Abstracts of the

1stInternational Symposium
on Aihara Moonshot Project
June 6th - 8th, 2022

This symposium is motivated by our aim to "Realization of Ultra-Early Disease Prediction andIntervention by 2050" as the Moonshot Goal 2, which is supported by MOONSHOT Research &Development Program at the Japan Science and Technology Agency (JST). In this symposium, we
would like to discuss how we can detect a "pre-disease" state, which has not yet been precisely
defined but suggested as the risky state approaching the timing leaving the normal state ofhealth. Precision medicine at a very early stage is targeted by detection of pre-disease states.
For this purpose, we would like to exchange our recent research progresses, including
personalized medicine, biological and mathematical modeling, control theory, and data analysis.

Organizers : Keita Iida, Koji Noshita, and Shingo Iwami

For Registration:

https://u-tokyo-ac-jp.zoom.us/webinar/register/WN̲H1l-qa12SlmanmaYsj22sQ

Program (JST)6th June
15:00 - 15:10
15:10 - 15:20
15:20 - 15:40
15:40 - 16:10

16:30 - 17:00
17:10 - 18:00
19:10 - 20:00
20:00 - 20:50

Welcome Gen Sobue
Welcome Masato Wakayama
Opening remark Kazuyuki Aihara“Identification and control of brain dynamics underpinning neuropsychiatric conditions”
Takamitsu Watanabe
Break“Phenotyping studies using morphometric descriptors for multi-omics analysis” Koji Noshita
Break“What TDA can say about the morphology of diseased neurons” David Beers
Break“Topological Flow Data Analysis for Blood Flows Inside a Heart” Takashi Sakajo“Modelling dynamics in the presence of bounded noise” Jeroen S.W. Lamb

7th June
15:00 - 15:30

15:40 - 16:30
17:00 - 17:50
18:00 - 18:30
19:30 - 20:20
20:20 - 20:50“Predicting diseases by dynamic network biomarker with network fluctuations”
Luonan Chen
Break
“Nonequilibrium phase transitions and critical phenomena” Eckehard Schöll
Break“Mechanisms of protective immunity in SARS-CoV-2 infection” Miles Davenport
Break“Modeling and characterizing vaccine-elicited antibody responses” Shingo Iwami
Break“From mechanisms to prediction: Designing personalised treatment strategies for eczema”
Reiko Tanaka“Statistical genetics, disease biology, drug discovery, and personalized medicine”
Yukinori Okada

8th June
15:00 - 15:50
16:00 - 16:30
17:00 - 17:50
18:00 - 18:30

19:30 - 20:20
20:20 - 20:50
20:50 - 21:00“Non-genetic heterogeneity arising from biological networks” Mariko Okada
Break“Re-stabilization of gene network systems via pole placement with HDLSS data” Xun Shen
Break“Can we define attractor states in biology?” Kumar Selvarajoo
Break“Understanding the disease-triggering inter-organ crosstalk using a genetic animal model”
Kazutaka Akagi
Break“Two new ideas for dynamical time-series analysis” Hiroshi Kokubu“Clustering single-cell and spatial transcriptomes through multifaceted biological aspects”
Keita Iida
Closing remark

1stInternational Symposium
on Aihara Moonshot Project

Program Director

Sub Program Director

Project Manager

Gen
Sobue

MasatoWakayama

Kazuyuki
AiharaChairperson,
President, Aichi
Medical University

Fundamental
Mathematics
Research Principal,
NTT Institute for
Fundamental
Mathematics

University Professor,
Professor Emeritus,
The University of
Tokyo

Professor Emeritus,
Kyushu University

6th 15:40-

WPI-IRCN The
The University
University ofof TokyoTokyo
Takamitsu Watanabe
Watanabe // WPI-IRCN
Takamitsu

Identification andand control
control ofof brainbrain dynamics
dynamics
Identification
underpinning neuropsychiatricneuropsychiatric conditions
conditions
underpinning

