特集 世界を救え 〜 SFC バイオの挑戦［招待論文］

Conceptualising Cell Signaling andTranscriptome-wide Response forTargeted Experimentations
標的実験に向けた細胞シグナル伝達とトランスクリプトームワイドな応答の概念化
Kumar Selvarajoo
Project Associate Professor, Graduate School of Media and Governance, Keio University
クマール セルバラジュ
慶應義塾大学大学院政策・メディア研究科特任准教授

Abstract:

The recent systems biology efforts are revealing complexities beyond
traditional static causal relationship understanding. Our research has focused on
dynamic cellular behaviors to uncover novel network and global response properties.
We investigated instructive cell signaling and high-throughput transcriptome-wide
behaviors of immune, cancer, and embryonic development cells. Our data revealthat cultured populations display deterministic signaling that can be modeled using
linear response rules. Adopting the rules, we have computationally predicted andverified experimentally novel signaling features and key targets for controlling
proinflammatory and cancer responses. In addition, our transcriptome-wide
statistical analyses of macrophage immune response and single cell embryonicdevelopmental process, from oocyte to blastocysts cell stage differentiation, revealed
interesting global patterns where even lowly expressed genes play a role.
近年のシステムバイオロジーの取り組みによって、これまでの静的な因果関係では分かり得なかった複雑性が明らかとなっている。我々は、新規ネットワークやグローバルな応答の特性を明らかにするために、細胞の動的な振る舞いに着目した。免疫応答やガン、そして胚発生に関わる有益な細胞のシグナル伝達とハイスループットな転写全体の振る舞いを研究した。そこで我々の成果によって、細胞集団の振る舞いは、線形応答のルールを用いる事でモデル化でき、決定論的なシグナルである事を示した。そしてこれらのルールを用いて、新規のシグナル伝達の特性及び、炎症反応やガンの応答をコントロールするための鍵となるターゲットをコンピューターで予測し、実験的に立証した。さらに、マクロファージによる免疫応答、及び卵母細胞から胚盤胞までの 1 細胞の胚発生の過程をトランスクリプトームの観点から統計解析した結果、低発現している遺伝子が役割を持っているという興味深い全体のパターンを明らかにした。

Keywords: cell signaling, cancer, immune, development, systems biology76

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1 Introduction
Living organisms are highly complex dynamical systems that areable to survive, proliferate, and evolve under variable conditions forthousands, if not, billions of years. Over their evolutionary period,
each living species has been able to detect, process, respond, andadapt to different environmental information. The immune system is
one such mechanism by which living systems are able to neutralize
potential threats by detecting external agents and invoking appropriate
intracellular response for protection. Living systems are not only able
to defend themselves individually, in many instances they are able topool together, or self-organize, to protect the entire population. Thus,
they are not only complex within their own physiology, but are alsosophisticated in their dynamical cooperative behavior in adaptation totheir surroundings.
For the past decade, our research has been focused onunderstanding the complex and dynamic perturbation-response ofcellular systems. In particular, we have centred on cell signaling or
the tracking of extracellular stimuli affecting numerous intracellularmolecules to trigger the transcription of genes for myriad cellular
processes such as differentiation, proinflammatory response andapoptosis. These processes are vital to investigate as they not only
provide understanding of the normal functioning of living cells, but may
also provide clues on disease occurrence or targets for disease control.
In this paper, I will discuss our research, adopting systems
biology approaches, to understand and tackle proinflammatory
responses of the immune and cancer cells, and shed some insights
into the complexities of embryonic stem cell differentiation. Our
strategy involves utilizing mathematical, computational, and statistical
approaches to analyse dynamical cellular datasets, from lowthroughput western-blot, enzyme-linked immunosorbent assay (ELISA)KEIO SFC JOURNAL Vol.15 No.1 201577

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or electrophoretic mobility shift assay (EMSA) readouts, to highthroughput gene expression profiles derived from DNA microarrays
and next generation (RNA) sequencing techniques. Using crossdisciplinary approaches, our aims were to better understand complex
network behaviors and their governing principles, and using thederived knowledge to systemically determine novel targets for more
optimal regulated response. In the following sections, I will provide
details of our goals and achievements.

