Next-Generation Integrated Simulation of Living Matter

HPCI Strategic Program Field 1 Supercomputational Life Science

BioSupercomputing
5
Newsletter2011.11Vol.

June 2011 Supercomputer "K computer"
Takes First Place In World

1st place certificate in the TOP500 ranking
Preparation status of "K computer" as of June 2011

CONTENTS●SPECIAL INTERVIEW●Report on Research●SPECIAL INTERVIEW●Report on Research◦The time has come for biosupercomputing to get results with the world's No. 1 supercomputer"K computer", and take up the challenge of "prognostic biology".
Deputy Program Director of Computational Science Research Program, RIKEN Ryutaro Himeno◦What should we do to promote industrial use of sophisticated computer resourcesand development applications?
Chief Coordinator of Foundation for Computational Science Masahiro Fukuda
Chief Researcher of Urban Innovation Institute and Executive Board Member and Bureau Chief of BioGrid Center Kansai Ryuichi Shimizu◦ Analysis of molecular mechanism of enzymatic reactions by QM/MM Free Energy Method
Graduate School of Science, Kyoto University Shigehiko Hayashi (Molecular Scale WG)◦Computational Mechanobiology of Actin Cytoskeleton
Institute for Frontier Medical Science, Kyoto University Yasuhiro Inoue (Cell Scale WG)◦ Development of Blood flow Analysis Method for Simulation of Thrombus Formation
Department of Mechanical Engineering, The University of Tokyo Satoshi Ii (Organ and Body Scale WG)◦Development of Data Assimilation Technology for Simulation of Living Things
The Institute of Statistical Mathematics Tomoyuki Higuchi (Data Analysis Fusion WG)◦Pioneering the Future of Computational Life Science toward Understanding and Predictionof Complex Life Phenomena
Program Director of RIKEN HPCI Program for Computational Life Sciences Toshio Yanagida
Deputy- Program Director of RIKEN HPCI Program for Computational Life Sciences Akinori Kidera
Deputy- Program Director of RIKEN HPCI Program for Computational Life Sciences Yukihiro Eguchi◦Simulation Applicable to Drug Design

Research Center for Advanced Science and Technology, The University of Tokyo Hideaki Fujitani (Field 1- Program 2)◦ An Ultra-fast Analysis System for Next-Generation DNA Sequencer Data

Graduate School of Information Science and Engineering, Tokyo Institute of Technology
Yutaka Akiyama, Takashi Ishida, Masanori Kakuta, Shuji Suzuki (Field 1- Program 4)●Report

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◦BioSupercomputing Summer School 2011

Computational Science Research Program, RIKEN Yasuhiro Ishimine (Organ and Body Scale WG)
Research and Development Center for Data Assimilation, Institute of Statistical Mathematics Masaya Saito (Data Analysis Fusion WG)
Niigata University of International and Information Studies Eisuke Chikayama (Cell Scale WG)
School of Medicine, Tokai University Yohei Nanazawa (Cell Scale/Organ and Body Scale WG)
Computational Science Research Program, RIKEN Takashi Handa (Brain and Neural Systems WG)
Computational Science Research Program, RIKEN Gen Masumoto (High-performance Computing Team)
Computational Science Research Program, RIKEN Kei Moritsugu (Molecular Scale WG)◦“Next-Generation Integrated Simulation of Living Matter (ISLiM)”,
a web page dedicated to new applications, has opened.
Computational Science Research Program Integrated Simulation of Living Matter Group●Event information

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PECIAL INTERVIEW

Biosupercomputing Pioneers the Future of Life Science

The time has come for biosupercomputing
to get results with the world's No. 1
supercomputer “K computer”, and take up
the challenge of "prognostic biology".

Deputy Program Director of Computational Science Research Program, RIKEN

Ryutaro Himeno● One third of the software has started tests in the environment of “K computer”.
In 20 0 6, the grand challenge (ISLiM:Nex t- Generation Integrated
Simulation of Living Matter) started with the mission of understanding
the super n-body multi-level problem, which is an extremely complex life
phenomenon, using K computer's peta-scale computation ability with
unprecedented performance, and develop software that will contributeto drug discovery and medical care. Five years has passed, and softwaredevelopment has now reached a new phase to proceed with super-large
scale parallelization and machine tuning using “K computer”.
According to the 37th TOP500 announced June of this year (2011), “K
computer” has measured 8.162 PFLOPS on the LINPACK benchmark(execution efficiency of 93.0%), and achieved 1st place even though it was
still under construction. Currently, our 31 software projects that are to run
on this highly acclaimed “K computer” are under development. 11 of thesehave already passed the development phase for large scale parallelization(8,192 parallel), and are being tested on “K computer”. The "MD coreprogram for large-scale parallel computers (cppmd)" is one of them, has
an execution efficiency that exceeds 30%, and reported 1.3PFLOPS as ofthe end of March. It is required for these software developments to makeas much use of the performance of "K computer" as possible. We wantto have a high line-up of software that exceeds at least 1PFLOPS whenusing the whole machine (10PFLOPS), and as for the mean time, one of
the softwares has already achieved the target performance. Including
cppmd, four softwares (cppmd, ZZ-EFSI, CafeMol, ZZ-HIFU) have exceededan execution efficiency of 20%, and two (cppmd, ZZ-EFSI) have reachedapproximately 40% as of now (October 2011). Keeping in mind that wewere able to start working on “K computer” from the beginning of thisyear, performance tuning has progressed at a rapid pace, and performance
results are rather satisfactory. However, further improvements are under
way in some softwares which have poor single performance values or havegood parallel performance but do not have good single performance.
Of course, “K computer” itself is a machine still under construction, andcompilers and network libraries will be enhanced. These enhancements
will probably improve the software performance, but we have no time
to waste. The time allocated to us to have priority access to “K computer”
is until the end of October, 2012, when its shared services will start. Until
then, we must organize calculations and produce results that have astrong scientific value. Therefore, we need to make our best efforts, suchas optimizing code, to withdraw the highest performance in the shortest
amount of time.
We of course are planning to run on “K computer” all of the 31 softwares
that are being developed and get results. But since time is limited, we
will have to decide on a detailed plan for how to allocate calculation size
and time. There may be 10, or maybe even less, that can be tested using
the entire system. Furthermore, we need to debate on the setup, such as
reducing calculation size but spending a longer time. One big challenge
will be devising a way to maximize our accomplishments by utilizing
calculation resources in a limited time.

● Grand challenge sows seeds for opening doors to the new generation of life science
In parallel to the grand challenge, the "HPCI STRATEGIC PROGRAM
Supercomputational Life Science", started this year. The main mission of
the grand challenge is software development which is science-oriented,
and not needs-oriented. With our desire to lead the entire life sciencefield towards computer simulation and data analysis, we have createdsoftware packages for a wide variety of research fields, such as genetics,
biomolecules, organs, and also the whole body. By producing further
results and publicizing them, our goal is to lower the threshold of thisgenre, and thus allow it to be used by people who wish to pursue newresearch. Therefore, our software lineup does not necessarily consistof software that may be desired by the pharmaceutical and medical
industries. On the other hand, for the HPCI Strategic Program, we will
maximize the use of high computing resources such as “K computer”, andaim to produce results that will have social value. Therefore, taking thesame computational software for molecular dynamics as an example,
development on “K computer” is its mission to meet the grand challenge,
and as for the Strategic Program, it is expected to get results that will bebeneficial to drug discovery. Like "development" and "usage", its use may
be entirely different even if the same topic is addressed. It is as though
we have planted seeds and are striving our best for beautiful flowers tobloom. With this analogy, we can say it is the job of the Strategic Programto nurture it until it bears fruit, and then to harvest it.

2 BioSupercomputing Newsletter Vol.5

We, in a sense, have been running towards a goal that has been set up
without considering the needs of society. However from now on, it is also
expected for the project to be applied for actual drug discovery and fordevelopment of medical equipment, and it will be required to add newfunctions, work along those who want use it for collaborative research,and to convene lectures. Through these activities, I believe the results ofthe grand challenge will be carried on with the Strategic Program. Therealready are high hopes in the findings by the molecule-scale research teamand the data analysis integration research team, especially in the drug
discovery field. Attention has been drawn to the fact that this software
can already be useful. It may take some more time for development by
the Organ and Body Team, but they too are also expected to contributeto drug discovery and medical care. I hope that by adding new functions
and combining them based on the software, the software will growin unpredictable ways. But to begin with, what we must first do is toenhance and perfect the software. With these efforts, there is no doubt
we will achieve research results that will surprise the world, namely, theblossoming of a beautiful flower of science.

