Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción
Los organismos vivos emplean redes de transcripción para regular su expresión génica, permitiéndoles adaptarse y responder dinámicamente a su entorno. Estas redes se constituyen a partir de patrones fundamentales frecuentes en sistemas biológicos, como el bucle de retroalimentación directa (FFL, por...
- Autores:
-
Velásquez Rojas, Rafael Enrique
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/73900
- Acceso en línea:
- https://hdl.handle.net/1992/73900
- Palabra clave:
- Redes transcripcionales
Procesos estocásticos
Teoría de información
Biología de sistemas
Redes de retroalimentación directa
Transcriptional networks
Stochastic processes
Information theory
Systems biology
Feedforward loops networks
Física
- Rights
- openAccess
- License
- Attribution-ShareAlike 4.0 International
| id |
UNIANDES2_798af9a16655b33d1d3dbaee8f43ad38 |
|---|---|
| oai_identifier_str |
oai:repositorio.uniandes.edu.co:1992/73900 |
| network_acronym_str |
UNIANDES2 |
| network_name_str |
Séneca: repositorio Uniandes |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| dc.title.alternative.eng.fl_str_mv |
Analysis of metabolic cost and information transmission in feedforward loops in transcription networks |
| title |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| spellingShingle |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción Redes transcripcionales Procesos estocásticos Teoría de información Biología de sistemas Redes de retroalimentación directa Transcriptional networks Stochastic processes Information theory Systems biology Feedforward loops networks Física |
| title_short |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| title_full |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| title_fullStr |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| title_full_unstemmed |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| title_sort |
Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripción |
| dc.creator.fl_str_mv |
Velásquez Rojas, Rafael Enrique |
| dc.contributor.advisor.none.fl_str_mv |
Pedraza Leal, Juan Manuel |
| dc.contributor.author.none.fl_str_mv |
Velásquez Rojas, Rafael Enrique |
| dc.contributor.jury.none.fl_str_mv |
Botero Mejía, Alonso |
| dc.contributor.researchgroup.none.fl_str_mv |
Facultad de Ciencias::Biofísica |
| dc.subject.keyword.spa.fl_str_mv |
Redes transcripcionales |
| topic |
Redes transcripcionales Procesos estocásticos Teoría de información Biología de sistemas Redes de retroalimentación directa Transcriptional networks Stochastic processes Information theory Systems biology Feedforward loops networks Física |
| dc.subject.keyword.none.fl_str_mv |
Procesos estocásticos Teoría de información Biología de sistemas Redes de retroalimentación directa Transcriptional networks Stochastic processes Information theory Systems biology Feedforward loops networks |
| dc.subject.themes.spa.fl_str_mv |
Física |
| description |
Los organismos vivos emplean redes de transcripción para regular su expresión génica, permitiéndoles adaptarse y responder dinámicamente a su entorno. Estas redes se constituyen a partir de patrones fundamentales frecuentes en sistemas biológicos, como el bucle de retroalimentación directa (FFL, por sus siglas en inglés). El FFL, que involucra la interacción de tres genes, presenta ocho posibles configuraciones cuya frecuencia de aparición en redes biológicas varía. En el presente proyecto de grado, exploramos la transmisión de información y el costo metabólico de estas configuraciones para entender las razones detrás de su prevalencia desigual, proponiendo hipótesis sobre las causas de estas diferencias en las redes de transcripción biológicas. A partir de los resultados obtenidos, se propuso una métrica de beneficio-costo que revela que FFL coherente de tipo 1 (C1) e incoherente de tipo 1 (I1) son los más frecuentes debido a su equilibrio óptimo entre costo y beneficio. Aunque basada en simplificaciones, esta métrica proporciona un primer paso hacia la comprensión de las redes de transcripción, planteando preguntas para futuras investigaciones sobre la adaptación celular a su entorno fluctuante. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-02-05T16:05:05Z |
