Indhold: Udvikling af medicin og matematisk modellering. Blodkoagulation.
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Transcript of Indhold: Udvikling af medicin og matematisk modellering. Blodkoagulation.
Indhold:
1) Udvikling af medicin og matematisk modellering.
2) Blodkoagulation.
3) Insulinproducerende beta celler.
4) Sammenfatning.
Matematik i biologi og farmaceutisk industri.
Mads Peter Sørensen
DTU Matematik, Kgs. Lyngby
Årskursus i matematik, kemi og fysik, Rosborg Gymnasium, Vejle, 24/10, 2008
1) Nina Marianne Andersen, DTU Matematik og Novo Nordisk
2) Steen Ingwersen, Biomodellering, Novo Nordisk.
3) Ole Hvilsted Olsen, Hæmostasis biokemi, Novo Nordisk.
4) Morten Gram Pedersen, Department of Information Engineering, University of Padova, Italy.
5) Oleg V. Aslanidi, Institute of Cell Biophysics RAS, Pushchino, Moscow, Russia.
6) Oleg A. Mornev, Institute of Theoretical and Experimental Biophysics RAS, Pushchino, Moscow, Russia.
7) Ole Skyggebjerg, Novo Nordisk.
8) Per Arkhammar og Ole Thastrup, BioImage a/s, Søborg.
9) Alwyn C. Scott, DTU Informatik og University of Arizona, Tucson AZ, USA.
10) Peter L. Christiansen, DTU Fysik og DTU Informatik.
11) Knut Conradsen, DTU Informatik
Samarbejdspartnere.
Sponsorer: Modelling, Estimation and Control of Biotechnological Systems (MECOBS). EU Network of Excellence BioSim.
Udviklingsomkostninger for ny medicin.
Ref.: Erik Mosekilde, Ingeniøren 10. oktober, side 9, (2008).
EU Network of Excellence BioSim. http://biosim-network.eu
Udviklingsprocessen for ny medicin.
Ide, hypotese, forskning.
Dyremodeller. Dyreforsøg.
1) Opdagelse.
Udviklingsfase. Dyreforsøg.
Protokol for sikkerhed og effektivitet.
Mekanisme og potentiel giftpåvirkning af organer.
2) Prækliniske forsøg.
Godkendelse fra regulerende myndigheder.
Test på mennesker.
Test for sikkerhed og effektivitet.
>50% af udviklings tiden.
1 ud af 10-15 medikamenter overlever til fase 3
3) Kliniske forsøg.Regulerende
myndigheder.
Godkendelse af medikamentet.
Marketing autorisation.
Sikker og effektiv medicin.
4) Godkendelse.
Lægemiddelovervågning
5) Kontrol.
Matematisk modellering som et redskab i udviklingen af ny medicin
Udviklingsomkostningerne for et nyt medikament ligger typisk mellem 1 og 7 milliarder kr.
Udviklingstid: 10 – 15 år.
Anvendelse af moderne modellerings og computer simuleringsværktøjer til udvikling af ny medicin. Kompleksitet.
Mere rationel og hurtigere udviklings proces med færre økonomiske omkostninger.
Forbedret behandling af patienter. Bedre, mere sikker og mere individuel behandling.
Reduktion i anvendelse af dyre eksperimenter.
Computer model af menneske.
Disorders of Coagulation
Hypocoagulation:
Hemophilia A
Hemophilia B
Others
Hypercoagulation:
Cardiovascular diseases:
Arthroscleroses
Emboli and thrombi development
Ref: http://www.ambion.com/tools/pathway/pathway.php?pathway=Blood%20Coagulation%20Cascade
Cartoon of the blood coagulation pathway.
Perfusions eksperiment og modellering
Perfusions kammer
Glaslåg coated med collagen
Thrombocyter (blodplader), røde og hvide blod celler.
Faktor X i fluid fase
X
Faktor VIIa I fluid fase
VIIa
Aktive thrombocyter (Ta) binder til et collagen coated låg. vWF.
Rekonstrueret blod.
Indhold: Thrombocyter (T), Erythrocyter.
[T] = 14 nM (70,000 blodplader / μ litre blood)
Enzym kinetic
PECES Reaktions skema:
Reaktions ligningerne:
Bemærk at:
sekckkdtde
121 )(
1k
1k
2k
sekckdtds
11
ckksekdtdc
)( 211 ckdtdp
2
0ece
Enzym kinetic
Skalering:
Matematisk model:
0ss
0ec
x 01
21
skkk
0
0
s
e01
1
skk
)( xdd
)(
xddx
Kvasistationær tilstand:
)/( x
)(
dd
Ref.: J. Keener and J. Sneyd, Mathematical Physiology, Springer, New York, (1998).M.G. Pedersen, A.M. Bersani and E. Bersani, Jour. of Math. Chem. 43(4), pp1318-1344, (2008).