Information processing
processing behindbehind aa wide
wide range
range ofof complex
complex human
human cognition
cognition isis considered
considered toto heavily
heavily rely
rely onon thetheInformation
dynamics ofof thethe whole-brain
whole-brain activity
activity patterns.
patterns. Also,
Also, its
its atypicality
atypicality seen
seen inin neuropsychiatricneuropsychiatric conditions
conditions isis often
often
dynamics
linkedwith
withthetheirregularity
irregularityof
ofsuchsuchtemporal
temporalproperties
propertiesof
ofthe
themacroscopic
macroscopicneuralneuralactivity.
activity.Here,
Here,IIwill
willfirst
firsttalktalklinkedaboutaadata-driven
data-drivenmannermannerthat
thatenables
enablesus
usto
toextract
extractthethebrainbraindynamics
dynamicsfrom
fromhuman
humanneuralneuraldata
dataobtained
obtainedby,
by,
about
for example,
example, functional
functional MRI
MRI andand EEG.
EEG. The
The method—so-called
method—so-called energy
energy landscapelandscape analysis—cananalysis—can not
not only
only identify
identify
forbrain-state dynamics
dynamics underpinningunderpinning human
human intelligence
intelligence but
but also
also pin
pin down
down neuralneural activity
activity patterns
patterns specificspecific totobrain-state
multiple neuropsychiatricneuropsychiatric conditions,conditions, including
including autism,
autism, ADHD
ADHD andand schizophrenia.
schizophrenia. II will
will also
also introduce
introduce aa novel
novel
multiple
non-invasive brain
brain stimulation
stimulation approach
approach thatthat combines
combines thethe energy
energy landscapelandscape analysis
analysis with
with aa real-timereal-time neuralneuralnon-invasive
activityprocessing
processingsystem.
system.The
Themethod
methodcan
cantrack
trackbrain
brainstate
statedynamics
dynamicsalmost
almostsimultaneously,
simultaneously,which
whichallows
allowsusustotoactivity
efficientlyinterveneintervenein
insuchsuchneuralneuraldynamics
dynamicswith
withweakerweakerstimulation
stimulationand,and,ultimately,
ultimately,control
controltypical
typicalandandatypicalatypicalefficiently
humancognitive
cognitiveandandbehavioural
behaviouraltendencies.
tendencies.
human

6th 16:30-

Kyushu University
University
Koji Noshita
Noshita // Kyushu
Koji

Phenotyping studies
studies using
using morphometricmorphometricPhenotyping
descriptors forfor multi-omics
multi-omics analysis
analysisdescriptors

Themorphologicalmorphologicaltraits
traitsof
ofcells,
cells,tissues,
tissues,and
andorgansorganscontributecontributeto
tofunctional
functionalandanddiseasediseasefeatures.
features.Although
AlthoughthetheThe
importanceof
ofphenotyping
phenotypingtechniques
techniquesfor
forthese
thesemorphologicalmorphologicaltraits
traitsisisincreasing
increasingwith
withadvancesadvancesin
inmulti-omics
multi-omics
importancestudies,there
therearearebarriers
barriersto
toits
itsadvancement,
advancement,including
includingthe
thegapgapbetween
betweenmeasured
measureddata
dataandandphenotypic
phenotypicvalues,
values,studies,
lowquantitativity,
quantitativity,and
andlowlowthroughput
throughputcaused
causedby
bythe
thelack
lackofofmodelsmodelsfor
forrepresenting
representingmorphologicalmorphologicaltraits.
traits.
low
In thisthis talk,talk, II introduce
introduce morphologicalmorphological descriptors
descriptors thatthat cancan bebe usedused forfor phenotyping
phenotyping morphologicalmorphological traits.
traits.
In
Geometric morphometricmorphometric approaches
approaches pave
pave thethe way
way toto aa general-purpose
general-purpose method
method applicableapplicable toto single
single units.
units.
Geometric
Hierarchical structures
structures composedcomposed ofof anan indefinite
indefinite number
number ofof multiple
multiple elements,
elements, which
which isis often
often observed
observed in
in
Hierarchicalbiological structures,
structures, cancan bebe quantified
quantified inin terms
terms ofof theirtheir multi-scale
multi-scale topological
topological characteristics.
characteristics. Theoretical
Theoreticalbiologicalmorphological models
models capture
capture specificspecific anatomical
anatomical structures
structures ifif recognized.
recognized. These
These morphologicalmorphological descriptors
descriptors
morphologicalprovideus
uswithwiththetheadvantages
advantagesof
ofmodel-basedmodel-basedphenotyping,phenotyping,including
includingrobust
robustquantification
quantificationof
oflimited
limiteddatasets.
datasets.
provide6th 17:10-

David Beers / University of Oxford
What TDA can say about the morphology of
diseased neurons

