X Peta (1015)
Flops
20

15

Sequia(IBM)Titan(Cray)

Kei 京(Riken)

10HPCI2010

京= 1016

2011

2012

2013

Kei 京(Riken)(High performance of computer infra)

project

生命科学のパラダイムシフト
分子生物学

パラダイムシフト

生命動態システム科学

次世代DNAシークエンサー

組合わせ爆発厳密解析

網羅的アプローチ

遺伝子操作

生命の原理

“複雑動システムを制御するしくみ”

細胞状態（発生分化、病態など）の予測操作

要素の解析（要素還元的）動システムの解析
先端
構成論的解析
計測技術
要素反応を対象にした分子ターゲッティングによる操作

システム操作細胞操作

イノベーション
1細胞診断治療創薬
再生医療

病態予測治療工学応用

2

Press man: How is transportation of vesicles in cell controlled?
Prof. Yoshimori: No idea. If I had to answer, it would be doneby God.

輸送システム全体の制御
神

個々の輸送（要素過程）
人間複雑動システムのゆらぎ制御、細胞操作（脳、情報科学との融合）
1ピコワット細胞は細胞の研究だけでは理解できない？ 脳細胞

超大自由度
遺伝子発現
ネットワーク原子ネットワーク（組み合わせ爆発）

１ワット

複雑動ネットワーク

分化，発生
成長

環境に応じた細胞シグナリング、細胞運命の決定

細胞状態のゆらぎ

薬剤耐性菌

癌状態（アトラクター）
間のゆらぎ探索
状況にマッチした状態（アトラクター）の選択

Wi-Fi
LTE

複雑化により10年後には原子力発電機１０基以上必要

厳密制御の破綻

自発ゆらぎで小自由度（状態、アトラクター）への自律的落とし込み
分子構造のゆらぎ

情報ネットワーク

140億の神経細胞結合の組み合わせ（ほぼ無限大）

膨大な組み合わせQBiC

生物の原理（ゆらぎ制御）の応用
活動パターンから状態、意図のデコーディングとそのゆらぎ観測に成功！

脳のゆらぎを制御してアトラクターの選択を操作 運動など機能障害のリハビリに成功

状態系

状態の良さ

ゆらぎ

ゆらぎ方程式
電力消費千分の１、故障、変動に１０倍以上頑強（シミュレーションで検証
企業とテストベッドで実用試験へ）

状態（アトラクター）の決定

生物の原理（ゆらぎ制御）脳情報通信融合研究センター

5筋肉で働く分子モーターのしくみ
１分子ナノ計測組織（センチ）細胞（ミクロン）

分子（ナノ）
10億分の１ｍ

Simultaneous observation of
single ATP turnovers and mechanical steps

Too rapid to be observedby optical trap nanometry
Ishijima, et al. Cell 92 (1998)
分子モーター

Detection of substeps by scanning nanometry
A pair of photodiodes Nanometry
Probe
Evanescent field
Water
Glass
OilMyosin head

Evanescent field

Whisker

Displacement
Actin filament

Objective lensLaser inLaser out

Single-molecule imaging
Laser
Kitamura, Tokunaga, Iwane,…TY
Nature 397 ’99,
Biophyscs, 1, 2005
Okada et al. BBRC, 2007

Images of single myosin heads

Substeps in raising phases of displacements
1 ATP cycle

Displacement
Displacement

Expanded

Process
Process

0.1pN

1

Load dependence of substeps
High loads>1pN

Kitamura, Tokunaga, Iwane…TY, Biophysics 1 (2005)

Low loads<1pN
Expanded

2.0pN
max

Expanded
0.02-0.1 pN/nm

0.5pN
max

The stepsize (5.5nm) is unchanged but the dwell time increases
and the number of backward steps increases .ミオシンのブラウンステップ

MyosinActin

Movie

5.5 nmKitamura, Tokunaga, Iwane, et al
Nature 397 ’99,
Biophyscs, 1, 2006

ミオシンはストレインセンサーで一方向を選択
Rapidly (μs) move

ADPPi

Laser trap

Weak binding

Backward
ForwardADP

Myosin has astrain sensor

Pi

Weak Binding

ADP

Pi

X30
Attachment
enhanced

X5
Detachment
enhanced
ADP

Pi
ADP

Iwaki et al. Nature Chem. Biol. 2009
Nishikawa et al. Cell 2010detached

Strongly binding分子から筋肉へ
モデル、計算機シミュレーション
ミオシン分子アクチンフィラメント

ブラウンステップとストレインセンサーをベースにしたモデル‘57 Model

Attach - detach

Atteched state‘71 Model

Marcucci L, Yanagida T. PLoS One. 2012;7(7):e40042. doi:Marcucci L. & Yanagida T. Phys. Rev. E. 2013

Sliding speed (V/Vmax)

Adaptive force generation of muscle is explained byBrownian (stochastic) steps
Steady and transient
behaviors
LengthTensionTension (T/T0)
Oscillatory behavior

Brownian steps

Velocity of contraction at different external loads. Applied tension is reduced or increased with respect toisometric tension at a given time. Simulated Tension vs. Time traces are shown in the insert. Tensions arenormalized to the isometric tension, T0. For different tensions, velocities are calculated from the linear
portion of the shortening traces and normalized to the maximum velocity. The simulated velocities vs.
tensions (blue triangles) are compared with experimental data (black dots) from [Piazzesi G, Lombardi V(1996) J Muscle Res Cell Motil 17]. In the concentric region (T < T0), the simulations fit the experimental
data well even if the velocity is underestimated in the central part. Experimental data for the eccentricregion (T > T0) are not shown, but a qualitative correspondence can be observed with a plateau region athigh tensions.

