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Physical Biology of Cellular Information Processing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Physical Biology of Cellular Information Processing./
Author:	Razo-Mejia, Manuel.
Description:	1 online resource (323 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Teaching. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30548436click for full text (PQDT)
ISBN:	9798379853976

Physical Biology of Cellular Information Processing.
Razo-Mejia, Manuel.

Physical Biology of Cellular Information Processing. - 1 online resource (323 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2022.

Includes bibliographical references

The state of matter that we define as *life* is different from anything else we have encountered so far in the universe. Living systems not only perpetuate their existence out of equilibrium against the will of the second law of thermodynamics, but they do so while keeping up with an ever-changing environment. A key part of this capacity to adapt to environmental changes is the ability of organisms to gather information from their surroundings to put together an adequate response to the challenges presented to them. This thesis presents an effort to understand, from first principles, this fundamental feature of information gathering that all life on earth shares. We dig into the physics behind one of the most pervasive mechanisms through which living systems sense and respond to the environment-the ability to turn *on* and *off* genes. In doing so, we hope to uncover general principles of how organisms deal with the problem of collecting information about the world that surrounds them.In Chapter 1, we develop the theoretical and conceptual tools to navigate the rest of the thesis. I introduce the idea of gene regulation, as well as different theoretical models of this pervasive biological phenomenon. We also delve into the realm of information theory and learn how the plastic concept of information can be mathematically defined and quantified.The second stop in our exploration (Chapter 2) asks the following question: can we understand, from first principles, how it is that proteins allow cells to regulate their genes on-demand upon sensing environmental cues? For this, we explore the physics behind transcriptional control due to allosteric transcription factors. Using simple quasi-equilibrium models of the two processes involved in this type of regulation-the regulation of the gene by the binding and unbinding of the transcription factor, and the regulation of the activity of the transcription factor itself by the binding and unbinding of an effector molecule-we are able to predict the input-output function of a simple genetic circuit, and compare such predictions with experimental determinations of the mean response of a population of bacterial cells.We then expand on these insights to ask questions about the inescapable cell-tocell variability that isogenic cells encounter. For this, we have to leave behind the pure thermodynamic framework and work in the language of chemical kinetics. This allows us to make predictions beyond the mean input-output gene expression response of cells by reconstructing full gene expression distributions. With these probabilistic input-output functions, in Chapter 3 we formalize the question of the *amount of information* that cells can gather from the environment. For this, we turn to information-theoretic concepts of maximal mutual information (otherwise known as channel capacity) between the state of the environment and the gene expression response from bacterial cells. Finally, we compare our predictions of the maximum amount of information-measured in bits-that cells can gather with single-cell inferences of this quantity.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379853976Subjects--Topical Terms:

517098
Teaching.
Index Terms--Genre/Form:

542853
Electronic books.

Physical Biology of Cellular Information Processing.
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100 1 $a Razo-Mejia, Manuel. $3 3701792
245 1 0 $a Physical Biology of Cellular Information Processing.
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300 $a 1 online resource (323 pages)
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338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Phillips, Robert.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2022.
504 $a Includes bibliographical references
520 $a The state of matter that we define as *life* is different from anything else we have encountered so far in the universe. Living systems not only perpetuate their existence out of equilibrium against the will of the second law of thermodynamics, but they do so while keeping up with an ever-changing environment. A key part of this capacity to adapt to environmental changes is the ability of organisms to gather information from their surroundings to put together an adequate response to the challenges presented to them. This thesis presents an effort to understand, from first principles, this fundamental feature of information gathering that all life on earth shares. We dig into the physics behind one of the most pervasive mechanisms through which living systems sense and respond to the environment-the ability to turn *on* and *off* genes. In doing so, we hope to uncover general principles of how organisms deal with the problem of collecting information about the world that surrounds them.In Chapter 1, we develop the theoretical and conceptual tools to navigate the rest of the thesis. I introduce the idea of gene regulation, as well as different theoretical models of this pervasive biological phenomenon. We also delve into the realm of information theory and learn how the plastic concept of information can be mathematically defined and quantified.The second stop in our exploration (Chapter 2) asks the following question: can we understand, from first principles, how it is that proteins allow cells to regulate their genes on-demand upon sensing environmental cues? For this, we explore the physics behind transcriptional control due to allosteric transcription factors. Using simple quasi-equilibrium models of the two processes involved in this type of regulation-the regulation of the gene by the binding and unbinding of the transcription factor, and the regulation of the activity of the transcription factor itself by the binding and unbinding of an effector molecule-we are able to predict the input-output function of a simple genetic circuit, and compare such predictions with experimental determinations of the mean response of a population of bacterial cells.We then expand on these insights to ask questions about the inescapable cell-tocell variability that isogenic cells encounter. For this, we have to leave behind the pure thermodynamic framework and work in the language of chemical kinetics. This allows us to make predictions beyond the mean input-output gene expression response of cells by reconstructing full gene expression distributions. With these probabilistic input-output functions, in Chapter 3 we formalize the question of the *amount of information* that cells can gather from the environment. For this, we turn to information-theoretic concepts of maximal mutual information (otherwise known as channel capacity) between the state of the environment and the gene expression response from bacterial cells. Finally, we compare our predictions of the maximum amount of information-measured in bits-that cells can gather with single-cell inferences of this quantity.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Teaching. $3 517098
650 4 $a Gene expression. $3 643979
650 4 $a Happiness. $3 531559
650 4 $a Physics. $3 516296
650 4 $a Writing. $3 551664
650 4 $a Microscopy. $3 540544
650 4 $a Information processing. $3 3561808
650 4 $a Biology. $3 522710
650 4 $a Parameter estimation. $3 567557
650 4 $a Bioinformatics. $3 553671
650 4 $a Genetics. $3 530508
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0306
690 $a 0605
690 $a 0715
690 $a 0369
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a California Institute of Technology. $b Chemistry and Chemical Engineering. $3 3701793
773 0 $t Dissertations Abstracts International $g 85-01B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30548436 $z click for full text (PQDT)