Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス（立川）、H23.7.14ポスター発

Ryo Yoshida

吉田 亮

English

RISM (Repository of the Institute of Statistical Mathematics) / 統計数理研究所 学術研究リポジトリ

2011年7月14日 統計数理研究所 オープンハウスデータ同化を使って全ゲノム転写動態シミュレーションを実現する吉田 亮 モデリング研究系 准教授Rapid advance of biotechnology and data assimilation Whole gene transcription simulation of human lung cancer systems      Cutting-edge biotechnology has resulted in ever-increasing amounts of information        The invention of DNA microarray chip has enabled us to measure transcript levels of moredisseminating within our society. The advent of high speed DNA sequencers is now bringinginto reality a new era of personal genome and personalized medicine Within the next severalthan 20,000 genes in human genome, simultaneously. Our collaborators (Professor Miyano andhis research team at Institute of Medical Science the University of Tokyo) successfully.years, this technological breakthrough could cause significant changes in entire research fields,monitored temporal change of all genes in two phenotypes of human lung cancers under theof life science, involving every -omics studies, in terms of both quantity and quality. Touncover a complex world of cellular systems from such a vast amount of information We aimstreatment with an anticancer agent; one demonstrating exquisite sensitivity to the drugtreatment and the other being resistant (Fig1 and Fig2) Recently several studies have,to create a new research infrastructure of life science data assimilation systems involving. ,advocated relatively rapid acquisition of resistance within a few years after initiation of theexperimental bioscience, bio-modeling, simulation, and state-of-art statistical science. anticancer drug treatment. The experimental data obtained now will certainly be vital touncovering molecular basis of maintaining the viability of drug resistant cancer population.For the first time in the world, we succeeded in the development of whole gene transcriptionz Human NSCLC-derived cell lines: simulators that are highly reproducible to the observed gene expressions of the drug sensitiveand resistant cancer cells (Fig4) The developed simulation models will be utilized in the   • PC9 (Sensitive to treatments with gefitinib) Miyano and Gotoh lab @HGC .discovery of key molecules that are promising for pharmacological treatment to improve drugi d i i l i i i i i h i bili h i d d i• PC9GR (Resistance)  eff cacy, an a pr nc p e of act on n ma nta n ng t e v a ty of t e acqu re rug res stance.z Gene expression arrays:Agilent whole human genome (4×44K) oligo microarray (G4112F)z Time points: 26 time points (24 hours)z Treatments: None, EGF, GFT (gefitinib), EGF+GFTSt tiarva on(hours)EGF (100 ng/ml)- 48 0 6 10 14 17- 2 4 53210.5 7 8 9 11 12 13 15 16 19 2218 20 21 2423gefitinib (0 5 μM) RNA Sampling( - ) : 0 hr ~ 24 hrEGF : 0 hr ~ 24 hr.        EGF + Gefitinib : 0 hr ~ 24 hrGefitinib : 0 hr ~ 24 hr     Fig1 DNA microarray experiments and normalized gene expression profiles At the indicated. .26 time points during 24 hr, transcriptional changes of parental PC9 and PC9GR (drugresistance) cells were measured by using a 44K Agilent Whole Human Genome OligoMicroarray Following starvation for 48 hr before starting the expression profiling each cell. ,line was processed after being untreated (None), exposure to either EGF (EGF: 100 ng/mltreated at 0 hr) or gefitinib (GFT: 0.5 uM treated for 2 hr prior to the start of expressionprofiling) or coadministration of EGF and gefitinib (EGF+GFT) Fig2 Heatmap displays of the normalized time course data obtained from the conditioned, . .microarray experiments. 17,654 genes appear in the same order across the eight images thatwere arranged according to a cluster analysis of the data in which all of the eight expressionprofiles were unifiedModeling transcription dynamics and statistical inferencesz Cell lines:     MCF-7 (human breast cancer cell line).The biological circuit is modeled as a set of differential equations that defines rates of changein concentrations of p biological entities x(t)=(x1(t) x (t)) over continuous times Each z Treatments:  EGF and HRGz Microarray: GeneChip Human Genome U133 Plus 2.0 Array, , ..., p , .variable (i th variable) is regulated by the parent variables Pa(i) with a rate equation having a       Nakakuki et al., 2010set of kinetic parameters. Conduction of in vivo or in vitro time course experiments enables usto measure changes in concentrations of target molecules y = (yi ) during discrete time pointsn n .To proceed with the statistical learning, we here relate the differential equations to thei l d i h d l i i i l i l hGFexper menta ata us ng t e state space mo e . Bayes an nvers on ana ys s exp ores t eunknown parameters in the model, involving initial state x(0) and reaction kinetics θ, throughEGthe posterior distributions P(x(0), θ|Y, Pa) and P(Pa|Y) under which a circuit structure Pa, setf f h i bl i ifi d k d i i k l d io reactants or eac var a e, s spec e or un nown, an a pr or now e ge on react onkinetics is expressed in a prior distribution.( ) ( )  with  ,   and  0H H O ~ , Rn n n n ny x w x x wn N= + = ≥RG(a) Block diagonal, and (b) non‐negativity~17000 dimensional expression vector H :H( )d for 1, ,ix x pβ φ L= =∑ ∑ i( )    d1ijk ij kk Pa i j Φ∈ ∈⎧t( )1  with  edge:  0 otherwisee epk iik ijk ij k ikk jx xxβ φΦ∈→= = ⎨⎩∑ ∑ Fig3. The identified 46 expression clusters of PC9 and PC9GR would be universal, as they areshared by many other expression profiles involving Different tissues treatments other cellular=e g Dictionary : Hill functions with a range of reaction parametersΦ, , ,environments, experimental platforms, or organisms. .               Once starting simulation the system reaches steady state very quicklySimulation  ,    - ,  Drug-sensitive line ResistanceT(+)6 rsPC9  -) GFTDataof 46usterPEGF(wn on cluSimulationkdowsionGR  FT(+)nockpressPC9GF(-) G DataKnexpEGBaseline change of 46 expression clusters     Fig4 The newly-derived whole gene transcription simulators could reproduce the observed expression dynamics of 17 654 genes in the drug sensitive and resistant NSCLC cells (PC9 and. ,PC9GR) treated with gefitinib (right). Based upon further simulation experiments, such as RNA knockdown in silico (left), we can identify or predict key molecules that are promising forpharmacological treatment to improve drug efficacy, and a principle of action in maintaining the viability of the acquired drug resistance.

データ同化を使って全ゲノム転写動態シミュレーションを実現する

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データ同化を使って全ゲノム転写動態シミュレーションを実現する

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データ同化を使って全ゲノム転写動態シミュレーションを実現する

Abstract

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RISM (Repository of the Institute of Statistical Mathematics) / 統計数理研究所 学術研究リポジトリ

RISM (Repository of the Institute of Statistical Mathematics) / 統計数理研究所学術研究リポジトリ