| dc.date.available.none.fl_str_mv |
2024-02-05T16:05:05Z |
| dc.date.issued.none.fl_str_mv |
2024-02-02 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/1992/73900 |
| dc.identifier.instname.none.fl_str_mv |
instname:Universidad de los Andes |
| dc.identifier.reponame.none.fl_str_mv |
reponame:Repositorio Institucional Séneca |
| dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
| url |
https://hdl.handle.net/1992/73900 |
| identifier_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
[1] P. Vesely, “Molecular biology of the cell. by bruce alberts, alexander johnson, julian lewis, martin raff, keith roberts and peter walter. isbn 0-8153-3218-1; hardback; 1,616 pages; 110,00garlandscienceinc.,newyork,2002, 2004. [2] U. Alon, An introduction to systems biology: design principles of biological circuits.CRC press, 2019. [3] D. T. Suzuki, A. J. Griffiths, et al., An introduction to genetic analysis. WH Freeman and Com- pany., 1976. [4] P. J. Russell and K. Gordey, IGenetics. No. QH430 R87, Benjamin Cummings San Francisco, 2002. [5] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, “Network motifs: simple building blocks of complex networks,” Science, vol. 298, no. 5594, pp. 824–827, 2002. [6] P.Erdos,“Onrandomgraphs,”Mathematicae,vol.6,pp.290–297,1959. [7] M.Serban,“Exploringmodularityinbiologicalnetworks,”PhilosophicalTransactionsofthe Royal Society B, vol. 375, no. 1796, p. 20190316, 2020. [8] S. Mangan and U. Alon, “Structure and function of the feed-forward loop network motif,” Proceedings of the National Academy of Sciences, vol. 100, no. 21, pp. 11980–11985, 2003. [9] T.J.StevensandI.T.Arkin,“Domorecomplexorganismshaveagreaterproportionofmem- brane proteins in their genomes?,” Proteins: Structure, Function, and Bioinformatics, vol. 39, no. 4, pp. 417–420, 2000. [10] A.Narayan,S.Gopi,D.Fushman,andA.N.Naganathan,“Abindingcooperativityswitchdriven by synergistic structural swelling of an osmo-regulatory protein pair,” Nature communications, vol. 10, no. 1, p. 1995, 2019. [11] T.R.SorrellsandA.D.Johnson,“Makingsenseoftranscriptionnetworks,”Cell,vol.161,no.4, pp. 714–723, 2015. [12] D.S.Latchman,“Transcriptionfactors:anoverview,”Theinternationaljournalofbiochemistry & cell biology, vol. 29, no. 12, pp. 1305–1312, 1997. [13] B. Alberts, R. Heald, A. Johnson, D. Morgan, M. Raff, K. Roberts, and P. Walter, Molecular Biology of the Cell: Seventh International Student Edition with Registration Card. WW Norton & Company, 2022. [14] J.Paulsson,“Summingupthenoiseingenenetworks,”Nature,vol.427,no.6973,pp.415–418, 2004. [15] C. W. Gardiner et al., Handbook of stochastic methods, vol. 3. springer Berlin, 1985. [16] D.Lavalette,M.A.Hink,M.Tourbez,C.Tétreau,andA.J.Visser,“Proteinsasmicroviscosimeters: Brownian motion revisited,” European Biophysics Journal, vol. 35, pp. 517–522, 2006. [17] M.ThattaiandA.VanOudenaarden,“Intrinsicnoiseingeneregulatorynetworks,”Proceedings of the National Academy of Sciences, vol. 98, no. 15, pp. 8614–8619, 2001. [18] P.S.Swain,M.B.Elowitz,andE.D.Siggia,“Intrinsicandextrinsiccontributionstostochasticity in gene expression,” Proceedings of the National Academy of Sciences, vol. 99, no. 20, pp. 12795– 12800, 