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Models and Measuresof
Virus Growth and Infection Spread
John YinDepartment of Chemical and Biological Engineering
University of Wisconsin-Madison, [email protected]
Our Genome in the News
President Clinton Announces the Completion of the First Survey of theEntire Human Genome (June 25, 2000)Hails public and private efforts leading to this historic achievement
Craig Venter Francis Collins
transcriptome
A Challenge for the Century
Genome (Homo sapiens)
Given a Genome Predict the Organism
FertilizedEgg
Proteome“Interactome”
Metabolome
Mechanisms ofdifferentiation& development
Susceptibility todisease & treatment
What are the most important traits for an organism?
Charles Darwin(1809-1882)
"In the struggle for survival, the fittest winout at the expense of their rivals because theysucceed in adapting themselves best to theirenvironment."
Traits that impact fitness: growth & adaptation
Systems Biology: from genome to organism
20th CenturyMolecular
Biology
21st Century“SystemsBiology”
Organism(traits)
Genome
OrganTissue
CellProteinGenetic
ProcessesParts
Networks
Molecules
Why focus on viruses?
1. As organisms that carry the smallest genomes, viruses encode the mostbasic “developmental processes”
i) virus finds and enters host cellii) makes virus progenyiii) releases virus progeny to environment
2. Many viruses have been well-characterized:sequencegenes (fully annotated !)regulation of transcription/replicationprotein-protein interactions
3. Synchronized virus-growth experiments are readily performed, providinga quantitative phenotype (virus fitness)
4. Viruses cause many important human diseases (e.g., AIDS, influenza,SARS, cancer).
5. Viruses are useful (vaccines; expression vectors, gene therapy; oncolytictherapy; antibiotics)
Human Virus
Physical size (m) 100 10-8 to 10-7
Genome size (bp) 109 103 to 106
Number of genes 104 100 to 103
Generation time (y) 25 10-5 to 10-2
Offspring per generation 2 102 to 104
Humans versus Viruses: by the Numbers
How does a virus view the world?Relative Length Scales
!?
10-7 m
1 m
1 m
Vesicular Stomatitis Virus (VSV)
The VSV virion is 180 nm long and75 nm wide.
Causes symptoms in livestocklike foot-and-mouth disease.Non-pathogenic in humans
Negative-sense RNA genome11 kilobases, 5 genes; related tomeasles, rabies, ebola virus
Infects diverse cell types
Biochemically well-studied
Potential applications vaccine (HIV, RSV,flu) oncolytic therapy
http://www.virology.net/Big_Virology/EM/VSV-EM.GIF
100 nm
Surface
glycoprotein (G)
Nucleocapsidprotein (N)
Matrixprotein (M)
ss-RNAgenome
L and P proteins(RNA polymerase)
P M G LN3’ 5’
VSV Genome (11,161 bases)ACCCGGUAUCAUUCUCGCAGAAGAAAGACGGUGGCGCAUUCAGCAUCGACCCUGAGGAACUCAUCAAGGAAGUCGAGGAAGUCGCACGACAGAAAGAAAUUGACCGCGCUAAGGCCCGUAA
AGAACGUCACGAGGGGCGCUUAGAGGCACGCAGAUUCAAACGUCGCAACCGCAAGGCACGUAAAGCACACAAAGCUAAGCGCGAAAGAAUGCUUGCUGCGUGGCGAUGGGCUGAACGUCAA
GAACGGCGUAACCAUGAGGUAGCUGUAGAUGUACUAGGAAGAACCAAUAACGCUAUGCUCUGGGUCAACAUGUUCUCUGGGGACUUUAAGGCGCUUGAGGAACGAAUCGCGCUGCACUGGC
GUAAUGCUGACCGGAUGGCUAUCGCUAAUGGUCUUACGCUCAACAUUGAUAAGCAACUUGACGCAAUGUUAAUGGGCUGAUAGUCUUAUCUUACAGGUCAUCUGCGGGUGGCCUGAAUAGG
UACGAUUUACUAACUGGAAGAGGCACUAAAUGAACACGAUUAACAUCGCUAAGAACGACUUCUCUGACAUCGAACUGGCUGCUAUCCCGUUCAACACUCUGGCUGACCAUUACGGUGAGCG