SPECIAL INTERVIEW

An array of the quadrillion
computer “K computer”
in the computer room
(lef t). One chassis isequipped with 24 systemboards (right).

Building of the Advanced Institutefor Computational Science in which“K computer” is being installed.

● Being able to use “K computer” has strengthened a sense of unity between researchers
When the grand challenge was in blueprint, an overwhelming majorityof people around us felt that, in order to accelerate life science research,enhancing experimental devices would be much more effective thanspending money on supercomputer. The grand challenge started withvoiced doubts as to whether life science software could produce decent
results using a supercomputer. Already at this point, however, one thirdof the software being developed had started testing on “K computer”,
and produced results that exceed 10 thousand parallels. The number ofsoftwares with 10% or higher execution efficiency is rising. Of course,
this is only stating the performance values for the software program, andaccomplishments that have scientific value will be coming henceforth.
But we can say for certain that the opinions of people concerned with thisproject are changing. Expectations in the possibilities of supercomputing
are growing, thick and fast.

is now the world best's in hardware, next it's for us to achieve somethingwith it", and "it's up to us to try our best". Once again, it was made clear tome that the project is moving forward with everyone having a high senseof purpose and togetherness.
Our mission is to produce results at the same time “K computer” goes into
action. The time left for us is only one more year. The final year shouldbe the most important to make a beautiful flower blossom, and go on
to the next phase, "usage". In getting ready for the prospective year, thebiggest concern is next year's budget. To secure our budget is also a major
challenge.

At the same time, the project members have also changed. Since themembers came from a wide range of research fields, many only hadinterests in their own field, and in the beginning when we started where
there were no common topics to discuss. Facing the same table, wewere able to identify each other’s faces and were able to have someunderstanding among one another, but a dramatic change occurred
when “K computer” became available to us. “What can be done in order toachieve high performance with “K computer”?” This question is shared byevery researcher involved in the project. The sense that they are together
in driving the same project forward with “K computer” at its heart has
been born through shared experiences and a common language, and thebond between each member has strengthened. One example can be seenin the mailing list recently founded for young researchers working on “Kcomputer”. Passionate messages can be seen there, such as, "“K computer”

Aiming at development of software for contributing to the understanding of life phenomena
and medical care by utilizing the full performance of the "K computer".

Of 31 kinds of software under development, 11 achieved the scheduled massive
parallelization and are being tested with “K computer” (above). There are 10 kinds ofsoftware which have achieved 10 thousands parallelization or more (below) (as of
October 2011).

BioSupercomputing Newsletter Vol.5 3

S

PECIAL INTERVIEW

Biosupercomputing Pioneers the Future of Life Science

What should we do to promote industrial use ofsophisticated computer resources anddevelopment applications?
Chief Coordinator of Foundation for Computational Science

Masahiro Fukuda (left photo)

Chief Researcher of Urban Innovation Institute andExecutive Board Member and Bureau Chief of BioGrid Center Kansai

Ryuichi Shimizu (right photo)● Why computer simulation is not widespread in private

did not participate in the first place. We planned the development
without pharmaceutical companies.

━ You two are members of the Preparation working group on
promotion of industrial use in the HPCI consortium. First, would you tell
us what kind of work you are doing now? Incidentally, Mr. Fukuda wasengaged in the development and operation of the Numerical Window
Tunnel (NWT), the supercomputer that once achieved the world speedrecord at the National Aerospace Laboratory of Japan, and then has
consistently worked in computational science and contributed to its
development.

FUKUDA : I see. This is why I asked. I often hear that although researchersdevelop applications, private companies cannot master them easily.
They need supporters teaching them step-by-step how to use suchapplications. Therefore, those applications do not get used by privatecompanies.

companies?

FUKUDA (Honorifics omitted) : Now I belong to the Foundation for
Computational Science which was established to motivate Japaneseindustry by disseminating computational science and simulation
technology, and help various private companies with research anddevelopment utilizing supercomputers. Our foundation also has asupercomputer for industry, the “FOCUS Supercomputer,” which
promotes advanced technology in the industry. I hope you makeextensive use of it.
SHIMIZU : I’d like to speak about the BioGrid Center Kansai I am involved
in. Originally, Osaka University promoted the “BioGrid Project.” It
aimed at running development applications for bio and medical carein a grid computing environment. In order to get the technology
and research products widely utilized in, for example, the industrial
arena, we established the BioGrid Center Kansai (NPO). In 2004, westarted a testbed and appealed for its use, but initially pharmaceuticalco mp anies to o k no ac tion. We res er ve d Os ak a Uni ver sit y ’s
supercomputer, installed applications in it and were ready for runningthem, but pharmaceutical companies would not use them, saying“we cannot provide our data” or “we are not allowed to connect our
network to outside.” So, we decided to get results by ourselves. We
cooperated with a venture company from Osaka University whichbegan operation and started a Development Project for Creationof an Intellectual Cluster. The year after, the National Institute for
Biomedical Innovation asked us if we could do in silico drug discovery
with them. Then, the “Drug Discovery Value Chain Project” (Wenamed it “Pharmaceutical Innovation Value Chain”) which creates
pipeline compounds based on computer simulation was started. It
was a time when the trend toward in silico drug discovery was about
to take off. That might be also helpful.
FUKUDA : Were applications available at the BioGrid Center Kansai
developed by professors at Osaka University?
SHIMIZU : We use various applications. But we basically use a docking
simulation (myPresto) developed by Professor Haruki Nakamura, fromthe Institute for Protein Research, Osaka University, and Mr. Yoshifumi
Fukunishi, from the National Institute of Advanced Industrial Scienceand Technology.
FUKUDA : After starting the project, were detailed calculations done bypeople from the pharmaceutical companies?
SHIMIZU : We outsourced them to a company. Pharmaceutical companies

4 BioSupercomputing Newsletter Vol.5

SHIMIZU : There are certain levels of acceptance in the pharmaceuticalindustry. About 10 of Japan’s largest companies have personnel who
have solid knowledge of computational science and enough ability touse supercomputer by themselves. As they want to know the details,
they ask for explanations on applications. After that, they only needsome basic training. Among those companies, about three companies
are actively using university supercomputers for computing with us.
FUKUDA : Several companies have already gotten that far, right?
SHIMIZU : Although I don’t know what level of computational resources
they have, I think major companies have an in-house compound
library and a system good enough to do reactive tests by themselves
to find the compound. However, there are many companies which
ask for teaching from the basics up. Some companies say they want
to study to work on drug discovery using supercomputers like major
companies in the future, and participate in our research project asobservers, of course after signing confidentiality agreements.
FUKUDA : There are some companies which are determined to takea proactive approach to supercomputers among middle-classpharmaceutical companies, right?
SHIMIZU : Yes, but I doubt such a proac tive approach will spreadthroughout the pharmaceutical industry. I get the impression that it
has not yet become the mainstream, and that many companies do a
bit of simulation for drug discovery when their experimental trials go
nowhere.
FUKUDA : You mean that it is not yet the mainstream that the in silico teamfirst does a simulation, which is finished up by experiment?
SHIMIZU : There seems to be a problem of time. They say they can see
results faster by doing experiments than waiting for computational
results for half a month. Due to such time-lag problems and otherthings, the synthesis team and in silico team do not collaborate wellin their systems, I think. Since some have been doing what they
can do with their conventional computational resources, they don’t think they can do large-scale computation and are discouraged
from the very start. From the beginning, they cannot imagine doing asimulation aggressively with an unusual resource like “K computer”.

● Pharmaceutical companies are changing theirunderstanding of simulation.

━ Is it still the same, the situation you mentioned before that theycannot take data outside the company, or they are banned fromSPECIAL INTERVIEW
What is PharmaceuticalInnovation Value Chain

Wet system

In silico system

Pharmaceutical Innovation Value Chain
Wet team

Proteincrystallization

Wet team

Proteinstructure
analysis

Search for
Compoundbasic skeleton optimization Synthesis

・Structural approach・Statistical analysis
approach・Structural approach・Structure activity
correlation

EXIT

Results of BioGrid Project
Source: BioGrid Center Kansai (NPO)connecting to the network?