Konkurrerende inhibitor (hæmningsstof)
PECES 1Reaktions skema:
Inklusion af flow og diffusion:
1k
1k
2k
sekcksvsDts
s 111)(
2CIE 3k
3k
Diffusionskonstant: sD Konvektions flow hastighed:
v
Reaktionsskema ved rand:
BPBP 0BBPB
Bindingssites på rand:
B4k
4k
)( 044 bbkbpkdtdb
To dimensionalt eksempel med flow, diffusion og bindingssites på randen
xy
P
Bindingssites på randen:
Cartoon model of the perfusion experiment
Activated Platelet
Va:XaVVIIa XaX Va
II IIaIIa
Unactivated PlateletUnactivated Platelet Activated PlateletActivated PlateletIIaIIa
IIaIIa
Reaction schemes, one example.
Ref: P.M. Didriksen, Modelling hemostasis - a biosimulation project, internal report, Dept. 252 Biomodelling, Novo Nordisk
TaIITaII 10Ta
10F
IIaTaXaTaXaTaII 16R
IIaTaVaXaTaVaXaTaII 3S
Factor II (prothrombin): II
Factor IIa (thrombin): IIa
Prothrombinase complex: Xa_Va_Ta
A total of 17 equations.
TaIITaVaXaSTaIITaXaRdtTadII
316
TaIIFTaIITa 1010
11750016 sMR
11103
7 sMS
1110
sF
114300010
sMTa
Reaction rates:
Numerical results.
T
VIIa Ta
IIa
Initial conditions: FVIIa = 50 nM FX = 170 nM T = 14 nM sTa = 0.1*14 nM FII = 0.3 nM
Reaction diffusion model with convection
Reaction scheme for T, Ta and IIa.
IIaTasTaIIaT 6T
7T
Corresponding model equations in the space Ω.
))((27 TayvTaDsTaT
dtdTa
Ta
))((26 TyvTDIIaTT
dtdT
T
))((276 sTayvsTaDIIaTaTIIaTT
dtdsTa
sTa
))((267 IIayvIIaDIIaTTsTaT
dtdIIa
IIa
Poiseuille’s flow
)/1()( Hyayyv
Boundary conditions and parameters
Boundary condition x=0 )(102.1 16 yfnMIIa
Ref.: M. Anand, K. Rajagopal, K.R. Rajagopal. A Model Incorporating some of the Mechanical and Biochemical Factors Underlying Clot Formation and Dissolution in Flowing Blood. Journal of Theoretical Medicine, 5: 183-218, 2003.
)(1014 29 yfnMT
0Ta 0sTa
Boundary condition x=l: Outflow boundary conditions.
Top and bottom boundary condition: No flow crossing.
Numerical results. Time = 0.6 sec.
T-IIa
IIa
T
Ta
Numerical results. Time = 5 sec.
T IIa
T-IIa Ta
Numerical results. Time = 10 sec.
T-IIa
T IIa
Ta
Future work: Boundary attachment of Ta
Reaction schemes on
Corresponding model equations on.
TaBTa TaBT
IIaTaBCTaBII 2
2k 3k
4k 5k
TaBIIkdtdII 4
25 CkdtdIIa
TkCkTaBIIkTakdt
dTaB 32542
2542 CkTaBIIk
dtdC
Including pro-coagulant and anti-coagulant thrombin
Ref.: V.I. Zarnitsina et al, Dynamics of spatially nonuniform patterning in the model of blood coagulation, Chaos 11(1), pp57-70, 2001.
E.A. Ermakova et al, Blood coagulation and propagation of autowaves in flow, Pathophysiology og Haemostasis and Thrombosis, 34, pp135-142, 2005.
Model consisting of 11 PDEs in 2+1 D, including diffusion
Sammenfatning og fremtidig arbejde
1. Modellering af perfusionseksperiment for blod-koagulation.
2. Reduceret PDL model, som inkludere blod flow og diffusion.
3. Modellering af vedhæftning af aktive thrombocyter på collagen coated rand.
4. Fuld PDL model.
5. Model af in vivo blod koagulation.
Synthesis and secretion of insulin
B
Transcription Pre-proinsulin
ProinsulinEndoplasmatic reticulum
InsulinGolgi complex
packed in granules
Exocytosis of insulincaused by increased Ca concentration
The β-cell
Ion channel gates for Ca and K
B
Mathematical model for single cell dynamics The modified Hodgkin-Huxley model for a single β-cell
CRACCaKATPKsKCa IIIIIIdtdv
C )()(
Ion currents due to the ion-gates
))(( CaCaCa vtvmgI
))()(( KKK vtvtngI
))()(( sss vtvtsgI
))(()()( KATPKATPK vtvgI
Ref.: A. Sherman, (Eds. Othmar et al), Case studies in mathematical modelling, ecology physiology and cell biology, Prentice Hall (1996), pp.199-217.
Ref.: Fall, Marland, Wagner, Tyson, Computational Cell Biology, Springer, (2002).