The morphology of neurons is important for their function and therefore is often used to classify them. The
topological morphology descriptor, a recent method developed to compare neuronal geometry, assigns amorphology descriptor called a barcode to a neuron equipped with a specified function, such as the path distanceor Euclidean distance from the root of the neuron. These barcodes can be converted into matrices called
persistence images, which can then be averaged across groups and to provide an interpretable summary ofmorphology. We show that persistence images are stable with respect to perturbations of input data, andintroduce topological morphology functions, a class of functions similar to Sholl functions, that can be recoveredfrom the associated topological morphology descriptor. To demonstrate this topological approach, we compare
healthy cortical and hippocampal mouse neurons to those affected by progressive tauopathy. We find a significant
difference in the morphology of healthy neurons and those with a tauopathy at a postsymptomatic age. We use
persistence images to conclude that the diseased group tends to have neurons with shorter branches as well as
fewer branches far from the soma.

6th 19:10-

Takashi Sakajo / Kyoto University
Topological Flow Data Analysis for Blood
Flows Inside a Heart
Blood flow in the heart has complex vortex patterns, playing an important role in an efficient blood flow supply
from the heart to the organs, and these patterns are disturbed by heart diseases. Recent progress in medical
imaging and computer technology yields blood flow visualization tools in echocardiography and cardiac MRI.
However, there are still few mathematical theories to clearly define the vortex flow structures such as size andlocation, or change over time in the main chamber in the heart: the left ventricle, since the flow is highly
unsteady and turbulent. Therefore, the efficiency of the physiological function of the heart as a pump andpathophysiological mechanisms of cardiovascular diseases has not been fully revealed yet. Since the function of
the vortex blood flow inside the left ventricle has been known to be determined by the mainstream observed in
the central plane of the symmetrical anatomy of the left ventricular system, we propose a mathematical theory to
extract and classify topological features of the flows in the heart in terms of symbolic graph expressions, called
partially cyclically rooted COT representations. Consequently, we identify well-organized vortex flow structures as
topological vortex structures, thereby characterizing healthy blood flows as well as inefficient flow patterns in the
diseased heart.

6th 20:00-

Jeroen S.W. Lamb / Imperial College London
Modelling dynamics in the presence of bounded
noise

Mathematical models of dynamics that include "noise" often include rather strong assumptions on the nature of
the noise. In particular, a common assumption is that the noise is de facto unbounded. While common
assumptions on the nature of noise may facilitate the mathematical analysis, it may also give rise to unrealistic
and incorrect models. In this talk, I will discuss ways in which bounded noise may be implemented and what kindof analysis and results can be obtained in this context, specifically in the context of bifurcation.

7th 15:00-

Luonan Chen / The University of Tokyo
Predicting diseases by dynamic network
biomarker with network fluctuations
We develop a DNB (dynamic network biomarker) method with the criterion of network fluctuations, i.e., temporal
network flow entropy (TNFE) to detect the critical states of complex diseases on the basis of each individual. By
applying this method to a simulated dataset and various real diseases, including respiratory viral infections andtumors with omics data, the critical states before deterioration were detected and their dynamic network
biomarkers (DNBs) were identified successfully. The results on the simulated dataset indicate that the TNFE
method is robust under different noise strength, and is also superior to the existing methods on detecting thecritical states. Moreover, the analysis on the real datasets demonstrated the effectiveness of TNFE for providing
early-warning signals on various diseases. In addition, we also predicted disease deterioration risk and identified
drug targets for cancers based on stage-wise data.

7th 18:00-

Shingo Iwami / Nagoya University
Modeling and characterizing vaccine-elicitedantibody responses

Recent studies have provided insights into the effect of vaccine boosters on recall immunity. Given a limited
global supply of the vaccinations, identifying vulnerable populations with lower sustained vaccine-elicitedantibody titers is important to decide target individuals for the booster. Here we investigated longitudinal data
among the cohort of the same individuals of 2,526 people in Fukushima, Japan from April 2021 to December
2021. Antibody titers following a two-dose SARS-CoV-2 vaccination were repeatedly monitored along with theinformation on lifestyle habits, comorbidities, adverse reactions, and medication. By employing mathematicalmodeling and machine learning, we characterized the elicited immune response following a two-dose SARS-CoV-2
vaccination.