UUUAGCUCGCGAACAGUUGGCCCUUGAGCAUGAGUCUUACGAGAUGGGUGAAGCACGCUUCCGCAAUCUCACAGUGUACGGACCUAAAGUUCCCCCAUAGGGGGUACCUAAAGCCCAGCCAA
UCACCUAAAGUCAACCUUCGGUUGACCUUGAGGGUUCCCUAAGGGUUGGGGAUGACCCUUGGGUUUGUCUUUGGGUGUUACCUUGAGUGUCUCUCUGUGUCCCUAUCUGUUACAGUCUCCU
AAAGUAUCCUCCUAAAGUCACCUCCUAACGUCCAUCCUAAAGCCAACACCUAAAGCCUACACCUAAAGACCCAUCAAGUCAACGCCUAUCUUAAAGUUUAAACAUAAAGACCAGACCUAAAG
ACCAGACCUAAAGACACUACAUAAAGACCAGACCUAAAGACGCCUUGUUGUUAGCCAUAAAGUGAUAACCUUUAAUCAUUGUCUUUAUUAAUACAACUCACUAUAAGGAGAGACAACUUAA
AGAGACUUAAAAGAUUAAUUUAAAAUUUAUCAAAAAGAGUAUUGACUUAAAGUCUAACCUAUAGGAUACUUACAGCCAUCGAGAGGGACACGGCGAAUAGCCAUCCCAAUCGACACCGGGG
UCAACCGGAUAAGUAGACAGCCUGAUAAGUCGCACGAAAAACAGGUAUUGACAACAUGAAGUAACAUGCAGUAAGAUACAAAUCGCUAGGUAACACUAGCAGCGUCAACCGGGCGCACAGU
GCCUUCUAGGUGACUUAAGCGCACCACGGCACAUAAGGUGAAACAAAACGGUUGACAACAUGAAGUAAACACGGUACGAUGUACCACAUGAAACGACAGUGAGUCACCACACUGAAAGGUG
AUGCGGUCUAACGAAACCUGACCUAAGACGCUCUUUAACAAUCUGGUAAAUAGCUCUUGAGUGCAUGACUAGCGGAUAACUCAAGGGUAUCGCAAGGUGCCCUUUAUGAUAUUCACUAAUA
ACUGCACGAGGUAACACAAGAUGGCUAUGUCUAACAUGACUUACAACAACGUUUUCGACCACGCUUACGAAAUGCUGAAAGAAAACAUCCGUUAUGAUGACAUCCGUGACACUGAUGACCU
GCACGAUGCUAUUCACAUGGCUGCCGAUAAUGCAGUUCCGCACGAUUGACCUCUGGGAAGACGCAGAAGACUUGCUCAAUGAAUACUUGGAGGAAGUCGAGGAGUACGAGGAGGAUGAAGA
GUAAUGUCUACUACCAACGUGCAAUACGGUCUGACCGCUCAAACUGUACUUUUCUAUAGCGACAUGGUGCGCUGUGGCUUUAACUGGUCACUCGCAAUGGCACAGCUCAAAGAACUGUACG
AAAACAACAAGGCAAUAGCUUUAGAAUCUGCUGAGUGAUAGACUCAAGGUCGCUCCUAGCGAGUGGCCUUUAUGAUUAUCACUUUACUUAUGAGGGAGUAAUGUAUAUGCUUACUAUCGGU
CUACUCACCGCUCUAGGUCUAGCUGUAGGUGCAUCCUUUGGGAAGGCUUUAGGUGUAGCUGUAGGUUCCUACUUUACCGCUUGCAUCAUCAUAGGAAUCAUCAAAGGGGCACUACGCAAAU
GAUGAAGCACUACGUUAUGCCAAUCCACACGUCCAACGGGGCAACCGUAUGUACACCUGAUGGGUUCGCAAUGAAACAACGAAUCGAACGCCUUAAGCGUGAACUCCGCAUUAACCGCAAGA
UUAACAAGAUAGGUUCCGGCUAUGACAGAACGCACUGAUGGCUUAAAGAAAGGUUAUAUGCCCAAUGGCACACUAUACGCUGCAAAUCGGCGAAUAGUGAGAACUUGGCGAGAGAACAACC
UCGAACGCCGCAAGGACAAGAGAGGGCGGCGUGGCAUAGACGAAAGGAAAAGGUUAAAGCCAAGAAACUCGCCGCACUUGAACAGGCACUAGCCAACACACUGAACGCUAUCUCAUAACGA
ACAUAAAGGACACAAUGCAAUGAACAUUACCGACAUCAUGAACGCUAUCGACGCAAUCAAAGCACUGCCAAUCUGUGAACUUGACAAGCGUCAAGGUAUGCUUAUCGACUUACUGGUCGAG
AUGGUCAACAGCGAGACGUGUGAUGGCGAGCUAACCGAACUAAAUCAGGCACUUGAGCAUCAAGAUUGGUGGACUACCUUGAAGUGUCUCACGGCUGACGCAGGGUUCAAGAUGCUCGGUA
AUGGUCACUUCUCGGCUGCUUAUAGUCACCCGCUGCUACCUAACAGAGUGAUUAAGGUGGGCUUUAAGAAAGAGGAUUCAGGCGCAGCCUAUACCGCAUUCUGCCGCAUGUAUCAGGGUCG
UCCUGGUAUCCCUAACGUCUACGAUGUACAGCGCCACGCUGGAUGCUAUACGGUGGUACUUGACGCACUUAAGGAUUGCGAGCGUUUCAACAAUGAUGCCCAUUAUAAAUACGCUGAGAUU
GCAAGCGACAUCAUUGAUUGCAAUUCGGAUGAGCAUGAUGAGUUAACUGGAUGGGAUGGUGAGUUUGUUGAAACUUGUAAACUAAUCCGCAAGUUCUUUGAGGGCAUCGCCUCAUUCGACA
UGCAUAGCGGGAACAUCAUGUUCUCAAAUGGAGACGUACCAUACAUCACCGACCCGGUAUCAUUCUCGCAGAAGAAAGACGGUGGCGCAUUCAGCAUCGACCCUGAGGAACUCAUCAAGGAA
GUCGAGGAAGUCGCACGACAGAAAGAAAUUGACCGCGCUAAGGCCCGUAAAGAACGUCACGAGGGGCGCUUAGAGGCACGCAGAUUCAAACGUCGCAACCGCAAGGCACGUAAAGCACACAA
AGCUAAGCGCGAAAGAAUGCUUGCUGCGUGGCGAUGGGCUGAACGUCAAGAACGGCGUAACCAUGAGGUAGCUGUAGAUGUACUAGGAAGAACCAAUAACGCUAUGCUCUGGGUCAACAUG
UUCUCUGGGGACUUUAAGGCGCUUGAGGAACGAAUCGCGCUGCACUGGCGUAAUGCUGACCGGAUGGCUAUCGCUAAUGGUCUUACGCUCAACAUUGAUAAGCAACUUGACGCAAUGUUAA
UGGGCUGAUAGUCUUAUCUUACAGGUCAUCUGCGGGUGGCCUGAAUAGGUACGAUUUACUAACUGGAAGAGGCACUAAAUGAACACGAUUAACAUCGCUAAGAACGACUUCUCUGACAUCG
AACUGGCUGCUAUCCCGUUCAACACUCUGGCUGACCAUUACGGUGAGCGUUUAGCUCGCGAACAGUUGGCCCUUGAGCAUGAGUCUUACGAGAUGGGUGAAGCACGCUUCCGCAAUCUCACA