2 Cell Signaling in Immune Response and Cancer
Immune diseases and cancer deaths still remain a major global
challenge affecting people from all walks of life. Despite the vast
amount of time, efforts and funds put to challenge the diseases worldwide, medical treatments remain suboptimal and are largely unchanged
over the past 40 years. To tackle some of the difficulties facing
current research, we undertook an integrative, multidisciplinary
approach for investigating the mammalian innate immune response andits link to cancer.

3 Uncovering Novel Features in Toll-like Receptor Signaling
We initially, about a decade ago, began our research on the
innate immune response invoked by the Toll-like receptors (TLRs).
TLRs are transmembrane proteins that function to recognize
conserved pathogen-associated molecular patterns (PAMPs) related
to microorganisms, such as lipopolysaccharide (LPS) from gramnegative bacteria and double-stranded RNA (dsRNA) from viruses.
There are 13 known members of the TLRs in mammals. TLRs 1, 2,
4, 5, 6 are located at cell surface, while TLRs 3, 7, 8, 9, 11, 13 arebound to the intracellular endosomes [1]. Each TLR recognizes specific
PAMPs and trigger microbial clearance and induce the production78

【第 1 章 SFC が先導した生命科学】
Conceptualising Cell Signaling and Transcriptome-wide Response for Targeted Experimentationsof immunoregulatory chemokines, cytokines, and cell surface andcostimulatory molecules. The TLRs also increase effector functions
such as phagocytosis, and present antigen to adaptive immune cells [1].
Thus, the activation of TLRs is a first line of mammalian’s immune
defense system.
The most well characterized PAMP is the TLR4. Upon LPS
binding, TLR4 triggers two major intracellular pathways, theMyD88- and TRAM- dependent pathways. The MyD88-dependent
pathway mainly induces proinflammatory cytokines such as TNF, IL6, and SOCS3 through activation of MAP kinases p38, ERK, JNK
and NF-κB [2]. The TRAM-dependent pathway, on the other hand,predominantly induces type I interferons (IFNs) and chemokines suchas IP-10 (encoded by Cxcl10) and interferon (IFN)-induced proteinsthrough activation of IRF -3 or -7 and NF-κB. Thus, both pathways
complement each other in the production of pro-inflammatory
mediators. Although there are detailed experimental works on
TLR4 signaling, the dynamical response of the MyD88- and TRAMdependent pathways remained poorly understood.
We investigated the TLR4 response [3] in wildtype and several
mutant conditions using a computational model based on theperturbation-response approach (Box 1), followed by experimental
verification. Our initial task was to investigate molecular mechanisms
for the impaired and delayed kinetics of NF-κB activation in MyD88
knock-out (KO) murine macrophages [4]. According to our modelingapproach, the impairment was due to lower signaling flux (affinity)
towards the TRAM-dependent pathway, compared with MyD88dependent pathway, in LPS stimulation and the delay in NF-κB was
due to several unknown signaling intermediates or process acting
upstream of TBK in the TRAM-dependent pathways (Fig. 1A). In
addition to these, the model also predicted delayed induction of TNF
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and enhanced expression of Cxcl10 in MyD88 KOs (Fig. 1B).
To validate the model predictions, we subsequently performedexperiments on murine macrophages [5]. Firstly, we proved the delayed
induction of Tnf and enhanced expression of Cxcl10 in MyD88 KO
murine macrophages (Fig. 1C). Next, we showed the mechanism forthe enhanced activation of Cxcl10 in MyD88 KOs is due to signaling
flux redistribution or SFR (Fig. 1D). The experiment also proved thepoint of lower signaling flux (affinity) towards the TRAM-dependent
pathways in the absence of MyD88, when TRAM preferentially boundto intracellular TLR4 domain (Fig. 1E). Notably, the speculationthat the TRAM-dependent pathway consisted of additional signaling
intermediates or processes was confirmed by later studies,demonstrated by the sequential activation of CD14, ITAM-mediatedprocess of tyrosine kinase Syk and its downstream effector PLCγ2 forthe endocytosis of TLR4 prior to TRIF/TRAM activation [6, 7], and thephosphorylation of TRAM by PKCεfor IRF-3 activation [8].

