ポスター一覧
M-1: Platypus-MM/CG マルチコピー・マルチスケール分子シミュレーション法開発の基盤となるクラスライブラリ

169

M-2: Platypus-REIN レプリカ交換インターフェースプログラム

170

M-3: MARBLE 全原子分子動力学計算

171

M-4: CafeMol 粗視化モデル計算

172

M-5: ProteinDF 密度汎関数法に基づくタンパク質全電子波動関数計算

173

M-6: Platypus-QM/MM-FE ハイブリッド QM/MM 反応自由エネルギー計算

174

M-7: Platypus-QM 量子化学計算

175

M-8: Platypus-QM/MM 量子化学計算／分子動力学連成計算

176

M-9: Platypus-CGM/MM 粗視化モデル計算／分子動力学連成計算

177

C-1: RICS 細胞シミュレーションプラットフォーム

178

C-2: RICS-Pre 細胞シミュレーションプラットフォーム (RICS) プリシステム

179

C-3: RICS-platelets RICS による血小板細胞シミュレーションと力学的作用による偽足的変形についての検討180C-4: RICS-Insulin Granule Dynamics インスリン分泌シミュレーターの研究開発

181

O-1: ZZ-EFSI 全身ボクセルシミュレーション（ボクセル構造流体連成解析プログラム）

182

O-2: TK-Thrombocyte 細胞シミュレーションプラットフォームを血栓止血の主役細胞である血小板に応用することによる統合的な血栓止血シミュレーターの基盤モデルの開発研究

183

O-3: ZZ-DOSE 全身ボクセルモンテカルロ重粒子線計算プログラム

184

O-4: ZZ-HIFU 集束超音波による低侵襲治療のための HIFU シミュレータ

185

O-5: ZZ-LUNG 肺呼吸・肺循環シミュレーション

186

O-6: UT Heart マルチスケール・マルチフィジックス心臓シミュレータ

187

D-1: ParaHaplo ハプロタイプ関連解析に於ける統計検定を行うためのソフトウェア

188

D-2: NGS analyzer 次世代シークエンサーの出力データを高速に解析し，個人間の遺伝的差異や
がんゲノムの突然変異を高い正確さで同定する

189

D-3: ExRAT 拡張 RAT 法による 2SNP 組合せの全ゲノム関連解析ソフトウェア

190

D-4: SiGN-BN ベイジアンネットワークによる大規模遺伝子ネットワーク推定プログラム

191

D-5: SiGN-L1 L1 正則化法による大規模遺伝子ネットワーク推定プログラム

192

D-6: SiGN-SSM 状態空間モデルによる大規模遺伝子ネットワーク推定プログラム

193

D-7: SBiP データ解析融合プラットフォーム

194

D-8: LiSDAS 生命体データ同化プログラム

195

D-9: MEGADOCK 網羅的タンパク質ドッキング解析プログラム MEGADOCK の開発と応用

196

B-1: NEST Towards brain simulations

197

B-2: CMDN 大規模並列計算で探る大脳局所神経回路の機能

198

B-3: VMS 全視覚系モデルによる視覚情報処理の解析 ( 視覚系シミュレーションのための共有プ
ラットフォーム )199

B-4: NeuroMorphoKit 神経細胞形態シミュレーションキット

200

B-5: IOSSIM カイコガ運動指令形成回路のシミュレーションの構築の試行

201

B-5: IOSSIM 昆虫嗅覚系全脳シミュレータ

202

H-1: cppmd 大規模並列用 MD コアプログラム

203

H-2: LSV 大規模データ解析ソフトウェア LSV の開発

204

H-3: SPHERE Simulation Framework for Large scale Coupled Analysis

205

H-4: VLSVL 合成可能な化学構造および反応スキームからなる大規模仮想ライブラリ206ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-1: Platypus-MM/CG䝬䝹䝏䝁䝢䞊䞉䝬䝹䝏䝇䜿䞊䝹ศᏊ䝅䝭䝳䝺䞊䝅䝵䞁ἲ㛤Ⓨ䛾ᇶ┙䛸䛺䜛䜽䝷䝇䝷䜲䝤䝷䝸㛤Ⓨ㈐௵⪅㻦 ᮌᑎペ⣖ 㻔⌮໬Ꮫ◊✲ᡤศᏊ䝇䜿䞊䝹◊✲㛤Ⓨ䝏䞊䝮㻕ศᏊືຊᏛἲ䛾┠ⓗゎỴἲs ⏕య㧗ศᏊ䛜ᢸ䛖㔜せ䛺⏕࿨⌧㇟䜢䝁䞁䝢䝳䞊䝍ୖ䛻෌⌧䛩䜛s 䝬䝹䝏䝁䝢䞊䞉䝬䝹䝏䝇䜿䞊䝹䝷䜲䝤䝷䝸䛾ᵓᡂ䝅䝭䝳䝺䞊䝅䝵䞁䛾ᐇ⾜– ィ⟬ᑐ㇟䛾䝁䝢䞊䜢」ᩘ䠄⢒ど໬䝺䝧䝹䛜␗䛺䜛䜒䛾䜒ྵ䜐䠅⏝ព– ⮬Ⓨⓗ䛺❧యᵓ㐀䞉㧗ḟᵓ㐀ᙧᡂ– ศᏊㄆ㆑䞉ศᏊ㛫┦஫స⏝– ❧యᵓ㐀ኚ໬– 䝁䝢䞊ྠኈ䜢┦஫స⏝䛥䛫䜛䛣䛸䛷䚸❧యᵓ㐀✵㛫᥈⣴ຠ⋡䜢㧗䜑䜛– 䝁䝢䞊㛫䛾┦஫స⏝䛻䜘䜛䝞䜲䜰䝇䜢ྲྀ䜚㝖䛟– ⮬⏤䜶䝛䝹䜼䞊ィ⟬s ከ䛟䛾⏕࿨⌧㇟䛾᫬㛫䝇䜿䞊䝹䛿䚸䝭䝸⛊䛛䜙⛊䛾䜸䞊䝎䞊– ⏕య㧗ศᏊ䛾ෆ㒊⮬⏤ᗘ䛜኱䛝䛟䚸┦஫స⏝䛜」㞧䛺䛯䜑

䝅䝭䝳䝺䞊
䝅 䞁
䝅䝵䞁

䝣䜯䜲䝹䛾ㄞ䜏㎸䜏䝷䜲䝤䝷䝸䛾≉ᚩs ィ⟬ᑐ㇟䜢䜸䝤䝆䜵䜽䝖䛸䛧䛶ᢅ䛖s Amber䚸Charmmຊሙ䛻ᑐᛂs OpenMP/MPI䛻䜘䜛୪ิ໬᭷㝈䝬䝹䝏䝇䜿䞊䝹ᣑᙇ䝃䞁䝥䝸䞁䜾(MSES)ἲ

┦஫స⏝⥅ Interfaceᢎ䝬䝹䝏䝁䝢䞊䞉䝬䝹䝏䝇䜿䞊䝹ἲ
䛾㛤Ⓨ䜢ᨭ᥼䛩䜛䝷䜲䝤䝷䝸䜢᪂䛯䛻㛤Ⓨs ㏻ᖖ䛾ศᏊືຊᏛἲ䛷䛿䚸ィ⟬᫬㛫䛜䛛䛛䜚䛩䛞䛶෌⌧䛷䛝䛺䛔

⥅
ᢎ

Core
ධฟຊ

s ⤫ィຊᏛ

ၥ㢟ⅬApps

ᗘ䝇䝖䝸䞁䜾ἲ┠ⓗ䠖 㧗⢭ᗘ䛺඲ཎᏊ䝰䝕䝹䠄MM䠅䛸㧗㏿䛻㐠ື䛩䜛⢒ど໬䝰䝕䝹䠄CG䠅䛸䛾㐃ᡂ䝅䝭䝳䝺䞊䝅䝵䞁䛻䜘䜚䚸⏕య㧗ศᏊ䛾඲ཎᏊᵓ㐀䝃䞁䝥䝸䞁䜾䜢ຠ⋡ⓗ䛻ᐇ⌧┠ⓗ䠖 䝍䞁䝟䜽㉁䛜ᵓ㐀ኚ໬䛩䜛㝿䛾ᑬ䜒☜⋡䛾㧗䛔⤒㊰䜢᥈⣴䛩䜛䚹⤒㊰ୖ䛾㑄⛣≧ែ䜢≉ᐃ䛩䜛䛣䛸䛻䜘䜚 ᵓ㐀ኚ໬䛾䝪䝖䝹䝛䝑䜽䛸䛺䜛ṧᇶ䜢ྠᐃ䛩䜛䜘䜚䚸ᵓ㐀ኚ໬䛾䝪䝖䝹䝛䝑䜽䛸䛺䜛ṧᇶ䜢ྠᐃ䛩䜛᪉ἲ䠍䠖 䝬䝹䝏䝇䜿䞊䝹᪉ἲ䠖 䝬䝹䝏䝁䝢䞊䞉䝬䝹䝏䝇䜿䞊䝹ᵓ㐀ኚ໬䜢グ㏙䛩䜛⢒ど໬✵㛫ෆ䛻䛚䛔䛶䚸ᵓ㐀ኚ໬⤒㊰䜢ᐃ⩏䛧䚸⤒㊰䜢㞳ᩓ໬䛧䛯Ⅼ࿘䜚䛾඲ཎᏊ䝬䝹䝏䝁䝢䞊MD䛻䜘䜚᭱ᑬ⤒㊰䜢᥈⣴䛩䜛

H MM  H CG  k MMCG ª¬ș UMM  UCG º¼H2඲ཎᏊ䝰䝕䝹ィ⟬䜢⏝䛔䛶⢒ど໬✵㛫䛻䛚䛡䜛᭱ᑬ⤒㊰䜢᥈⣴closed᪉ἲ䠎䠖 䝬䝹䝏䝁䝢䞊H0, kMMCG,0 = 0

MM

H1, kMMCG,1

H2, kMMCG,2

H3, kMMCG,3

openᛂ⏝౛䠖 䜰䝕䝙䝹㓟䜻䝘䞊䝊䛾ᵓ㐀ኚ໬

CG
Exchange⢒ど໬✵㛫Exchange䝝䝭䝹䝖䝙䜰䞁䝺䝥䝸䜹஺᥮ἲ

Exchange
FukunishiͲWatanabeͲTakada,
J.Chem.Phys.116,9058(2002)

'䝞䜲䜰䝇䛾䛺䛔DDᵓ㐀䜰䞁䝃䞁䝤䝹ᛂ⏝౛䠖 䝅䝙䝵䝸䞁䝣䜷䞊䝹䝕䜱䞁䜾䛾ᵓ㐀䝃䞁䝥䝸䞁䜾extended

MSES

Matsunaga etal.,submitted.

ᛶ⬟䠖 ி䛷䛾䝧䞁䝏䝬䞊䜽⤖ᯝůĂƉƐĞĚƚƚŝŵĞ΀Ɛ΁

2000

200

GB
Ewald

20

඲ཎᏊMD

120

CG䛸㐃ᡂ

480

960

1920

3840

EƵŵďĞƌŽĨWhƐnativeMoritsuguͲTeradaͲKidera,J.Chem.Phys.133,224105(2010)240ᚑ᮶䛾ᡭἲ䛸୍⮴169

ᮏ䝫䝇䝍䞊䛾⤖ᯝ䛾୍㒊䛿䚸⌮໬Ꮫ◊✲ᡤ䛜ᐇ᪋䛧䛶䛔䜛
ி㏿䝁䞁䝢䝳䞊䝍䛂ி䛃䛾ヨ㦂฼⏝䛻䜘䜛䜒䛾䛷䛩䚹ᛶ⬟್䛻䛴䛔䛶䛿䚸ᩚഛ୰䛾䝅䝇䝔䝮䛻䜘䜛ᬻᐃⓗ䛺ᩘ್䛷䛩䚹ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-2: Platypus-REINÜķĪĵďcĊĹĚĻĩČĻėĪĸĒĴį1
1,2,3
Ì¼¬ÛÜljzØØØØØØØØØÙØØÜ"=\Ü4KÜlº"=@Ú2ÜÜÜÜØØØØØØÜÜÜÜÜÜÜÜÜÜÜÜļØØÜ"=\Ü-ĕėĞįĘĹĚĻÜ;v­ĕĮĲķĻĕĳĹěĻįĽ3
2,3,4
ÜØØÜÌ¬ÛÜAFÜÜÜÜļØØÜ"=\Ü¸¡=vtÜ£;¦ěĻįĽØ4
1
ÜÜÜ#¬ÛÜmØØØrØØØØØØÜØÙØØÜ"=\Üw¸¡=ÌĪĸĒĴįÜ;ėēĻĶěĻįĽÜ

IntroductionķĪĵď c;!={ļĎķĹĖĽö
;!=ĕĮĲķĻĕĳĹÜļ¾ÝÔÝªĽÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜÜ³ÑøPć`óĚĹħđ½útÅĀ½úZø÷úûÝÄIú; !={ļMDĽĀıĹĞďĶĸ{ļMCĽ¸¡õûÝtÅļZĽÍúG\iEZùbýăÝ9ç
ú5+ÝxìátÅĀZćµóèĄ å1ÓõàĄÞ
ÜķĪĵď c;!={ļREMDĽûÝľĽ¦ú³eúĔĨĻļķĪĵďĽćáðøĄLĀÁÒ^nø÷úħĴİĻĚć`óMDć?±ìÝĿĽÐă+âħĴİĻĚć`óķĪĵďćàĄ,kõİğĸĬĵėđĴ
ĊĞĵĉùSòô cëîĄf{õàĄÞLTmöZmć`óiúķĪĵďúĥĮĶğĢĉĹć
Hm(pi,qi) = Km(pi) + Um (qi) öíĄöÝiöjúķĪĵďú c ûúuùøĄÞķĪĵď = m(Um (qj)-Um (qi))-n(Un (qj)-Un (qi))

min[1, exp(-)],c;!={(REMD)ú}ąðñìÝØØm = 1/(kB Tm), Tm: L, kB: īĶĝĭĹ0;õàĄÞ
ÜêąùāătÅûG\iEZún«äĂ·dëąÝußøZļtÅĽüÈå(­öøĄÞ©¸ć±â õiÇøtÅĀZåõæĄÞýð×¤Lú®čģĶĐĻĪĸĩĈĊĶåTĂąĄú
õÝĚĹħđ½ÍĀĚĹħđ½Ļ°ļE;ĽÍø÷ú ú·ùÿQóÞÜPlatypus-REINûÝg<ú;!=ĪĸĒĴįø÷ćïúýýìôÝùôÝķĪĵď cĕĮĲķĻĕĳĹć±âęĩğċčĉõàĄÞ

ĔĹĨĲĻĚĀPCđĴėĚ

K. Hukushima, and K. Nemoto, J. Phys. Soc. Jpn. 65, 1604, (1996)
Y. Sugita, and Y. Okamoto, Chem. Phys. Lett. 314, 141–151, (1999)
Y. Sugita, A. Kitao, and Y. Okamoto, J. Chem. Phys., 113, 6042-6051,(2000)

Replica-exchange interface (REIN)
ĚĹħđ½Ā;útÅĺ¨+ĺtÅ8"å¢ùõæĄĔĹĨĲĻĚćáð5+Ý%;ćáð:æø¦Ā³Ñø6ùàĄ¦úĕĮĲķĻĕĳĹåkRõæĄVľŁÜg<ú;!=(MD)ø÷úĪĸĒĴįćĦĊġĵPNļÿìçûsourcePNĽõ§ÃþĄÜÜÜÜĊĹĪĜğPNúāçòôáĄg<úMDĪĸĒĴįćòðķĪĵď

c;!=(REMD)ĕĮĲķĻĕĳĹåõæĄ

ÝNAMD2öMARBLEùDXÞversionćéĄùSáv­ć7ĀìÝ_OYÿéĄ>ÞMARBLEû

ĔĹĨĲĻĚùDXíĄÝĔĹĨĲĻĚõûREIN/MARBLEćáĄÞ¨+

Fortran95, MPI2úv­ć

REINĦĊġĵPNsourcePNg<ú;!=ĪĸĒĴįø÷U

VĿŁÜ9wķĪĵďc(LÝÁÒ^nø÷)ùDX

ÀĔĹĨĲĻĚúĵęĻėćjREPLICAMARBLE
NAMD2AMBER
GROMACS
etc…

REPLICACPU CPU|ìð¸¡åõæĄCPU CPU)2ùôÝ¾¯ú$û*ßúķĪĵďúMD¸¡õáĄĤĻĠÍÄÞĎķĹĖ¯ú$ûķĪĵď
cúÏúķĪĵďÍÄÞMDúÄöķĪĵďÍúÄćèôáĄÞMDÄûCõàĄåÝ
ķĪĵďÍÄûõàĄÞ9wķĪĵďc(MREM)õûÕIù9çúķĪĵďć&ă]âðþÝ

ĔĹĨĲĻĚø÷úÀĔĹĨĲĻĚåW´öøĄÞVŀŁÜREMDĕĮĲķĻĕĳĹùW´øsupporting software
RETUNEĘĜğĉĜĪø÷REMDĕĮĲķĻĕĳĹúĥĻĠĶćéĄ
REIN_pack: ķĪĵďREIN_packcùW´øinputć[ÞLú®!setupÝkĩĈĊĶ¹>Þ

RETUNE (Replica-exchange tunneling time estimator):Ü·o¸¡ùāăĕĮĲķĻĕĳĹhÍćļķĪĵď

cúSetupĽ

REMAP (Replica-exchange multi-histogram analysis program):ÜĿwúWHAM·oĪĸĒĴį

REIN

    

ÜN3¥Ëö'Böú¨+ûÝX ø÷Ê´ø-»ö~çÎĆòôáĄÞĒĵĔĕĠ¨+úqÂYúðþÝ?Öõ¥ËútÅćaãĄúûsþô1ÓõàĄÞïêõÝķĪĵď c;!
=ĕĮĲķĻĕĳĹùāăyN3¥ËútÅć±òðÞïú¨pÝ¥ËĻ'B¨+úYĀ¶.YúÆáåÝ9uøÉMYúJ²Höïú8"ù¿0ìôáĄêöå/ëąðÞ
REIN/NAMD2 on RICC
260 hours/512 core
~10,000 atoms,
300K-500K
64 replica x 52 ns = 3.3 micro sec.
S. Re, N. Miyashita, Y. Yamaguchi, Y. Sugita,Ü
Biophys. J. 101, L44-L46, (2011)

170

REIN

ļĕĮĲķĻĕĳĹhÍĽ

REMAPļWHAM·osoftwareĽᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-3: MARBLE
඲ཎᏊศᏊືຊᏛィ⟬㛤Ⓨ㈐௵⪅㻦 ụཱྀ‶ᚨ 㻔ᶓ὾ᕷ❧኱Ꮫ㻕◊✲ᴫせMARBLEฟຊ䠖ᗙᶆ䛾ᒚṔ

඲ཎᏊMD䝋䝣䝖䜴䜶䜰

ゎᯒ䝋䝣䝖䜴䜶䜰⩌ᐇ㦂䝕䞊䝍䛻䜘䜛㻹㻭㻾㻮㻸㻱䛾᳨ド౛
NMR䛷䛾᳨ド඲ཎᏊศᏊືຊᏛ䝅䝇䝔䝮MARBLE䛾≉ᚩ

ィ⟬ᑐ㇟
⏕యศᏊ

㏆㊥㞳ຊ䛾୪ิ໬ຠ⋡X⥺⁐ᾮᩓ஘ᐇ㦂䛷䛾᳨ドிࠥ170୓ཎᏊ
OpenMP 8 cores 䝅䞁䝥䝺䜽䝔䜱䝑䜽㒊ศ๛య᫬㛫✚ศἲ± ⢭ᗘ䛾㧗䛔඲䜶䝛䝹䜼䞊ಖᏑከ๣᤼ฟ䝖䝷䞁䝇䝫䞊䝍䞊
AcrB䛾඲ཎᏊศᏊືຊᏛ
䝅䝭䝳䝺䞊䝅䝵䞁
9ศᏊ䝇䜿䞊䝹䝏䞊䝮䛷ඹ
㏻䛾䝍䞊䝀䝑䝖
9⭷䝍䞁䝟䜽㉁9ᵓ㐀ኚ໬䛜ᶵ⬟䛻㔜せ9䜖䜙䛞䛸ᵓ㐀ኚ໬

DNA⤖ྜ䝍䞁䝟䜽㉁PhoB

 ┦஫స⏝

ᵓ㐀䜖䜙䛞᪉ྥ(㉥)䛸
ไ㝈㓝⣲EcoO109I ᵓ㐀ኚ໬᪉ྥ(㟷)䛾୍⮴

Ỉ䜢௓䛧䛯
ศᏊㄆ㆑

í㏆㊥㞳ຊ䛸㛗㊥㞳ຊ䠄'))7䜢฼⏝䠅䛛䜙䛺䜛 ୪ิ໬䝥䝻䜾䝷䝭䞁䜾

S2䝅䝭䝳䝺䞊䝅䝵䞁⤖ᯝ䛾ゎᯒᢏ⾡䛾㛤Ⓨ
9 ❧యᵓ㐀ኚ໬
9 ❧యᵓ㐀䜖䜙䛞
9 ⮬⏤䜶䝛䝹䜼䞊
9 ᐇ㦂䛸䛾ᑐᛂ
NMR䠈SAXS䛺䛹⏕య㉸ศᏊ⏝඲ཎᏊ䝅䝭䝳䝺䞊䝅䝵䞁ᢏ⾡䛾㛤Ⓨ
9 ㉸୪ิ໬䝋䝣䝖䜴䜶䜰MARBLE9 ୡ⏺ᶆ‽䜰䝹䝂䝸䝈䝮䜢⿦ഛ䠄PME➼䠅
9 ඛ➃䜰䝹䝂䝸䝈䝮䜒⿦ഛ䠄䝅䞁䝥䝺䜽䝔䜱䝑䜽๛యἲ➼䠅± 2SHQ03ᑐᛂ± ✵㛫ศ๭䛻䜘䜛୪ิ໬ᵓ㐀䝎䜲䝘䝭䜽䝇䛾᳨ド䠄㉥䠖ィ⟬䚸㟷䠖ᐇ㦂䠅ᩓ஘䝥䝻䝣䜯䜲䝹䠄㟷䠖ィ⟬䚸䝢䞁䜽䠖ᐇ㦂䠅

(JACS, 2010)(Biophys J, 2009)ト䠖ி䛿⌧ᅾ㛤Ⓨ୰䛷䛒䜚䚸䛣䜜䜙䛾ᩘᏐ䛿⌧≧䛾್䛷䛒䜛䚹ከ๣᤼ฟ䝖䝷䞁䝇䝫䞊䝍䞊䛾඲ཎᏊศᏊືຊᏛ䝅䝭䝳䝺䞊䝅䝵䞁

⫼ᬒ

ᵟᶁᶐᵠ

‫ٶ‬д੎ЈἚἻὅἋἯὊἑὊ

‫ٻ‬ᐂᓏễỄỉἂἻἲᨏࣱኬᓏỆ‫נ܍‬ẴỦἩἿἚὅᬝѣỉ‫ٶ‬д੎ЈἚἻὅἋἯὊἑὊỉɟᆔኬᏘỆஊܹễᆔẉỉཋឋίီཋὸửኬᏘ‫ٳ‬ồ੎ЈẴỦ΁Ẩửਤếἑὅἣἁឋ

㻰㻺AcrBỉἛἳỶὅನ঺ኽӳ‫׹‬඲ཎᏊศᏊືຊᏛ䝅䝭䝳䝺䞊䝅䝵䞁䛻䜘䜚䚸
AcrB䛾ከ๣᤼ฟ䛾䝯䜹䝙䝈䝮䜢ゎ᫂䛩䜛䚹

䖃TolC䚸AcrA䛸࿧䜀䜜䜛⺮ⓑ㉁䛸」ྜయ䜢ᙧᡂ䛧䛶ᶵ⬟䛧䛶䛚䜚䠄ᅗ䠍䠅䚸⸆๣᤼ฟ䛾୰ᚰⓗ䛺ᙺ๭䜢䛺䛩䚹䖃᤼ฟ䛩䜛䜒䛾…ᢠ⏕≀㉁䚸ᾘẘ๣䚸ᢠ䛜䜣๣䚸䖃᤼ฟ䛩䜛䜒䛾
ᢠ⏕≀㉁ ᾘẘ๣ ᢠ䛜䜣๣Ⰽ⣲ᛶẘ⣲䚸⏺㠃άᛶ๣䛺䛹䛸䛟䛻䚸ྛ≧ែ䛾䝥䝻䝖䞁㌿⛣䝃䜲䝖䛾䝥䝻䝖䞁໬≧ែ䜢ㄪ䜉䜛䚹㼀㼛㼘㻯䝗䝑䜻䞁䜾䝗䝯䜲䞁䖃⸆๣䛾⤒㊰䛾ᙧ≧䛜␗䛺䜛䚸⤖ྜᆺ䚸᤼ฟᆺ䚸ྲྀ㎸ᆺ䛸࿧䜀䜜䜛䝰䝜䝬䞊䛛䜙䛺䜛䝩䝰3㔞య䜢ᙧᡂ䛩䜛䠄ᅗ䠎䠅⸆๣䛾⤒㊰䠄㟷䠅
䠄ᅗ䠎䠅
੎Ј‫׹‬
䖃䝥䝻䝖䞁㥑ືຊ䛷␗䛺䜛䠏≧ែ㛫䜢㡰␒䛻ᵓ㐀ኚ໬䜢䜢䛩䜛䛣䛸䛷⸆๣䜢᤼ฟ䛩䜛䠄ᅗ䠏䠅ӕᡂ‫׹‬䠄ᅗ䠍䠅኱⭠⳦䛻䛚䛡䜛ከ๣᤼ฟ䝅䝇䝔䝮㻔㻭㼏㼞㻮㻛㻭㼏㼞㻭㻛㼀㼛㼘㻯㻕┠ⓗ㻰㻯䖃኱⭠⳦䛾䜒䛴⸆๣᢬ᢠᛶ䛾୺せᅉ䖩⬡㉁⭷ୖ䛻Ꮡᅾ䛩䜛䖩ከ䛟䛾⏕≀䛻Ꮡᅾ䛧䛶䛔䜛䖩୍✀㢮䛾䝖䝷䞁䝇䝫䞊䝍䞊䛷䚸ᵝ䚻䛺␗≀䛻ᑐᛂ䛷䛝䜛䚹䖩⣽⬊ 䛸䖩⣽⬊䛻䛸䛳䛶᭷⏝䛺䜒䛾䛿እ䛻ฟ䛥䛺䛔᭷ 䛺䜒 䛿እ ฟ䛥䛺䖩㝔ෆឤᰁ䠄⑓ཎᛶ⣽⳦䛾ᢠ⏕≀㉁⪏ᛶ໬䠅䖩䛜䜣⣽⬊䛾ᢠ䜺䞁๣⪏ᛶ໬

㻼㻯㻝

㻼㻯㻞

㻼㻺㻞

ĺ ྛ≧ែ䛾ᵓ㐀䜢Ᏻᐃ䛻⥔ᣢ䛩䜛䛣䛸䛾䛷䛝䜛䝥䝻䝖䞁໬≧ែ䜢ㄪ䜉䜛㻼㻺㻝䝫䞊䝍䞊䝗䝯䜲䞁

90ᶟ

䝥䝻䝖䞁䛾⣽⬊ෆ䜈䛾ྲྀ䜚㎸䜏䛻䛛䛛䜟䜛䜰䝭䝜㓟䠄䝥䝻䝖䞁㌿⛣䝃䜲䝖䠅䠄ᅗ䠏䠅ᶵ⬟ⓗᅇ㌿
ӕᡂ‫׹‬

੎Ј‫׹‬

Thr978

Thr978
Asp407

H+

Th 9 8
Thr978

Lys940Asp407

㼀㻹㻝
㼀㻹㻢

ᕤд

ӕᡂ‫׹‬

ƌƵŐ

ኽӳ‫׹‬

ኽӳ‫׹‬

Asp407

ᕤд

Asp408

Lys940Asp408

Asp408⭷㈏㏻䝗䝯䜲䞁

⤖ᯝᮏ◊✲䛷⾜䛳䛯䝅䝭䝳䝺䞊䝅䝵䞁䝥䝻䝖䞁㌿⛣䝃䜲䝖䛾䝁䞁䝣䜷䝯䞊䝅䝵䞁

100ns

50ns

407 í408H

Asp408 í

Asp408 í

407 í 408 í -II(100ns)407 í 408 í -III

(100ns)Asp407

Asp408Initial structure

Simulation Time407ï408ï-I

í

í

X-ray

100 ns407ï408H

í

H

X-ray

200 ns407H408ï

H

í

X-ray

100 ns407H408H

H

H

X-ray

100 ns407ï408ï-IIíí

407ï408H, 100 ns

100 nsí

407ï408H, 50 ns

100 nsí

Asp407
Lys940407í408 í -I_Binding

Lys940407 í 408 í -I_Access

Asp408᤼ฟᆺ
Thr978

K940-D407
K940-D408
K940-T978

Lys940Asp407
Time (ns)K940-D407
K940-D408
K940-T978

Asp408

䠄Lys940-Asp408 ӑ Lys940-Thr978 > Lys940-Asp407䠅

᤼ฟᆺ䠄Asp407, 408䜢ᵝ䚻䛺䝥䝻䝖䞁໬≧ែ䛷ィ⟬䠅
Asp408䜢⬺䝥䝻䝖䞁໬

Asp408䜢䝥䝻䝖䞁໬
407 í408H_Extrusion

407 í 408 í -I_Extrusion

407H408 í _Extrusion

Time (ns)Time (ns)Distance (Å)

Time (ns)AcrB䛷᝿ᐃ䛥䜜䜛Ỉ䛾㏱㐣䝯䜹䝙䝈䝮

Lys940Asp408(Lys940-Thr978 > Lys940-Asp407 § Lys940-Asp408)

Asp407䛚䜘䜃Asp408䜢⬺䝥䝻䝖䞁≧ែ䛷䚸䝥䝻䝖䞁㌿⛣䝃䜲䝖䛾ᵓ㐀䜢Ᏻᐃ䛻⥔ᣢྛ䝅䝭䝳䝺䞊䝅䝵䞁䛸䜒䚸⤖ྜᆺ䛸ྲྀ㎸ᆺ䛾䝰䝜䝬䞊䛾Asp407, Asp408䛿⬺䝥䝻䝖䞁໬≧ែ䛷⾜䛳䛯䚹䝥䝻䝖䞁㌿⛣䛾䝥䝻䝉䝇

Thr978

Asp407

Asp407

Asp408-Alternative Gating mechanism-

Thr978Time (ns)᤼ฟᆺ䛾Asp407䛚䜘䜃Asp408䛾䝥䝻䝖䞁໬≧ែ䛾␗䛺䜛4✀㢮䛾䝅䝭䝳䝺䞊䝅䝵䞁䜢⾜䛳䛯䚹ǆƚƌƵƐŝŽŶ
Thr978

ྲྀ㎸ᆺ

⤖ྜᆺ

200ns

Distance (Å)

407H408 í

407ï408ï-IIIᵓ㐀ኚ໬䛾䝏䜵䝑䜽䛻⏝䛔䛯ཎᏊ㛫㊥㞳⤖ྜᆺ䞉ྲྀ㎸ᆺ䠄Asp407, 408䜢⬺䝥䝻䝖䞁໬䠅

407 í 408H

Distance (Å)

SystemProtein䠖(PDB ID: 2DHH)䠄Non drug containing structure䠅
Missing part region(499-512) were added fromanother crystal structure (PDB ID: 2J8S)
䠄47,724 atoms䠅
Lipid bilayer䠖POPE 809 molecules (101,125 atoms䠅
Water䠖TIP3P model 108,385
108 385 molecules (325,155(325 155 atoms䠅
+Ion䠖Na (36 atoms)
Total䠖~474,000atoms
Box size: 160ÅX160ÅX180Å

407 í 408 í -I

Distance (Å)

Program : MARBLE (Ikeguchi)
Force field: CHARMM 27
Electrostatic interaction:
Non cut-off (Particle-mesh
Ewald)
Ensemble: NPT,
Temperature:300K,
Time step:2fs

K940-D407
K940-D408
K940-T978407H408H_Extrusion

Asp408䛜䝥䝻䝖䞁໬

Time (ns)407 í408 í-III_Extrusion

407 í408 í-II_Extrusion䝨䝸䝥䝷䝈䝮ഃ,н

Asp407

Asp408

ᕤд

ィ⟬᪉ἲ

⤖ྜᆺ

ྲྀ㎸ᆺ
Thr978

Lys940Asp407
Lys940᤼ฟᆺ䛾㻸㼥㼟㻥㻠㻜䛾䝁䞁䝣䜷䝯䞊䝅䝵䞁䛜⤖ྜᆺ䞉ྲྀ㎸ᆺ䛸␗䛺䜛

H+

੎Ј‫׹‬

Thr978

Asp408ᕤд
Murakami et al., 1DWXUH , 419, 587-593 (2006)

᤼ฟᆺ

⤖ྜᆺ
Thr978
Asp407

Lys940Asp408

Lys940

ӕᡂ‫׹‬

㼀㻹㻣
㼀㻹㻥
㼀㻹㻝㻜
㼀㻹㻤
㼀㻹㻡

Distance (Å)ᕤдᕤд

Distance (Å)

H+

㼀㻹㻝㻞

㼀㻹㻞
㼀㻹㻠
㼀㻹㻟

ྲྀ㎸ᆺ

Distance (Å)

੎Ј‫׹‬

,н

᤼ฟᆺ

⤖ྜᆺ

Distance (Å)

ኽӳ‫׹‬
ኽӳ

㼀㻹㻝㻝

Time (ns)Time (ns)