2002. [19] S.S.Shen-Orr,R.Milo,S.Mangan,andU.Alon,“Networkmotifsinthetranscriptionalregula- tion network of escherichia coli,” Nature genetics, vol. 31, no. 1, pp. 64–68, 2002. [20] M.A.Rowland,A.Abdelzaher,P.Ghosh,andM.L.Mayo,“Crosstalkandthedynamicalmodula- rity of feed-forward loops in transcriptional regulatory networks,” Biophysical Journal, vol. 112, no. 8, pp. 1539–1550, 2017. [21] D. S. Lemons and A. Gythiel, “Paul langevin’s 1908 paper “on the theory of brownian mo- tion”[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)],” American Journal of Physics, vol. 65, no. 11, pp. 1079–1081, 1997. [22] L.Ham,M.A.Coomer,andM.P.Stumpf,“Thechemicallangevinequationforbiochemical systems in dynamic environments,” The Journal of Chemical Physics, vol. 157, no. 9, p. 094105, 2022. [23] M.P.Hui,P.L.Foley,andJ.G.Belasco,“Messengerrnadegradationinbacterialcells,”Annual review of genetics, vol. 48, pp. 537–559, 2014. [24] S.Goutelle,M.Maurin,F.Rougier,X.Barbaut,L.Bourguignon,M.Ducher,andP.Maire,“The hill equation: a review of its capabilities in pharmacological modelling,” Fundamental & clinical pharmacology, vol. 22, no. 6, pp. 633–648, 2008. [25] M. Santillán, “On the use of the hill functions in mathematical models of gene regulatory networks,” Mathematical Modelling of Natural Phenomena, vol. 3, no. 2, pp. 85–97, 2008. [26] R.Silva-RochaandV.deLorenzo,“Mininglogicgatesinprokaryotictranscriptionalregulation networks,” FEBS letters, vol. 582, no. 8, pp. 1237–1244, 2008. [27] R.Grima,“Linear-noiseapproximationandthechemicalmasterequationagreeuptosecond- order moments for a class of chemical systems,” Physical Review E, vol. 92, no. 4, p. 042124, 2015. [28] P. C. Parks, “Am lyapunov’s stability theory—100 years on,” IMA journal of Mathematical Control and Information, vol. 9, no. 4, pp. 275–303, 1992. [29] T. S. Roy, M. Nandi, A. Biswas, P. Chaudhury, and S. K. Banik, “Information transmission in a two-step cascade: interplay of activation and repression,” Theory in Biosciences, vol. 140, pp. 295–306, 2021. [30] D. J. MacKay, Information theory, inference and learning algorithms. Cambridge university press, 2003. [31] A. Lyon, “Why are normal distributions normal?,” The British Journal for the Philosophy of Science, 2014. [32] F.A.Haight,“Handbookofthepoissondistribution,”(NoTitle),1967. [33] A.PapoulisandS.UnnikrishnaPillai,Probability,randomvariablesandstochasticprocesses. 2002. [34] A. Rényi, “On measures of entropy and information,” in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, vol. 4, pp. 547–562, University of California Press, 1961. [35] S.KullbackandR.A.Leibler,“Oninformationandsufficiency,”Theannalsofmathematical statistics, vol. 22, no. 1, pp. 79–86, 1951. [36] T.M.Cover,Elementsofinformationtheory.JohnWiley&Sons,1999. [37] A. B. Barrett, “Exploration of synergistic and redundant information sharing in static and dynamical gaussian systems,” Physical Review E, vol. 91, no. 5, p. 052802, 2015. [38] D.M.Bieretal.,“Theenergycostsofproteinmetabolism:leanandmeanonunclesam’steam,” The role of protein and amino acids in sustaining and enhancing performance, pp. 109–119, 1999. [39] R.W.King,R.J.Deshaies,J.-M.Peters,andM.W.Kirschner,“Howproteolysisdrivesthecell cycle,” Science, vol. 274, no. 5293, pp. 1652–1659, 1996. [40] H.A.Orr,“Fitnessanditsroleinevolutionarygenetics,”NatureReviewsGenetics,vol.10,no.8, pp. 531–539, 2009. [41] D. T. Gillespie, “Exact stochastic simulation of coupled chemical reactions,” The journal of physical chemistry, vol. 81, no. 25, pp. 2340–2361, 1977. [42] M. Nandi, A. Biswas, S. K. Banik, and P. Chaudhury, “Information processing in a simple one-step cascade,” Physical Review E, vol. 98, no. 4, p. 042310, 2018. [43] M.S.A.Momin,A.Biswas,andS.K.Banik,“Coherentfeed-forwardloopactsasanefficient