GUGUACGGACCUAAAGUUCCCCCAUAGGGGGUACCUAAAGCCCAGCCAAUCACCUAAAGUCAACCUUCGGUUGACCUUGAGGGUUCCCUAAGGGUUGGGGAUGACCCUUGGGUUUGUCUUU
GGGUGUUACCUUGAGUGUCUCUCUGUGUCCCUAUCUGUUACAGUCUCCUAAAGUAUCCUCCUAAAGUCACCUCCUAACGUCCAUCCUAAAGCCAACACCUAAAGCCUACACCUAAAGACCCA
UCAAGUCAACGCCUAUCUUAAAGUUUAAACAUAAAGACCAGACCUAAAGACCAGACCUAAAGACACUACAUAAAGACCAGACCUAAAGACGCCUUGUUGUUAGCCAUAAAGUGAUAACCUU
UAAUCAUUGUCUUUAUUAAUACAACUCACUAUAAGGAGAGACAACUUAAAGAGACUUAAAAGAUUAAUUUAAAAUUUAUCAAAAAGAGUAUUGACUUAAAGUCUAACCUAUAGGAUACUU
ACAGCCAUCGAGAGGGACACGGCGAAUAGCCAUCCCAAUCGACACCGGGGUCAACCGGAUAAGUAGACAGCCUGAUAAGUCGCACGAAAAACAGGUAUUGACAACAUGAAGUAACAUGCAG
UAAGAUACAAAUCGCUAGGUAACACUAGCAGCGUCAACCGGGCGCACAGUGCCUUCUAGGUGACUUAAGCGCACCACGGCACAUAAGGUGAAACAAAACGGUUGACAACAUGAAGUAAACA
CGGUACGAUGUACCACAUGAAACGACAGUGAGUCACCACACUGAAAGGUGAUGCGGUCUAACGAAACCUGACCUAAGACGCUCUUUAACAAUCUGGUAAAUAGCUCUUGAGUGCAUGACUA
GCGGAUAACUCAAGGGUAUCGCAAGGUGCCCUUUAUGAUAUUCACUAAUAACUGCACGAGGUAACACAAGAUGGCUAUGUCUAACAUGACUUACAACAACGUUUUCGACCACGCUUACGAA
AUGCUGAAAGAAAACAUCCGUUAUGAUGACAUCCGUGACACUGAUGACCUGCACGAUGCUAUUCACAUGGCUGCCGAUAAUGCAGUUCCGCACUACUACGCUGACAUCUUUAGCGUAAUGG
CAAGUGAGGGCAUUGACCUUGAGUUCGAAGACUCUGGUCUGAUGCCUGACACCAAGGACGUAAUCCGCAUCCUGCAAGCGCGUAUCUAUGAGCAAUUAACGAUUGACCUCUGGGAAGACGC
AGAAGACUUGCUCAAUGAAUACUUGGAGGAAGUCGAGGAGUACGAGGAGGAUGAAGAGUAAUGUCUACUACCAACGUGCAAUACGGUCUGACCGCUCAAACUGUACUUUUCUAUAGCGACA
UGGUGCGCUGUGGCUUUAACUGGUCACUCGCAAUGGCACAGCUCAAAGAACUGUACGAAAACAACAAGGCAAUAGCUUUAGAAUCUGCUGAGUGAUAGACUCAAGGUCGCUCCUAGCGAGU
GGCCUUUAUGAUUAUCACUUUACUUAUGAGGGAGUAAUGUAUAUGCUUACUAUCGGUCUACUCACCGCUCUAGGUCUAGCUGUAGGUGCAUCCUUUGGGAAGGCUUUAGGUGUAGCUGUAG
GUUCCUACUUUACCGCUUGCAUCAUCAUAGGAAUCAUCAAAGGGGCACUACGCAAAUGAUGAAGCACUACGUUAUGCCAAUCCACACGUCCAACGGGGCAACCGUAUGUACACCUGAUGGGU
UCGCAAUGAAACAACGAAUCGAACGCCUUAAGCGUGAACUCCGCAUUAACCGCAAGAUUAACAAGAUAGGUUCCGGCUAUGACAGAACGCACUGAUGGCUUAAAGAAAGGUUAUAUGCCCA
AUGGCACACUAUACGCUGCAAAUCGGCGAAUAGUGAGAACUUGGCGAGAGAACAACCUCGAACGCCGCAAGGACAAGAGAGGGCGGCGUGGCAUAGACGAAAGGAAAAGGUUAAAGCCAAG
AAACUCGCCGCACUUGAACAGGCACUAGCCAACACACUGAACGCUAUCUCAUAACGAACAUAAAGGACACAAUGCAAUGAACAUUACCGACAUCAUGAACGCUAUCGACGCAAUCAAAGCAC
UGCCAAUCUGUGAACUUGACAAGCGUCAAGGUAUGCUUAUCGACUUACUGGUCGAGAUGGUCAACAGCGAGACGUGUGAUGGCGAGCUAACCGAACUAAAUCAGGCACUUGAGCAUCAAGA
UUGGUGGACUACCUUGAAGUGUCUCACGGCUGACGCAGGGUUCAAGAUGCUCGGUAAUGGUCACUUCUCGGCUGCUUAUAGUCACCCGCUGCUACCUAACAGAGUGAUUAAGGUGGGCUUU
AAGAAAGAGGAUUCAGGCGCAGCCUAUACCGCAUUCUGCCGCAUGUAUCAGGGUCGUCCUGGUAUCCCUAACGUCUACGAUGUACAGCGCCACGCUGGAUGCUAUACGGUGGUACUUGACGC
ACUUAAGGAUUGCGAGCGUUUCAACAAUGAUGCCCAUUAUAAAUACGCUGAGAUUGCAAGCGACAUCAUUGAUUGCAAUUCGGAUGAGCAUGAUGAGUUAACUGGAUGGGAUGGUGAGUU
UGUUGAAACUUGUAAACUAAUCCGCAAGUUCUUUGAGGGCAUCGCCUCAUUCGACAUGCAUAGCGGGAACAUCAUGUUCUCAAAUGGAGACGUACCAUACAUCACCGACCCGGUAUCAUUC
UCGCAGAAGAAAGACGGUGGCGCAUUCAGCAUCGACCCUGAGGAACUCAUCAAGGAAGUCGAGGAAGUCGCACGACAGAAAGAAAUUGACCGCGCUAAGGCCCGUAAAGAACGUCACGAGG
GGCGCUUAGAGGCACGCAGAUUCAAACGUCGCAACCGCAAGGCACGUAAAGCACACAAAGCUAAGCGCGAAAGAAUGCUUGCUGCGUGGCGAUGGGCUGAACGUCAAGAACGGCGUAACCA
UGAGGUAGCUGUAGAUGUACUAGGAAGAACCAAUAACGCUAUGCUCUGGGUCAACAUGUUCUCUGGGGACUUUAAGGCGCUUGAGGAACGAAUCGCGCUGCACUGGCGUAAUGCUGACCGG
AUGGCUAUCGCUAAUGGUCUUACGCUCAACAUUGAUAAGCAACUUGACGCAAUGUUAAUGGGCUGAUAGUCUUAUCUUACAGGUCAUCUGCGGGUGGCCUGAAUAGGUACGAUUUACUAAC
UGGAAGAGGCACUAAAUGAACACGAUUAACAUCGCUAAGAACGACUUCUCUGACAUCGAACUGGCUGCUAUCCCGUUCAACACUCUGGCUGACCAUUACGGUGAGCGUUUAGCUCGCGAACA
