Next-Generation Integrated Simulation of Living Matter
Strategic Programs for Innovative Research Field 1 Supercomputational Life Science2013.38Vol.

4th Biosupercomputing Symposium
December 3 -5 , 2012

Tokyo International ForumCONTENTS●SPECIAL INTERVIEW●Report on Research◦ Grand Challenge opens the way to the future of life science through innovative approach
Program Director, Computational Science Research Program, RIKEN Koji Kaya
◦ A Landmark Project that Brought on an Innovation in the Field of Life Scienc
Deputy-Program Director, Computational Science Research Program, RIKEN Ryutaro Himeno◦ Multi-scale, multi-physics heart simulator UT-Hear
Graduate School of Frontier Sciences, the University of Tokyo

Toshiaki Hisada, Seiryo Sugiura, Takumi Washio, Jun-ichi Okada, Akihito Takahashi

2-3
4-5
6

（Organ and Body Scale WG）◦ Simulation Model for Insulin Granule Kinetics in Pancreatic Beta Cells
Graduate School of System Informatics, Kobe University Hisashi Tamaki（Cell Scale WG）◦ The road to brain-scale simulations on K

Brain Science Institute, RIKEN, Institute of Neuroscience and Medicine (INM-6), Juelich Research Center
Medical Faculty, RWTH Aachen University Markus Diesmann（Brain and Neural Systems WG)●SPECIAL INTERVIEW◦ MD Core Program Development for Large-scale Parallelization
Computational Science Research Program, RIKEN Yousuke Ohno（High-performance Computing Team)◦ Aiming to realize hierarchical integrated simulation in the circulatory organ systemand the musculoskeletal / cerebral nervous systems

Professor, Department of Mechanical Engineering and Department of Bioengineering The University of Tokyo

Shu Takagi（Theme3 GL)◦Leading-edge large-scale sequence data analysis with K computer in order topromote the understanding of life programs and their diversity

Professor, Human Genome Center, The Institute of Medical Science, The University of Tokyo
Satoru Miyano（Theme4 GL)●Report◦4th Biosupercomputing Symposium Report

Computational Science Research Program, RIKEN Eietsu Tamura◦ “K Computer” Compatible Computer: Installation of SCLS Computer System
HPCI Program for Computational Life Sciences, RIKEN Yoshiyuki Kido
●Event information and news

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PECIAL INTERVIEW

Biosupercomputing Pioneers the Future of Life Science

Looking back on Six and a Half Years of the“Next-Generation
Integrated Simulation of Living Matter”

Grand Challenge opens the way to the futureof life science through innovative approach
Program Director, Computational Science Research Program, RIKEN

Koji Kaya● Six-and-a-half-year support of the “K computer” from the viewpoints of hardware and software
When RIKEN took the central role in the development of today’s “Kcomputer” and worked with many public research institutions anduniversities in its development, and made environmental arrangements
for its usage, we carried out many discussions at RIKEN Science Council
which I chaired at that time. Although studies utilizing supercomputers
had progressed across a wide range of areas including material science,high end computation did not play an important role in the life sciencefield. In these circumstances, RIKEN ended up making the proposalthat supercomputer-based studies would become very important in
the life science field, and RIKEN should take a proactive stance. I thinkadministration officers including the Ministry of Education, Culture, Sports,
Science & Technology which took the initiative in the project probably
held active discussions. However, I believe that our proposal contributed
to appointment of RIKEN as the core agency.
Along with developing a supercomputer, the R&D of Grand Challenge
Application software to allow maximum utilization of the new computer
became one of the major goals of the project. Regarding “Next-Generation
Integrated Nanoscience Simulation Software,” a supercomputer had
already been introduced in the early 2000s when I was Director of theInstitute for Molecular Science, National Institutes of Natural Sciences.
Then, theorists involved in nano-technology gathered to start joint studyof computational science. Therefore, the Institute for Molecular Science
served as a center to start the development of Grand Challenge simulation
sof tware for understanding and predicting the characteristics andphenomena of nanomaterials through electronic-, atomic- or molecularlevel sophisticated, large-scale computation. However, regarding thelife science field, the computational science approach had hardly been
explored. Still, Dr. Ryutaro Himeno and Dr. Makoto Taiji et al insisted
that research and development should be promoted in this field, andplayed a driving force for Grand Challenge “Next-Generation Integrated
Simulation of Living Matter Software.” As their insight was brilliant andthe introduction of computational science in life science was a must, 13
institutes (ultimately 15 institutes) gathered to establish the R&D system. It
officially started in September 2006 after its application was approved by
the Ministry of Education, Culture, Sports, Science & Technology in June
2006. Since I did not major in life science, I honestly hesitated for a moment
when I was asked to assume the position of Program Director. Howeveras I was once involved in computational science in the field of nanotechnology, I thought I could be of some help in organizational control
and decided to assume the position. From 2008, I have been concurrently
serving as Deputy Director of the RIKEN Next-Generation Supercomputer
R&D Center. As a result, I have watched over the development of the “Kcomputer” from the viewpoints of both hardware and software.

● Tangible results produced by Grand Challenge
While no one in the material science field had doubts about the researchand development of simulation software, it was initially very difficult togain understanding from those involved in the life science field. Afterall, we had to address a considerably broad spectrum ranging from themolecular level to whole-body scale. What we had to do was a multiscale simulation. In addition, we had hardly any established theory foreach hierarchy. Regarding cells for example, RIKEN of course had been
studying them by experiments. However, cells are not simple, anyway. The
cell contains various substances including proteins, whose concentrationsin the cell are incredibly high. It is almost unbelievable that proteins canbe dissolved in water at such high concentrations. Everyone doubted
whether they could replicate it correctly and calculate cell functions. Cell
simulation was such a very difficult theme. However, as a result, research
has progressed in those 6 years so that we can see some daylight. Of
course it is also a fact that actual replication of the cell is far beyond thecomputational capability of the “K computer.”

After I had just assumed the position of Program Director of Grand
Challenge, our group including Dr. Himeno met many leading professors
in the life science field. Many of them gave us rather dismissive answers.
They neither showed active interest in nor extended support to ourattempt. Only 6 to 7 years ago, it was a trend in the life science field. Now
our research and development in life science has advanced by leaps andbounds. Simulation studies have advanced understanding at a cellularlevel, and also at the molecular level we can now replicate various proteinfunctions. Brain research has advanced such that the “K computer” wouldin a real sense replicate functions like those of a real brain on a computer
for the first time in the world. The Organ and Body Scale WG has enabledvery excellent heart simulation. In blood flow simulation, a radically new
fluid-structure interaction analysis method was developed for replicationof the thrombogenic process. Sonic simulation studies which may leadto the development of ultrasonic therapy apparatus also show successful
results. These are very important achievements also in terms of the

Building of Advanced Institute for Computational Science
under construction (April, 2009)

2 BioSupercomputing Newsletter Vol.8

Supercomputer room under construction was shown
to the press (February, 2010)

SPECIAL INTERVIEW

Commemorative picture for completion of“K computer”

The 3rd BioSupercomputing Symposium (March, 2011)

contribution to medical care and medical engineering. In the field of data
analysis fusion, some are initially doubtful about processing bioinformatics
data by a petaflops-class supercomputer. When the environment necessaryfor easier genome analysis is put in place, the era of personalized medicine
which requires the processing of enormous amounts of data will arrive. In
this context, the supercomputer with high calculation performance wasproven to be useful in various processing tasks.
Initially, many people doubted whether a computational science-based
approach can provide adequate results in the complex and broad-ranging

life-science field. In spite of this background, there is no doubt that we got
some positive response and discovered many possibilities during those 6
to 7 years. Until we reached this stage, the High-performance Computing
Team led by Dr. Taiji took a key role in the development of software
technologies and supported this project. I cannot forget their importantrole. Of course our accomplishments are still a far cry from clarificationof complex life phenomena and mechanisms. However, it is a fact thattangible results were obtained. I think Grand Challenge made a powerful
impact. I do feel that high end computation will make a strong positive
impact on the biology and medical care in the 21st century.