7th 19:30-

Imperial College
College London
LondonReiko J.
J. Tanaka
Tanaka // Imperial
Reiko

From mechanisms to prediction: Designingpersonalised treatment strategies for eczema

Atopic dermatitis (or eczema, AD) is the most common inflammatory skin disease characterised by inflamed, dry
and itchy skin leading to substantial quality of life impairment and significant socioeconomic impact. Designingpersonalised treatment strategies for AD is challenging, given the apparent unpredictability and large variation in
AD symptoms and treatment responses within and across individuals. A first step toward developing personalised
treatment strategies is to better predict the consequences of possible treatments at an individual level, rather
than at population level, to deal with the variability across patients.
We developed a mechanistic model of AD pathogenesis which provided a coherent mechanistic explanation of the
dynamic onset, progression, and prevention of AD, as a result of interactions between skin barrier, immune
responses and environmental stressors. Model predictive control using the mechanistic model suggested a
possibility for designing personalized treatment strategies. We also adapted the structure of the mechanistic
model to real patient data and developed a Bayesian mechanistic model tailored to each individual, that can
predict the next day’ s AD severity score given their score and treatments used on that day.
These works presented hopes and challenges in designing personalized treatment strategies for AD, for which
detailed dynamic data is not often available.

7th 20:20-

Yukinori Okada / Osaka University / The University of Tokyo /RIKEN Center for IMS

Statistical genetics, disease biology, drug
discovery, and personalized medicine

Statistical genetics is a research field that evaluates causality of human genetic variations on diseases, using
statistical and bioinformatics approaches. Recent developments of sequencing technologies have provided humandisease genome data of hundreds of thousands of the subjects, and successfully identified comprehensive
catalogues of genetic susceptible loci. However, little is known regarding how to develop methodology tointegrate large-scale human genome data with diverse biological resources. We have developed such methods andapplied to a pioneering example of large-scale genetic association studies on a variety of human complex traits.
Tran-layer omics analysis identified the cell types and microbiomes implicated in disease biology. Network analysis
between the disease risk genes and the drug target genes could identify novel candidates of drug repositioning.
Integration of cell type-specific gene expression profiles estimated from GWAS with compound perturbation
databases can pinpoint novel therapeutic targets and compounds. Application of the machine learning methods
into population genome data can classify the samples without prior biological information. These results shouldempirically show the value of statistical genetics to dissect disease biology, novel drug discovery, and personalized
medicine. Finally, we would like to introduce our activity on young researcher developments ( “Summer school ofstatistical genetics” in Osaka Univeristy).

8th 15:00-

Mariko Okada / Institute for Protein Research, Osaka University
Non-genetic heterogeneity arising from
biological networks
Signaling networks play an important role in connecting the extracellular environment with intracellular genetic
information. NF-kappa B is a transcription factor that regulates the expression of a wide variety of genes in
inflammation, cancer and immune systems in response to the environmental changes, and is also known for its
threshold and oscillatory dynamics, which are attracting theoretical and experimental attentions. We
quantitatively study the dynamics of NF-kappa B and its target gene expression using epigenetic data, live cell
imaging and mathematical modeling. Our study reveals that NF-kappa B activates enhancers for gene expression
in immune B cells in both cooperative and non-cooperative manners. The cooperative mechanism requires
lineage-specific open chromatin DNA regions of B cells for all-or-none gene responses. Imaging analysis together
with single RNA-sequence analysis suggests that the enhanced heterogeneity in expression of NF-kappa B target
genes is associated with higher-order of macromolecular assembly similar to the mechanism of liquid-liquid phaseseparation. We propose that this is the one of the origins of non-genetic heterogeneity contributing to clonal
expansion of B cells. We also introduce patient-specific modeling that allows us to understand the patient
heterogeneity resulting from a cancer signaling network.

8th 16:00-

Xun Shen / Tokyo Institute of Technology
Re-stabilization of gene network systems via
pole placement with HDLSS data

The Dynamical Network Biomarkers (DNBs) theory has been proposed to detect early-warning signals of criticaltransitions in gene network systems only with High-Dimension Low-Sample-Size (HDLSS) data of the system state.
Besides, recent experimental results have shown that the manipulation of multiple DNB gens immediately before
critical transitions leads to remarkable changes in malignant phenotypes of lung cancer. Towards giving atheoretical foundation for ultra-early medical treatment, this paper proposes the re-stabilization of gene network
systems by pole placement with HDLSS data, where the dominant eigenvector of the system matrix is focused. In
addition, we conduct an approximation to decrease the number of assigned inputs for the pole placement. The
feasibility and effectiveness have been investigated by theoretical analysis and numerical simulations.