GUUGGCCCUUGAGCAUGAGUCUUACGAGAUGGGUGAAGCACGCUUCCGCAAUCUCACAGUGUACGGACCUAAAGUUCCCCCAUAGGGGGUACCUAAAGCCCAGCCAAUCACCUAAAGUCAAC
CUUCGGUUGACCUUGAGGGUUCCCUAAGGGUUGGGGAUGACCCUUGGGUUUGUCUUUGGGUGUUACCUUGAGUGUCUCUCUGUGUCCCUAUCUGUUACAGUCUCCUAAAGUAUCCUCCUAA
AGUCACCUCCUAACGUCCAUCCUAAAGCCAACACCUAAAGCCUACACCUAAAGACCCAUCAAGUCAACGCCUAUCUUAAAGUUUAAACAUAAAGACCAGACCUAAAGACCAGACCUAAAGAC
ACUACAUAAAGACCAGACCUAAAGACGCCUUGUUGUUAGCCAUAAAGUGAUAACCUUUAAUCAUUGUCUUUAUUAAUACAACUCACUAUAAGGAGAGACAACUUAAAGAGACUUAAAAGAU
UAAUUUAAAAUUUAUCAAAAAGAGUAUUGACUUAAAGUCUAACCUAUAGGAUACUUACAGCCAUCGAGAGGGACACGGCGAAUAGCCAUCCCAAUCGACACCGGGGUCAACCGGAUAAGUA
GACAGCCUGAUAAGUCGCACGAAAAACAGGUAUUGACAACAUGAAGUAACAUGCAGUAAGAUACAAAUCGCUAGGUAACACUAGCAGCGUCAACCGGGCGCACAGUGCCUUCUAGGUGACU
UAAGCGCACCACGGCACAUAAGGUGAAACAAAACGGUUGACAACAUGAAGUAAACACGGUACGAUGUACCACAUGAAACGACAGUGAGUCACCACACUGAAAGGUGAUGCGGUCUAACGAA
ACCUGACCUAAGACGCUCUUUAACAAUCUGGUAAAUAGCUCUUGAGUGCAUGACUAGCGGAUAACUCAAGGGUAUCGCAAGGUGCCCUUUAUGAUAUUCACUAAUAACUGCACGAGGUAAC
ACAAGAUGGCUAUGUCUAACAUGACUUACAACAACGUUUUCGACCACGCUUACGAAAUGCUGAAAGAAAACAUCCGUUAUGAUGACAUCCGUGACACUGAUGACCUGCACGAUGCUAUUCA
CAUGGCUGCCGAUAAUGCAGUUCCGCACUACUACGCUGACAUCUUUAGCGUAAUGGCAAGUGAGGGCAUUGACCUUGAGUUCGAAGACUCUGGUCUGAUGCCUGACACCAAGGACGUAAUC
CGCAUCCUGCAAGCGCGUAUCUAUGAGCAAUUAACGAUUGACCUCUGGGAAGACGCAGAAGACUUGCUCAAUGAAUACUUGGAGGAAGUCGAGGAGUACGAGGAGGAUGAAGAGUAAUGUC
UACUACCAACGUGCAAUACGGUCUGACCGCUCAAACUGUACUUUUCUAUAGCGACAUGGUGCGCUGUGGCUUUAACUGGUCACUCGCAAUGGCACAGCUCAAAGAACUGUACGAAAACAACA
AGGCAAUAGCUUUAGAAUCUGCUGAGUGAUAGACUCAAGGUCGCUCCUAGCGAGUGGCCUUUAUGAUUAUCACUUUACUUAUGAGGGAGUAAUGUAUAUGCUUACUAUCGGUCUACUCACC
GCUCUAGGUCUAGCUGUAGGUGCAUCCUUUGGGAAGGCUUUAGGUGUAGCUGUAGGUUCCUACUUUACCGCUUGCAUCAUCAUAGGAAUCAUCAAAGGGGCACUACGCAAAUGAUGAAGCA
CUACGUUAUGCCAAUCCACACGUCCAACGGGGCAACCGUAUGUACACCUGAUGGGUUCGCAAUGAAACAACGAAUCGAACGCCUUAAGCGUGAACUCCGCAUUAACCGCAAGAUUAACAAGA
UAGGUUCCGGCUAUGACAGAACGCACUGAUGGCUUAAAGAAAGGUUAUAUGCCCAAUGGCACACUAUACGCUGCAAAUCGGCGAAUAGUGAGAACUUGGCGAGAGAACAACCUCGAACGCC
GCAAGGACAAGAGAGGGCGGCGUGGCAUAGACGAAAGGAAAAGGUUAAAGCCAAGAAACUCGCCGCACUUGAACAGGCACUAGCCAACACACUGAACGCUAUCUCAUAACGAACAUAAAGG
ACACAAUGCAAUGAACAUUACCGACAUCAUGAACGCUAUCGACGCAAUCAAAGCACUGCCAAUCUGUGAACUUGACAAGCGUCAAGGUAUGCUUAUCGACUUACUGGUCGAGAUGGUCAAC
AGCGAGACGUGUGAUGGCGAGCUAACCGAACUAAAUCAGGCACUUGAGCAUCAAGAUUGGUGGACUACCUUGAAGUGUCUCACGGCUGACGCAGGGUUCAAGAUGCUCGGUAAUGGUCACU
UCUCGGCUGCUUAUAGUCACCCGCUGCUACCUAACAGAGUGAUUAAGGUGGGCUUUAAGAAAGAGGAUUCAGGCGCAGCCUAUACCGCAUUCUGCCGCAUGUAUCAGGGUCGUCCUGGUAU
CCCUAACGUCUACGAUGUACAGCGCCACGCUGGAUGCUAUACGGUGGUACUUGACGCACUUAAGGAUUGCGAGCGUUUCAACAAUGAUGCCCAUUAUAAAUACGCUGAGAUUGCAAGCGAC
AUCAUUGAUUGCAAUUCGGAUGAGCAUGAUGAGUUAACUGGAUGGGAUGGUGAGUUUGUUGAAACUUGUAAACUAAUCCGCAAGUUCUUUGAGGGCAUCGCCUCAUUCGACAUGCAUAGCG
GGAACAUCAUGUUCUCAAAUGGAGACGUACCAUACAUCACCGACCCGGUAUCAUUCUCGCAGAAGAAAGACGGUGGCGCAUUCAGCAUCGACCCUGAGGAACUCAUCAAGGAAGUCGAGGA
AGUCGCACGACAGAAAGAAAUUGACCGCGCUAAGGCCCGUAAAGAACGUCACGAGGGGCGCUUAGAGGCACGCAGAUUCAAACGUCGCAACCGCAAGGCACGUAAAGCACACAAAGCUAAGC
GCGAAAGAAUGCUUGCUGCGUGGCGAUGGGCUGAACGUCAAGAACGGCGUAACCAUGAGGUAGCUGUAGAUGUACUAGGAAGAACCAAUAACGCUAUGCUCUGGGUCAACAUGUUCUCUGG
GGACUUUAAGGCGCUUGAGGAACGAAUCGCGCUGCACUGGCGUAAUGCUGACCGGAUGGCUAUCGCUAAUGGUCUUACGCUCAACAUUGAUAAGCAACUUGACGCAAUGUUAAUGGGCUGA
UAGUCUUAUCUUACAGGUCAUCUGCGGGUGGCCUGAAUAGGUACGAUUUACUAACUGGAAGAGGCACUAAAUGAACACGAUUAACAUCGCUAAGAACGACUUCUCUGACAUCGAACUGGCU
