背幅：6.5mm

京都大学化学 研 究 所 共 同 利 用 ・ 共 同 研 究 拠 点 第期
平成 年度 成果報告書

29

京都大学化学研究所
共同利用・共同研究拠点

第期

化学関連分野の深化・連携を基軸とする先端・学際研究拠点

平成 29 年度

成果報告書

京都大学化学研究所
共同利用・共同研究拠点

化学関連分野の深化・連携を基軸とする
先端・学際研究拠点

平成 29 年度

成果報告書

－i－

－ii－

－iii－

－iv－

－v－

２．国際学会、シンポジウム・研究報告会

 国際会議・ICRIS-NMR ’17: DNP-NMR Workshop

111

 シンポジウム・研究会
・第 2 回 pMAIRS ワークショップ：薄膜の分子配向解析を研究に活かす・一般社団法人レーザー学会学術講演会 第 38 回年次大会

114
116

121

３．成果発表論文（平成 29 年 2 月までに刊行された論文で、平成 22－28 年度の成果報告書に掲載されていないもの）

紙数の都合により、*を付けた論文のみについて、別刷りを本報告書に記載する。

*Study of Perfluoroalkyl Chain-Specic Band Shift in Infrared Spectra on the Chain Length, J.
Phys. Chem. A, 121, 8425–8431(2017).

*Critical Controllability Analysis of Directed Biological Networks Using Efficient Graph
Reduction, Sci. Rep., 7, 14361 (2017).
Crystallographic studies of aspartate racemase from Lactobacillus sakei NBRC 15893, Acta
Crystallogr. Sect. F Struct. Biol. Cryst. Commun., 71, 1012–1016 (2015).
Synthesis and Inhibitory Activity of Substrate-Analog Fructosyl Peptide Oxidase Inhibitors,
Bioorg. Med. Chem. Lett., 25, 3910–3913 (2015).
Stereospecific Inhibitory Effects of CCG-1423 on the Cellular Events Mediated by
Myocardin-Related Transcription Factor A, PLoS One, 10, e0136242 (2015).
Phosphonate-Based Irreversible Inhibitors of Human γ-Glutamyl Transpeptidase (GGT).
GGsTop is a Non-Toxic and Highly Selective Inhibitor with Critical Electrostatic Interaction
with an Active-Site Residue Lys562 for Enhanced Inhibitory Activity, Bioorg. Med. Chem., 24,
5340–5352 (2016).
The magnetic properties of Fe3O4/nonmagnetic metal/Fe hybrid systems, Appl. Phys. Lett., 110,212402 (2017).
Germabenzenylpotassium: A Germanium Analogue of a Phenyl Anion, Angew. Chem. Int. Ed.,56, 4588–4592 (2017).
Dodecaallylhexasilacyclohexane, IUCrData, 2, x170807 (2017).
Synthesis and Structure of an Open-cage C69O Derivative, Chem. Lett,. 46, 543–546 (2017).
Cycloadditions of Benzyne to Naphthalene-Fused Tetracene with a Twisted π-Surface, Chem.
Lett., 46, 591–593 (2017).
4,7-Bis[3-(dimesitylboryl)thien-2-yl]benzothiaziazole:
Solvato-,Thermo-,andMechanochromism Based on the Reversible Formation of an Intramolecular B-N Bond, Chem.
Eur. J., 23, 3784–3791 (2017).
D-π-A Dyes with Diketopyrrolopyrrole and Boryl-substituted Thienylthiazole Units for
Dye-Sensitized Solar Cells with High JSC Values, Chem. Lett., 46, 715–718 (2017).
A Stable, Soluble, and Crystalline Supramolecular System with a Triplet Ground State, Angew.
Chem. Int. Ed., 56, 4261–4265 (2017).
Unsymmetric Twofold Scholl Cyclization of 5,11-Dinaphthyltetracene: Selective Formation ofPentagonal and Hexagonal Rings via Dicationic Intermediates, Angew. Chem. Int. Ed., 56,
5082–5086 (2017).
－vi－Isolation of the Simplest Hydrated Acid, Sci. Adv., 3, e1602833 (2017).
Fullerene C70 as a "Nano-flask" to Reveal Chemical Reactivity of a Nitrogen Atom, Angew.
Chem. Int. Ed., 56, 6488–6491 (2017).
Rh-Catalyzed Dehydrogenative Cyclization Leading to Benzosilolothiophene Derivatives via
Si-H/C-H Bond Cleavage, Org. Lett., 19, 2564–2567 (2017).
Solvent-Coordinated Tin Halide Complexes as Purified Precursors for Tin-based Perovskites,ACS Omega, 2, 7013–7021 (2017).
Organic Synthesis of Small-Molecule Endohedral Fullerenes and Their 1H NMR Signal, NMR
Bull. Nuc. Mag. Res. Soc. Jpn., 8, 33–37 (2017).
Synthesis and Evaluation of the Inhibitory Activity of the Four Stereoisomers of the Potent andSelective Human γ-Glutamyl Transpeptidase Inhibitor GGsTop, Bioorg. Med. Chem. Lett., 27,
4920–4924 (2017).
An Improved Synthesis of the Potent and Selective γ-Glutamyl Transpeptidase Inhibitor
GGsTop together with an Inhibitory Activity Evaluation of its Potential Hydrolysis Products,
Tetrahedron Lett., 58, 3700–3703 (2017).
Synthesis and Functional Assessment of a Novel Fatty Acid Probe, ω-Ethynyl Eicosapentaenoic
Acid Analog, to Analyze the in Vivo Behavior of Eicosapentaenoic Acid, Bioconjug. Chem., 28,
2077–2085 (2017).
A Dioxygenase Catalyzes Steroid 16α-Hydroxylation in Steroidal Glycoalkaloid Biosynthesis,
Plant Physiol., 175, 120–133 (2017).
Vitamin D metabolite, 25-Hydroxyvitamin D, regulates lipid metabolism by inducing
degradation of SREBP/SCAP, Cell Chem. Biol., 24, 207–217 (2017).
Solution-processable thermally activated delayed fluorescence emitters for application inorganic light emitting diodes, J. Soc. Inf. Disp., 25, 480–485 (2017).
Fabrication of hydrophobic polymethylsilsesquioxane aerogels by a surfactant-free method
using alkoxysilane with ionic group, J. Am. Ceram. Soc., 5, 104 (2017).
Transparent Ethenylene-Bridged Polymethylsiloxane Aerogels: Mechanical Flexibility andStrength and Availability for Addition Reaction, Langmuir, 33, 4543–4550 (2017).
Blue organic light-emitting diodes realizing external quantum efficiency over 25% using
thermally activated delayed fluorescence emitters, Sci. Rep., 7, 284 (2017).
Dynamics of Rouse Chains undergoing Head-to-Head Association and Dissociation: Difference
between Dielectric and Viscoelastic Relaxation, J. Rheol., 61, 1151–1170 (2017).
Growths of Mechanical Elasticity and Electrical Conductance of Graphene
Nanoplatelets/Poly(lactic acid) Composites Under Strong Electric Field: Correlation with Time
Evolution of Higher Order Structure of Graphene Nanoplatelets, Rheol. Acta, 56, 871–885(2017).
Creation of Large, Periodic Temperature Gradient via Plasmonic Heating from Mesoscopic
Planar Lattice of Metal Domains, Int. J. Therm. Sci., 118, 247–258 (2017).
Re-Examination of Terminal Relaxation Behavior of High-Molecular-Weight Ring Polystyrene
Melts, Rheol. Acta, 56, 567–581 (2017).
Interplay between Structure and Property of Graphene Nanoplatelet Networks Formed by anElectric Field in a Poly(lactic acid) Matrix, J. Rheol., 61, 291–303 (2017).
Iron-Catalyzed anti-Selective Carbosilylation of Internal Alkynes, Angew. Chem. Int. Ed., 56,
13298–13301 (2017).
－vii－

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X

L-

Thermococcus litoralis
Tl
Tl
Tl

tert
Tl

tert

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－11－

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Chem. Lett., 2018 47－12－2017-13－13－

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4

150
3
100

2

50

1
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Energy (eV)

－16－

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0

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Scientific Reports
BMC Systems Biology－23－

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Cancer Res
Bioorg. Med. Chem
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CS Nano 2012 6
Nat. Chem 2017 9－30－

2017-31

293sChem. Soc. Rev. 2012 41
2017 46
J. Org. Chem. 2012 77－31－2017-32

(CPP,

1)
CPP
CPP

(CPP2+)
CPP2+
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2
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CPP2+

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Franck-Condon
1) Fujitsuka, M.; Kayahara, E.; Yamago, S.;
Majima, T. in preparation

2. [n]CPP2+－32－

2017-33－33－

2017-34－34－

2017-35－35－

2017-36

L1
Al(L1)3

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Ga(L1)3
In(L1)3

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2017-37

J. Phys. Chem. C.
ACS Omega

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2

121

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Scientific Reports
6Int. J. Thermal Sciences 118－45－

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Fig.

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2017-50－50－

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1. L. Asano, et al., Cell Chem. Biol., 2017, 24, 207-217.
－53－

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45 oC

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－73－

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Introduction

Experimental

Results and Discussion

Outcomes

Invited lectures at international conferences
Brisbane,
Boston,－75－

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dConclusions and future plan

References
NatureNatureCommun.
Phys. Rev. Lett.

－89－

2017-89－90－

(3) Isotope ratios of Ni, Cu, and Zn in sinking particles.

Conference
et al
Paper
et al

－91－

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2a

2b
Scheme 1.

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5
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Chem. Lett 2017 46－93－2017-91

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F13C6

F13C6

J. Fluorine Chem.

－102－2017-98

1

2

Chart

2

3

4

3
3

4

2

4
5
6

5
6
6

2
6
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SchemeChem. Lett. 2017 46－103－

2017-99－104－2017-100－105－

2017-101

1

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2b

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3b d

3b

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Org. Biomol. Chem.

－106－2017-102－107－

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2017-104

ACS Nano.
－109－

NMR2017

DNP-NMR

1.

29

11

10

29

10

6 9 11

2.
NMR

2017 DNP-NMRICRIS-NMR '17: DNP-NMR Workshop
3.

4.

NMR
CEA Grenoble, France
North Carolina State University
5.

(

)

(
)

)
)
)

(
(

NMR

)
(

)

NMR
)

(
(

)

(

(

NMR

Gaël De Paëpe (CEA Grenoble) Takeshi Kobayashi (U.S. Department of Energy, Ames National

Laboratory)

Alex I. Smirnov (North Carolina State University)

6.
131
7.