4 Systems Biology to Regulate TNF Signaling
Following the successful prediction and validation of our TLR4
model, we next embarked on studying the tumor necrosis factor (TNF)signaling. As noted above, TNF is one of the key cytokine induced by
the proinflammatory response of TLR4. TNF, which is also producedby various other signaling cascades, plays a major role in regulating
myriad cellular processes [9]. Chronically elevated levels of TNF
have led to several major illnesses including rheumatoid arthritis andcertain types of cancers [10, 11].
We embarked on the development of a new TNF signaling model,
and using the model intended to find a key optimal target that will
selectively and effectively suppress, but not abolish, TNF-induced
proinflammatory response [12]. This is because, total abolishment of80

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Fig. 1(A)Fig. 1(B)Fig. 1(C)
Tnf/Graph

1.2

Tnf

0.8
0.4
0.0

Cxcl10/Graph

Cxcl10

1.2
0.8
0.4
0.0

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Fig. 1(D)Fig. 1(E)Figure 1 (A) Schematic of TLR4 signaling with modif ied topology (grey ar rows)
including novel intermediates. Dynamic computational simulations (B) and experimental
expression (C) of Tn f (top) and C xcl10 (bottom) genes in wildtype (black) and MyD88
KO (dotted grey). (D) Signaling f lux redistribution (SFR) is a theoretical hypothesis thatsuggests removing or suppressing signaling molecules (MyD88) at pathway junction will
enhance the activations of molecules at alternative (TRAM-dependent) pathways, and viceversa, through the law of signaling f lux conservation. (E) Experimental evidence f or S F R:
Increasing MyD88 (aMyc) reduces TRAM (aFlag) binding to TLR4 (GST) using in vitro
competition assay. Figure modif ied f rom [5].

TNF response will immuno-compromise subjects to pathogenic threats.
First, we curated the literature and theoretically derived the
TNF signaling topology (Fig. 2A, black arrows). A dynamic massaction model (Box 1) was developed based on this topology withparameter values chosen to fit the temporal profiles of p38 and IκBαa in wildtype and several mutant conditions (Fig. 2B). Subsequently,
we extended the model to simulate 3 major groups of upregulated
proinflammatory genes in TNF stimulation (Fig. 2C).
It is important to emphasize that our computational models
fitted to the wildtype experimental data often failed to recapture82

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profiles in mutant conditions [1, 13-16]. The main reason for this, to ourknowledge, is the general lack of detailed network information of oursignaling topologies. To overcome this limitation, we have developed
9 response rules that will help guide us to correct any grossly missing
information. Note that our works, and that of others in related
studies, have demonstrated that biological networks are often sensitive
to network topology rather than parameter values [1, 16-18]. Hence, theuse of response rules (Box 2) to modify and re-investigate signaling
topologies is an appropriate next step.
For the TNF signaling, although our model was able to fit bothFig. 2(A)KEIO SFC JOURNAL Vol.15 No.1 201583

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Fig. 2(B)Fig. 2(C)Fig. 2(D)Fig. 2(F)84

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Fig. 2(G)Figure 2 (A) Schematic of TNF signaling with modif ied novel feedback mechanisms in greyarrows. (B) Temporal experimental prof iles (lef t) and dynamic computational simulations (right)of IκBα (top) and p38 (bottom) activity in wildtype (black) and various KO conditions (colors).
(C) Average expression prof iles of genes in groups I (red), II (green), and III (blue) in 3T3
f ibroblasts stimulated with TNF. Simulation prof iles of the 3 groups of genes using initial TNF
model (D) and modif ied model (E) with transcriptional delay and novel feedback mechanisms(solid lines) or with transcriptional delay and without feedback mechanisms (dotted lines). (F)Simulation prof iles of the 3 groups of genes in wildtype and 3 key KO conditions (IκBα KO,
RIP1 KO, and TAK1 complex KO) using the modif ied model with feedback (note that for allother KO simulations, see [12] for details). (G) Temporal gene expressions of groups I (T n f ai3p,
Il6, Jun, N f kbia), II (Ccl7, V cam1, C xcl10), and III (M m p3, M m p13, En p p2) genes in TNFstimulated BALB/3T3 (top) and MEF (bottom) cells, treated without (dark color) and with (lightcolor) Nec-1. (A) reproduced f rom [40] and (B -G) adapted f rom [12].