GCUAUCCCGUUCAACACUCUGGCUGACCAUUACGGUGAGCGUUUAGCUCGCGAACAGUUGGCCCUUGAGCAUGAGUCUUACGAGAUGGGUGAAGCACGCUUCCGCAAUCUCACAGUGUACGG
ACCUAAAGUUCCCCCAUAGGGGGUACCUAAAGCCCAGCCAAUCACCUAAAGUCAACCUUCGGUUGACCUUGAGGGUUCCCUAAUUGCAAGCGACAUCAUUGAUUGCAAUUCGGAUGAGCAUG
AUGAGUUAACUGGAUGGGAUGGUGAGUUUGUUGAAACUUGUAAACUAAUCCGCAAGUUCUUUGAGGGCAUCGCCUCAUUCGACAUGCAUAGCGGGAACAUCAUGUUCUCAAAUGGAGACGU
ACCAUACAUCACCGACCCGGUAUCAUUCUCGCAGAAGAAAGACGGUGGCGCAUUCAGCAUCGACCCUGAGGAACUCAUCAAGGAAGUCGAGGAAGUCGCACGACAGAAAGAAAUUGACCGCG
CUAAGGCCCGUAAAGAACGUCACGAGGGGCGCUUAGAGGCACGCAGAUUCAAACGUCGCAACCGCAAGGCACGUAAAGCACACAAAGCUAAGCGCGAAAGAAUGCUUGCUGCGUGGCGAUG
GGCUGAACGUCAAGAACGGCGUAACCAUGAGGUAGCUGUAGAUGUACUAGGAAGAACCAAUAACGCUAUGCUCUGGGUCAACAUGUUCUCUGGGGACUUUAAGGCGCUUGAGGAACGA
Genome design
P M GL N3’ 5’
P M GN3’ 5’L
Why does wild-type VSV order its genes as
but not
?
Note: for 5 genes we have 120 gene-order permutations
Genome Design
VSV regulates transcription bypartial termination between genes
Le
3'
N
100
P
75
M GTr
5'
LφP φM φLφG
56 42 2100
φ: extent of attenuation
Levels of synthesis: N > P > M > G > L
gene order influences gene expression level
genomepol
polN anti-genome
M G
replicationtranscriptiontranslation
pol
M
virusG
virusvirus
virus
32
1
N
cell
Three decisions in VSV development
Makegenomes
Makeproteins
YESNO
[N] large?
Make proteins or genomes
YES
NO
[M] large?
Makevirus
Makegenomes
[pol]large? YES
NO
Make proteins
Key idea
Dynamics of virus ‘development’ depends onkinetics of information flow in host cell
Kinetics is nature’s way of preventing everything from happening all at once.
S.E. LeBlanc
Accounting for VSV nucleocapsid protein (N)
d(mRNAN )
dt= f4 (VSV ribonucleoprotein complex, VSV pol) ! f5 (degradation)
d(ProteinN )
dt= sources ! sinks
= f1(mRNAN , translation resources) ! f2 (degradation)
! f3(VSV full ! length RNA, ProteinN )
transcription
translation protein-RNAinteractions
Selected VSV and cell parameters
VSV polymerase elongation rate 3.7 nucleotides / sec Iverson & Rose, 1981Spacing between polymerases on RNA template 170 nucleotides Gao & Lenard, 1995Intergenic transcriptional attenuation Ball et al, 1999; Rose & Whitt 2001
Iverson & Rose, 1981; Gao & Lenard, 1995leader-N 0.0N-P 0.25P-M 0.25M-G 0.25G-L 0.95
Degradation ratesVSV mRNA 1.9 x 10-4 sec-1 Pennica et al, 1981protein N 3.5 x 10-5 sec-1 Knipe et al, 1977 protein P 1.4 x 10-6 sec-1 Canter & Perrault, 1996
protein M 1.5 x 10-4 sec-1 Knipe et al, 1977 protein G 5.7 x 10-5 sec-1 Knipe et al, 1977
protein L 1.2 x 10-5 sec-1 Canter & Perrault, 1996Proteins per virus particle N 1258 Thomas et al, 1985
P 466M 1826G 1205L 50
Ribosomes per cell 5 x 106 Bielka, 1982Ribosome elongation rate 6 amino acids / sec Spirin, 1986
Parameter Value ReferenceTranscription/replicationrates
Transcriptional regulation
Translation rate
Degradationrates
Virus particle stoichiometry
Tracks intracellular levels of all 13 viral species genomic and anti-genomic RNA mRNA, proteins nucleocapsid complexes viral progeny
140 differential equations 25 algebraic equations 45 parameters 3 estimated, 2 fit
Lim et alPLoS Comp. Bio. (2006)
Kinetic model for single-cycle growth of VSV
12 VSV gene-order permutations have been made
Wertz et al, PNAS 1998Gene rearrangement attenuates expression and lethality of a non-segmented negative strandRNA virus.