᤼ฟᆺ

H2 O

䝨䝸䝥䝷䝈䝮ഃ䛛䜙䝥䝻䝖䞁䛜⛣ື䝥䝻䝖䞁㌿⛣
䝃䜲䝖

,н
H2 O

⣽⬊⭷䛜㈏ධ䛧䛺䛔䜘䛖䛻䛩䜛௙⤌䜏䛜Ꮡᅾ䛩䜛䛸⪃䛘䜙䜜䜛

Asp407

Lys940Asp408

⤖ྜᆺ

ኽӳ‫׹‬

᤼ฟᆺ

ྲྀ㎸ᆺ

⤖ᬗᵓ㐀 (Ỉ䛾⤒㊰)⤖ྜᆺ⸆๣䛾⤒㊰䠄PC2䝃䝤䝗䝯䜲䞁䛾Cᮎ㡿ᇦ䞊TM8䠅

᤼ฟᆺ

Thr978,н䝃䜲䝖䝥䝷䝈䝮ഃAsp408䛾䜏䜢䝥䝻䝖䞁໬䛧䛯䝅䝭䝳䝺䞊䝅䝵䞁䠄407í408H)䛷䛿䚸᤼ฟᆺ䛾≧ែ䜢Ᏻᐃ䛻ಖᣢ

PC2

PC2
PN1

PN1

Loop PC2-TM8

Loop PC2-TM8

᤼ฟᆺ䛷䛿㛢䛨䛶䛔䜛⸆๣䛾ྲྀ䜚㎸䜏ཱྀ䛜㛤䛟ᵝᏊ䛜
ほᐹ䛥䜜䛯

᤼ฟᆺ
䝥䝻䝖䞁㌿⛣
䝃䜲䝖⬺䝥䝻䝖䞁໬

,н⤖ᬗᵓ㐀䛻䚸ୖ䛷᝿ᐃ䛧䛯Ỉ䛾㏱㐣䝯䜹䝙䝈䝮䜢♧၀䛩䜛䜢♧၀䛩䜛䚹

䝃䜲䝖䝥䝷䝈䝮ഃ䜈䝥䝻䝖䞁䛜⛣ື⸆๣䛾ධ䜚ཱྀ䛻ὀ┠TM⤖ᬗᵓ㐀䠄᤼ฟᆺ䚸ྲྀ㎸ᆺ䠅⸆๣䛾⤒㊰䠄ྲྀ㎸ᆺ䠅

TM䝅䝭䝳䝺䞊䝅䝵䞁䛾᭱⤊ᵓ㐀䠄407í408H, 407í408í -I)⸆๣䛾⤒㊰䠄407í408í -I䠅Ỉ䛾፹௓䛻䜘䜚䝥䝻䝖䞁䛜⛣ື䛩䜛䛸⪃䛘䜙䜜䜛ㅰ㎡ᮏ◊✲䛾⤖ᯝ䛾୍㒊䛿䚸⌮໬Ꮫ◊✲ᡤ䛜ᐇ᪋䛧䛶䛔䜛ி㏿䝁䞁䝢䝳䞊䝍䛂ி䛃䛾ヨ㦂฼⏝䛻䜘䜛䜒䛾䛷䛩䚹

䜎䛸䜑
AcrB3㔞య䛾⭷㈏㏻㡿ᇦ䛻Ꮡᅾ䛩䜛䚸䝥䝻䝖䞁㌿⛣䝃䜲䝖䛾䝥䝻䝖䞁໬≧ែ䛻䛴䛔䛶䚸ศᏊືຊᏛ䝅䝭䝳䝺䞊䝅䝵䞁䛻䜘䜚ㄪ䜉䛯䚹䖩⤖ྜᆺ䚸ྲྀ㎸ᆺ䛾ྛ≧ែ䛾䝥䝻䝖䝬䞊䛿䚸Asp407, 408䛜⬺䝥䝻䝖䞁໬䛧䛯≧ែ䛜Ᏻᐃ䛷䛒䜚䚸᤼ฟᆺ䛷䛿䚸Asp408䛜䝥䝻䝖䞁໬䛧䛯≧ែ䛜Ᏻᐃ䛷䛒䜛䚹䖩᤼ฟᆺ䛾⸆๣ྲྀ䜚㎸䜏ཱྀ䛜㛤䛟ᵝᏊ䜢ほᐹ䛩䜛䛣䛸䛜䛷䛝䛯䠄407í408—I)䖩䛣䛣䛷♧䛥䜜䛯䚸ྛ≧ែ䛾䝥䝻䝖䞁໬≧ែ䛛䜙⪃ᐹ䛥䜜䜛䝥䝻䝖䞁㌿⛣䛾䝥䝻䝉䝇䛿䚸⤖ᬗᵓ㐀䛻ぢ䜙䜜䜛Ỉ䛾䝟䝇䛸▩┪䛧䛺䛔䚹

171ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

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Kenzaki et al
J. Chem Theo Comp
2011 
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Yao et al Nature Comm 2010
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ᐦᗘỗ㛵ᩘἲ䛻ᇶ䛵䛟䝍䞁䝟䜽㉁඲㟁ᏊἼື㛵ᩘィ⟬㻌㛤Ⓨ㈐௵⪅䠖㻌
㛤Ⓨ⪅䠖㻌㻌

బ⸨ᩥಇ㻌㻔ᮾி኱Ꮫ⏕⏘ᢏ⾡◊✲ᡤ㻕㻌ᖹ㔝ᩄ⾜䚸ୖᮧ඾Ꮚ䚸ᜏᕝ┤ᶞ䚸ᯇ⏣₶୍㻌㻔ᮾி኱Ꮫ⏕⏘ᢏ⾡◊✲ᡤ㻕㻌WƌŽƚĞŝŶ&䛸䛿䠖䞉䝍䞁䝟䜽㉁䛜ᣢ䛴඲䛶䛾㟁Ꮚ䛾ศᏊ㌶㐨䛜㻌 ィ⟬䛷䛝䜛㔞Ꮚ໬Ꮫィ⟬䝋䝣䝖䜴䜵䜰ᮏ䝥䝻䝆䜵䜽䝖䛿䠖䞉䛂ி䛃䜈䛾ᐇ⿦䛜䝭䝑䝅䝵䞁Ύ䞉ගྜᡂ཯ᛂ⣔䛜䝍䞊䝀䝑䝖ΎΎ䞉䝇䝖䝺䝇䛾ᑡ䛺䛔ᇶ♏䞉ᛂ⏝◊✲ᒎ㛤䛻㈉⊩ᶓ⥺୍䛴୍䛴䛜  ศᏊ㌶㐨䛾䜶䝛䝹䜼䞊  HOMO  

LUMO+2  HOMO-­1  LUMO+1  HOMO-­2  LUMO  Ύ ி㏿䝁䞁䝢䝳䞊䝍ி䛾ヨ㦂฼⏝䜢฼⏝䛔䛯䛧䜎䛧䛯䚹ΎΎ ி㏿䝁䞁䝢䝳䞊䝍ி䜢฼⏝䛩䜛ணᐃ䛷䛩䚹ගྜᡂ཯ᛂ୰ᚰ䞉䜰䞁䝔䝘䝍䞁䝟䜽㉁

ᮏ䝥䝻䝆䜵䜽䝖䛷䛿䚸䝥䝻䜾䝷䝮䜢䛂ி䛃䛻ᑐᛂ䛥䛫䜛䛰䛡䛷䛺䛟䚸ṇ䛧䛔኱つᶍ&dィ⟬䜢ᐇ⾜䛩䜛䛯䜑䛾◊✲䜒ྠ᫬䛻⾜䛳䛶䛔䜎䛩䚹䐟㞴䛧䛔^&ィ⟬䜢཰᮰䛻ᑟ䛟ᢏ⾡ィ⟬஦౛Ε」㞧䝍䞁䝟䜽㉁඲㟁Ꮚィ⟬㐩ᡂ䛾䛯䜑䛻Ε䝦䝮;㕲䠉䝫䝹䝣䜱䝸䞁Ϳ䜢ϰ䛴ᣢ䛴ĐǇƚ͘cϯ䛾ϯ>zWศᏊ㌶㐨ィ⟬䐠㉸኱つᶍ&dィ⟬䛷䜒ಙ㢗䛷䛝䜛஺᥮┦㛵㻌 㻌 㡯ィ⟬ἲ䛾㛤Ⓨ䐡㔞Ꮚ໬Ꮫィ⟬⤖ᯝ䜢ศᏊຊᏛィ⟬䜈㑏ඖ㻌 㻌 䛩䜛᪉ἲ䛾◊✲䝍䞁䝟䜽㉁㔞Ꮚ໬Ꮫィ⟬䛻䜘䜛ᛂ⏝◊✲䜒㐍䜣䛷䛔䜎䛩ůĞĐƚƌŽŶƚƌĂŶƐĨĞƌƐŝŵƵůĂƚŝŽŶŝŶƚŚĞƉƌŽƚĞŝŶŝƐĂǀĂŝůĂďůĞ͘z͘dŽŬŝƚĂ;^ŽŶǇŽƌƉŽƌĂƚŝŽŶͿ͕et.  al.͕
J.  Am.  Chem.  Soc.͕ϭϯϬ͕ϱϯϬϮ;ϮϬϬϴͿ䝠䝗䝻䝀䝘䞊䝊䛻䜘䜛,Ϯ䛾⏕⏘ZƵŝƐK䛻䜘䜛Ⅳ⣲ᅛᐃ⏣ᮧ㝯ඛ⏕;ᒸᒣ኱ᏛͿ䛸䛾ඹྠ◊✲◊✲ᐊ⊂⮬◊✲౛  ⏘ᴗ⏺䛷䛾౑⏝౛  173ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)03ODW\SXV4000)(㻌䝝䜲䝤䝸䝑䝗㻽㻹㻛㻹㻹཯ᛂ⮬⏤䜶䝛䝹䜼䞊ィ⟬㻌㛤Ⓨ㈐௵⪅㻦㻌 ᯘ㻌 㔜ᙪ㻔ி㒔኱Ꮫ኱Ꮫ㝔⌮Ꮫ◊✲⛉ศᏊ䝇䜿䞊䝹◊✲㛤Ⓨ䝏䞊䝮㻕㻌㻌 㻌㻌ᴫせ㻌 ᪂つ䛺  QM/MM  ⮬⏤䜶䝛䝹䜼䞊ἲ䠄QM/MM-­RWFE-­SCF  ἲ䠅䜢㛤Ⓨ䛧䛯䚹QM/MM  ⮬⏤䜶䝛䝹䜼䞊ἲ䛷䛿䚸MD   䝅䝭䝳䝺䞊䝅䝵䞁䛻䜘䜚䝃䞁䝥䝹䛥䜜䛯   MM   㒊ศ䛾ᵓ㐀ศᕸ䛻䜘䜚ᐃ⩏䛥䜜䜛⮬⏤䜶䝛䝹䜼䞊᭤㠃ୖ䛷䚸QM   ἲ䛻䜘䛳䛶ྲྀ䜚ᢅ䜟䜜䜛άᛶ㒊఩ศᏊ䛾᭱㐺⮬⏤䜶䝛䝹䜼䞊ᵓ㐀䛜Ỵᐃ䛥䜜䜛 䚹ᡃ䚻䛿䚸ᖹᆒሙ㏆ఝ䛸⤫ィⓗ  reweighting  䛾ᡭἲ䜢⤌䜏ྜ䜟䛫䚸䛥䜙䛻  QM-­MM  㛫䛾㛗㊥㞳䜽䞊䝻䞁┦஫స⏝䜢  Ewald  ἲ䛻䜘䜚㐺ษ䛻⪃៖䛩䜛䛣䛸䛻䜘䜚䚸㠀ᖖ䛻⢭ᗘ䛾㧗䛔ຠ⋡ⓗ䛺ᡭἲ䜢㛤Ⓨ䛧䛯䚹≉䛻䚸ᮏᡭἲ䛾䝇䜻䞊䝮䛷䛿䚸QM/MM   ἲ㒊ศ䛾ィ⟬䛸  MM   ᵓ㐀䝃䞁䝥䝸䞁䜾䛾䛯䜑䛾   MD   䝅䝭䝳䝺䞊䝅䝵䞁㒊ศ䛜᏶඲䛻ศ㞳䛥䜜䜛䚹ᚑ䛳䛶䚸MD   䝅䝭䝳䝺䞊䝅䝵䞁䛻᪤Ꮡ䛾Ὑ⦎䛥䜜䛯  MD  䝥䝻䜾䝷䝮䜢౑⏝䛩䜛䛣䛸䛜ྍ⬟䛻䛺䜚䚸ィ⟬㐙⾜ୖ䛾኱䛝䛺ᰂ㌾ᛶ䜢㐩ᡂ䛧䛶䛔䜛䚹  ⌮ㄽ  QM/MM  䝝䝭䝹䝖䝙䜰䞁  HÖ (r, R, X)⮬⏤䜶䝛䝹䜼䞊ỗ㛵ᩘ  F[< MF ;; R] E 1 ln ³ dX exp E E[< MF (r;; R);; R, X]

Fock  or  Kohn-­Sham  ᪉⛬ᘧ  

HÖ 0 (r, R)  HÖ QM-­MM (r, R, X)  E MM (X)fÖ QM/MM r;; R

NQM

fÖ 0 r;; R  ¦ qÖ A r;; R VA R, X
A㟼㟁ᖹᆒሙ  

Reweighting  factor  VA R, X

X, E ª¬q d ,R ;;R , X º¼

Z d, d ref ;; R, R ref ;; X⮬⏤䜶䝛䝹䜼䞊䛾᰾ᗙᶆᚤศ  VA R, X Z d, dref ;; R, R ref ;; X

X , E ª¬q d , R ;;R , X º¼
X , E ª¬q d ref ,R ref ;;R ref , X º¼

exp  E'E QM-­MM d, d ref ;; R, R ref ;; X
exp  E'E QM-­MM d, d ref ;; R, R ref ;; X

wF ª¬ < MF ;; R º¼

wE ª¬ < MF r;; R ;; R, X º¼

wRwR

X , E ª¬q d ref , R ref ;;R ref , X º¼

X , E >q ;;R , X@ィ⟬⤖ᯝ  
90  ns  21  ns  ᕥᅗ䠖  QM  㡿ᇦ䛾᭱㐺໬ᵓ㐀䚹⇕ᦂ䜙䛞䛾ຠᯝ䛻䜘䜚໬Ꮫⓗ≧ែ䛻ᛂ䛨䛯㢧ⴭ䛺ᵓ㐀ኚ໬䛜ぢ䜙䜜䜛䚹  ୖᅗ䠖   άᛶ㒊఩௜㏆䛾䝍䞁䝟䜽㉁ᵓ㐀ኚ໬䚹ึᮇᵓ㐀䠄㟷䠅䛛䜙኱䛝䛺ኚ໬䛜ぢ䜙䜜䜛䚹䜎䛯䚸໬Ꮫ཯ᛂ䛻┦㛵䛩
䜛  L2  䝹䞊䝥䛾኱䛝䛺ᵓ㐀ኚ໬䛜ᚓ䜙䜜䛯䚹  䝥䝻䜾䝷䝮㛤Ⓨ  



ㅰ㎡  

ᖹᆒሙ  QM-­MM  ┦஫స⏝䛾୪ิ໬䛿䝃䞁䝥䝹ศ๭䚹  QM  㒊ศ䛿᳨ウ୰䚹  174ᮏ䝥䝻䜾䝷䝮䛾㛤Ⓨ䛿䚸኱Ꮫ㝔⏕䛾ᑠᮡ㈗ὒ
䛥䜣䛸஭ୖ㞝௓䛥䜣䚸༤ኈ◊✲ဨ䛾⚄㇂ᇶྖ༤ኈ䛸䛾ඹྠ◊✲䛷䛒䜛䚹  ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-7: Platypus-QM

㔞Ꮚ໬Ꮫィ⟬
㛤Ⓨ㈐௵⪅㻦 ୰ᮧ᫓ᮌ 㻔኱㜰኱Ꮫ⺮ⓑ㉁◊✲ᡤ㻕1 ᴫせ1.
ศᏊ㌶㐨ἲ䛚䜘䜃ᐦᗘỗ㛵ᩘ⌮ㄽ䠄DFT䠅䜢⏝䛔䛯⺮ⓑ㉁䛺䛹⏕యศᏊ⣔䛾඲㟁Ꮚ➨୍ཎ⌮ィ⟬䜢⾜䛖䚹ᮏ◊✲䛷䛿⏕య୰䛷䛾ບ㉳≧ែ➼䚸໬Ꮫ⤖ྜ䛾⤖ྜゎ㞳䚸ศᏊ㛫ຊ䛾グ㏙䜢⾜䛖䛯䜑䚸Quantum
Mechanics(QM)ィ⟬䛾㧗ᗘ໬䜢⾜䛳䛶䛝䛯䚹ලయⓗ䛻䛿䚸Platypus-QM䜈䛾CIS䚸CIS-DFT䚸CIS(D)䚸
CASSCF䚸MP2䛾ᐇ⿦䜢⾜䛳䛯䚹CIS䛿⏕యⰍ⣲䛺䛹䛾ບ㉳≧ែ䜢グ㏙䛩䜛Ⅽ♏ ㄽ 䛒䜚䚸
䚸ບ㉳≧ែ䜢グ㏙䛩䜛Ⅽ䛾ᇶ♏⌮ㄽ䛷䛒䜚䚸CIS-DFT䚸CIS(D)䛿䛭䛾㧗⢭ᗘ⌮ㄽ䛻ヱᙜ䛩䜛䚹䛣䜜䜙䛾ᐇ⿦䛻䜘䜚⏕యⰍ⣲䛻䜘䜛ග྾཰(ගྜᡂ⣔)䛺䛹䛾㧗⢭ᗘィ⟬䛜ྍ⬟䛻䛺䛳䛯䚹䜎䛯CASSCF䛿໬Ꮫ⤖ྜ䛾⤖ྜゎ㞳㐣⛬䜢グ㏙䛩䜛Ⅽ䛾⌮ㄽ䛷䛒䜚䚸௒ᅇ䛾ᐇ⿦䚸㧗ຠ⋡໬䛻䜘䜚䚸໬Ꮫ཯ᛂ㐣⛬䛜グ㏙ྍ⬟䛸䛺䛳䛯䚹ཪ䚸MP2䛾ᐇ⿦䛻䜘䜚䚸ศᏊෆỈ⣲⤖ྜ➼䛾ศᏊ㛫ຊ䛻㉳ᅉ䛩䜛⏕໬Ꮫ㐣⛬
Phodobacter sphaeroides⏤᮶䛾ගྜᡂάᛶ୰ᚰpග䜒ィ⟬ྍ⬟䛸䛺 䛯䜒ィ⟬ྍ⬟䛸䛺䛳䛯䚹䛾䝞䜽䝔䝸䜸䜽䝻䝻䝣䜱䝹a஧㔞య䠄䝇䝨䝅䝱䝹䝨䜰䠅
䛾HOMO

2. ி䛷䛾ᛶ⬟ホ౯ி䝁䞁䝢䝳䞊䝍ୖ䛻䛚䛔䛶䝝䜲䝤䝸䝑䝗୪ิ䛸SIMD໬䜢㐍䜑䛶䛚䜚䚸䜶䝛䝹䜼䞊ィ⟬䛻䛚䛡䜛✚ศィ⟬䛚䜘䜃Fock⾜ิ䛾2㟁Ꮚ㡯ィ⟬䛾䜹䞊䝛䝹䝁䞊䝗䜢సᡂ䛧䛯䚹AOᩘ2728
䛾⣔䛻䛚䛔䛶䚸୪ิ໬⋡
99.96% (8CPUx1 䝇 䝺 䝑 䝗 䛸
1024CPUx8 䝇 䝺 䝑 䝗 䛛 䜙 ⟬ฟ)䛜ᚓ䜙䜜䛯䚹512CPUx8䝇䝺䝑䝗䛷䛿40%䜢㉸䛘䜛୪ิ໬ຠ⋡䜢ほ 䛷䛝䛯䚹⣔䠖䝇䝨䝅䝱䝹䝨䜰䠄ཎᏊᩘ280)䛾cc-pVDZᇶᗏ㛵ᩘ⣔䠄AOᩘ2728)

3. Platypus-DFT䛻䜘䜛ᛂ⏝ィ⟬ගྜᡂ䛾Ỉศゎ཯ᛂ୰ᚰ䛾ィ⟬ྍどග䜢⏝䛔䛶Ỉ䜢ศゎ䛧㓟⣲䛸Ỉ⣲(䝥䝻䝖䞁+㟁Ꮚ)䜢Ⓨ⏕䛥䛫䜛䛣䛾཯ᛂ䛿⌧ᅾ䛾䜶䝛䝹䜼䞊ၥ㢟䞉ᆅ⌫ᬮ໬ၥ㢟䜢ゎỴ䛩䜛㘽䛸䛺䜛཯ᛂ䛷䛒䜛䚹
䛭䛾ゐ፹୰ᚰ䛿㓟⣲ᯫ
ゐ፹୰ᚰ 㓟⣲ᯫᶫ䝬䞁䜺䞁ᅄ᰾䜘䜚䛺䜛䜽䝷䝇䝍䞊䛷䛒䜛䚹㔜ཎᏊ䛾఩⨨䜢ᅛᐃ䛧䚸ᯫᶫ䜰䝭䝜㓟䛾ഃ㙐୰䛾䜰䝉䝔䞊䝖䜢㓑㓟䛻䝰䝕䝹໬
䛧䛶䚸Kok䝃䜲䜽䝹䜢᝿ᐃ䛧䛶㓟໬≧ែ䜢ኚ䛘䛺䛜䜙䚸䝁䜰㒊ศ䛾᭱Ᏻᐃ㟁Ꮚ≧ែ䜢ㄪ䜉䛯䚹

PS II䛾䝬䞁䜺䞁䜽䝷䝇䝍䞊䛾ྍ⬟䛺඲䛶䛾䝇䝢䞁ᵓ㐀䝬䞁䜺䞁䜲䜸䞁䛜䝇䝢䞁ศᴟ⋡䛛䜙ぢ䜜䜀᏶඲䛺☢ᛶ≧ែ䛻䛒䜛䛾䛻䜒㛵䜟䜙䛪䚸䝇䝢䞁䜔㟁Ⲵ䛜ྛ䝃䜲䝖㛫䜢ᐜ᫆䛻㌿⛣䛩䜛Labile䛺㟁Ꮚᵓ㐀䜢ᣢ䛴஦䚸䜎䛯䝃䜲䝖㛫䛾☢Ẽⓗ┦஫స⏝ゎᯒ䛾⤖ᯝ䛛䜙䛿䚸䜽䝷䝇䝍䞊඲య䛾☢Ẽⓗ䛺䜹 䝥䝸䞁䜾䛾⤖ᯝ඲⣔䛾䝇䝢䞁≧ែ䛜Ỵᐃ䛧䛶䛔䜛஦䛜᫂䜹䝑䝥䝸䞁䜾䛾⤖ᯝ඲⣔䛾䝇䝢䞁≧ែ䛜Ỵᐃ䛧䛶䛔䜛஦䛜᫂䜙䛛䛻䛺䛳䛯[1-2]䚹

ཧ⪃ᩥ⊩

1. Kanda et al.,Chem.Phys.Lett. 506, 98-103 (2011)
Kok䝃䜲䜽䝹䛸䝬䞁䜺䞁䜽䝷䝇䝍䞊䛾㓟໬ᩘ䠄㉥䛜᝿ᐃ䛧䛯≧ែ䠅 2. Yamanaka et al., Chem.Phys.Lett. 511, 138-145 (2011)175ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-8: Platypus-QM/MM㔞Ꮚ໬Ꮫィ⟬䠋ศᏊືຊᏛ㐃ᡂィ⟬㛤Ⓨ㈐௵⪅㻦 ୰ᮧ᫓ᮌ 㻔኱㜰኱Ꮫ⺮ⓑ㉁◊✲ᡤ㻕

1 ᴫせ
1.

3ODW\SXV4000 4XDQWXP PHFKDQLFV 0ROHFXODU PHFKDQLFV 䛷䛿䚸⏕య㧗ศᏊ䜢⤒㦂ⓗศᏊຊሙ䜶䝛䝹䜼䞊㛵ᩘ䛸㠀⤒㦂ⓗศᏊ㌶㐨ἲ䛷グ㏙䛧䛶ศᏊືຊᏛ䝅䝭䝳䝺䞊䝅䝵䞁䜢㐙⾜䛷䛝䜛䚹⤒㦂ⓗศᏊຊሙ䜶䝛䝹䜼䞊㛵ᩘ䛷䛿ྛཎᏊ䛿㉁㔞䛸㟁Ⲵ䜢ᣢ䛱࿘㎶ཎᏊ䛸䛾⤖ྜ㛵ಀ䜢⤒㦂ⓗ䛻ண䜑ᐃ䜑䜙䜜䛯䝟䝷䝯䞊䝍䞊䛻䜘䛳䛶ィ⟬䛩䜛䚹୍᪉䚸㠀⤒㦂ⓗ䛺ศᏊ㌶㐨ἲ䛷䛿䚸ྛཎᏊ䛿ཎᏊ᰾䛾㟁Ⲵ䛸㟁ᏊἼື㛵ᩘ䛻䜘䛳䛶䛾䜏グ㏙䛥䜜䜛䚹ᡃ䚻䛿䝪䝹䞁䞊䜸䝑䝨䞁䝝䜲䝬䞊㏆ఝ䛾⠊ᅖෆ䛷䚸ཎᏊ᰾䛜㟁Ꮚᵓ㐀䛸䛿᩿⇕ⓗ䛻ศ㞳䛧䛶ྲྀ䜚ᢅ䛘䜛䛸௬ᐃ䛧䛯䛖䛘䛷 䝝䜲䝤䝸䝑䝗䜶䝛䝹䜼䞊㛵ᩘ䛷グ㏙䛥䜜䛯⣔䛾䝝䝭䝹䝖䝙䜰䞁䜢ྂ඾ⓗ䛻✚ศ䛧䛶⣔䜢᫬㛫Ⓨᒎ䛥ᐃ䛧䛯䛖䛘䛷䚸䝝䜲䝤䝸䝑䝗䜶䝛䝹䜼㛵ᩘ䛷グ㏙䛥䜜䛯⣔䛾䝝䝭䝹䝖䝙䜰䞁䜢ྂ඾ⓗ䛻✚ศ䛧䛶⣔䜢᫬㛫Ⓨᒎ䛥䛫䛶䛔䜛䚹3ODW\SXV4000䛿䛣䛾⤒㦂ⓗศᏊຊሙ䝴䝙䝑䝖䛸㠀⤒㦂ⓗ䝴䝙䝑䝖䜢㐃ᡂ䛥䛫䛶4000ศᏊືຊᏛ䝅䝭䝳䝺䞊䝅䝵䞁䜢ᐇ᪋䛩䜛䚹䛣䛾㝿䚸00✵㛫䛛䜙䛾㟼㟁ⓗᐤ୚䜢ᚑ᮶ἲ䛻ẚ䜉䛶䛿䜛䛛䛻ṇ☜䛻ྲྀ䜚㎸䜐᪉ἲ䛸䛧䛶䚸᪂䛯䛻㛤Ⓨ䛧䛯=HUR'LSROH 6XPPDWLRQ䛾䜰䝹䝂䝸䝈䝮䜢ྲྀ䜚ධ䜜䛯䚹䛥䜙䛻䚸&KDLQRI6WDWHἲ䛻ᇶ䛵䛟␯⤖ྜ㉸୪ิィ⟬䛻䜘䛳䛶䚸᭱ᑠ⮬⏤䜶䝛䝹䜼䞊⤒㊰Ỵᐃ䛩䜛䝇䜻䞊䝮䜢ᑟධ䛧䛯䚹

2. Zero-dipole summation 䜰䝹䝂䝸䝈䝮䛾MDィ⟬䜈䛾㐺⏝­F ( rc ) 21
2 ½
qi q j ®V ( rij )  V (rc ) (rij  rc )¾¦¦
2 iN jN
2rc¯¿
( rij  rc ,j zi )D ½­V (r ) r2
 ® c  c F (rc ) ¾¦ qi
4S ¿iN¯ 2& &erfc (D | ri  rj |)& &| ri  rj |V (rij )








F (r )


U|



:ROI䜙䛻䜘䛳䛶ᥦ᱌䛥䜜䛯࿘ᮇ⣔䛻䛚䛔䛶ேᕤⓗ䛺୰ᛶ⣔䜢స䜛䛣䛸䛻䜘䛳䛶㟼㟁┦஫స⏝䜶䝛䝹䜼䞊䛾⢭ᗘ䜢ಖ䛴ᕤኵ>@䛿䚸⚟⏣䜙䛻䜘䛳䛶཮ᴟᏊ䝰䞊䝯䞁䝖䜒䝊䝻䛸䛩䜛⣔䛸䛩䜛䜰䝹䝂䝸䝈䝮
䛻Ⓨᒎ䛥䜜䛯>@䚹䛣䛾䜰䝹䝂䝸䝈䝮䛷䛿䚸࿘ᮇ⣔䛻ᚲ㡲䛾⿵ㄗᕪ㛵ᩘ䛻䛚䛡䜛䝎䞁䝢䞁䜾䝟䝷䝯䞊䝍䜢㝈䜚䛺䛟ᑠ䛥䛟䛩䜛䛣䛸䛜ྍ⬟䛷䛒䜚䚸㠀࿘ᮇⓗ䛺⣔䜈䛾ᣑᙇ䜒ྍ⬟䛷䛒䜛䚹䛣䛾䜰䝹䝂䝸䝈䝮䜢ỈศᏊ䛾⣔䛚䜘䜃⭷⺮ⓑ㉁䛾⣔䛻ᛂ⏝䛧䛣䛾䜰䝹䝂䝸䝈䝮䜢ỈศᏊ䛾⣔䛚䜘䜃⭷⺮ⓑ㉁䛾⣔䛻ᛂ⏝䛧䚸
(ZDOGἲ䛻䜘䜛䜶䝛䝹䜼䞊䛚䜘䜃ຊ䛾್䛸ẚ㍑䛧䛯䛸䛣䜝䚸ᴟ䜑䛶Ⰻ䛔⢭ᗘ䛷୍⮴䛩䜛䛣䛸䛜☜ㄆ䛥䜜䛯䚹䛥䜙䛻䚸䛣䛾䜰䝹䝂䝸䝈䝮䛻䜘䜛0'ィ⟬䛾⤖ᯝ䚸䛭䜜䜙䛾⣔䛾䝎䜲䝘䝭䝑䜽䛺ᛶ㉁䜒ಖ䛯䜜䛶䛔䜛䛣䛸䛜☜ㄆ䛥䜜䛯䚹

wV
wr





3. ᭱㐺⮬⏤䜶䝛䝹䜼䞊⤒㊰᥈⣴ἲ䠖Chain-of-State + hybrid-QM/MD䝅䝭䝳䝺䞊䝅䝵䞁㓝⣲䛺䛹䛾」㞧䛺ศᏊ㐣⛬䛾⮬⏤䜶䝛䝹䜼䞊ィ⟬䛻ᑐ䛩䜛㧗ຠ⋡䛾ィ⟬䛾䛯䜑䚸&KDLQRI6WDWHἲ䛻䜘䜛␯㓝⣲䛺䛹䛾」㞧䛺ศᏊ㐣⛬䛾⮬⏤䜶䝛䝹䜼ィ⟬䛻ᑐ䛩䜛㧗ຠ⋡䛾ィ⟬䛾䛯䜑 &KDLQ RI 6WDWHἲ䛻䜘䜛␯⤖ྜ୪ิィ⟬䜢⏝䛔䛯䚸」ᩘ䛾䝟䝷䝯䞊䝍್䛜ྠᮇ䛧䛶㢮ఝ䛾ィ⟬䜢⾜䛖஧㔜໬䛧䛯୪ิ໬䝇䜻䞊䝮䜢ᑟධ䛧
䛯䚹&KDLQRI6WDWHἲ䛾ึᮇ㓄⨨䛸䛧䛶䚸䛒䜛ᖹ⾮≧ែ5䛸3㛫䜢⤖䜆ヨ⾜⤒㊰䠄ୗグ䛾౛䛷䛿┤⥺䠅ୖ䛻➼㛫㝸䛻ィ⟬䝅䝭䝳䝺䞊䝅䝵䞁Ⅼ䜢㓄⨨䛩䜛䚹ྛィ⟬䝅䝭䝳䝺䞊䝅䝵䞁Ⅼ䛷䛿䛚஫䛔௚䛸䛿␗䛺䜛఩┦✵㛫ୖ䛾䝟䝷䝯䞊䝍䞊䠄཯ᛂᗙᶆ䠅䜢ᣢ䛱ᐃ䜑䜙䜜䛯᫬㛫㛫㝸୰䠄䝣䜵䞊䝈䠅䛻ྛ䚻఩┦✵㛫Ⅼ㏆ഐ䛾⮬⏤䜶䝛䝹䜼䞊㠃䛻㛵䛩䜛ィ⟬䜢ᐇ⾜䛩䜛䚹ྛ䚻䛾ィ⟬䛜཰᮰䛧䛯ᚋ䛻䚸䝩䝇䝖䛻䛣䜜䜙䛾ィ⟬᝟ሗ䜢㞟⣙䛧䛶ホ౯䛩䜛䛣䛸䛷ḟ䛾᫬㛫㛫㝸䛻ྛ䝅䝭䝳䝺䞊䝅䝵䞁䛜౑⏝䛩䜛䝟䝷䝯䞊䝍䞊䜢Ỵᐃ䛧䛶䝤䝻䞊䝗䜻䝱䝇䝖䛧ḟ䛾䝣䜵䞊䝈䛾ィ⟬䜢⥆⾜䛩㛫㛫㝸ྛ䝅䝅 䞁 ౑⏝䛩䜛 䝷
䝍 䜢Ỵᐃ
䜻䝱
ḟ
ィ⟬䜢⥆⾜䛩
䜛䚹䝟䝷䝯䞊䝍䞊䛜཰᮰䛩䜛䜎䛷䛣䛾ィ⟬䛸䝟䝷䝯䞊䝍䞊㞟⣙䛾㐣⛬䜢⧞䜚㏉䛩䛣䛸䛷᭱㐺䛺䝟䝷䝯䞊䝍䞊䜢Ỵᐃ䛧䚸5䛸3䜢⤖䜆᭱㐺⤒㊰䛜Ỵᐃ䛥䜜䜛䛣䛸䛻䛺䜛䚹ᚑ᮶䛾&KDLQRI6WDWHἲ䛿䚸䜋䛸䜣䛹䛜ྂ඾ຊᏛ䛾⠊␪䛷䛺䛥䜜䛶䛝䛯䛜䚸ᡃ䚻䛿䚸3ODW\SXV40䛷㛤Ⓨ䛧䛯㟁Ꮚ≧ែィ⟬䜢ᑟධ䛩䜛䛣䛸䛻䜘䛳䛶䚸㟁Ꮚᵓ㐀䛜ኚ໬䛩䜛໬Ꮫ཯ᛂ䛾⮬⏤䜶䝛䝹䜼䞊⤒㊰䛾ィ⟬䜢ྍ⬟䛸䛧䛯䚹Ỉ⁐ᾮ୰䛷䛾䜰䝷䝙䞁䞉
R
䝆䝨䝥䝏䝗䛾
ᵓ㐀ኚ໬䛻䛴䛔䛶䚸5䠄ȕᵓ㐀䠅䛛䜙3
䠄ĮKHOL[䠅䜈
䛾ኚ໬䛾᭱
㐺⮬⏤䜶䝛
P
䝹䜼 ⤒㊰
䝹䜼䞊⤒㊰
䛾᥈⣴౛䚹
Ỉ䛿00䚸䝨
䝥䝏䝗䛿40䛸䛧䛶ィ⟬䚹
>@:ROIHWDOJ. Chem. Phys., 110,   >@)XNXGDHWDOJ. Chem. Phys. 134,   176ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