Mathematical model for single cell dynamics
The gating variables
n
tnndtdn
)(
s
tssdtds
)(
x
x
s
vvx
exp1
1
OC k
k
Ref.: Fall, Marland, Wagner, Tyson, Computational Cell Biology, Springer, (2002).
Ref.: Fall, Marland, Wagner, Tyson, Computational Cell Biology, Springer, (2002).
Dynamics and bifurcations
Ref.: E.M. Izhikevich, Neural excitability spiking and bursting, Int. Jour. of Bifurcation and Chaos, p1171 (2000).
Dynamics and bifurcations
Simple polynomial model
zIxxydtdx 23 3
yxdtdy 251
zxxsrdtdz )( 1
2/)51(1 x
Parameters
001.0r 4s
Ref.: J. Keener and J. Sneyd, Mathematical Physiology, Springer, New York, (1998).
Sketch of the homoclinic bifurcation
crzz crzz
crzz
Mathematical model for single cell dynamics Topologically equivalent and simplified models. Polynomial model
with Gaussian noise term on the gating variable.
zwufdtdu )( )()( twug
dtdw ))(( zuh
dtdz
Ref.: M. Panarowski, SIAM J. Appl. Math., 54 pp.814-832, (1994).
Voltage across the cell membrane: )(tuu Gating variable: )(tww
Gaussian gate noise term: )(t where 0)( t
)()0()( tt
Slow gate variable: )(tzz
Ref.: A. Sherman, (Eds. Othmar et al), Case studies in mathematical modelling, ecology physiology and cell biology, Prentice Hall (1996), pp.199-217.
The influence of noise on the beta-cell bursting phenomenon.
0
1.0
3.0
Ref.: M.G. Pedersen and M.P. Sørensen, SIAM J. Appl. Math., 67(2), pp.530-542, (2007).
Mathematical model for coupled β-cells
j
jiijATPKsKCai vvgIIII
dt
dvC )()(
Coupling to nearest neighbours.
Coupling constant: ijg
Gap junctions between neighbouring cells
Ref.: A. Sherman, (Eds. Othmar et al), Case studies in mathematical modelling, ecology physiology and cell biology, Prentice Hall (1996), pp.199-217.
Coupled β-cells
Image analysis experiments of in vitro islets of Langerhans
Experiments on Islets of Langerhans
ijg
The gating variables
))(( CaiiCaCa vvvmgI
The gating variables obey.
Calcium current:
Potassium current: ))(( KiKK vvtngI
)()()( KiATPKATPK vvgI ATP regulated potassium current:
Slow ion current: ))(( Kiss vvtsgI
n
nvndtdn
)(
s
svsdtds
)(
)/)(exp(11
)(xxi
i svvvxx
snmx ,,
Glycose gradients through Islets of Langerhans
Ref.: J.V. Rocheleau, et al, Microfluidic glycose stimulations … , PNAS, vol 101 (35), p12899 (2004).
Coupling constant:
Glycose gradients through Islets of Langerhans. Model.
pSipSg ATPK 1)1(120)( Ni ,...,2,1
Continuous spiking for: pSg ATPK 90)(
Bursting for: pSgpS ATPK 16290 )(
Silence for: )(162 ATPKgpS
Note that 43i corresponds to pSg ATPK 162)(
Wave blocking
Units tkt tphys uku uphys
msgck Cat 3.5/
mVsk mu 12
Glycose gradients through Islets of Langerhans
pSgij 50
PDE model. Fisher’s equation
Continuum limit of
)2(),( 11 iiiciii vvvgsvF
dt
dv
Is approximated by the Fisher’s equation xxt uaufu );(
where )1)(();( uauuauf
2exp1
1),(
00 vtxx
txuSimple kink solution 2/)21( av
Velocity:
Ref.: O.V. Aslanidi et.al. Biophys. Jour. 80, pp 1195-1209, (2001).
Numerical simulations and comparison to analytic result
Sammenfatning 1) Støj på ion porte reducerer burst perioden.
2) Blokering af bølgeudbredelse ved rumlig variation af den ATP regulerende Na ion kanal.
3) Koblingen mellem beta celler fører til en forøget excitation af ellers inaktive celler.
1) Bio-kemiske processer er meget komplekse og kræver omfattende modellering.
2) Simple og overskuelige modeller kan give kvalitativ indsigt.
3) Der er lang vej til pålidelige kvantitative modeller.
4) Matematiske modeller forventes dog at kunne bidrage til hurtigere og mere sikker udvikling af medicin med færre dyreforsøg.
Ref.: M.G. Pedersen and M.P. Sørensen, SIAM J. Appl. Math., 67(2), pp.530-542, (2007).M.G. Pedersen and M.P. Sørensen, To appear in Jour. of Bio. Phys. Special issue on Complexity
in Neurology and Psychiatry, (2008).
Studieretningsprojekter for gymnasiet
Se:
http://www.dtu.dk/Moed_DTU/Studieretningsprojekter.aspx