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p38 and IκBα in wildtype and several mutant conditions, when themodel was extended to simulate gene expressions, the wildtype model
was insufficient to fit group 3 gene profile: the simulation clearly
underestimated the transcription levels after 2 h (Fig. 2D). According
to our response rules (Rules 2 & 4, Box 2), a post-transcriptional
feedback mechanism, specifically to IkappaB-alpha, is required for thecontinuous activation of group 3 genes. Adding this hypothesis to themodel resulted in significant improvement to the dynamical simulations(Fig. 2E).
Next, we tested, in silico, the effect of down-regulating all
signaling proteins in the TNF topology for controlling the expressionof the 3 major groups of proinflammatory genes which are usually
upregulated in proinflammatory diseases. Among the data, we foundRIP1 knock down (KD) simulations produced moderate regulations of
all 3 groups of genes (Fig. 2F). This data indicated that RIP1 may
be an attractive target for controlling TNF-induced proinflammatory
response. Hence, to validate this important result, which may provide
significant benefit for proinflammatory therapeutics, we performed
actual experiments on 2 cell types (MEF and 3T3).
Necrostatin-1, or Nec-1, is a well-known specific inhibitorof RIP1 [19]. We compared 10 proinflammatory gene expressions in
MEF and 3T3 cells stimulated with TNF, with and without Nec-1
pretreatment (Fig. 2G). The experimental results confirmed our model
simulations, and also demonstrated that Nec-1 could potentially be
used to treat proinflammatory diseases such as rheumatoid arthritis
or osteoarthritis.

5 Enhancing Apoptosis by Tinkering TRAIL Signaling
Cancer cells are highly variable and largely resistant totherapeutic intervention. The TNF-related apoptosis-inducing ligand86

【第 1 章 SFC が先導した生命科学】
Conceptualising Cell Signaling and Transcriptome-wide Response for Targeted Experimentations(TRAIL) induces apoptosis in malignant cells, while leaving other cells
mostly unharmed. However, several carcinomas remain resistant to
TRAIL [1]. To investigate the resistance mechanisms in TRAIL-stimulated
human fibrosarcoma (HT1080) cells, we developed a computational modelto analyze the temporal activation profiles of cell survival (IκB, JNK, p38)
and apoptotic (caspase-8 and -3) molecules in wildtype and several (FADD,
RIP1, TRAF2 and caspase-8) KD conditions [20].
Our initial model was based on the well-known TRAIL signaling(Fig. 3A-2, black arrows). Similarly to our TLR4 and TNF works, theinitial computational TRAIL model could only fit the wildtype dynamical
activation profiles of the 5 signaling molecules (IκB, JNK, p38, caspase-8,
and caspase-3), and failed to match mutant conditions. Next, by carefully
applying the response rules (Box 2), the TRAIL signaling topology was
modified in silico step by step until all tested molecules matched the 4available mutant conditions (RIP1 KD, FADD KD, caspase-8 KD, andTRAF2 KD) (Fig. 3A-1). As a result, two novel molecules have been
predicted by the revised model, i) molecule Y acting independent of FADD
and able to activate JNK and p38, and ii) molecule Z specifically activating
JNK via p62 (Fig. 3A).
Using the revised TRAIL model, we next investigated which of
the two novel molecules is key to suppress cell survival activation or
increase cell death pathway activity. Notably, the simulations suggested
that knocking down molecule Z will significantly increase apoptosis andmay result in 95% cell death (Fig. 3B). In other words, knocking down
or inhibiting molecule Z in TRAIL-stimulated human fibrosarcoma will
likely sensitize the large majority of resistant cells to death. To identify
molecule Z, we performed a search on the protein-protein interaction
database for p62 interacting partners, and obtained protein kinase C(PKC) family members as likely candidates. However, which PKC member
might be molecule Z remained to be identified.
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Consequently, we utilized PKC inhibitor, bisindolylmaleimide I(BIM-I), to experimentally verify whether TRAIL-stimulated HT1080
cells will indeed significantly induce apoptosis in the presence of PKC
inhibition [21]. In addition to HT1080, we also tested another TRAILresistant cancer cell, the human colon adenocarcinoma (HT29) cells.
Notably, as predicted by the computational model, PKC inhibition
during TRAIL stimulation produced over 95% cell death for both
HT1080 and HT29 cells, with relatively insignificant effect on normal
control TIG-1 and MRC5 cells (see Fig. 1 of ref. 21). These results
confirmed our model simulations that a PKC family member isFig. 3(A-1) Wildtype