M-9: Platypus-CGM/MM⢒ど໬䝰䝕䝹ィ⟬䠋ศᏊືຊᏛ㐃ᡂィ⟬㛤Ⓨ㈐௵⪅㻦 ୰ᮧ᫓ᮌ 㻔኱㜰኱Ꮫ⺮ⓑ㉁◊✲ᡤ㻕1 ᴫせ1.
⺮ⓑ㉁䛾ศᏊືຊᏛ䛻⨨䛔䛶䜒䛳䛸䜒୍⯡ⓗ䛻౑⏝䛥䜜䛶䛔䜛඲ཎᏊ䝰䝕䝹䠄䠝䠝䠩䠅䛿䚸ᵓ㐀᝟ሗ䛜ヲ⣽䛷䛒䜛䛜䜖䛘䛻ᵓ㐀✵㛫䛾᥈⣴䛻᫬㛫䛜䛛䛛䜛䚹≉䛻䝗䝯䜲䞁㐠ື䛾䜘䛖䛺⌧㇟䜢ᢅ䛖䛻䛿୙ྥ䛝䛷䛒䜛䚹୍᪉䛷⢒ど໬䝰䝕䝹䠄䠟䠣䠩䠅䛿㛗᫬㛫䛾䝅䝭䝳䝺䞊䝅䝵䞁䛻ྥ䛔䛶䛿䛔䜛䛜䚸ཎᏊ䝺䝧䝹䛾┦஫స⏝䜢グ㏙䛩䜛䛣䛸䛿䛷䛝䛺䛔䚹䛣䛾䛯䜑⺮ⓑ㉁䛾ᵓ㐀ண 䜔పศᏊ໬ྜ≀䛸䛾⤖ྜ䜢ྲྀ䜚ᢅ䛖䛣䛸䛿䛷䛝䛺䛔䚹䛭䛣䛷䠝䠝䠩䛾ಙ㢗ᛶ䛸䠟䠣䠩䛾ຠ⋡ᛶ䛾཮᪉䜢ά⏝䛩䜛䝬䝹䝏䝇䜿䞊䝹䝅䝭䝳䝺䞊䝅䝵䞁ᡭἲ䜢ᥦ᱌䛧䚸䛭䛾ᡭἲ䛷⏝䛔䜛Ⅽ䛾
䠟䠣䠩䝅䝭䝳䝺䞊䝍䞊䠄platypus-CGM䠅䜢㛤Ⓨ䛧䛶䛝䛯䚹

2 ᡭἲ䛾᳨ド䠄Chignolin)2.
ᡭἲ䛾᳨ド䠄Chignolin)*1

ChignolinศᏊ(PDBID:1uao)㓄ิ:ิ GYDPETGTWG

䜶䝛䝹䜼䞊䛸Q್䛾ᩓᕸᅗ(ᕥ䠅AAM/CGM䚸䠄ྑ䠅ᚑ᮶䛾all-atom multicanonical䜶䝛䝹䜼䞊䛸Q್䛾FEL(ᕥ䠅AAM/CGM(ᕥ䠅AAM/CGM䚸䠄ྑ䠅ᚑ᮶䛾all-atom multicanonical

AAM/CGM䛾⤖ᯝ䛸䠝䠝䠩䝅䝭䝳䝺䞊䝅䝵䞁䛾⤖ᯝ䛜䜘䛟୍⮴

3. ᵓ㐀䝃䞁䝥䝸䞁䜾䛾ຠ⋡໬*2
Wang-Landauἲ䛷䛿䜶䞁䝖䝻䝢䞊S䜢䜶䝛䝹䜼䞊E䛸᫬㛫t䛾㛵ᩘ䛷⾲䛧䚸S=S+f᫬㛫㛵ᩘ ⾲䛧䚸
((f䛿ศᕸ㛵ᩘP(E)䛜ᖹ䜙䛿ศᕸ㛵ᩘ ( ) ᖹ䜙䛻䛺䜛䛯䜃䛻ῶᑡ䛩䜛䝟䝷䝯䞊䝍䠅䛸ኚ᭦䛩䜛䚹䝃䞁䝥䝸䞁䜾ึᮇ䛾ẁ㝵䛾S䛾ฝพ䜢䛾䛮䛟䛯䜑䚸S䛾᭤⋡䜢᭱ᑠ໬䛩䜛ኚศཎ⌮䜢ᑟධ䛧䚸⮬ືⓗ䛻⢭ᗘ䜘䛟ᵓ㐀䜢䝃䞁䝥䝸䞁䜾䛩䜛᪉ἲ䜢☜❧䛧䛯䠄Smooting
Wang-Landauἲ:SWLἲ䠅䚹

4. ௒ᚋ䛾ィ⏬
s37,ἲ䛾⬡⫫㓟ྜᡂ㓝⣲䝅䝭䝳䝺䞊䝅䝵䞁䜈䛾ᛂ⏝㯮୸䛿䠝䠟䠬䛸䠩䠝䠰䝗䝯䜲䞁䛷䚸▮༳䛾㛗䛥䛜R䚹R䛻ᑐ䛧䛶䠯䠳䠨ἲ䜢ᛂ⏝䛩䜛䛣䛸䛷䚸ᣑᩓ䜶䞁䝖䝻䝢䞊䜢ຠ⋡ⓗ䛻ồ䜑䜛䛾䛜≺䛔䚹s䝥䝻䜾䝷䝮䛾䛥䜙䛺䜛㧗㏿໬䠖┦஫స⏝䝔䞊䝤䝹䝥 䜾䛥 䛺䜛㧗㏿ ┦ స䝤
䛾㏻ಙ䛾ຠ⋡໬┿✵୰䛾Alanine-dipeptide䜢SWLἲ䜢⏝䛔䛶ィ⟬䛧䛯 ཧ⪃ᩥ⊩䜹䝜䝙䜹䝹ศᕸ䠄㉥s㟷䠅䛸䜹䝜䝙䜹䝹䝅䝭䝳䝺䞊䝅䝵䞁䛾⤖ 1. Shimoyama et al., J.Chem.Phys. 133, 135101 (2010)
2. Shimoyama et al., J.Chem.Phys. 134, 024109 (2011)ᯝ䠄䝢䞁䜽s⥳䠅䠙䠚୍⮴177

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)&5,&6⚦⢩䉲䊚䊠䊧䊷䉲䊢䊮䊒䊤䉾䊃䊐䉤䊷䊛5,&6䋺5,.(1,QWHJUDWHG&HOO6LPXODWRU㐿⊒⽿છ⠪㪑 ᮮ↰⑲ᄦ 㗇᳗ᵏᒄ 㩿ℂൻቇ⎇ⓥᚲ⚦⢩䉴䉬䊷䊦⎇ⓥ㐿⊒䉼䊷䊛㪀ᵫᶍᶒᶇᶔᵿᶒᶇᶍᶌᾉ ኬᏘƸᙐᩃƳ‫࢟࢟ٳ‬ཞǍϋᢿನᡯǛਤƪ‫ئ‬৑ƴǑƬƯˊᜓӒࣖƕီƳǔŵ࢟ཞǛᎋॾƠƯኬᏘϋӒࣖǛᚘምƠƨƍ

ᵭᶓᶐᴾᵟᶎᶎᶐᶍᵿᶁᶆᾉ ኬᏘǷȟȥȬȸǷȧȳወӳǷȟȥȬȸǷȧȳȗȩȃȈȕǩȸȠƷ᧏ႆኬᏘἉἱἷἾὊἉἹὅἩἻἕἚἧỻὊἲ
ᵰᵧᵩᵣᵬᴾᵡᶃᶊᶊᴾᵱᶇᶋᶓᶊᵿᶒᶍᶐᴾᵆᵰᵧᵡᵱᵇ4+%5ƷǿȸǲȃȈ⢄⤳⚦⢩䋨⢄ዊ⪲䋩⿒ⴊ⃿῍ኬᏘϋỉ‫ئ‬ửᎋॾẲẺဃ҄‫ܖ‬ӒࣖἉἱἷἾὊἑὊ῍ᐏᡢᢅὺਘ૝ὺဃ҄‫ܖ‬Ӓࣖίˊᜓὸ␹⚻⚦⢩䊶ㅪ⛯૕䈫䈚䈩⚦⢩ౝ䉕⴫⃻䊶ⓨ㑆䉕䊗䉪䉶䊦ⓨ㑆䈪⴫⃻䊶⚦⢩ౝዊེቭ䈭䈬䈱ⶄ㔀䈭ⓨ㑆᭴ㅧ䈮䉅ኻᔕ䊶ⶄᢙ⚦⢩䉕หᤨ䈮⸘▚น⢻䊶⚦⢩⤑䈱ᯏ⢻䉕⴫⃻น⢻

ⴊዊ᧼

⤟⤳ȕ⚦⢩

⤳ེ䈫⚦⢩䉕䈧䈭䈓䉲䊚䊠䊧䊷䉲䊢䊮䉕⋡ᜰ䈚䈩䈇䉁䈜䇯᫋ࣇᦟဒ΂ẦỤ˺঺ẲẺᵑഏΨ࢟ཞϋỂỉᚘምᚘምኽௐᲢ߼ᲴӒࣖƋǓŴӫᲴӒࣖƳƠᲣᤨ㑆ᰴరౣ᭴▽䈚䈢⚦⢩ᒻ⁁౒ὶὐ䊧䊷䉱䊷㗼ᓸ㏜↹௝
䋨+HS*⚦⢩䋺⢄⤳⚦⢩䋩⸘▚᧦ઙಽഀᢙ䋨⸃௝ᐲ䋩䋺㬍㬍
ਗ೉ᣇᴺ䋺㗔ၞಽഀਗ೉ᢙ䋺ਗ೉ǻW 䋨⑽䋩
⸘▚6WHSᢙ䋺6WHS૶↪䈚䈢⸘▚ᯏ䋺
5,&&⿥ᄙ⋡⊛䉪䊤䉴䉺䊷⸘▚ታᤨ㑆䋺
෻ᔕ䈭䈚䋺 ಽ෻ᔕ䈅䉍䋺 ᤨ㑆&D䈱᜛ᢔଥᢙ䋺㬍䋨PV䋩

5,&66ROYHU䈪⸘▚෻ᔕ䋺24෻ᔕ
‛⾰ᢙ䋺10୘⚦⢩ౝ䉦䊦䉲䉡䊛✭ⴣ෻ᔕ෻ᔕ䈏䈅䉎䈫⷗䈎䈔䈱᜛ᢔㅦᐲ䈏ㆃ䈒䈭䉎⚦⢩ౝ䈱ዪᚲ⊛䈭෻ᔕ䈏⚦⢩ౝ䈱‛⾰䈱Ớᐲᄌൻ䈮ᓇ㗀䉕ਈ䈋䈩䈇䉎䈖䈫䉕␜ໂ䈚䈢䇯RICCỉನ঺૾ᆉࡸ
ਗ೉ൻല₸㩿୘೎㪀Ყ㩿㪍㪋㪺㫆㫉㪼㪆㪺㫆㫉㪼㪀wIl ,mCl Il ,m  El ,m  Ql ,mwt

᭴ᚑᣇ⒟ᑼ㧦

᜛ᢔEl , m‛⾰↢ᚑ㗄㧦

⤑ㅘㆊ㗄㧦

Il ,m
ECell l , mam , n

Cl

m

䋺ᇦ⾰

m

Ql ,m

l

ౝ䈱‛⾰
l

n

ౝ䈱‛⾰

䋺ᇦ⾰

㑆䈱⤑㕙Ⓧ

l

䋺‛⾰

䈱᜛ᢔଥᢙ

¦

⤑ㅘㆊ

't

't o0

䈱Ớᐲ

䋺ᇦ⾰

m, n

lim

෻ᔕ

ECelll ,m ^I*,m `, 't  Il ,mVol  Vf m
Vol䈱෻ᔕᓟ䈱Ớᐲvl ,m,n

m

䋺ᇦ⾰

ᜓᠴ

⤑
᜛ᢔ

㪈㪋㪅㪇

ઍ⻢
ో૕

㪈㪉㪅㪇

ℂᗐ

ᧄ⊒⴫䈱⚿ᨐ䈱৻ㇱ䈲䇮ℂൻቇ⎇ⓥᚲ䈏ታᣉ䈚䈩䈇䉎੩ㅦ䉮䊮䊏䊠䊷䉺㵱੩㵱䈱⹜㛎೑↪䈮䉋䉎䉅䈱䈪䈜䇯

㪏㪅㪇
㪍㪅㪇
㪋㪅㪇

䈱䊗䉪䉶䊦ౝ૕Ⓧ₸䋺䋱䊗䉪䉶䊦䈱૕Ⓧ
l

Ṷ▚䈱䉂
㪈㪍㪅㪇

㪈㪇㪅㪇

am,n ^ vl ,m,n Il ,m  Il ,n `
Vfm

㪈㪏㪅㪇

m, n

䋺‛⾰ 䈱ᇦ⾰㑆䈱⤑ㅘㆊ₸

㪉㪅㪇
㪇㪅㪇
㪇

㪉㪇㪇㪇

㪋㪇㪇㪇

㪍㪇㪇㪇

㪏㪇㪇㪇

㪈㪇㪇㪇㪇 㪲㪺㫆㫉㪼㪴RICCửဇẟẺ‫ᙹٻ‬೉ɳЗ

178ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

C-2: RICS-Pre⚦⢩䉲䊚䊠䊧䊷䉲䊢䊮䊒䊤䉾䊃䊐䉤䊷䊛(RICS)䊒䊥䉲䉴䊁䊛㐿⊒⽿છ⠪㪑 ᮮ↰⑲ᄦ 㗇᳗ᵏᒄ 㩿ℂൻቇ⎇ⓥᚲ⚦⢩䉴䉬䊷䊦⎇ⓥ㐿⊒䉼䊷䊛㪀ᵫᶍᶒᶇᶔᵿᶒᶇᶍᶌᾉ ܱᨥƷኬᏘƸᙐᩃƳϋᢿನᡯǛਤƪ‫ئ‬৑ƴǑƬƯˊᜓӒࣖƕီƳǔƨǊᙐᩃƳ࢟ཞǍИ஖வˑǛјྙǑƘ4+%5ƴᚨ‫ܭ‬ƠƨƍᲨ

ᵭᶓᶐᴾᵟᶎᶎᶐᶍᵿᶁᶆᾉ ኬᏘǷȟȥȬȸǷȧȳወӳȗȩȃȈȕǩȸȠȗȪǷǹȆȠ
Ტ4+%52TGᲣƷ᧏ႆᵰᵧᵡᵱᵱᶍᶊᶔᶃᶐ Ể࣏ᙲễᚨ‫ܭ‬ἧỳỶἽ
CAD & ↹௝ᵱᵴᵶἧỳỶἽ䊶ᒻ⁁䈫⸃௝ᐲ䈱⸳ቯ䋨⚦⢩䇮䉥䊦䉧䊈䊤䇮ᄖㇱⅣႺ䈭䈬䋩䊶‛⾰䈱Ớᐲ䈫᜛ᢔଥᢙ䇮ಽᏓ䈱⸳ቯ䊶෻ᔕ᧦ઙ䈱⸳ቯ䊶⸘▚᧦ઙ䈱⸳ቯ䋨ǻt䇮ਗ೉ᢙ䈭䈬䋩䊶⤑⸳ቯ䊶ᵰᵧᵡᵱᵋᵮᶐᶃầẝỦể↢ൻቇ෻ᔕᑼ
(E-Cell)ǹȚǷȣȪǹȈưƸƳƘƯǋ, ኬᏘǷȟȥȬȸǷȧȳƷȢȇȫ˺঺ƕӧᏡƴƳǔᲨ
9 ؕஜ࢟ཞƷ˺঺ƕܾତҥኝƳؕஜ࢟ཞƸCADǛဇƍƣ RICS-Preư˺঺9ᚐௌவˑƷᚨ‫ܭ‬ƕܾତλщȕǩȸȠƴࢼƬƯ࣏ᙲƳऴ‫إ‬Ǜᚨ‫ܭ‬ᵶᵫᵪἧỳỶἽᵣᵫᵪἧỳỶἽᵰᵧᵡᵱᵋᵱᶍᶊᶔᶃᶐ

ᵴᶍᶖᶃᶊἙὊἑỉᚐ΂ࡇ‫୼٭‬ኬƔƍ᫋ࣇᦟȇȸǿƔǒችࡇǑƘቫƍᚐ΂ࡇƷ࢟ཞȢȇȫǛ˺঺ᵰᵧᵡᵱᵋᵮᶐᶃỉೞᏡ

⿒䋺⚦
⢩⾰
㕍䋺ᩭ
✛䋺䊚
䊃䉮䊮
䊄䊥䉝

9 ܱኬᏘ࢟ཞӏƼCAD࢟ཞƷӕǓᡂǈ
9 ؕஜ࢟ཞƷဃ঺
9 ࢟ཞƷ̲ദ
9 ኬᏘϋƷ‫ۥ‬ឋ, ཋឋȑȩȡȸǿሁƷᚨ‫ܭ‬
9 ӒࣖኺែӏƼᐏೞᏡǛɤഏΨȇȸǿǁᚨ‫ܭ‬
9 ᚘምவˑƷᚨ‫ܭ‬䋳ᰴరౣ᭴▽䈚䈢HepG2⚦⢩
䋨300x300x30䋬Pitch:20,20,50䋩↹௝䊂䊷䉺Pixelᢙ
(1Voxel䈱ഀว)

300㬍300㬍30(=2,700,000 voxels)

26㬍26㬍15 (=10,140
voxels)ᵰᵧᵡᵱᵋᵮᶐᶃỉἧἿὊ

⥄േ↢ᚑ
⸘▚᧦ઙ
㩿㪯㪤㪣㪀

8QZGN1TKIKP'NGOPCOGEGNNQZ
+PRWV&CVC
'NGOPCOG/
Q[
DCUGFKTKP
KF
Q\
+P(KNG CVVT/
2CTCO8QZGN5K\G
HQTOCVUXZPCOGOGVCDQNKUOKZ
HPCOG/UXZFV[RG564+0)
LZOWNVKPQXCNWGGONOCVG
MZ
+P(KNG CVVT/ON8QZGN9KFVJ
HQTOCVUXZ
2CTCOYZ
HPCOG/UXZPCOGU[UVGOY[
FV[RG564+0) OWNVKPQ
Y\
+P(KNGXCNWG
CVVT/''NGOHQTOCVURJ
'NGOPCOG/
HPCOG'URJ
KFOWNVKPQ'NGO+PRWV&CVC
'NGOᜓᠴᧄ⊒⴫䈱⚿ᨐ䈱৻ㇱ䈲䇮ℂൻቇ⎇ⓥᚲ䈏ታᣉ䈚䈩䈇䉎੩ㅦ䉮䊮䊏䊠䊷䉺㵱੩㵱䈱⹜㛎೑↪
䈮䉋䉎䉅䈱䈪䈜䇯

179

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)&5,&6SODWHOHWV㻌㻾㻵㻯㻿䛻䜘䜛⾑ᑠᯈ⣽⬊䝅䝭䝳䝺䞊䝅䝵䞁䛸ຊᏛⓗస⏝䛻䜘䜛ഇ㊊ⓗኚᙧ䛻䛴䛔䛶䛾᳨ウ㻌୐⃝㻌ὒᖹ䚸⏣ᮧ㻌 ඾Ꮚ䚸ᚋ⸨㻌 ಙဢ㻌㻔ᮾᾏ኱Ꮫ་Ꮫ㒊㻌 ⮚ჾ/⣽⬊䝇䜿䞊䝹◊✲㛤Ⓨ䝏䞊䝮㻕㻌㻌 㻌㻌㻌 ᮏ◊✲䛷䛿䚸⾑ᑠᯈ⣽⬊䛻䛴䛔䛶  䐟⣽⬊䛾⏕໬Ꮫⓗ཯ᛂ  䐠ຊᏛⓗస⏝䛻䜘䜛㒊ศⓗ䛺ኚᙧ  䛾2Ⅼ䛻╔┠䛧䚸◊✲䜢㐍䜑䛶䛔䜛䚹  
RICS䜢⏝䛔䛯⾑ᑠᯈ⣽⬊䛻䜘䜛」ᩘ⣽⬊䛻䜘䜛䝅䝭䝳䝺䞊䝅䝵䞁䛸ෆ㒊཯ᛂ௦ㅰ䝬䝑䝥䛾⢭⦓໬  㻌 ⣽⬊ෆ㒊䛾௦ㅰ཯ᛂ/䝅䜾䝘䝹ᣑᩓ䛻╔┠䛧䚸⣽⬊ෆ䛾≀㉁/䜸䝹䜺䝛䝷䛾఩⨨᝟ሗ䜢⪃៖䛧䛯⾑ᑠᯈ⣽⬊ෆ䛷䛾௦ㅰᣑᩓ཯ᛂ䛻䛴䛔䛶䚸⌮◊⣽⬊䝅䝭䝳䝺䞊䝅䝵䞁⤫ྜ䝥䝷䝑䝖䝣䜷䞊䝮
䠄RICS䠅䜢⏝䛔䛶䛾ィ⟬ᶵ䛻䜘䜛䝅䝭䝳䝺䞊䝅䝵䞁䜢⾜䛺䛳䛯䚹RICS䛸䛿䚸⣽⬊䜢」ᩘ䛾䝪䜽䝉䝹䛻༊⏬䛧䚸䛭䛣䛻ᐇ 䝕䞊䝍䜘䜚ᚓ䜙䜜䛯⣽⬊ෆ䛾≀㉁㔞/⛣ື㔞䛺䛹䛾᝟ሗ䜢ྲྀ䜚㎸䜏䚸⣽⬊ෆ䛾⌧㇟䜢⾲⌧ྍ⬟䛸䛩䜛⣽⬊䝅䝭䝳䝺䞊䝅䝵䞁⤫ྜ䝥䝷䝑䝖䝣䜷䞊䝮䛷䛒䜛䚹  㻌 ๓ᖺᗘ䜎䛷䛻䚸RICS䜢⏝䛔䛯⾑ᑠᯈ⣽⬊䝰䝕䝹䜢ᵓ⠏䛧䠄ᅗ䠍䠅䚸⣽⬊እ䛛䜙䛾⣽⬊⭷䜈䛾่⃭䛻ᛂ䛨䛯䚸⾑ᑠᯈ⣽⬊ෆ䜸䝹䜺䝛䝷䛷䛒䜛⃰ᰁ㢛⢏䛛䜙䛾ADP䚸ATP䚸䝉䝻䝖䝙䞁䛺䛹䛾ෆໟ≀㉁ᨺฟ䜢ྛ✀⏕໬Ꮫ཯ᛂ䛾✚䜏㔜䛽䛻䜘䜚⾲⌧䜢ྍ⬟䛸䛧䛯䚹  㻌 ᮏᖺᗘ䛾ᡂᯝ䛸䛧䛶䚸」ᩘ䛾⾑ᑠᯈ⣽⬊䜢ィ⟬ᶵ୰䛻タᐃ䛧䚸䛒䜛⣽⬊䛻䛿GP1bĮ䜈䛸ᒁᡤⓗ䛻vWF䛻䜘䜛่⃭䜢୚䛘䚸䛭䛾⟠ᡤ䛛䜙ጞ䜎䜛άᛶ໬䝥䝻䝉䝇䜢⤒䛶⃰ᰁ㢛⢏䛛䜙䛾ෆໟ≀㉁ᨺฟ䚸᭦䛻ู⣽⬊䜈䛸ᨺฟ≀㉁䛻䜘䜛άᛶ໬䛾ㄏ㉳䜢☜ㄆ䛧䛯䠄ᅗ2䠅䚹  㻌 ⣽⬊ෆ௦ㅰ཯ᛂ䝬䝑䝥䛾⢭⦓໬䜢㐍䜑䚸ᐇ㦂䛻䜘䜚 ᐃྍ⬟䛺≀㉁䜢⤌䜏㎸䜏䚸ィ⟬⤖ᯝ䛸ᐇ㦂⤖ᯝ䛾ẚ㍑䜢ྍ⬟䛺䜒䛾䛸䛧䛯䠄ᅗ3䠅䚹  ⾑ᑠᯈ⭷ୖ⺮ⓑ㉁GPIbĮ䛸vWF䛸䛾᥋╔᫬䛻⏕䛨䜛ഇ㊊䜢⪃៖䛧䛯⾑ᑠᯈ㐠ື䝰䝕䝹  㻌 PGE1䛻䜘䜚GPIIbIIIa䛾άᛶ໬䜢㜼ᐖ䛧䛯⾑ᑠᯈ⣽⬊䛜䚸  䝣䝻䞊䝏䜵䞁䝞䞊䛾vWF௜╔㠃䜈䛸GPIbĮ-­vWF⤖ྜ䛻䜘䜛  ᥋╔᫬䛻⏕䛨䜛⣒≧䛾Ꮡᅾ䠄ഇ㊊䠅䛾ᛶ㉁᳨ド䜢䚸⾑ᑠᯈ⣽  ⬊䜢ᴃ෇యᙧ≧䛾๛య䛸௬ᐃ䛧䚸㐠ື䝰䝕䝹䜢⏝䛔䛶ィ⟬  ᶵ䝅䝭䝳䝺䞊䝅䝵䞁䛻䛶⾜䛳䛯䚹  㻌 ᮏᖺᗘ䛿䚸⾑ὶ䛛䜙⏕䛨䜛ὶయຊ䛾ィ⟬䜢཮ᖹ㠃䛸䛫䜣᩿  ㏿ᗘ䛾ᙳ㡪䜢⪃៖䛧䚸㐠ື䝰䝕䝹䛾⢭⦓໬䜢⾜䛳䛯䚹1䛴䛾  ⾑ᑠᯈ⣽⬊䛻䛴䛔䛶✀䚻ィ⟬᮲௳䜢ኚ䛘䜛䛣䛸䛷䚸䜘䜚䝉䞁  䝅䝔䜱䝤䛻ഇ㊊䛾㛗䛥ศᕸ䛻ᙳ㡪䜢୚䛘䜛せᅉ䛾᳨ド䜢⾜䛳  )LJ  5,6&࡟࡚ࠊ」ᩘ⣽⬊ࢆ⪃៖ࡋ࡚ࡢ䛯䚹  ⾑ᑠᯈ⣽⬊ࣔࢹࣝࡢᵓ⠏  )LJ   .ᒁᅾࡍࡿ*3EǂࡀY:)࡟ࡼࡾ่⃭ࡉࢀࡿ   . *3Eǂ࠿ࡽᨺฟࡉࢀࡿࢩࢢࢼࣝ≀㉁࡟ࡼࡿ
่⃭࡟ࡼࡾ&D㈓ⶶᗜ࠿ࡽ&Dࡀᨺฟ   . ⣽⬊ෆ&D⃰ᗘୖ᪼࡟ࡼࡗ࡚⃰ᰁ㢛⢏ࡀ่⃭ࡉࢀࡿ   . ⃰ᰁ㢛⢏࡟ྵࡲࢀࡿ≀㉁ࡢᨺฟࠋ࿘ᅖ⣽⬊࡬࡜άᛶ໬࡟⧅ࡀࡿ≀㉁ࢆఏ࠼ࡿࠋ  )LJ  ⾑ᑠᯈ⣽⬊ෆ௦ㅰ཯ᛂ࣐ࢵࣉࡢᣑᙇࠊཬࡧࠊ ᐃྍ⬟≀㉁࡜ࡢẚ㍑    180ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

C-4 RICS-Insulin Granule Dynamics

䉟䊮䉴䊥䊮ಽᴲ䉲䊚䊠䊧䊷䉺䊷䈱⎇ⓥ㐿⊒㗰☸䈱↢ᚑ₸ (㗰☸/⑽)㗰☸䈱↢ᚑ₸ (㗰☸/⑽)㗰☸䈱಴⧘₸䋨㗰☸/⑽䋩

⢛᥊䈫⋡⊛

ᤨ㑆䋨⑽䋩

ᤨ㑆䋨⑽䋩

࿑6䋮䉯䊦䉳ⵝ⟎䈪䈱䉟䊮䉴䊥䊮㗰☸䈱಴⧘ᢙ䈱⸳ቯ

䉟䊮䉴䊥䊮㗰☸䈱⤑Ⲣว㗫ᐲᣣᧄ䈪䈲ᚑੱ䈱⚂6ੱ䈮1ੱ䈏♧ዩ∛䉅䈚䈒䈲♧ዩ∛੍஻ァ䈫⸒䉒䉏䈩䈇䉎䋨ෘ↢ഭ௛⋭ᐔᚑ19ᐕ࿖᳃ஜᐽ䊶ᩕ㙃⺞ᩏ䋩䇯ᣣᧄੱ䈱♧ዩ∛䈱ਥ䈭ේ࿃䈱䋱䈧䈲䇮⤟⤳ȕ⚦⢩䈎䉌䈱䉟䊮䉴䊥䊮ಽᴲ⢻䈱ૐਅ䈪䈅䉎䇯䈚䈎䈚䇮⤟⤳ȕ⚦⢩ౝ䈪䈱䉟䊮䉴䊥䊮㗰☸䈱േᘒ䈮㑐䈚䈩䈲ਇ᣿䈭ὐ䈏ᄙ䈇䇯ᧄ⎇ⓥ䈪䈲⤟⤳ȕ⚦⢩ౝ䈪䈱䉟䊮䉴䊥䊮㗰☸േᘒ䈱䊜䉦䊆䉵䊛䉕⸃᣿䈜䉎䈢䉄䈮䇮ో෻኿ဳⰯశ㗼ᓸ㏜䋨TIRFM䋩䈪ขᓧ䈚䈢േ↹䉋䉍䉟䊮䉴䊥䊮㗰☸䈱േᘒ䊝䊂䊦䉕䇮䉁䈢ȕ⚦⢩䈱ઍ⻢േᘒ䊝䊂䊦䉕RICS䉕↪䈇䈩૞ᚑ䈚䇮䉟䊮䉴䊥䊮ಽᴲ䉲䊚䊠䊧䊷䉺䊷䉕㐿⊒䈜䉎䈖䈫䉕⋡⊛䈫䈜䉎䇯㗰☸䈱⒖േㅦᐲ䋨nm/⑽䋩

㐿⊒⽿છ⠪㪑 ᷡ㊁ ㅴ 㩿␹ᚭᄢቇᄢቇ㒮කቇ⎇ⓥ⑼⚦⢩ಽሶකቇ䇮⚦⢩䉴䉬䊷䊦⎇ⓥ㐿⊒䉼䊷䊛㪀㩷

࿑7䋮㗰☸䈱ᓸዊ▤਄䈪䈱⒖േㅦᐲ䈱⸳ቯ 䉫䊦䉮䊷䉴ᔕ╵ᕈ䉟䊮䉴䊥䊮ಽᴲ䈱․ᓽ䈪䈅䉎ᕆፋ䈭╙䋱⋧䈫ᜬ⛯ᕈ䈱╙䋲⋧䈏⴫⃻䈪䈐䈢䇯 ⚦⢩⤑䈮Ⲣว䈜䉎䉟䊮䉴䊥䊮㗰☸ᢙ䉅ታ㛎⚿ᨐ䈮ㄭ䈇䉲䊚䊠䊧䊷䉲䊢䊮⚿ᨐ䈏ᓧ䉌䉏䈢䇯
ᤨ㑆䋨⑽䋩

࿑8䋮䉲䊚䊠䊧䊷䉲䊢䊮⚿ᨐ㪋㪇㪇㪇㪈㪐㪇㪈㪅㪌

࿑1䋮ᧄ⎇ⓥ䈱᭎⇛㪠㪢㪘㪫㪧㩷㩷㩿㪽㪘㪀㪘㪫㪧㩷䋨P㪤㪀㪈㪐㪇㪈㪅㪇
㪈㪐㪇㪇㪅㪌
㪈㪐㪇㪇㪅㪇
㪈㪏㪐㪐㪅㪌

ATPᗵฃᕈK+
䋨KATP䋩䉼䊞䊈䊦

GLUT㪈㪏㪐㪐㪅㪇
㔚૏ଐሽᕈCa2+䉼䊞䊈䊦

⣕ಽᭂ

Kir6.2

SUR1
ઍ⻢

䊥䉪䊦䊷䊃

⤑Ⲣว

䊄䉾䉨䊮䉫

㪇
㪇

㪉㪅㪇

M1

Ⱟశ䉺䊮䊌䉪⾰䈪ᮡ⼂䈚䈢䉟䊮䉴䊥䊮㗰☸

M2
16

Y(4.0, 8.0, 8.0)

(23.0, 8.0, 8.0)

X
䊗䉪䉶䊦ᢙ
䊏䉾䉼
Ӡ䌴
᜛ᢔଥᢙ
v
m
n
h

28㬍16㬍16
1.0
1.0e-04
100.0
1.0
1.0
1.0
1.0

Cm
gK
gNa
gL
vK
vNa
vL
Iapp

1.0
36.0
120.0
0.3
-12.0
115.0
10.613ᐳᮡ(4.0,8.0,8.0)(23.0, 8.0, 8.0)

16ᤨ㑆䋨m⑽䋩

2. Restless newcomer䋺䊥䉪䊦䊷䊃ೝỗ䈮䉋䉍⚦⢩ౝ䈎䉌ᣂ䈢䈮䊥䉪䊦䊷䊃䈘䉏䈢㗰☸
䈏⍍ᤨ䈮⤑Ⲣว

⤑Ⲣว

ᤨ㑆

䊥䉪䊦䊷䊃
⤑Ⲣว
䊄䉾䉨䊮䉫

᦭䉍

䉫䊦䉮䊷䉴 (16.7 mM)

6Resting newcomer
Old face (predocked)