● Hoping for young people’s contribution to development of future life science
Another achievement of Grand Challenge is that as simulation sciencegrew in the field of life science, many young researchers became interested
and joined the field, and we can now collaborate with people from a wide
range of research fields. I believe those young scientists will become avery important human resource which supports future research. However,the problem is that the new computational science, which those young
researchers will be engaged in, has not yet won enough recognition in
Japan. In the USA where computer science enjoys greater recognition,
reasonable posts are offered to researchers engaged in computational
science, but the Japanese life science field doesn’t even go that far. Still
today, vacant posts tend to be allocated to experimental scientists.
Simulation studies will definitely have a key role in future life science.
However, I cannot help feeling that professors who have been leading lifescience researchers do not seem so sure about that. In addition, we have to
make the best of today’s tight research budget. I think this is one of reasons
why it is not easy to increase the number of posts for the computational
bio-science.

The Grand Challenge Program ends in March 2013. While producing
excellent results, there still remain many courses of research anddevelopment which have to be challenged again by the collaborations
among colleagues in the Grand Challenge Program. I believe how to inheritand expand it will be a major challenge from now on. The life science field
had a very limited interaction and collaboration with other research fields.
However, in Grand Challenge, researchers across various fields gathered
to create a new life science. I hope more and more young researchers
overcome the barrier, absorb the knowledge and information in various
fields, and pave the way for a new world.
In any age, it is always hard to open up a new field, and we may face manydifficulties in doing so. However, one thing that I can say without fear isthat life science is facing an era of revolutionary change. More and moreinnovations must be produced from now. I believe Grand Challenge is arunway for that. It is my fervent hope that as many young researchers as
possible take off from this runway, and continue the effort to blaze a newpath to the future.

Monomeric HOMO
Protein DF - Protein All Electron Calculation

Ｃa+ kinetics simulation（ HepG2）

BioSupercomputing Newsletter Vol.8 3

S

PECIAL INTERVIEW

Biosupercomputing Pioneers the Future of Life Science

Looking back on Six and a Half Years of the“Next-Generation
Integrated Simulation of Living Matter”

A Landmark Project that Brought onan Innovation in the Field of Life Science
Deputy-Program Director, Computational Science Research Program, RIKEN

Ryutaro Himeno● Phenomenal Findings by the Project
The “Next-Generation Integrated Simulation of Living Matter,” a researchand development project which we have committed to since Octoberof 2006, essentially aimed to develop a “show-case model” of simulation
software in the life science field as part of the “Next-Generation Super
Computer Project ”. This project was set up to prove that “new andastounding studies could be performed” with a supercomputer with aperformance at the 10 PFLOPS level.
Looking back on the last six and a half years, there were many things we
could and could not accomplish. But in the perspective of research anddevelopment of simulation software, I believe we were able to produce alarge number of software streamlined for the “K computer”, which wereable to display a higher performance than the theoretical performancethat we originally expected.
Not only did the “MD core program for large-scale parallel computers(cppmd)”, the molecular dynamics sof tware streamlined for the “Kcomputer”, result in a performance value close to 40% as expected,
the “Fluid-structure interaction analysis program for whole body voxel
simulation (ZZ-EFSI)” also attained a remarkable performance value of 43%.
Further, the “multi-scale multi-physics heart simulation (UT-Heart)” also
attained a performance value close to 30%, and “non-invasive treatment
simulation (HIFU)” also attained a performance value higher than 20%.
Originally, our goal was to develop simulation software that exceededa performance of 1 PFLOPS using the entire “K computer”, and we were
hoping to possibly develop two software. But these four software willdefinitely result in performance values higher than 20% if they are actually
run on the entire “K computer”. Therefore, it might sound a little overstated,
but I feel we can say we surpassed our initial goal. Regardless of how we
say it, simulation software that yields high performance values has beencreated. There are thirteen software including these four that were able toproduce solid results on ten thousand nodes of the “K computer”. The factthat one third of approximately thirty software programs in total attained
honorable records is a success that surpasses our original expectations.

Ryutaro Himeno, Deputy-Program Director, who gave his lecture at “NextGeneration Integrated Simulation of Living Matter Symposium 2007”

4 BioSupercomputing Newsletter Vol.8

You may think, “Isn’t that a little over dramatic for 30%?”, but in the year
2006 when the project started, there was much speculation and criticism
regarding development of simulation software in the life science field forthe supercomputer. In those terms, to be able to develop thirteen usable
software is a wonderfully remarkable achievement.
Looking back, it was good that we were able to produce respectablef indings ever y year. People have gradually star ted to accredit our
accomplishments through these yearly achievements. Most of all, theactual researchers who are developing the software understand that theirefforts for improving parallel performance have been fed back to themas achievements, and are at a stage where they say, “We can’t operate
without the “K computer”, actually even the “K computer” isn’t enough”. It
is now hard to believe that there were worries like, “Can we fully utilize a 10
PFLOPS machine?” when we first started.
Especially in the year 2012, we only had a period of two months starting inJuly to be granted priority to use 10 PFLOPS together with the five fields of
the Strategic Programs for Innovative Research. We had a difficult time on
how to handle everyone’s concerns regarding the lack of calculation time.
Reflecting on our initial worries, we can say it is a gratifying miscalculation,
but it is very disappointing that some were not able to produce sufficientscientific findings due to lack of time to use on a priority basis. For example,
the simulation software “Neural Simulation Tool (NEST)”, which replicates
input and output of the entire brain in order to reproduce the growth andlearning capabilities of the brain, has the largest calculation in the field ofbrain science in the world when it uses the entire “K computer”, but wecould not get enough calculation time. In that context, I believe a lot more
could be accomplished if we had another year.
Likewise, there are many topics that are left uncompleted, but I feel thatthe project was able to secure respectable achievements overall. Without
this project, I believe there still would have been many researchers thatwould say “the “K computer” is not necessary for the field of life sciences”.

SPECIAL INTERVIEW

Poster Session held at the Joint Computational Science Workshop 2009
(July, 2009)

Poster Session held at the 2nd Biosupercomputing Symposium(March, 2010)● Integration Born from Summer School
The “K computer” has a relatively fast network and is constructed withhighly distinguished hardware. The work of the High-performance
Computing Team whose researchers found bugs in compiler before others
use it and made various efforts to bring out full performance values cannot
be ignored. Their work is wonderful. Through their steady efforts, the “Kcomputer” has been built as a machine that can be used with ease, and I
believe the existence of this team has led to the development of a large
number of software that attain high efficiency. For improving computation
speed, it is critical to share knowhow among all software development
researchers. The role they played in sharing a precise pool of information
for generating higher performance was exceptional.

not lead to integration. In the midst of this, the summer school in which
we collected younger researchers was effective. While each researcher
worked on his/her new model, people from varying research fields
used the computers in a similar manner but with a different approach,and this observation directly stimulated everybody. Through summer
school, researchers became to understand what researchers from otherfields were pursuing, which led to the overcoming of research field andtechnical language barriers, and eventually progressed to integration.
You can say that the simulation of blood clotting through the fluidstructure interaction analysis method was also born out of this new kind of
integration.

The word “sharing” brings to mind the early stage of this project.
Personnel with research backgrounds of various scales from many research
institutions and universities joined the project, but since they spoke theirown technical language and abbreviations, it was difficult to understandeach other when we initially held symposiums (laughing). Althoughexplanations became more explicit and understandings deepened, it did

I am very pleased that this project stimulated growth in each field due tothe new integration. This itself cannot be explained in a research paper,and may not be perceived as an achievement. Even so, I hope there will
be a day when people will say that in the long run this project became theturning point for generating new connections, and led to wide-ranging
progress in research and development.