8.
NMR

NMR
(Dynamic Nuclear Polarization, DNP)
20

1

－111－

NMR

10

DNP-NMRDNP-NMR(Institute for Chemical Research International Symposium, ICRIS)
DNP-NMR

3

50

NMR

ICRIS-NMR

131

DNP-NMRDNP-NMRDNP-NMRDNP-NMRMICEJSPS(A) (No. 17H01231)

DNP-NMR(SPIRITS)

Nuclear magnetic resonance (NMR) is ever-growing, and researches and developments on NMR based on
novel concepts have been still ongoing. Among them, tremendous efforts have been made for sensitivity
enhancements from various perspectives to solve a crucial issue on NMR, low sensitivity. Recently, morethan twenty commercially-available dynamic nuclear polarization-enhanced NMR (DNP-NMR) systems
have been introduced in Europe and the United States, and these DNP-NMR systems have achieved great
success in the sensitivity enhancement in these countries. Under such circumstances, we installed a
DNP-NMR system in ICR, Kyoto University, this year. The system is the first commercial DNP-NMR
product in Japan. On the occasion, we had an opportunity to hold the DNP-NMR workshop as an ICR
International Symposium,-NMR '17: DNP-

. Three years ago, we had ICRIS-NMR'14. At that time, we had 50 attendees. This time we had 131 attendees, indicating that DNP-NMR attracts
significant attention as a forthcoming analytical technique. In this workshop, we had ten world-famous
researchers as invited speakers from all over the world. We expect future developments of DNP-NMR andresearchers related to DNP-NMR.
This workshop was financially supported by Kyoto Convention Bureau (MICE support) and several
companies. Travel and accommodation expenses were supported by Japan Society and the Promotion of
Science (JSPS) Grant-in-Aid for Scientific Research (A) (No. 17H01231). We also acknowledge Kyoto
University for Supporting Program for Interaction-Based Initiative Team Studies (SPIRITS) and theCollaborative Research Program of Institute for Chemical Research, Kyoto University (grant # 2015-90) for
the introduction of DNP-NMR.

－112－

Program
November 6
14:00-15:30
15:30-18:00
November 99:30-11:00

Satellite Seminar by Dr. Gaël De Paëpe (CEA Grenoble, France)
DNP in Grenoble: Radical design and advanced instrumentation for challenging applications
Individual Discussion

11:00-12:00
12:00-18:00
18:15-20:15

Satellite Seminar by Prof. Alex I. Smirnov (North Carolina State University, USA)
Combining solid state NMR, DNP, DEER and nanotechnology for structural studiesof membrane proteins in native lipid bilayer environment
Individual Discussion
Excursion for Invited Speakers
Dinner for Invited Speakers

November 10
9:30-10:30
10:30-10:40

Registration
Welcome speech

10:40-11:10

11:10-11:40

11:40-12:10
12:10-13:10
13:10-13:40
13:40-14:10
14:10-14:30
14:30-15:00

15:00-15:30

15:30-16:00
16:00-16:20
16:20-16:50
16:50-17:20

17:20-17:30
17:30-18:00
18:00-19:30
November 11
14:00-15:30
15:30-16:30(()
Norihiro Tokitoh (The Director of ICR, Kyoto Univ.))[]

Masahiro Shirakawa (Kyoto Univ.) [Japanese lecture]
Cell biology by using Optically Detected Magnetic Resonance (ODMR)
(
)[
]
DNP
Makoto Negoro (Osaka Univ.) [Japanese lecture]
The present and future of triplet-DNP
Alex I. Smirnov (North Carolina State Univ.) [English lecture]
Towards DNP of macroscopically oriented samples: Biradical molecular tags and instrumentation
Lunch Break
Jean-Paul Amoureux (Univ. of Lille) [English lecture]
Dynamic Nuclear Polarization: new insights into the structure of hybrid and inorganic materialsGaël De Paëpe (CEA Grenoble) [English lecture]
Efficient high field DNP at low temperature and fast MAS
Group Photo(U.S. Department of Energy, Ames National Laboratory) [
]
DNP
NMR
Takeshi Kobayashi (U.S. Department of Energy, Ames National Laboratory) [Japanese lecture]
Applications of dynamic nuclear polarization (DNP)-enhanced solid-state NMR
in the study of materials science (tentative)
(
)[
]
1000
NMR -16.4T, 30K
DNPYoh Matsuki (Osaka Univ.) [Japanese lecture]
Over-1000 times more sensitive MAS NMR -DNP at 16.4T and 30KFabien Aussenac (Bruker BioSpin, France) [English lecture]
Recent applications in solid state Dynamic Nuclear Polarization
Break
Melanie Rosay (Bruker BioSpin, USA) [English lecture]
Instrumentation development for dynamic nuclear polarization at 263 GHz to 593 GHz()[]
DNP-NMR
Hironori Kaji (Kyoto Univ.) [Japanese lecture]
The introduction of DNP-NMR and toward the future developments
Closing remarks
DNP-NMR/800 MHz NMR lab. Tour
Banquet (Cafe Restaurant Kihada)
Satellite Seminar by Dr. Takeshi Kobayashi(U.S. Department of Energy, Ames National Laboratory)
New developments in the studies of surfaces by DNP-enhanced solid-state NMR methods
Individual Discussion

－113－

2

2017

11

pMAIRS

22

13

17
2 CB-215, 217
2.

12

pMAIRS

2

p-MAIRS
51

3.

IoT

10
pMAIRS
pMAIRS
2007

4.

MAIRS

FT-IR
5.

pMAIRS

6.

pMAIRS

7.

pMAIRSSciX2017
pMAIRS
2018

3

pMAIRS
pMAIRS2
51.

pMAIRS－114－

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Reprinted from J. Phys. Chem. A, 121, 8425–8431 (2017)with permission from American Chemical Society.

ArticleCite This: J. Phys. Chem. A 2017, 121, 8425-8431

pubs.acs.org/JPCA

Study of Perﬂuoroalkyl Chain-Speciﬁc Band Shift in Infrared Spectraon the Chain Length
Takafumi Shimoaka,† Masashi Sonoyama,‡ Hideki Amii,‡ Toshiyuki Takagi,§ Toshiyuki Kanamori,§
and Takeshi Hasegawa*,††

Laboratory of Chemistry for Functionalized Surfaces, Division of Environmental Chemistry, Institute for Chemical Research, Kyoto
University, Gokasho, Uji, 611-0011 Kyoto, Japan‡
Division of Molecular Science, Faculty of Science and Technology, Gunma University, Kiryu, 376-8515 Gunma, Japan§AIST Tsukuba Central 5, National Institute of Advanced Industrial Science and Technology, 1-1-1 Higashi, Tsukuba, 305-8565
Ibaraki, JapanS Supporting Information*

ABSTRACT: The CF 2 symmetric stretching vibration(νs(CF2)) band of a perﬂuoroalkyl (Rf) group in an infrared(IR) spectrum exhibits a unique character, that is, an apparent
high wavenumber shift with increasing the chain length, which
is an opposite character to that of the CH stretching vibration
band of a normal alkyl chain. To reveal the mechanism of theunusual IR band shift, two vibrational characters of an Rf chain
are focused: (1) a helical conformation of an Rf chain, (2) the
carbon (C) atoms having a smaller mass than the ﬂuorine (F)atom dominantly vibrate as a coupled oscillator leaving F
atoms stay relatively unmoved. These indicate that a “coupledoscillation of the skeletal C atoms” of an Rf chain should be
investigated considering the helical structure. In the present
study, therefore, the coupled oscillation of the Rf chain dependent on the chain length is investigated by Raman spectroscopy,
which is suitable for investigating a skeletal vibration. The Raman-active νs(CF2) band is found to be split into two bands, thesplitting is readily explained by considering the helical structure and length with respect to group theory, and the unusual peakshift is concluded to be explained by the helical length.

■

mode.4,5 This Rf chain-speciﬁc character of the peak shift is, infact, useful for distinguishing the Rf chain length.2,3 However,
the mechanism of the unusual shift is not revealed. The aim of
the present study is thus revealing the origin of the unusual Rfspeciﬁc shift.
On considering the vibration of a molecular chain composedof many repeat units, the coupled oscillation must be
considered.6 Although the shift of the νs(CF2) band should
depend on the shape of the coupled oscillation as commonly
found for a normal alkyl chain, the vibration of an Rf chain has
a complicated character as follows. The coupled oscillation of
the CH2 symmetric stretching vibration (νs(CH2)) mode of anormal alkyl chain is explained brieﬂy, followed by discussion of
the diﬀerence of the νs(CF2) mode from νs(CH2), so that themechanism of the shift would be revealed.
Coupled Oscillation of a Normal Alkyl Chain. Molecular
structure of a normal alkyl chain having the all-trans zigzag
conformation corresponds to the point group of D2h, on which

INTRODUCTION
The intermolecular interaction between perﬂuoroalkyl (Rf)chains has recently been revealed to be induced by the dipole−
dipole interaction, which is generated by the large dipole
moment along each C−F bond.1,2 The dipole−dipole
interaction induces a close and tight molecular packing of Rf
chains in a two-dimensional manner, which readily explains the
Rf compounds-speciﬁc bulk properties represented by the waterand oil repellency.2,3 The molecular packing of Rf chains is
discussed by infrared (IR) spectroscopy4 using the peak
position of the CF2 symmetric stretching vibration (νs(CF2))
band at ca. 1150 cm−1.2,3 Thanks to the large dipole moment of
the C−F bond, the band strongly appears in an IR spectrum
even for a monolayer sample, which makes quantitative
discussion easier. Although the IR-active νs(CF2) band ispractically useful for analyzing the molecular packing of Rf
compounds,3 the band has a unique character of an apparent“higher wavenumber shift” with increasing the chain length, n(number of CF2 units), that is, 1126 cm−1 (n = 3), 1135 cm−1
(n = 5), 1146 cm−1 (n = 7), 1149 cm−1 (n = 9), and 1151 cm−1
(n = ∞) as presented in Figure 1,2 which is an opposite shift tothat of the “CH2” symmetric stretching vibration (ν(CH2))© 2017 American Chemical Society

Received: September 7, 2017
Revised: September 29, 2017
Published: October 5, 2017
8425