42↢ൻቇ෻ᔕ䋨⍫ශ䋩䋺㪉㪋෻ᔕ䋨⚿วಽሶ䈫䈱⋧੕૞↪䋩ታ✢䋺ਥ෻ᔕ䇮⎕✢䋺⺞▵෻ᔕ㪚㪸㪉㪂⚿วಽሶ䋨㕍ਣ䋩䋺㪈㪇୘

0
0

1

2

3

4

5

6

7

8

9 10
ᤨ㑆䋨ಽ䋩

࿑4䋮䉟䊮䉴䊥䊮㗰☸䈱㐿ญಽᴲ᭽ᑼ䋨Ꮐ䋩䈫䉟䊮䉴䊥䊮㗰☸േᘒ䋨ฝ䋩䉟䊮䉴䊥䊮㗰☸䈲䉝䉪䉼䊮㛽ᩰ਄䉕⒖േ䈜䉎䇯䉟䊮䉴䊥䊮㗰☸
䈲ᓸዊ▤਄䉕
⒖േ䈜䉎䇯ᄖㇱጀή䈚䋨Ca2+䈱න⚐᜛ᢔ䈱䉂䋩

Restless newcomer࿑13䋮⚦⢩ౝ䈪䈱Ca2+෻ᔕ䈫᜛ᢔ䈱䊝䊂䊦ൻౝㇱጀ䋺䉯䊦䉳ⵝ⟎䈪಴⧘䈚䈢䉟䊮䉴䊥䊮㗰☸䈏䇮ᓸዊ▤਄䉕⚦⢩⤑䈮ะ䈎䈦
䈩⒖േ䈜䉎䇯

࿑14䋮Ca2+䈱෻ᔕ䈫᜛ᢔ䈱ㅪᚑ䋨䊗䊥䊠䊷䊛䊧䊮䉻䊥䊮䉫䋩 5,&6਄䈪ATPỚᐲᄌൻ䇮KATP䉼䊞䊈䊦㔚ᵹ䉕䊝䊂䊦ൻ䈚䈢䇯 5,&6਄䈪⤑㔚૏䉕⴫⃻น⢻䈭䉲䉴䊁䊛䉕㐿⊒䈜䉎䈖䈫䈪䇮⤑㔚૏વ᠞⸘▚ᯏ⢻䉕ታⵝ䈚䈢䇯 ⚦⢩ౝ䈱Ca2+✭ⴣ૞↪䉕⸳ቯ䈚䈢႐ว䇮↢ൻቇ෻ᔕ䈏ή䈎䈦䈢ᤨ䈮Ყ䈼䇮Ca2+䈱⷗䈎䈔䈱᜛ᢔㅦᐲ䈱ૐਅ䈏⹺䉄䉌䉏䈢䇯 ታ㓙䈱⤟ȕ⚦⢩䈪䉂䉌䉏䉎Ca2+േᘒ䉕RICS਄䈪䊝䊂䊦ൻ䈪䈐䉎䈖䈫䈏␜ໂ䈘䉏䈢䇯

TIRFM⸃ᨆ䈮䉋䉍䇮䉫䊦䉮䊷䉴䈪ೝỗ䈘䉏䈢⤟ȕ⚦⢩䈪䈲䇮䉟䊮䉴䊥䊮㗰☸䈲䋲⋧ᕈ䈱⤑Ⲣว䉕␜䈚䇮䈾䈫䉖䈬䈏restless newcomer䇮䈜䈭䉒䈤⚦⢩ౝㇱ䈎䉌ᣂ䈢䈮䊥䉪䊦䊷䊃䈘䉏䈢㗰☸䈮䉋䉎䉅䈱䈪䈅䈦䈢䇯಴⧘࿑12䋮⤑㔚૏ㅪ⛯ೝỗ䊁䉴䊃

RICS䈪⸳ቯ䈚䈢䊌䊤䊜䊷䉺䊷

࿑11䋮Hodgkin-Huxley䈱ㅪ⛯ೝỗ䊁䉴䊃䈱⸳ቯG2.8⤑Ⲣว䈱㗫ᐲ䋯⚦⢩200 Pm2䉟䊮䉴䊥䊮㗰☸䈱⤑Ⲣวౝㇱጀ

M1(4.0, 8.0, 8.0)
X⚦⢩ౝ䈱Ca2+⚿วಽሶ
⤑Ⲣว

䉯䊦䉳ⵝ⟎

M2
Y

Iapp䉕એਅ䈱䉋䈉䈮ᜰቯ䈚䇮
20000 step䉁䈪ታⴕ䊒䊧䊄䉾䉪⚦⢩⤑䈮䈜䈪䈮䊄䉾䉪䈚䈩䈇䉎㗰☸䈱⤑Ⲣว

ᤨ㑆

㪉㪅㪇

࿑10䋮⚦⢩⤑਄䈪䈱⤑㔚૏䈱᜛ᢔ⚿ᨐ䈫䉁䈫䉄

ೝỗ䈮䉋䉍⚦⢩ౝ䈎䉌ᣂ䈢䈮䊥䉪䊦䊷䊃䈘䉏䈢㗰☸䈏䊄䉾䉪䈚䈢ᓟ䈮⤑Ⲣว

㪈㪅㪌

M3

࿑3䋮䊙䉡䉴⤟ȕ⚦⢩䈱TIRFM↹௝

3. Resting newcomer䋺㪈㪅㪇ᤨ㑆 㩿⑽㪀㩷

26

1. TIRFM䉕↪䈇䈩䇮⤟ȕ⚦⢩䈱䉫䊦䉮䊷䉴ᔕ╵ᕈ䉟䊮䉴䊥䊮㗰☸േᘒ䉕⸃ᨆ䈜䉎䇯
2. 䉟䊮䉴䊥䊮㗰☸േᘒ䉕䊝䊂䊦ൻ䈜䉎䇯
3. ⤟ȕ⚦⢩䈱䉫䊦䉮䊷䉴ઍ⻢䉕⚦⢩䉲䊚䊠䊧䊷䉲䊢䊮⛔ว䊒䊤䉾䊃䊐䉤䊷䊛䋨RICS䋩䈪䊝䊂䊦ൻ䈜䉎䇯

ᤨ㑆

㪇㪅㪌

wt

ᣇᴺ

1. Old face䋺㪈㪅㪌䊶⤑㔚૏䈱વ᠞ᯏ⢻䋺㔚૏䉕‛⾰䈫䈚䈩ᛒ䈇䇮⤑㔚૏䈱વ᠞䉕᜛ᢔ䈪⴫⃻䈜䉎䇯
䊶Hodgkin-Huxley䊝䊂䊦䈱ታⵝ䋺␹⚻⥝ᅗ䈮䉋䉎⤑㔚૏䈱ᄌൻ䉕ห䊝䊂䊦䈪⸘▚䈜䉎䇯䋨⃻Ბ㓏䈪䈲␹⚻⚦⢩䈱䊝䊂
䊦䉕೑↪䋩
wV
ၮ␆ᑼ
  C V  H⿒✢䋺䉟䊮䉴䊥䊮㗰☸䈱ዊ⢩ャㅍ䊚䊃䉮䊮䊄䊥䉝㪈㪅㪇ᤨ㑆 㩿⑽㪀㩷

16㕍✢䋺䉫䊦䉮䊷䉴ઍ⻢

ATP/ADP㪇㪅㪌㪈㪇㪇㪇࿑9䋮⤟ȕ⚦⢩䈮䈍䈔䉎ATPỚᐲ䋨Ꮐ䋩䈫KATP䉼䊞䊈䊦㔚ᵹ䋨ฝ䋩䈱䊝䊂䊦ൻ

࿑2䋮⤟⤳ȕ⚦⢩䈮䈍䈔䉎䉫䊦䉮䊷䉴ᔕ╵ᕈ䉟䊮䉴䊥䊮ಽᴲ

Ca2+

K+

㪇

㪉㪇㪇㪇

Iapp䋺⽶⩄䈚䈢㔚ᵹ䋨mA䋩

䉫䊦䉮䊷䉴

㪊㪇㪇㪇

⎇ⓥෳട⠪
␹ᚭᄢቇᄢቇ㒮Ꮏቇ⎇ⓥ⑼ᢎ᝼ ₹⟎ ਭ␹ᚭᄢቇᄢቇ㒮කቇ⎇ⓥ⑼ቴຬಎᢎ᝼ ⑔ፉశᄦ␹ᚭᄢቇᄢቇ㒮කቇ⎇ⓥ⑼⻠Ꮷ ᩊፒᔘ㓶␹ᚭᄢቇᄢቇ㒮Ꮏቇ⎇ⓥ⑼ୃ჻⺖⒟1ᐕ ↰⇌⋥♿
ℂൻቇ⎇ⓥᚲ ᮮ↰⑲ᄦℂൻቇ⎇ⓥᚲ 㗇᳗ᵏᒄᄖㇱጀ䋺䉟䊮䉴䊥䊮㗰☸䈏ᓟ䉐䈎䉌᛼䈚಴䈘䉏䈩䇮✂⋡⁁䈮ᒛ䉍Ꮌ䉌䈘䉏䈢䉝䉪䉼䊮㛽ᩰ਄䉕⒖േ䈜䉎䇯࿑5䋮䉟䊮䉴䊥䊮㗰☸േᘒ䊝䊂䊦䈱᭎⇛ᧄ䊝䊂䊦䈲䉫䊦䉮䊷䉴ೝỗ䈘䉏䈢ȕ⚦⢩䈮䈍䈔䉎䉟䊮䉴䊥䊮㗰☸䈱⤑Ⲣวㆊ⒟䈣䈔䈪䈭䈒䇮䉟䊮䉴䊥䊮㗰☸䈱⵬లㆊ⒟䉕ข䉍౉䉏䈢䊝䊂䊦䈪䈅䉎䇯181

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

O-1: ZZ-EFSI
඲㌟䝪䜽䝉䝹䝅䝭䝳䝺䞊䝅䝵䞁䠄䝪䜽䝉䝹ᵓ㐀ὶయ㐃ᡂゎᯒ䝥䝻䜾䝷䝮䠅㛤Ⓨ㈐௵⪅㻦 㧗ᮌ࿘ 㻔⌮໬Ꮫ◊✲ᡤ ḟୡ௦ィ⟬⛉Ꮫ◊✲㛤Ⓨ䝥䝻䜾䝷䝮㻕ィ⟬䛾ጇᙜᛶ (ὶయᵓ㐀/⭷㐃ᡂᶵ⬟䛾ᐇ⿦)

㛤Ⓨ䛻⮳䜛⫼ᬒ䜻䝱䝡䝔䜱ὶ䜜୰䛾㉸ᙎᛶయ⢏Ꮚ䛫䜣᩿ὶ୰䛾⭷䜹䝥䝉䝹s་⒪⏬ീ䠄CT䝇䜻䝱䞁䠈MRI➼䠅䛸┦ᛶ䛾Ⰻ䛔ὶయᵓ㐀㐃ᡂゎᯒ䛻䜘䜚䠈ಶேẖ䛾デ᩿䛻༶䛧䛯἞⒪ᨭ᥼䜢ᐇ⌧䛧䛯䛔䠊s ㉥⾑⌫䞉⾑ᑠᯈ䜢ྵ䜐⾑ὶ䜢ᑐ㇟䛸䛩䜛㉸኱つᶍ୪ิィ⟬䜢ᐜ᫆䛻ᐇ⌧䛧䠈⾑ᑠᯈ⾑ᰦ䛻⮳䜛ᶵᵓ䜢᫂䜙䛛䛻䛧䛯䛔䠊- - Zhao, Freund & Moser(2009, J. Comput. Phys. 227)
SMAC䠇 Lagrangian for solid䠉 Present (ACM + full Eulerian)㛤Ⓨ䝋䝣䝖䜴䜵䜰䛾≉ᚩ኱つᶍ୪ิィ⟬䛻㐺䛧䛯ὶయᵓ㐀/⭷㐃ᡂゎᯒ䜢ᐇ⌧
Eulerᆺὶయ䞉ᵓ㐀/⭷㐃ᡂゎᯒἲHow is the solid deformation༑ศ䛻᳨ド䛥䜜䛯ィ⟬⤖ᯝ䜢Ⰻ䛟෌⌧⌧ᅾ㛤Ⓨ/ᐇ᪋୰䛾ィ⟬஦౛ (⾑ᰦ䝅䝭䝳䝺䞊䝍)⾑ᑠᯈ⾑ᰦ䛻⮳䜛ᶵᵓゎ᫂䜢┠ᣦ䛩
described?
s ᅛᐃ䝯䝑䝅䝳ୖ䛷䠈඲䛶䛾≀
Lagrangian frame
Eulerian frame⾑ὶ䛸Ligand-Receptor⤖ྜ䛾䝬䝹䝏䝇䜿䞊䝹/⌮㔞䜢᭦᪂䛷䛝䜛䜘䛖䛻ᐃᘧ䝣䜱䝆䝑䜽䝇ゎᯒἲ䛾ᴫせ໬䠊s VOXEL䝕䞊䝍䛾฼⏝䛻䜘䜚䠈䝯䝑䝅䝳⏕ᡂ䞉෌ᵓᡂ䜢୙せ䛻䠊s 」㞧䛺ቃ⏺ᙧ≧䜢ᣢ䛴/ከᩘ䛾ศᩓయ䜢ྵ䜐ၥ㢟䛾ᢅ䛔
by left Cauchy-Greenby the displacement of
MATERIAL POINTS THEMSELVES deformation tensor
䜢ᐜ᫆䛻 䠊
ືⓗ䝟䝷䝯䞊䝍䜢ᣢ䛴ᨃఝᅽ⦰ᛶἲ
(Artificial Compressibility Method; ACM)s ᅽຊಟṇ䛻㝿䛧䛶䠈㏿ᗘⓎᩓ䜢᭱ᑠ໬䛩䜛ฎ⌮䜢ᑟධ䠊s ᩘ್ⓗ䛻Ᏻᐃ䛷᏶඲㝧ⓗ䛺᫬㛫✚ศ䠊s 㧗䛔ᐇຠᛶ⬟䠈୪ิᛶ⬟䜢ᐇ⌧䠊⌧᫬Ⅼ䛷䛾㛤Ⓨ≧ἣs 㛤Ⓨゝㄒ䛸䝷䜲䝤䝷䝸
FORTRAN, C++, MPI, OpenMP, VSpheres 䛂ி䛃䛾䝬䝙䝳䜰䝹䛻ᚑ䛔䠈䝥䝻䝣䜯䜲䝷䠈PA䝒䞊䝹䜢౑䛳䛶ᛶ⬟䝏䝳䞊䝙䞁䜾䠊s 䛂ி䛃䛷䠈䝇䝔䞁䝅䝹ィ⟬䛸䛧䛶ᴟ䜑䛶㧗䛔ᐇຠᛶ⬟䠈ྎᩘຠᯝ䜢㐩ᡂ䠊ᐇຠᛶ⬟䛸୪ิྎᩘ
䝏䝱䝛䝹ὶ䜜ෆBICONCAVE(weak scaling)
neo-Hookeయ⢏Ꮚ⾑ᑠᯈ௜╔䛾ᵝᏊ䠈㉥⾑⌫䛾ᙳ㡪⾑ᰦᙧᡂ䛾➨୍ẁ㝵䛻䛚䛡䜛䠈㉥⾑⌫䛾Ꮡᅾ䛾㔜せᛶ䠊э௒ᚋ䛿䠈ከᩘ䛾㉥⾑⌫䛜Ꮡᅾ䛩䜛᮲௳䛷䠈⾑ᑠᯈ䛾௜╔䛛䜙⾑ᰦ䛾ึᮇ㐣⛬䜢䛂ி䛃䛷ゎᯒཧ⪃ᩥ⊩Sugiyama, K., Ii, S., Takeuchi, S., Takagi, S. and Matsumoto, Y. (2010) Full Eulerian simulations of biconcave neo-Hookean
particles in a Poiseuille flow, Comput. Mech., 46: 147-157.
Nagano, N., Sugiyama, K., Takeuchi, S., Ii, S., Takagi, S. and Matsumoto, Y. (2010) Full-Eulerian finite-difference simulation of
fluid flow in hyperelastic wavy channel, J. Fluid Sci. Tech., 5: 475-490.
Sugiyama, K., Ii, S., Takeuchi, S., Takagi, S. and Matsumoto, Y. (2011) A full Eulerian finite difference approach for solving fluidstructure coupling problems, J. Comput. Phys., 230: 596-627.
Ii, S., Sugiyama, K., Takeuchi, S., Takagi, S. and Matsumoto, Y. (2011) An implicit full Eulerian method for the fluid-structureinteraction problem, Int. J. Numer. Meth. Fluids, 65: 150-165.
Ii, S., Gong, X., Sugiyama, K., Wu, J., Huang, H., and Takagi, S. (2011) A full Eulerian fluid-membrane coupling method with asmoothed volume-of-fluid approach, Comm. Comput. Phys. (accepted).
Takagi, S., Sugiyama, K., Ii. S., and Matsumoto, Y. (2011) A review of full Eulerian methods for fluid-structure interaction
problems, J. Appl. Mech. (accepted).

182ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

O-2: TK-Thrombocyte
SZ¤§¬¯¨®£©¢¯¥^7<^(SZ y^$5
+H~UJ^7<^¤§¬¯¯K¦«nINP¯nIgXuv)]rs4?k

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血小板細胞放出シミュレーション

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\i¯«j/

b. 臓器全身スケールシミュレーター
（血小板細胞接着シミュレーター）
a. 細胞スケールシミュレーター（RICSの血小板細胞への応用）

C2A nI¢-6

ー実証実験にて計測された細胞内小器官の局在、細胞表面上に局在するGPIbとvon Willebrand因子の相互作用による活性化シグナルの広がり、細胞内の濃染顆粒放出を組み込んだ。

ー実証実験に裏打ちされ、血小板細胞上の特異的接着蛋白（von Willebrand因子と結合するGPIb）の細胞表面上の局在を考慮し、接着した血小板細胞が受ける流体力による受動的形態変化（偽足形成）を組み込んだ血小板細胞接着シミュレーター
を作成した。

hm

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%°cQ: D±%°cQ: 9

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リン酸化シグナル蛋白VASP-Pの局在
ー実証実験による活性化刺激
後のシグナル蛋白の細胞内
非刺激時
刺激時
局在を定量しシミュレー
ターを精緻化
ー「京」を用いたa, bの大規模連携計算により、細胞ス
ケールと臓器全身スケール ー流れ場にて、von Willebarand因子に接着した血小板の偽足長を実測してシミュレーターを精緻化の統合的理解を目指す

183

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)O-3: ZZ-DOSE based on PHITS

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'1+ Ăç>ª¡

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Ｘ線は通り道にまんべんな
く影響をあたえ、正常組織に後遺症が出ることも 線量の集中性が高い（ピンポイントに照射可能）





重粒子線は、がんのあるせまい領域だけを集中攻撃 強い生物効果（細胞を死滅させる力!(')
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が強い）

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 他の治療法では治らないがん（メラノーマ、肝臓がん、手術できない肉腫）が治る

 同じ治るにしてもより短期間で安全#

1984年 重粒子線治療センター
建設計画スタート
1994年 炭素イオン線臨床試験開始
2003年 高度先進医療として承認に治りQOLが高い（Ｘ線なら20〜40
回照射、重粒子線なら１回照射） 高度先進医療として、近年、国民の放射線医学総合研究所での患者数

注目が高まっている

重粒子線は目に見えないので、3D-CT画像を使って線量を計算し、治療計画を立てる

3

42985798!/<1* (%+&'-
).,
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実際には3Dで計算

342

98

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既存の計算手法：ペンシルビーム法等

長所：簡便で計算時間が短い（数分）

342

短所：不均質な媒質中（骨の近くなど）で誤差大 

98$$57).,<1#

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がん病巣へは不十分な線量、正常組織に不要な被ばく
様々な組織から構成される不均質な体系についても高精度

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1994.6-2009.2の期間に4504人 (年々増え2008年度は684人)

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ペンシルビーム法

で線量計算できる手法が必要
がん病巣への線量を十分に、正常組織の被ばくを低減
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53+8'0!'-")',-245)66

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モンテカルロ法を用いた重粒子線線量計算シミュレーターを開発rvy

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fĀĈ¬N<P]TY 53')66-2+0)1)27K±©ýYÌÃf­Ä=±èâïÆÙUZċW@KÞ¹Api¶Rc¿;¿Ýnm{jxBðíÚwl¦«ă¸YrvoõH?

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184

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O-4: ZZ-HIFU㞟᮰㉸㡢Ἴ䛻䜘䜛ప౵く἞⒪䛾䛯䜑䛾㻴㻵㻲㼁䝅䝭䝳䝺䞊䝍㛤Ⓨ㈐௵⪅㻦 ᯇᮏὒ୍㑻 㻔ᮾி኱ᏛᕤᏛ⣔◊✲⛉ᶵᲔᕤᏛᑓᨷ㻕

1. Background and Objectives

3. Simulation Assisted Focus Control
Experimental Apparatus

High-Intensity Focused Ultrasound therapy
 Minimally-invasive treatment, which provides
the heat coagulation of tissue around the focal
point of ultrasound.
 Benefits
 Short hospital stay
 Radiation free
 Repeatable treatment
 Approved for tumors such as the prostate
hyperplasia and uterine fibroids.

40 mm

TriggerT]\oTrigger[

61ch Amplifier

Needle hydrophone
Geometrical Focus

Water

Acrylic plate (3 mm)
c=2660m/s

Oscilloscope

http://www.insightec.com

䞉56 elements
䞉Frequency 2 MHz
䞉Focal distance: 100 mm

The acrylic plate as a phantom of bone is inserted between the transducer and target. The angle of theacrylic plate can be changed
changed. The transducer is driven by 61ch amplifier under the control of
LabVIEW. The acoustic field is measured by using needle hydrophone 3-dimensionally

HIFU for tumor behind bones
Brain tumor

56ch PZTTransducer

LabVIEW™

Kennedy JE., Nat. Rev. Cancer, 5:321-27, 2005

MRI-guided Focused Ultrasound Surgery has
been developed for various tumors.
 Pre and Post contrast imaging for treatment
validation
 Accurate targeting Real-time monitoring of temperature
http://www.ushifu.com

Stage

Stage controller

Liver tumor

Method & Results
I. Estimation of control parameters using the NUMERICAL SIMULATION

Displacement and diffusion of focal point due to the reflection
and refraction of ultrasound at the interface of bones

2. Take cross correlations of the received
pressure and choose the amplitude and phasedelay to make the cross correlation maximum.

1. Ultrasound is emitted from a target point
and recieved by the array transducer

Focus control by array transducer

Simulation assisted focusing methods

Cross correlation

 A layered wavevector-frequency domain model using input from CT scans(Clement & Hynynen, 2002)
 A time-reversal process based on prior CT scans using the wave propagation simulation(Aubry et al., 2003)
 Digital human model is constructed based on CT images
 Control parameters of array transducer is obtained by a simulation

Phase shift map

Amplitude map

Objectives
 Realization
Reali ation of the appropriate focus
foc s control in the bod
body using
sing arra
array transd
transducercer Prediction of the treatment region for the preoperative planning Support of the development of HIFU device from design to approval
II. Transducer is driven by the estimated parameters in the EXPERIMENT\

2. HIFU Simulator]

The media of a volume model ofhuman body (voxel phantom) is
discretized into cubic elementswith the voxel data for a living
human body collected by
CT/MRI technologies. On theother hand, the shape of thetransducer defined by CAD isrepresented by the volume dataof the signed distance function.
Then, the ultrasound propagationfrom the transducer through thevoxel phantom is performed bylarge-scale parallel computing.

z=0mm

[MPa]

x=-4mm

z=0mm

[MPa]

x=-1mm[ZRIRFXVFRQWUROZIRFXVFRQWURO

㼻
\
]

z=0mm

[MPa]

x=0mm

[MPa]

x=0mm

[

Okita et al., Int. J. Numer. Meth. Fluids 2011; :43–66

ZRIRFXVFRQWURO

Basic equations
The mass conservation equation for mixture:
The equation of state of media: (Tait’s equation)
1
f p wU p
1 wp wu j
wE§ p  A1 · J
U0 cs20Sound source due to the transducers
2
U U0 ¨ p0
where A1¸
U m csmwt wx jU p wE T , p w t
J© p0  A1 ¹
1
fk
where U m ¦ f k U k and¦k U c 2
2
U m csmk skkThe momentum conservation equation for mixture:
wu
1
wp
w ­
§
·½
Um i 
®] m ekk G ij  2 P m ¨ eij  ekk G ij ¸ ¾
3wt
wxi wx j ¯
©
¹¿

and

J

B
1
A

z=0mm

Non-linear coefficientZIRFXVFRQWUROThe heat equation for mixture:wT
U mC pm
wW

w §
wT ·
1
2
2½
­
¨Nm
¸  ] m ekk  2 P m ®eij eij  ekk ¾
wx j ¨© wx j ¸¹
3¯¿

Since the sound speed of the acrylic plate is faster than water, ultrasounds focused away fromthe target in both cases without focus control. By driving the array transducer with the control
parameters which were previously obtained by the numerical simulation, the focal points
were assigned to the target correctly in the experiments with focus control. Therefore, thesimulation assisted focus control is effective and the numerical simulation is so accurate thatthe focus control is enabled.

Numerical method
 Basic equations are discretized by the Finite Difference Method of 6-th order central differencein space and are explicitly integrated in time based on FDTD method.
 Perfectly Matched Layer is employed for the non-reflecting boundary.
 Parallel computing is performed by the domain decomposition with MPI + OpenMP.

185

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)O-­5:  ZZ-­LUNG⢖๭ๆ䊶⢖ᓴⅣ䉲䊚䊠䊧䊷䉲䊢䊮㐿⊒⽿છ⠪: ⍹ፃᐽᶈ (ℂൻቇ⎇ⓥᚲ⤳ེోり䉴䉬䊷䊦⎇ⓥ㐿⊒䉼䊷䊛)䉸䊐䊃㐿⊒䈱⢛᥊⸘▚੐଀ෘ↢ഭ௛⋭䈱ੱญേᘒ⛔ ᘟᕈ㐽Ⴇ⸘䋨2010ᐕ 䋩䈮䉋䉎䈫䇮ᣣᧄ ᕈ⢖∔ᖚ
(1.4%) 䈠䈱ઁᖡᕈᣂ↢‛ੱ䈱ᱫ࿃㗅૏䈮䈍䈇䈩⢖Ἳ
(29.5%)
䈏䋴૏䇮ᘟᕈ㐽Ⴇᕈ⢖∔ᖚ䈏䋹૏䉕භ䉄䈩䈇䉎䋨࿑䋱䋩䇯䈖䈱⣖ ᔃ∔ᖚ⢖Ἳ ⴊ▤ (15.8%)
䉋䈉䈮๭ๆེ♽∔ᖚ䈲䇮ᄙ䈒
(9.9%) ∔ᖚ䈱࿖᳃䈱↢๮䈮㑐䉒䉎㊀ᄢ
(10.3%)
䈭∔ᖚ䈪䈅䉎䈮䉅㑐䉒䉌䈝䇮࿑䋱䋮ᣣᧄੱ䈱ᱫ࿃㗅૏⮎೷ᴦ≮䉇ᄖ⑼ᴦ≮䈱ㆬᛯ⢇䈏ਲ䈚䈒䇮 ലᨐ⊛䈭ᴦ≮ᴺ䈱㐿⊒䈏ᄢ䈐䈭⺖㗴䈫䈭䈦䈩䈇䉎䇯䉁䈢䇮㊀◊䈭∔ᖚ䈱㓙䈮๭ๆ⵬ഥ䈮೑↪䈘䉏䉎䈖䈫䈏ᄙ䈇ੱᎿ๭ๆེ䈮㑐䈚䈩䉅䇮ᄖㇱ䈎䉌ᒝ೙⊛䈮឵᳇䉕ⴕ䈉䈖䈫䈪⢖䈱ᧃ᪳⚵❱䈮៊்䈏⊒↢䈚䇮ᖚ⠪䈱੍ᓟ䉕ᖡ䈒䈜䉎ᄢ䈐䈭ⷐ࿃䈫䈭䈦䈩䈇䉎䇯䈖䉏䉌䈱໧㗴䈱⸃᳿䉕㒖䉃ᩮᧄ⊛䈭ⷐ࿃䈫䈚䈩䇮⢖䈱ᧃ᪳ㇱ૏䈮䈍䈔䉎ⓨ᳇䈱ᵹ䉏䈫䈠䉏䈮઻䈉⚵❱䈱ᄌᒻ䈱㑐ଥ䈏᣿⏕䈮䈘䉏䈩䈇䈭䈇䈫䈇䈉੐ታ䈏䈅䉎䇯⢖䈲䇮⚂200䊚
䉪䊨䊮䈱⢖⢩䈫๭䈳䉏䉎ᚱ⁁䈱⚵❱䈏ㅪ⚿䈚䈢ⶄ㔀䈭ᒻ⁁䉕䈚䈩䈍䉍䇮ᮮ㓒⤑䈱਄ਅㆇേ䈮䉋䈦䈩⢖⢩䈱㓸ว૕䈏ฃേ⊛䈮ᄌᒻ䈚䇮䈠䉏䈮䉋䈦䈩⢖ౝㇱ䈮ⓨ᳇䈱ᵹ䉏䈏⊒↢䈜䉎䇯䈚䈎䈚䇮᳇ᵹ䈱ㅦᐲ䉇⚵❱䈮૞↪䈜䉎ᔕജ䉕↢૕ౝ䈪⸘᷹䈜䉎䈖䈫䈲䇮ᦨవ┵䈱⸘᷹ᛛⴚ䉕↪䈇䈩䉅ᭂ䉄䈩࿎㔍䈪䈅䉎䇯৻ᣇ䈪䇮⸘▚ᯏᕈ⢻䈱ะ਄䈮䉋䈦䈩䇮ⶄ㔀䈭ᒻ⁁䈱‛૕䈫ᵹ૕䈱⋧੕૞↪䉕⸃ᨆ䈜䉎䉲䊚䊠䊧䊷䉲䊢䊮䈏ㄭᐕ䇮䉋䈉䉇䈒ታ↪䊧䊔䊦䈪น⢻䈮䈭䉍䈧䈧䈅䉎䇯䈠䈖䈪䇮ᚒ䇱䈲䇮␹ᚭ䈮ᑪ⸳ਛ䈱੩ㅦ䉮䊮䊏䊠䊷䉺䇸੩䇹䉕↪䈇䈢ᄢⷙᮨ⸘▚䈮䉋䈦䈩䇮๭ๆ䈪↢䈛䉎⢖䈱ᧃ᪳ㇱ૏䈮䈍䈔䉎䉻䉟䊅䊚䉾䉪䉴䉕⸃᣿䈜䉎䈢䉄䇮䉲䊚䊠䊧䊷䉲䊢䊮䉸䊐䊃䈱㐿⊒䉕ㅴ䉄䈩䈇䉎䇯⃻Ბ㓏䈪䈲䇮䉮䊷䊄䈱ା㗬ᕈ䉕⏕⹺䈚䈧䈧䇮⸃ᨆ䈜䈼䈐⃻⽎䈱ၮᧄ⊛䈭ᝄ䉎⥰䈇䉕ᛠី䈜䉎䈢䉄䇮⢖ᧃ᪳ㇱ䈱ᒻ⁁䉕䉲䊮䊒䊦䈭ᐞ૗ᒻ⁁䈪ᮨᡆ䈚䈢♽䈮䈍䈇䈩⸘▚䉕ⴕ䈦䈩䈇䉎䇯䈜䈭䉒䈤䇮⢖⢩▤䉕౞ᩇ䇮䈠䈱๟䉍䉕ข䉍࿐䉃⢖⢩䉕⃿䈪ᮨᡆ䈚䈢ᐞ૗ᒻ⁁䉕᭴▽䈚䇮䈠䈱ౝㇱ䈮๭ๆ䈮䉋䉎᳇ᵹ䈏⊒↢䈜䉎䉋䈉䈮䈚䈩䈇䉎䇯䈘䉌䈮䇮⃿䈱๟䉍䈮ⷙೣ⊛䈭ⓣ䈱ⓨ䈇䈢⃿Ზ䉕㈩⟎䈚䇮ⴊᵹ䈏ᵹ䉏䉎Ძ⚦ⴊ▤䉕ᮨᡆ䈚䈢䋨࿑䋳䋩䇯䈠䈱⸘▚଀䉕␜䈚䈢䉅䈱䈏࿑䋴䈪䈅䉎䇯㐿⊒䉮䊷䊄䈱᭎ⷐ੹ᓟ䈱ዷ㐿ᧄ㐿⊒䉮䊷䊄䈲䇮᦭㒢Ꮕಽᴺ䈮ၮ䈨䈒ోり䊗䉪䉶䊦䉲䊚䊠䊧䊷䉲䊢䊮᭴ㅧᵹ૕ㅪᚑ⸃ᨆ䊒䊨 䉫䊤䊛
ZZ-EFSI䉕䊔䊷䉴䈫䈚䈩䈇䉎䋨⹦⚦䈲䇮 O-­1䋺ℂൻቇ⎇ⓥᚲ䊶㜞ᧁ๟䈱䊘䉴䉺䊷䉕䈗ⷩ䈒䈣䈘䈇䋩䇯⸘▚㗔ၞ䈲䇮ⓨ㑆਄䈮࿕ቯ䈚䈢৻᭽䈭䋳ᰴర䈱⋥ᣇ૕ᩰሶ䈪ో૕䉕ಽഀ䈚䇮 VOF㑐ᢙ䈮䉋䈦䈩䇮ⓨ᳇䈏ᵹ䉏䉎᳇㆏㗔ၞ䇮ⴊᶧ䈏ᵹ䉏䉎Ძ⚦ⴊ▤㗔ၞ䈭䉌䈶䈮⢖⚵❱䉕඙೎䈚䈩䈇䉎䋨࿑䋲䋩䇯䈖䈱䈢䉄䇮ਗ೉⸘▚䈮ᒰ䈢䈦䈩䈲䇮⍱ᒻ㗔ၞ䉕ဋ╬ಽഀ䈜䉎䈖䈫䈪䇮ല₸⊛䈭䊨䊷䊄䊋䊤䊮䉴䉕ታ⃻䈜䉎䈖䈫䈏น⢻䈪䈅䉎䇯
䉁䈢䇮ℂൻቇ⎇ⓥᚲ䈱䌖䌃䌁D䉲䉴䊁䊛⎇ⓥ䊒䊨䉫䊤䊛䈪᭴▽䈘䉏䈢䌖-­䌓phere䈫๭䈳䉏䉎㐿⊒ⅣႺ䉕೑↪䈚䈩MPI䈫OpenMP䈱䊊䉟䊑䊥䉾䊄ਗ೉䉕ታ⃻䈚䈩䈇䉎䇯㐿⊒⸒ ⺆ 䉅 C++ 䈫 Fortran90 䈱 䊊 䉟 䊑䊥䉾䊄䈫䈭䈦䈩䈇䉎䇯

a

0

0.5

1

࿑䋴䋮⸘▚䈪ᓧ䉌䉏䈢ㅦᐲಽᏓ䈱䊗䊥䊠䊷䊛䊧䊮䉻䊥䊮䉫䇯࿑䋳䈱⸘▚ᒻ⁁䈱ਅ┵䉕ᒝ೙⊛䈮਄ਅ䈮ᝄേ䈘䈞䈭䈏䉌䇮Ძ⚦ⴊ▤ౝㇱ䈮䈣䈔᳓ᐔᣇะ䈮࿶ജ൨㈩䈏
⊒↢䈜䉎䉋䈉䈮Ⴚ⇇᧦ઙ䉕⸳ቯ䈚䈩⸘▚䉕ⴕ䈦䈢䇯䈢䈣䈚䇮࿑䈪␜䈘䉏䈩䈇䉎䈱䈲䇮⸘▚ᒻ⁁䈱৻ㇱ䈱䉂䇯