● To Expand on the Accomplishments of the Project
To simulate the phenomenon of life which is a multi-body and multilayered system, it is necessary to explore new phases through new
integrations. In this project, new integrations have been formed in eachresearch and development team, and interesting findings are coming tolight. The main focus of the project was software development, but at thispoint interests are shifting towards the research findings generated fromthe new software which is an exciting result. You can also say we werescience-driven and software had been developed as seedlings for the
researchers’ interests, but some could possibly be put to practical use for
actual drug discovery and medical care.
The project will end in the end of fiscal 2012, and each research anddevelopment team will be dissolved, but those that have the potential
for future needs will be incorporated into the Strategic Programs forInnovative Research (field 1). Other software that should be inherited

Ultrasound treatment simulator: ZZ-HIFU

and extended, rather than ending up as they are, will be undertaken by
RIKEN’s Advanced Center for Computing and Communication for further
development, updates, and support. We hope they will be actually usedby the research centers at RIKEN. We will need to maintain a runningenvironment, or else efforts will end with the development stage. By
continuing the efforts in RIKEN’s research topics, I hope many will realize
and appreciate the value of operating this project at RIKEN.
I mentioned earlier, the young researchers whose research attitudes
have changed through the summer school. We have founded a biosupercomputing research group in order to support them, and hope tocontinue to help this research group evolve. It will be a wonderful place tocontinue to cultivate these budding young researchers that possess new
insights.

Neural network activity simulation

BioSupercomputing Newsletter Vol.8 5

Report on ResearchMulti-scale, multi-physics
heart simulator UT-Heart
Graduate School of Frontier Sciences, the University of Tokyo

Toshiaki Hisada, Seiryo Sugiura,
Takumi Washio, Jun-ichi Okada, Akihito Takahashi(From the above)（Organ and Body Scale WG）

The human heart comprises multi-physics problems
that widely cover various electric (ion current, excitation
propagation, electrocardiogram, etc.), chemical (masstransp or t, reac tion, energ y conver sion, etc.), andmechanical (myocardial tension, blood pressure, blood
flow, etc.) phenomena originated from the biochemical
reaction by ATP (adenosine triphosphate) hydrolysis as anenergy source. In terms of spatial scale, it comprises multiscale problems that range from protein molecules (up to 10nm) to organs (up to cm) via cells (up to 100μm) and tissues(up to mm), and finally ejection of blood is achieved.
ATP is consumed as energy for the relative sliding
movement between the numerous actin filaments andmyosin filaments that comprise 50 to 60 myofibrils present
in a single cardiac muscle cell. It is known that the relative
sliding of the filaments results from the tractive force thatis produced by the myosin molecule heads extending
from the myosin filaments combining with the actin
filaments. Hypotheses for the specific mechanism involved
are however diverse, and still remain under discussion.
Figure 1 (right) shows a hypothesis of the rotation of
myosin heads. In the meantime, cardiac hypertrophy is
generally interpreted as a phenomenon where the wall
thickness (myocardial cross section) increases to copewith a situation of high blood pressure such as valvulardiseases and hypertension. In a disease called hypertrophic
cardiomyopathy, however, the heart wall is thickened forno reason at normal blood pressure. In an initial study,
the thickening had been interpreted as occurring tocompensate for degrading cardiac function due to point
mutation of myosin molecules (represented by mutation of
the 403rd amino acid at which the myosin molecule binds
with the actin molecule). In a later study, however, it wasreported that the mutated myosin is rather hyperactive[1].
Then, what happens to the heart by admixture with thehyperactive myosin?
We h av e d e v e l o p e d c a r d i a c m u l t i - s c a l e a n a l y s i stechnologies on massively parallel computers so far [2], andare earnestly addressing this issue using the “K computer”.
More specifically, we had inevitably tried to solve theprobabilistic binding state of actin molecules and myosin
molecules by conceiving an average molecule, andapplying it to an equation expressing the state transition in
the past, due to limitations of computer power [3]. However,
we came to be able to seamlessly solve the tissue andorgan movements through cell contraction by startingwith a concrete simulation of the probabilistic movementsof the individual myosin heads with intramolecularelastic elements modeled on a spring as shown in Fig. 1,
by taking advantage of the characteristics of massively
parallel computers. The myosin head movement has acharacteristic, known as “Cooperativity,” whereby bindingof a certain myosin head with an actin filament encourages
its neighboring myosin heads to bind with the actin. This
computation model enables the introduction of such acharacteristic as it is, and thus the analysis of heartbeat
in the case of admixture with the previously-mentioned
hyperactive myosin. The following specific procedures will
be undertaken:(1) Simulate the probabilistic micro-scale behavior of
myosin molecules with cooperativity directly by the Monte
Carlo method;(2) Embed the above-mentioned Monte Carlo model inthe muscle fiber portion of the mesoscale cell structuralelement model including the intercellular space and soon to properly address the dynamic interactions between
scales physically; and(3) Bind the movements of the above secondary structuralelement model and the macro-scale organ model by thehomogenization method.
The realization of the above analytical method enables thesimulation-based analysis of such issues as what effects
are produced by the molecular level state change law
and cellular tissue level structural elements on heartbeat
performance and energy efficiency and, conversely, what
effects are produced by the feedback of macromuscular
contraction and relaxation on the molecular level statechange. Such a simulation platform may also serve asa means for molecular biologists to verify their own
hypotheses from a new perspective in future.
The research and development and validation of the UTHeart are jointly implemented by the Fujitsu Limited
Next Generation Technical Computing Unit (a groupled by Yoshimasa Kadooka, General Manager and Head,Application Research and Development Division), andthe University of Tokyo Hospital (a group led by Visiting
Professor Ryozo Nagai). The clinical study was approved by
the Institutional Review Board at the University of Tokyo.
[References][1] Belus A, Piroddi N, Scellini B, Tesi C, Amati GD, Girolami
F, Yacoub M, Cecchi F, Olivotto I, Poggesi C, The familial
hypertrophic cardiomyopathy-associated myosin mutation
R403Q accelerates tension generation and relaxation ofhuman cardiac myofibrils, J Physiol, 586(Pt 15), 3639-3644,
2008[2] Hosoi A, Washio T, Okada J, Kadooka Y, Nakajima K, Hisada
T, A Multi-Scale Heart Simulation on Massively Parallel
Computers, SC10, NewOrleans, proceedings 1-11, 2010
[3] Washio T, Okada J, Sugiura S, Hisada T, Approximation for
Cooperative Interactions of a Spatially-detailed Cardiac
Sarcomere Model, Cellular and Molecular Bioengineering,5, 113-126, 2012

Fig. 1: K computer-based Multi-scale Heartbeat Simulation from Sarcomere Kinetics

6 BioSupercomputing Newsletter Vol.8

Report on Research

Simulation Model for Insulin Granule
Kinetics in Pancreatic Beta Cells

Graduate School of System Informatics, Kobe University

Hisashi Tamaki(Cell Scale WG)

In insulin granule kinetics or the transportation process of insulin granules
produced in pancreatic cells to the cell membrane for secretion, thegranules are believed to move along the cell skeleton. For constructing
the simulation model, we divided the model into four stages based on
the skeleton of a pancreatic cell and the principle of granule kinetics, i.e.,
granule producing site, interior layer, exterior layer and granule secretory
part (Fig. 1).
Regarding the phenomena we aim to reproduce, we considered that

Cell Nucleus
Golgi Body

Microtubule

Actin filament

Cell membrane・Insulin Synthesis
・Insulin Granulation

・One cell skeleton
・Motion path for the granule・Multiple microtubules arepresent in the cell・One cell skeleton
・Motion path for the granule・Network

・Spots that can open ands e c r e te a r e s c at te r e dabout the cell membrane
・Presence of RRP

Modeling by focusing on the structure of the cell skeleton and principle of granule kinetics
Granule producing site
Number of granules
produced per unit time

Interior layer
Required transit time of the
granule in the interior layer

Exterior layer
Required transit time of the
granule in the exterior layer

Granule secretion part
Granule secretion rate
Number of granules secreted perunit time

As an example simulation, Fig. 2 shows the results for two cases simulatinga healthy subject and a diabetic patient, respectively. Although thedifference in parameter setup between Case 1 and Case 2 is only the
number of secreted granules at the granule secretion site under glucose
stimulation, it was confirmed that biphasic insulin secretion and insulin
secretion lacking Phase I, both of which are found in real observation, wereable to be reproduced in Case 1 and Case 2, respectively.
Currently we are searching for a parameter setup for reproducing insulin
granule secretion after drug administration. Additionally, we think it isalso vital to conduct a more detailed study on the impact of the individual
parameter setup on kinetics and secretion by improving simulation results
which can reproduce typical secretion patterns. Further, we would like toperform studies for setting parameters based on available data of granulekinetics in pancreatic beta cells (which would be considerably limited andfragmentary), creating a satisfactory simulation scenario and refining thesimulation through coordination with metabolic system simulation.