－121－

DOI: 10.1021/acs.jpca.7b08940
J. Phys. Chem. A 2017, 121, 8425−8431

The Journal of Physical Chemistry A

Articleactive Ag mode, and the other (ϕ = π) is assigned to the IRactive B2u mode.
The vibrational diﬀerence on ϕ inﬂuences the peak position.
In fact, the νs(CH2) bands of polyethylene in Raman and IR
spectra appear at slightly diﬀerent positions of 2848 and 2850
cm−1, respectively.4,10 This noncoincidence is practically foundin the spectrum of a normal alkane, and the peak exhibits alower wavenumber shift with increasing the chain length5
because of the decrease of the elements in the matrix of G of
the GF-matrix method.14,15
It sounds strange that the observed diﬀerence of thewavenumber positions of ϕ = 0 and π is only ca. 2 cm−1,
although the shapes of the two oscillations are largely diﬀerent
from each other. This is because the mass of hydrogen (H) ismuch less than that of carbon (C); that is, the oscillation is
intrinsically represented by H atoms, and C atoms are relatively
unmoved. On considering the νs(CF2) mode, the situation
should change greatly, since the mass of ﬂuorine (F) is larger
than that of C. In fact, the νs(CF2) mode appears at ca. 1150
and 730 cm−1 in IR and Raman spectra, respectively, and thediﬀerence is fairly large by more than 400 cm−1. This implies
that the coupled-oscillation phenomena of an Rf group should
be signiﬁcantly diﬀerent from those of a normal alkyl group.
Coupled Oscillation of an Rf Chain. The critical
diﬀerence of F from H is not only for elemental mass but
also for the radii of the atoms.2,3,16 The larger radii of F than H
by ca. 20% induces the Rf chain-speciﬁc helical structure due tothe steric repulsion between the adjacent CF2 units. The
twisting rate of the Rf chain depends on the phase diagram ofpolytetraﬂuoroethylene (PTFE, known as Teﬂon).17−20 At an
ambient temperature in the phase IV (19−30 °C), the Rf chain
has a symmetry of the 157-helix about the chain axis; that is, thedirection of the symmetric axis of the CF2 unit is rotated by
180° over 15 CF2 units. This helical structure corresponds tothe point group of D15.
The vibrational modes of PTFE have also been studied
already by the factor group analysis using D15.21−24 The point
group of D15 has nine irreducible representations, A1, A2, E1, E2,···, E7, and the irreducible representations of IR and Raman
Raman
spectroscopies are calculated to be ΓIR
vib = 3A2 + 8E1 and Γvib
= 4A1 + 8E1 + 9E2, respectively. The frequencies of thevibrational modes of PTFE were evaluated theoretically by the
GF calculation using a classical force constant,22−24 and theνs(CF2) band at ca. 1150 cm−1 in an IR spectrum of PTFE is
assigned to the IR-active mode of ϕ = π (E1) as presented in
Figure 2d. The dispersion curve of the νs(CF2) mode is also
reported, and the Raman band at ca. 730 cm−1 is assigned to
the Raman-active νs(CF2) band (ϕ = 0).22−24 The schematic of
the Raman mode is presented in Figure 2c.
The diﬀerence of relative mass of F and H to carbon (C)changes the shape of the normal mode greatly. Contrary to the
CH stretching vibration modes, the C atoms having a smaller
mass than the F dominantly vibrate leaving the F atoms stayrelatively unmoved as presented in Figure 2c,d. Since the C
atoms in the Rf chain are covalently bonded with the single
bonds (−C−C−), the “coupled oscillation of the skeletal C
atoms” must be considered. The character speciﬁc to thecoupled oscillation of the skeleton has already been pointed out
for the CF3 symmetric stretching vibration (νs(CF3)) mode.25
Although the CH3 symmetric stretching vibration (νs(CH3))
mode of a normal alkyl chain is nearly localized at the terminal
methyl group, the νs(CF3) mode is spread over the entire Rfgroup because of the coupled oscillation, which means that the

Figure 1. C−F stretching vibration region of IR spectra of an Rf chain
with diﬀerent lengths measured by using the ATR technique with an
ATR prism of Ge at the angle of incidence of 45°.2 The νs(CF2) band
exhibits a unique high-wavenumber shift with the Rf length.

the vibration is analyzed by the factor group analysis.5,7−13 On
the discussion of the coupled oscillation of the alkyl chain, thephase diﬀerence (ϕ) between adjacent two CH2 units is
crucial.6 The coupled oscillation has many shapes on the phase(0 ≤ ϕ ≤ π), and the frequency of the νs(CH2) mode
dependent on ϕ (dispersion curve) is revealed by the GF
calculation method.11 Fortunately, Raman and IR spectroscopies only observe the vibration bands of ϕ = 0 and ϕ = π,respectively, both of which correspond to the wavenumber
vector of nil (k = 0) because of the restriction by the Brillouincenter condition. The schematics of the νs(CH2) modes of an
ethylene unit having ϕ = 0 and ϕ = π are presented in Figure
2a,b. When a methylene group alone in the schematics isfocused, two hydrogen (H) atoms seem to oscillate with thesame phase, which is thus denoted as the νs(CH2) mode. As amatter of fact, this vibration mode is of a repeat unit of CH2,
however, which has diﬀerent shapes on the wavenumber vector
or the phase, and they are assigned to diﬀerent symmetry
species: the former vibration of ϕ = 0 is assigned to the Raman-

Figure 2. Schematic images of the coupled oscillation of an alkyl chain
having the phase diﬀerence of (a) ϕ = 0 and (b) ϕ = π. Those of an Rf
chain having (c) ϕ = 0 and (d) ϕ = π, and the potential energy
distribution (%) of vibrational components in the vibration modes (c,d) reported by Hannon et al. (ref 22).
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ArticleFigure 3. Raman spectra of MA-Rfns (n = 3, 5, 7, and 9) and a PTFE tape.

transition moment of the νs(CF3) mode is parallel to the Rf
chain.25 As a result, the intensity of the νs(CF3) band is useful26
for discussing the orientation of Rf chains in a thin ﬁlm.2,3,25 In
this manner, investigation of the coupled oscillation of theskeletal C atoms is crucial to reveal the unusual IR-activeνs(CF2) band shift.
Aim of the Present Study. To reveal the origin of theunusual IR shift dependent on the Rf length, the νs(CF2) bandis investigated by using “Raman” spectroscopy, which is suitablefor investigating a skeletal vibration. As the samples, myristic
acid derivatives having an Rf group (CF3(CF2)n(CH2)mCOOH; MA-Rfn, where n = 3, 5, 7, 9 and n + m = 12) werechosen as used in previous works.3,25 The IR spectra shown in
Figure 1 (n = 3−9) are the results of these compounds. As aresult, the Raman-active νs(CF2) band is split into two bands
for n = 7 or shorter. By considering the Rf chain structure interms of group theory, the splitting is readily explained by the
conformational change of the Rf group. After a spectral
simulation based on density functional theory (DFT), thelower-wavenumber peak is assigned to the Raman-activeνs(CF2) band, which exhibits intrinsically the same shift as
the IR-active νs(CF2) band. The unusual peak shift is concluded
to be explained by this conformational change.

of 10× with a confocal aperture with a pinhole of 25 μm width
were used for both laser irradiation and collection of the Raman
scattered light. The laser spot size is 10 μm, which corresponds
to the spatial resolution (xy) limit on the condition, and it also
determines the depth resolution (z), 30 μm. Since the linearlypolarized light was irradiated on the sample, the band intensity
should be inﬂuenced by molecular orientation. In the present
study, however, it has no problem, because only the band
position is discussed. The laser was operated at a constant
power of 10.0 mW at the sample surface. The Raman spectrum
was obtained with an exposure time of 4 s and with a totalacquisition time of 150 s.
Density functional calculation for a spectral simulation was
performed by using the Gaussian 09 program.26 The optimal
geometry, harmonic wavenumbers, and Raman intensities of asingle MA-Rfn molecule were obtained using 6-311++G basis
set. For the DFT calculations, Becke’s three-parameterexchange functional together with correlation functional of
Lee−Yang−Parr (B3LYP) was employed.27,28 A harmonic
wavenumber, ν̃harm , was corrected by the linear scaling factor,= 0.9613 ·νharmν̃scaled :νscaled
̃
̃ .29

■

Splitting of the Raman-Active νs(CF2) Bands ofPartially Fluorinated Myristic Acids. Figure 3 presents
Raman spectra of MA-Rfn (n = 3, 5, 7, and 9) at an ambient
temperature (>19 °C), which corresponds to the phase IV. As asample of an extremely long Rf chain (n = ∞), a PTFE tape
was also measured. As mentioned in the Introduction, thehelical structure of the Rf chain in the phase IV has thesymmetry represented by the point group of D15. The Ramanpeaks observed in the spectrum of PTFE are, thus, assigned to
the Raman-active vibration modes of D15 (A1, E1, and E2) asshown in Figure 3. Since MA-Rfn is a diblock compoundconsisting of the Rf and normal alkyl parts, Raman spectra of
MA-Rfn also have bands derived from the normal alkyl part,which are highlighted by blue, whereas the bands derived fromthe Rf part are highlighted by pink. The lower two peaks (ca.
384 cm−1 and ca. 290 cm−1) are assigned to the CF2 bending
and twisting vibration bands, respectively.22 The band at ca. 730
cm−1 is the key band for the present study, which is assigned to■

EXPERIMENTAL SECTION
11,11,12,12,13,13,14,14,14-Nonaﬂuorotetradecanoic acid (MARf3), 9,9,10,10,11,11,12,12,13,13,14,14,14-tridecaﬂuorotetradecanoic acid (MA-Rf5), 7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,14-heptadecaﬂuorotetradecanoic acid (MA-Rf7), and5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,14-henicosaﬂuorotetra-decanoic acid (MA-Rf9) were synthesized asreported in our previous work.3 A PTFE tape was purchasedfrom AS ONE Corporation (Tokyo, Japan).
Raman spectra were measured by using a Thermo Fischer
Scientiﬁc (Madison, WI) DXRxi Raman imaging microscope
having a diode-pumped laser with an excitation wavelength of
532 nm, a grating having 900 lines mm−1, and an edge ﬁlter for
eliminating the Rayleigh scattered light. On this spectrometer,
Raman spectra in the range of 3500−50 cm−1 with theresolution of 5 cm−1 are measured. The bulk sample of MA-Rfn
in the solid state was put on a glass plate, and an objective lens
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Articlethe Raman-active νs(CF2) mode with ϕ = 0 (A1). This band
exhibits a remarkable change that the peak is split into two
peaks for n = 7, and the splitting width becomes larger withdecreasing n. The mechanism of the band splitting is discussed
as follows.
The splitting is known to appear for a coupled oscillator
composed of repeat units having a high symmetry as found in anormal alkyl chain with D2h, which includes the mirror (orglide) plane as a symmetry element, because all the C atoms arein a plane. The factor group analysis on D2h reveals that anormal alkyl chain has two skeletal vibration modes, in which
the C−C bond length changes without a signiﬁcant change of
the C−H bond length.7 One of the two modes has thesymmetry species of Ag, in which the C atoms vibrate in theradial direction (y direction) with ϕ = 0 as presented in Figure
4a. The other mode is the B1g mode, in which the C atoms(n = 3, 5, 7) has a long “normal alkyl” part, which yields the twoν(CC) bands at ca. 1100 (Ag) and 1060 cm−1 (B1g) (Figure 3).
In this manner, the D2h symmetry is the origin of the band
splitting of a ν(CC)-related vibration band. Since the mass of C
is smaller than F, as mentioned above, the carbon chain works
as the core part of a normal mode on an Rf chain, which is trueof the νs(CF2) mode as mentioned below.
Fortunately, a former study using the GF calculation of
PTFE reveals the potential energy distribution (%) ofvibrational components in both IR and Raman-active νs(CF2)modes,22 which are presented in the squares in Figure 2c,d. The
IR-active E1 mode (Figure 2d) accompanies the F−C−F andC−C−C bending vibrations (δ(FCF) and δ(CCC), respectively). The Raman-active A1 mode (Figure 2c) accompanies anadditional vibration component of the C−C stretchingvibration (νCC) as well as the δ(FCF) and δ(CCC) vibrations.
These details of the νs(CF2) mode are comprehensively
understood by considering the ν(CC) vibration as a key
vibration. The νs(CF2) vibration is thus a result induced by the
ν(CC) vibration, and the δ(FCF) and δ(CCC) vibrations arealso generated as vice products. In other words, the essence ofthe νs(CF2) mode is of the ν(CC) vibration over the Rf group,and the name of “the νs(CF2) mode” is a “representation of a
local image” of the νs(CF2) vibration at a CF2 group. Thisvibrational image agrees with the unusual motion of an Rf
group considering the larger mass of F than that of C.25
Here, note that a short Rf group having three CF2 groups is
known to have a planar skeleton with D2h.16 On the one hand, if
the vibration of an Rf group is driven by the skeleton vibration,then MA-Rf3 should therefore exhibit doubly split bands for theνs(CF2) mode, which is conﬁrmed in Figure 5a. On the other
hand, if the symmetry of the molecular skeleton is lowered, thesplitting is lost to have a single band, which readily explains the
single νs(CF2) band at 734 cm−1 for PTFE having thesymmetry of D15.
It is worth noting that the other compounds of MA-Rfn
having intermediate Rf lengths (n = 5−9) exhibit a gradualchange of the splitting. To discuss the splitting width
depending on the Rf length (n), the “twisting rate” of the Rf
chains is introduced for convenience. Twisting angles of the
two CF2 groups at both ends of the Rf chain with n = 9, 7, andFigure 4. Schematic image of the skeletal vibration (ν(CC)) of analkyl chain: (a) Ag and (b) B1g modes.