੹ᓟ䈲䇮䉋䉍⃻ታ⊛䈭♽䈮䈍䈇䈩⸘▚䉕ታᣉ䈚䇮ᧃ᪳᳇㆏䈮䈍䈔䉎᳇ᵹ䈱㐽Ⴇ䈏⢖⚵❱䈱ᔕജ႐䈮䈬䈱䉋䈉䈭ᓇ㗀䉕ਈ䈋䇮⢖⢩᭴ㅧ䈱⎕უ䉕ᒁ䈐⿠䈖䈜䈎䈮䈧䈇䈩⸃ᨆ䉕ㅴ䉄䈩䈇䈒੍ቯ䈪䈅䉎䇯䈘䉌䈮䈲䇮䈖䈱⸃ᨆ䉕ㅢ䈚䈩䇮⢖Ἳ䉇ᘟᕈ㐽Ⴇᕈ⢖∔ᖚ䈭䈬䈱∛ᘒ䉕䉋䉍ᷓ䈒ℂ⸃䈚䇮ലᨐ⊛䈭ᴦ≮䈮䈧䈭䈏䉎␜ໂ䉕ᓧ䈢䈇䈫⠨䈋䈩䈇䉎䇯ੱᎿ๭ๆེ䈮䉋䉎⢖៊்䉕ᛥ೙䈪䈐䉎༐⿠䈱೙ᓮᚻᴺ
䉕ᮨ⚝䈜䉎䈖䈫䉅㊀ⷐ䈭䊃䊏䉾䉪䈣䈫⠨䈋䈩䈇䉎䇯㉄⚛䈫ੑ㉄ൻ὇⚛䈱േᘒ䉕⸃ᨆ䈜䉎䊝䉳䊠䊷䊦䉅♖✺ൻ䈚䇮ᓴⅣེ♽䈱ℂ⸃䈮㊀ⷐ䈭䉧䉴੤឵䊒䊨䉶䉴䈮䈧䈇䈩䉅⸃ᨆ䉕ㅴ䉄䈩䈇䈒ᣇ㊎䈪䈅䉎䇯䈭䈍䇮䉸䊷䉴䉮䊷䊄䈲዁᧪⊛䈮䈲ήఘ䈪౏㐿䈜䉎੍ቯ䈪䈅䉎䈏䇮⃻ᤨὐ䈪䈲㐿⊒ㅜ਄䈪䈅䉎䈢䉄䇮೑↪Ꮧᦸ⠪䈲䇮౒ห⎇ⓥ䈫䈇䈉ᒻᑼ䈪䇮㐿⊒⠪䈫ද⼏䈚䈭䈏䉌ㆡ↪▸࿐䉕ᬌ⸛䈚䈩䈇䈒䈖䈫䉕ᗐቯ䈚䈩䈇䉎䇯Is࿑䋲䋮 (a) ᵹ૕᭴ㅧ✵ᚑ⸃ᨆ䈮↪䈇䉌䉏䉎䈖䈫䈏ᄙ䈇㕖᭴ㅧᩰሶ䈱৻଀䋨಴ౖ : Gao & Hu J.
Comp. Phys., 2009䋩䇯
᭴ㅧ૕䈱Ⴚ⇇ઃㄭ䈮ᄙ䈒䈱ᩰሶ䉕㈩⟎䈚䇮᭴ㅧ૕䈱⒖േ䈫䈫䉅䈮ᩰሶ䉅⒖േ䉅䈚䈒䈲ౣቯ⟵䈜䉎䇯(b) ⓨ㑆䈮࿕ቯ䈚䈢⋥ᣇ૕ᩰሶ䈱৻଀䇯᭴ㅧ૕䈱ㆇേ䈮઻䈦䈩ᤨ㑆⊒ዷ䈜䉎VOF㑐ᢙ䉕↪
䈇 䈩ᵹ ૕ 䈫 ᭴ ㅧ ૕ 䉕඙೎䈜䉎䇯c࿑䋳䋮 (a) ⢖ᧃ᪳ㇱ૏䈱ᮨᑼ࿑䋨಴ౖ䋺‐ᧁ䊶ዊᨋ⪺䇸ੱ૕䈱ᱜᏱ᭴ㅧ䈫ᯏ⢻ I ๭ๆེ䇹䋩 (b) ⸘▚䈮↪䈇䈢᳇㆏ᒻ⁁䈱㠽⍑࿑ (c) Ძ⚦ⴊ▤ᒻ⁁䈱㠽⍑࿑䇯⢖⢩▤䈫䋱䋲୘䈱⢖⢩䈎䉌ᚑ䉎♽䉕128x128x256䈱⸘▚ᩰሶ䈪૞ᚑ䇯⢖⢩▤䈭䉌䈶䈮⢖⢩䈱⋥ᓘ䈲50ᩰሶ䇮Ძ⚦ⴊ▤䈱ෘ䈘䈲7ᩰሶ䈪⴫⃻䈘䉏䈩䈇䉎䇯

a

b

b

⻢ㄉ
ᧄ⎇ⓥ䈲䇮ᢥㇱ⑼ቇ⋭ ᦨవ┵䊶㜞ᕈ⢻᳢↪䉴䊷䊌䊷䉮䊮䊏䊠䊷䉺䈱㐿⊒೑↪䇸ᰴ਎ઍ↢๮૕⛔ว䉲䊚䊠䊧䊷䉲䊢䊮䉸䊐䊃䉡䉢䉝䈱⎇ⓥ㐿⊒䇹䈱ᡰេ䉕ฃ䈔ⴕ䉒䉏䈢䉅䈱䈪䈜䇯
䉁䈢䇮ℂൻቇ⎇ⓥᚲ䈏ታᣉ䈚䈩䈇䉎੩ㅦ䉮䊮䊏䊠䊷䉺䇸੩䇹䈱⹜㛎೑↪䈮䈍䈇䈩䇮㐿⊒䉮䊷䊄䈱䊁䉴䊃⸘▚䉕䈘䈞䈩䈇䈢䈣䈐䉁䈚䈢䇯⸥䈚䈩ᗵ⻢䈇䈢䈚䉁䈜䇯186

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)287+HDUW㻌䝬䝹䝏䝇䜿䞊䝹䞉䝬䝹䝏䝣䜱䝆䝑䜽䝇ᚰ⮚䝅䝭䝳䝺䞊䝍㻌㛤Ⓨ㈐௵⪅㻦㻌 ஂ⏣ಇ᫂㻌㻔ᮾி኱Ꮫ㻕㻌㻌 㻌㻌ୖグ䝕䞊䝍䛿ி䛾ヨ㦂฼⏝୪䜃䛻⌮໬Ꮫ◊✲ᡤ᝟ሗᇶ┙䝉䞁䝍䞊RICC  䛻䜘䜚ᚓ䜙䜜䛯䚹  ி䛿⌧ᅾ㛤Ⓨ୰䛷䛒䜚䚸䛣䜜䜙䛾ᩘᏐ䛿⌧≧䛾್䛷䛒䜛䚹  187ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-1: ParaHaploÔ×ÜÎÁ×

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2¿mN+¨½¤¯±º¼`8 )¿=½;T®½
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7¯7¿V»©¯¨½¯e²7¿{v¯¨ s7µ½( Ô×ÜÎÁ×®ÇÓØÝÁÒ ~K¿¨½¤¯®e²7¿{v¯¨ s7¿V»¦º¼'¡²`8 )¿c{® ½¤¯F4¥¾½©¤»¥»±`8 )¿ÎßÇÏÑ±w"¹SbT²c´¯o£½¤¯¿d<¨(Misawa et al. 2008)
([CZ²cÍÖÑÂÃÀ±¬­( ParaHaplo³PCÅÚÊÎ®8,000ÓßÒ²7s¿,¦ÈÞÕÙßÎ®·7s½¤¯g¥¾­½®³ÈÀ·,z¦­½(Misawa and Kamatani
2009, 2010, 2011)
( [%³/²¿d<¦SIMD¿x«­½(,²ÐßÎ¿]ParaHaplo¿¦ª(H?¯@T( ParaHaplo±º¼JBG 44 àJPT)²ÉÞ×Û¯$ 45 (CHB) ²ÉÞ×Û²Ô×ÜÎÁ×2¿Q¦mN+¿x«ª(lL( ParaHaplo±º½Ô×ÜÎÁ×2²N+²lLÄË±h¥¾­½
( Znº¼6I²@T®)2²0N® ª&:,nº¼ParaHaplo®
)2²0N® ª&:®½
( 23G²MuYU®y¥¾­½(p( 6ITº¼·'¡²)2²0N® ½¤¯½(
5³> |O²,²`8ÐßÎ¿]`8 )¿{¬¢½¤¯¿d<¨( kC±½²®ÈÞÕÙßÎ¿]¨½

参考文献
Misawa K, Fujii S, Yamazaki T, Takahashi A, Takasaki J, Yanagisawa M, Ohnishi Y, Nakamura Y, Kamatani N (2008)
New correction algorithms for multiple comparisons in case-control multilocus association studies based on
haplotypes and diplotype configurations. J Hum Genet 53:789-801
Misawa K, Kamatani N (2009) ParaHaplo: A program package for haplotype-based whole-genome association study
using parallel computing. Source Code Biol Med 4:7
Misawa K, Kamatani N (2010) ParaHaplo 2.0: a program package for haplotype-estimation and haplotype-based
whole-genome association study using parallel computing. Source Code Biol Med 5:5
Misawa K, Kamatani N (2011) ParaHaplo 3.0: A program
package for imputation and a haplotype-based whole188
genome association study using hybrid parallel computing. Source Code Biol Med 6:10

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-2: NGS analyzer5 ~t y`VsQ-m \tXB@ukz
tG:@y`j6Elqnx
[AUIdeP?W"ab>DF$ DF[Aca
[A%!IdeL,+8ab>DF$ DF[Acae

e0N
5 ~t y`VsQ-mf \tXB@ukz
tG:@y`j6Elqnxg
e]'SHt)7
29Y snxrE<SHspjo1#/SH)7C&7svxM tP t)7^ 
e[ARTr}
Perl, Ce=;qtSHO3
t*, ~Q-Nat Genet 42: 931–6 JkzQ-Nature 464, 993-8 PC2000{qMZa4 TBf|Za100 TB
e54hiqtSHO3
500tkzY Q-svxG:@
64{qMZa2PBf|Za50 PB
kzQ-}}
    #"#  $!

Mapping

MappingtXB1#(1Mbp!owtSNPt mutation caller      

kztG:@.K @r(@ 

SNPt_ 

189

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D-3: ExRAT拡張RAT法による2SNP組合せの全ゲノム関連解析ソフトウェア\9T FpN6Xbc4<?$§®±=<?ª¶«·db

,K
bbbbbY ]; 598µ©¥ 
7!^WY C §®±&Bjzvx]C VH(1zvx]W[I¸;^¹E#A(1º>` JF
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bbbbbreefbuxtfbw~|}ux

32Q@M-p ihzvxkhhh«¤¯¶¦¬·«jzvx
C
 lhCkhhhzvx¬·« ytrrbnioj¨¢²³µZbigibsq¬£©¥Zbjbsq/ .Q@M- +G zvxiM-jzvxCO* jlhhCikm7!zvx¬·« ²³µZbjhbsq¬£©¥Zbjb{q¡
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190

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-­4:  SiGN-­BN  SiGN  㛤Ⓨ࣓ࣥࣂ࣮  :  ⋢⏣  ჆⣖㸪ᓥᮧ  ᚭᖹ㸪ᒣཱྀ  㢮㸪᪂஭⏣  ཌྖ㸪ᩧ⸨  ࠶ࡺࡴ㸪㛗ᓮ  ṇᮁ㸪஭ඖ  Ύဢ㸪ᐑ㔝  ᝅᴫ        せࢯࣇࢺ࢙࢘࢔௙ᵝ

SiGN㸦ࢧ࢖ࣥ㸧ࡣ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗ
ࢱࢆ⏝࠸࡚㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࢆண ࣭᥎ᐃࡍࡿࢯࣇࢺ࢙࢘
࢔⩌࡛࠶ࡿ㸬㑇ఏᏊࢿࢵࢺ࣮࣡ࢡゎᯒ࡟ࡼࡾ㸪ほ ࢹ࣮ࢱ࠿ࡽ⸆๣ᶆⓗ㑇ఏᏊࡢྠᐃ㸪๪స⏝ࡢண ࣭ᅇ㑊㸪๰⸆ࢱ࣮ࢤࢵ
ࢺ࣭ẘᛶ㛵୚ࣃࢫ࢙࢘࢖᥈⣴࡞࡝ࡀᐜ᫆࡟࡞ࡿࡇ࡜ࡀᮇᚅࡉ
ࢀࡿ㸬SiGN ࡛ࡣᵝࠎ࡞ᐇ㦂ࢹ࣮ࢱࡢゎᯒ࡟ᑐᛂࡍࡿࡓࡵ࣋
࢖ࢪ࢔ࣥࢿࢵࢺ࣮࣡ࢡ㸪≧ែ✵㛫ࣔࢹࣝ㸪ࢢࣛࣇ࢕࢝ࣝ࢞࢘
ࢩ࢔ࣥࣔࢹࣝ㸪࣋ࢡࢺࣝ⮬ᕫᅇᖐࣔࢹࣝࡀ㑇ఏᏊࢿࢵࢺ࣮࣡
ࢡࣔࢹࣝ࡜ࡋ࡚฼⏝ྍ⬟࡛࠶ࡿ㸬SiGN-­BN ࡣࡇࢀࡽࡢ࠺ࡕ࣋
࢖ࢪ࢔ࣥࢿࢵࢺ࣮࣡ࢡࢆ⏝࠸ࡓ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ᥎ᐃࢯࣇ
ࢺ࢙࢘࢔࡛㸪ࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱࢆ⏝࠸ࡓ୪ิィ⟬࡟ࡼࡾࣄࢺ඲ࢤࣀ࣒ࢆ⥙⨶ࡋࡓᩘ୓㑇ఏᏊࡢ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࢆ
▷᫬㛫࡛᥎ᐃྍ⬟࡛࠶ࡿ㸬

ฟຊ

ᴫせ࣭≉ᚩ

ฟຊࢹ࣮ࢱ㸸㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ㸦ࢸ࢟ࢫࢺ /CSML㸧-­  ࣀࣥࣃ࣓ࣛࢺࣜࢵࢡᅇᖐ࡟ࡼࡿ࣋࢖ࢪ࢔ࣥࢿࢵࢺ

᥎ᐃࡉࢀࡓᯞ࡟ಙ㢗ᗘ➼ࡢ᝟ሗ௜ࡁ

࣮࣡ࢡࢆ⏝࠸࡚㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽ㑇ఏᏊࢿࢵࢺ

Cell  Illustrator ࡛ゎᯒྍ⬟࣮࣡ࢡࢆ᥎ᐃࡍࡿࢯࣇࢺ࢙࢘࢔
-­  ࠕிࠖୖ࡟࡚㉸୪ิ࡛ືసࡋ㧗㏿࡟㑇ఏᏊࢿࢵࢺ࣮࣡
ࢡ㸦᭷ྥࢢࣛࣇ㸧ࢆィ⟬ྍ⬟୪ิᛶ⬟ (HC+Bootstrap)-­  NNSR ࢔ࣝࢦࣜࢬ࣒ࢆ⏝࠸ࡿ࡜ࡇࢀࡲ࡛ᚲせࡔࡗࡓ᭱኱ືసࢥ࢔ᩘ㸸30,720 ࢥ࢔㸦3,840 ࣀ࣮ࢻ㸧ࠕ㑇ఏᏊ㑅ᢥࠖࢆࡏࡎ࡟㸪඲ࢤࣀ࣒࡟ࡼࡿ኱つᶍ㑇ఏ୪ิ໬ຠ⋡㸸0.91  (30,720  vs  15,360 ࢥ࢔᫬ )㸦ࢫࢺࣟࣥࢢࢫࢣ࣮ࣜࣥࢢ㸧Ꮚࢿࢵࢺ࣮࣡ࢡࡢ᥎ᐃࡀྍ⬟-­  HC+Bootstrap ἲࢆ⏝࠸ࡿࡇ࡜࡟ࡼࡾ㧗ಙ㢗ࢿࢵࢺ

ͤ  2011/12/1 ⌧ᅾ      「ிࠖࡣ⌧ᅾ㛤Ⓨ୰࡛࠶ࡾ㸪ࡇࢀࡽࡢᩘᏐࡣᩚഛ୰ࡢࢩࢫࢸ࣒࡟
ࡼࡿᬻᐃⓗ࡞್࡛࠶ࡿ

࣮࣡ࢡࢆィ⟬ྍ⬟-­  ࣐࢖ࢡࣟ࢔ࣞ࢖ࡔࡅ࡛࡞ࡃᵝࠎ࡞஦๓▱㆑ࢆࢿࢵࢺ
࣮࣡ࢡ᥎ᐃ࡟฼⏝ྍ⬟ࠕிࠖ௨እ࡛ࡢࢱ࣮ࢤࢵࢺ⎔ቃධຊධຊࢹ࣮ࢱ㸸࣐࢖ࢡࣟ࢔ࣞ࢖࡞࡝ࡢ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ౛ 㟼ⓗ㸸ࣀࢵࢡࢲ࢘ࣥᐇ㦂㸪ᝈ⪅⏤᮶ࢧࣥࣉࣝ
ືⓗ㸸⸆๣ᛂ⟅᫬⣔ิࢹ࣮ࢱᮾி኱Ꮫ  ་⛉Ꮫ◊✲ᡤ  ࣄࢺࢤࣀ࣒ゎᯒࢭࣥࢱ࣮
(HGC) ࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱࢩࢫࢸ࣒
㸦Xeon/Opteron  PC  ࢡࣛࢫࢱ㸧ࢯࣇࢺ࢙࢘࢔㓄ᕸᙧែࢧࣥࣉࣝ ( ࢔ࣞ࢖㸧ᩘ㸸50  ࠥ  1,000  ࢔ࣞ࢖᫬⣔ิࢹ࣮ࢱࡣ」ᩘᅇࡢ⧞ࡾ㏉ࡋィ ࢆ᥎ዡ ࠕி࣮ࠖࣘࢨ࡟ᐇ⾜ࣂ࢖ࢼࣜࢆ㓄ᕸࣀ࣮ࢻ㸦㑇ఏᏊ㸧ᩘ㸸100ࠥ100,000  ࣀ࣮ࢻHGC ࡛ࡣࢩࢫࢸ࣒࡟࢖ࣥࢫࢺ࣮ࣝணᐃ

Visit SiGN Web Site at http://sign.hgc.jp

ࡇࢀࡲ࡛ࡢᛂ⏝஦౛

Tamada et al. (2011a).

㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ⫵⒴⣽⬊ᰴ PC-­9㸩Gefitinib ᢞ୚࡟ࡼࡿ⸆๣ᛂ⟅ࣃࢫ࢙࢘࢖ゎᯒ= 㑇ఏᏊⓎ⌧ࡢ౫Ꮡ㛵ಀࢆࢿࢵࢺ࣮࣡ࢡ࡛⾲⌧ࡋࡓࣔࢹࣝ

౑⏝ࢹ࣮ࢱ

㑇ఏᏊ

...

Ⓨ⌧ࡢ
౫Ꮡ㛵ಀ

x

ィ ࢹ࣮ࢱ㸸26  ᫬Ⅼ ×  1  ᅇィ 㸻26 ࢔ࣞ࢖ࢹ࣮ࢱ

࣐࢖ࢡࣟ࢔ࣞ࢖࡞࡝࡟ࡼࡿ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ- ࣀࢵࢡࢲ࢘ࣥᐇ㦂ࡸᝈ⪅ࢧࣥࣉࣝ- ⸆๣ᛂ⟅᫬⣔ิࢹ࣮ࢱ」㞧࡞㑇ఏᏊ㛫ࡢไᚚࡸ౫Ꮡ㛵ಀࢆⓎ⌧ࢹ࣮ࢱ࠿ࡽ᥎ᐃࡋࡓ࠸y┠ⓗ⣽⬊㸸⫵⒴⣽⬊ᰴ  PC-­9⸆㸸Gefitinib( ࢖ࣞࢵࢧ :  ᢠࡀࢇ๣㸧࣐࢖ࢡࣟ࢔ࣞ࢖㸸Agilent  44K  Whole  Human  Genome  Oligo  Microarray
EGF,Gefitinib ᢞ୚࣭㠀ᢞ୚ࡢ 4 ᮲௳⤖ᯝGefitinib ࢆᢞ୚ࡋࡓ⫵⒴⣽⬊ᰴ PC-­9 ࡛ࡢ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࡢືⓗኚ໬ࢆぢࡿࡇ࡜࡛㸪ᮍ▱స⏝
ᶵᗎࡢྠᐃ
฼⏝ᡭἲ
ື ⓗ ࣋ ࢖ ࢪ ࢔ ࣥ ࢿ ࢵ ࢺ ࣡ ࣮ ࢡ (HC) ࡟ ࡼ ࡿ
Node-­Set  Separation  ἲ

Node-­Set  Separation ἲ࡟ࡼࡾ᫬Ⅼẖࡢࢿࢵࢺ࣮࣡ࢡࡀᚓࡽࢀ᫬㛫࡟ࡼࡿኚ໬ࡀゎᯒྍ⬟኱つᶍ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽࡢ኱つᶍ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࡢ᥎ᐃ࡟ࡣ⭾኱࡞ィ⟬ࡀᚲせ࡛࠶ࡿ㸬ࡋࡓࡀࡗ࡚㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ
ゎᯒ࡟ࡣࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱ࡟ࡼࡿ኱つᶍィ⟬ࡀḞ࠿ࡏ࡞࠸㸬x ࠿ࡽ y ࡬ࡢᯞ㸸㑇ఏᏊ x ࡢⓎ⌧ࡀ㑇ఏᏊ y ࡢⓎ⌧࡟ᙳ㡪ࢆ୚࠼ࡿࣀࣥࣃ࣓ࣛࢺࣜࢵࢡᅇᖐ࡟ࡼࡿ࣋࢖ࢪ࢔ࣥࢿࢵࢺ࣮࣡ࢡ
Bayesian Network Model

Imoto et al. (2002).
Representation of the joint probability distribution
X2 by the DAG (Directed Acyclic Graph) structure

X1

X3

X5

Gene Expression = Random Variable
Node = Gene
Directed Edge = Dependency

X4

f (X1, X 2 , , X 6 ) f1 (X1 ) f 2 (X 2 ) f 3 (X 3 | X1 )X6The network structure is determined by maximizing
the posterior probability given the expression dataࣄࢺ⮖ᖏ㟼⬦ෆ⓶⣽⬊ (HUVEC) ࡟ࡼࡿ࢔࣏ࢺ࣮ࢩࢫ㛵୚㑇ఏᏊྠᐃ Affara et al. (2007).


G: gene network      X:  gene expression data౑⏝ࢹ࣮ࢱThe parent-­child relationship is modeled by the B-­spline
nonparametric regression

f 6 (X 6 | X 3 , X 4 )Bayesian Network

Parallel Algorithms for Estimating Gene Network Structures
ࠥ100,000
genes┠ⓗ⣽⬊㸸ࣄࢺ⮖ᖏ㟼⬦ෆ⓶⣽⬊㸦HUVEC㸧่⃭㸸  ⾑Ύ㝖ཤ  &  siRNA ࣀࢵࢡࢲ࢘ࣥ
࣐࢖ࢡࣟ࢔ࣞ࢖㸸 CodeLink  UniSet  Human  20K  gene  chipィ ࢹ࣮ࢱ㸸8  ᫬Ⅼ ×  3  ᅇィ   ᫬⣔ิࢹ࣮ࢱ
8  siRNA  ࣀࢵࢡࢲ࢘ࣥ࢔ࣞ࢖
                                ィ 32 ࢔ࣞ࢖ࢹ࣮ࢱ
⤖ᯝ

Tamada et al. (2011b).
Construct a single gene networkby accumulating the estimatedsubnetworks.

฼⏝ᡭἲsiRNA ࣀࢵࢡࢲ࢘ࣥ࢔ࣞ࢖࡟ࡼࡿ஦๓᝟ሗࢆ⏝࠸ࡓ
HC+Bootstrap ࡟ࡼࡿືⓗ࣋࢖ࢪ࢔ࣥࢿࢵࢺ࣮࣡ࢡ

ࢿࢵࢺ࣮࣡ࢡ᥎ᐃ࠾ࡼࡧゎᯒࡢ⤖ᯝ GABARAP ࡀࣁࣈ㑇ఏᏊ㸦Ꮚࡢከ࠸ࢿࢵࢺ࣮࣡ࢡ୰ࡢࣀ࣮ࢻ㸧࡜ࡋ࡚ᢳฟࡉࢀᐇ㦂ⓗ࡟ࡶ HUVEC
࡟࠾࠸࡚࢔࣏ࢺ࣮ࢩࢫ࡬ࡢ㛵୚ࢆ☜ㄆ㸬

The NNSR (Neighbor Node Sampling & Repeat) Algorithm
Randomly pick up genes with
Perform subnetwork probabilities proportional to the
network score.
estimation࢔࣏ࢺ࣮ࢩࢫࢆㄏᑟࡋࡓ HUVEC ࢆ᫬⣔ิ࡟ほ ࡋࢿࢵࢺ࣮࣡ࢡゎᯒࢆࡍࡿࡇ࡜࡟ࡼࡾ࢔࣏ࢺ࣮ࢩࢫไ
ᚚ࡟㛵ࢃࡿ㛵㐃㑇ఏᏊࡢྠᐃ

Perform a random walk to collectgenes that are close to each otherin terms of the network.

ࡑࡢ௚ࡢ஦౛
-­  HUVEC/Fenofibrate ࢆ⏝࠸ࡓ㧗⬡⾑⑕⸆᪂つࢱ࣮ࢤࢵࢺ㑇ఏᏊࡢྠᐃ Imoto et al. (2006).
-­  HUVEC/Fenofibrate ࡟ࡼࡿ㠀⬡㉁௦ㅰ⣔స⏝ࣃࢫ࢙࢘࢖ࡢྠᐃ Araki et al. (2009).

Repeat this many times independently-­  ࣓ࣛࣀ࣮࣐⣽⬊ /Paclitaxel ࡟ࡼࡿ᪂つᢠ⒴๣ࢱ࣮ࢤࢵࢺࡢྠᐃ
ࠥ1,000genes

Greedy Hill-­Climb (HC) + Bootstrap Algorithm Imoto et al. (2002). Tamada et al. (2009).
Network
estimation  X1

Randomsampling withreplacement  X3

X5

Original expression
dataset  Re-­sampleddataset  

X2

PR

X4

X6

ᩥ 㒊 ⛉ Ꮫ ┬    ᪂ Ꮫ ⾡ 㡿 ᇦ ◊ ✲ ࠕ ࢩ ࢫ ࢸ ࣒ ࡀ ࢇ ࠖ

SiGN is also used in the Systems Cancer Project that started
newly in 2010. Visit our web site at http://systemscancer.hgc.jp
and follow our tweets @SystemsCancer in Twitter for updates.

Estimated network  Acknowledgments
ࠥ30
genes

Parallel Optimal Search (Para-­OS) Algorithm Ott et al. (2004).

Tamada et al. (2011c).

Computational time was provided by the Super Computer System, Human Genome
Center, Institute of Medical Science, The University of Tokyo, RIKEN Integrated
Cluster of Clusters (RICC) system, and early access to the K computer at the RIKEN
Advanced Institute for Computational Science.

-­ Parallel version of the optimal search algorithm bydynamic programming.

ReferenceīĂƌĂ͕D͘ĞƚĂů͘;ϮϬϬϳͿ͘hŶĚĞƌƐƚĂŶĚŝŶŐĞŶĚŽƚŚĞůŝĂůĐĞůůĂƉŽƉƚŽƐŝƐ͗tŚĂƚĐĂŶƚŚĞƚƌĂŶƐĐƌŝƉƚŽŵĞŐůǇĐŽŵĞĂŶĚƉƌŽƚĞŽŵĞƌĞǀĞĂů͍͕WŚŝůŽƐŽƉŚŝĐĂůdƌĂŶƐĂĐƟŽŶƐŽĨZŽǇĂůSocietǇ62;ϭϰϴϰͿ͕ϭϰϲϵͲϭϰϴϳ͘ƌĂŬŝ͕,͘ĞƚĂů͘;ϮϬϬϵͿ͘ŶĂůǇƐŝƐŽĨWWZɲͲĚĞƉĞŶĚĞŶƚĂŶĚ
WWZɲͲŝŶĚĞƉĞŶĚĞŶƚƚƌĂŶƐĐƌŝƉƚƌĞŐƵůĂƟŽŶĨŽůůŽǁŝŶŐĨĞŶŽĮďƌĂƚĞƚƌĞĂƚŵĞŶƚŽĨŚƵŵĂŶĞŶĚŽƚŚĞůŝĂůĐĞůůƐ͕
Angiogenesis12;ϯͿ͕ϮϮϭͲϮϮϵ͘

ᮏⓎ⾲ࡢ⤖ᯝࡢ୍㒊ࡣ㸪⌮໬Ꮫ◊✲ᡤࡀᐇ᪋ࡋ࡚࠸ࡿி㏿ࢥࣥࣆ࣮ࣗࢱࠕிࠖࡢヨ㦂฼⏝࡟ࡼࡿࡶࡢ࡛ࡍ㸬/ŵŽƚŽ͕^͘ĞƚĂů͘;ϮϬϬϮͿ͘ƐƟŵĂƟŽŶŽĨŐĞŶĞƟĐŶĞƚǁŽƌŬƐĂŶĚĨƵŶĐƟŽŶĂůƐƚƌƵĐƚƵƌĞƐďĞƚǁĞĞŶŐĞŶĞƐďǇƵƐŝŶŐĂǇĞƐŝĂŶŶĞƚǁŽƌŬĂŶĚŶŽŶƉĂƌĂŵĞƚƌŝĐƌĞŐƌĞƐƐŝŽŶ͕WĂĐ͘^ǇŵƉ͘ŝŽĐŽŵƉƵƚ͘7͕ϭϳϱͲϭϴϲ͘/ŵŽƚŽ͕^͘ĞƚĂů͘;ϮϬϬϲͿ͘ŽŵƉƵƚĂƟŽŶĂůƐƚƌĂƚĞŐǇĨŽƌĚŝƐĐŽǀĞƌŝŶŐĚƌƵŐŐĂďůĞŐĞŶĞŶĞƚǁŽƌŬƐĨƌŽŵŐĞŶŽŵĞͲǁŝĚĞEĞǆƉƌĞƐƐŝŽŶƉƌŽĮůĞƐ͕WĂĐŝĮĐ^ǇŵƉŽƐŝƵŵŽŶŝŽĐŽŵƉƵƟŶŐ;W^ϮϬϬϲͿ͕ϭϭ͕ϱϱϵͲϱϳϭ͘KƩ͕^͘ĞƚĂů͘;ϮϬϬϰͿ͘&ŝŶĚŝŶŐŽƉƟŵĂůŵŽĚĞůƐĨŽƌƐŵĂůůŐĞŶĞŶĞƚǁŽƌŬƐ͘WĂĐ͘^ǇŵƉ͘ŝŽĐŽŵƉƵƚ͘9͕ϱϱϳͲϱϲϳ͘dĂŵĂĚĂ͕z͘ĞƚĂů͘;ϮϬϬϵͿ͘hŶƌĂǀĞůŝŶŐĚǇŶĂŵŝĐĂĐƟǀŝƟĞƐŽĨĂƵƚŽĐƌŝŶĞƉĂƚŚǁĂǇƐƚŚĂƚĐŽŶƚƌŽůĚƌƵŐͲƌĞƐƉŽŶƐĞƚƌĂŶƐĐƌŝƉƚŽŵĞŶĞƚǁŽƌŬƐ͕WĂĐŝĮĐ^ǇŵƉŽƐŝƵŵŽŶŝŽĐŽŵƉƵƟŶŐ;W^ϮϬϬϵͿ14͕ϮϱϭͲϮϲϯ͘dĂŵĂĚĂ͕z͘ĞƚĂů͘;ϮϬϭϭĂͿ͘^ŝ'E͗>ĂƌŐĞͲƐĐĂůĞŐĞŶĞŶĞƚǁŽƌŬĞƐƟŵĂƟŽŶĞŶǀŝƌŽŶŵĞŶƚĨŽƌŚŝŐŚƉĞƌĨŽƌŵĂŶĐĞĐŽŵƉƵƟŶŐ͕