Number of granules secreted

Since many studies have been carried out on the insulin secretionmechanism of the cell membrane, considerable progress has been made.
However, it is also a fact that the intracellular mechanism of granulekinetics has still hardly been clarified. Insulin granule kinetics after glucose
stimulation has recently become observable. Based on the result of thispartial observation and biological evidence, we have prepared a simulation
model for insulin granule kinetics in pancreatic cells. With this simulation
model, we are trying an approach to acquire new insight into insulin
granule kinetics by reproducing typical phenomena found in insulin
granule secretion.

the insulin granule secretion process consists of three major phases, (1)Phase I: Steep increase in secretion occurring in connection with glucose
stimulation, (2) Phase II: Moderate increase in secretion after glucose
stimulation, and (3) Phase III: Small amount of continuous secretion calledbasal secretion. Then, we focused on the typical secretion patterns foundin healthy subjects and diabetic patients, and after drug administration,
respectively. (a) Healthy subjects: Phase I, II and III are observed successively.
(b) Diabetic patients: Phase I does not appear. (c) At drug administration:
Both Phase I and II appear strongly. We aim to reproduce the secretion
processes with those typical patterns.

Case1:
Insulin secretion
characteristic ofhealthy subjects

Phase I

Phase II
Phase IIITime

Number of granules secreted

Diabetic patients have been increasing dramatically all over the world today,
especially among Asians. Most Asian diabetic patients suffer from Type 2
diabetes caused by inadequate insulin secretion. Healthy people produce
insulin granules in the Golgi body of pancreatic cells. Then the granules aretransported to the cell membrane for secretion. Type 2 Diabetic patients
have insulin granules in the cell though their secretion is inhibited by somefactor. For revealing the factor and dealing with it properly, it is necessary to
elucidate the insulin secretion mechanism in pancreatic cells.

Disappearance of Phase I

Case 2:
Secretion characteristicof diabetic patients

Secretion from granule poolDirect secretion from the inside of the cell

Time

Fig. 1 : Outline of insulin granule kinetics modelFig. 2 : Simulation Results

BioSupercomputing Newsletter Vol.8 7

Report on Research

The road to brain-scale simulations
on K
Brain Science Institute, RIKEN
Institute of Neuroscience and Medicine (INM-6), Juelich Research Center
Medical Faculty, RWTH Aachen University

Markus Diesmann（Brain and Neural Systems WG)

This is the story of the endeavor of the Brain and Neural Systems Team (BNT)
to make the computational power of K available to the field of computational
neuroscience. An extended version of this report can be found at [1].
The human brain comprises about 1011 neurons, each connected to 10000
others. In computational neuroscience, the bottom-up approach often
starts from a mathematical description of neurons and their interactions inorder to investigate network dynamics [2]. The NEST simulator [3] is tailoredto this resolution. Neurons are represented as small systems of differential
equations, which interact by δ-impulses [4,5] to form networks of natural sizeand complexity.

came into sight, and was reached in May [10] utilizing just 12288 nodes of K.
The upcoming NEST 2.2 release is based on this kernel.
The contacts between neurons exhibit different types of dynamics andplasticity [11,12]. In the limit where a neuron has either none or only a single
synapse on a given compute core, however, this heterogeneity collapses to asingle type. The 4g kernel presented at the 9th meeting in Sep 2012 exploits
this uniqueness. The figure shows the maximum network size (triangles)
for a given number of nodes and the runtime (dots). The 4g kernel enables
simulations of 109 neurons on K. Neither kernel compromises on generality;
functionality and user interface of NEST remain the same [13,14].

The top-down approach starts from an abstract description of a particularbrain function and investigates how this function is implemented at theneuronal level (e.g. [6]). The functional circuits of the brain typically involve
several areas. Due to the demand of computer memory [7] , however,simulations were previously constrained to the local microcircuit (e.g. [8,9]) or
non-spiking macroscopic models. With supercomputers like K, the multiscale connectivity of the brain is within reach.

We hope that the simulation technology now available will help
neuroscientists to integrate the data on the anatomy and dynamics of thebrain into models to gain insights into brain function.

At the time of the 1st BNT meeting (Nov 2008), NEST (2nd generation kernel,2g) scaled to 1024 processors for 105 neurons. At the 3rd meeting (Oct 2009)
the JUGENE computer at Juelich reached 106 neurons. By Feb 2010 we had
set milestones for K with network size increasing from 106 to 108 and above.
NEST compiled Nov 2010 on a prototype, May 2011 on the test system, andin Sep on K as reported on meetings 6 and 7.

www.csn.fz-juelich.dewww.nest-initiative.org

In the 3g kernel, the data structures account for the sparseness of neurons
with local targets and enable the efficient storage of information about nonlocal neurons [7]. At the 8th BNT meeting in Mar 2012, the second milestone

Our challenges ahead are to make the 4g technology available as a NEST
release and to develop software concepts for the exa-scale machines now in
preparation.

Acknowledgements
Partly supported by early access to K at RIKEN AICS, Next-Generation
Supercomputer Project of MEXT, Helmholtz Association: HASB and portfolio
theme SMHB, JARA and EU Grant 269921 (BrainScaleS).

[References][1] Diesmann (2012) p83-85 In Proc 4th BioSupercomputing
Symposium, Tokyo[2] Lansner and Diesmann (2012) Chap 10 In Computational
Systems Biology, Le Novére ed., Springer[3] Gewaltig and Diesmann (2007) Scholarpedia 2(4):1430[4] Morrison et al. (2008) Biol Cybern 98:459-78[5] Hanuschkin et al. (2010) Front Neuroinform 4:113[6] Potjans et al. (2011) PLoS Comput Biol 7(5): e1001133[7] Kunkel et al. (2012) Front Neuroinform 5:35
[8] Potjans et al. (2012) Cerebral Cortex doi:10.1093/cercor/bhs358[9] Wagatsuma et al. (2011) Front Comput Neurosci 5:31
[10] Helias et al. (2012) Front.Neuroinform 6:26
[11] Potjans et al. (2010) Front Comput Neurosci 4:141[12] Pfeil et al. (2012) Front Neuromorph Eng 6:90
[13] Eppler et al. (2009) Front Neuroinform 2:12
[14] Gewaltig et al. (2012) Chap 18 In Computational Systems
Biology, Le Novére ed., Springer

8 BioSupercomputing Newsletter Vol.8

Report on Research

MD Core Program Developmentfor Large-scale Parallelization

Computational Science Research Program, RIKEN

Yousuke Ohno（High-performance Computing Team)
Our team has been developing a MD core program for large-scaleparallelization in order to provide a high speed core program library for the"K computer", and to attain and collect application speedup technology
knowhow on the "K computer".
Molecular dynamics (MD) simulation is a method for estimating the
movement of molecules and changes in structure through calculation. In
life sciences, biomolecules such as protein which are the foundation of life
phenomena are targets of calculation. In the simulations, all movements
of atoms are calculated by calculation of the all forces between atoms andnumerically integration of the equation of motion. The amount of calculation
for the numerical integration of the equations of motion and forces ofcovalent bond approximated by classical is proportional to the number ofatoms. On the other hand, Van der Waals forces (intermolecular forces) andCoulomb forces incur a calculation load that is proportional to the numberof pairs of atoms, which means the square of the number of atoms. Van
der Waals forces are very small after a distance of 1.4nm, therefore pairs of
atoms that are distant are ignored which is called a cutoff method. However,
Coulomb force is inversely proportional to the square of the distance, andcannot be blindly ignored. Therefore the PME (Particle Mesh Ewald) method,which reduces the calculation load of long distance Coulomb force as low as
O (N log N ) by using FFT, is a popular way to calculate Coulomb force. Yet, FFT
requires a high amount of communication when parallelized, which leads tolower performance in large-scale parallelization. Therefore, methods like theFMM (Fast Multiple Method) which demands less communication comparedto FFT are starting to be employed. In addition, the PME method is very strict
under periodic boundary conditions, and intracellular targets which do nothave periodic cycles may have problems in using a pseudo-period. In thatcase, a cutoff method that corrects electrical charge and dipole moments
under neutral conditions may be applied [1, 2]. The cutoff method requires
communication between adjacent nodes, which is suitable for large-scaleparallelization.
In our research, optimizing a cutoff method on the "K computer" became
the center of our study because some form of cutoff calculation is the
major part of calculation time regardless of the methods used. We attained
a calculation efficiency of 60% for the cutoff calculation portion on asingle CPU using a cutoff method with the best conditions. In the all nodeevaluations of the "K computer" (Note 1) in October 2012, we attained 4 PFlops
effective calculation speed and 38% efficiency on 5.4 hundred million atoms
with a cutoff distance of 2.8nm and once per four-step potential calculation.
When potential is calculated in each step, effective calculation speed was 4.4
PFlops and 43% efficiency for a calculation of a 79872 node with 5.2 hundred
million atoms.
Fig. 1 shows the parallel performance. The green dotted line represents
parallelism when the calculation size (number of atoms) per node isconstant (weak scaling). The number of atoms per node is 52338, 6542,
Table 1: Calculation performance of the cutoff method on a scale worth all nodesof the "K computer". Cutoff distance of 2.8nm .