vibrate nearly along the chain in the x−y plane (Figure 4b).
Both modes are denoted as the C−C stretching vibration
ν(CC) mode. In fact, MA-Rfn having a relatively short Rf chain

Figure 5. (a) Magniﬁed spectra in the Raman-active νs(CF2) region. (b) Splitting width between the two bands shown in the νs(CF2) region (Δ)
dependent on n.
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Article5 are 108°, 84° and 60°, respectively, and the twisting rate (%)
is calculated, so that the angle of n = 15 (180°) corresponds to100% as presented in the third column of Table 1. In addition,

assignable to the symmetry species of Ag of D2h. On the other
hand, the other band at 766 cm−1 corresponds to thevibrational motion presented in Figure 6b; that is, the C
atoms at both ends (ﬁrst and fourth) of the Rf chain vibrate in
the x−y plane, which is assignable to B1g. As a result, the twoνs(CF2) bands for n = 3 have the same symmetry species (Agand B1g) as those of the skeletal vibration of a normal alkyl
chain presented in Figure 4. Since the B1g mode is Ramanactive but IR-“inactive”, the split two peaks are available only in
Raman spectra, whereas the IR spectra have only the lower
wavenumber band (Figure 2d).
Although the entire Rf chain of n = 5 and 7 has the helicalconformation, the two bands in the νs(CF2) region for n = 5 and7 suggest that the vibrational mode involves the planar
character due to the low twisting rate. To conﬁrm thisspeculation, the calculated result of n = 7 is discussed as an
example. The vibrational mode of the lower wavenumber bandat 722 cm−1 (Figure 5a) is assigned to the A1 mode aspresented in Figure S2a. The image from top view (FigureS2a′) clearly indicates that the C atoms vibrate in the radial
direction of the chain. In the same manner as n = 3, the C
atoms in the center (marked by the pink square) mainly vibrate,
whereas those at both ends have very minor motion. In thevibrational motion of the higher wavenumber band at 739 cm−1
presented in Figure S2b, however, the C atoms on both ends
(marked by the blue square) vibrate with a large amplitude“along” the Rf chain. On closer inspection, the arrows on thethree C atoms at each end lie in the plane involving the three C
atoms. This indicates that the individually vibrating part
(marked by the blue square) belongs to D2h. The individually
vibrating part has a motion assigned to the Raman-inactive B3u
mode. When the entire molecule is taken into account,however, C atoms in the two squares vibrate in the opposite
direction. Therefore, the vibration mode of the entire Rf chain
is assigned to the Raman-active totally symmetric A1 mode of
D15 along the chain direction. As a result, the higher
wavenumber band is thus concluded to be induced by the planar
structure. The two νs(CF2) bands for n = 5 are readily explainedin the same fashion.
The A1 mode is IR-inactive, which readily explains the single
IR band in the νs(CF2) region for n = 7 as well as n = 3. As aresult, the lower wavenumber bands for n = 3−7 are all
explained by the A1 mode in terms of the vibration of the
carbon atoms in the radial direction. The higher wavenumber
band shift (indicated by the pink line in Figure 5a) withincreasing n corresponds to the decreasing Δ of the split “two”
bands in Raman spectra.
The higher wavenumber shifts of the Raman (A1) and IR(E1) νs(CF2) bands are interesting, if the band positions arecompared as plotted in Figure 7 against the twisting rate. The
Raman band positions are presented by the green circles (seethe right axis). The change is gradual along the green dashedcurve, which shows a good correlation between the Raman
band and the twisting rate. This indicates that thecorrespondence of n = ∞ to 100% is reasonable, and the Rf
chain of n = 3 certainly has the planar structure. Since thehelical structure generates a strain in the carbon skeleton, thehigher peak shift can also be interpreted as an increased strainof the Rf chain as a result of an increased force constant of the
Rf chain.
On the other hand, the νs(CF2) band positions of IR spectra
shown in Figure 1 are plotted by the red cross (see the left axis)
in Figure 7 and exhibit a similar trend to the Raman band

Table 1. Twisting Rate of Rf Chain Dependent on the Chain
Lengthnumber of CF2
units ntwisted angle of the two CF2 group at bothends, deg

twisting
rate, %

3
5
7
9
⋮
15

0
60
84
108
⋮
180

0
33
47
60
⋮
100

since the Rf chain with n = 3 has exceptionally the all-transzigzag conformation, the twisting rate for n = 3 is made 0%.
The splitting width (Δ) against the twisting rate is plotted aspresented in Figure 5b.
The spectrum of PTFE and n = 9 has a single νs(CF2) band,and thus Δ is zero. It is interesting that Δ in the n = 3−9 isfound to have a “linear correlation” with the twisting rate. This
indicates that the splitting width reﬂects the conformationalchange of the skeleton from the planar structure (D2h) to thehelical structure (D15). Through this analysis, the Rf chain
having n = 9 is found to have a vibrational character, as if thehelix were completed, although the twisting rate is only 60%.
This implies that the νs(CF2) mode of MA-Rf5 and 7 has anintermediate character between the planar structure (D2h) andthe helical structure (D15) as the molecular vibration. In thenext section, the assignment of the two bands is mentioned,
and the origin of the unusual peak shift in IR spectra is revealed.
Assignment of the Two Bands in the νs(CF2) Region
and the Origin of the Peak Shift of the IR-Active νs(CF2)Band. To make the assignment of the two Raman bands in theνs(CF2) region, QC calculations were performed. The
calculated Raman spectra of MA-Rfn (n = 3, 5, 7 and 9) arepresented in Figure S1 in Supporting Information, which arecompared with the measured spectra. Almost all the measured
peaks are readily reproduced by the calculation. Of course, thecalculated spectrum for n = 9 has a single band in the νs(CF2)region, and the vibrational motion is thus assigned to the A1
mode such as the schematic image shown in Figure 2c; that is,the chain of C atoms vibrates in the perpendicular (radial)
direction to the Rf chain axis. The calculated spectra shown in
Figure S1 also reproduce the band splitting for n = 7 or shorter,and MA-Rf3 exhibits the largest splitting. The vibrationalmotion of the lower wavenumber band presented in Figure 5a
indicates that the center C atoms (second and third) in the Rf
chain vibrate nearly along the y direction with ϕ = 0 (Figure6a). Therefore, on the one hand, the lower band at 689 cm−1 isFigure 6. Calculated vibrational motions of the split two bands for n =3: (a) 689 and (b) 766 cm−1.
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Articlespectra is inactive for IR spectroscopy, the reason why the IRband exhibits the unique higher wavenumber shift without aband splitting has readily been revealed.

■

ASSOCIATED CONTENT

S Supporting Information*

The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.jpca.7b08940.
Calculated Raman spectra of MA-Rfn (n = 3, 5, 7 and 9),
vibrational images of the split two bands for n = 7 (PDF)■

Figure 7. IR-active (red, left axis) and Raman-active (green, right axis)
νs(CF2) band positions plotted against the twisting rate.

AUTHOR INFORMATION

Corresponding Author*E-mail: htakeshi@scl.kyoto-u.ac.jp. Fax: +81 774 38 3074.
ORCID

except an apparent wavenumber jump between 33% (n = 5)
and 47% (n = 7). On the one hand, the entire trend is similar tothat of Raman, which can be explained by the representative
vibrational components commonly involved in the Raman- andIR-active modes as summarized beside Figure 2c,d. On theother hand, the IR-speciﬁc apparent jump is attributed to afundamental diﬀerence between Raman and IR spectroscopies.
The Raman bands are derived from the molecular polarizability change of the Rf chain, whereas the IR bands depend
on a change of the transition dipole moment. Therefore, the IRband strongly reﬂects an atmosphere of a dipole. In ourprevious study, the aggregation property of MA-Rfn dependent
on n was studied by using IR spectroscopy. The study revealed
that MA-Rfn having n = 7 or longer forms a close molecular
packing via the strong dipole−dipole interaction, whereas thosehaving n = 5 or shorter exhibit a poor packing.2,3,30 Since thestrong dipole−dipole interactions make the molecules tightly
aggregated only for n = 7 or longer, the atmosphere about a C−
F bond would largely change from n = 5 to 7. In other words,
the diﬀerence of the dipole’s atmosphere induces a
discontinuous IR band shift between n = 5 and 7. In short,
IR bands of an Rf compound are inﬂuenced by not only theskeletal structure but also the packing of the Rf chain, which is abeneﬁt of using IR spectroscopy for investigating the packing.

Takeshi Hasegawa: 0000-0001-5574-9869
Notes

The authors declare no competing ﬁnancial interest.

■

ACKNOWLEDGMENTS
This work was ﬁnancially supported by Grant-in-Aid for
Scientiﬁc Research (B) (No. 23350031) and Grant-in-Aid forYoung Scientists (B) (No. 17K14502) from Japan Society for
the Promotion of Science, and Priority Areas (23106710) from
the Ministry of Education, Science, Sports, Culture, andTechnology, Japan. This work was supported by the
Collaborative Research Program of Institute for Chemical
Research, Kyoto Univ. (Grant No. 2012-38).