RIP1 KDFADD KD

caspase-8 KD

TRAF2 KD

r=0.123

r=0.051

r=0.036

r=0.032

r=0.018

r=0.016

r=0.101

r=0.044

r=0.074

r=0.053

r=0

r=0

r=0

r=0.103

r=0.088

Simulation only

Simulation only

Simulation only

r=0.069

r=0.009

Simulation only

Simulation only

Simulation only

r=0.092

Time(min)Time(min)Time(min)caspase-3 KD

caspase-8 KD

IkB(a.u.)

JNK(a.u.)p38(a.u.)

r=0.092

Time(min)88

Time(min)【第 1 章 SFC が先導した生命科学】
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Fig. 3(A-2)

Fig. 3(B)Fig. 3(C)Figure 3 (A) Schematic of TRAIL signaling with modif ied novel pathways in grey (A2),and simulation prof iles (curves) compared with experiments (shapes) of p38, IκB, JNK,
caspase-8 and -3 in wildtype, RIP1 KD, FADD KD, caspase-8 KD, and TRAF2 KD (lef t)
f or TRAIL stimulated HT1080 cancer cells(A-1). (B) Survival ratios, experimental (darkgrey) and evaluated based on simulations (light grey), f or all conditions including novel
molecules Y and Z KD f or simulations. (C) Experimental survival ratios f or increasinglevels of TRAIL stimulation with and without increasing doses of PKC inhibitor (BIM-I)
in HT1080 cells. (A,B) taken f rom [20] and (C) adapted f rom [21].

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molecule Z, and its inhibition significantly sensitizes TRAIL-induced
cell death (Fig. 3C).
Next, to identify which PKC member, among 10 known, is
molecule Z, we tested all of them experimentally. Notably, we foundthat PKCδ siRNA produced the same amount of cell death at 3h
compared with BIM-I treatment (see Fig. 4C of ref. 21). Thus, weconcluded our study by revealing that specific PKCδ inhibition in
TRAIL-based therapy has great prospects for malignant cancers thatare resistant to TRAIL [21].
Overall, from our systems biology research of using dynamic
computational modeling to identify novel cell signaling mechanisms
or targets, and subsequently performing experiments to verify themin TLR4, TNF, and TRAIL signaling has been highly successful.
We believe our modeling successes are not through“chance”, butdemonstrate the presence of simple rules guiding complex behaviors
through physical laws on a macroscopic top-down scale [1, 14, 22]. Our data
provide further evidence for the utility of systemic approaches to tackle
and treat complex diseases more effectively. In the following section,
our other works on high-throughput omics data will be discussed.

6 Transcriptome-wide Analysis
The previous sections on cell signaling were based on a limited
set of signaling proteins and genes. Although the findings shown arepromising, a living cell actually contains several thousands of genes,
proteins, and metabolites. To grasp the complex large scale systemlevel properties, the development and analysis of high-throughput
experimental technologies for genomics, proteomics, and metabolomics
are required. Over the last two decades, these methods have been
intensively investigated, and today they generate large quantity of
biological data at different scales crucial for unraveling the detailed90

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molecular composition and complexity of living organisms.
For instance, what types or kinds of genes are induced by
the immune cells in response to invading pathogens? In a study toinvestigate the dynamic response of innate immune cells (macrophages)
exposed to pathogenic agents (LPS), cDNA microarrays revealed
that almost 3000 genes were expressed over a period of 24 hours [23].
Apart from immune-related genes, genes belonging to diverse cellular
processes were also expressed. Thus, the study was one of the firstto show, from a global analysis, that a single receptor stimulation can
result in the response of thousands of genes not directly related tothe original function. Such valuable information suggests that highthroughput analysis of biological components are crucial for providing
another dimension (of scale) to the understanding of complex cell
behaviors.