What is the drug discovery
Laboratory of Japan. When we introduced a supercomputer, wevalue chain?

SHIMIZU : I think their attitude is changing little by little. There may
be circumstances that they cannot af ford to concentrate theirpersonnel only on computation. It seems that there is a growing
movement to allow outsourcing of routine computation as far as aninterorganizational confidentiality agreement is signed.
FUKUDA: I think reliable organizations must be provided that take care ofcomputation for important industries. For example, RIKEN takes careof computation for the pharmaceutical industry. Such a system should
be prepared as national policy. By doing this, you can make clear who
is responsible. Not to mention cost-effectiveness, you can gather all
sorts of know-how. The FOCUS supercomputer of our foundation was
introduced exactly with the concept of being used by the industrial
sector. Although its level is 25TFLOPS meaning not so powerful, I am
pretty sure there is enough demand after visiting many companies
to talk to them. Many companies we have visited are in the field of
CAE (Computer Aided Engineering). We have not yet visited so many
pharmaceutical companies, but I think there is enough demand fromwhat I hear from you.
SHIMIZU : The pharmaceutical industry is now in a transition stage. They
had been defensive, but they are starting to think they should use
outside resources. On the other hand, No.1 in the world, “K computer”
is starting to gear up for industrial use. I feel the time is right. Only wehave to present successful examples, or when a tangible achievement
is made, some pharmaceutical companies may get interested andbegin using supercomputers.
FUKUDA : In this regard, the result of the RIKEN grand challenge has
become very crucial.
SHIMIZU : No other country has put more effort into the development ofbiosimulation than Japan. I can say Japan is the world leader in thisfield. Since the wisdom of the Japanese people has come this far, it
would be a waste not to use it. What a shame. We should all publicizeit.

● Sprouts do not appear without sowing seeds, although
you water the ground.

━ We cannot produce new drugs easily today, but what is the
expectation for computer simulation in the pharmaceutical industry as amethod to find candidate compounds?
SHIMIZU: I don’t know the full details, but I think a willingness to docomputer simulation is being fostered. However, they don’t havethe necessary know-how and might still be hesitating. Also, top
executives may mistrust us. For example, they may think, “Can drugs
be developed in such a manner?” I think it is our challenge to change
their attitude.
FUKUDA : There was a story when I still worked in the National Aerospace

told major companies in the industry that simulation must be used
more in the development of aircraft. However, the middle managers
at that time expressed little enthusiasm. What I did next was talkwith young frontline engineers who were eager to absorb newtechnologies. Then, I had them use applications we had developed
as much as they liked. Next, I went to see the director of the company
and said, “Japanese technology won't go anywhere unless we startnow”. However, he still didn't lift a finger. Then, I said, “Your young
and active engineers are very enthusiastic”. Then, he got interested,saying, “I would listen to your idea if we have such young engineers,”
and I was able to proceed further. That was a real experience. It is
crucial to find out how to input data in the company. It is not always
good to rush things and negotiate only with senior management.
Sprouts do not appear without sowing seeds, although you water the
ground as much as you can.

SHIMIZU : In fact, similar things are going on in pharmaceutical companies.
While we are under taking joint research with them by using
applications developed at the university, they will bring in theirassignments, and are going to feed them back into the computation.
FUKUDA : After getting a result, someone in a higher position only has togive the top management a kick in the butt (laugh).

━ Do you have something you would like researchers developing
applications to know as regards industrial use?
FUKUDA : Applications are easy to understand when a sample calculationis ready. For example, this input results in this computation and so on.
In addition, it would be good to have additional functions that can
respond to various requests from companies, for example a programthat can be applied to a slightly different calculation. By meeting such
requests willingly, a relationship of trust would be built up fostering
a willingness to work together in a team environment. Of course, itdoes not mean that you do everything the company suggests from A
to Z.
SHIMIZU : I want them first to build tidy applications and then to prepare
basic manuals, because the user interface and so on are not the areaof researchers. If possible, the application should focus on versatility.
FUKUDA : It is not good that an application can be used for one group ofcompounds but not another.
SHIMIZU : Anyway, we aim at understanding and widespread use of
supercomputer simulation, and would like to spread the word mostly
among pharmaceutical companies that you can do these applicationsor that we have these great applications.

━ We may still have big challenges to overcome, but I feel I can lookforward to the future. Thank you for coming today.

BioSupercomputing Newsletter Vol.5 5

Report on Research

Analysis of molecular mechanism of enzymatic
reactions by QM/MM Free Energy Method

Graduate School of Science, Kyoto University

Shigehiko Hayashi(Molecular Scale WG)

Our group is developing the QM/MM free energy method for analyzing
the molecular mechanism of enzyme reactions. Many molecular functionsof biomolecules are coupled to chemical reactions with enzyme catalytic
activity. Therefore, understanding the mechanism of enzymatic chemical
reactions is important in control and design of biological molecularfunctions. The mainstream method for this purpose is currently the hybrid
QM/MM approach in which quantum mechanics (QM) method is combinedwith the molecular mechanics (MM) method. In this method, very highcomputational efficiency is attained by describing the local active siteof the enzyme catalyst by the QM method, and the other vast protein
environment by the computationally cheap MM method. So far our grouphas elucidated molecular mechanisms of various enzyme reactions suchas photoreceptor and molecular motor proteins by using the QM/MM
approach.
In spite of the usefulness of the QM/MM approach, the calculation of
enzymatic chemical reactions coupled to molecular function still remained
difficult. In many cases, molecular function appears with accompanying
by large conformational changes in biomolecules. Such conformationalchange is usually analyzed by molecular dynamics (MD) simulation basedon the MM method. However, calculation of enzymatic chemical reactions
coupled to conformational change requires a description based on the
QM/MM method. In the QM/MM approach, however, QM calculation iscomputationally much more expensive compared to MM calculation,even though its application is limited to local active sites. Therefore, it is
impossible to obtain enough MD sampling time to adequately take into
account the slow relaxation of the biomolecular system.

Fig 1 : Enzyme reaction scheme of α-amylase (above), and structural change fromreactant to product (middle and below). Many parts of optimized structure on thefree energy surface are different from the structure on the potential energy surface(0K), but it is also observed that the distance between atoms which have strong
electronic interaction in the product is maintained in a thermal fluctuation.

6 BioSupercomputing Newsletter Vol.5

In order to solve this problem, we have developed a novel QM/MM freeenergy method (QM/MM-RWFE-SCF method). In the QM/MM free energy
method, the optimum free energy structure of the active site molecule
treated by the QM method is determined on a free energy surface definedby the structural distribution of the MM region sampled by MD simulation.
We developed a highly accurate and very efficient method by combining amean field approximation with a statistical reweighting method, and thenconsidering appropriately long-range coulomb interaction between QM
and MM by the Ewald method. Especially in the scheme of this method,
the calculations of the QM/MM method part are completely separate from
the MD simulation part for MM structure sampling. Therefore, existingsophisticated MD programs can be applied to the MD simulation, which
achieves great flexibility in performing calculations.
As a test of the method, it was applied to hydrolysis of the glycosidebond ofα-amylase shown in the reaction scheme in Fig 1. The protein
system in water represented by a periodic boundary condition consists of
68,000 or more atoms, and QM molecules of the active site are describedwith abundant base functions of over 600 bases. By using this method,
we determined the optimum energy structures of the reactant and the
product of the reaction. In the result, as shown in Fig 2, their optimum
structures were determined by the accompanying 90- and 21-ns large
structural relaxation of protein loops surrounding active sites, respectively.
Such a QM/MM free energy structural optimization calculation close to
sub-microseconds has no precedent. More specifically, the structural
optimization calculation per formed by this method can follow therelaxation process of a protein structure ten-thousand times longer
compared to direct QM/MM MD calculation, and describe relaxation
100 to 1000 times longer compared to other similar QM/MM free energy
calculations. In addition, this calculation suggests the possibility thatadjacent loops assume different structures (Fig 2). Such a major change ofprotein structure correlating with the enzyme reaction is a finding whichcannot be obtained through conventional methods. It is believed to offer
a new avenue for elucidating, controlling and designing new molecularmechanisms of enzyme reactions.