■

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■

SUMMARY
To reveal the Rf-compound speciﬁc IR band shift of theνs(CF2) dependent on the Rf chain length, n (number of CF2
units: n = 3, 5, 7, and 9), the skeletal vibration of the Rf chain
has been investigated by using Raman spectroscopy. While theRaman-active νs(CF2) band of n = 9 has a single band at ca. 730
cm−1 as well as PTFE, the band is split into two bands for n =3−7, and the splitting becomes larger with decreasing n. To
investigate the origin of the splitting, a twisting rate of the Rf
chain is introduced, which is a useful index for interpreting theconformational change with n. The band splitting width was
found to have a good correlation with the twisting rate, and thevariation of the width was readily attributed to the conformational change between D15 and D2h. The “lower” wavenumber
band of the split two bands in the spectrum of n = 3−7
corresponds to the νs(CF2) band, which shows a similar higher
wavenumber shift to the IR band. The νs(CF2) band position
also has a good correlation with the twisting rate, and themechanism of the unusual shift has been concluded to be
attributed to the “strain” of the skeletal chain dependent on the
chain length. A similar trend is found for the IR band position.
Since the “higher” wavenumber band found in the Raman
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Reprinted from Sci. Rep., 7, 14361 (2017)with permission from Nature Publishing Group.

www.nature.com/scientificreports

OPEN

Received: 24 July 2017
Accepted: 6 October 2017
Published: xx xx xxxx

Critical controllability analysis of
directed biological networks using
efficient graph reduction
Masayuki Ishitsuka1, Tatsuya Akutsu2 & Jose C. Nacher1
Network science has recently integrated key concepts from control theory and has applied them to theanalysis of the controllability of complex networks. One of the proposed frameworks uses the Minimum
Dominating Set (MDS) approach, which has been successfully applied to the identification of cancerrelated proteins and in analyses of large-scale undirected networks, such as proteome-wide protein
interaction networks. However, many real systems are better represented by directed networks.
Therefore, fast algorithms are required for the application of MDS to directed networks. Here, we
propose an algorithm that utilises efficient graph reduction to identify critical control nodes in largescale directed complex networks. The algorithm is 176-fold faster than existing methods and increases
the computable network size to 65,000 nodes. We then applied the developed algorithm to metabolic
pathways consisting of 70 plant species encompassing major plant lineages ranging from algae toangiosperms and to signalling pathways from C. elegans, D. melanogaster and H. sapiens. The analysis
not only identified functional pathways enriched with critical control molecules but also showed thatmost control categories are largely conserved across evolutionary time, from green algae and earlybasal plants to modern angiosperm plant lineages.
An understanding of the functional processes and mechanisms of living cells requires combined knowledge ofmultiple life molecules. However, these biological functions are not the result of single molecules but rather tend
to emerge from the interactions among molecules. Complex diseases are not an exception; in fact, these diseases
are often the result of failures in interactions and molecules that can exert effects on distant pathways1,2, and thisview naturally leads to the concept of biological networks. To restore functional features or reverse a disease state,
one prospective way is to apply intervention or control to the disrupted pathways and molecular processes. Since
it is impossible to directly alter the states of all genes or molecules, it is needed to find a small number of nodes
in a given network control of which drives the whole network to a desired state, where these kinds of nodes arecalled driver nodes.
Indeed, recent research has led to the development of several frameworks for this controllability problem incomplex networks. Liu et al. employed the structural controllability framework for linear systems and showed thatthe minimum set of driver nodes can be found by computing the Maximum Matching (MM) in graph theory 3.
However, they also showed that this approach requires a large number of driver nodes in scale-free networks 3. Asan alternative approach, Nacher and Akutsu proposed the Minimum Dominating Set (MDS) approach 4, where
MDS is also a well-known concept in graph theory. They showed that the MDS approach needs a smaller fractionof nodes in scale-free networks. Since most of the observed complex networks in nature are scale-free networks
and the MDS approach can cope with non-linear systems4,5, analysing the controllability of real networks using
the MDS approach appears reasonable. Actually, the MDS approach has been applied to analysis of various biological networks and reported to be useful for finding important genes and molecules not only by our group but
also by other groups6–19.
Although finding the MDS in a given network is known as a hard problem (NP-hard problem), the MDS canbe found in reasonable CPU time in many practical cases by using Integer Linear Programming (ILP) if the network has a scale-free property. However, the MDS is not uniquely determined, that is, there might exist multiple
minimum dominating sets. In order to cope with this non-uniqueness issue, the concepts of critical, intermittent
and redundant nodes were applied to the MDS framework16, where these concepts were originally proposed for1

Department of Information Science, Faculty of Science, Toho University, Funabashi, 274-8510, Japan.
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. Correspondence andrequests for materials should be addressed to J.C.N. (email: nacher@is.sci.toho-u.ac.jp)
2

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the MM framework20. Critical nodes are nodes included in all MDSs, whereas nodes that belong to some but not
all MDSs are called intermittent, and those nodes that do not participate in any MDS are called redundant16. The
computation of these control categories is very intense and requires solving the MDS problem n times, where
n is the number of nodes. For undirected networks, in order to address this computational difficulty, a novel
algorithm that benefits from heuristics pre-processing procedures has been proposed10. This algorithm was successfully used to investigate controllability properties by integrating a proteome-wide protein interaction networkwith transcriptome information.
However, many real-world networks, including metabolic pathways and signal pathways, are directed. To
efficiently examine directed controllability features in these systems, we propose a new algorithm that includes
heuristics to speed up the computation time and increase the computable network size. The performance of thealgorithm was examined using artificial complex networks. The results showed that the algorithm expanded thecomputable size to 65,000 nodes and increased the computational speed 176-fold.
The significant improvement in performance obtained with the proposed algorithm allows the first directed
MDS controllability analysis of large-scale directed biological networks, such as metabolic networks and signal
pathways. The results provide novel insights and findings on specific functional pathways that are significantly
enriched in critical control molecules and other control categories and show that most control categories arelargely conserved across evolutionary time, from green algae and early basal plants to modern angiosperm plantlineages.

Results

Theoretical and computational analysis. Theoretical analyses of control categories led to an efficient MDS

algorithm. The formal definition of an MDS in a directed network is as follow: Let G(V, E) be a directed graph,
where V and E are sets of nodes and directed edges, respectively. Then, each node pair (u, v) ∈ E denotes a directededge from u to v. In a directed network, S (S ⊆ V) is a Dominating Set (DS) if each node in V is an element of S or
has directed edge(s) from one or more elements of S to the node. If the size of S is smallest among all existing DSs,
it is called an MDS. The computation of an MDS is an NP-hard problem21,22. Therefore, the development of analgorithm that can solve the problem in polynomial time is not plausible. However, we formalised the MDS problem using Integer Linear Programming (ILP), in which xv = 1 and xv = 0 indicate that node ν is in an MDS and
not in an MDS, respectively:
Minimise

∑ xv

v∈V

subject toxv +∑ xu ≥ 1 ∀ v ∈ V ,(u , v ) ∈ V

xv ∈ {0, 1} ∀ v ∈ V .

(1)Here, by solving the ILP (Eq. 1), an MDS is given by the set {v|xv = 1}.
It should be noted that an MDS satisfies the structural controllability condition, as demonstrated in Theorem
1 in our previous work4. However, this computational approach has some problems. First, the solution for a given
MDS problem is not unique, which means that multiple node configurations might have the same minimum
node size but contain different nodes. This problem is particularly serious when analysing real data. Previous
studies defined control categories (critical, intermittent and redundant) to address this issue in both MM and
MDS approaches16,20. A node is critical if it belongs to all MDS solutions. A node is considered intermittent if it
belongs to at least one but not all MDS solutions, and a node is redundant if it does not belong to any MDS configuration. To identify these control categories, particularly the critical set of nodes, algorithms for undirected anddirected networks have been proposed16. These algorithms successfully identify the control categories in complex
networks. However, the network size that can be handled by these algorithms is limited to a few thousand nodes
at most, and the computational time is not fast because the MDS problem needs to be solved n times, where n is
the number of nodes. Therefore, pre-processing steps inspired by theoretical advances are necessary for the efficient computation of large networks consisting of tens of thousands of nodes.
Here, we present a new algorithm that computes controllability categories in directed networks using the MDS
approach combined with efficient graph reduction techniques. The main novelty of the proposed method is theincorporation of heuristics in a pre-processing step to significantly improve the computation efficiency in termsof both computable size and computational speed. The details of the algorithm are shown in the Methods section.
This manuscript also describes theoretical results obtained using the graph reduction process.
First, we invoke two important propositions for controllability in directed networks16:
Proposition 2.3 If ν is a node with indegree 0, ν is a critical node.
Proposition 2.4 If ν is a node with at least two directed edges to nodes with outdegree 0 and indegree 1, ν is a
critical node.
These two propositions are related to the identification of critical nodes. However, a large fraction of redundant nodes could also be predetermined based on the following new proposition:
Proposition 1: If ν is a node with outdegree 0 and has an incoming link from a critical node, then ν is a redundant node.
The combination of these three propositions led to the design of an efficient algorithm for the computation ofcontrol categories in large directed complex networks (see the Methods section). The operations performed by the2

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algorithm are illustrated in Fig. 1, and an example of the classification of nodes in real metabolic and signalling
pathway networks into control categories is shown in Fig. 2.

Computational experiments using artificial networks. To assess the performance of the algorithm, weconstructed scale-free networks of different sizes, ranging from 500 to 65,000 nodes, with a degree distributionthat follows a power law k−γ . We also investigated the performance of the proposed algorithm when varying two
topological parameters, such as the degree exponent γ and the average degree <k>. First, for each parameter pair,ten undirected scale-free networks were constructed using the Havel–Hakimi algorithm with random(Monte-Carlo) edge swaps (HMC model)23. The direction for each undirected link was then randomly selected,
leading to the generation of directed networks with the same incoming and outgoing degree exponents γin = γout .
This procedure was repeated ten times, yielding a total of 100 networks for each data point. Figure 3(a–c) shows
the average results obtained with the algorithm. The specific degree exponents examined were γin = γout = 2.2,
2.4. 2.6, 2.8, and 3.0, and the average degree values were <k> = 2, 2.5 and 3. Compared with our previous algorithm (black line), the proposed method (red line) increased the computation speed 176-fold, as determinedusing the computational time for N = 4,500 nodes with <k> = 2 as a reference. For a network size of N = 7,000,
the computation speed was 156-fold faster than that of existing algorithms. Moreover, the computable network
size was expanded to 65,000 nodes for some topologies. In general, we observed that increasing the average
degree < k > from 2 to 3 increased the computational time (Fig. 3(a–c)) because the density of the edges increased,complicating the solution of the MDS. In contrast, scale-free networks with lower degree exponents were solved
faster, suggesting the existence of high-degree nodes.
Although our new method significantly expanded the size of networks for which the critical, intermittent, andredundant nodes can be obtained, the MDS problem is NP-hard and thus exponential time is still needed in theworst case. Indeed, the log-log plots (see Fig. 3) suggest this tendency because if the computation time were polynomial, the curves would be almost straight. Therefore, it is reasonable that there exists some limit on the size ofavailable networks. However, if much faster ILP solvers are developed in the future, larger networks might behandled. Since ILP solvers have been continuously improved, we can expect such improvements. Furthermore,when γin = γout = 2.2 is small, our method could compute the critical, intermittent, and redundant nodes for large
networks with 65,000 nodes.
Analysis of directed biological networks.

In this study, we applied the proposed efficient MDS algorithm to directed biological networks such as metabolic and signalling pathways. For metabolic networks, weused the latest update of the Plant Metabolic Network Database (PMND) 24, which includes 70 plant speciesclassified into the following four major groups in the green plant lineage (with the number of species in each lineage shown in parentheses): green algae (6), early land plants (2) and angiosperms, which are further subdivided
into monocots (17) and eudicots (45)25. The analysis was performed using an enzyme/reaction-centric network
assembled from the full metabolic pathways. The number of plants included in each main evolutionary group is
shown in Table S1.
In addition, we assembled networks downloaded from the SignaLink 2.0 database corresponding to signalling pathways from three organisms, namely, C. elegans, D. melanogaster and H. sapiens26. The statistics for theconstructed signal networks are shown in Table S2. The signal proteins in these pathways are classified into sevenmajor pathways that contain all main developmental signalling mechanisms, namely, the Notch, NHR (nuclear
hormone receptor), RTK (including EGF/MAPK and IGF/insulin), TGF-B, Wingless/Wnt, Hedgehog and JAK/STAT pathways.