Fig. 1 : Parallel performance of the cutoff method. Cutoff distance of 2.8nm.
Horizontal axis represents number of nodes, vertical axis represents calculation
time per step (milliseconds). The green dotted line is a fixed number of atoms per
node (weak scaling), the red line is when the number of all atoms is fixed (strong
scaling).

818, 102 in descending order. Calculation time is fixed in every case anda high level of parallelism is attained. The red line represents parallelism
when the entire calculation size is fixed (strong scaling) and the number ofatoms is 418707, 3349656, 7536726, 26797248, 214377984 from left to right.
Parallelism depends on the number of atoms per node, and performance
improved proportionally to the number of nodes when there were 6000
and more atoms. But when the numbers of nodes were multiples of 8 or 64
and the numbers of atoms were 800 or 100 per node, parallel performancefell to 50% or 20%. Ten milliseconds for calculating one step on a node of
100 or so atoms seems to be the margin for practical use. Regarding thetest evaluation of long distance Coulomb calculation using FMM, parallel
performance did not weaken as much compared to cutoff calculations.
Further, we confirmed that performance levels did not weaken much on ascale that is equivalent to a scope using all nodes of the "K computer".
The optimization on the "K computer" attained in our research has been
applied in the development of applications and optimizing by other teams.
Further, this MD core program will be released either as an example and areference code for the optimization on the "K computer", or as an optimized
MD code that can be reused.

Node configuration

48x52x32

48x52x32

48x48x36*

Number of nodes

79872

79872

82944

Number of atoms

522546336

522546336

542644272

Calculation time per 1000
steps (sec)116.357

112.414112.085

Frequency of potential
calculation

Every time stepOnce per four
steps

Once per foursteps

Effective calculation speed(PFlops)

4.387

3.871

4.031

Efficiency of calculation

0.429

0.379

0.38

* Note: It differs from the actual network configuration of the "K computer" which is 48x54x32,
but this does not greatly affect the results.

Our research is joint research project with the High-performance
Computing Team whose members are Hiroshi Koyama*, Gen
Masumoto, Aki Hasegewa, Gentaro Morimoto, Noriaki Okimoto,and Hidenori Hirano.
* Hiroshi Koyama is now a researcher at National Institute forMaterials Science (NIMS).
Note 1
Use of RIKEN's super computer "K computer" as a research programusing the HPCI system (program number:hp120068)[References][1] Wolf, D. and Keblinski, P. and Phillpot, SR and Eggebrecht, J., J.
Chem. Phys. 110 8254 (1999)[2] Fukuda, I. and Yonezawa, Y. and Nakamura, H., J. Chem. Phys.
134, 164107 (2011)

BioSupercomputing Newsletter Vol.8 9

S

PECIAL INTERVIEW

Strategic Programs for Innovative Research Field 1 Supercomputational Life Science

Theme 3 Hierarchical integrated simulation for predictive medicine

Aiming to realize hierarchical integrated
simulation in the circulatory organ system andthe musculoskeletal / cerebral nervous systems
Professor, Department of Mechanical Engineeringand Department of Bioengineering The University of Tokyo

Shu
Takagi
左より、木寺、柳田、江口（Theme3 GL)●Elucidate life systems in top-down approach
The ultimate goal of Theme 3 is to elucidate the meaning of “being alive.”To this end, we will work on hierarchical integrated simulation, which formsa hierarchical human body model, with the aim to understand life as anintegrated system.
Our lives are sustained by ingesting nutrition from the mouth, which istransported throughout the body via the blood and by breathing air toobtain oxygen in the lungs, which is transported throughout the humanbody again via the blood. Through these processes, the cells constituting thehuman body play their respective functions with the nutrition and oxygen
delivered to them. After this process, a direction for further nutrient intake
will be transmitted to our brain and our brain will feel hungry and encourage
us to eat more. There are naturally various smaller processes, which arerequired for the sustainability of life, with a system that transfers heat, mass,
and information in various forms. In plain words, Theme 3 aims to elucidate
life systems in such an approach as explaining what is happening in the
body with the above phenomena as a starting point.
For example, when a disease was cured by an effective medicine, it is correct
to explain, on the molecular level, that “the disease was cured as a result of
normal cellular functioning with a certain function of protein inhibited by
the medicine.” If the disease was not cured, however, there are many cases
where the reason cannot be explained, only locally on the molecular level,
why the disease was not cured. In fact, there are many medicines that are
not effective although they should take effect on the molecular level. Whensymptoms of a disease disappear, your brain will feel that “the disease has
been cured.” In that case, “the cure of your disease” is felt in the brain finally
via the nervous system after your cells normally function with the proteinfunctions changed, and then your tissues and organs are normally activatedas populations of such cells rather than the individual functioning of proteinsor cells. In other words, outcomes are mostly not determined only by local

events. Accordingly, if a medicine is not effective, you should think of othervarious factors involved. In order to understand “the meaning of being
alive,” various factors need to be grasped from the molecular level to thesystemic level. It is difficult, however, to elaborately simulate all factors fromthe molecular level to the systemic level and there are inevitable limitations.
What is important is the methodology as to how the hierarchies should be
integrated.
There are two approaches to the understanding of life phenomena where
various events are inter-related in a complicated manner both in terms ofscale and time. One is a bottom-up approach that builds up small factors
to lead to the understanding of larger phenomena, and the other is a topdown approach that starts with a condition, where the whole body is wellmaintained, and goes down to smaller factors to explore how the small
factors function and what is effective in maintaining the whole body. The
Theme 3 Team aims to elucidate life phenomena with the latter approach.
Our body is equipped with a function called homeostasis that tries torestore to a normal condition. Thanks to homeostasis, a slightly disturbed
condition will not aggravate immediately. The condition will drastically
become pathological, however, when it exceeds a certain level. Although it
may be difficult to grasp quantitatively what significant changes will occur
at which stages due to what effective functions, we believe that a top-down
approach is better to find what significant changes will occur at what levelsof which factors. On the contrary, it is very difficult in a bottom-up approach
to find what factor remains last. Hierarchical integration is coarse graining
in the sense that it tries to focus on different hierarchies by cutting off
something. It should be noted that simple averaging will erase the functions
and features that may appear in the next hierarchies. What should be leftdepends on the functions and features that will appear in upper hierarchies
in the end. Which factors should be linked to what parts and how they can

We will aim to reproduce the scenario leading tomyocardial infarction by integrating the thrombosis
simulator and the heart simulator. We will reproduce
vascular occlusion in a contracting/dilating coronary
artery through the coupled analysis of heartbeat byfurther expanding the multi-scale thrombus simulator,
incorporating the previous simulation ranging fromprotein-molecular-level interactions to fluid-dynamicslevel blood flow phenomena.