information transmitting motif,” Physical Review E, vol. 101, no. 2, p. 022407, 2020. [44] M.S.A.MominandA.Biswas,“Extrinsicnoiseofthetargetgenegovernsabundancepattern of feed-forward loop motifs,” Physical Review E, vol. 101, no. 5, p. 052411, 2020. [45] W. R. Inc., “Mathematica, Version 13.3.” Champaign, IL, 2023. [46] S.J.BryantandB.B.Machta,“Physicalconstraintsinintracellularsignaling:thecostofsending a bit,” Physical Review Letters, vol. 131, no. 6, p. 068401, 2023. [47] S. Iyer Biswas, Applications of methods of non-equilibrium statistical physics to models of stochastic gene expression. PhD thesis, The Ohio State University, 2009. [48] J. Grandell, Mixed poisson processes, vol. 77. CRC Press, 1997. |
| dc.rights.en.fl_str_mv |
Attribution-ShareAlike 4.0 International |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-sa/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution-ShareAlike 4.0 International http://creativecommons.org/licenses/by-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
83 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad de los Andes |
| dc.publisher.program.none.fl_str_mv |
Física |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ciencias |
| dc.publisher.department.none.fl_str_mv |
Departamento de Física |
| publisher.none.fl_str_mv |
Universidad de los Andes |
| institution |
Universidad de los Andes |
| bitstream.url.fl_str_mv |
https://repositorio.uniandes.edu.co/bitstreams/091e6973-9530-44ed-b07a-50ae4260fe93/download https://repositorio.uniandes.edu.co/bitstreams/e169e94d-9ca9-46c2-af22-37629dc6f8b8/download https://repositorio.uniandes.edu.co/bitstreams/9ffdbd9b-38b9-46eb-841e-50491e8a6fbb/download https://repositorio.uniandes.edu.co/bitstreams/8b5804ed-fb4e-431a-b6bd-b48a45d9aa56/download https://repositorio.uniandes.edu.co/bitstreams/6fa4fc99-08c3-42c0-811e-c30433857d6e/download https://repositorio.uniandes.edu.co/bitstreams/74093396-8880-4351-a9bd-9b1b90bb6147/download https://repositorio.uniandes.edu.co/bitstreams/18e3a0c1-b3a3-4c5e-b99e-a322f98dbd23/download https://repositorio.uniandes.edu.co/bitstreams/9899c5cf-74fe-41b4-8587-207eba8ee1bb/download |
| bitstream.checksum.fl_str_mv |
84a900c9dd4b2a10095a94649e1ce116 ae9e573a68e7f92501b6913cc846c39f e7bb410a90db15d358c9084c68e59cdd 3f4e73cc3520a2ab3d1df8f91b089416 49b9113a846950ce851d3db0d7e24e2f 7412940740bea6188ed0a9014b1c8d94 0bd31c326b2d4dd94f5087e396f5c2a2 f9f457517399420a7a32aea3264b7ea6 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio institucional Séneca |
| repository.mail.fl_str_mv |
adminrepositorio@uniandes.edu.co |
| _version_ |
1808390461103013888 |
| spelling |
Pedraza Leal, Juan ManuelVelásquez Rojas, Rafael EnriqueBotero Mejía, AlonsoFacultad de Ciencias::Biofísica2024-02-05T16:05:05Z2024-02-05T16:05:05Z2024-02-02https://hdl.handle.net/1992/73900instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Los organismos vivos emplean redes de transcripción para regular su expresión génica, permitiéndoles adaptarse y responder dinámicamente a su entorno. Estas redes se constituyen a partir de patrones fundamentales frecuentes en sistemas biológicos, como el bucle de retroalimentación directa (FFL, por sus siglas en inglés). El FFL, que involucra la interacción de tres genes, presenta ocho posibles configuraciones cuya frecuencia de aparición en redes biológicas varía. En el presente proyecto de grado, exploramos la transmisión de información y el costo metabólico de estas configuraciones para entender las razones detrás de su prevalencia desigual, proponiendo hipótesis sobre las causas de estas diferencias en las redes de transcripción biológicas. A partir de los resultados obtenidos, se propuso una métrica de beneficio-costo que revela que FFL coherente