7 Global Immune Response
From the second half of last decade, we have developed simple
statistical techniques to investigate high-throughput gene expression
dataset [24-30]. Instead of using arbitrary threshold cut-off to eliminate
genes of very low expressions (or high signal-to-noise ratio), which
often results in removing 90% of genome data, we used powerlaw, correlation and noise approaches to analyze the entire global
response patterns. Such statistical techniques have been widelyused to investigate deterministic patterns of highly noisy data ofother complex systems such as the weather [31], stock markets [32], andcosmology [33].
We examined the time-series genome-wide (22,690 Affymetrixbased microarray probes) response of LPS stimulation (TLR4
signaling) in wildtype and 3 mutant conditions (MyD88 KO, TRIF KO,
and MyD88/TRIF Double KO) of murine macrophages [24]. The aim of
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the study was to compare the global gene expression patterns invokedby wildtype and the mutant innate immune cells (macrophages) toenvironmental threat (LPS). We do recognize the fact that microarrayor even the recently developed RNA-Seq datasets are prone to a large
degree of technical error or biases, especially for the lowly expressed
genes. Nevertheless, our goal was not to specifically identify individual
novel genes expressed in all four conditions. Instead, we examined
the global collective behaviors of the LPS-induced innate immune
response.
Pearson correlation analysis [28], which provides a measure ofdeviation from unity as a source of difference between the samples
was adopted. In our case, the Pearson correlation coefficient shows
the compressed (averaged) information of the genome-wide response.
We developed a scheme to compare the correlation coefficients
between (i) the same genotype at different times (e.g., wildtype 0 h vs.
wildtype 1 h, called auto-correlation) and, (ii) the same time point withdifferent genotypes (e.g., wildtype 1 h vs. MyD88 KO 1 h, called crosscorrelation).
From the correlation plots, DKO auto-correlations were
surprisingly similar to single KOs on the temporal scale (Fig. 4A, top).
This data indicated that LPS is able to invoke gradual intracellularresponse, as seen by the monotonic deviation of correlation coefficientfrom unity, independent of the key adaptor signaling moleculesof MyD88 and TRIF. The cross-correlations revealed that DKO
response, compared with wildtype, is the least similar (Fig. 4B, top).
This result indicated that DKO genome response, compared withsingle KOs, is most distinct from the wildtype response. Overall,although impaired, it was surprising that DKO, previously known toabolish all significant gene expressions [34], is able to invoke global gene
expression response.

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Fig. 4(B)Fig. 4(A)Figure 4 (A) Auto- and (B) cross- cor relations of whole genome (top) and 157
proinf lammatory or immune genes (bottom) af ter LPS stimulation. x-axis represents timeand y-axis represents the correlation coeff icients. Figure obtained f rom [24].

To confirm whether DKO induces genome response, correlation
coefficients of whole genome were compared with an ensemble
comprising only of well-known proinflammatory genes (Fig. 4A,B,
bottom). Notably, for the selected group of 157 proinflammatory
genes, the auto-correlation of DKO was almost unchanged with time,
indicating no noticeable ensemble response, consistent with otherstudies [34, 35]. Thus, these results indicated the presence of novel
pathways, independent of MyD88 and TRIF, to activate novel gene
expressions in DKO. Although we had pointed out a few biologicalprocesses not specifically related to immunity using the Gene
Ontology database, we were unable to experimentally verify the
specific DKO or TLR4-independent response of LPS at that time.
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Nevertheless, recently, two research teams have identified TLR4independent LPS activation of caspase-11, which plays a pivotal rolein shaping inflammasome [36, 37]. Thus, our temporal Pearson statisticalanalysis was able to predict novel regulatory response from genomewide expression dataset with further experiments required for
confirmation.