Fraction and enrichment of control categories for each functional metabolic reaction class.

The algorithm was able to identify the controllability categories for each analysed metabolic network. The fractionof metabolic reactions in each control category computed for all plant species in each major evolutionary groupand classified into specific functional classes of enzymes showed a heterogeneous distribution. Specifically, some
pathways contained a larger fraction of critical nodes (e.g., detoxification), whereas other pathways were largely
depleted of critical controllers (e.g., energy metabolism and intermediate metabolism; see Fig. 4). This tendency
was largely conserved across evolutionary groups, from green algae to eudicots. However, there are some exceptions, such as specialised metabolism; for this pathway, a large fraction of critical nodes was identified in green
algae, but this fraction progressively decreased over evolutionary time. In general, for all examined functionalclasses, the smallest and largest fractions of nodes corresponded to the critical and redundant nodes, respectively.
We then computed the enrichment or depletion of metabolic reactions associated with each control category and further classified these into functional pathways in each lineage-specific group. The results, which are
shown in Fig. 5, indicated that the enrichment of controllability categories for functional classes of enzymes waslargely conserved across major plant lineages from algae to angiosperms. However, some tendencies regarding
the enrichment and depletion of control categories in each functional class were observed. For example, energy
metabolism was largely depleted of critical controllers, whereas the detoxification pathway was largely enrichedwith critical controllers. Moreover, specialised metabolism also showed positive enrichment encompassing all
major plant lineages. The highest enrichment of the redundant control category was also observed in energy
metabolism. In contrast, nucleotide metabolism was found to be largely enriched with intermittent controllers.
The statistical significance of each enrichment, which was assessed by computing the corresponding two-tailed
p-value using Fisher’s exact test (see the Methods section for details), is shown in the figure. Furthermore,
the fractions of nodes classified into each control category for each analysed plant species are shown in the
Supplementary Information (Figs S1–S2). We observed that the fractions were conserved not only within each3

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Figure 1. Illustration of the steps in the fast MDS algorithm. This graphical illustration follows the algorithm
description shown in the Methods section. (a) Consider a directed graph of 13 nodes. (b) The application of
proposition 2.3 to each node identifies a preliminary set of critical nodes. (c) Proposition 2.4 is then applied
to each remaining unclassified node. (d) The incoming links to the newly identified critical node are deleted.
(e) The application of proposition 1 to unclassified nodes leads to the identification of redundant nodes. (f)The incoming links to the newly identified redundant nodes are deleted. (g–k) Because links were deleted inprevious steps, propositions 2.4 and 1 are re-applied to delete additional links. (l) ILP is used to find an MDS
in the reduced graph, and the critical algorithmic procedure is applied to each node that belongs to the MDS
and that has not already been identified as critical to determine its critical role. (m) The redundant algorithmic
procedure is applied to each node that does not belong to the MDS to determine its redundant role. Bothremaining nodes (2 and 3) are classified as intermittent.

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Figure 2. Network visualisation showing the classification of the nodes in a real plant metabolic network of A.
thaliana (a) and in a signal network of H. sapiens (b) into control categories.

major plant lineage but also across evolutionary time. The analysis of the enrichment of control categories acrossfunctional classes for each individual plant species is shown in the Supplementary Information (Figs S3–S11).

Fraction and enrichment of control categories of proteins in signal networks and their functional pathways. To further apply the proposed algorithm to real biological systems, we assembled networks

downloaded from the SignaLink 2.0 database corresponding to signalling pathways from three organisms, specifically, C. elegans, D. melanogaster and H. sapiens, and then analysed these three networks in our study. First, we
assembled signalling interactions representing experimentally verified physical interactions between moleculesthat have been classified as (1) directed protein-protein interactions and (2) transcriptional interactions. The latter represents physical protein-DNA binding interactions predicted based on promoter-gene data. Moreover, (3)a combined dataset consisting of both sets of collected data was also prepared.
The fraction of control categories was then computed for each of the assembled networks for the three organisms. The results show that for all three signalling networks, the fraction of critical nodes decreased with increasing organism complexity, i.e., from C. elegans to H. sapiens (see Fig. 6). Second, the fraction of critical nodes was
similar to that of redundant nodes in directed PPI networks. However, a striking difference was observed in thefraction of intermittent nodes. The results of the combined networks showed that the intermittent nodes constituted the smallest fraction in all of the organisms, particularly H. sapiens (Fig. 6). This finding highlights the
structural differences between metabolic pathways and signalling networks.
The subsequent enrichment analysis of each major functional pathway showed that most of the pathways weredepleted of critical nodes (see Fig. 7), and this finding was obtained due to the existence of few star-type protein
hubs, which perform critical roles connecting and controlling multiple molecules (see Fig. 2). An exception was
found for the NHR pathway, which showed an enrichment of critical control nodes. The fraction of intermittent
nodes was largely enriched in several signalling pathways related to the transcriptional networks of D. melanogaster and H. sapiens. The Hedgehog and NHR pathways showed the greatest enrichment of intermittent nodes
in the combined network. The statistical significance, which was determined based on the two-tailed p-value
computed using Fischer’s exact test, is shown in the figure.

Discussion

In this work, we propose an efficient algorithm for analysing controllability in directed biological networks using
the MDS approach. The existing algorithms require solving the MDS problem n times, where n is the number ofnodes16, which makes the application of existing algorithms to large and dense biological networks unpractical.
In contrast, the proposed algorithm performs efficient graph reduction in a pre-processing step, which aids theidentification of a large fraction of critical and redundant controllers and results in a notable simplification of the
network. The resulting network is therefore smaller, making it easier to detect the remaining unclassified nodes.
The computational results obtained using artificially constructed scale-free directed networks showed that the
proposed algorithm solves the control category problem at a speed up to 176-fold faster than that achieved withexisting algorithms. More importantly, the algorithm is able to solve networks consisting of up to 65,000 nodes,
indicating that it is suitable for the analysis of genome-scale biological networks.
The algorithm was applied to directed biological networks, such as metabolic pathways and signalling pathways. The analysis of the results revealed that although most control categories are largely conserved across evolutionary time, from green algae and early plants to modern angiosperm plant lineages, some specific metabolic
functions tend to have more critical nodes than others. Therefore, we were able to associate biological functions tocertain control categories. For example, detoxification reaction classes show a high enrichment of critical nodes.
In addition, the detoxification pathway contains a class of proteins and enzymes specialised in degradation and/or
elimination of endogenous and exogenous toxins27, and the concentration of these enzymes represents a responseto an increase of toxin stimulation. Our analysis showed that these enzymes perform structural control functions in the network. In contrast, the genes associated with specialised metabolism encode enzymes that reflect
evolutionary mutations that occurred in response to specific environmental changes28,29. Our results show that asignificant fraction of the enzymes in the specific functional category also participates in critical network control.

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Figure 3. Results from the application of the MDS algorithm to artificially generated complex networks. The
figure shows the computational time as a function of the network size N for scale-free networks with thefollowing average degrees on log-log scale: (a) <k> = 2, (b) <k> = 2.5 and (c) <k> = 3.0. The results for
networks with different degree exponents from γ = 2.2 to γ = 3.0 are shown in each figure. The colour legend forthese values is shown in Fig. 3(c). Note that γ refers to γin = γout because only directed scale-free networks wereconsidered.

Figure 4. Fraction of enzymes in each control category computed for all plant species in each major
evolutionary group and classified into specific functional classes of enzymes. The red fraction indicates thecritical nodes, the white fraction denotes the intermittent nodes, and the blue fraction denotes the redundantnodes.
Signalling pathways show a unique network control category distribution. In fact, as shown in Fig. 6, signalling
pathways include a small fraction of intermittent nodes, and this is a consequence of the structural differences
between metabolic and signal networks. Among the functional pathways, the NHR pathway showed a conserved
enrichment of critical control proteins (Fig. 7). Signal proteins associated with the NHR pathway represent a classof ligand-activated proteins that perform on-off transcriptional switches when bound to specific DNA sequences
within the cell nucleus. These functional switches play roles in the control of the development, growth and differentiation of skin, bone and behavioural areas in the brain, the determination of sex, the continual regulation of
reproductive tissues, and the regulation of cell metabolism.
We believe that analysing the controllability of biological networks is a useful technique for identifying criticalcontrol-related molecules associated with not only specific biological functions but also human disorders. In
future, we encourage further studies on the identification of critical disease-related proteins in directed biologicalnetworks.

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Figure 5. Enrichment or depletion of metabolic enzymes associated with each control category and further
classified into functional pathways in each lineage-specific group. The statistical significance was analysed using
Fischer’s exact test, and the corresponding two-tailed p-value is shown next to each pathway with the following
notations: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001. The colours provide the same information
as in Fig. 4.

Figure 6. Classification of proteins in all analysed signalling networks of the three organisms into control
categories. The colours provide the same information as in Fig. 4.

Methods

Datasets.

The data used in this study correspond to metabolic pathways of 70 plant species downloaded
from the Plant Metabolic Network Database version 16.0. These species were classified into major plant lineages
as follows: six species correspond to green algae, two species are early land plants, 17 species are monocots,and 45 species are eudicots25. The analysis was performed using an enzyme/reaction-centric network assembled
from the full metabolic pathways. The bipartite network is therefore transformed into a unipartite network inwhich nodes corresponds to the enzymes. The statistics for the assembled networks are shown in Table S1 in the
Supplementary Information (SI). The analysed signalling pathways of directed protein-protein and transcriptional interactions corresponded to those from three organisms, namely, C. elegans, D. melanogaster and H. sapiens26. The data were collected from the SignaLink 2.0 database. The statistics for the signalling pathway networks
are shown in Table S2 in the SI.

Algorithm for the efficient computation of critical and redundant dominating subsets indirected complex networks. The combination of the propositions introduced in the Results section led tothe development of the following algorithm for the efficient computation of control categories in large directedcomplex networks (see Fig. 1 for an illustration of the algorithm steps):

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Figure 7. Enrichment or depletion of proteins associated with each control category and further classified intofunctional signalling pathways in each organism. The statistical significance was analysed using Fischer’s exact
test, and the corresponding two-tailed p-value is shown next to each pathway with the following notations:*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001. The colours denote the same information as in Fig. 4.

1. The critical proposition 2.3 is applied to each node.
2. The critical proposition 2.4 is applied to each unclassified node, and the incoming links to the newly identified critical nodes are deleted.
3. The redundant proposition 1 is applied to each unclassified node, and the incoming links to the newly
identified redundant nodes are deleted.
4. If Steps 2 and 3 involved the deletion of one or more links, propositions 2.4 and 1 in Steps 2 and 3, respectively, are re-applied.
5. The simplified MDS problem for the resulting network is applied after adding information obtained from
Steps 1~4 into the ILP.
6. After an MDS solution is obtained, the control category algorithm for critical and redundant roles is applied to classify the remaining nodes as follows:
6.1 The critical algorithmic procedure is applied to each node that belongs to the MDS16 and has not alreadybeen identified as critical to determine its critical role.
6.2 The redundant algorithmic procedure is applied to each node that does not belong to the MDS16 to determine its redundant role.
For artificial network analysis, the formalised Integer Linear Programming (ILP) problem was solved using
the GNU Linear Programming Solver (glpsol). For data analysis, the ILP problem was solved using the IBM ILOG
CPLEX Optimiser package version 12.0.