Experiment
Our model

Predicted efficiency at different velocities. Simulation of the efficiency of contraction, as predicted by themodel compared to experimental data (circles from [Barclay CJ (1998) J Muscle Res Cell Motil 19],
green line). Efficiency is computed as tension times velocity divided by the chemical energy consumed(triangles) and following the method described in [Sekimoto K (1997) Journal of The Physical Society ofJapan 66] (squares).

Experiment
Our model

Fast tension recovery. Simulations and experimental data for a small and fast length step applied
to a muscle fiber in isometric contraction. The simulated tensions after the imposed
step, T1 (red triangles), and after actomyosin re-equilibration, T2 (blue triangles), are shown andcompared to the experimental results (circles; data from [Piazzesi G, Lucii L, Lombardi V (2002) J
Physiol 545]). All tensions are normalized with respect to isometric tension T0. Simulated Tension
vs. Time traces are shown in the insert, T2 is estimated by the tangent method.

A.F. Huxley Proposal
The biological molecular machine (myosin)
does not overcome but rather use Brownian motions(thermal noise), of which stochastic nature allows
adaptive operation of complex and dynamic system(muscle) without strict external control (self-organizedsystem, SOS).

A.F. Huxley diedon 30 May, 2012

分子から心臓の運動をシミュレーション
Continuum model
FEM (Finite Element Method)Time step size : 2.5ms

分子の集合

UT heart

筋肉細胞の束Fluid-Structure interaction
FEM analysis

超大規模計算心臓

１兆 ｘ１００万 個
分子モーター
分子モーターUT heart
HPCI project
Hierarchical integrated simulation for predictive medicine GroupMulti-scale, multi-physics cardiac simulation Team(Tokyo Univ. Grad. of Frontier Sciences)

Toshiaki Hisada

Takumi Washio

Seiryo Sugiura

Jun-ichi Okada

Akihito Takahashi

Three scale analysis : Sarcomere – Cell - Heart UT heart
Stochastic sarcomere model
MC (Monte Carlo Method)Time step size : 5µs
32 sarcomere samples toeach myofibril element

Continuum model
FEM (Finite Element Method)Time step size : 2.5ms
A cell complex FEM model
to each element

Fluid-Structure interaction
FEM analysis

Equation for propagationof cellar excitation•MC and FEM is coupled by imposing theconsistency of the mechanical energy.
• Cell and Heart models are coupled byhomogenization method.
fiber and laminar structure

Stochastic activities of the myosin model
Cooperativity with neighboring myosins
No binding state
γ=80 is adopted

knpγ nTransition in power stroke (one step)
U PreR

Chemical energy in the head

Warm (L )Strain energy in the arm

kpnγ − ntropomyosin

U PostR
Warm (L + x0 ) Eh0 exp − PostR  k BT Binding statetroponin

UT heart

Lnatural + L E h0 exp − PreR  k BT 

n=0

Lnatural + L + x0Energy(chemical energy in head + stain energy in arm)

E PreR = U PreR + Warm (L )E PostR = U PostR + Warm (L + x0 )n=1
Boltzmann factor

n=2

 Eexp − PreR  k BT  Eexp − PostR  k BT One stepUT heart

Sarcomere Model

T/T unit model
Ca2+Ca2+No binding

Ca2+Ca2+

Two-stage stroke myosin modelNot yetCa2+

Based on the Huxley & Simons 1971 Modelwith the Rice cooperative XB model (2003)

Two step
Weak binding

Strong binding＋6nm

Pre-rotation －6nm 1st stroke＋5nm－5nm 2nd strokeEffects of Two-stage stroke

UT heartTwo-stage stroke modelMore intensive
andsustainabledistribution

Ejection
68.7ml ⇒78.3ml
Efficiency
25.5 % ⇒28.3%

Two stroke
One stroke

Pressure (Pa)10000
Two Stroke
One Stroke

5000

One-stage stroke model

04060

80

Population

15000

05
Myosin-arm stretch [nm]100 120

Volume (ml)Pressure-Volume loop

Arm stretch distribution

Cell complex
at EndoSarcomere

UT heart

決定論的オン・オフモデル
Cooperativity with neighboring myosins
No binding state
γ=80 is adopted

knpγ nTransition in power stroke (one step)
U PreR

Chemical energy in the head

Warm (L )Strain energy in the arm

kpnγ − ntropomyosin

Warm (L + x0 ) Eh0 exp − PostR  k BT 

Binding state
troponin

U PostR

Lnatural + Lx

 E h0 exp − PreR  k BT n=0

Lnatural + L + x0

Energy(chemical energy in head + stain energy in arm)

E PreR = U PreR + Warm (L )E PostR = U PostR + Warm (L + x0 )n=1Boltzmann factor

n=2 Eexp − PreR  k BT  Eexp − PostR  k BT 

Conventional
deterministic XB生物物理と臨床のブリッジ
cumulative survival rate(%)

Unknown Cardiomegaly

Braunwald’s Heart Disease 7th ed.
Elsevier Saunders

NormalBraunwald’s Heart Disease 7th ed.
Elsevier Saunders

Tangled structure

Watkins H et al. New Engl J Med 1992
020

Unkown mechanizm
connecting the micro
and macro scales

40

Age60

80
Braunwald’s Heart Disease 7th ed.
Elsevier Saunders
Braunwald’s Heart Disease 7th ed.
Elsevier Saunders↓Abnormalitiesof myosin(Sarcomeredisorder）

No efficient treatment
Protein Database

Mutation sites

A.D.A.M Interactive physiology