de tipo 1 (C1) e incoherente de tipo 1 (I1) son los más frecuentes debido a su equilibrio óptimo entre costo y beneficio. Aunque basada en simplificaciones, esta métrica proporciona un primer paso hacia la comprensión de las redes de transcripción, planteando preguntas para futuras investigaciones sobre la adaptación celular a su entorno fluctuante.Living organisms employ transcription networks to regulate their gene expression, allowing them to adapt and respond dynamically to their environment. These networks are constituted from fundamental patterns common in biological systems, such as the direct feedback loop (FFL). The FFL, which involves the interaction of three genes, presents eight possible configurations whose frequency of occurrence in biological networks varies. In the present undergraduate project, we explore the information transmission and metabolic cost of these configurations to understand the reasons behind their unequal prevalence, proposing hypotheses about the causes of these differences in biological transcription networks. From the results obtained, a benefit-cost metric was proposed that reveals that coherent type 1 (C1) and incoherent type 1 (I1) FFLs are the most prevalent due to their optimal cost-benefit balance. Although based on simplifications, this metric provides a first step toward understanding transcription networks, raising questions for future research on cellular adaptation to their fluctuating environment.FísicoPregradoBiología de sistemas83 páginasapplication/pdfspaUniversidad de los AndesFísicaFacultad de CienciasDepartamento de FísicaAttribution-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Análisis del coste metabólico y de la transmisión de información en bucles feedforward en redes de transcripciónAnalysis of metabolic cost and information transmission in feedforward loops in transcription networksTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPRedes transcripcionalesProcesos estocásticosTeoría de informaciónBiología de sistemasRedes de retroalimentación directaTranscriptional networksStochastic processesInformation theorySystems biologyFeedforward loops networksFísica[1] P. Vesely, “Molecular biology of the cell. by bruce alberts, alexander johnson, julian lewis, martin raff, keith roberts and peter walter. isbn 0-8153-3218-1; hardback; 1,616 pages; 110,00garlandscienceinc.,newyork,2002, 2004.[2] U. Alon, An introduction to systems biology: design principles of biological circuits.CRC press, 2019.[3] D. T. Suzuki, A. J. Griffiths, et al., An introduction to genetic analysis. WH Freeman and Com- pany., 1976.[4] P. J. Russell and K. Gordey, IGenetics. No. QH430 R87, Benjamin Cummings San Francisco, 2002.[5] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, “Network motifs: simple building blocks of complex networks,” Science, vol. 298, no. 5594, pp. 824–827, 2002.[6] P.Erdos,“Onrandomgraphs,”Mathematicae,vol.6,pp.290–297,1959.[7] M.Serban,“Exploringmodularityinbiologicalnetworks,”PhilosophicalTransactionsofthe Royal Society B, vol. 375, no. 1796, p. 20190316, 2020.[8] S. Mangan and U. Alon, “Structure and function of the feed-forward loop network motif,” Proceedings of the National Academy of Sciences, vol. 100, no. 21, pp. 11980–11985, 2003.[9] T.J.StevensandI.T.Arkin,“Domorecomplexorganismshaveagreaterproportionofmem- brane proteins in their genomes?,” Proteins: Structure, Function, and Bioinformatics, vol. 39, no. 4, pp. 417–420, 2000.[10] A.Narayan,S.Gopi,D.Fushman,andA.N.Naganathan,“Abindingcooperativityswitchdriven by synergistic structural swelling of an osmo-regulatory protein pair,” Nature communications, vol. 10, no. 1, p. 1995, 2019.[11] T.R.SorrellsandA.D.Johnson,“Makingsenseoftranscriptionnetworks,”Cell,vol.161,no.4, pp. 714–723, 2015.