Enrichment analysis.

To assess the extent to which each control category is present in each metabolic andsignalling pathway, we used the following enrichment factor. We first computed the fraction of enzymes or proteins identified for a given control category (S) and existing in a given enzyme functional class or pathway (P) NSP
divided by the total number of enzymes or proteins identified for the given category (S) NS, yielding f SP = NSP /NS.
We then calculated the fraction of enzymes or proteins that are located in a given enzyme functional class orpathway NP divided by the total number of nodes (enzymes or proteins) N in a given metabolic network or signal
network, i.e., f P = N P /N . The enrichment factor for a control category (S) in a given pathway (P) was then computed as ESP = ln(f SP /f P ). We then defined a contingency table and evaluated the statistical significance of theenrichment or depletion of the identified critical, intermitted and redundant enzymes or proteins in each functional class or pathway using Fisher’s exact test. The two-tailed exact p-values are denoted in each figure using thefollowing notations: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001.

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In the analysis of signalling networks, only one organism was included in each enrichment computation. The
computation of the enrichment of a control category (S) in a given enzyme functional class or pathway (P) for allorganisms included in a major plant lineage involved NSP , NS, NP, and N, where each variable has the same meaning as defined above but aggregates the total number of counts observed in each major lineage.

Data Availability.

The metabolic pathways were downloaded from the publicly available Plant Metabolic
Network Database Version 12.0 (PMN) www.plantcyc.org. The signal pathways were downloaded from the publicly available SignalLink 2.0 Database http://signalink.org/. We did not perform biological experiments, therefore, we did not generate any new biological dataset in this study. All the computational results are presented infigures and included in this manuscript and in the Supplementary Information that accompanies this paper.

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Acknowledgements

J.C.N. was partially supported by MEXT, Japan (Grant-in-Aid 25330351). T.A. was partially supported by MEXT,
Japan (Grant-in-Aid 26540125) and (Grant-in-Aid 26240034), respectively. This work was also supported in partby research collaboration projects of the Institute for Chemical Research, Kyoto University.

Author Contributions

M.I. and J.C.N. conducted the principal research. M.I. analysed the empirical data, implemented the algorithms
and prepared the figures. J.C.N. designed the research and prepared the manuscript. T.A. contributed to thetheoretical discussion and data analysis interpretation. M.I, T.A. and J.C.N. reviewed the manuscript.

Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-14334-8.
Competing Interests: The authors declare that they have no competing interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.

