國立虎尾科技大學 |

Mathematical Models for Understanding Dynamic Cellular Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Mathematical Models for Understanding Dynamic Cellular Systems./
Author:	Mattingly, Henry Hughes.
Description:	1 online resource (113 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Contained By:	Dissertation Abstracts International79-05B(E).
Subject:	Bioengineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355480276

Mathematical Models for Understanding Dynamic Cellular Systems.
Mattingly, Henry Hughes.

Mathematical Models for Understanding Dynamic Cellular Systems. - 1 online resource (113 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Cellular processes, such as fate decisions and mitotic divisions, are dynamic. Complex behaviors emerge from networks of interactions among numerous components: time-varying extracellular signals induce expression of specific genes, and periodic changes in intracellular molecule concentrations coordinate mitotic entry and exit. Quantitative understanding of these processes requires mathematical models. Models can make predictions about system dynamics in conditions that are difficult to probe experimentally, explain how systems-level behaviors emerge from a network of interactions, and convert observed data into constraints on future behaviors. This thesis uses mathematical modeling in each of these ways to learn about various dynamical biological phenomena. In the first chapter, models are used to estimate time-varying signals controlling meiosis that are difficult to measure experimentally. In the second, a simple model of the embryonic cell cycle is used to understand how robust oscillations arise in that system. And in the third and ongoing chapter, a model is used to explore how much we can expect learn about a biochemical mechanism from planned experiments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355480276Subjects--Topical Terms:

598252
Bioengineering.
Index Terms--Genre/Form:

554714
Electronic books.

Mathematical Models for Understanding Dynamic Cellular Systems.
LDR:02485ntm a2200349Ki 4500 001 910580
005 20180517123957.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355480276
035 $a (MiAaPQ)AAI10635206
035 $a (MiAaPQ)princeton:12341
035 $a AAI10635206
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Mattingly, Henry Hughes. $3 1181939
245 1 0 $a Mathematical Models for Understanding Dynamic Cellular Systems.
264 0 $c 2017
300 $a 1 online resource (113 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Advisers: Stanislav Y. Shvartsman; Ioannis G. Kevrekidis.
502 $a Thesis (Ph.D.) $c Princeton University $d 2017.
504 $a Includes bibliographical references
520 $a Cellular processes, such as fate decisions and mitotic divisions, are dynamic. Complex behaviors emerge from networks of interactions among numerous components: time-varying extracellular signals induce expression of specific genes, and periodic changes in intracellular molecule concentrations coordinate mitotic entry and exit. Quantitative understanding of these processes requires mathematical models. Models can make predictions about system dynamics in conditions that are difficult to probe experimentally, explain how systems-level behaviors emerge from a network of interactions, and convert observed data into constraints on future behaviors. This thesis uses mathematical modeling in each of these ways to learn about various dynamical biological phenomena. In the first chapter, models are used to estimate time-varying signals controlling meiosis that are difficult to measure experimentally. In the second, a simple model of the embryonic cell cycle is used to understand how robust oscillations arise in that system. And in the third and ongoing chapter, a model is used to explore how much we can expect learn about a biochemical mechanism from planned experiments.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Bioengineering. $3 598252
650 4 $a Chemical engineering. $3 555952
655 7 $a Electronic books. $2 local $3 554714
690 $a 0202
690 $a 0542
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Princeton University. $b Chemical and Biological Engineering. $3 845568
773 0 $t Dissertation Abstracts International $g 79-05B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10635206 $z click for full text (PQDT)