191'ĞŶŽŵĞ/ŶĨŽƌŵĂƟĐƐ͕Ϯϱ;ϭͿ͕ϰϬͲϱϮ͘dĂŵĂĚĂ͕z͘ĞƚĂů͘;ϮϬϭϭďͿ͘ƐƟŵĂƟŶŐ'ĞŶŽŵĞͲǁŝĚĞ'ĞŶĞEĞƚǁŽƌŬƐhƐŝŶŐEŽŶƉĂƌĂŵĞƚƌŝĐĂǇĞƐŝĂŶEĞƚǁŽƌŬDŽĚĞůƐŽŶDĂƐƐŝǀĞůǇWĂƌĂůůĞůŽŵƉƵƚĞƌƐ͘/ͬDdƌĂŶƐ͘ĐŽŵƉƵƚ͘ďŝŽů͘ďŝŽŝŶĨŽƌŵĂƟĐƐ͕ϴ;ϯͿ͕ϲϴϯͲϲϵϳ͘dĂŵĂĚĂ͕z͘ĞƚĂů͘;ϮϬϭϭĐͿ͘WĂƌĂůůĞůĂůŐŽƌŝƚŚŵĨŽƌůĞĂƌŶŝŶŐŽƉƟŵĂůĂǇĞƐŝĂŶŶĞƚǁŽƌŬƐƚƌƵĐƚƵƌĞ͕:ŽƵƌŶĂůŽĨDĂĐŚŝŶĞ>ĞĂƌŶŝŶŐZĞƐĞĂƌĐŚ͕ϭϮ͕ϮϰϯϳоϮϰϱϵ͘ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-5: S iG N-L 1
L1ṇ๎໬ἲ䛻䜘䜛኱つᶍ㑇ఏᏊ䝛䝑䝖䝽䞊䜽᥎ᐃ䝥䝻䜾䝷䝮㛤Ⓨ㈐௵⪅: ᐑ㔝 ᝅ (ᮾி኱Ꮫ ་⛉Ꮫ◊✲ᡤ 䝠䝖䝀䝜䝮ゎᯒ䝉䞁䝍䞊)
SiGN 㛤Ⓨ䝯䞁䝞䞊: ⋢⏣ ჆⣖䚸ᓥᮧ ᚭᖹ䚸ᒣཱྀ 㢮䚸᪂஭⏣ ཌྖ䚸ᩧ⸨ 䛒䜖䜐䚸㛗ᓮ ṇᮁ䚸஭ඖ Ύဢ䚸ᐑ㔝 ᝅCase  4.  ␗䛺䜛䝃䞁䝥䝹䛻ᑐ䛩䜛㑇ఏᏊⓎ⌧䝕䞊䝍䛛䜙䝃䞁䝥䝹ྛ䚻䛾䝛䝑䝖䝽䞊䜽䜢᥎ᐃ  ᴫせSiGN  (䝃䜲䞁)  䛿㑇ఏᏊ䝛䝑䝖䝽䞊䜽Ⓨ⌧䝕䞊䝍䛛䜙䝇䞊䝟䞊䝁䞁䝢䝳䞊䝍䜢⏝䛔䛶㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢ண 䞉᥎ᐃ䛩䜛䝋䝣䝖䜴䜵䜰⩌  (SiGN-­‐BN,  SiGN-­‐L1,  SiGN-­‐SSM)  䛷䛒䜛䚹䛣䛾䛖䛱䚸SiGN-­‐L1  䛿  L1  ṇ๎໬ἲ䛻䜘䜛䝇䝟䞊䝇䛺⤫ィⓗ䜾䝷䝣䜱䜹䝹䝰䝕䝹䠄䜾䝷䝣䜱䜹䝹䜺䜴䝅䜰䞁䝰䝕䝹䚸䝧䜽䝖䝹⮬ᕫᅇᖐ䝰䝕䝹䚸ᵓ㐀᪉⛬ᘧ䝰䝕䝹䠅䜢⏝䛔䛯㑇ఏᏊ䝛䝑䝖䝽䞊䜽᥎ᐃ䝋䝣䝖䜴䜵䜰䛷䛒䜚䚸䝇䞊䝟䞊䝁䞁䝢䝳䞊䝍䜢⏝䛔䛯୪ิィ⟬䛻䜘䜚䚸䝠䝖඲㑇ఏᏊ䜢ᑐ㇟䛸䛧䛯኱つᶍ㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ䛩䜛䛣䛸䛜ྍ⬟䛷䛒䜛䚹᥎ᐃ䛥䜜䛯㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢ゎᯒ䛩䜛䛣䛸䛻䜘䜚䚸ほ 䝕䞊䝍䛛䜙⸆๣ᶆⓗ㑇ఏᏊ䛾ྠᐃ䚸๰⸆䝍䞊䝀䝑䝖䞉ẘᛶ㛵୚䝟䝇䜴䜵䜲䛾᥈⣴䛺䛹䛾ᛂ⏝䛜ᮇᚅ䛥䜜䜛䚹  NetworkProfiler  SiGN-L1䛾≉ᚩs 3 ✀㢮䛾⤫ィⓗ䜾䝷䝣䜱䜹䝹䝰䝕䝹 (䜾䝷䝣䜱䜹䝹䜺䜴䝅䜰䞁䝰䝕䝹䚸䝧䜽䝖䝹⮬ᕫᅇᖐ䝰䝕䝹䚸ᵓ㐀᪉⛬ᘧ䝰䝕䝹) 䛻䜘䜚䚸㑇ఏᏊⓎ⌧䝕䞊䝍䛛䜙㑇ఏᏊ䝛䝑䝖䝽䞊䜽䝰䝕䝹䜢ᵓ⠏s L1 ṇ๎໬ἲ䜢⏝䛔䛶䝇䝟䞊䝇䛺䝰䝕䝹䛾䝟䝷䝯䞊䝍✵㛫䜢᥈⣴䛩䜛䛣䛸䛻䜘䜚䚸
100,000 䝜䞊䝗䜎䛷䛾኱つᶍ㑇ఏᏊ䝛䝑䝖䝽䞊䜽᥎ᐃ䛜ྍ⬟s ᮾி኱Ꮫ་⛉Ꮫ◊✲ᡤ䝠䝖䝀䝜䝮ゎᯒ䝉䞁䝍䞊䠄HGC䠅䛾䝇䞊䝟䞊䝁䞁䝢䝳䞊䝍䝅䝇
䝔䝮䠄Xeon/Opteron PC 䜽䝷䝇䝍䠅ୖ䛷䚸Sun Grid Engine 䜢⏝䛔䛯୪ิ໬䛻䜘䜚1,024 䝁䜰䛾୪ิ໬䜢㐩ᡂ䠊௒ᚋ䚸RICC䚸RICC-FX䚸䛂ி䛃ୖ䛷ືస䜢᳨ドணᐃ䠊䛣䜜䜎䛷䛾ᛂ⏝஦౛㑇ఏᏊ䝛䝑䝖䝽䞊䜽 = 㑇ఏᏊⓎ⌧㛫䛾౫Ꮡ㛵ಀ䜢⾲䛧䛯䜾䝷䝣ᵓ㐀ධຊ䛸ฟຊ஧䛴䛾␗䛺䜛ᡂ㛗ᅉᏊ䛻䜘䜛⣽⬊ෆ䛾᝟ሗఏ㐩䝟䝇䜴䜵䜲䛾ẚ㍑  Case  1.  Sample  A  䛻ᑐ䛩䜛㑇ఏᏊ◚ቯᰴⓎ⌧䝕䞊䝍䛛䜙㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ  䝕䞊䝍: 8ᐇ㦂᮲௳ୗ䛻䛚䛔䛶8᫬Ⅼ䛷ィ 䛧䛯᫬⣔ิ㑇ఏᏊⓎ⌧䝕䞊䝍(Nagashima et al., 2007)┠ⓗ: EGF䚸HRGྛ䚻䛾ᡂ㛗ᅉᏊ䛻䜘䛳䛶ㄏᑟ䛥䜜䜛 8 ᐇ㦂᮲௳ୗ䛷䛾㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ䛧䚸EGF่⃭䚸HRG่⃭ୗ䛾䝅䝇䝔䝮䛾㐪䛔䜢ẚ㍑

Sparse  Graphical  Gaussian  Model  Case  2.  Sample  A  䛻ᑐ䛩䜛᫬⣔ิ㑇ఏᏊⓎ⌧䝕䞊䝍䛛䜙㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ  Sparse  Vector  Autoregressive  Model  ෌Ⓨ䝸䝇䜽䛻㛵䜟䜛᪥ᮏே⫵⒴ᝈ⪅䛾㑇ఏᏊ䝛䝑䝖䝽䞊䜽䛾ẚ㍑  Case  3.  Sample  A  䛻ᑐ䛩䜛␗䛺䜛ᐇ㦂᮲௳ୗ䛾᫬⣔ิ㑇ఏᏊⓎ⌧  䝕䞊䝍䛛䜙ྛ䚻䛾ᐇ㦂᮲௳ୗ䛷䛾㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ  Condition  1  NetComparator  Condition  1  䝕䞊䝍: 226⑕౛䛾⫵⥺䛜䜣ᝈ⪅䛾㑇ఏᏊⓎ⌧䝕䞊䝍䠄ᅜ❧䛜䜣䝉䞁䝍䞊ᶓ⏣ඛ⏕䚸Ἑ㔝ඛ⏕䛸䛾ඹྠ◊✲䠅┠ⓗ: ෌Ⓨ䝸䝇䜽䜢䝰䝆䝳䝺䞊䝍䞊䛸䛧䛯䛸䛝䛾 226 ⑕౛ྛ䚻䛾㑇ఏᏊ䝛䝑䝖䝽䞊䜽䜢᥎ᐃ䛧䚸෌Ⓨ䝸䝇䜽䛜㧗䛔ᝈ⪅䛸ప䛔ᝈ⪅䛾䝅䝇䝔䝮䛾㐪䛔䜢ẚ㍑

Condition  2  Condition  2  Condition  3  Condition  3  ཧ⪃ᩥ⊩s Shimamura et al. (2007) Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.
Genome Informatics, 19, 142-153.
s Shimamura et al  2ECURSIVE REGULARIZATION FOR INFERRING GENE NETWORKS FROM TIME-course gene expression profiles.
BMC Systems Biology, 3, e41.
s Shimamura et al  )NFERRING DYNAMIC GENE NETWORKS UNDER VARYING CONDITIONS FOR TRANSCRIPTOMIC NETWORK COMPARISON
Bioinformatics, 26(8), 1064-1072.
s Shimamura et al. (2010) Collocation-based sparse estimation for inferring continuous-time dynamic gene networks.
Genome Informatics, 24, 164-178.
s Shimamura et al  ! NOVEL NETWORK PROFILING ANALYSIS REVEALS SYSTEM CHANGES IN EPITHELIAL-mesenchymal transition.
PLoS ONE 6(6): e20804, doi:10.1371/journal.pone.0020804, 2011.

CTGF  (Connective  Tissue  Growth  Factor)  ቑṪ䞉ศ໬䜢ไᚚ䛩䜛TGF-­‐ɴ䛾ୗὶᅉᏊ䛷䚸  TGF-­‐ɴ䛾㛫㉁⧄⥔໬ಁ㐍స⏝䜢௰௓  ቑṪಁ㐍䚸㐟㉮䚸⣽⬊እᇶ㉁⟬ฟ䚸⾑⟶᪂⏕స⏝䜢࿊䛩䜛  ㅰ㎡#OMPUTATIONAL TIME WAS PROVIDED BY THE 3UPER #OMPUTER 3YSTEM (UMAN 'ENOME #ENTER )NSTITUTE OF -EDICAL 3CIENCE 4HE 5NIVERSITY OF
4OKYO 2)+%. )NTEGRATED #LUSTER OF #LUSTERS 2)## SYSTEM AND EARLY ACCESS TO THE + COMPUTER AT THE 2)+%. !DVANCED )NSTITUTE for
Computational Science.

192ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-­6:  SiGN-­SSM≧ែ✵㛫ࣔࢹࣝ࡟ࡼࡿ኱つᶍ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ᥎ᐃࣉࣟࢢ࣒ࣛ
SiGN  㛤Ⓨ࣓ࣥࣂ࣮  :  ⋢⏣  ჆⣖㸪ᓥᮧ  ᚭᖹ㸪ᒣཱྀ㢮㸪᪂஭⏣ཌྖ㸪ᩧ⸨࠶ࡺࡴ㸪㛗ᓮ  ṇᮁ㸪஭ඖ  Ύဢ㸪ᐑ㔝  ᝅᴫ        せࢯࣇࢺ࢙࢘࢔௙ᵝ

SiGN㸦ࢧ࢖ࣥ㸧ࡣ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗ
ࢱࢆ⏝࠸࡚㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࢆண ࣭᥎ᐃࡍࡿࢯࣇࢺ࢙࢘
࢔⩌࡛࠶ࡿ㸬㑇ఏᏊࢿࢵࢺ࣮࣡ࢡゎᯒ࡟ࡼࡾ㸪ほ ࢹ࣮ࢱ࠿ࡽ⸆๣ᶆⓗ㑇ఏᏊࡢྠᐃ㸪๪స⏝ࡢண ࣭ᅇ㑊㸪๰⸆ࢱ࣮ࢤࢵ
ࢺ࣭ẘᛶ㛵୚ࣃࢫ࢙࢘࢖᥈⣴࡞࡝ࡀᐜ᫆࡟࡞ࡿࡇ࡜ࡀᮇᚅࡉ
ࢀࡿ㸬SiGN ࡛ࡣᵝࠎ࡞ᐇ㦂ࢹ࣮ࢱࡢゎᯒ࡟ᑐᛂࡍࡿࡓࡵ࣋
࢖ࢪ࢔ࣥࢿࢵࢺ࣮࣡ࢡ㸪≧ែ✵㛫ࣔࢹࣝ㸪ࢢࣛࣇ࢕࢝ࣝ࢞࢘
ࢩ࢔ࣥࣔࢹࣝ㸪࣋ࢡࢺࣝ⮬ᕫᅇᖐࣔࢹࣝࡀ㑇ఏᏊࢿࢵࢺ࣮࣡
ࢡࣔࢹࣝ࡜ࡋ࡚฼⏝ྍ⬟࡛࠶ࡿ㸬SiGN-­SSM ࡣࡇࢀࡽࡢ࠺ࡕ
≧ែ✵㛫ࣔࢹࣝࢆ⏝࠸ࡓࢿࢵࢺ࣮࣡ࢡ᥎ᐃࢯࣇࢺ࢙࢘࢔࡛㸪ࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱࢆ⏝࠸ࡓ୪ิィ⟬࡟ࡼࡾ㸪᫬⣔ิ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽᩘ༓㑇ఏᏊࡢࣔࢪ࣮ࣗࣝ࠾ࡼࡧ㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࡢ᥎ᐃ㸪࠾ࡼࡧண ࣔࢹࣝࡢᵓ⠏ࡀྍ⬟࡛࠶ࡿ㸬

ฟຊ

ᴫせ࣭≉ᚩ

ฟຊࢹ࣮ࢱ㸸
ࢩࢫࢸ࣒ᵓ㐀ࣃ࣓࣮ࣛࢱ ≧ែ᥎ᐃ್㸦ࢸ࢟ࢫࢺ㸧㸦㑇ఏᏊࢿࢵࢺ࣮࣡ࢡࠊࣔࢪ࣮ࣗࣝࢿࢵࢺ࣮࣡ࢡண ࣔࢹࣝᵓ⠏࡟౑⏝ྍ㸧-­  ≧ែ✵㛫ࣔࢹࣝ࡟ࡼࡾ᫬⣔ิ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽ㸪ࣔࢪ࣮ࣗࣝࢿࢵࢺ࣮࣡ࢡ㸪㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ࠾ࡼࡧ
ືⓗⓎ⌧ண ࣔࢹࣝࢆᵓ⠏-­  ḟඖ⦰⣙࠾ࡼࡧ୍ពᛶไ⣙࡟ࡼࡾ㧗ḟඖ▷᫬⣔ิ࡛࠶ࡿ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽືⓗࢩࢫࢸ࣒ࡢ᥎ᐃྍ⬟୪ิᛶ⬟-­  ୪࡭᭰࠼᳨ᐃ࡟ࡼࡿࢿࢵࢺ࣮࣡ࢡ᥎ᐃ

᭱኱ືసࢥ࢔ᩘ㸸8,192 ࢥ࢔-­  ⧞ࡾ㏉ࡋィࢆᣢࡘ᫬⣔ิ㸪Ḟ࠾ࡼࡧ୙➼㛫㝸ィ୪ิ໬ຠ⋡㸸0.98  (256 ࢥ࢔᫬ )㸦ࢫࢺࣟࣥࢢࢫࢣ࣮ࣜࣥࢢ㸧ࢆྵࡴ᫬⣔ิࡢ᝟ሗࡶ㐺ษ࡟ᢅ࠺ࡇ࡜ࡀྍ⬟-­ ືⓗண

࡟ᇶ࡙ࡃࢣ࣮ࢫ࣭ࢥࣥࢺ࣮ࣟࣝ᫬⣔ิࡢẚ㍑

㸦ไᚚᵓ㐀࡟ᕪ␗ࢆ㑇ఏᏊ⩌ࡢண

ͤ  2011/10/31 ⌧ᅾ
5,&&㸪5,&&);
+*&ࢫࣃࢥࣥୖ᳨࡛ド୰㸧ࠕிࠖ௨እ࡛ࡢࢱ࣮ࢤࢵࢺ⎔ቃධຊᮾி኱Ꮫ  ་⛉Ꮫ◊✲ᡤ  ࣄࢺࢤࣀ࣒ゎᯒࢭࣥࢱ࣮
(HGC) ࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱࢩࢫࢸ࣒
㸦Xeon/Opteron  PC  ࢡࣛࢫࢱ㸧ධຊࢹ࣮ࢱ㸸࣐࢖ࢡࣟ࢔ࣞ࢖࡞࡝ࡢ㑇ఏᏊⓎ⌧ࠉࠉࠉࠉࠉࠉ᫬⣔ิࢹ࣮ࢱ౛㸸⸆๣ᛂ⟅᫬⣔ิࢹ࣮ࢱࢧࣥࣉࣝ ( ࢔ࣞ࢖㸧ᩘ㸸10  ࠥ  30  ࢔ࣞ࢖᫬⣔ิࢹ࣮ࢱࡣ」ᩘᅇࡢ⧞ࡾ㏉ࡋィ ࢆ᥎ዡࣀ࣮ࢻ㸦㑇ఏᏊ㸧ᩘ㸸100ࠥ2,000  ࣀ࣮ࢻVisit SiGN Web Site at http://sign.hgc.jp

http://sign.hgc.jp/signssm/index.html

ࡇࢀࡲ࡛ࡢᛂ⏝஦౛ṇᖖ⫵ୖ⓶⣽⬊ SAEC㸩EGF(+Gefitinib) ᢞ୚ୗEGF ᛂ⟅ࣃࢫ࢙࢘࢖ゎᯒ≧ែ✵㛫ࣔࢹࣝ࡟ࡼࡿ᫬⣔ิ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽࡢ᝟ሗᢳฟ

౑⏝ࢹ࣮ࢱ⣽⬊㸸⫵ṇᖖ⣽⬊  SAEC่⃭  ⸆㸸EGFGefitinib( ࢖ࣞࢵࢧ :  ᢠࡀࢇ๣㸧࣐࢖ࢡࣟ࢔ࣞ࢖㸸Agilent  44K  Whole  Human  Genome  Oligo  Microarray≧ែ✵㛫ࣔࢹࣝ 6WDWH6SDFH0RGHOィḟඖ⦰⣙࡟ࡼࡿ

ࢹ࣮ࢱ㸸19 ᫬Ⅼ ×  1  ᅇィLQ ᫬㛫⤖ᯝ㑇ఏᏊⓎ⌧ࢹ࣮ࢱ࠿ࡽࡢ

㑇ఏᏊ㛫ไᚚᵓ㐀࡟ᕪ␗ࡢ࠶ࡿ㑇ఏᏊ⩌ࡢணࢩࢫࢸ࣒᥎ᐃ

┠ⓗGefitinib ࡢᙳ㡪࡟ࡼࡾ㑇ఏᏊ㛫ไᚚᵓ㐀࡟ኚ໬ࡢ࠶ࡗࡓ㑇ఏᏊ⩌ࡢண ࠾ࡼࡧ ⫵ࡀࢇணᚋண ࣂ࢖࣐࣮࢜࢝᥈⣴฼⏝ᡭἲࢥࣥࢺ࣮ࣟࣝࢹ࣮ࢱ࡟ࡼࡾᵓ⠏ࡉࢀࡓࣔࢹࣝ࠿ࡽࡢ
ࢣ࣮ࢫࢹ࣮ࢱࡢண ࡟ᇶ࡙ࡃࢩࢫࢸ࣒␗ᖖ᳨▱EGF,Gefitinib ᢞ୚࣭㠀ᢞ୚ࡢ 4 ᮲௳㧗ḟඖ▷᫬⣔ิp 10 3N  20ࢯࣇࢺ࢙࢘࢔㓄ᕸᙧែ࣮࢜ࣉࣥࢯ࣮ࢫ࡜ࡋ࡚බ㛤GNU  Affero  General  Public  License࠾ࡼࡧ⫵ࡀࢇணᚋண

ࢩࢢࢿࢳ࣮ࣕ࡬ࡢᛂ⏝࡟ᡂຌ

Control

㑇ఏᏊᩘ
᫬Ⅼᩘ

Case

ࢩࢫࢸ࣒ᵓ㐀ࣃ࣓࣮ࣛࢱ᥎ᐃືⓗⓎ⌧ண {H , F , R, x0 }Ⓨ⌧ไᚚᵓ㐀᥎ᐃࣔࢪ࣮ࣗࣝࢿࢵࢺ࣮࣡ࢡ
㸦ඹⓎ⌧㑇ఏᏊ⩌㛫㸧ไᚚᵓ㐀࡟ᕪ␗ࡢ࠶ࡿ
㑇ఏᏊ⩌ࡢⓎ⌧ࣃࢱ࣮ࣥ࡟
ᇶ࡙ࡃ⫵ࡀࢇᝈ⪅⩌ணᚋண⫵ࡀࢇ⣽⬊ᰴ PC9/PC9GR ࡟ᑐࡍࡿ Gegitinib ືⓗຠᯝண
౑⏝ࢹ࣮ࢱ

┠ⓗ

⣽⬊㸸⫵ࡀࢇ⣽⬊ᰴ  PC9  および PC9GR

㑇ఏᏊࢿࢵࢺ࣮࣡ࢡ
㸦ಶู㑇ఏᏊ㛫㸧่⃭  ⸆㸸EGFGefitinib( ࢖ࣞࢵࢧ :  ᢠࡀࢇ๣㸧࣐࢖ࢡࣟ࢔ࣞ࢖㸸 Agilent  44K  Whole  Human  Genome  Oligo  Microarrayィࢹ࣮ࢱ㸸26 ᫬Ⅼ ×  1  ᅇィLQ26 ᫬㛫⸆๣⪏ᛶ⬟ࡢ␗࡞ࡿࡀࢇ⣽⬊࡬ࡢ⸆๣స⏝Ⅼ࠾ࡼࡧస⏝ᶵᗎࡢண 㸸
PC9㸦⸆๣ឤཷᛶ㸧
PC9GR㸦⸆๣⪏ᛶ㸧฼⏝ᡭἲ⸆๣ືⓗຠᯝࣔࢹࣝ㡯ࢆ⤌ࡳ㎸ࢇࡔࣔࢹࣝ࡟ࡼࡿ
⸆๣స⏝ୗࢹ࣮ࢱࡢண ࡟ᇶ࡙ࡃ≧ែ᥎ᐃ⸆๣ຠᯝࣉࣟࣇ࢓࢖ࣝ
PC9 ࡜ PC9GR ࡢ㛫࡛␗࡞ࡿ⸆๣ຠᯝࣉࣟࣇ࢓
࢖ࣝࢆᣢࡘࣔࢪ࣮ࣗࣝࢆྠᐃ㸬ᙜヱࣔࢪ࣮ࣗࣝᨭ㓄ୗ࡟࠶ࡿ㑇ఏᏊ⩌ࡀ⸆๣⪏ᛶ⬟ࡢᕪ␗࡟㛵ࢃࡿྍ⬟ᛶࢆ♧၀㸬⤖ᯝ≧ែኚᩘ㛫㞄᥋⾜ิ

F

PC9GR

PC9

Time

ࡑࡢ௚ࡢ஦౛

ືⓗⓎ⌧ண ࣔࢹࣝ㸦ࢩ࣑࣮ࣗࣞࢩࣙࣥ㸧-­  ஙࡀࢇ⣽⬊ MCF7 ⸆๣่⃭ࢹ࣮ࢱ࠿ࡽࡢ⸆๣ᛂ⟅ࣃࢫ࢙࢘࢖᥎ᐃ
-­  HUVEC データからの遺伝子／モジュールネットワーク推定㑇ఏᏊ㛫㞄᥋⾜ิ-­  ࣓ࣛࣀ࣮࣐⣽⬊ /Paclitaxel ࡟ࡼࡿ᪂つᢠ⒴๣ࢱ࣮ࢤࢵࢺࡢྠᐃ

 D FD

AcknowledgmentsணศᕸComputational time was provided by the Super Computer System, Human Genome
Center, Institute of Medical Science, The University of Tokyo, RIKEN Integrated
Cluster of Clusters (RICC) system, and early access to the K computer at the RIKEN
Advanced Institute for Computational Science.

P( yn | y1 ,…, yn 1;; )Reference
Yamaguchi	  and	
  Higuchi.	
  State-­‐space	
  approach	
  with	  the	  ŵĂǆŝŵƵŵůŝŬĞůŝŚŽŽĚƉƌŝŶĐŝƉůĞƚŽŝĚĞŶƟĨǇƚŚĞƐǇƐƚĞŵͲŐĞŶĞƌĂƟŶŐƟŵĞĐŽƵƌƐĞŐĞŶĞĞǆƉƌĞƐƐŝŽŶĚĂƚĂŽĨǇĞĂƐƚ͘/Ŷƚ:ŽĨĂƚĂDŝŶŝŶŐĂŶĚŝŽŝŶĨŽƌŵĂƟĐƐ͕ϭ;ϭͿ͗ϳϳͲϴϳ͕;ϮϬϬϲͿ͘
Yamaguchi	
  et	
  al.	
  Finding	  module-­‐based	
  gene	  networks	  ǁŝƚŚƐƚĂƚĞͲƐƉĂĐĞŵŽĚĞůƐͲDŝŶŝŶŐŚŝŐŚͲĚŝŵĞŶƐŝŽŶĂůĂŶĚƐŚŽƌƚƟŵĞͲĐŽƵƌƐĞŐĞŶĞĞǆƉƌĞƐƐŝŽŶĚĂƚĂ͘/^ŝŐŶĂůWƌŽĐĞƐƐŝŶŐDĂŐĂǌŝŶĞ͕Ϯϰ;ϭͿ͗ϯϳͲϰϲ;ϮϬϬϳͿ͘,ŝƌŽƐĞĞƚĂů͘ůƵƐƚĞƌŝŶŐǁŝƚŚƟŵĞĐŽƵƌƐĞŐĞŶĞĞǆƉƌĞƐƐŝŽŶƉƌŽĮůĞƐĂŶĚƚŚĞŵŝǆƚƵƌĞŽĨƐƚĂƚĞƐƉĂĐĞŵŽĚĞůƐ͘'ĞŶŽŵĞ/ŶĨŽƌŵĂƟĐƐ͕ϭϴ͗ϮϱϴͲϮϲϲ͕;ϮϬϬϳͿ͘,ŝƌŽƐĞĞƚĂů͘^ƚĂƟƐƟĐĂůŝŶĨĞƌĞŶĐĞŽĨƚƌĂŶƐĐƌŝƉƟŽŶĂůŵŽĚƵůĞͲďĂƐĞĚŐĞŶĞŶĞƚǁŽƌŬƐĨƌŽŵƟŵĞĐŽƵƌƐĞŐĞŶĞĞǆƉƌĞƐƐŝŽŶƉƌŽĮůĞƐďǇƵƐŝŶŐƐƚĂƚĞƐƉĂĐĞŵŽĚĞůƐ͘ŝŽŝŶĨŽƌŵĂƟĐƐ͕Ϯϰ͗ϵϯϮͲϵϰϮ͕;ϮϬϬϴͿ͘zĂŵĂŐƵĐŚŝĞƚĂů͘WƌĞĚŝĐƟŶŐĚŝīĞƌĞŶĐĞƐŝŶŐĞŶĞƌĞŐƵůĂƚŽƌǇƐǇƐƚĞŵƐďǇƐƚĂƚĞƐƉĂĐĞŵŽĚĞůƐ͘'ĞŶŽŵĞ/ŶĨŽƌŵĂƟĐƐ͕Ϯϭ͗ϭϬϭͲϭϭϯ͕;ϮϬϬϴͿ͘ŽĞƚĂů͘ǆƉůŽƌŝŶŐƚĞŵƉŽƌĂůƚƌĂŶƐĐƌŝƉƟŽŶƌĞŐƵůĂƟŽŶƐƚƌƵĐƚƵƌĞŽĨƐƉĞƌŐŝůůƵƐĨƵŵŝŐĂƚƵƐŝŶŚĞĂƚƐŚŽĐŬďǇƐƚĂƚĞƐƉĂĐĞŵŽĚĞů͘
D'ĞŶŽŵŝĐƐ͕ϭϬ͗ϯϬϲ͕;ϮϬϬϵͿ͘zĂŵĂŐƵĐŚŝĞƚĂů͘EĞƚǁŽƌŬͲďĂƐĞĚƉƌĞĚŝĐƟŽŶƐĂŶĚƐŝŵƵůĂƟŽŶƐďǇďŝŽůŽŐŝĐĂůƐƚĂƚĞƐƉĂĐĞŵŽĚĞůƐ͗ƐĞĂƌĐŚĨŽƌĚƌƵŐŵŽĚĞŽĨĂĐƟŽŶ͕193:ŽĨŽŵƉ^ĐŝĂŶĚdĞĐŚ͕Ϯϱ;ϭͿ͗ϭϯϭͲϭϱϯ͕;ϮϬϭϬͿ͘dĂŵĂĚĂĞƚĂů͘^ŝ'EͲ^^D͗ŽƉĞŶƐŽƵƌĐĞƉĂƌĂůůĞůƐŽŌǁĂƌĞĨŽƌĞƐƟŵĂƟŶŐŐĞŶĞŶĞƚǁŽƌŬƐǁŝƚŚƐƚĂƚĞƐƉĂĐĞŵŽĚĞůƐ͘ŝŽŝŶĨŽƌŵĂƟĐƐ͕Ϯϳ;ϴͿ͗ϭϭϳϮͲϭϭϳϯ͕;ϮϬϭϭͿ͘dĂŵĂĚĂĞƚĂů͘^ŝ'E͗ůĂƌŐĞͲƐĐĂůĞŐĞŶĞŶĞƚǁŽƌŬĞƐƟŵĂƟŽŶĞŶǀŝƌŽŶŵĞŶƚĨŽƌŚŝŐŚƉĞƌĨŽƌŵĂŶĐĞĐŽŵƉƵƟŶŐ͘'ĞŶŽŵĞ/ŶĨŽƌŵĂƟĐƐ͕Ϯϱ;ϭͿ͗ϰϬͲϱϮ;ϮϬϭϭͿ͘ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-­7:  SBiP䝕䞊䝍ゎᯒ⼥ྜ䝥䝷䝑䝖䝣䜷䞊䝮㛤Ⓨ㈐௵⪅: ᐑ㔝ᝅ (ᮾி኱Ꮫ ་⛉Ꮫ◊✲ᡤ 䝠䝖䝀䝜䝮ゎᯒ䝉䞁䝍䞊)
SBiP  㛤Ⓨ䝯䞁䝞䞊  :  㛗ᓮ  ṇᮁ䠈㛵㇂  ᘺ⏕䠈ᩧ⸨  䛒䜖䜐䠈ᒣཱྀ  㢮䠈஭ඖ  Ύဢ䠈ᓥᮧ  ᚭᖹ䠈᪂஭⏣  ཌ䠈⋢⏣  ჆⣖䠈ᐑ㔝  ᝅ  SBiP  䛿䚸䝕䞊䝍ゎᯒ⼥ྜ䜰䝥䝸䜿䞊䝅䝵䞁㛤Ⓨ䝏䞊䝮䛷㛤Ⓨ䛧䛯ᢏ⾡䛸䝥䝻䜾䝷䝮䛾䛖䛱䚸≉䛻  SiGN䚸LiSDAS  䜢ゎᯒ䝟䜲䝥䝷䜲䞁䛾䝁䞁䝫䞊䝛䞁䝖䛸䛧䛶䝴䞊䝄䛜⡆༢䛻฼⏝䛷䛝䜛㧗ᶵ⬟  GUI  䜢ഛ䛘䛯䝋䝣䝖䜴䜵䜰䝥䝷䝑䝖䝣䜷䞊䝮䜢䝴䞊䝄䛻ᥦ౪䝋䝣䝖䛷䛒䜛䚹ி䛻⏝ព䛥䜜䛶䛔䜛䝆䝵䝤䝇䜿䝆䝳䞊䝸䞁䜾䝅䝇䝔䝮䛸㐃ᦠ䛧䚸ி䛻䛶ྛゎᯒ䝟䜲䝥䝷䜲䞁䛾ฎ⌮䛾୍㒊䜢ᐇ⾜䛧䚸䛭䛾⤖ᯝ䜢䚸
SBiP  䛾どぬ໬䝁䞁䝫䞊䝛䞁䝖⩌䜢⏝䛔䛶ಖᏑ䛷䛝䜛䜘䛖┠ᣦ䛧䛶䛔䜛䚹  䝴䞊䝄䛿  SBiP  䛻⏝ព䛥䜜䛶䛔䜛䛥䜎䛦䜎䛺ゎᯒ䝁䞁䝫䞊䝛䞁䝖䜢⤌䜏ྜ䜟䛫䛶䜹䝇䝍䝬䜲䝈䛧䛯ゎᯒ䝣䝻䞊䜢ᐇ⾜ྍ⬟䛷䛒䜛䚹౛䛘䜀SiGN-­SSM䚸  SiGN-­L1䚸SiGN-­BN  䛺䛹䛾㑇ఏᏊ䛾Ⓨ⌧᝟ሗ䛛䜙䝛䝑䝖䝽䞊䜽䜢᥎ᐃ䛩䜛䝁䞁䝫䞊䝛䞁䝖⩌䜢ி䛾ୖ䛷ᐇ⾜䛧䚸䛭䜜䜙䛾⤖ᯝ䜢䝴䞊䝄䛻ᩚ⌮䛧どぬ໬䛧䛶⾲♧䛩䜛⤫ྜゎᯒ䝥䝷䝑䝖䝣䜷䞊䝮䛸䛺䜛ணᐃ䛷䛒䜛䚹  SBiP  (Systems  Biology  integrative  Pipeline)  䝕䞊䝍䛾ᆺ   䝣䝻䞊   䝁䞁䝫䞊䝛䞁䝖  ⤫ྜゎᯒ  
Step  1  -­‐  2:  Set  Time-­‐Series  Data  Step  1  –  1:  Set  CSML  and  Parameter  Search  Space    CSML  ⣽⬊ෆ䛾཯ᛂ䜢䝛䝑䝖䝽䞊䜽䛸䛧䛶⾲⌧䛧䚸䝅䝭䝳䝺䞊䝅䝵䞁䚸䜎䛯どぬ໬䛷䛝䜛䝕䞊䝍ᵓ㐀  Java  