3'-N-P-M-G-L-5' (N1) Wild-type3'-P-N-M-G-L-5' (N2)3'-P-M-N-G-L-5' (N3)3'-P-M-G-N-L-5' (N4)
Ball et al, J. Virology 1999Phenotypic consequences of rearranging the P, M, and G genes of vesicular stomatitis virus
3'-N-P-G-M-L-5’ 3'-N-M-P-G-L-5’ 3'-N-M-G-P-L-5’ 3'-N-G-M-P-L-5'3'-N-G-P-M-L-5’
Flanagan et al, J. Virology 2000Moving the glycoprotein gene of vesicular stomatitis virus to promoter-proximal positionsaccelerates and enhances the protective immune response
3'-G-N-P-M-L-5' 3'-P-M-G-N-L-5' 3'-G-P-M-N-L-5’
VSV growth is sensitive to position of N
-3-2-101234
0 5 10 15 20 25Time (hr)
log
(viru
s p
er c
ell)
N1N2
N3
N4
N1 > N2 > N3 > N4
Experiment
Lam et al, Biotech. Bioeng.2005
0
1000
2000
3000
4000
5000
6000
7000
Gene-shuffled VSV strain
Viru
s pe
r cel
l N2N1
N4N3 N1 > N2 > N3 > N4
Simulation of all 120 gene-order mutants
N1 (wild-type)
N2
N3
N4
Wertz et al, PNAS 1998
N P M G L
NP M G L
NP M G L
NP M G L
Kwang-il Lim
VSV growth is sensitive to order of first and last gene
L
0
1000
2000
3000
4000
1 2 3 4 5Gene location
0
1000
2000
3000
4000
1 2 3 4 5Gene location
G
0
1000
2000
3000
4000M
1 2 3 4 5Gene location
1 2 3 4 5Gene location
0
1000
2000
3000
4000 P
0
1000
2000
3000
4000
Viru
s pe
r cel
l
1 2 3 4 5Gene location
N
N1
L5
Wild type = N P M G L Kwang-il Lim
Simulated wild-type growth is optimal in thepresence of regulated gene expression
regulated un-regulated(shut-off inter-gene attenuation)
wild type
wild type
Kwang-il Lim
Figure SA2
0
1000
2000
3000
4000
5000
6000
7000
0 5 10 15 20 25
Time, hr
Vir
ion
pro
du
cti
on
(#/c
ell)
BHK
DBT
Viru
s pr
oduc
tion
(per
cel
l)
BHK cells
DBT cells
Virus production depends on environment (host cell)
Random
0
2000
4000
6000
8000
0 2000 4000 6000 8000
BHK
DBT
Optimality of wild-type is independent of host typeV
SV
gro
wth
on
DB
T
VSV growth on BHK
wild-typeWild-type virus
is a “generalist.”
Kwang-il Lim
2500 simulated VSV mutants with wild-type gene orderand randomized transcriptional attenuation
Where may virus models offer new perspectives?
Anti-viral strategies Antimicrob. Agents Chemo. (2000, 2004)
Transcriptome-proteome inference tools Metabolic Engineering (2000)
“Nature versus nurture” J. Bact. (2002), Biotech. Bioeng. (2004)
Genetic interactions (robustness versus plasticity, epistasis) Genetics (2002), Biophys. J (2005), IEE Systems Biology (2006)
Genome design PNAS (2000), PLoS Comp.Bio (2006)
“Nature versus nurture”
(Effects of environment on virus growth)
Top-Down versus Bottom-UpModeling of Biological Systems
BehaviorSystem Output
Macro-scalePhenotype
Molecular partsSystem InputMicro-scaleGenotype
Bottom
TopTop-Down: Howdoes behaviordepend on parts?
“genetic approach”
Bottom-Up: Howdo parts affectbehavior?
“biochemistryapproach”
Virus Growth
[ ]102
- 104
Parentvirus
Host cell
Progenyviruses
Genotype (G)Environment (E)
Phenotype (P)
Bottom-Up Model: Calculate P, given G and E
“Bottom-Up” model of virus (phage) growth
Endy, et al, Biotech. Bioeng. 1997
Endy, et al, PNAS 2000
KEY ASSUMPTIONGenotype(G) defines Phenotype(P)
P does not depend on environment(E)
GIVEN: virus genome & biochemistryCALCULATE: growth dynamics in cells
Bottom-Up model
Experiments show P dependence on EVirus growth depends on physiological state of host cell
host growth rate = 0.7 doublings/hr
1.0
1.2 1.7
minutes post infection
Viru
s p
er c
ell
You, et al
J. Bact., 2002
How can cell environment affect virus growth?
Top-Downcorrelations linking cellresources and cell growth
Bremer & Dennis (1996)E. coli and Salmonella:Cell. and Mol. Bio., 2nd Ed.