[12] D.S.Latchman,“Transcriptionfactors:anoverview,”Theinternationaljournalofbiochemistry & cell biology, vol. 29, no. 12, pp. 1305–1312, 1997.[13] B. Alberts, R. Heald, A. Johnson, D. Morgan, M. Raff, K. Roberts, and P. Walter, Molecular Biology of the Cell: Seventh International Student Edition with Registration Card. WW Norton & Company, 2022.[14] J.Paulsson,“Summingupthenoiseingenenetworks,”Nature,vol.427,no.6973,pp.415–418, 2004.[15] C. W. Gardiner et al., Handbook of stochastic methods, vol. 3. springer Berlin, 1985.[16] D.Lavalette,M.A.Hink,M.Tourbez,C.Tétreau,andA.J.Visser,“Proteinsasmicroviscosimeters: Brownian motion revisited,” European Biophysics Journal, vol. 35, pp. 517–522, 2006.[17] M.ThattaiandA.VanOudenaarden,“Intrinsicnoiseingeneregulatorynetworks,”Proceedings of the National Academy of Sciences, vol. 98, no. 15, pp. 8614–8619, 2001.[18] P.S.Swain,M.B.Elowitz,andE.D.Siggia,“Intrinsicandextrinsiccontributionstostochasticity in gene expression,” Proceedings of the National Academy of Sciences, vol. 99, no. 20, pp. 12795– 12800, 2002.[19] S.S.Shen-Orr,R.Milo,S.Mangan,andU.Alon,“Networkmotifsinthetranscriptionalregula- tion network of escherichia coli,” Nature genetics, vol. 31, no. 1, pp. 64–68, 2002.[20] M.A.Rowland,A.Abdelzaher,P.Ghosh,andM.L.Mayo,“Crosstalkandthedynamicalmodula- rity of feed-forward loops in transcriptional regulatory networks,” Biophysical Journal, vol. 112, no. 8, pp. 1539–1550, 2017.[21] D. S. Lemons and A. Gythiel, “Paul langevin’s 1908 paper “on the theory of brownian mo- tion”[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)],” American Journal of Physics, vol. 65, no. 11, pp. 1079–1081, 1997.[22] L.Ham,M.A.Coomer,andM.P.Stumpf,“Thechemicallangevinequationforbiochemical systems in dynamic environments,” The Journal of Chemical Physics, vol. 157, no. 9, p. 094105, 2022.[23] M.P.Hui,P.L.Foley,andJ.G.Belasco,“Messengerrnadegradationinbacterialcells,”Annual review of genetics, vol. 48, pp. 537–559, 2014.[24] S.Goutelle,M.Maurin,F.Rougier,X.Barbaut,L.Bourguignon,M.Ducher,andP.Maire,“The hill equation: a review of its capabilities in pharmacological modelling,” Fundamental & clinical pharmacology, vol. 22, no. 6, pp. 633–648, 2008.[25] M. Santillán, “On the use of the hill functions in mathematical models of gene regulatory networks,” Mathematical Modelling of Natural Phenomena, vol. 3, no. 2, pp. 85–97, 2008.[26] R.Silva-RochaandV.deLorenzo,“Mininglogicgatesinprokaryotictranscriptionalregulation networks,” FEBS letters, vol. 582, no. 8, pp. 1237–1244, 2008.[27] R.Grima,“Linear-noiseapproximationandthechemicalmasterequationagreeuptosecond- order moments for a class of chemical systems,” Physical Review E, vol. 92, no. 4, p. 042124, 2015.[28] P. C. Parks, “Am lyapunov’s stability theory—100 years on,” IMA journal of Mathematical Control and Information, vol. 9, no. 4, pp. 275–303, 1992.[29] T. S. Roy, M. Nandi, A. Biswas, P. Chaudhury, and S. K. Banik, “Information transmission in a two-step cascade: interplay of activation and repression,” Theory in Biosciences, vol. 140, pp. 295–306, 2021.[30] D. J. MacKay, Information theory, inference and learning algorithms. Cambridge university press, 2003.[31] A. Lyon, “Why are normal distributions normal?,” The British Journal for the Philosophy of Science, 2014.[32] F.A.Haight,“Handbookofthepoissondistribution,”(NoTitle),1967.[33] A.PapoulisandS.UnnikrishnaPillai,Probability,randomvariablesandstochasticprocesses. 2002.[34] A. Rényi, “On measures of entropy and information,” in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics, vol. 4, pp. 547–562, University of California Press, 1961.[35] S.KullbackandR.A.Leibler,“Oninformationandsufficiency,”Theannalsofmathematical statistics, vol. 22, no. 1, pp. 79–86, 1951.