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Novel Quanti.ati.e Approach for Predi.ti.g mRNA/Protei. Counts i. Li.i.g Cells

Appli.d Mathemati.s 2017年第8期8卷 1128-1139页

作者： Henri.C. Ji.bo seraphin i. ngongo Achi.le Mbassi.Ni.olas G. Andji.a Department of Appli.d Mathemati.s and Stati.ti.s Waseda University Tokyo Japan Department of Appli.d Mathemati.s University of Paris 1 Pantheon Sorbonne Paris France Department of Urology Central Medical Hospital CEMAC Region Yaounde Cameroon Department of Mathemati.s Higher National School (ENS) Yaounde Cameroon

One of the most complex questi.ns i. quanti.ati.e bi.logy i. how to manage noi.e sources and the subsequent consequences for cell functi.ns. Noi.e i. geneti. networks i. i.evi.able, as chemi.al reacti.ns are probabi.i.ti. and often, genes, mRNAs and protei.s are present i. vari.ble numbers per cell. Previ.us research has focused on counti.g these numbers usi.g experi.ental methods such as complex fluorescent techni.ues or theoreti.al methods by characteri.i.g the probabi.i.y di.tri.uti.n of mRNAs and protei.s numbers i. cells. i. thi. work, we propose a modeli.g based approach;we bui.d a mathemati.al model that i. used to predi.t the number of mRNAs and protei.s over ti.e, and develop a computati.nal method to extract the noi.e-related i.formati.n i. such a bi.logi.al system. Our approach contri.utes to answeri.g the questi.n of how the number of mRNA and protei.s change i. li.i.g cells over ti.e and how these changes i.duce noi.e. Moreover, we calculate the entropy of the system;thi. turns out to be i.portant i.formati.n for predi.ti.n whi.h could allow us to understand how noi.e i.formati.n i. generated and expanded.

关键词： Appli.d Mathemati.s Embedded Control System Geneti. Algori.hm Opti.i.ati.n Bi.dynami.s Stochasti. Modelli.g Si.ulati.ns Stochasti.i.y Bi.engi.eeri.g and Medi.i.e

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Portfoli. Opti.i.ati.n under Cardi.ali.y Constrai.ts: A Comparati.e Study

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Open Journal of Stati.ti.s 2017年第4期7卷 731-742页

作者： Henri.Claver Ji.bo i.i.ore seraphin ngongo Ni.olas Gabri.l Andji.a Takeru Suzuki.Charles Awona Onana Department of Appli.d Mathemati.s and Stati.ti.s Laboratory of Big Data and Stochasticity Waseda University Tokyo Japan Department of Appli.d Mathemati.s Laboratory SAMM (Statistics Analysis and Multidisciplinary Modeling) University of Paris 1 Pantheon Sorbonne Paris France Department of Mathemati.s Faculty of Science University of Yaounde 1-ENS-Higher Training College Yaounde Cameroon

The Cardi.ali.y Constrai.t-Based Opti.i.ati.n problem i. i.vesti.ated i. thi. note. i. portfoli. opti.i.ati.n problem, the cardi.ali.y constrai.t allows one to i.vest i. assets out of a uni.erse of N assets for a prespeci.i.d value of K. i. i. generally agreed that choosi.g a “small” value of K forces the i.plementati.n of di.ersi.i.ati.n i. small portfoli.s. However, the questi.n of how small must be K has remai.ed unanswered. i. the present work, usi.g a comparati.e approach we show computati.nally that opti.al portfoli. selecti.n wi.h a relati.ely small or large number of assets, K, may produce si.i.ar results wi.h di.ferenti.ted reli.bi.i.i.s.

关键词： Cardi.ali.y Constrai.ts Di.ersi.y Portfoli. Selecti.n Portfoli. Reli.bi.i.y Parametri. Stati.ti.s

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