Simulation of myocardial infarction
Simulation of platelet adhesion to arteriosclerosis lesion

Molecule

TissueCell
PlateletPlatelet

Organ

Myocardial
infarction

PlateletCollagen exposed on vessel wall

Molecular-level interaction
between platelet surface
GP1bα protein and bloodvessel wall vWF protein(molecular dynamics method)

Thrombus growth startingfrom interactions between
platelet-vWF and platelet(Monte Carlo method)

10 BioSupercomputing Newsletter Vol.8

Stenosis due to thrombus
growth in coronary arterywith arteriosclerosis(Eulerian fluid-structureinteraction analysis)

Ischemia due tovascular occlusion→ myocardial
infarction (coupledanalysis with heart
simulator)

SPECIAL INTERVIEW
be linked together to leave cardiac functions for the heart and leave bloodflow functions for blood flow properly based on the theories of the lower

hierarchies ─ it is no exaggeration to say that most of the study subjects,
which we have dealt with so far, focused on the handling of this point●Exploit the results of the Grand Challenge Program for further development
In the Grand Challenge Program, which has been carried out for the past
five years, various living body simulators with excellent performance havebeen developed such as the thrombus simulator, the heart simulator, themusculoskeletal simulator, and the cerebral nervous system simulator.
In Theme 3, we wish to improve the infrastructure tool for reproducing
complicated processes relating to various diseases by integrating thesesimulators, which have been developed with the “K computer” on an
individual basis so far, by positively exploiting the previous results with aneye to developing the efforts into construction of frames for integration
into a hierarchical human whole-body simulation in future. In addition, we
will work on applying disease-related simulation results to the prediction ofpathological conditions, and support for treatment.

aggregation occurs in the initial stage of thrombus formation, but also thescenario, in which the growth of a secondary thrombus (fibrin thrombus)
initiated by blood coagulation function develops into vascular occlusion,by expanding the thrombus simulator developed in the Grand Challenge
Program. In addition, we will perform coupled analysis of the heart simulator
developed by Toshiaki Hisada (The University of Tokyo) et al., to simulate
the thrombus growth process from thrombotic adhesion to arteriosclerotic
lesions in the coronary artery surrounding the heart to myocardial
infarction with the effects of the heart simulator incorporated into thesimulation result. We also wish to provide important findings useful for thedevelopment of new drugs by evaluating the effects of thrombus-related
drugs.

We selected myocardial infarction and Parkinson disease as two specificsimulation targets. As mentioned above, the circulatory organ system isa mass transfer network and the nervous system is a signal transduction
network, which are vital in the human body for transfer of heat, mass, andinformation for the sustainability of life, and function as what may be called
human body trunk line networks. The circulatory organ system and thenervous system are also very important for future construction of a systemic
integrated simulator. With such importance taken into consideration, we
selected myocardial infarction and Parkinson disease as targets.

We also aim to reproduce the simulation of Parkinson disease as an integral
part of the musculoskeletal simulator and the cerebral nervous systemsimulator. Parkinson disease is considered to cause limb tremor and physicalrigidity even in a healthy musculoskeletal body due to abnormal signals
transmitted from the brain. We want to contribute to the pathologicalprediction and treatment support for Parkinson disease, which is one of
the functional motility disorders resulting from a cranial nerve disease, byreproducing a pathology called tremor that spike signals from the braintransmit to the muscle fibers via motor neurons and cause tremors, and apathology called rigidity that stiff muscles cause a rigid posture.

In the simulation of myocardial infarction, we aim to reproduce not onlya primary thrombus (platelet thrombus), where platelet adhesion and●Promote studies with an eye to the construction of human whole-body integrated simulator
In addition to the ongoing simulation of myocardial infarction and Parkinson
disease, we also intend to work on the construction of infrastructure
software for an integrated simulator that can be applied to a wide variety
of diseases by integrating musculoskeletal and circulatory organ simulators
via the nervous system. For the heart part, various diseases are expected tobe simulated to study therapeutic methods and evaluate efficacy including
abnormal cardiac functions due to myocardial infarction, angina pectoris,
dilated cardiomyopathy, hypertrophic cardiomyopathy, and microvasculardamage, and influence of stress and excitement. For Parkinson disease, wewish to develop the simulation into prediction of an optimum prescription
such as concomitant use of electric stimulation with dosing based on data

Brain simulator: NEST
(Doya Team)of individual patients on symptoms and motor tests, for example, which
are different from the conventional symptomatic treatment with electric
stimulation, by elucidating the mechanism and evaluating therapeutic
methods through a whole-body integrated simulation including the
cerebral nervous system, with consideration given to the effects of mass andsignal transfer on the neuron level and the effects of signal transduction. In
addition, we believe that the use of such a simulator will enable the analysis
of a wide range of phenomena such as prediction of changes in bodybalance and damage in the event of overturning, and the difference in theeffects of muscular or brain fatigue in the future.

Whole-body musculoskeletal
model: K-Body(Nakamura Team)We will aim to reproduce Parkinson`s disease, which
is one of the motor dysfunction resulting froma cranial nerve disease, by integrating the brain
nervous system simulator and the musculoskeletal
simulator.

Spinal cord
model
Multi-scale skeletal muscle simulator: HI-MUSCLE(Takagi Team)

BioSupercomputing Newsletter Vol.8 11

S

PECIAL INTERVIEW

Strategic Programs for Innovative Research Field 1 Supercomputational Life Science

Theme 4 Large-scale analysis of life data

Leading-edge large-scale sequence
data analysis with K computer in orderto promote the understanding of life
programs and their diversity
Professor, Human Genome Center, The Institute of Medical Science
The University of Tokyo

Satoru Miyano（Theme4 GL)●Elucidation of life systems through large-scale data analysis
In Theme 4 “Large-scale analysis of life data”, af ter establishing an
infrastructure for leading-edge large-scale sequence data analysis optimized
for High Performance Computing Infrastructure (HPCI) centering on the
K computer, we are proceeding with research, in which we consider life
programs found in cancers and cell differentiation processes as a system
and understand their complexity and diversity based on the genomes.
We also conduct biomolecule network analysis research centered on thedata based on genomes. Through this research, we aim to contribute topractical applications such as better predictions of drug efficacy and adverse
reactions, projections of causes of toxicities, personalized medicine, andprediction of survival.
“Life data” in the title of course includes the genome. It also covers a massive
amount of data based on the genomes such as the epigenome (the entire
set of DNA modifications) and transcriptome (all transcription products).
They are key data for understanding life programs.
As an example to understand their importance, let’s consider why we getcancer. Cancer is a kind of disorder in life systems induced by the complex
combination of genetic factors inherited from parents, DNA modification(epigenome) by environmental factors and genetic mutation accumulated
in tumor cells. Systems disorder may result in malfunction of a selfdestruction system for the prevention of abnormality (apoptosis) or selfordering for growth due to malfunction of external order for arresting
growth. In addition, cancer cells join hands with normal cells such as
vascular endothelial cells, immunocytes and inflammatory cells to acquire
drug resistance, or spread anywhere through infiltration and metastases.
Due to this, cancer is a complex and abnormal cell group that evolves
spatiotemporally. It is the genome that regulates the malignancy grading ofcancer, responsiveness to treatment and likeliness to have adverse reactions.
In order to elucidate systems disorder, we have to examine the genome,
epigenome, trascriptome as well as proteome (the entire set of proteins),
metabolome (all metabolic products), and interactome (the entire set ofinteractions). However, we cannot understand a treatment approach or stateof disease with these alone. For elucidating the diversity, complexity anddynamism of cancer, it is crucial to understand the system by large-scaledata analysis and mathematical modeling taking advantage of mathematics
and supercomputers.