8 Noise in Human Developmental Cell Differentiation
The cell population responses, dealt so far, have been
instrumental not only for cell signaling, but also for large-scale orglobal understanding of myriad deterministic biological processes
such as immune response, growth, and metabolism. However, eachcell within a population is not identical in its morphology or shape,
and the intracellular molecular environment is highly inhomogenous.
Furthermore, even genetically identical cells produce diverse
phenotypes, such as a single stem or progenitor cell can produce
distinct lineages, which can be tilted even by small external
perturbations. Population-wide average techniques (e.g. linear
response models) are not suitable for the investigation of cellularvariability and inhomogeneity [38, 39].
Recent single-cell experimental techniques have revealed
fluctuations in gene and protein expressions over time. Suchfluctuations, measured by transcription, phosphorylation, morphology,
and motility, have been key for generating cellular heterogeneity.
Increasingly, investigators recognize that a combination of intrinsic
and extrinsic factors contribute to cellular stochasticity: i) intrinsic
or‘uncorrelated’noise; the random nature of biochemical reactions,e.g. due to low copy numbers of intracellular molecules in a Poissonprocess, and ii) extrinsic or ‘correlated’ noise; non-Poisson
fluctuations in other cellular components or states that indirectly94【第 1 章 SFC が先導した生命科学】
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affect the expression of a specific gene or protein.
To understand global gene expression structure and noise
patterns of single cells during mammalian developmental stages, weinvestigated transcriptome-wide RNA-Seq expressions of human andmouse developmental cells [29]. A total of 7 in human and 10 in mouse,from oocytes to blastocysts, were analysed using correlation metrics,
Shannon entropy and noise analyses.
Using gene expressions scatter plots, Pearson correlation and
Shannon entropy between single cells data, we observed sharply
increased variability from 2-cell to 4-cell stage onwards in bothhuman and mouse (Fig. 5A). Next, global noise of single cells wasinvestigated by quantifying the squared coefficient of expression
variations over mean expression values which showed transition ofnoise patterns occurring between 2-cell and 8-cell stage (Fig. 5B).
To understand the possible mechanisms for increased noise
patterns for 4-cell stage onwards, we developed a stochastictranscriptional model based on ordinary differential equations andfitted the model to experimental noise patterns (Fig. 5C). From thesimulation results, we concurred that the early developmental stages
were mainly dominated by low transcriptional activity dominatedby Poisson noise. The increase in transcriptome-wide noise for the
middle stage developmental cells was due to stochastic transcriptional
amplification, which generated heterogeneity in gene expressions
between individual cells. Such heterogeneity has been shown to benecessary for cell fate diversifications (see review in ref. 38). For thelater stages, on top of high transcriptional process, the cells possess
quantal activation of most transcription factors, or are subject
to more extrinsic variability such as phenotypic diversity amongindividual cells. These factors increase the general expression scatterand noise levels.
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Fig. 5(A)96【第 1 章 SFC が先導した生命科学】
Conceptualising Cell Signaling and Transcriptome-wide Response for Targeted Experimentations

Fig. 5(C)Fig. 5(B)Figure 5 (A) Gene expression distribution between 2 single cells (lef t), Pearson
correlations (top right), and Shannon entropy (bottom right) of development cells f rom
oocytes to blastocysts in human and mouse. (B) Experimental and (C) Simulated noise η( 2)versus mean (μ) expression patterns f or each development stage in human (top) and mouse(bottom). Figure reproduced f rom [29].

Overall, the investigations into the transcriptome-wide
expressions of the early mammalian developmental stages revealed
increasing variability and noise patterns across the mammaliandevelopment process. These data suggested different stages of thecell differentiation process can be better understood by investigating
the transcriptome-wide noise patterns. To summarize, oursystemic approach provided novel insights into the transcriptomewide expression and noise patterns for development cells, and theunderlying nature of the transcriptional mechanisms.
In conclusion, in this paper, our previous works on specific
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instructive cell signaling in immune and cancer response, as well as,
the global gene expression analyses of immune and developmental cells
have been reviewed. Our multidisciplinary techniques have allowed
the elucidation of novel properties of dynamic cell behavior, bothfrom deterministic and stochastic perspectives, not possible using
traditional low throughput and steady-state data of molecular species.
In coming years, we expect to witness further growth in complex
systems biology approaches.

Acknowledgement
The author thank Vincent Piras, Kentaro Hayashi, Masaru Tomita, Masa Tsuchiya, andAlessandro Guiliani for collaborations on reviewed works. Tsuruoka city, Yamagata Prefecture
and the JSPS Grants-in-Aid for Scientific Research FX132008K3 are acknowledged for theirfinancial support.