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－144－中村 正治
山子 茂

東京大学大学院・理学系研究科化学専攻

横浜国立大学・大学院工学研究院

2017-20 山口 佳隆

帯広畜産大学・グローバルアグロメディシン研究センター

信州大学・学術研究院農学系

京都大学・薬学研究科

広島大学・大学院理学研究科

大阪大学・産業科学研究所

University of Science and Technology of China

2017-28 茅野 光範

2017-29 大神田 淳子

2017-30 西川 元也

2017-31 安倍 学

2017-32 真嶋 哲朗

2017-33 松尾 豊

緒方 博之

国立研究開発法人海洋研究開発機構

岐阜大学・工学部・電気電子情報工学科

高知大学・農林海洋科学部

2017-25 長﨑 慶三

2017-26 髙見 英人

東京理科大学・理学部

2017-27 志賀 元紀

緒方 博之

東邦大学理学部情報科学科

2017-23 ホセ ナチェル

2017-24 武村 政春

山子 茂

山子 茂

山子 茂

二木 史朗

二木 史朗

馬見塚 拓

馬見塚 拓

緒方 博之

阿久津 達也

阿久津 達也東京大学大学院・薬学系研究科

九州工業大学・大学院情報工学研究院

2017-21 内山 真伸

2017-22 竹本 和広

中村 正治

中村 正治

金光 義彦

金光 義彦

2017-19 ILIES Laurean

上越教育大学・学校教育研究科

2017-16 小川 佳宏

金光 義彦

情報通信研究機構・未来ICT 研究所

千葉大学・大学院理学研究科

2017-15 山田 泰裕

金光 義彦

熊本大学・大学院生命科学研究部薬学系

長岡技術科学大学・技術科学イノベーション専攻

2017-14 山田 昇

竹内 勝彦

2017-18 杉浦 正晴

近畿大学・理工学部

2017-13 松尾 司

小澤 文幸

藤井 知実

藤井 知実

岩下 芳久

岩下 芳久

岩下 芳久

岩下 芳久

井上 峻介

阪部 周二

阪部 周二

橋田 昌樹

橋田 昌樹

化研内研究協力者

2017-17 井原 章之

弘前大学・大学院理工学研究科

高エネルギー加速器研究機構 加速器研究施設

2017-9 佐伯 学行

2017-12 岡崎 雅明

高エネルギー加速器研究機構 加速器研究施設

2017-8 早野 仁司

香川大学・国際希少糖研究教育機構

産業技術総合研究所・分析計測標準研究部門

2017-7 木野 幸一

関西大学・化学生命工学部

量子科学技術研究開発機構・量子ビーム応用研究部門

2017-6 羽島 良一

2017-11 加藤 志郎

大阪大学レーザーエネルギー学研究センター

2017-5 有川 安信

2017-10 老川 典夫

大阪大学・レーザーエネルギー学研究センター

光産業創成大学院大学

2017-4 加藤 義章

摂南大学・基礎理工学機構

2017-3 時田 茂樹

大阪産業大学・工学部

研究代表者所属2017-2 長島 健

研究代表者

2017-1 草場 光博

課題番号化学研究所共同利用・共同研究拠点 H29年度採択課題（応募137件、採択 104件）

分野選択型

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型

実施
国際
状況
レーザー吸収制御による金属の微細加工に関する基礎研究

MAPLE とGENIES を組み合わせたゲノム・メタゲノム配列からの新たな代謝機能予測法

赤潮頻発性閉鎖海域における巨大ウイルス探索: dsDNA ウイルス進化を巡る新仮説構築に向けて

日本の温泉水から分離した新たな大型DNAウイルスのゲノム解析研究

最小支配集合による複雑ネットワークの制御と解析

次数相関のある複雑ネットワークの解析

シクロパラフェニレンの電子構造の解明と物質創製

アニオン性の3座配位子を有する第一列後周期遷移金属錯体の研究

鉄触媒による不斉炭素—炭素結合生成反応

酸化反応を触媒するポリカルボン酸鉄錯体の開発

新規レーザー顕微分光技術を活用したナノ発光材料の量子物性の研究

半導体量子ドットにおけるキャリア間相互作用の研究

ハロゲン化金属ペロブスカイト半導体のキャリア伝導機構の解明

ペロブスカイト半導体を用いた新規冷却デバイスの実現可能性検討

ピンサー型ホスファアルケン配位子を有する遷移金属錯体の合成と触媒反応への応用

遷移金属および高周期典型元素含有反応場における小分子活性化

X線結晶解析によるL-アスパラギナーゼの耐熱性及び基質特異性の研究

X 線構造解析によるマレイル酢酸還元酵素の反応機構研究

超伝導空洞の内面における窒素ドープ処理と薄膜生成処理による高性能化の研究

ＩＬＣ推進に向けた将来加速器技術開発

リチウムイオン二次電池の電極材内の充放電反応可視化を目的とした、六極磁石によるパルス中性子透過イメージ拡大の研究

化学反応の量子制御を目指したCEP安定化自由電子レーザの提案、これを実現するための省電力型超伝導加速空洞の開発

繰り返しレーザー駆動中性子源の開発

レーザー駆動量子ビームを用いたリチウムイオン電池の診断

レーザー駆動高強度テラヘルツ表面波の研究

シクロパラフェニレンジカチオンの励起状態ダイナミクス

マクロ環骨格内に導入した開殻系分子の化学

高活性多足型構造核酸(polypodna)複合体の創出と細胞内送達

細胞透過性ペプチドによるたんぱく質間相互作用阻害剤の細胞内デリバリ

統計的機械学習による効率的な分子ネットワーク解析法

4 発展的 国際 シクロパラフェニレンーフラーレン誘導体のホストーゲスト化学

4 発展的

4 発展的

4 萌芽的

4 発展的

3 発展的

課題名

高輝度テラヘルツ波相互作用による材料表面への新規機能性付与

3 萌芽的 国際 ゲノムの多様な補助情報を用いるためスパース正則化学習

3 萌芽的

3 萌芽的

3 萌芽的

3 萌芽的

3 萌芽的

2 発展的

2 萌芽的

2 萌芽的

2 発展的

2 萌芽的

2 萌芽的

2 萌芽的

2 萌芽的

2 萌芽的

2 発展的

1 発展的

1 発展的

1 発展的

1 発展的

1 発展的

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1 発展的

1 発展的

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選択
分野

－145－

大阪工業大学・工学部 生命工学科

立命館大学・生命科学部 生物工学科

広島大学・大学院理学研究科

名古屋大学大学院工学研究科結晶材料工学専攻

弘前大学・大学院理工学研究科

2017-66 三原 久明

2017-67 本間 謙輔

2017-68 畑野 敬史

2017-69 増野 敦信

電気通信大学 情報理工学研究科

2017-59 仲谷 栄伸

2017-65 大森 勇門

名古屋工業大学・物理工学科

2017-58 田中 雅章

近畿大学・農学部 応用生命化学科

東京大学・大学院工学系研究科

2017-64 倉田 淳志

岐阜大学・工学部・化学・生命工学科

2017-56 山田 啓介

2017-57 千葉 大地

金沢大学大学院・薬学系

北海道大学工学研究院物質化学部門固体反応化学研究室

2017-55 長浜 太郎

岐阜薬科大学・薬学部

国立研究開発法人理化学研究所 環境資源科学研究センター

2017-54 伊藤 昭博

2017-63 国嶋 崇隆

東京農工大学 工学部生命工学科

2017-53 長澤 和夫

2017-62 伊藤 彰近

理化学研究所・環境資源科学研究センター

2017-52 Jong-Myong Kim

神戸大学大学院・工学研究科

京都大学・生存圏研究所

2017-51 矢崎 一史

立教大学・理学部化学科

小野 輝男

広島大学・生物圏科学研究科

2017-50 冨永 るみ

2017-61 山中 正浩

小野 輝男

東北大学 多元物質科学研究所

島川 祐一

菅 大介

阪部 周二

栗原 達夫

栗原 達夫

栗原 達夫

川端 猛夫

川端 猛夫

川端 猛夫

梶 弘典

小野 輝男

小野 輝男

小野 輝男

上杉 志成

上杉 志成

青山 卓史

青山 卓史

青山 卓史

長谷川 健

村田 靖次郎、若宮 淳志

名古屋大学・大学院情報科学研究科

2017-49 山本 俊介

課題提案型

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分野選択型

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松宮 由実

分野選択型

分野選択型

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分野選択型

分野選択型

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分野選択型

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分野選択型

2017-48 吉田 久美

2017-60 福島 達也

分野選択型
分野選択型

渡辺 宏

寺西 利治

寺西 利治

竹中 幹人

宗林 由樹

宗林 由樹

村田 靖次郎村田 靖次郎

若宮 淳志

村田 靖次郎渡辺 宏

大阪大学大学院理学研究科

2017-46 片島 拓弥

村田 靖次郎村田 靖次郎2017-47 Sathish Kumar Sukumaran 山形大学・大学院有機材料システム研究科

防衛医科大学校 医用工学講座

日本女子大学・理学部数物科学科

九州大学・カーボンニュートラル・エネルギー国際研究所

2017-43 山内 美穂

2017-44 石原 美弥

一般財団法人総合科学研究機構・中性子科学センター

2017-42 宮崎 司

2017-45 島田 良子

京都教育大学・教育学部理学科

2017-41 向井 浩

2017-38 家 裕隆

奈良先端科学技術大学院大学・物質創成科学研究科

大阪大学 産業科学研究所・産業科学ナノテクノロジーセンター

2017-37 佐伯 昭紀

近畿大学・理工学部

大阪大学大学院工学研究科

2017-36 村田 理尚

2017-40 中口 譲

大阪工業大学・工学部応用化学科

2017-35 菅 誠治

2017-39 鈴木 充朗

久留米工業高等専門学校

岡山大学大学院・自然科学研究科

2017-34 黒飛 敬

4 萌芽的

新規機能性色素を指向したヘテロアズレンオリゴマーの創製架橋様式の異なる高分子網目中のゲスト鎖のダイナミクス

金属ドメイン周期格子のプラズモン発熱における共鳴効果の検証

金属ナノ粒子を用いた光診断治療一体型機能性材料の創生

単結晶Pd ナノ粒子内における水素の分布状態の解明

トリブロック共重合体の粘着メカニズム解明のための表面・界面構造の解析

イオン液体含浸膜を用いた金属イオンの液膜輸送の高効率化に関する研究

東シナ海における生物活性微量金属のフラックス研究

前駆体法を鍵技術とする高結晶性有機半導体材料の分子エンジニアリング

新規な有機半導体材料で構成される有機太陽電池

非鉛ペロブスカイト太陽電池の探索と高効率有機ホール輸送層の開発

三次元π共役錯体の合成と有機半導体材料としての性能

脱水型縮環反応によるπ拡張チエノフラン誘導体の合成とその物性評価

微生物の金属呼吸における金属誘導性膜タンパク質の機能解析

食品タンパク質中の結合態D-アミノ酸の機能と生成機構の解析

腸内細菌が生産する膜小胞の特性と形成機構

機能性触媒を用いた膜局在性生体分子の選択的化学修飾

位置及び立体選択的酸化触媒の開発

4-ピロリジン‐ピリジン型分子触媒による化学選択的アシル化反応の理論的解析

有機デバイスにおける構造－機能相関に関する研究

電界によるスカーミオンの移動

垂直磁化型の強磁性絶縁体薄膜によるトンネル型スピンフィルター効果に関する研究

電界による磁性の誘起や閉じ込めを利用した磁性ナノ構造の生成

多層構造を有する磁性ナノワイヤーにおける巨大磁気抵抗効果の観測

新規スピントロニクス材料としての高性能スピネル型フェライト酸化物薄膜の開発

アシルドーパミンの新機能

ビタミンＤの新機能の調節

転写因子 ARR1の植物染色体DNA上における動態の研究

植物細胞の脂質分泌経路の研究

植物表皮細胞の分化における制御機構の研究

π 共役系ユニット含有単分子膜集積体のpMAIRS 法を用いた精密構造解析

フラボノイド系色素を組み合わせた色素増感太陽電池の作製と特性評価、並びに効率向上を目指した計算化学研究

発展的

発展的

高温高圧処理中のガラスの結晶化過程と物性変化

電界効果による遷移金属酸化物の新機能特性の探求

発展的 国際 真空内四光波混合の探索

発展的

発展的

萌芽的

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発展的

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5 萌芽的

5 萌芽的

5 発展的 国際 高分子融液における分子運動の相関

5 発展的

5 萌芽的

5 萌芽的

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5 発展的

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4 発展的

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4 萌芽的

4 萌芽的

－146－

齊藤 高志

名古屋大学・大学院工学研究科

神戸大学・大学院農学研究科

京都大学・大学院農学研究科

岡山理科大学・理学部化学科

弘前大学・教育学部

2017-82 高野 敦志

2017-83 水谷 正治

2017-84 中川 好秋

2017-85 岩永 哲夫

2017-86 島田 透

時任 宣博

東北大学・大学院理学研究科

近畿大学・理工学部

福岡大学・理学部化学科

立教大学・理学部

奈良県立医科大学・医学部

東京理科大学・理学部第一部応用化学科

2017-101 長洞 記嘉2017-102 箕浦 真生

2017-103 秦野 修

2017-104 根岸 雄一

磯﨑 勝弘

磯﨑 勝弘

時任 宣博

時任 宣博

時任 宣博

時任 宣博

＊選択分野：１．ビーム科学分野、２．元素科学分野、３．バイオ情報学分野、４．物質合成分野、５．現象解析分野

国立研究開発法人理化学研究所・創造物性科学研究センター

2017-100 橋爪 大輔

2017-99 松尾 司

時任 宣博

茨城大学・工学部生体分子機能工学科

倉田 博基

倉田 博基

倉田 博基

2017-98 岩本 武明

2017-94 宮本 光貴

梶 弘典

2017-97 吾郷 友宏

島根大学・大学院総合理工学研究科

2017-93 犬飼 宗弘

渡辺 宏

東京理科大学･工学部

徳島大学大学院・理工学研究部

2017-92 井上 正志

二木 史朗

九州大学・大学院総合理工学研究院融合創造理工学部門大阪大学理学研究科

2017-91 Sandrine SAGAN

辻井 敬亘

高野 祥太朗2017-96 斉藤 光

フランス国立科学研究センター(CNRS)/パリ第6 大学(UPMC)

2017-90 後藤 淳

島川 祐一

長谷川 健

長谷川 健

村田 靖次郎

渡辺 文太

渡辺 文太

渡辺 宏

松宮 由実

水落 憲和

水落 憲和

水落 憲和

二木 史朗

辻井 敬亘

2017-95 河合 武司

Academia Sinica・Research Center for Environmental Changes

南洋理工大学・理学部・物理数学科学院・化学生物化学科

2017-89 Ho, Tung-Yuan

北海道大学低温科学研究所雪氷新領域宇宙物質科学

Sungkyunkwan University • School of Chemical Engineering

2017-81 Youngdon Kwon

國立臺灣大學 凝態科學研究中心

東京工業大学・大学院理工学研究科工学院電気電子系

2017-80 波多野 睦子

2017-87 羽馬 哲也

金沢大学・理工研究域 電子情報学系

2017-79 徳田 規夫

2017-88 陳 威廷

産業技術総合研究所・先進パワーエレクトロニクス研究センター

2017-78 牧野 俊晴

寺西 利治

東京工業大学・フロンティア材料研究所

名古屋大学・トランスフォーマティブ生命分子研究所

2017-76 真島 豊

2017-75 橘 泰宏

2017-77 望田（桑田）啓子

寺西 利治

共立女子大学・家政学部被服学科

RMIT 大学 工学研究科

2017-74 村瀬 浩貴

梅谷 重夫
大野 工司

奈良教育大学・教育学部

国立研究開発法人産業技術総合研究所・機能化学研究部門

2017-73 山本 貴広

宗林 由樹

2017-72 山崎 祥子

産業技術総合研究所・材料・化学領域

大阪府立大学工業高等専門学校 環境物質化学コース

2017-70 正井 博和

2017-71 倉橋 健介

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施設・機器利用型

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連携・融合促進型

連携・融合促進型

連携・融合促進型

連携・融合促進型

連携・融合促進型

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萌芽的

高圧合成によるアルカリケイ酸塩ガラスの構造制御と機能開発

電子線・X線・中性子線を用いた高分子複合材料が有する階層構造の精密構造解析

ポリマーブラシ付与微粒子／光応答性液晶複合ゲルにおけるゲル状態間転移の光制御に関する研究

レアメタルに高選択的な抽出、吸着試薬の開発

13 属元素分離における界面活性剤の協同効果試薬としての利用

超高感度センサに向けたダイヤモンドNV中心の評価

ダイヤモンド表面付近におけるNV 中心のスピンコヒーレンス時間の長時間化

ダイヤモンド中のNV中心スピンの電気的制御と電気的検出

癌標的化を目指した膜透過ペプチドの相互作用タンパク質の同定

Au25 クラスターを用いた常温動作単電子トランジスタ

萌芽的

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発展的

萌芽的

配位子交換反応中に生成する中間体クラスターの単離とその質量分析

質量分析イメージングと標的プロテオミクスによるステロイドホルモン産生機構の研究

複数の９−トリプチシルメチル基を速度論的安定化に用いるに高反応性典型元素化学種の合成と構造

含リン芳香族化合物の合成と発光特性の解明

有機ケイ素化合物の実験的電子密度分布解析

ゲルマニウム不飽和化合物の合成と分子構造の解明

非対称型ジシリンおよび関連π電子系の合成と物性解明

単結晶X線構造解析を用いた、含フッ素共役分子の結晶構造におけるフルオラス相互作用の解明

電子エネルギー損失分光によるNd-Fe-B磁石の局所磁気モーメント分析

キラル型メタマテリアルを目指したらせん状極細金ナノワイヤーの合成

核融合プラズマ対向材中の水素・ヘリウム挙動に関する電子分光学的研究

配位高分子における結晶－非結晶相転移の機構解明

国際 東アジア圏の若手レオロジストのための第13回ワークショップ

国際 アルブミンを血中保持担体に用いた薬物送達

国際 有機触媒リビングラジカル重合を用いた光機能性ポリマーの精密合成

国際 安定同位体比分析に基づく海洋における溶存態・粒子態微量金属の循環解明

国際 Exploring for novel functional transition-metal oxides by high-pressure synthesis

水界面に吸着したジカルボン酸の構造における偶奇効果とその地球大気化学における意義

多角入射分解分光法を用いた表面増強赤外吸収（SEIRA）

トリフェニルアミン骨格を組み込んだ大環状化合物の合成とその応用

ベンゾイルフェニルウレア系殺虫剤の分子レベルにおける作用機構解明

ナス科植物のステロイドグリコアルカロイド生合成機構の解明

２成分多元ブロック共重合体のシーエンス制御とバルおよび表面性質

萌芽的 国際 末端会合/解離平衡下の直鎖状A 型Rouse 鎖の誘電緩和

発展的

発展的

発展的

発展的

萌芽的

発展的 国際 新規狭バンドギャップ半導体ナノ結晶の合成と物性評価

萌芽的

発展的

萌芽的

萌芽的

背幅：6.5mm

京都大学化学 研 究 所 共 同 利 用 ・ 共 同 研 究 拠 点 第期
平成 年度 成果報告書

29

京都大学化学研究所
共同利用・共同研究拠点

第期

化学関連分野の深化・連携を基軸とする先端・学際研究拠点

平成 29 年度

成果報告書