●Breakthrough for understanding life systems through mathematical analysis by use of the K computer
In 2003, the 13-year “Human Genome Project”, which cost 100 billion yen,
finally succeeded in decoding the human genome. This year, the National
Institutes of Health (NIH) provided a roadmap after decoding the humangenome, which says “Biology is changing fast into a Science of Information
Management.” In 2003, it surprised many Japanese researchers, who thought
it was an exaggerated claim. Now, 10 years since then, everyone keenly
feels that the claim is coming to be realized. From 2003, the International
HapMap Project to elucidate individual differences on a DNA level was put
into practice mainly in the USA, and a basis for efficiently finding genes
involved in human diseases and drug responsiveness was established. The
International Cancer Genome Consortium started to prepare a genomic
aberration catalogue of major cancers from 2008, and has analyzed wholegenome information on 50 thousand human genomes, consisting of cancer
samples and normal cells of 25 thousand humans. Meanwhile, Barack
Obama who assumed the presidency in 2008 introduced “The Genomics
and Personalized Medicine Act” when he was a senator. In 2009, “the Genetic
Information Nondiscrimination Act” was enacted. Consequently, the USA
steadily implemented medical and healthcare strategies based on genomic
information. NIH started to allocate its budgets to the $1,000 Genome (aproject to drop the cost of full genome sequencing per human to roughly$1,000) from as early as 2004. Thanks to this, development and practicalapplication of sequencing technology was promoted, and now Biology is
becoming a science to analyze and interpret massive amounts of genome
data.
These days, sequencing technology is advancing rapidly. The nextgeneration sequencer using conventional optical sequencing technology
is an expensive instrument, and also has some problems such as need ofextravagant fluorescent reagents and limited lead length. However, today,
cutting-edge sequencers such as a semiconductor sequencer using lowcost silicon chips have been developed, and an instrument which can

12 BioSupercomputing Newsletter Vol.8

make personal DNA sequence information available “at $1,000 or JPY 100
thousand within several hours” will be put to practical use in 2013. It will
give steady service a couple of years later. Moreover, sequencers providing
personal genome information “at $100 within an hour” are said to be “aroundthe corner”. With the spread of super-low price, high-speed, high-precision
sequencers, personalized medicine, in which disease diagnosis and selectionof therapeutic strategy and type and dosage of the drug are decided basedon clinical sequence to check your own DNA, will rapidly become popular.
I believe the day is not far off when everyone has his or her own DNA
sequence. When the personal genome era begins in earnest, an enormous
amount of genome data will be produced, and new breakthroughs will beapplied to the understanding of life systems.
The first results have already been produced. It is the “discovery of causative
genes for chronic myelodysplastic syndrome (MDS).” Many cases of MDS
transform to acute myelocytic leukemia since bone marrow cannot produce
normal blood. Although its cause had been unknown, the team of Seishi
Ogawa (Cancer Board, University of Tokyo Hospital) and his colleagues
fully employed the supercomputer system at Human Genome Center(The Institute of Medical Science, The University of Tokyo) and discoveredfrom the thorough analysis of patient samples’ exomes (remnant after an
RNA splicing reaction) using next-generation sequencers that the cause
was mutations in 4 genes in the RNA splicing pathway. This is said to be ahistoric result in cancer research, since it is not only the first discovery of
the causative genes for this disease, but has also shown that abnormal RNA
splicing plays a role in the development of cancer for the first time in theworld. This can moreover be said to be the result of a successful combinationof sequencing technology, supercomputer and a statistical mathematics
analysis team. Also in the “Large-scale analysis of life data” of Strategic
Programs for Innovative Research Field 1, in which we are now engaged, wewould also like to achieve such breakthroughs.

SPECIAL INTERVIEW●Content of the research Theme 4address
The major objective of Theme 4, “Large-scale analysis
of life data,” is the understanding of the life programand its diversity. The first thing required to achieve thisgoal is the high-speed processing of massive amountsof next-generation sequencer data. For this purpose,
we made great efforts to establish the basis for highspeed analysis of massive amounts of next-generation
sequencer data optimized for “the K computer” from2011 to 2012. In this analysis system, we are trying
to realize high-speed analysis of massive genome
sequence information by achieving the performance of
10 million reads per hour and performing the world`s
deepest high-speed sequence searching which candetect more challenging smaller homology. In this way,
we are aiming to create a system to ensure a 10-to-1
advantage over the rest of the world.
The basic data for the analysis includes not only publicdata but also the Cancer TCGA Project and the Metagenome Project data, and we collaborate with theInternational Cancer Genome Consor tium which
researchers of Theme 4 participate in, the Innovative
Areas “Systems Cancer Research” and “Adipocyte
Project.” Validation by experiment is also carried out inthose projects.

Research advancement plan of Theme 4

The specif ic content of the research is roughly
grouped into “Understanding of the lineage of cancer and its diversity,”“Deepening of personal genome understanding,” “Personal medical
intervention prediction,” “Cell differentiation network,” “Drug response
network” and “Personal caner network.” Although the research will be
extensive, it has 3 goals. One is “Deepening the understanding of thebiological diversity and personal genomes.” We especially focus on the
metagenome and the diversity of cancer. In addition, by advancing
the understanding of personal genomes, we believe we can deepen
understanding of the biological diversity. The second is “Unravelingsystems characteristics/heterogeneity of cells/cancer and networks.”
The third is “Control of disease state” by leading those findings to
prediction of medical intervention. We are going to advance our own
research to achieve those three goals.
Simulation studies are being vigorously performed in Life Science.
However, the life system has not yet been sufficiently clarified tosimulate even E. coli fully. Therefore, although understanding of
the human life system is advancing, it is probably still very difficult
nowadays to draw new biological findings or develop a drug effective
for everyone by constructing a mathematical model for simulation. In
Theme 4, we start from data for data-driven science. It is our approachto develop methods to seek a clue to the cause for disease of eachindividual by analyzing data in an integrated way, and identifying
something visible from that.

Network of 400 mRNA+32 microRNA

MicroRNA/mRNA gene network of lung adenocarcinoma. Overall view of the causal
relations among genes (by courtesy of Professor Takashi Takahashi (School of Medicine,
Nagoya University)).

Introduction of the gene TTF-1/NKX2-1
changed the gene expression of surrounding
network that shows a significant relationship tosurvival. It is seen that the switch to determine
whether or not lung adenocarcinoma survives
has been altered (by courtesy of Professor
Takashi Takahashi (School of Medicine, Nagoya
University)).

BioSupercomputing Newsletter Vol.8 13

Report

4th Biosupercomputing
Symposium Report

Computational Science Research Program, RIKEN

Eietsu Tamura“Next-Generation Integrated Simulation of Living Matter (ISLiM)” hasbeen comprehensively working on the Grand Challenge for Life Science,
which is research and development of software in the field of life sciencetargeting the “K computer” since the start of the project in October 2006.
This project, which is organized by six research and development teams
for Molecular Scale, Cell Scale, Organ and Body Scale, Brain and Neural
Systems, Data Analysis Fusion, and High Performance Computing, will becompleted at the end of the fiscal 2012.
At the final phase of the program, the “ISLiM International Symposium:
4th Biosupercomputing Symposium” was held from December 3 to 5,
2012, at Tokyo International Forum (Hall D) by inviting the following sevenrenowned overseas researchers in the field of modeling and simulations
for expanding international visibility and collaborations:Pharmacueticals – Sandra R. B. Allerheiligen (Merck)
Brain Science – Sten Grillner (Karolinska Institutet)
Cell – Ion I. Moraru (University of Connecticut Health Center)
Medicine – Grace Peng (National Institute of Health)
Organ – Aleksander Popel (Johns Hopkins University)
Molecules – Karissa Sanbonmatsu (Los Alamos NationalLaboratory)
Molecules – Ruhong Zhou (IBM T.J. Watson Research Center)
Koji Kaya, Program Director, gave an opening speech, and two keynote
speakers, seven invited speakers and 14 ISLiM speakers presented
highlights on the research and development that have been conducted inthis program.
There were 145 participants successfully joining the symposium.

14 BioSupercomputing Newsletter Vol.8

The following are comments of some invited speakers from abroad.
・ The symposium was very broad and I have expertise in only some andnot all areas, but it is a very stimulating symposium and learned a lot.
・ The speeches of Program Director KAYA and Deputy Program Director
HIMENO were both helpful in presenting a great picture of the ISLiM
Project, and all the other speeches were also well-designed.
・ The ISLiM Project is evidently a success story. It is extremely importantto let other countries know about the sof tware researched anddeveloped in the ISLiM project and its experience.
During three days of symposiums, invited speakers and ISLiM teammembers communicated actively together. This was a good opportunity
to present all the work of ISLiM and its results to major overseas
researchers in the field, and gain their international visibilities.
The proceedings of this symposium is available in the Web site (URL:
http://www.csrp.riken.jp/4thbscs/program.html).