⾜ิ䚸䝕䞊䝍䝣䝺䞊䝮䛺䛹䛾䝕䞊䝍ゎᯒ䛻ᚲせ䛺  Java  䛷⾲⌧䛥䜜䜛䝕䞊䝍ᵓ㐀  Graph  R  Step  4:  Results  and  Parameter  Distributions  Step  2:  Link  CSML  with  Time-­‐Series  Data  䝕䞊䝍ྠ໬䛻䜘䜛䝰䝕䝹᥎ᐃ  MIX  ༢⣧䛺䜾䝷䝣ᵓ㐀  ⤫ィゎᯒ䝋䝣䝖  R  䛾䝕䞊䝍ᵓ㐀  ୖグ䛾䝕䞊䝍䛜㞟䜑䜙䜜䛯䝕䞊䝍ᵓ㐀  㑇ఏᏊไᚚ䛾ẚ㍑ゎᯒ䛾౛  (䛣䛣䛷䛿㠀ᖖ䛻ᑡ䛺䛔㑇ఏᏊᩘ䜢ᅗ♧)  Step  3:  Run  Data  Assimilation  SBiP䛷䛿䚸⣽⬊䞉⏕࿨䝥䝻䜾䝷䝮䛾⤫ྜⓗ⌮ゎ䛾䛯䜑䛻᪂つᶵ⬟䜢ᑟධ䛧䚸GUI䛸⤫ྜ䛩䜛䛣䛸䛷䚸ᐇ㦂䝕䞊䝍䛸䝁䞁䝢䝳䞊䝍ゎᯒ䜢䝅䞊䝮䝺䝇䛻䛴䛺䛞䚸⣽⬊䝅䝇䝔䝮䛾ゎᯒ䜢䝴䞊䝄䛜⡆༢䛻⾜䛖䛣䛸䛜䛷䛝䜛䝋䝣䝖䜴䜵䜰䝥䝷䝑䝖䝣䜷䞊䝮䜢┠ᣦ䛧䛶䛔䜛䚹ᶵ⬟䛧䛶䛔䜛㒊ศ䛻䛴䛔䛶䛿䚸䝗䜻䝳䝯䞁䝔䞊䝅䝵䞁ཬ䜃ᑟධ䞉฼⏝ἲ䛾ㄝ᫂䠄ⱥᩥ䞉࿴ᩥ䠅䜢ᩚഛ䛧䚸බ㛤䛧䛶䛔䜛䚹    http://sbip.hgc.jp/  䛆ලయⓗ䛺㛤Ⓨෆᐜ䛇      ᪂つᶵ⬟䛾㏣ຍ䛸䝴䞊䝄䝡䝸䝔䜱䛾ྥୖ䜢䛿䛛䛳䛯䚹  Cell  Illustrator  Online  䛻䜘䜛䝅䝭䝳䝺䞊䝅䝵䞁ゎᯒ  㑇ఏᏊ䝛䝑䝖䝽䞊䜽᥎ᐃ䞉⣽⬊ᶵᗎゎᯒ  䠍䠊SBiP䛾ゎᯒ䝁䞁䝫䞊䝛䞁䝖䛻ᑐ䛧䛶䚸ྛ✀䛾⾲♧䞉⟶⌮䜴䜱䞁䝗䜴䜢㛤Ⓨ䛧䚸ᐇ⾜᫬䛻䛚䛡䜛䝴䞊䝄䝡䝸䝔䜱䜢ྥୖ䛥䛫䛯䠄䝟䜲䝥䝷䜲䞁䝣䝻䞊ไᚚ䝇䜽䝸䝥䝖タᐃ䞉ᐇ⾜ᶵ⬟䚸䝕䝣䜷䞊䝹䝖್䛾
preferenceᶵ⬟䚸ᐇ⾜᫬䜶䝷䞊䝺䝫䞊䝖ᶵ⬟䚸ධฟຊ䝟䝷䝯䞊䝍್䛾ᐇ⾜᫬ᣦᐃᶵ⬟䚸➼䠅䚹䝽䞊䜽䝣䝻䞊䛾ᥥ⏬䞉ྍど໬䝷䜲䝤䝷䝸䞊JGraph䛾㧗ᶵ⬟໬䛻క䛔SBiP䛾䝁䞊䝗䜢኱ᖜ䛻ಟṇ䛧䚸どぬⓗ䛻䜟䛛䜚䜔䛩䛔䝣䝻䞊సᡂ䜢ᐇ⌧䛧䛯䚹䜎䛯䝁䞁䝫䞊䝛䞁䝖䛾䝟䝑䜿䞊䝆໬䛻䜘䜚SBiP䛾䝎䜴䞁䝻䞊䝗᫬㛫䜢▷⦰䛷䛝䜛䜘䛖䛻䛧䛯䚹SBiP䛿Java䛷㛤Ⓨ䛥䜜䛶䛔䜛䛜䚸⤫ィィ⟬䛸䜾䝷䝣䜱䝑䜽䝇䛾䛯䜑䛾ゝㄒR䜢୙⮬⏤䛺䛟౑䛘䜛䜘䛖䛻䝋䝣䝖䜴䜵䜰䜢ᩚഛ䛧䛯䚹䜎䛯䚸䝕䞊䝍ゎᯒ䜔䝛䝑䝖䝽䞊䜽ゎᯒ䞉ẚ㍑ゎᯒ䛺䛹䛾䝁䞁䝫䞊䝛䞁䝖䜢᪂䛯䛻㏣ຍ䛧䛯䚹䝅䝭䝳䝺䞊䝅䝵䞁䝒䞊䝹Cell  Illustrator䛾ᨵⰋ䛻䜘䜚䝴䞊䝄䝡䝸䝔䜱䛾ྥୖ䜢ᅗ䛳䛯䚹  䠎䠊SBiP䜰䝥䝸䜿䞊䝅䝵䞁䝃䜲䝖䞉䝦䝹䝥䝃䜲䝖䜢సᡂ䛧䚸Java  Web  Startᶵ⬟䜢౑䛳䛶㉳ື䛩䜛online∧䚸zip䝣䜯䜲䝹䜢ᒎ㛤䛧䛶䝻䞊䜹䝹䛷ᐇ⾜䛩䜛offline∧䛜฼⏝䛷䛝䜛䜘䛖䛻䛧䛯䚹  㑇ఏᏊ䝛䝑䝖䝽䞊䜽᥎ᐃ䞉⣽⬊ᶵᗎゎᯒ  䠏䠊SBiP䜢⏝䛔䛶䝕䞊䝍ゎᯒ䝟䜲䝥䝷䜲䞁䜢ᵓ⠏䛧䚸䝕䞊䝍ゎᯒ䜢ᐇ᪋䛧䛯䠄Gefitinibឤཷᛶ⫵䛜䜣⣽⬊ᰴ䛸⪏ᛶᰴ䛾䝛䝑䝖䝽䞊䜽ẚ㍑䚸ங䛜䜣⣽⬊ᰴ䛾䝸䜺䞁䝗ᛂ⟅᫬⣔ิ䝕䞊䝍䜢౑䛳䛯䝛䝑䝖䝽䞊䜽᥎ᐃἲ䛾ẚ㍑䚸➼䠅䚹  EGF่⃭ୗGefitinibᢞ୚ୗ䛻䛚䛡䜛Gefitinibឤཷᛶᰴ䠄ᕥᅗ䠅䛸⪏ᛶᰴ䠄ྑᅗ䠅䛾㑇ఏᏊ䝛䝑䝖䝽䞊䜽  194ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-8: LiSDAS⏕࿨య䝕䞊䝍ྠ໬䝥䝻䜾䝷䝮㛤Ⓨ㈐௵⪅㻦 ᵽཱྀ▱அ 㻔⤫ィᩘ⌮◊✲ᡤ䝕䞊䝍ゎᯒ⼥ྜ◊✲㛤Ⓨ䝏䞊䝮㻕䕿ᩪ⸨ṇஓ ྜྷ⏣ு ୰㔝ៅஓ 㛗ᑿ኱㐨 㻔⤫ィᩘ⌮◊✲ᡤ䞉䝕䞊䝍ྠ໬◊✲㛤Ⓨ䝉䞁䝍䞊㻕ూବz ᐇ㦂䛻䜒䛸䛵䛟䝍䞁䝟䜽㉁㛫┦஫స⏝䜔㌿෗ไᚚᅉᏊ䛾ാ䛝䛻䛴䛔䛶䛾▱㆑䛾䜏䛛䜙䚸⌧㇟䜢෌⌧䛩䜛ຊᏛ䝰䝕䝹䜢ᵓᡂ䛩䜛䛣䛸䛿ᮍ䛰ᅔ㞴䛷䛒䜛䚹z 䛣䛾ᅔ㞴䛾᰿ᮏⓗ䛿䚸⣽⬊㛫䛾ಶయᕪ䛾䛯䜑䛻ィ ್䛾ㄗᕪ䛜኱䛝䛔䛣䛸䜔䚸䛚䛭䜙䛟ᐇ㦂䛷᳨ド䛥䜜䛶䛔䛺䛔཯ᛂ㐣⛬䛜Ꮡᅾ䛩䜛䛯䜑䛷䛒䜛䛸⪃䛘䜙䜜䜛䚹z 䛭䛣䛷䚸LiSDAS䛷䛿䚸⏕యෆศᏊ䛾ィ 䝕䞊䝍䜢ཧ↷್䛸䛧䛶䛒䛯䛘䜛䛣䛸䛷 䝅䝭䝳䝺䞊䝅䝵䞁䛾෌⌧ᛶ䞉ண್䛸䛧䛶䛒䛯䛘䜛䛣䛸䛷䚸䝅䝭䝳䝺䝅䝵䞁䛾෌⌧ᛶ ணຊ䜢ᨵၿ䛩䜛䛯䜑䛾䝟䝷䝯䞊䝍䝏䝳䞊䝙䞁䜾䜔䝰䝕䝹䛾ᨵⰋ䜢䝧䜲䝈⤫ィ䛾ᯟ⤌䜏䛷ᐇຠ䛩䜛䚹ਙચ೾৒ਙચ೾৒

哺乳動物の概日周期転写ネットワーク
z 䝰䝕䝹䛾つᶍs 䝅䝇䝔䝮ኚᩘ䛾ಶᩘ: 29s ほ ኚᩘ䛾ಶᩘ: 7s 䝰䝕䝹䝟䝷䝯䞊䝍ᩘ: 116z ほ 䜢䝖䝺䞊䝇䛩䜛䝟䝷䝯䞊䝍䛜Ⓨぢ䛷䛝䜛䛣䛸䜢☜ㄆ䚹
䖃

ほ

䝕䞊䝍

䞊 ᭱⤊ⓗ䛺MAPゎ䞊 MAPゎ䛾᭦᪂ᒚṔ३५ॸ঒ଡਛ

ଁੑ৓ঔॹঝেਛ
(ম৬धभੌ੢॑ਫ਼ୈ)

হ৐ীഘ
ଡਛஓ

ほ
䝕䞊䝍

s ㌿෗ไᚚᅇ㊰
ศゎ

䝰䝕䝹

ॹॕঞॡঞ
೴়ૌங

ᨵⰋ
䝰䝕䝹

䝰䝕䝹
㌿෗άᛶ

㌿෗ᢚไ

(ト䠖ி䛿⌧ᅾ㛤Ⓨ୰䛷䛒䜚䚸䛣䜜䜙䛾ᩘᏐ䛿⌧≧䛾್䛷䛒䜛)」ྜయ⏕ᡂి৷হ୻ཡऋ॒಍ཝभఇදૢ௦

dxj/dt = -dkj + tckjA(xa(j)| cj, pj)R(xr(j)| dj, qj) + bkxb(j,1)xb(j,2)( ||c,, p) = xp/(x( p + cp), R(x( ||d,, q) = cq/(x( q + dq)s A(x䝅䝭䝳䝺䞊䝅䝵䞁䞉䝰䝕䝹䛾ᵓᡂᡭ⥆䛝䝅䝭 䝺 䝅 䞁 䝕䝹䛾ᵓᡂᡭ⥆䛝s yi,n:᫬Ⅼn䛷䛾㑇ఏᏊi䛾⃰ᗘ (i = 1,…,17654)
s xj,n:᫬Ⅼn䛷䛾j␒┠䛾₯ᅾኚᩘ䚹Ⓨ⌧䝟䝍䞊䞁䛜ఝ䛶䛔䜛㑇ఏᏊ䛾䜽䝷䝇䝍 (j = 1,…, 46 (௵ព))
s ほ ⾜ิ H = (hij)䛾᥎ᐃs yi,n = hij xj + İi, İi䡚 N(䞉|0,wi), xj,n 䡚 N(䞉|0,vj)
s 㠀㈇ᑐゅ໬᮲௳:ѩk.(hik > 0ҍѦj.(j  k э hij = 0))s ₯ᅾኚᩘୖ䛾䝛䝑䝖䝽䞊䜽䜢Ỵ䜑䜛a(j), r(j)䛾᥎ᐃs dx
d j/dt = -dkj + tckjA(xA( a(j)| cj, pj)R(x)R( r(j)| dj, qj)
s ྛi 䛻䛴䛝, ṇ㈇䛭䜜䛮䜜䛻,䝖䝺䞁䝗䛾┦㛵䛜᭱䜒㧗䛔䜒䛾䜢, 䛭䜜䛮䜜a(j), r(j) 䛸䛩䜛䚹s 䝰䝕䝹䝟䝷䝯䝍 ș = (xj(0), dkj, ckj, bk, cj, pj)䛾ᖹᆒ䞉ศᩓsほ᫬⣔ิ D = (yi,n)ੑ઴ুਢऌ

ॹॕঞॡঞ೴়ૌங॑ਹ৷खञঃছও‫ॱش‬௓৒੝ଐऔोञীഘ

ʌ2ʌ

ほ 䝕䞊䝍 㻰

ʌ3

3ș

1
2ș

ʌ1

1ș

1

1

ᑬᗘ䛻䜘䜛㔜䜏௜䛡३঑গঞ‫ش‬३ঙথधৰୡभૻຎ

ᑬᗘ䛾㧗䛔Ⅼ䛾࿘㎶䛛䜙䝃䞁䝥䝸䞁䜾䛩䜛s D: ほ 䝕䞊䝍s ș,ʌ:
ș 䝰䝕䝹䝟䝷䝯䞊䝍䝰䝕䝹䝟䝷䝯 䝍 (཯ᛂ㏿ᗘ,(཯ᛂ㏿ᗘ ㌿෗ᅉᏊ䛧䛝䛔್)s “a 䡚 b”: ศᕸb䛛䜙䝃䞁䝥䝸䞁䜾䛧䛶a䛻௦ධ
For i = 1,…, N [N 䡚106]s For j = 1,…, m: ș(j) 䡚 p(䞉|ʌ*). [m =128]
s w* = j p(D|ș(j))/m. [p(D|ʌ(i)) § w* 䛾㏆ఝ䛻౑䛖]s If i ӌ n then [ಖᣢ䛩䜛 ʌ 䛾ಶᩘ n =128]s ʌ(i) := ʌ*, w(i) := w*.
s ʌ* 䡚 G0(䞉). [LiSDAS䛜๓ฎ⌮䛷タィ]s Elses ș* 䡚 j p(D|ș(j))į(ș - ș(j))/m.
s ʌ* 䡚 p(ʌ| ʌ(1),…, ʌ(n), ș*)s ҃ 1/(Ȗ + n)㽢i p(ș*|ʌ(i))į(ʌ - ʌ(i))
s+ Ȗ/(Ȗ + n)㽢p(ș*|ʌ) G0(ʌ).
s If ѩk Ѯ{1,…,n}. p(D|ʌ(k)) < p(D|ʌ*)thens ʌ(k) := ʌ*, w(k) := w*.

ఇදິਙઙ䝃䞁䝥䝸䞁䜾
ʌi

G0(ʌ)ৢৢଞऋ॒಍ཝ

੦ೲ೾২ G0(ʌ)

ㅰ㎡ி䛷䛾ィ⟬䛻㛵䛧䛶䛿ி㏿䝁䞁䝢䝳䞊䝍ி䛾ヨ㦂฼⏝䚸䛚䜘䜃ᮏᖺ3᭶ 䛷䛾≉ู㐠⏝䛷䛾⤖ᯝ䛷䛩䚹䜎䛯䚸PC䜽䝷䝇䝍䛷䛾ᛶ⬟ィ 䛻㛵䛧䛶䛿⌮໬Ꮫ◊✲ᡤ᝟ሗᇶ┙䝉䞁䝍䞊䛾RICC䜢䛧䛶䛿⌮໬Ꮫ◊✲ᡤ᝟ሗᇶ┙䝉䞁䝍
䛾RICC䜢 ౑⏝䛧䛶䛔䜎䛩䚹౑⏝䛧䛶䛔䜎䛩

૞અધ൴

Yoshida R, et al., (2010) Bayesian experts in exploring reaction
kinetics of transcription circuits, Bioinformatics, 26(18), i589-595.
Yoshida R, et al. (2010) Bayesian learning in sparse graphical factor
MODELS VIA VARIATIONAL MEAN FIELD annealing, Journal of Machine
Learning Research, 11:1771-1798.
Yoshida R, et al. (2008) Bayesian learning of biological pathways on
genomic data assimilation, Bioinformatics, 24(22):2592-2601.

195ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

D-9: MEGADOCK¥abTÎêÚÆtÔÐÅêÇkDÞèÇäáMEGADOCK·S´5O
Sr d¥Y.¥HB0F"$"$6 N0$X[Zl]0$+9¥¥¥¥¥¥¥¥¥C/QN WPs!"

MEGADOCKは、タンパク質立体構造に基づいて細胞内のÎêÚÆtU

タンパク質を網羅的にドッキングすることで、タンパク

網羅的ドッキング

質間相互作用ネットワークを予測するプログラムである。
我々は、剛体ドッキングの際に、形状相補性について従P1

OK·I¼
相互作用予測

P2

P3

P4

P5

来と比べて単純化することで計算量を減じた評価モデル（rPSCモデル）を新規に提案した。さらに静電相互作用も導入した評価関数を実装し、高速フーリエ変換（FFT）
により効率的に計算することを可能とした。また、OpenMPとMPIを用いたハイブリッド並列化を行うことで、大規模並列計算を可能にした。
標準的なベンチマークデータセットや実問題としてバクテリアの走化性の問題に適用し、タンパク質間ネット

P1<iËÉ¿:£·&h $
  &+ ,  
=   +i

ワーク予測精度としてそれぞれ F値0.43, 0.45を得た。



京速コンピュータ「京」において、12,288ノードで良好な並列化効率 (ストロングスケーリング0.96)を得た。今後は肺がんに関連の深いEGF受容体シグナル伝達系を対象に2,000 x 2,000級の組み合わせのドッキング問題にチャレ
ンジする。



ÔÐÅêÇl]·ÜèëÏâëÓ

ZDOCK=  +i



=





=



+i





çÌÞÎë)åÄêÔß¿åËÓ·o¸v¸ß¿1çÌÞÎë0 åÄêÔ0

MPI¶¹»çÌÞÎë)åÄêÔß¿:æëÞnkDß¿2çÌÞÎë1 åÄêÔ1çÌÞÎë)åÄêÔß¿ß¿3çÌÞÎë2 åÄêÔ2* *#!
22 +,çÌÞÎë

åÄêÔuj3:æëÞîuj3·åÄêÔl]

highR¸v¸l]

OpenMP¶¹»

Score

ËÉ¿·]ìwí`E·ÍëÓ`E·m

Trypsinogen andtrypsin inhibitor(PDB id : 1CGI)

)©º

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low

`E·

§¨³·n

5Op-).

1×ëÔ³·ËÈëäÛåÑÀ¥ìËçÐÔíe¬¾zì ^í·| #ÖÐÓéëÆ¶-®»ÎêÚÆtc½*q´­¯abT K¥

01//

978
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7ËçÐÔ&g>¶0ËçÐÔ&g>·_4)53·x3

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143

/
0

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 90/7&* '0·ÔÐÅêÇ+2/&g­¯1,

2

3

4

5

6

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e¬¾fz| #ìB"(~X['8í¶®»ÎêÚÆtÒëÎ

約1///$1///級のドッキング計算を実施
新規相互作用の発見を目指す

183ÎêÚÆt
ì_04//\ Gyí

7/&///ノード利用の場合
全計算を約半日で終了する見込み7#

ËÓèêÇËÈëåêÇì #ï! "!ï "ï "Green: MEGADOCK 2.1
predicted ,Red: X-ray structure)µ­

03///
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  %
 $&

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ì386\ Gy,0)3時間$1&///$1&///)7/&///ノード
901(4時間

ØÁÝåÐÔí

×ëÔ¤É¿ïÉ¿½{7ÏãëÕêÇp

0.96

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0.92¥l]>

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5&033 01&177

#
18&271+#! ) ,J,:ML]4°
J,:MèëÔÅâÐÊã¿ÆÌË4°

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


¢£¡ ¢£¡ ¢£¡ ¢£¡ ¢£¡ ¢£¡ ¢£¡ ¢£¡ 謝辞
結果の一部は、理化学研究所が実施している
京速コンピュータ「京」の試験利用によるものです。
「京」における計測値は;·ÊËÑá¶¹»?%Tµ:³®¦ì1/0025&8&00@lKíᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)%1(67㼀㼛㼣㼍㼞㼐㼟㻌㼎㼞㼍㼕㼚㻌㼟㼕㼙㼡㼘㼍㼠㼕㼛㼚㼟㻹㼍㼞㼗㼡㼟㻌㻰㼕㼑㼟㼙㼍㼚㼚㻌㻔㻯㼛㼙㼜㼡㼠㼍㼠㼕㼛㼚㼍㼘㻌㼍㼚㼐㻌㻿㼥㼟㼠㼑㼙㼟㻌㻺㼑㼡㼞㼛㼟㼏㼕㼑㼚㼏㼑㻘㻌㻵㻺㻹㻙㻢㻘㻌㻾㼑㼟㼑㼍㼞㼏㼔㻌㻯㼑㼚㼠㼑㼞㻌㻶ü㼘㼕㼏㼔㻘㻌㻳㼑㼞㼙㼍㼚㼥㻾㻵㻷㻱㻺㻌㻮㼞㼍㼕㼚㻌㻿㼏㼕㼑㼚㼏㼑㻌㻵㼚㼟㼠㼕㼠㼡㼠㼑㻘㻌㼃㼍㼗㼛㻌㻯㼕㼠㼥㻘㻌㻿㼍㼕㼠㼍㼙㼍㻘㻌㻶㼍㼜㼍㼚㻕
NEST is a simulator for large biological neuronal networks. Small systems of differential equations describe
individual neurons which are coupled to form networks of natural size and complexity. The code runs on avariety of computer architectures: from single- and multi-core desktop computers to large clusters and HPC
facilities such as K and JUGENE. NEST is subject to continuous development driven by the needs of theneuroscience community. These efforts are coordinated by the NEST Initiative (www.nest-initative.org), aninternational collaboration of several research institutes.
In the course of the Next-Generation Supercomputing Project we adapted NEST to systems withmore than 10,000 cores enabling the simulation of full size networks at cellular resolution:s
Model of memory and optimizations
Spike-timing and neuromodulation dependent
plasticity
new implementation of storage for neurons
NEST can simulate models of(solid blue) and synapses (solid orange) employs
synaptic plasticity depending on
sparse representation
 activity of pre- and postreduced memory consumption (dashed: old impl.)
synaptic neurons
enables efficient distribution on M 10000 cores presence of neuromodulators
about 1000
neurons
per core

Morrison A et al. (2007) Neural Comput. 19, 1437-1467
Potjans W et al. (2010) Front Comput Neurosci 4:141
Potjans W et al. (2011) PLoS CB 7(5): e1001133

about 1000
neurons
per core

Kunkel et al. (2011)(submitted)

Model of the local microcircuit
1 mm2 cortex with layer- andcell type specific connectivity
reproduces layer-dependent
rate (A, B) and irregularity (C),and propagation

NEST implements the MUSIC interface
NEST supports the MUSIC interface toCommunicate with other simulatorsdistributed simulators A, B, and C
exchange of data during runtime

3 times larger network
than on Jugene in same timeenables multi-simulator models

Potjans et al. (2011) (submitted)
Potjans et al. (2011) arXiv:1106.5678v1
Wagatsuma et al. (2011)
Front. Comput. Neurosci. 5:31
Lindén et al (2011) Neuron (in press)

Djurfeldt M et al. (2010) Neuroinformatics 8(1):43-60

High degree of parallelization

Compositionality in networks of synfire chains 3㽢106 neurons

high-capacity embedding of speedup Į=0.9@16384 cores

synfire chains hybrid OpenMP/MPI code

propagation probability p(h) achieved phase III on K

depends on # waves hactivity settles at critical point

Plesser HE et al. (2007)
Springer-Verlag LNCS 4641, 672-681
Kunkel S et al. (2009) Front. Neuroinform.
doi:10.3389/conf.neuro.11.2009.08.044
Trengove (2011) (submitted)

Roadmap (22 Feb. 2010) and outlook
Step Description

Network type

Nrns.

Syn.

1

reduced primate visual cortex(32 areas 㽢 1 local microcircuit)random at micro andmacro scale

3㽢10

3㽢101 technically achieved on
0
K on 28.Aug 2011

㻙 㼏㼔㼍㼞㼍㼏㼠㼑㼞㼕㼦㼑㻌㼟㼠㼍㼠㼕㼛㼚㼍㼞㼥㻌㼟㼠㼍㼠㼑㻦㼞㼍㼠㼑㻘㻌㼕㼞㼞㼑㼓㼡㼘㼍㼞㼕㼠㼥㻙 㼏㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼛㼒㻌㼘㼛㼣㻙㼒㼞㼑㼝㼡㼑㼚㼏㼥㻱㻱㻳㻌㼍㼚㼐㻌㻸㻲㻼㻌㼠㼛㻌㼑㼤㼜㼑㼞㼕㼙㼑㼚㼠㼟

2

like 1, but respecting relative
sizes of areas (32) and distancedependent connectivity

spatially organized at 108
micro scale1012㼏㼍㼜㼍㼎㼕㼘㼕㼠㼥㻌㼠㼛㻌㼟㼕㼙㼡㼘㼍㼠㼑㻌㼒㼡㼘㼘㻌㼟㼏㼍㼘㼑㻌㼙㼛㼐㼑㼘㻌㼛㼒㻌㼍㻌㼒㼡㼚㼏㼠㼕㼛㼚㼍㼘㻌㼡㼚㼕㼠3

human brain model (approx. 100
areas)

spatially organized6< 1011 < 1015

197

Status Nov 2011

- technically possible
with current code- tests in progress

Expected results㼍㼟㼟㼑㼟㼟㼙㼑㼚㼠㻌㼛㼒㻌㼍㼎㼕㼘㼕㼠㼥㻌㼠㼛㻌㼟㼕㼙㼡㼘㼍㼠㼑㻌㼒㼡㼘㼘㻌㼟㼏㼍㼘㼑㻌㼙㼛㼐㼑㼘ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)%&RUWLFDO0LFURFLUFXLW'HYHORSHGRQ1(67኱つᶍ୪ิィ⟬䛷᥈䜛኱⬻ᒁᡤ⚄⤒ᅇ㊰䛾ᶵ⬟㻌㛤Ⓨ㈐௵⪅㻦㻌 ῝஭᭸ᶞ㻌㻔⌮໬Ꮫ◊✲ᡤ㻌 ⌮໬Ꮫ◊✲ᡤ  ḟୡ௦ィ⟬⛉Ꮫ◊✲㛤Ⓨ䝥䝻䜾䝷䝮  ⬻⚄⤒⣔◊✲䝏䞊䝮,  ⌮◊⬻䝉䞁䝍䞊䚸䝅䝙䜰䝏䞊䝮䝸䞊䝎㻕㻌㻌 㻌㻌䞊 どぬὀព䛾䝅䝭䝳䝺䞊䝅䝵䞁 䞊⫼ᬒ“WHERE”  pathwayཷᐜ㔝⬻ᶵ⬟䜢⏕䜏ฟ䛩⚄⤒ᅇ㊰䝯䜹䝙䝈䝮䜢ゎ᫂䛩䜛䛣䛸䛜䚸⬻◊✲䛾ୡ⏺ⓗ₻ὶ䛻䛺䛳䛶᮶䛴䛴䛒䜛䚹䛭䛾䜘䛖䛺◊✲䛾᪂䛧䛔ᡭẁ䛸䛧䛶䚸኱つᶍ୪ิ䝅䝭䝳䝺䞊䝅䝵䞁䛜䚸ᙉຊ䛺ᡭἲ䜢୚䛘䜛ྍ⬟ᛶ䛻ὀ┠䛥䜜䛶䛔䜛䚹"౛䠖 Human  Brain  Project  (EU䛾13◊✲ᶵ㛵䛜㐃ᦠ)

“WHAT”  pathway┠ⓗ኱つᶍ⚄⤒ᅇ㊰䛾୪ิ䝅䝭䝳䝺䞊䝅䝵䞁ᢏ⾡䜢㛤Ⓨ䛧䚸䛭䜜䜢⏝䛔䛶㟋㛗㢮䛾⬻䛷≉䛻Ⓨ㐩䛧䛶䛔䜛኱⬻⓶㉁6ᒙᒁᡤᅇ㊰䛾䝰䝕䝹䜢ᵓ⠏䛧䚸䛭䛾ィ⟬ཎ⌮䛻㏕䜛䠊

䖃どぬ㔝䠄V2/V4)䛾⣽⬊䛻䛿䚸ど㔝ෆ䛾ᢸᙜ㡿ᇦ䠄ཷᐜ㔝䠅䛜䛒䜛䜰䝥䝻䞊䝏᭱᪂䛾ゎ๗Ꮫⓗ◊✲䞉㟁Ẽ⏕⌮Ꮫᐇ㦂䛾▱ぢ䜢ྲྀ䜚ධ䜜䛶䚸ᒁᡤ⚄⤒ᅇ㊰䛾ᵓ㐀䜢Ỵᐃ䛩䜛䚹ᙧែᵓ㐀䜢ᣢ䛯䛺䛔䝙䝳䞊䝻䞁䝰䝕䝹䜢᥇⏝䛧䚸䝅䝘䝥䝇䛻䛴䛔䛶䛿༑ศ䛺ᩘ䛸ྍረᛶ䜢☜ಖ䛩䜛䚹ᵓ⠏䛧䛯䝰䝕䝹䜢⬻ᶵ⬟䛾ㄝ᫂䛻㐺⏝䛧䚸⛉Ꮫⓗ᭷ຠᛶ䜢᳨ド䛩䜛䚹䖃ྛ⣽⬊䛤䛸䛻ዲ䛝䛺่⃭䛾᪉ྥ䛜䛒䜛䖃ὀព䜢ྥ䛡䜛䛣䛸䛷⣽⬊䛾ᛂ⟅䛜ኚ໬䛩䜛
;ZĞǇŶŽůĚƐĞƚĂů͕͘ϭϵϵϵͿ

ィ⟬஦౛㻝WƌĞĨĞƌƌĞĚ

䖃ὀព䜘䜛どぬ⚄⤒⣽⬊䛾ᛂ⟅ኚ໬䛾䝅䝭䝳䝺䞊䝅䝵䞁

EŽŶͲƉƌĞĨĞƌƌĞĚ

ϴϬ͕ϬϬϬ>/&ͲŶĞƵƌŽŶƐ

‫ׅ‬ែȢȇȫᲢ㻸㻞㻛㻟Ღ㻸㻡Უ ƸŴ᬴ܱưᙸǒǕǔදॖƷјௐǛǑƘϐྵƢǔ㻚ŽŶŶĞĐƚŝǀŝƚǇ

ůĞĐƚŽƌŽƉǇƐŝŽůŽŐǇ

ŝŶǌĞŐŐĞƌĞƚĂů͘ϬϰdŚŽŵƐŽŶĞƚĂů͘ϬϮȢȇȫƸ㻸㻠ƴ᧙ƠƯŴီƳǔኽௐǛʖᚕ㻚;WŽƚũĂŶƐΘŝĞƐŵĂŶŶ͕ϮϬϭϭͿィ⟬஦౛㻞䝅䝭䝳䝺䞊䝅䝵䞁ᐇ㦂䞊 ✵㛫ὀព䛸≉ᚩὀព䛾䝅䝭䝳䝺䞊䝅䝵䞁 䞊
Wagatsuma,  Potjans,  Sakai,  Diesmann,  Fukai≉ᚩὀពExc-­Exc(Martinez-­Trujillo  &  Treue,  2004;;  Ling,  Liu  &  Carrasco,  2009)
Exc-­Inh✵㛫ὀព(McAdams  &  Maunsell,  1999;;  Ling,  Liu  &  Carrasco,  2009)⏕≀Ꮫⓗ௜ຍ౯್䛾௜୚㻌㻌ᢚไ⣽⬊㛫䛾㟁Ẽⓗ䝅䝘䝥䝇䝇䝟䜲䜽᫬㛫౫Ꮡ䛾ྍረᛶ㻔㻿㼀㻰㻼㻕㻌䛾ᐇ⿦tpre

Bi  and  Poo  (1998)  

spre

ti

໬Ꮫⓗ䝅䝘䝥䝇䛸㟁Ẽ䝅䝘䝥䝇䛜ඹᏑÆ ຠ⋡ⓗ䛺䝅䝭䝳䝺䞊䝅䝵䞁䛜ᅔ㞴

Vi _ V jneuron  id=spre+dsyn -­ti
spre

㏻ಙ㈇Ⲵ䜢ᢚ䛘䛺䛜䜙ィ⟬ຠ⋡䜢☜ಖ䛧䛯䛔tneuron  j

spost

neuron  i't tpost  tpre

NEST  =  event-­driven  simulatort+dsynneuron  j
dsyn㟁Ẽ䝅䝘䝥䝇䛛䜙ὶධ䛧䛯㟁ὶ䛾㐣ཤ䠄ࠥ1ms䠅 䛾ᖹᆒ್䜢⏝䛔䚸ᮍ᮶䛾㟁఩ኚ໬䜢ண᝿䝇䝟䜲䜽䜹䞊䝛䝹㛵ᩘ

