyclase with first order kinetics – Handout

-adrenergic receptors

• Integral membrane protein with 7 TM regions – serpentine receptor

• Epinephrine (or adrenaline) binds and causes a conformational change that stimulates a G protein, which in turn stimulates adenylyl cyclase

Epinephrine transduction

G Protein has a built-in timing mechanism

The adenylyl cyclase reaction

Modeling this reaction

-receptor is physically separated and activates the enzyme by “collision coupling”

• Modeled as a first order reaction in the presence of non-hydrolyzable GTP analogue

• Expressing the results mathematically

Activation of adenylate cyclase by adenosine

• In contrast to collision coupling, the adenosine receptor is modeled as permanently coupled to adenylate cyclase

• This predicts a distinct rate constant dependence for cyclase activity (cyclase activation)

• Adenosine activation of adenylate cyclase is predicted to be independent of receptor concentration (k3 is unaltered), but the maximum catalytic units will decrease upon receptor activation

Braun and Levitzki

• Examine figure 3; o-adenosine is a competitive inhibitor that does not affect the catalytic rate regarding adenosine activation of adenylate cyclase

• This result is consistent with their model• Additional support comes from independence of

adenosine activation from membrane fluidity

• Relax, “permanent” means k3>>k1

Use simple models to build complicated ones …

• http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/T7list.html

• http://discover.nci.nih.gov/kohnk/fig6a.html

• http://www.genesis-sim.org/GENESIS/

Signal transduction

• http://www.sciencemag.org/cgi/reprint/284/5411/92.pdf

• Bhalla and Iyengar

• Signaling pathways are wires, since not separated by insulators – signaling molecules are distinct

A role for cAMP

Desensitization from persistent signal

Other second messengers

• Phospholipase C cleaves membrane lipid phosphatidylinositol 4,5 bisphospate into two messengers diacylgllycerol and inositol 1,4,5 trisphosphate (IP3)

• IP3 in turn activates release of calcium ions that act as a messenger and activate protein kinase C (numerous isozymes with tissue specific roles, for instance in cell division)

PLC mediated signal transduction

Regulation of cell cycle by protein kinases

Cyclin-dependent protein kinases control cell cycle

• By phosphorylating specific proteins at precise time intervals these kinases orchestrate the metabolic activities of the cell for cell division

• Heterodimers – one regulatory subunit (cyclin) and one catalytic subunit (cyclin-dependent protein kinase [CDK])_

Post-translational regulation through phosphorylation and proteolysis

Four mechanisms to control CDK activity

• Phosphorylation– Phosphorylate tyrosine prevents ATP binding– Removal of phosphate from tyrosine and

phosphorylation of threonine allows substrate binding

• Controlled degradation– Feedback loop involving DBRP

• Regulated synthesis of CDKs and cyclins– MAPK mediated activation of Jun and Fos

• Inhibition of CDK– Specific proteins such as p21 bind and inactivate CDK

Observe variations in the activities of specific CDKs during cell cycle

Whither MAPK?

MAPK kinase cascade

• Many signals stimulate MAPK kinase cascade, but the wire is well conserved in biology – Handout

• Why does MAPK kinase use three kinases instead of one?

• Allows conversion of graded inputs into switch-like outputs

Regulation of passage from G1 to S

Neuron function and signal transduction

• Voltage- and ligand-gated

• ion channels

Allosteric effectors of protein structure/function

Glutamate receptor

http://www.ibcp.fr/GGMM/Nimes/O11.html

Forming memories

• http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/L/LTP.html

• Mini-review handout

• http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11807168&dopt=Abstract

Integrating circuits

• Circuits exhibit synergy within a cellular context• Bhalla and Iyengar modeling signal transduction

in the brain and long-term potentiation (LTP) (Fig 8.15)

• http://doqcs.ncbs.res.in/~bhalla/doqcs/template.php?x=home&y=index

• PKC activates MAPK, while MAPK helps activate PKC (Figure 8.16)

Why does it take 100 minutes of 5 nM EGF to reach LTP?

• 10 min at 5 nM or 100 min at 2 nM EGF is insufficient for LTP (Fig 8.18)

• Fig 8.19 result of determining concentration dependence of MAPK activation of PKC and the converse

• Three intersection points – MM 8.2 “Cobweb”– A indicates high activity for both enzymes– B indicates low activity for both– T is threshold stimulation, if EGF is sufficient to activate

either PKC or MAPK above T – both will reach A (T serves as a switch between A and B)

Turning off LTP

• Use a phosphatase to knock MAPK below threshold

• AA (arachidonic acid) generated by PLA2 persists, which makes it hard to turn off

• Takes awhile to de-phosphatase

Integrating more circuits

• Start with MAPK circuit

• Add calcium activation, etc.