Fig 2 : Change of protein structure between reactant and product. The structureof the loops adjacent to active sites (L1 and L2 in red) obtained by free energy
structure optimization are largely different from the structure before structural
optimization (in blue). The structure of the L2 loop differs substantially between
reactant and product.

Report on Research

Computational Mechanobiology ofActin Cytoskeleton

Institute for Frontier Medical Science, Kyoto University

Yasuhiro Inoue(Cell Scale WG)

The actin cytoskeleton forms higher structures such as a network structure
and bundle structure through interaction of various auxiliary proteins using
actin filaments as the basic skeleton, in which actin monomers are coupledwith one another, and constitutes an important mechanical and chemicalbase which supports cell shape and motion. For example, actin beneath thecell surface forms structures for motility such as filopodia and lamellipodia.
They are essential for formation of neuronal growth cone and cell motion.
In actomyosin filaments consisting of actin and myosin, the motor
motion of myosin generates a force, which is crucial in cell deformation inmorphogenesis, cytoplasmic division by a contractile ring, and turnover ofcell adhesion. Those structures emerge from molecular systems centeredon actin, and can change their structures and characteristics dynamicallyby going the round of mechanically and chemically metastable states. We
are studying the relation of actin cytoskeleton dynamics to cell functions
by using a computational mechanobiology approach. Our recent studies
revealed that cell shape in cell motion is coupled with actin polymerization

via thermal fluctuation [1], and the relation between the force generated
in the actomyosin network and the myosin density is bilinear in responseto network structure change [2]. These studies give important insights in
the relation between cell-scale expression of function and molecularscale dynamics. Since actin mechanobiology can be simulated by using
computers, it is not a totally unrealizable dream to recreate a whole cellon the computer. Of course, we have a lot of challenges to overcome. As
for these challenges, we have to work with researchers inside and outside
the Next-Generation Integrated Simulation of Living Matter Group. A
Computational Science Algorithm Study Group is regularly held to createsuch an opportunity. There, researchers share knowledge of mathematicalmodels and computational approaches beyond the scope of their fields,
and pursue collaboration with researchers in related boundary areas.

[References][1] Inoue, Y and Adachi, T. Biomech. Model.
Mechanobiol. 2011 10:495-503[2] Inoue, Y. Tsuda, S., Nakagawa K., Hojo, M., Adachi T.
J Theor Biol. 2011 21;281(1):65-73

Fig 1 : Computational simulation of migrant cell: Cell
shape obtained by simulation (solid line) coincides withactually-observed cell shape (fluorescence image). In
addition, it was found that the actin polymerization
speed at the tip of the cell (longitudinal axis) is in
proportion to the curvature radius at the tip of the cell(horizontal axis).

Fig 2 : Autonomous structural change of actomyosinn e t w o r k : D u e to m o to r m o t i o n o f my o s i n i n t h e
ac tomyosin net work , the struc ture of ac tomyosin
network changes autonomously. Simulation showedthat the force generated in the network according to
restructuring is bilinear with respect to myosin density.

BioSupercomputing Newsletter Vol.5 7

Report on Research

Development of Blood flow Analysis Method
for Simulation of Thrombus Formation
Department of Mechanical Engineering, The University of Tokyo

Satoshi Ii(Organ and Body Scale WG)

When the inner wall of a blood vessel is damaged, a thrombus is formedat the site of damage. Due to the thrombus, bleeding from a slight injury
usually stops after a certain amount of time. This is the major role of athrombus. The thrombus formed is lysed after bleeding stops. For somereason, it sometimes does not disappear and becomes enlarged. Then, it
may block the blood vessel, or the enlarged thrombus may slough off andocclude a more distal vessel to block blood flow. Such a thrombus causes
brain infarct and cardiac infarct. It is also a cause of the development of
economy-class syndrome or flight-related DVT. For these reasons, antithrombosis agents are used in clinical practice. However, since they havethe potential to affect normal thrombus involved with hemostasis, theyshould be given cautiously. However it is known to be very difficult toassess the effect of a medicine in the bloodstream of the human bodyin advance. Against this background, our group is aiming to reproduce
thrombus formation through simulation. For example, we are thinking of
its use for understanding drug efficacy in the bloodstream in developing
new drugs.
In the thrombus formation process, adhesion of platelets to the damaged
vessel wall, agglutination of platelets and coagulation incorporating
erythrocytes, involving various factors such as the bloodstream and cell
metabolism, occur in a step-by-step manner. In this research, we focus on
the early process, platelet primary aggregation in which platelets adhere
to the damaged cell wall. In this process, platelets adhere to the damaged
cell wall through binding of vWF (von Willerbrand factor) attached tocollagen beneath vascular endothelial cells and GPIbα (glycoprotein Ibα)
found on the platelet surface. In order to analyze such phenomena, we aredeveloping an approach to deal with bloodstream phenomena including
erythrocytes and platelets, and GPIbα-vWF binding phenomena between
the platelet surface and damaged cell wall in a unified manner (Fig.1).
Since erythrocytes have a very high bulk ratio of around 45% in the
bloodstream, when the vessel diameter is micrometer-size, they affectthe flow properties of the blood stream and become strongly involved inplatelet aggregation. Since erythrocytes are very flexible and deformable,
they cause an interac ting phenomenon with the f low f ield. Since
these phenomena must be analyzed in a system containing numerous
erythrocytes, our group proposes a new analytical approach which
excels in calculation efficiency and has a good compatibility with bloodflow analysis [1,2]. We performed an analysis by use of an experimentallyobtained physical property of erythrocytes. As shown in Fig 2, we obtained
a valid result as compared to the deformation behavior of real erythrocytes
in the flow field.
In the analysis of platelet primary aggregation, binding of GPIbα on theplatelet surface and vWF in the damaged cell wall has to be considered.
They are molecular-scale phenomena, but their interactive analysis
in combination with blood flow is currently impossible. Therefore,
we introduced a technique in which we can deal with such binding
phenomena statistically, and combined it with blood flow analysis. Fig 3shows an example of the analysis. It reproduces how binding of GPIbαon the platelet and vWF at the bottom of the wall (simulating a damaged
vessel wall) takes place, and how platelets adhere to the cell wall.
Hereafter, we will deal with aggregation of platelets by modeling themetabolic response of platelets, and reproduce the thrombus formation
process in a step-by-step manner.
[1] K. Sugiyama, S. Ii, S. Takeuchi, S. Takagi and Y. Matsumoto, A full Eulerian finite
difference approach for solving fluid-structure coupling problems, J. Comput.
Phys., 230 (2011) 596-627.
[2] S. Ii, X. Gong, K. Sugiyama, J. Wu, H. Huang and S. Takagi, A full Eulerian fluidmembrane coupling method with a smoothed volume-of-fluid approach,Commun. Comput. Phys. (2011) accepted.
[3] P. Gaehtgens, C. Dührssen and K.H. Albrecht, Motion, deformation, andinteraction of blood cells and plasma during flow through narrow capillary tubes.
Blood Cells, 6 (1980) 799-817.

Fig 1 : View showing a frame format of analysis toward
platelet primary aggregation

Fig. 2 : Deformation behavior of erythrocytes in theflow field [3], and result of simulation

8 BioSupercomputing Newsletter Vol.5

Fig 3 : How platelets adhere in fluid analysis taking GPIbα-vWF
binding into accountReport on Research

Development of Data Assimilation
Technology for Simulation of Living Things
The Institute of Statistical Mathematics

Tomoyuki Higuchi(Data Analysis Fusion WG)

Through the East Japan Great Earthquake (the big earthquake that hit
Eastern Japan this year), we researchers recognized that it is difficult tounderstand and provide protection against or control complicated systems,
and that we continuously need to bring together our wisdom to solve
problems. When we try to understand and control complicated systems
such as the earth as well as living things, it is effective to evaluate andcorrect the progress of research by using our ability to predict phenomenaon the assumption that information about an object is always incomplete.
This approach has been demonstrated in statistics, and it has always
contributed greatly to human prosperity on earth.

quantities of space data collected hourly from all over the world and thelargest world scale simulated calculation result on a super computer using
Bayesian statistics, and then improving simulation models in real time. Also,
it has been pointed out that the simulator SPEEDI, which estimates theeffects of atomic radiation and became a hot topic recently, was not able
to demonstrate its power sufficiently partly because it had no function toreflect real observation data in the simulator in real time.
The integration of observation data and the result of the model is referred
to as data assimilation, and it has recently been attracting attention in thefield of simulation science. If the idea of data assimilation is applied to thesimulation of living things, it at least will lead to a steady improvement ofprediction ability, and consequently, help understanding and control of
the complex system. With this fervent desire, we have worked on researchand development of data assimilation technology for simulation of living
things as members of the Data Analysis Fusion Team every day. Current
living thing simulation models are like ready-made clothes when compared
to clothes. Even if there is a variation, there might be a difference in size
like S, M, and L at most. On the other hand, each human body system isdifferent. We are looking forward to the day when a custom-made or evensemicustom-made living matter simulator, which is suitable for a patient,can automatically be built from medical information about patients who
suffer from side-effects of medicine or a treatment method.