Bottom-Upmodel of virus growth
Endy, et al,Biotech. Bioeng. (1997)
Cell growthrate
Virus growthdynamics
Cell Environment
RNA polymeraselevel and elongation rate
Ribosomelevel and elongation rate
DNA contentAmino acid pool size
NTP pool sizeCell volume
Hybrid Top-Down—Bottom-Up ModelAccounts for genetics AND environment on virus growth phenotype
host growth rate = 0.7 doublings/hr
1.0
1.2 1.7
minutes post infection
Viru
s pe
r ce
ll
eclipse time
Riserate
You, et al.J. Bact., 2002
Hybrid model
Hybrid model captures trendsin rise rate and eclipse time
host growth rate (doublings/hour)
T7 p
arti
cles
/min
min
utes
rise rate eclipse time
Hybrid model
Hybrid model
one-parameteradjustment
Model highlights effects of hostenvironmental factors on virus growth
host growthrate =1.5 hr-1
You, et alJ. Bact. 2002
Virus growth is most sensitive to host protein synthesis resources.
1 virus particle per cell
Generate and isolate single cells infectedby single virus particles
P M G LN3’ 5’
P M G LN3’ 5’GFP
VSV (wild type)
VSV-GFP
MOI 0.01
Infect BHK cells with VSV-GFP at MOI 0.01, isolate single cells
infected by single virus particles,measure their virus production.
Real cells produce broad distribution of virus yields
0.05
0.15
0.10
0.2010
~500
501~
1000
1001
~150
0
1501
~200
0
2001
~250
0
2501
~300
0
3001
~350
0
3501
~400
0
4001
~450
0
4501
~500
0
5001
~550
0
5501
~600
0
6001
~650
0
6501
~700
0
7000
~750
0
>750
0
0
Yield of virus progeny per cell
frequ
ency
192 cells collected134 cells produce detectable virus
Why is the distribution of virus production so broad?
To what extent is the diversity genetic?
0.05
0.15
0.10
0.2010
~500
501~
1000
1001
~150
0
1501
~200
0
2001
~250
0
2501
~300
0
3001
~350
0
3501
~400
0
4001
~450
0
4501
~500
0
5001
~550
0
5501
~600
0
6001
~650
0
6501
~700
0
7000
~750
0
>750
0
0
Yield of virus progeny per cell
frequ
ency
134 cellsLow (n=7)
High (n=8)
mean2600
High-yield isolates: yields = 2600±200Low-yield isolates: yields = 2600±200 (n=5),
1100 and 1600
→ Genetic variation accounts for only 2-of-15 of selected extreme-yield cells.
To what extent is the diversity environmental?Infection experiments on synchronized cells
Early S Late S G0G1 G2M
Arrest cells at G1/S with aphidicolinRelease arrest, confirm cell cycle progression
Infect synchronized cellsFit model
model
3250 virus per cell
1370 virus per cell
3340 virus per cell
8680 virus per cell
To what extent is the diversity environmental?Infection experiments on synchronized cells
Yield diversity may reflect effects of host-cell cycle
Low-level virus yields remain a mystery
0.05
0.15
0.10
0.2010
~500
501~
1000
1001
~150
0
1501
~200
0
2001
~250
0
2501
~300
0
3001
~350
0
3501
~400
0
4001
~450
0
4501
~500
0
5001
~550
0
5501
~600
0
6001
~650
0
6501
~700
0
7000
~750
0
>750
0
0
Yield of virus progeny per cell
frequ
ency
Cell cycle can account for virus yields
?
Chemical Kinetics Becomes “Noisy” for SmallNumbers of Reacting Molecules
First-order series reaction: A → B → C k1 k2
time
A
B
C
[A]0= 1 mM
[B]0= [C]0= 0
Deterministic
time
NANC
NB
NA= 100 molecules
NB= NC= 0
Stochastic
Con
cent
ratio
n,
mM
N
From Chemical Reactor Analysis and Design FundamentalsRawlings and Ekerdt, 2002
Core Reaction Network for the Growth of Virus
Virus Progeny
Initiation of virus growth by a viral genome (N = 1 molecule)
Hybrid stochastic-deterministic modeling enablessimultaneous tracking of relevant intermediates
Time (h)
(-)RNA
N mRNA
L mRNA
N protein
L protein
100
104
0 9
deterministic
stochastic
Every simulation run produces a unique trajectory
Tracking of rapidly fluctuating speciescreates computational challenges
Time (h)
(-)RNA
N mRNA
L mRNA
N protein
L protein
100
104
0 9
deterministic
stochastic
N protein is rapidlymade and consumed
VSV genomic RNA
Fully encapsidated VSV genomic RNA
Encapsidation: consumes N protein
VSV genomic RNA
N proteinmRNA (N)
Translation: makes N protein
See posterRishi Srivastava
Simulations suggest structure inviral genomic populations
Number of VSV genomes
N = 1000 infection simulations
Number of VSV genomes
mRNA (L) at 1.5 hours
Stochastic gene expression may affect yield distribution
0.05
0.15
0.10
0.2010
~500
501~
1000
1001
~150
0
1501
~200
0
2001
~250
0
2501
~300
0
3001
~350
0
3501
~400
0
4001
~450
0
4501
~500
0
5001
~550
0
5501
~600
0
6001
~650
0
6501
~700
0
7000
~750
0
>750
0
0
Yield of virus progeny per cell
frequ
ency
Cell cycle can account for virus yields
Stochastic processes?
What happens when virus growth couples with virus movement?
Infections spread!
0 h 0.1 h 0.5 h 1 h 5 h 10 h
0 h 10 h 25 h 40 h
0.001
0.01
0.1
1
10
100
1000
10000
0 5 10 15 20 25
Time (h)
Viru
s pr
ogen
y pe
r cel
l0123456789
0 20 40 60 80 100 120
Time (h)D
ista
nce
(mm
)
1.8 mm /day
virus
cell
Pandemic in a Petri Dish
Micro-scale growth and spread of viruses
“plaque growth”
Micro-scale virus growth and spread in focal infections
Expose cell monolayer to localized virusAgar Agar
Allow virus adsorption and entry into cells
Virus infection propagates over multipleinfection cycles
virus
cell
96 hr
48 hr
18 hr
Micro-scale infection spreadFo
cal
Radi
us
Vesicular Stomatitis Virus (VSV)on BHK cellsFix and stain VSV-G. Image andmeasure.