• Result in Figure 8.23– PKC– MAPK– cAMP– Calcium

A network algorithm

• Derived in analogous fashion to protein interaction algorithm

• Use RegulonDB as training set• Set up a matrix where the score = 1 if an operon

(j) encodes a transcription factor that regulates another operon (I) to detect network motifs

• Random model – maintain number of connections but partners are chosen randomly

Applied to several model networks

• Biochemistry

• Ecology

• Neurobiology

• Engineering (WWW)

The similarity of networks

• Although components are unique among these models, the topologic properties of various networks share similarities.

• Universal organizing principles apply to all networks from cell to WWW?

Does gene order matter?

• Cis-regulatory elements, proteins, and messengers are integrated into biological circuits.

• Does gene location in the genome affect the circuit?

• Genome evolution – gene order does matter that’s why we observe synteny

Gene order in T7

• T7 produces 59 proteins from 56 genes…only 33 have known function

• T7 infection is unique, first 850 bp are inserted, transcription begins, then the remainder is pulled in

• E. coli polymerase pulls the first 15% of genomic DNA into the cell at ~45 bp/sec through transcription at 5 promoters– what a cool molecular machine

Gene 1

• T7 RNA polymerase

• Uses 17 different promoters in the remaining 85% of genome

• Pulls at a rate of 200 bp per second.

• What happens if Gene 1 is moved elsewhere on the genome?

In silico analysis

• http://model.mit.edu/cgi-bin/t7web/t7v2.5

• Measured optimal time for phage-induced lysis for 72 distinct T7 genomes

• Some genotypes were better than others

• T7 is suboptimal? Where’s the data?

Experiments

• Three phage genome constructs were generated and tested at positions 1.7, 3.8 and 12 (controls had random DNA inserted at these positions or a late promoter inserted early in genome)

• Little agreement between predicted and experimental data

Systems biology

• Watson School of Biological Sciences at CSH• “…The systems approach defines all of th

eelements in a system and then studies how each behaves in relation to the others as the system is functioning. Ultimately the systems approach requires mathematical model which will both describe the nature of the system and its systems properties.”

“Systems Biology Superstars”

• Integration of multiple -omes:– Metabolomics– Proteomics– Genomics

• “Looking at individual silos of genomics, proteomics, or metabolomics is akin to using a laser pointer in a dark office to describe its contents…”

Galactose metabolism in yeast as an example

• Define all genes in the genome and the subset of genes, proteins and other molecules constituting the galactose pathway..build a model

• Perturb each pathway component using genetics or environmental challenges

• Utilize microarrays and ICAT to collect gene expression data

• Refine model

Functional genomics

• Grew wild type and deletion strains and assessed gene expression via microarrays

• Used Northerns as controls

• How reliable are microarrays?

Proteomics

• http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/ICAT/ICAT.html

• Measured 289 proteins using ICAT, only 30 observed differences; 15 of which showed no change in RNA levels, post-transcriptional control

Going system…

• http://depts.washington.edu/sfields/• Ideker uses Fields protein interaction data to

identify 997 mRNA and 15 proteins whose expression is altered by galactose

• Discovery questions 7-9 in Chapter 9• I relent on the writing: “Typically, if good

data conflict with your model, trust your data”

Clinical Proteomics

Identifying Biomarkers

• Recall a web videocast regarding this topic from NIH

• A test using mass spectroscopic analysis of proteins predicts ovarian cancer 95% of the time – is this good? 20 out of 100,000 women afflicted – Bayes Rule

Personalized medicine

• Herceptin – aimed for 25-30% of women with breast cancer

• Drug development opens door for diagnostics, not vice-versa (diagnostics not being generated for diagnostics sake)

• Isn’t there some legislation looking to pass that relieves pharmaceutical companies from legal responsibilities for their products and side effects?

Investigating Disease

• Clinical Presentation– Biopsy and Labs

• Family pedigree– DNA is inherited

• Karyotyping and Linkage analysis

• DNA sequence analysis

Duchenne’s MD

• Following this process – identified dystrophin as causative gene/protein

• How do you work towards a solution?– An animal model – mice– Dystrophin has a paralog – utrophin, which is

ubiquitously expressed, distinct domains within these proteins lead to distinct localization and protein interactions

Finding dystrophin’s molecular partners

• Immunoprecipitation – leads to Figure 10.7• But then what?• (graph theory and critical nodes in Figure 10.8)• Mutations in any of the genes encoding these gene

products can lead to MD• Ensuing discussion on the inaccuracy of one gene-

one function-one phenotype posed as attending a meeting

Pharmacogenomics

Drug Delivery

• Viral vehicles– Provides specificity for cell type, can be

performed in vitro or in vivo– Liposomes offer an alternative

• Protein carriers– Protein-transduction domain

• Nucleic acids

Drug dilemmas

• The inefficacy of aspirin and Cox proteins

• Want to inhibit Cox-2, which produces prostaglandins that result in PAIN

• However, aspirin has 100X more affinity for Cox-1 than Cox-2