Prediction ability is an integrated index of effectiveness, which consistsof two major functions. One of them is the descriptive power of a forward
computing model, and the other is the cognitive power that catches
the present state of the object (current state). To put it simply, forward
computing represents a repeat assignment operation. For example, it is like
a computing method where, when a value is put into the right side of an
equation, the resulting value comes out of the left side, then the resulting
value is also put into the right side, and the resulting value of the next stepcomes out of the left side. Many simulation computations explicitly solving
time development adopt this method, and long-term prediction is achievedby repeating this forward computing. On the other hand, the latter isrelated directly to innovation of measurement methods. New instruments
can provide larger amounts of more precise information thanever before by innovations with epoch-making measurement
Analysis and prediction based on a simulation personalized for an
methods, and they are a great attraction to researchers in any
individual or an environment are important in the future.
field, especially life sciences, where new instruments have driven
Data assimilation is the integral technology for realizing this objective.
development.
However, leaning toward research and development of
measurement devices is not a good strategy from the viewpointof improving prediction ability. This is not only because directly
measuring the whole object has limitations in theory, but it isextremely effective to strengthen the descriptive power of theforward computing model for increasing prediction ability. In
the research fields that have a long history of simulation, suchas earth and space sciences and solid-state physics, governing
equations that are the basis of forward computing have usually
been established, and it is important for success to implement
calculations approximately based on the governing equations on
a super computer. Improvement of this approximate calculation
is equivalent to improvement of the forward computing model.
One of the major goals of BioSupercomputing is to dramatically
improve the descriptive power of this forward computation
model by taking full advantage of the scale of computing
hardware. Unfortunately, it is not an exaggeration to say that
there is no principle corresponding to the governing equation inlife sciences, so forward computing models themselves must be
based on a wide variety of ideas and become less general.
Then, is the systematic improvement of forward computing
model dif ficult in life sciences? As mentioned above, theability to predict phenomena can be improved by enhancing
both the measurement method and the forward computing
model. Therefore, it looks more natural to arrange the forward
computing model so that prediction ability may improve than tomodify the model in accordance with its own evaluation criteria.
This means that learning functions fed back from measurement
data are added to the forward computing model. In fact, inthe weather and oceanography fields that are state-of-the-art
areas of simulation research, it is usual that weather forecasting
services improve prediction performance by integrating large

Experiment and Measurement

Modeling

Designing Models
Evaluation and Selection
Rebuilding Models

Building Hypothesis
Predicting Drug Response
Designing Experiments
Simulation: PC9:GFT

Simulation

Experimental data: PC9:GFT
Simulation: PC9GR:GFT
Experimental data: PC9GR:GFT

Figure 1 : Conceptual diagram that shows how to use the application under development,
LiSDAS (Life Science Data Assimilation Systems). LiSDAS took its name from the well-known
Automated Meteorological Data Acquisition System, AMeDAS. Data acquired from anexperiment or a measurement site (upper left part) and an existing set of models (upper rightpart) are combined to perform the data assimilation. Data assimilation allows for evaluation
and rebuilding of models at the same time. The calculation result and the measured data in
the assimilated simulator are shown in the lower part. The result of the data assimilation is
utilized to build a new hypothesis, and to design the following experiment. LiSDAS is such a
calculation platform which achieves a series of intelligence cycles.

BioSupercomputing Newsletter Vol.5 9

S

PECIAL INTERVIEWHPCI Strategic Program Field 1
Supercomputational Life Science

HPCI Strategic Program Field 1 Special Discussion

Pioneering the Future ofComputational Life Science toward
Understanding and Prediction ofComplex Life Phenomena

Dr. Kidera, Mr. Eguchi and Dr.Yanagida (from the left)

Toshio Yanagida
Deputy- Program Director of RIKEN HPCI Program for Computational Life Sciences Akinori Kidera
Deputy- Program Director of RIKEN HPCI Program for Computational Life Sciences Yukihiro Eguchi
Program Director of RIKEN HPCI Program for Computational Life Sciences

The HPCI Strategic Program, which aims to produce the world’s best research products in a wide variety of fields by utilizing
Japanese High-Performance Computing Infrastructures (HPCI) centered on “K computer” and returning their fruits to society,got into full swing this year. What breakthrough will Field 1, “Supercomputational Life Science” bring to life science? We had
Director Toshio Yanagida, Deputy Program Director Akinori Kidera, and Deputy Program Director Yukihiro Eguchi talk aboutthat (This article appears in this and following numbers).

● Our goal is to recreate life phenomena on the computer.
YANAGIDA (Honorif ics omit ted) : In anutshell, our dream is to reproduce
life phenomena representing highly
complex, non-linear and dynamic
systems on the computer. It is our
final target. For example, in orderto reproduce various cell functions,
we have to have enough data to
input to the computer. Of course, wecannot understand the system onlywith the data, and need a concept toprocess and analyze the data. After
formulating it properly, we set up amodel based on it and reproduce cellfunctions with the computer. Of course, we want to reproduce not
only cells but also tissues and organs, and that's not easy. Therefore,
we must first have a firm understanding of partial functions such asprotein functions and reproduce them on the computer. It would
be wonderful if that could lead to drug discovery. Of course, we areinterested in the way how complex movements of proteins lead totheir function, but if we could manipulate protein functions, we would
be able to expect to translate it into drug discovery.
KIDERA : I have been studying protein
simulation. My final goal in the HPCI
Strategic Program is the same aswhat Dr. Yanagida said. I want to
replicate protein behavior plainly bysimulating proteins in the computer.
H o w e v e r, c o m p a r e d t o o t h e rf ields such as the materials f ield
in which computational science isadvanced, life science is still largely
undeveloped. If I were to speak out
without fear of being wrong, I would
say that simulation of life phenomena
is nearly impossible. Then, what is
possible to us? I think our challenge started from here. It is difficult

10 BioSupercomputing Newsletter Vol.5

to replicate cell behavior. Then, how about the more limited case ofa single protein molecule? Like this, we have studied what we canfind by limiting the theme until we can handle and do the simulation
properly. We made the theme smaller until we could handle it. As aresult, what happened? It became a matter of physics, not biology.
Everything starts from physics. However, many biologists ignored
it completely. Then, what is different between life phenomena andphysical phenomena? For example, nothing happens by putting asingle protein into the computer and simulating it. A partner is needed
to make something happen. To make the partner work, anotherpartner is required. And then, that partner needs yet another one.
Such a world of endless chain reactions takes place in an astonishingly
complex and heterogeneous cell environment. Formerly, we were
not able to observe it due to the limitation of computer power. With
the increased computer power, the range that we can reproduce
becomes larger spatially and temporally, and we don't have to minify
the limitation. Now at last, biologists are starting to show interest. In
the next step, we are going to observe something which is larger andmore real with “K computer”. Our research level at last is starting to
be recognized by biologists doing experiments. In addition, people
in the fields of medical care and drug discovery, who thought they
had nothing to do with computing, are starting to say, “Simulation
may be useful” or “We may be able to do something interesting with
K computer”. What was once thought to be impossible may become
feasible or probable. Well, that's an overstatement (laugh).
YANAGIDA : If you say, “Let’s improve this by five times,” you will havetrouble dealing with the reality, but if you say, “By one hundred
times,” you will feel relieved and get on with the research (laugh).
KIDERA : Since the level of the computer we used so far was 10TFLOPS andthat of “K computer” is one thousand times higher, 10PFLOPS, more
and more researchers should show an interest (laugh).
YANAGIDA : Aside from joking, it's actually very important that researchers
think something is “interesting.” If they think simulation is interesting
and useful, they come to think along those lines and their research
may take a new direction.