Igarashi,  Diesmann,  Fukai
㐣ཤ

tpost
Inh  1

ᮍ᮶ண᝿䝇䝟䜲䜽Ⓨ⏕V⥺ᙧ㏆ఝ䛧䛶䝕䞊䝍㌿㏦

Inh  2

No

V

Potjans,  Morrison,  Diesmann  (2010)

t
ඹྠ◊✲⪅

஬༑ᔒ ₶ 䠄ḟୡ௦ィ⟬⛉Ꮫ/ 㟁Ẽ䝅䝘䝥䝇䛚䜘䜃NESTᐇ⿦䠅

NEST

Markas Diesmann༙⏣ 㧗ྐ 䠄ḟୡ௦ィ⟬⛉Ꮫ/ ᒁᡤᅇ㊰ᐇ㦂䝕䞊䝍཰㞟䠅

Tobias  C  Potjans

ᡃጔ ఙᙪ 䠄Ꮫ᣺PD/ どぬὀព䛾ᒁᡤᅇ㊰䝰䝕䝹䠅Moritz  Heliasㅰ㎡Gen  Masumoto198 ி䛿⌧ᅾ㛤Ⓨ୰䛷䛒䜚䚸䝇䝷䜲䝗୰䛾ᩘᏐ䛿⌧≧䛾್䛷䛒䜛䚹 ிୖ䛾ᛶ⬟䛻㛵䛧䛶䛿ி䝁䞁䝢䝳䞊䝍䛾ヨ㦂฼⏝䚸䛚䜘䜃≉ู㐠⏝䛻䜘
䜛䚹㛤Ⓨ䛚䜘䜃㐠⏝䛻䛒䛯䜛㛵ಀྛ఩䛻ឤㅰ⏦䛧ୖ䛢䜛䚹䛸䛟䛻ிୖ䛾ᐇ⿦䛻䛴䛔䛶䛿䚸ISLiM㧗ᗘ໬䝏䞊䝮䛾༠ຊ↓䛟䛧䛶䛿ᅔ㞴䛷䛒䛳䛯䚹ឤㅰ⏦䛧ୖ䛢䜛䚹

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

B -­3:   VMS
඲どぬ⣔䝰䝕䝹䛻䜘䜛どぬ᝟ሗฎ⌮䛾ゎᯒ㻔どぬ⣔䝅䝭䝳䝺䞊䝅䝵䞁䛾䛯䜑䛾ඹ᭷䝥䝷䝑䝖䝣䜷䞊䝮㻕㛤Ⓨ㈐௵⪅: ⮻஭ᨭᮁ (⌮໬Ꮫ◊✲ᡤ⬻⚄⤒⣔◊✲㛤Ⓨ䝏䞊䝮㻕
網膜

外界像

眼球(眼光学)ढ़ওছभঞথ६‫؞‬ইज़‫ش‬ढ़५ਃચप
ৼਊ‫؛‬ణশ૎২્ਙ‫ؚ‬ణએઽ୷‫ؚ‬ቊሠभ્ਙऩन॑અൟखਗੀभිჍ൸॑ੑ

網膜ढ़ওছभইॕঝ঒पৼਊ‫؛‬ਗੀभිჍ

眼光学൸ऋ‫ؚ‬ऒऒदਗ਼ਞਦಀप૗ఌऔोॊ‫؛‬

網膜像ිჍद૪৶औोञੲਾमଳઋ৽॑ஂख
थઋ৽५ঃॖॡपेढथଳಁຜସष஫

઴

୸औोॊ

数理モデルでみると。。。

近視＋乱視

近視補正

Strong

乱視補正

Weak

入力イメージ

眼球運動制御眼球運動制御網膜細胞の応答५ঃॖॡधम‫ء‬ໍ৑৓प঱ಊ‫ৣ؞‬ఋघॊਗ਼ਞਦಀभ૗৲दधऋढञణ
஄पऩॊ‫؛‬౼৔दम‫ؚ‬ऒो॑ुधपੲਾभृॉ਄ॉऋ
ষॎोथःॊ

ଳಁ௺‫ܠ‬ढ़ওছ଺୼৬पढ़ওছ॑਱ऐॊਃચपৼਊ‫؛‬

視覚皮質প౼ຜସदੳ௙औोञੲਾपৌखथ‫ؚ‬ිჍऊै஫ॎॊઋ৽५ঃॖॡ॑ुधप

ৄञः্਱षଳ଍॑୎घਦಀऋ౼ୌ৖

खथ੟৬ृବ౦‫ؚ‬ऽञजभ౦॑ੳ௙ख

ীदੑ઴औोथःॊ‫؛‬ৰ੠म৬૎खथ

ञॉ‫ؚ‬ੳ௙खञੲਾ॑ਹ৷खथষ৿॑

ःऩः‫ুؚ‬ஷोपৼਊघॊेअऩ๶৵

യघ‫؛‬ঔঀ॑ৄॊৃ়म‫ؚ‬ଳ଍ऋ਑౪

視覚皮質ऩ৿ऌऋ੎ਏऩ௮ऌ॑घॊ

৸ଳಁ௺ঔॹঝभଡണ

औोॊଳಁ௺भঔॹঝ

঄ॺभଳಁम‫ؚཽ‬ૉ‫ිؚ௺৾୾ؚཽ‬Ⴭ‫ؚ‬౼ຜସपेॉଡਛऔोथउॉ‫ؚ‬ਗੀभੲਾम‫ؚ‬ऒोैभ৖ਜ਼द੗మಽ৓प૪৶औोॊऒधद‫ؚ‬੟भੳ௙ृଳ଍भ୎৿धःढञਃચऋৰ
ਠऔोथःॊ‫؛‬३ ঑গঞ‫ش‬३ঙথदम‫ؚ‬োৡॖও‫ش‬४पৌखथ‫ؚ‬जोझोभ৖ਜ਼

౼ਃચभ  णदँॊଳಁ‫ुَؚ‬भ॑ৄॊُऒध॑ॱ‫ش‬।ॵॺप‫ؚ‬ଳಁਃચऋन
भेअप౼दৰਠऔोथःॊऊ॑ੰ৥घॊ‫؛‬भলৡ ઋ৽ૢ௦ಉ‫ك‬भ૭ଳ৲‫ੰؚ‬ෲ॑ষः‫ؚ‬ଳಁਃચभੰ৥॑ਤीॊ‫؛‬఻৬৓प୦॑घॊभ‫ء‬ཽૉઈ৿‫ිؚ৾୾ؚཽ‬Ⴭऩैलपଳಁຜସऊैऩॊ৸ଳಁ௺॑ถ಍ऩপૠெঔॹ
ঝधखथଡണख‫ؚ‬ர‫ر‬भଳಁਠ଴॑३঑গঞ‫ش‬३ঙথपेॉੰෲघॊ

入力
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૚१ঈ३५ॸ঒ঞঋঝ 0LFUR ध৸ଳಁ௺ঞঋঝ 0DFUR भ௴ਡऊैଳ
ಁਠ଴पउऐॊଳಁ௺भ૽સ॑ੰ৥घॊ

眼光学モデル

網膜画像
網膜神経節細胞の応答

網膜画像
X  type  (OFF,  ON) Ytype  (OFF,  ON)

眼球運動生成
モデル

視線の位置

網膜モデル

神経活動

視覚皮質の応答 (V1,MT)१ঈঔॹঝଡണध 3/$72 पेॊଁ়

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視覚皮質
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視細胞 (Cone Mosaic)
2

0

10

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number  of  process

10

4

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視覚皮質
2

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external image

retinotopic map model (V1 or MT)

Response for white
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intensity

Response for yellow(light gray)
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入力

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Response for blue (dark gray)intensity

Response for black
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注視点制御
モデル

Processing  time  (sec)Processing  time  (sec):  接続部  :  数理モデル:  信号の流れ

Processing  time  (sec)ૃऽढथःॊ઺൸ऩ
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॥থॺছ५ॺपৌघॊଳ
ಁຜସ಍ཝૢ௦प჻ଳੴ
ಁपৌૢघॊੲਾऋ਀ਠ
औोथःॊऒध॑ંྮ

ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)¹

B-4: NeuroMorphoKit

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mapping  䝅䝘䝥䝇⤖ྜྍ⬟ᛶ䛾᥎ᐃ  䝅䝘䝥䝇⤖ྜ  ᙉᗘ᥎ᐃ  

⬻䜢䛴䛟䜛⚄⤒⣽⬊䠄䝙䝳䞊䝻䞁䠅䛾ᙧ䜔䛿䛯䜙䛝䛿᪻⹸䞉့ங㢮䜢䛸䛚䛧䛶ඹ㏻䛰䛜䚸⚄⤒⣔䛾つᶍ䛿ᑠ䛥䛔䚹␗✀ឤぬ⤫ྜ㐃ྜᏛ⩦ᐃ఩⾜ື䛺䛹⎔ቃ䛻㐺ᛂ䛩䜛䛯䜑඘ศ䛺」㞧䛺⾜ື䜢⾜䛖䚹⚄⤒䛾⏕⌮䞉ᙧែグ㘓  䛭䛾䛯䜑㻌 ᪻⹸⬻䛿ឤぬ䛛䜙⾜ື䜎䛷䛾⬻䛾᝟ሗฎ⌮䜢䝅䝭䝳䝺䞊䝅䝵䞁䛩䜛䛯䜑䛾䝰䝕䝹⣔䜢䛧䛶ඃ䜜䛶䛔䜛䚹䜹䜲䝁䜺䛾䝙䝳䞊䝻䞁䝕䞊䝍䝧䞊䝇䠄BoND䠅
䛸䝬䝹䝏䝇䜿䞊䝹ィ   ᇶ♏᪉⛬ᘧ  

ᙧែᢳฟ䝋䝣䝖;<EtZŝd䠅ᶆ‽⬻ᵓ⠏䝋䝣䝖䠄ƌĂŝŶŽŵƉŽƐĞƌ䠅䝅䝭䝳䝺䞊䝅䝵䞁๓䛾䝕䞊䝍䛾ὶ䜜  

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5000ಶ䛾⚄⤒⣽⬊䠄ྛ5000  䝁䞁䝟䞊䝖䝯䞁䝖䠅䜢0.025msec༢఩䛷        䝸䜰䝹䝍䜲䝮䛻ィ⟬䛩䜛䛻䛿䚸⣙300  TFLOPSᚲせ  䞉⌧ᅾ䛿᭱኱22.5  TFLOPS䠄8192
䝜䞊䝗᫬䠅䜢㐩ᡂ  䝅䝭䝳䝺䞊䝅䝵䞁䜢⏝䛔䛯⚄⤒䝟䝷䝯䞊䝍᥎ᐃㅰ㎡ி䛿⌧ᅾ㛤Ⓨ୰䛷䛒䜚䚸䝇䝷䜲䝗୰䛾ᩘᏐ䛿⌧≧䛾್䛷䛒䜛䚹䞉ᮏ䝋䝣䝖䜴䜵䜰䛾㛤Ⓨ䛻㝿䛧䛶䛿䚸ி㏿䝁䞁䝢䝳䞊䝍ி䛾ヨ㦂฼⏝䚸⌮໬Ꮫ◊✲ᡤ᝟ሗᇶ┙䝉䞁䝍䞊䛾RICC䚸ᮾி኱Ꮫ᝟ሗᇶ┙䝉䞁䝍䞊䛾
T2K䜢⏝䛔䛯䚹ிୖ䛾ᐇ⿦䛻䛚䛡䜛ISLiM㧗ᗘ໬䝏䞊䝮䚸≉䛻⯖ᮏẶ䚸኱㔝Ặ䛾䝃䝫䞊䝖䛻ឤㅰ䛩䜛䚹  
  
  

202

ଝᖓμᏯ⇝∅∋−∞⇝∍∙᧏ႆ⇧∞∆ᅕ߃ʰ࠯‒ ɳங᣻‫∝^∝⇮⇽⇌⇵ ‒ܨ‬ԗɟ‒ ьඑჷ്‒ ᭗ߢᎫ‒ ആʟ࣓ͤ‒ ‫ޥ‬உჷᬐ˱ᕲᨗ࠯‒ ܷஜ‫ٻ‬᠒‒ ౕӐʰ‒ ဋณྸӪᕲҾ᠗Ӫ‒ ޻ެᛮ‒ ˱ᕲᅵʶ
൷᣼ᒍМ‒ ჽʟग़ࡻ‒ ૼဋ͒‫܇‬ᙱ߷ᢷ‫ݱ ‒܇‬௎ʰ‫ݱ ‒ࢠ֒ެޛ ‒ ‒ ‒ٽ‬ன‫ࢀޒ‬‫ݱ‬᣼޽ᨙʂ‒ ‒ ʞҗߕӴ᠗ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)+FSSPG㻌኱つᶍ୪ิ⏝㻹㻰䝁䜰䝥䝻䜾䝷䝮㻌㛤Ⓨ㈐௵⪅㻦㻌 ኱㔝ὒ௓㻌㻔⌮໬Ꮫ◊✲ᡤ⏕࿨యᇶ┙䝋䝣䝖䜴䜵䜰㛤Ⓨ䞉㧗ᗘ໬䝏䞊䝮㻕㻌㻌 㻌㻌

ABSTRACT  We  report  all-­atom  simulations  of  molecular  crowding,  an  early  result   from  the   ³.   FRPSXWHU´   which   is   a   10-­PFLOPS   supercomputer   under  construction   in   Japan.   The   K   compute   nodes   consist   of   8-­core   SPARC64  VIIIfx   processors   attached   to   dedicated   routers   in   a   six-­dimensional  mesh/torus.   Using   27,648   such   nodes,   we   observed   1.3   PFLOPS   (37.5%  of  peak)  during  a  180  million-­atom  simulation.  We  discuss  the  performance  and   scaling   of   molecular   dynamics   simulations   on   ³.´   as   well   as  macromolecular  crowding  at  atomic  resolution.  

  Living  cells  are  crowded  because  macromolecules  comprise  ~30%  of  their  molecular  weight.  Recently,  the  effects  of  crowded  cellular  environments  on  protein   stability   have   been  revealed   through   in-­cell   NMR   spectroscopy.   To  study   these   effects,   we   performed   all-­atom   classical   molecular   dynamics  simulations  of  two  systems:  target  proteins  in  solvent  and  target  proteins  in  an  environment  of  molecular  crowders  that  mimic  the  conditions  of  a  living  cell.    Table  1.  FLOPS  efficiency  on  the  kernel  loop  Model  Number  of  atoms  Cutoff  radius  Number  of  pairs  per  atom  FLOP  counts  for  1,000   steps    Kernel  FLOPS  per  thread  SIMD%  FLOP  counts  per  atom  pair  Figure  1.  Section  of  the  K  computer.  Visible  are  four  computer  racks  with  384  nodes.  

,QYLWUR  418,707  28  Å  +  2  Å  margin  8,835(  +  2,031   in  margin)  469,564,792MFLOP  7,088  MFLOPS  (44.3%)  48.76%  103.2  FLOP  Table  4.  Conditions  of  the  simulation  for  the  peak  performance  Model  Number  of  atoms  Cutoff  radius  Number  of  pairs  per  atom    FLOP  counts  for  1,000   steps  Topology  of  computation  node  Number  of  nodes  Number  of  cores  Theoretical  peak  performance  Calculation  time  for  1,000  step  Sustained  performance  Efficiency  432  LQYLWUR  180,881,424  28  Å  +  2  Å  margin  8,835(  +  2,031   in  margin)  202,851,990,144   MFLOP  242448  27,648  
221,184  3.539  PFLOPS  154.29  sec  3)/236  
0.3716  tĞĂŬƐĐĂůŝŶŐ

Table  2.  Time  spent  in  blocks  in  millisecond  per  step  for  weak  scaling  (418,707  atom/64  nodes)  #  of  nodes  1node  #  of  atoms  1atom  Total7(1node)  Force  calculation    Communication  Other  

64  418,707  152.84  128.21  20.19  4.45  

256  
1,674,828  
153.66  128.21  20.99  4.47  

512  3,349,656  
153.34  128.36  20.50  4.48  

4096  
26,797,248  
153.88  128.34  20.87  4.67  

13824  90,440,712  153.92  128.06  21.21  4.64  

27648  180,881,424  154.29  128.15  21.55  4.60  

Table  3.  Time  spent  in  blocks  in  millisecond  per  step  for  strong  scaling  (1,674,828  atoms)  
256  
936  
26  
153.66  128.21  20.99  4.47  

512  
768  
44  
84.63  61.78  19.96  2.89  

2048  
504  124  28.57  15.42  11.75  1.40  

4096  446  174  18.98  8.33  
9.55  
1.10  

16384  342  342  13.58  2.68  
9.96  
0.94  

1.2

^ƚƌŽŶŐƐĐĂůŝŶŐ

vit1
viv
vit2 (amber)1

RMSF (Å)#  of  nodes  #  of  import  cells  #  of  communi-­cation  nodes  Total  Force  calculation  Communication  Other  

Figure   2.   Weak   scaling   for   6,542   atoms/node.  
Constant   wall   clock   times/step   show   the  perfect  scaling.  
Ratios  of  force  calculation/communication/the   others   are   also  constant,  and  force  calculation  is  dominant.  

0.8

0.6

Figure   6.   Vicinal   area   of   TTHA   for   the   LQ YLYR   system   (viv).   The  TTHA  molecule  is  drawn  using  the  ribbon  model,  and  ovalbumin  molecules   are   drawn   using   the   space-­filled   model.   Each  ovalbumin  molecule  is  colored  differently.  

Figure  3.  Strong  scaling  for  the  system  of  1,674,828  atoms.  

0.4

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20

30

40

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50

60

Figure  5.  Conformational  fluctuations  of  TTHA  for  LQ YLYR   (viv)   and   LQ YLWUR   systems   (vit1,   vit2).   The  green,  red,  and  blue  curves  indicate  the  RMSFs  of  TTHA   for   LQ YLYR   and   LQ YLWUR   systems,  respectively.   The   abscissa   axis   is   the   residue  number   of   TTHA   and   the   ordinate   axis   is   the  RMSF  value   (in   Angstrom).   Red   and   white   boxes  indicate  alpha-­helices  and  beta-­sheets,  respectively.  

^ĐŝĞŶƚŝĨŝĐƌĞƐƵůƚƐACKNOWLEDGMENTS  This   work   was   partially   supported   by   the   Integrated   Simulation   of   Living   Matter   Project,  commissioned  by  the  Ministry  of  Education,  Culture,  Sports,  Science  and  Technology,  Japan.  A  part   of   the   calculations   in   this   work  were   done   for   the   performance   evaluation   process   on   the  development   of   the   Next-­Generation   Supercomputer.   We   thank   the   Next-­Generation  Supercomputer   R&D   Center,   especially   Kazuo   Minami   and   Masaaki   Terai   (in   the   Application  Development   Team)   for   their   outstanding   support.   We   appreciate   Mikio   Hondou   and   Hikaru  Inoue,  from  Fujitsu  Co.  Ltd.,  for  their  optimization  support.  We  also  thank  the  RIKEN  Integrated  Cluster   of   Clusters   (RICC)   for   the   computer   resources   used   for   our   code   development   and  calculations.  We  gratefully  acknowledge  the  exceptional  support  provided  by  the  late  Takayuki  Shigetani   at   RIKEN   Advanced   Center   for   Computing   and   Communication   (ACCC).   We   also  thank  Toru  Takinaka  at  NEC  Informatec  Systems,  Ltd.  for  the  supporting  code  development.  

203ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)

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ᰴ਎ઍ↢๮૕⛔วࠪࡒࡘ࡟࡯࡚ࠪࡦ࠰ࡈ࠻࠙ࠚࠕߩ⎇ⓥ㐿⊒ (ISLiM) ᚑᨐႎ๔ળ 2011  (2011.12.21-­22)+9/69/㻌ྜᡂྍ⬟䛺໬Ꮫᵓ㐀䛚䜘䜃཯ᛂ䝇䜻䞊䝮䛛䜙䛺䜛኱つᶍ௬᝿䝷䜲䝤䝷䝸㻌㛤Ⓨ㈐௵⪅㻦㻌 ⯪ὠබே㻌㻔ᮾி኱Ꮫ㻌 ኱Ꮫ㝔ᕤᏛ⣔◊✲⛉㻕㻌㻌 㻌㻌㸯㸬⫼ᬒ  
䞉  ๰⸆䛾➨୍Ṍ䛿᭷ᮃ䛺䝸䞊䝗໬ྜ≀䛾Ⓨぢ䛷䚸䝝䜲䝇䝹䞊䝥䝑䝖䝇䜽䝸䞊䝙䞁䜾䠄HTS䠅䛜㔜せ䛺ᙺ๭䜢ᯝ䛯䛧䛶䛝䛯䚹䛧䛛䛧䚸HTS䛷᭷ᮃ䛺䝸䞊䝗໬ྜ≀䛜ぢ䛔䛰䛫䛺䛔䜿䞊䝇  㻌 䜒䛒䜚䚸䛭䛾᥈⣴ຠ⋡䛸䝁䝇䝖䛜ၥ㢟䛸䛺䛳䛶䛔䜛䚹  䞉  ⌧ᅾ䛿䚸䝁䞁䝢䝳䞊䝍ᨭ᥼䝞䞊䝏䝱䝹䝇䜽䝸䞊䝙䞁䜾䛻䜘䜛䝸䞊䝗໬ྜ≀᥈⣴䛜  ᥎㐍䛥䜜䛶䛚䜚䚸䛭䛾䛯䜑䛾䚸་⸆ရ䛻䛺䜚䛖䜛㉁䛾㧗䛔௬᝿໬ྜ≀⩌䛛䜙䛺䜛  ኱つᶍ䛺  㻌 䝞䞊䝏䝱䝹䝷䜲䝤䝷䝸䛾ᵓ⠏䛜ᮃ䜎䜜䛶䛔䜛䚹㻌 㻌 㻌 㻌   
䞉  䝞䞊䝏䝱䝹䝇䜽䝸䞊䝙䞁䜾䛻䛚䛔䛶䚸㧗䝇䝁䜰䛾䝸䞊䝗໬ྜ≀⩌䜢ᚓ䜙䜜䜛䛛䛹䛖䛛䛿䚸䝞䞊䝏䝱䝹䝷䜲䝤䝷䝸䜢ᵓᡂ䛩䜛௬᝿໬ྜ≀⩌䛾㉁䜔ከᵝᛶ䚸つᶍ䛺䛹䛻ᕥྑ䛥䜜䚸  㻌 ་⸆ရ㛤Ⓨ䛾ᡂྰ䛻኱䛝䛺ᙳ㡪䜢୚䛘䜛䚹⥲䛨䛶䚸䝞䞊䝏䝱䝹䝷䜲䝤䝷䝸䛾㉁䛸㔞䛜㔜せ䛺ព࿡䜢ᣢ䛴䛸䛔䛘䜛䚹  䞉  ୍᪉䚸䝇䜽䝸䞊䝙䞁䜾⤖ᯝ䚸㧗䝇䝁䜰䛾䝸䞊䝗໬ྜ≀⩌䜢⤠䜚㎸䜣䛷䜒䚸ᩥᏐ㏻䜚䝞䞊䝏䝱䝹䛷䛒䜛䛜ᨾ䛻䚸ᑐ㇟໬ྜ≀䜢ධᡭ䛩䜛䛯䜑䛾ྜᡂ᳨ウ䛻ከ኱䛺䝁䝇䝖䜢せ䛩䜛䚹  㻌 䛣䛾ၥ㢟䛻ᑐ䛩䜛ゎỴ⟇䛜ᚲせ䛸䛥䜜䛶䛔䜛䚹  㸰㸬┠ⓗ  

㸲㸬ࢩࢫࢸ࣒ࡢᵓᡂ  䠍.      䝸䞊䝗໬ྜ≀䛸䛺䜚䛘䜛௬᝿໬ྜ≀⩌䛛䜙䛺䜛䚸            㻌 㻌 㻌 㻌 㻌 㻌 㻌   㻌 㻌 㻌     ኱つᶍ䛺໬ྜ≀䝞䞊䝏䝱䝹䝷䜲䝤䝷䝸䠄VL䠅䛾ᵓ⠏    䠎.      VL䛻ྵ䜎䜜䜛௬᝿໬ྜ≀䛾ከᵝᛶ䜢☜ಖ䛧䚸  㻌 㻌 㻌 㻌 㻌 㻌 㻌 㻌 㻌 ௬᝿໬ྜ≀䛾ྜᡂ䝹䞊䝖䛻㛵䛩䜛᝟ሗᥦ౪䜢ྍ⬟䛸䛩䜛  㸱㸬᪉ἲ  
䐟  ໬Ꮫ཯ᛂ䝇䜻䞊䝮䛻⾲⌧䛥䜜䜛䝸䜰䜽䝍䞁䝖䛸䝥䝻䝎䜽䝖䛻䛴䛔䛶䚸  㻌 㻌 䛭䜜䛮䜜䛾཯ᛂ㒊఩᝟ሗ䛸ᵓ㐀≉ᚩ䛾ኚ໬䛛䜙グ㏙䛥䜜䜛transform䜢            ཯ᛂ䝕䞊䝍䝧䞊䝇䛛䜙ᢳฟ䞉㞟⣙䛩䜛䚹    䐠  ධຊ䛥䜜䛯䝗䝷䝑䜾䝷䜲䜽䛺䝍䞊䝀䝑䝖ᵓ㐀䛻ᑐ䛧䛶䚸䛣䛾transform䜢㐺⏝䛧䚸  㻌 㻌 䝍䞊䝀䝑䝖ᵓ㐀䜢ᚓ䜛䛯䜑䛾ྜᡂ๓㥑య䜢ᥦ᱌䛥䛫䚸⵳✚䛩䜛䚹    䐡  ฟຊ䛥䜜䛯ྜᡂ๓㥑య䜢䚸෌ᗘ䝍䞊䝀䝑䝖ᵓ㐀䛸䛧䛶䝅䝇䝔䝮䛻෌ධຊ䛧䚸  㻌     ྜᡂ๓㥑య䜢ᥦ᱌䛥䛫䜛䛸䛔䛖ฎ⌮䜢⧞䜚㏉䛩䚹  ኱つᶍ࡞཯ᛂࢹ࣮ࢱ࣮࣋ࢫ࠿ࡽtransformࢆ⮬ືᢳฟࡍࡿࡇ࡜࡟ࡼࡗ࡚ࠊ     ከᵝ࡞཯ᛂ஦౛࡟㐺ᛂ࡛ࡁࡿtransformࢆከ㔞࡟⵳✚ࡋࠊ    ࡑࡢ㐺⏝࡟ࡼࡗ࡚ㄏᑟࡉࢀࡿ௬᝿໬ྜ≀⩌ࡢከᵝᛶࢆ☜ಖࡍࡿࠋ    䐢  ධຊᵓ㐀䛻ᑐ䛧䛶䚸཯ᛂண ᪉ྥ䛻䜒transform䜢㐺⏝䛧䚸཯ᛂ⏕ᡂ≀䜒ᥦ᱌  㻌 㻌 䛥䛫䚸䛣䜜䜢⧞䜚㏉䛧䛶䚸ྜᡂ䝹䞊䝖ᶞ䜢ᙧᡂ䛩䜛໬ྜ≀䝷䜲䝤䝷䝸䛸䛩䜛䚹  㸳.  transform  䞉    transform䛻䛿䚸཯ᛂ䝇䜻䞊䝮䜢ᵓᡂ䛩䜛ྛ໬ྜ≀䛾཯ᛂ㻌   㻌 ୰ᚰ᝟ሗ䛜཰⣡䛥䜜䜛䚹  㻌 㻌 ලయⓗ䛻䛿䚸཯ᛂ୰ᚰ䛾㒊ศᵓ㐀䛸䛭䛾࿘ᅖ䛾ᵓ㐀≉ᚩ  㻌 ᝟ሗ䚸⤖ྜ䛾ษ᩿㒊఩䚸⤖ྜḟᩘ䛾ኚ໬䛧䛯㒊఩䛸䛭䛾  㻌 ḟᩘ䚸᪂䛯䛻⏕ᡂ䛧䛯༢⤖ྜ䛾㒊఩䚸⬺㞳䛧䛯⨨᥮ᇶ䛾  㻌 ✀㢮䛸㒊఩䚸ຍ䜟䛳䛯⨨᥮ᇶ䛾✀㢮䛸䛭䛾఩⨨䛺䛹䚹  㸴Ⓨ⏕໬ྜ≀⩌ࡢ ᪂つᛶ

㸵ࣂ࣮ࢳࣕࣝࣛ࢖ࣈࣛࣜࡢᵓᡂ
Virtual  Library  10൨ᵓ㐀  

䞉1൨ᵓ㐀  䞉1൨3000୓䝇䜻䞊䝮  5൨ᵓ㐀    䞉㻌 䝅䝇䝔䝮䛻ධຊ䛥䜜䛯໬Ꮫᵓ㐀䛻䚸ྛtransform䛻  㻌 㻌 グ㏙䛥䜜䛯㒊ศᵓ㐀䛜ྵ䜎䜜䜛ሙྜ䛿䚸ヱᙜ㒊఩䜢  㻌 㻌 transformᵓ㐀䛻⨨䛝᥮䛘䛯཯ᛂ䝇䜻䞊䝮䜢ᥦ᱌䛩䜛䚹㻌   

㏫ྜᡂ  
✀ᵓ㐀  

໬ྜ≀DB  

㡰ྜᡂ  

཯ᛂ౛  
O

O

O

O

5൨ᵓ㐀  +

䠖䝸䞊䝗໬ྜ≀䛻䛺䜚䛘䜛໬ྜ≀  䠖䝸䞊䝗໬ྜ≀䛻䛺䜙䛺䛔໬ྜ≀  ᢳฟ䛥䜜䛯䝖䝷䞁䝇䝣䜷䞊䝮౛  䠖཯ᛂ䝇䜻䞊䝮ᵓᡂせ⣲  

1  

1  

6  

2  

+
3  
4  

5  

䡎  

2  

6  

3  

5  

࣭ࢩࢫࢸ࣒࡟໬ྜ≀DB࠿ࡽ✀ᵓ㐀ࡀධຊࡉࢀࡿ࡜ࠊ㏫ྜᡂᥦ᱌ࡢሙྜࡣࠊ✀ᵓ㐀ࢆྜᡂࡍࡿࡓࡵࡢ๓㥑య   ࢆⓎ⏕ࡋࠊ㡰ྜᡂᥦ᱌ࡢሙྜࡣࠊ  ✀ᵓ㐀ࢆฟⓎ≀㉁࡟ྵࡴ཯ᛂ⏕ᡂ≀ࢆⓎ⏕ࡍࡿࠋ   ࡑࡇ࡛ᚓࡽࢀࡓ໬ྜ≀ࢆ✀࡜ࡋ࡚ࠊࡇࢀࡽࡢฎ⌮ࢆ㡰ḟ⧞ࡾ㏉ࡋ࡚ࠊ໬ྜ≀ࣛ࢖ࣈࣛࣜࢆᵓ⠏ࡍࡿࠋ  ࣭▮༳ࡣ཯ᛂࢫ࣮࣒࢟ࢆ⾲ࡋࠊࡑࡢ୧➃ࡢ୸༳ࡣࠊྛ཯ᛂࢫ࣮࣒࢟ࢆᵓᡂࡍࡿ໬ྜ≀ࢆ♧ࡋ࡚࠸ࡿࠋ  ࣭Ⓨ⏕ࡋࡓ໬ྜ≀࡟ࡣࠊ཯ᛂࢫ࣮࣒࢟ࡢ⤒㊰᝟ሗࡀ௜୚ࡉࢀࠊྜᡂ⤒㊰᳨ウࡢ᭷ຊ࡞᝟ሗ࡜࡞ࡿࠋ  4  䕿༳䠖ษ᩿㒊఩㻌 㻌 㻌 㻌 䕿༳䠖෌⤖ྜ㒊఩  

9.  /LSLQVNL¶V  rule  of  5  ࢆᣦᶆ࡜ࡋࡓホ౯౛  㸶ᵓ㐀ฟຊ౛

ศᕸ䛾␗䛺䜛✀ᵓ㐀⩌䛾ά⏝᳨ウ  ࣭ୗ࡟ࠊ࠶ࡿ་⸆ရࢆtarget࡜ࡋࡓሙྜ࡟ࠊࡇࡢࢩࢫࢸ࣒ࡀᥦ᱌ࡍࡿྜᡂ๓㥑య౛ࢆ♧ࡋࡲࡍࠋ  ࣭ࡇࢀࡽࡢ໬ྜ≀ࡣࠊ  ධຊᵓ㐀㸦target㸧ࡢᇶᮏ㦵᱁ࢆ཯ᫎࡋ࡚࠸ࡿࡇ࡜ࡀ☜ㄆ࡛ࡁࡲࡍࠋ   ࡑࡢࡓࡵࠊⓎ⏕໬ྜ≀ࡢ≀⌮໬Ꮫⓗࠊཬࡧࠊ⸆⌮Ꮫⓗ࡞ᛶ㉁ࡶ࠶ࡿ⛬ᗘཷࡅ⥅ࡀࢀ࡚࠸ࡿ࡜᥎ ࡉࢀࡲࡍࠋ  ࣭་⸆ရ࡜ࡑࡢ࣮ࣜࢻ໬ྜ≀࡟ࡘ࠸࡚ࡣࠊᵓ㐀ⓗ࡞ᕪ␗ࡣᑠࡉࡃࠊඹ㏻ࡢ㦵᱁ࢆᣢࡘ㸯㸧࡜ࡉࢀ࡚࠸ࡲࡍࠋ  ࣭ࡼࡗ࡚ࠊࢻࣛࢵࢢࣛ࢖ࢡࢿࢫ࡞໬ྜ≀ࢆ✀ᵓ㐀࡜ࡋ࡚ࠊᙜᡭἲ࡟ࡼࡾᵓ⠏ࡉࢀࡓࣂ࣮ࢳࣕࣝࣛ࢖ࣈࣛࣜ࡟ࡣࠊ   ་⸆ရࡢ࣮ࣜࢻ໬ྜ≀࡜ࡋ࡚ࡢせồࢆ‶ࡓࡍࠊ㉁ࡢⰋ࠸໬ྜ≀ࡀໟྵࡉࢀ࡚࠸ࡿ࡜ᮇᚅࡉࢀࡲࡍࠋ     
  

  

  

ධຊᵓ㐀  

䠍䠅J.R.Proudfoot,   Bioorg.   Med.  Chem.  Lett.,  12,  1647(2002)  

206

(謝辞)
本資料集に記載されている「京」での計算は、2011 年 3 月の「京」の特別運用およびその後の試験利用によって行われたものです。
また、本資料集に記載されている「京」を使用した測定値は、開発整備中の「京」による、測定時点での数値です。