[36] T.M.Cover,Elementsofinformationtheory.JohnWiley&Sons,1999.[37] A. B. Barrett, “Exploration of synergistic and redundant information sharing in static and dynamical gaussian systems,” Physical Review E, vol. 91, no. 5, p. 052802, 2015.[38] D.M.Bieretal.,“Theenergycostsofproteinmetabolism:leanandmeanonunclesam’steam,” The role of protein and amino acids in sustaining and enhancing performance, pp. 109–119, 1999.[39] R.W.King,R.J.Deshaies,J.-M.Peters,andM.W.Kirschner,“Howproteolysisdrivesthecell cycle,” Science, vol. 274, no. 5293, pp. 1652–1659, 1996.[40] H.A.Orr,“Fitnessanditsroleinevolutionarygenetics,”NatureReviewsGenetics,vol.10,no.8, pp. 531–539, 2009.[41] D. T. Gillespie, “Exact stochastic simulation of coupled chemical reactions,” The journal of physical chemistry, vol. 81, no. 25, pp. 2340–2361, 1977.[42] M. Nandi, A. Biswas, S. K. Banik, and P. Chaudhury, “Information processing in a simple one-step cascade,” Physical Review E, vol. 98, no. 4, p. 042310, 2018.[43] M.S.A.Momin,A.Biswas,andS.K.Banik,“Coherentfeed-forwardloopactsasanefficient information transmitting motif,” Physical Review E, vol. 101, no. 2, p. 022407, 2020.[44] M.S.A.MominandA.Biswas,“Extrinsicnoiseofthetargetgenegovernsabundancepattern of feed-forward loop motifs,” Physical Review E, vol. 101, no. 5, p. 052411, 2020.[45] W. R. Inc., “Mathematica, Version 13.3.” Champaign, IL, 2023.[46] S.J.BryantandB.B.Machta,“Physicalconstraintsinintracellularsignaling:thecostofsending a bit,” Physical Review Letters, vol. 131, no. 6, p. 068401, 2023.[47] S. Iyer Biswas, Applications of methods of non-equilibrium statistical physics to models of stochastic gene expression. PhD thesis, The Ohio State University, 2009.[48] J. Grandell, Mixed poisson processes, vol. 77. CRC Press, 1997.202013862PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81025https://repositorio.uniandes.edu.co/bitstreams/091e6973-9530-44ed-b07a-50ae4260fe93/download84a900c9dd4b2a10095a94649e1ce116MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82535https://repositorio.uniandes.edu.co/bitstreams/e169e94d-9ca9-46c2-af22-37629dc6f8b8/downloadae9e573a68e7f92501b6913cc846c39fMD52ORIGINALAnálisis del coste metabólico.pdfAnálisis del coste metabólico.pdfapplication/pdf61328641https://repositorio.uniandes.edu.co/bitstreams/9ffdbd9b-38b9-46eb-841e-50491e8a6fbb/downloade7bb410a90db15d358c9084c68e59cddMD53Autorizacion.pdfAutorizacion.pdfHIDEapplication/pdf524250https://repositorio.uniandes.edu.co/bitstreams/8b5804ed-fb4e-431a-b6bd-b48a45d9aa56/download3f4e73cc3520a2ab3d1df8f91b089416MD54TEXTAnálisis del coste metabólico.pdf.txtAnálisis del coste metabólico.pdf.txtExtracted texttext/plain102515https://repositorio.uniandes.edu.co/bitstreams/6fa4fc99-08c3-42c0-811e-c30433857d6e/download49b9113a846950ce851d3db0d7e24e2fMD55Autorizacion.pdf.txtAutorizacion.pdf.txtExtracted texttext/plain88https://repositorio.uniandes.edu.co/bitstreams/74093396-8880-4351-a9bd-9b1b90bb6147/download7412940740bea6188ed0a9014b1c8d94MD57THUMBNAILAnálisis del coste metabólico.pdf.jpgAnálisis del coste metabólico.pdf.jpgGenerated Thumbnailimage/jpeg7458https://repositorio.uniandes.edu.co/bitstreams/18e3a0c1-b3a3-4c5e-b99e-a322f98dbd23/download0bd31c326b2d4dd94f5087e396f5c2a2MD56Autorizacion.pdf.jpgAutorizacion.pdf.jpgGenerated Thumbnailimage/jpeg8731https://repositorio.uniandes.edu.co/bitstreams/9899c5cf-74fe-41b4-8587-207eba8ee1bb/downloadf9f457517399420a7a32aea3264b7ea6MD581992/73900oai:repositorio.uniandes.edu.co:1992/739002024-02-16 15:29:34.555http://creativecommons.org/licenses/by-sa/4.0/Attribution-ShareAlike 4.0 Internationalopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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 |