SPECIAL INTERVIEW● We will show that life science is becoming an information-driven science.
EGUCHI : Dr. Yanagida said it's a dream,
but I hope the day would come,when many researchers who once
performed many experiments withtrial and error say, “As I can now findthe result of this experiment throughcomputer simulation, I will leave it tosimulation.” For example, the safetyof automobiles is tested by collisionexperiments. It was once actually
done with many automobiles tochange collision angles and speeds,
but today most experiments are done
through simulation. This means thatcomputer simulation has become that much more reliable. I hopesomething similar would happen in life science.
YANAGIDA : Right, in the field of manufacturing, half of the experiments aredone by simulation. It is common to use simulation in experiments. In
this regard, life science may be behind in computer science. However,life science will also follow the same pattern. It is impossible to replace
everything, but we have to omit experiments and use simulation withdetermined parameters as much as possible. Then, the part we cannot
simulate should be solved by experiments to increase efficiency, andespecially in drug discovery. There are too many combinations for trial
and error.
KIDERA : There is another important thing in addition to what Mr. Eguchi
mentioned. I think it's to show that life science can be shown to
be a science which utilizes information. This means improving on

the limited way of conventional information processing with “Kcomputer” change the image of information itself, and make what
was once thought impossible, possible. I think it's one of the roles ofthe HPCI Strategic Program we are addressing to clearly point the way
in this direction, and greatly expand the possibilities. It's important toshow how to do data analysis or prototypes using massive computerresources like “K computer”, and widely demonstrate its possibilities
and results to the outside. Also, I’d like to speak a little on what Dr.
Yanagida said: life science lags behind manufacturing. Actually weare doing the same level of computation. However, it's not enoughwhen we come to life science. So it seems we can only handle smallthings. For example, there is whole body-scale simulation. The humanbody basically looks similar to an automobile, but it's different fromautomobiles which are a combination of steel sheets and an ironframe - even an organ is a gathering of a lot of very, very many kindsof cells. In addition, heat and substances go in and out of the organ
for chemical reactions. We must change our computations dependingon what we observe. Anyway, it's a very difficult problem.
YANAGIDA : Its crucial difference from the other fields is that life science
does not have fundamental equations. We can only search forparameters and find a certain predisposition or state of things. It's alsodifficult that what is going on has multiple factors. Since the targetsof life science are very complex and difficult, we still have a long way
to go. In addition, a high computing power is required due to many
combinations. However, if we clear those obstacles and our researchlevel reaches that of other fields, computer simulation would be able
to do a lot of things, i.e., contribute to drug discovery.

● Can you do your studies without enjoying them?
EGUCHI : It's tough enough to analyze only proteins. Cells have all sortsof other data. Dr. Yanagida, who is conversant with the difficulty ofcomplex, varied and non-linear life phenomena, is head of the projectand is always issuing communications. I think this is very important.
By doing this, I believe that new ideas will come out of new data tolead to further research. In order to enliven this future Field 1, we
will need to rebuild the path so that R&D teams bring up issues one
after another, or newcomers can participate. This is a 5-year project,
but I'd like to create a framework in which more new trends appear
after the 6th year and there are more young researchers who wantto participate in this field. Although the research is hard, I want to
get it across to the life science community and young researchers’communities that all members are actively getting results by making
the best use of new computer power, and expanding the horizons ofresearch.
YANAGIDA : Not only are researchers who are engaged in computer sciencebacking this field, but many biologists who use computers today.
So, promoting this trend leads to further improvement of science
itself. However, it's no use only to say that computers are important.
We need something playing the role of a driving force or becoming
a base. I think that symbol is “K computer” which demonstrated aworld-beating computing ability. Then, an actual project using “Kcomputer” starts rolling, impressive research is being done there, andJapan has world-class researchers. I believe that is what is important.
EGUCHI : Now, “K computer” has become No. 1 in the world. Because ofthat, I’d like professors to get good research results.
YANAGIDA : Since this research resource became No. 1, it, of course,
also hints that Japanese science research itself might become theworld’s No.1. To achieve this, it's crucial to expand the horizons ofinterdisciplinary studies, for example a successful marriage of Scienceand Engineering.

EGUCHI: It's great to do research with world-beater “K computer”. I would
like to see an environment in which researchers from various fields
participate, compete with and complement each other.
KIDERA : It's true that along with an increase in computer power, we can
not only do research in our field, but also do more advanced research.
We can use “K computer”. This offers a great advantage for our
research. For this reason, it may be the challenge of the future to builda system in which users do their research with a sense of thrill andexcitement, not pressure, although it's my position to urge them toproduce results (laugh).
EGUCHI : Please create an atmosphere in which everyone gets excited andwants to team up (laugh).
KIDERA : I really felt “K computer” was great when I put it into use. After
experiencing the speed, I really felt it is a terrific computer.
YANAGIDA : The triple-digit difference from conventional computers
makes you feel not only a quantitative difference but also a qualitativechange.
KIDERA : Everyone gets the feeling they can get great results with it. That’s why they also feel pressured. I don't want to say, “Hurry up and get
results,” as far as I can avoid it, but… (laugh)
YANAGIDA : As I recall, at the review board the other day someone said
about a research presentation, “Research must be carried out with asense of joy. Are you happy when you do your research?”
KIDERA : Of course we have responsibility, but after all you cannot do greatthings without enjoying them.

(Continued in the next issue)

BioSupercomputing Newsletter Vol.5 11

Report on Research

Simulation Applicable to Drug Design

HPCI Strategic Program Field 1
Supercomputational Life Science

Research Center for Advanced Science and Technology, The University of Tokyo

Hideaki Fujitani(field 1- Program 2)

The examination of interaction between a protein and a compound thatspecifically binds to the protein (ligand) is not only a major challenge inthe basic study of life phenomena, but also extremely important from the
aspect of application. Accurate evaluation of the free energy of binding
between proteins and drug molecules is a longstanding challenge in thefield of drug discovery. Although various methods including DockingSimulation have been tried, there has been no method that predicts thefree energy of binding to a protein with an accuracy effective enoughfor designing a drug molecule. For finding the difference in free energy
between two different states of thermal equilibrium, one in which theprotein and its ligand bind together in water (cell fluid) and the other inwhich they separately dissolve in water, a method
for accurate incorporation of the entropy change
due to conformational change of the protein
and molecular motion is required. For such a
calculation, the molecular dynamics method isused and its accuracy depends strongly on theforce-field parameter used.
Torsion interaction relative to the Ramachandranangle of the protein main chain is the mostimportant force-field parameter in defining the
cubic structure of proteins, and its energy profiles
in a glycine peptide are compared in Fig 1[1]. The
black solid line shows the profile defined througha molecular orbital calculation which is currently
the most accurate (LCCSD). It is compared withconventional molecular force fields such as AMBER(ff99, ff03) and OPLS-AA. Ff99sb is a force field inwhich only the main chain torsion interaction of
ff99 released in 2006 was modified, and definedso that it conforms to energy by high-accuracy
molecular orbital calculations at some angles.
For this reason, the energy values are close to
those shown by the black line when φ and ψ
are 0 degrees. However when φ is 80 to 180
degrees, an energy barrier higher by 1kcal/mol isobserved compared to the energy shown by theblack line. By use of the electric charge of AMBER
and the van der Waals parameter, we developed
a method to assign force-field parameters in a
unified manner to proteins, DNA, RNA or arbitrarycompounds, and named it the FUJI force-field[2].
With a relational expression between the freeenergy difference ΔG and non-equilibrium
work W [3] , we performed molecular dynamics
calculations independently at multiple
intermediate states from the state in which there
is full interaction between the ligand and theother molecule to the state in which there is nointeraction between them[4], and calculated thefree energy from the Work W required for shifting
to the next stage (Fig2). With the MP-CAFEE
method and the FUJI force field, it is possible toperform an accurate computation of proteinligand binding free energy.