1 mm
Virus-spread depends on cell type
0
2
4
6
8
0 20 40 60 80 100 120 140 160
time (h)
radi
us (m
m)
VSV on BHK cells
Infe
ctio
n ra
dius
[mm
]
Time [hr]
0
1
2
3
4
5
6
0 20 40 60 80 100 120 140 160
Infe
ctio
n ra
dius
[mm
]
Time [hr]
Focal Spread
radi
us (m
m)
time (h)
VSV on DBT cells
Cell-cell communication may affect infection spread
Interferons
Cells
Virus
dsRNA
Proteins
dsRNA
MxRNasePKRNOS
MxRNasePKRNOS
Proteins
Interferon signaling limits virus spread in DBT cells
0
1
2
3
4
5
6
7
0 20 40 60 80 100
Irad
ius
(mm
)nfe
ctio
nra
dius
[mm
]
Time [hr]
100 U AIFN
Control
Focal Spread
time (h)
100 U Anti-IFN
0
1
2
3
4
5
6
7
0 20 40 60 80 100
Infe
ctio
n ra
dius
[mm
]
Time [hr]
100 U IFN
Control
50 U IFN
Focal Spread
Irad
ius
(mm
)nfe
ctio
nra
dius
[mm
]
time (h)
100 U IFN
50 U Anti-IFN
Inhibition of signalingenhances spread
Enhancement of signalinginhibits spread
Agar Agar
virus
cell
How might cell-cell signaling depend on virus dose?
Focal infection enables control ofMultiplicity of Infection
MOI =number of added virus particles
number of initially accessible cells
MOI 0.01
MOI 0.1
MOI 1
MOI 1000
8
1000 Time Post-Infection (h)
Infe
ctio
n R
adiu
s (m
m)
Infe
ctio
n R
adiu
s (m
m)
8
Time Post-Infection (h) 100
Extent of infection spread depends on virus dose
Low virus dose
Low activationof cell defenses
Infectionspreads
High virus dose
High activationof cell defenses
Infectionstops
Duca, et alBiotech. Prog. 2001
Virus infection can activateanti-viral host responses
OAS
PKR
ISRE
IRF-E
NF-!BPRDIFN-"
PRD-LEIFN-# IRF-7
IRF-3IRF-7
IRF-7IRF-3
VIRUS
dsRNA
kinase
GASIRF-1
protein synthesis
P P
IFN-#IFN-"
IFN receptors on other cells
Jak/STAT
AAFISGF-3
IRF-1
iNOS
intracellular
extracellular
IFN-#
IFN-"
-+
New measures of virus growth by flow-enhanced infection spread
Agar overlay inhibits infection spread
Most virus progeny in spreading infections do not find new host cells
0. 00010. 0010. 010. 1
110
1001000
10000100000
100000010000000
100000000
0 5 10 15 20 25 30
theore
tical
maxim
um
Infection spread(observed)
Are
a of
cel
l dea
th (m
m)
Time post-infection (h)
Opportunity ?
Fluid-flow enhances spread of infections initiated bysingle virus particles
CometsLiquid overlay(flow)
PlaquesAgar overlay(no flow)
1 cm
Virus Particles
Virus: VSVHost: BHK cells
Anti-viral drug inhibits flow-enhanced infection spread
0 µg/ml 1 µg/ml 2 µg/ml 4 µg/ml 8 µg/ml
Drug (5-fluoro-uracil)
Summary
Models of virus intracellular networks enable prediction of virus growth. → suggest optimality and robustness of wild-type virus
Single-cell measures of virus growth exhibit broad distribution. → stochastic gene expression may contribute to observed diversity
Dynamics of infection spread can depend on activation of host defenses. → cell-cell communication affects infection spread
Fluid flows enhance the spread of virus infections in culture. → platform for measurement of distributions of virus growth.
Virus Infections Span Multiple Scales
Molecular andCell Biology
viruscell Tissue
Biology
Clinical Sciences
Epidemiology
Acknowledgements
Co-workers modeling: Eric Haseltine, Sebastian Hensel, Tobias Lang, Kwang-il Lim, Rishi Srivastava, Lingchong Youexperiments: Karen Duca, Vy Lam, Patrick Suthers, Kristen Thompson, Ying Zhu
Colleagues Tom Kurtz Mathematics, UW-MadisonJim Rawlings Chemical and Biological EngineeringGreg Rempala Mathematics, U. LouisvilleSean Whelan Medical School, Harvard (VSV-GFP)
Support NSF-FRG Stochastic models for intracellular reaction networks
NIH Phased Research Innovation Award DAAD German Academic Exchange NLM Computation and Informatics in Biology and
and Medicine (UW-Madison) UW-Madison Graduate School
µchannels
0
200
400
600
800
1000
1200
0 200 400 600 800 1000 1200
Predicted cell seedingdensity (cells / mm2)
Mea
sure
d ce
ll se
edin
g de
nsity
(cel
ls /
mm
2 )
Seed 1-micron fluorescent beads in6-well plate with 2 ml water. Monitor fluorescence near wall
Seed known number of cells into well or micro-channel. Compare measured with predicted cell density.
Micro-channel
Well
Flows in conventional culture wells createspatial heterogeneity
0
10
20
30
40
50
0 8 16 24 32 40 48 56 64
Drug (!g/ml)
Cell-d
eath
sig
nal
0
20
40
60
80
100
Pla
qu
e c
ou
nt
(b) (c)
3×106
3×105
3×104
3×103
3×102
3×10(d)(e)
0
2
4
6
8
µl PFU
0
2
4
8
16
32
64
Drug(µg/ml)
10 mm
Top view
Side view
32 mm2
mm8 mm
500 µm
250 µm
(a)
Take-home points of this talk
1. What factors impact virus yield from infected cells?
Multiplicity of infection (MOI)
1 virus particle per cellVirus yields span 104 reflecting variation in viral genetics,
host-cell state and other factors.
multiple virus particles per cellDefective virus-like particles co-infect cells and reduce
virus yields in a dose-dependent manner.
2. How might we more sensitively measure virus infectivity?
Use fluid flows to enhance spread and imagingto measure the resulting cytopathology