Report“K Computer”Compatible Computer:
Installation of SCLS Computer System

HPCI Program for Computational Life Sciences, RIKEN

Yoshiyuki Kido
Background and Mission

Providing a Test Bed Environment for Research Findings

The HPCI innovative research field 1 is proceeding with research activities
using supercomputers that are centered on the “K computer”, while taking
responsibility for the establishment of the research systems for computational
science such as the dissemination of research findings, cultivating humanresources, and creation of human networks. In that effort, we have decided to
install a SCLS computer system which is compatible with the “K computer”. Fig. 1
shows the field site for SCLS computer system installation. The purpose of thissystem is as follows:

In innovative research field 1 and the ISLiM project, applications that run
on super parallel computer machines such as the “K computer” are also
being developed. As a means for disseminating research findings, the SCLS
computer system will be developed as a test bed environment and will
promote the usage of these applications. Applications developed in the ISLiM
project range widely over six categories, resulting in development of a total of
31 applications [2]. These applications will be installed on the SCLS computer
system, and the SCLS computer system will be available as an environment for
their use and evaluation.

１. To promote the use of
supercomputers such as
the “K Computer” in life
science research

Creation of a Developer and User Community

２. To provide a test
bed environment for
research findings frominnovative research
field 1 and the ISLiM
project
３. To create a community
for developers and users Fig. 1 : SCLS computer system installation field siteof the “K computer”,
and “K computer compatible computers”.
Therefore, we are widely calling for participation of life scientists to use the
SCLS computer system [1]. The specifications of the SCLS computer system
which is to be installed are given in Table 1. The specifications of the “Kcomputer” are also provided as a reference for comparison.

Promoting the Use of Supercomputerssuch as the “K Computer” in Life Science Research
In the field of drug discovery, virtual screening and docking simulations
are performed using computers to narrow down candidate compounds for
improving efficiency. Furthermore, it is becoming realistic to perform precise
molecular dynamic simulations through calculations of intermolecular freebonding energy. To execute precise molecular dynamic simulations, thecalculation load rises exponentially when factors increase, and the calculation
time would be unrealistic without the use of supercomputers. However, it
is difficult for small and medium-sized enterprises and research institutions
to introduce supercomputers because they are highly expensive. Therefore,
we have developed the SCLS computer system as a super computing
environment that can be used casually on a small scale for life scientists.
We have prepared an open application system for using the SCLS computer
system, and programs that qualify and pass the open application may use the
SCLS computer system free of charge. Although the number of nodes of the
SCLS computer system is small, it is compatible with the "K computer", and wehope the know-how attained through the programs on the SCLS computer
system will lead to applications for using HPCI centered on the "K computer".

Table 1 : Comparison of SCLS computer system and K computer specifications

SCLS computer system

K computer

Logicalperformance

SPARC64TM IXfx

SPARC64TM VIIIfx

211GFLOPS(1.65GHz)

128 GFLOPS（2GHz）

Number of nodes

48

88,12810.1 TFLOPS

11.28 PFLOPS

32GB(whole system 1.5TB）

16GB(whole system 1.5PB）

Name

CPU

Number of cores

Whole
systemLogicalperformance

Memory per node

16

Many applications that run on the “K computer ” and “K compatible
computers” are developed by large projects, and maintenance of theseapplications will be difficult once the project ends. Such a situation would
be a large loss, therefore we aim to grow a developer community to allow
continuous maintenance of the applications running on the “K computer”
and “K compatible computers”. While providing computational resources,
the SCLS computer system will also provide a developer community withthe opportunity for lively discussions. Specifically, a Web-based system fordevelopers to freely read and write (see Fig. 2), and a mailing list, will be
provided. It is planned to operate the Web-based system as a place to containvarious information such as system troubleshooting and knowhow for paralleldevelopment, and a system operation engineer will be included in theeditorial staff.

Conclusion and Future Challenges
Life science, medical care, and drug discovery involve massive data andcomputational load that cannot be processed by humans. However, withthe progress of computers, this problem can be solved. In other words, you
can say that computers are absolutely necessary in the field of life sciences.
The “K computer” has taken first place on the 37th and the 38 th TOP500 listin 2011[3], and remains a top class computer in the world. In order to use the“K computer,” it is necessary to provide supercomputer use records suchas application scalability, which requires knowledge of supercomputers. It
is too difficult for life scientists who do not have this expertise to use large
scale computers like the “K computer”. Therefore, we believe it is necessary
to provide a supercomputer which is small-scale and can be used with easeby such life scientists. We hope that the SCLS computer system will become acatalyst in promoting the use of supercomputers.
[References][1] HPCI Innovative Research Field 1,
http://www.kobe.riken.jp/stpr1-life/en/[2] Next-Generation Integrated Simulation of Living Matter,
http: //www.csrp.riken.jp/index_e.html[3] TOP 500, http://www.top500.org
Fig. 2 : SCLS computer
system portal site
under test operation（Our English portal site
will be constructed in
the near future.）

8

BioSupercomputing Newsletter Vol.8 15

Event information and news
● Announcement of an open symposium

Open Symposium on the“High-performance Computing Infrastructure(HPCI)”by the Ministry of Education, Culture, Sports, Science andTechnology and the Strategic Programs for Innovative Research“Grand
Challenge application software development project”⃝Date : 10 : 00 - 18 : 20 on March 11 (Mon.) (A social gathering will be held after the symposium)⃝Place : Sanjo Conference Hall, The University of Tokyo (Bunkyo-ku, Tokyo)⃝Participation fee : Free (Social gathering participation fees will be charged)
For more details, visit our website (http://www.csrp.riken.jp/).

● Updated information on SCLS including events and research results is posted on our Twitter and Facebook at the earliest possible time.
Our twitter account is “@HPCI_Senryaku1” and our official Facebook page can be searched by the account “HPCI Senryaku1”. We hope
you will follow us on our Twitter and Facebook, and click the “Like” button with your comments.
● Information on acceptance of applications from the public to use the SCLS computer system (K-computer-compatible supercomputer
system) is available on the SCLS website (http://www.kobe.riken.jp/stpr1-life/).
Applications will be accepted in April, July and October in 2013. We look forward to receiving your application.
● In the Computational Biology Research Center (CBRC) of the National Institute of Advanced Industrial Science and Technology, “Strategic
Programs for Innovative Research Field 1 Human Resources Fostering Program” is being implemented. The program consists of awide range of activities, from training for novices in bioinformatics, to cultivation of human resources who can make full use of thesupercomputer, “K computer”, by providing seminars, workshops, tutorial training, and e-learning to the public, and by combining
computational science with life science in a sophisticated way. The details of activities for FY 2013 will be announced on the website(http://hpci.cbrc.jp) as soon as they are decided.

The Development and Use of the NextGeneration Supercomputer Project of the
Ministry of Education, Culture, Sports,
Science and Technology (MEXT)

Strategic Programs for Innovative
Research Field 1

Next-Generation Integrated Simulationof Living Matter
The “Next-Generation Integrated Simulationof Living Matter” is a project sponsored by the
Ministry of Education, Culture, Sports, Scienceand Technology(MEXT ), in which researchand development of simulation software tounderstand various phenomena occurring in the
biological systems, including molecules and thehuman body, have been undertaken to realize
a petascale simulation by making full use of theperformance of supercomputer “K computer”.

Supercomputational Life Science
SPIRE (Strategic Programs for Innovative Research)
is a MEXT program aiming to produce groundbreaking results in computer science technology
by maximizing the benefits of the HPCI (High
Performance Computing Infrastructure) centeredon the supercomputer “K computer”, andencouraging developments in five research fields
that need to be strategically addressed.
“Supercomputational Life Science” has conducted
research with the mission of understanding andpredicting life phenomena based on large-scale
simulation and advanced data analysis, and to
apply these results to design and implement
medicine and medical care with its research.

BioSupercomputing Newsletter Vol.8 2013.3
RIKEN

Research Cluster for Innovation
Computational Science Research Program
2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Tel : +81-48-462-1488 Fax : +81-48-462-1220
http://www.csrp.riken.jp

Issued: March 2013

HPCI Program for Computational Life Sciences
7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan
Tel : +81-78-940-5692 Fax : +81-78-304-8785
http://www.kobe.riken.jp/stpr1-life
RIKEN 2013-028