12 BioSupercomputing Newsletter Vol.5[1] H. Fujitani, A. Matsuura, S. Sakai, H. Sato, and Y. Tanida : J. Chem. Theory Comput., 5,1155 (2009).
[2] H. Fujitani, Y. Tanida, and Azuma Matsuura : Phys. Rev. E, 79, 021914 (2009).
[3] C. Jarzynski : Phys. Rev. Lett., 78, 2690-2693 (1997).
[4] H. Fujitani, Y. Tanida, M. Ito, G. Jayachandran, C. D. Snow, M. R. Shirts, E. J. Sorin,
and V. S. Pande : J. Chem. Phys., 123, 084108 (2005).

Fig 1 : Comparison of torsion interaction of protein main chain

Fig 2 : Massively parallel computation of absolute binding free energy, MP-CAFEE

Report on Research

An Ultra-fast Analysis System
for Next-Generation DNA Sequencer Data

HPCI Strategic Program Field 1
Supercomputational Life Science

Graduate School of Information Science and Engineering,Tokyo Institute of Technology(From the above)

Yutaka Akiyama, Takashi Ishida
Masanori Kakuta, Shuji Suzuki(field 1- Program 4)

The Next-Generation DNA Sequencer allows DNA sequencing of about
1 trillion bases with a single run by a dramatic technological innovation.
Although the "number of bases sequenced per dollar" doubled with amarvelous extension rate in about 19 months with what we call a firstgeneration sequencer, it doubled in only about 5 months after theappearance of Next-Generation Sequencers which are coming into
wide use everywhere. The largest bottleneck now is the calculation ofsemantic interpretation for read sequences. It is common knowledge thatsupercomputers are required for integrated advanced calculation such as
gene network prediction. But now, large-scale calculations are also required
for elementary processes for DNA fragments from sequencers.
A high-speed algorithm which is suitable to explore an exact matchwith a known sequence or highly similar character string can be used forthe study of subtle variations of human genes, and large-scale personal
computer clusters have managed to address it. However, in the studyof a comprehensive analysis of enteric microbe's genome fragments ofhumans or the study of significant sequence mutations, fragment mapping
procedure (finding correspondence with a portion of known genome
sequence) is not possible without detection of sequence similarity among
distant or largely mutated sequences. For these studies, incomparablylarge-scale calculation is required, and personal computer clusters are noteffective.
Human health is maintained by the properly balanced work of many
symbiotic microbes in the body. In addition, the balance is considered tohave several locally stable points. Comprehensive sequencing of genome
fragments of microorganism gastro-intestinal which exist in the body's
internal environment including oral, digestive, skin and urogenital system
has been implemented for about 500 people in the Human Microbiome
Project (HMP) in the USA and for about 300 people in the MetaHIT Projectin Europe. However, when analyzing these valuable data, the rate of
non-analyzable sequence fragments to be excluded will be significantly
increased only with insensitive methods hypothesizing highly similar
character strings.

Million read/hour

We firstly established the super parallel execution environment of theexisting BLASTX software with the cooperation of Prof. Ken Kurokawa(Tokyo Institute of Technology), who is an expert in genomic analysis. A
scalability up to 16,000 cores was confirmed on TSUBAME 2.0 of the Tokyo
Institute of Technology (Fig. 1). Binary-tree transfer was used for applying
the bandwidth of networks at the initial delivery of the database to eachcalculation node. After translating DNA fragment sequences into amino acidsequences in six possible frames, we flexibly compared them with existing
amino acid sequences. Event though the target DNA sequence fragments
mutated at the DNA sequence level, they are often consistent with residuesof existing sequences at the amino acid sequence level, or are changed into
residues with similar physicochemical properties. Therefore, our flexible
method which compares using a distance matrix between characters

High-speed
search ofcandidatelocation

improves sensitivity for sequence search
to explore distant genes with the samefunctions.
We then created a software, GHOSTX, as a
substitute for BLASTX, with drastic speed[1, 2]. GHOSTX can findup on the algorithm
similar short strings between databaseand quer y sequences immediately bymultito-multi comparison by describinga partitioned database stored in memory
using Suffix Array, translating a query
sequence to Suffix Array, and comparing
them e ffectively (Fig. 2). While BLASTX
compares only character strings witha f ixed - length, charac ter s trings forcomparison can be ex tended withvariable-length until they satisfy a similarity
threshold score in GHOST X, which
improves the sensitivity per calculation
cost. GHOSTX achieved about 20 times
processing speed even when an equivalentsensitivity an equivalent sensitivity with
BLASTX was required. When the sensitivity
is decreased to a degree of no difficulty,
GHOST X at tained over 100 times the
speed. Its sensitivity is much better and it is
2 to 3 times faster than the BLAT algorithm
which had high speed but was not able tobe used because of poor sensitivity. We
established a super parallel analysis system
named "GHOST-MP" by implementing the
GHOSTX algorithm on the ten quadrillion
sp e e d co m p u te r “ K co m p u te r ”, a n d
integrating thread parallelization using
OpenMP and data parallelism between
nodes using MPI.

For the principal parts of homologous
search, go o d scalabilit y up to 1,536
nodes (12,288 cores) was measured with
"K computer". Although the execution
time for each sequence fragment varies
widely, a scalability of up to several tens of
thousands of nodes is expected because of the load balancing mechanism.
In GHOST-MP, I/O operation is not completely intensive because its
calculations are proportional to the product (multiplication) of length of thedatabase and length of query sequence sets. Even though, careful I/O design
is required. In the case of "K computer", I/O transfer to and from I/O nodes
takes place in the z axis direction.
We aim to achieve highly- efficient
implementation up to several tens
Query sequencegroupsof thousands of nodes by adjustingdata input using representative nodes
and a broadcast mechanism, and by
Connecting data by insertion of separating
Connecting data by insertion of separating
characters to create Suffix Array
characters to create Suffix Array
improving dispersion and reduction of
I/O load.
Exploring pairs of partial characterswith arbitrary length which have ahigher score than the threshold level

Actualcalculationof alignment

CPU core number

Fig 1 : Parallel performance in BLASTX-based system

Extension to
the right and left
side without a gap

Extension to
the right and left
side with a gap

Fig 2 : Processing flowchart in GHOSTX algorithm

Output of results

References[1] Suzuki, Ishida, Akiyama (2010) IPSJTechnical report, 2010-BIO-23(21):1-6[2] Suzuki, Ishida, Akiyama (2011) IPSJTechnical report, 2011-BIO-25(32):1-8

BioSupercomputing Newsletter Vol.5 13

ReportBioSupercomputing Summer School 2011
Computational Science Research Program, RIKEN Yasuhiro Ishimine (Organ and Body Scale WG)

Research and Development Center for Data Assimilation, Institute of Statistical Mathematics Masaya Saito (Data Analysis Fusion WG)
Niigata University of International and Information Studies Eisuke Chikayama (Cell Scale WG)

School of Medicine, Tokai University Yohei Nanazawa (Cell Scale/Organ and Body Scale WG)

Computational Science Research Program, RIKEN Takashi Handa (Brain and Neural Systems WG)

Computational Science Research Program, RIKEN Gen Masumoto (High-performance Computing Team)
Computational Science Research Program, RIKEN Kei Moritsugu (Molecular Scale WG)(In Japanese alphabetical order)