8th 17:00-

Kumar Selvarajoo / Agency for Science, Technology andResearch / National University of Singapore / Nanyang
Technological University

Can we define attractor states in biology?
Living systems are known to display complex and non-linear behaviors, such as self-organization, cell fate decisions
and emergent properties. Commonly, biologists look at a very small subset of genes, proteins or metabolites toinvestigate such behaviors. These are often selected based on setting arbitrary threshold values relying mostly onvery highly expressed or variable ones. Here, I will share our works on transcriptome-wide data analyses of
immune, cancer and microbial cells without setting any threshold but still adopting a rigorous way to remove
technical noise or variation. Our analyses, based on sound statistical laws, led us to define statistical density plots
where we observed a fractal-like subset of genes’ dynamic response to fall into global cell fate trajectories. These
localizations may well define a way to investigate attractor states in biology.

8th 18:00-

Kazutaka Akagi / University of Toyama
Understanding the disease-triggeringinter-organ crosstalk using a genetic animal
modelConsumption of unhealthy Western diets is associated with the prevalence of type 2 diabetes and obesity, whichhave risen drastically for decades. These disorders are linked with disruption of metabolic homeostasis that leads
to chronic inflammation. Moreover, the morbidity associated with long-standing type 2 diabetes results fromserious complications including nephropathy, cancer and cardiovascular diseases. The current therapeutics fordiseases including type 2 diabetes are targeted to after its onset, but it’ s often starting too late. Hence, weneed to develop the new therapeutic approaches that targeting the pre-disease state in the context of eachdisease. However, the definition of the pre-disease state and its biomarkers have not well characterized.
We have successfully detected a pre-disease state in the mouse model of metabolic syndrome before the disease
onset using the dynamical network biomarkers (DNB) theory. We have also identified 147 genes, which represent
this pre-disease state from adipose tissue. To investigate the underlying biological mechanisms responsible forthese DNB genes, we took advantage of the Drosophila model system. Due to their short lifespan and ease ofgenetic manipulation; invertebrate models continue to be useful as models for understanding human diseases. In
particular the conservation of biological processes and signaling pathways between mammals and Drosophila
provides a unique opportunity to understand the common biological mechanisms across species. In this talk, I willsummarize our strategy to unveil the biological functions of DNB genes.

8th 19:30-

Hiroshi Kokubu / Kyoto University
Two new ideas for dynamical time-series
analysis

The method of time-delay coordinate embedding due to Floris Takens is a now classical idea for inferring
information of unknown dynamics from its time-series data by observation. It gives a mathematical basis ofreconstructing an attractor of the unknown dynamics under a mild condition, and has been used in many
application problems.
The purpose of this talk is to propose two new ideas on the dynamical time-series analysis which may improvesome insufficiencies of the Takens method. One idea is the Conley-Morse decomposition of the global dynamics,
and the other is the reservoir computing. The former uses many short time-series data from different points ofthe phase space of the unknown dynamical system, from which one can obtain a Morse decomposition of the
global structure of the dynamics including unstable invariant subsets. The latter provides us with a model of time
evolution constructed in the reservoir phase space that mimics the dynamics of the unknown system. An
advantage of having a concrete model for unknown dynamics is that one can learn unseen information of thetarget dynamics. We will give several examples that show such advantages of these ideas.

8th 20:20-

Keita Iida / Osaka University
Clustering single-cell and spatial transcriptomes
through multifaceted biological aspects

Single-cell transcriptomics have deepened our knowledge of biological complexity in terms of molecular
heterogeneity in cell populations. However, conventional signature gene-based approaches may be insufficient in
capturing such complexity as genes can interact with each other to regulate a certain biological function. Here,
we introduce ASURAT, a computational tool for simultaneous cluster analysis and interpretation of single-celldata in terms of cell type, disease, biological function, and signaling pathway activity. Mimicking Saussure’ sidea for defining “meaning” in semiology, we introduce correlation structures into annotated gene sets definedin knowledge-based databases, such as Cell Ontology, Disease Ontology, Gene Ontology databases, and Kyoto
Encyclopedia of Genes and Genomes. Then, we apply a novel correlation graph decomposition to each gene set,
producing a triplet we term “sign” containing biological description, subset of genes, and correlation structure,
which is the central concept of our analysis. To validate usability and clustering performance, we apply ASURAT
on single-cell RNA sequencing and spatial transcriptome datasets of human pancreatic ductal adenocarcinoma(Moncada et al., Nat. Biotechnol. 38, 2020). We demonstrate that ASURAT is able not only to cluster spatialtranscriptomic data into functionally distinct tissue regions but also identify de novo atypical regions.