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Box 1: Perturbation-Response Approach
The perturbation-response approach involves giving a small perturbation to theconcentration of one or more reactant species in a network and analyzes the response profiles ofother species within the network [1, 13-16]. To briefly examine, consider a linear-chain of reactions(X1 → X2 → X3 → ···) at steady-state condition. If the concentration of X1 is pulse perturbed,
the concentrations of X2, X3, etc., will increase, go through a maximum, and then decrease backto its steady-state value in sequential order (see Fig. 2.2 of ref. 1). The experiments, based on
the law of information conservation, connect the species between input and output fluxes througha linear superposition of propagation response waves (first-order response) [1, 13-16].
Despite the simplicity of the approach, linear response is visually apparent in the glycolytic
metabolite profiles, EGF (epidermal growth factor), TLR3, and TLR4 signaling dynamics (seeFig. 2.3 of ref. 1). Although the kinetics could vary slightly from sample to sample, the general
average response profiles are very well reproducible. In other words, regardless of how complex
a signaling topology might be, the species’average dynamic responses followed deterministic
formation and depletion waves [1,13,14,16]. Notably, it can be shown, theoretically, that no matter
how complex or non-linear the signaling system is, the dynamic response can be approximatedusing first-order terms if the perturbation levels are small [1,13,14,16].
Unlike typical kinetic models, which often use similar equations or sometimes with nonlinear expressions to model the dynamics of biological networks, our perturbation-response
approach considers the network as a sequence of events rather than molecules. As signaling100【第 1 章 SFC が先導した生命科学】
Conceptualising Cell Signaling and Transcriptome-wide Response for Targeted Experimentations
networks are largely not fully understood, this difference is crucial as it prevents rigidly fixing
the network topologies, and allows it to be modified according to experimental data so as toprevent overfitting problems and to identify novel features of signaling networks. In addition, as
signaling process involves large number (thousands) of intracellular molecular activations, it iscurrently not plausible to model the dynamics of all possible reactions with the generally limited
data. To overcome such difficulties, our approach permits the lumping of several molecules
into a signaling specie in the model network. In this way, although the model does not become
a comprehensive representation of an entire signaling process, however, it still allows theidentification of overtly missing key features.
To successfully identify novel features of signaling networks, we set a target that thecomputational model should be able to simulate not just one experimental condition (like mostmodels do), but in as many conditions as available (see maintext). Like any other modelingapproach, there are certain limitations that require mentioning. Firstly, the perturbationresponse approach discussed does not comprehensively represent the details of each signaling
reaction's kinetics. Secondly, the small perturbation assumption leading to the first-order massaction equations represents an average cell response and this cannot be used to study single cellstochastic behavior or non-linear behaviors such as bistability. Thirdly, the model predictions
will show relative, and not absolute, activation levels. However, the approach can be universally
applied to model any pathways that experimentally display formation and depletion waves.

Box 2 Response Rules
Rule 1, Controlling flux: Controlling the upstream parameter of a hypothetical molecule X2(See Fig. S1 of Ref. 12) mostly affects the slope of the formation part of the expression profile.
Alternatively, controlling the downstream parameter mainly modifies the expression profile’s
depletion part. Rule 2, Time delay: by comparing the time to reach peak activation, any timedelay in target signaling molecule’s activation represents‘missing’cellular features such as
directed transport machinery, protein complex formation, and novel molecular interactions. Rule3, Feedforward flux: A) Rapid kinetics: when simulation of a downstream molecule is noticeably
quicker than experimental dynamics, B) Similar kinetics: when removing a molecule along apathway does not completely abolish its downstream intermediates, C) Delayed kinetics: whenremoving a molecule along a pathway show significant delay. In all these cases, the superposition
principle suggests a novel feedforward pathway with different number of intermediates. Rule4, Feedback flux: when a response profile shows multiple peaks or continuous increase ofactivation not following pulse perturbation response, this indicates feedback pathways suchas posttranslational effect or secondary (autocrine/paracrine) signaling. Rule 5, Signaling Flux
Redistribution (SFR): At pathway junctions, removing a molecule enhances the entire alternative
pathways. Rule 6, No SFR: At pathway junctions, removing a molecule does not enhance thealternative pathway, suggesting novel i) intermediate(s) between the removed molecule and thepathway junction or ii) pathway link between the removed molecule and the alternative pathway.
Rule 7, Differential flux: quantifies each pathway branch by comparing activation levels between
wildtype and mutants data. Rule 8, Reversible flux: when a response profile show limiting decaythat cannot be modeled by first-order decay, the presence of reversible step is expected to
produce limiting decay. Rule 9, Non-linearity: When complex dynamics is observed, the linear
response approach breaks down, and non-linear approaches are needed.

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