On September 26 and 27, 2011, the “Biosupercomputing Summer School
2011” was held at the Awaji Yumebutai International Conference Centerin Awaji, Hyogo Prefecture. This is the third time as a young researchers’
workshop planned and organized by research associates in the NextGeneration Integrated Simulation of Living Matter (ISLiM) project,
following last year’s summer school and winter school. This school was
renamed in connection with the support from the “BioSuperComputing
Research Community” (BSCRC).
The ten-petaflops supercomputing, “K computer”, now being installed in
Kobe, Hyogo Prefecture, was ranked number one in the world at TOP500
in this June. ISLiM is developing softwares for numerical simulation andmassive data analysis for life science so that the K computer can achieve
its full performance. This summer school was held to demonstrate what
K computer is able to do and how it is able to contribute to life sciences,and then consisted of two sessions, “Computer and computational
methodology” and “Application for life science”. A total of 56 research
associates in the project and graduate students from the associatedresearch fields attended to have a close relationship with each other.
First, as keynote lectures, Prof.Koji Kaya, Director of the Next-Generation
Computational Science Research Program, delivered a lecture about thehistory of the ISLiM project and its future prospects. Then, Prof. Ryutaro
Himeno, Vice Director of the Next-Generation Computational Science
Research Program, delivered a lecture on the current performance andstatus of the application softwares now being developed at ISLiM, and thefuture schedule for the usage of the K computer.
In the following “Application” session, Prof. Masahiro Kami, the Instituteof Medical Science, University of Tokyo, gave a lecture as an invited
speaker and the three members from the ISLiM teams talked on living
matter softwares under development. Prof. Kami gave a talk from theviewpoint of a researcher as well as a clinician on genomic analysis, tailormade medical care, the doctor shortage problem, and his activity in
Fukushima Prefecture hit by the Great East Japan Earthquake. It is a known
fact for computational scientists that they must have data to analyze, but
his point was impressive that it is important to collect data actually by
way of extending the community of his own. Dr. Masaya Saito from theData Analysis Fusion Team spoke about the analysis of the mammalian
circadian cycle transcriptional network using LiSDAS, and introduced

14 BioSupercomputing Newsletter Vol.5

data assimilation library. Dr.Yasuhiro Sunaga from the Cell Scale Team
introduced the Cell simulation integrated platform (RICS), and its appliedresearch. Dr.Tomoki Kazawa from the Brain and Neural Systems Team told
us about a bombycid olfactory and motor nerve systems simulation andreal time imaging of nerve circuits.
In the subsequent banquet and poster session, there were 35 poster
presentations participated, and debates on their present works went on
until late at night. The poster award competition was held by the choices offive participating graduate students. Proposed by Prof. Himeno, debates
were also made about the forthcoming “exascale computing”.
In the “Computer” session on the second day, first Dr. Ikuo Miyoshi and
Dr. Yoshinori Sugizaki, both from the PA project of the Next-Generation
TC Development Section at FUJITSU, delivered keynote lectures as
invited speakers. Then, the three members from the ISLiM teams talked
about particle and fluid systems simulation and their high-performancecomputing techniques. Dr. Miyoshi described the hardware configurationand software development status of the K computer and Dr. Sugizaki
explained tuning techniques with the help of simple and useful examples.
In the subsequent question-and-answer session, we could obtain detailed
understandings from the knowledge of the K computer developers. Dr.
Yusuke Matsunaga from the Molecular Scale Team described Molecular
Dynamics (MD) simulation, especially how to deal with long-range forces,
and introduced the platypus-MM/CG under development. Dr. Yosuke Ohno
from the High-performance Computing Team explained MD speed-up
techniques particularly for short-range forces by giving useful examples
with the K computer. Dr. Hitoshi Ii from the Organ and Body Scale Team
spoke about the simulation of platelet thrombus formation by the Euler
approach, and Dr. Hiroshi Koyama from the High-performance Computing
Team delivered a lecture about the numerical errors in the solution ofdifferential equations and why they appear.
Through the three ISLiM young researchers’ workshops from last year,
the initial goal to encourage exchanges of the Islim members between
different teams seems to has been achieved. We expect that in the near
future this kind of workshop will become an opportunity for creating acommunity of young researchers in the fields of simulation and massive
data analysis in life sciences, together with the participants from theAdvanced Institute for Computational Science and the HPCI program.

Report“Next-Generation Integrated Simulation of
Living Matter (ISLiM)”, a web page dedicated to
new applications, has opened.
Computational Science Research Program
Integrated Simulation of Living Matter Group
Next-Generation Integrated Simulation of Living Matter started in 2006, andevolved application programs that can attain peta-flop class execution efficiency
by using "K computer", a peta-scale computer, with the core mission of "developing
software that can contribute to medical care with an analytical approach to basic
equations, an approach to extract experimental data that looms in on unknown
theorems using massive amounts of experimental data, and an approach tointegrate multiple levels to fully utilize the performance of a supercomputer having
an unprecedented peta-scale performance. This will make it possible to integrate
and organically link research and experimental data of varying scale, and tounderstand the highly varied phenomena of the biological body." Throughout its
research, the data and findings collected during development have been released
to academic conferences and journals, and a symposium was convened to freely
exchange views.
Experimental use of "K computer", a peta-scale computer, started from fiscal
year 2011, and some programs under development have achieved an execution
efficiency of peta-flop class. From now on, we would like many people to become
familiar with these new application programs, and to use them. Please take time tolook at our web page that showcases individual programs (see diagram).
This page introduces all application programs that are currently underdevelopment. As an effort to give back the project's accomplishments to thesociety, we are planning to gradually release their source code. It is also planned
to increase the content of the download page so that the public can use actual
programs.
A status report of the research and development of these application programs
will be released at the “Debrief Meeting 2011 on Next-Generation Integrated
Simulation of Living Matter (ISLiM)” (December 21 (Wed) – 22 (Thurs) 2011). Pleasefeel free to attend if you are interested.

Application Program Introduction Page
Japanese page: http://www.csrp.riken.jp/application_j.html
English page : http://www.csrp.riken.jp/application_e.html“Debrief Meeting 2011 on Research and Development of Next-Generation
Integrated Simulation of Living Matter (ISLiM)”, Notice page
http://www.csrp.riken.jp/2011/islim-houkokukai2011_j.html

An introductory video for “Next-Generation Integrated Simulation of Living
Matter” is available on the following page.
http://www.riken.jp/engn/r-navi/video/research.html
Video name: Approach to the Life Science Grand Challenge – Next-Generation
Integrated Simulation of Living Matter –BioSupercomputing Newsletter Vol.5 15

Event Information"Debrief Meeting 2011 on Next-Generation Integrated Simulationof Living Matter (ISLiM)"● Date : December 21 (Wed), 22 (Thurs) 2011
● Location : The University of Tokyo, Takeda Hall (Hongo, Bunkyo-ku, Tokyo)
For debrief program and details, please refer to the web page.
http://www.csrp.riken.jp/2011/islim-houkokukai2011_j.html

4th BioSupercomputing Symposium● Date : March 5 (Mon), 6 (Tue) 2012
● Location : Kobe city, Hyougo prefecture (TBA)
Details will be published on the web page (http://www.csrp.riken.jp) once fixed.

K computer ranked the fastest computer in the world.
"K computer" has been acclaimed No.1 in the "37th TOP500", the world's supercomputer computation performanceranking, which was released in June 2011. Its performance has marked 8.162 peta flops (performance of 8,162 tera
floating point calculations per second) on the LINPACK benchmark during its preparation phase (peak performanceof 8.774 peta flops). An execution performance of 93% is an outstanding record for a system of this scale. In
addition, it has been proven to have top class energy efficiency due to its ranking No.6 in Green500 for its calculation
performance per energy.
(Cover photo)

The Development and Use of the NextGeneration Supercomputer Projectof the Ministry of Education, Culture,
Sports, Science and Technology(MEXT).
Next-Generation Integrated Simulationof Living Matter
The “Next-Generation Integrated Simulationof Living Matter” is a project sponsored by the
Ministry of Education, Culture, Sports, Science andTechnology, in which research and developmentof simulation software to understand various
phenomena occurring in the biological systems,
including molecules and the human body, havebeen undertaken to realize a petascale simulation
by making full use of the performance of aten quadrillion speed computer known as “Kcomputer”.

Ministry of Education, Culture, Sports, Scienceand Technology Project for high-degreeapplication of high performance generalpurpose computer

HPCI Strategic Program Field 1
Supercomputational Life Science
The HPCI Strategic Program is a program of the Ministry of
Education, Culture, Sports, Science and Technology aiming to
produce ground-breaking results in computer science technology
by maximizing the benefits of the HPCI (High Performance
Computing Infrastructure) centered on the K computer, andencouraging developments in five research fields that need to be
strategically addressed.
"Supercomputational Life Science" as a representative
organization of Riken, has conducted research with the missionof understanding and predicting life phenomena based on largescale simulation and advanced data analysis, and to apply theseresults to design and implement medicine and medical care with
its research.

BioSupercomputing Newsletter Vol.5 2011.11
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: November 2011

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