[{"type":"dissertation","date_published":"2024-02-23T00:00:00Z","oa":1,"supervisor":[{"full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","first_name":"Gašper"}],"publication_identifier":{"issn":["2663 - 337X"]},"status":"public","related_material":{"record":[{"status":"public","id":"7553","relation":"part_of_dissertation"},{"id":"7606","relation":"part_of_dissertation","status":"public"},{"status":"public","id":"12081","relation":"part_of_dissertation"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"date_updated":"2024-02-23T13:50:53Z","file_name":"hledik thesis pdfa 2b.pdf","content_type":"application/pdf","date_created":"2024-02-23T13:50:53Z","file_size":7102089,"checksum":"b2d3da47c98d481577a4baf68944fe41","file_id":"15021","creator":"mhledik","relation":"main_file","access_level":"open_access","success":1},{"date_created":"2024-02-23T13:50:54Z","file_size":14014790,"checksum":"eda9b9430da2610fee7ce1c1419a479a","date_updated":"2024-02-23T14:20:16Z","content_type":"application/zip","file_name":"hledik thesis source.zip","relation":"source_file","access_level":"closed","file_id":"15022","creator":"mhledik"}],"has_accepted_license":"1","month":"02","project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385"},{"_id":"2665AAFE-B435-11E9-9278-68D0E5697425","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","grant_number":"RGP0034/2018"},{"_id":"bd6958e0-d553-11ed-ba76-86eba6a76c00","grant_number":"101055327","name":"Understanding the evolution of continuous genomes"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","keyword":["Theoretical biology","Optimality","Evolution","Information"],"language":[{"iso":"eng"}],"year":"2024","citation":{"ista":"Hledik M. 2024. Genetic information and biological optimization. Institute of Science and Technology Austria.","mla":"Hledik, Michal. <i>Genetic Information and Biological Optimization</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:15020\">10.15479/at:ista:15020</a>.","short":"M. Hledik, Genetic Information and Biological Optimization, Institute of Science and Technology Austria, 2024.","chicago":"Hledik, Michal. “Genetic Information and Biological Optimization.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:15020\">https://doi.org/10.15479/at:ista:15020</a>.","ieee":"M. Hledik, “Genetic information and biological optimization,” Institute of Science and Technology Austria, 2024.","ama":"Hledik M. Genetic information and biological optimization. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:15020\">10.15479/at:ista:15020</a>","apa":"Hledik, M. (2024). <i>Genetic information and biological optimization</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:15020\">https://doi.org/10.15479/at:ista:15020</a>"},"date_updated":"2025-06-30T13:21:09Z","abstract":[{"text":"This thesis consists of four distinct pieces of work within theoretical biology, with two themes in common: the concept of optimization in biological systems, and the use of information-theoretic tools to quantify biological stochasticity and statistical uncertainty.\r\nChapter 2 develops a statistical framework for studying biological systems which we believe to be optimized for a particular utility function, such as retinal neurons conveying information about visual stimuli. We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the expected utility. We explore how such priors aid inference of system parameters with limited data and enable optimality hypothesis testing: is the utility higher than by chance?\r\nChapter 3 examines the ultimate biological optimization process: evolution by natural selection. As some individuals survive and reproduce more successfully than others, populations evolve towards fitter genotypes and phenotypes. We formalize this as accumulation of genetic information, and use population genetics theory to study how much such information can be accumulated per generation and maintained in the face of random mutation and genetic drift. We identify the population size and fitness variance as the key quantities that control information accumulation and maintenance.\r\nChapter 4 reuses the concept of genetic information from Chapter 3, but from a different perspective: we ask how much genetic information organisms actually need, in particular in the context of gene regulation. For example, how much information is needed to bind transcription factors at correct locations within the genome? Population genetics provides us with a refined answer: with an increasing population size, populations achieve higher fitness by maintaining more genetic information. Moreover, regulatory parameters experience selection pressure to optimize the fitness-information trade-off, i.e. minimize the information needed for a given fitness. This provides an evolutionary derivation of the optimization priors introduced in Chapter 2.\r\nChapter 5 proves an upper bound on mutual information between a signal and a communication channel output (such as neural activity). Mutual information is an important utility measure for biological systems, but its practical use can be difficult due to the large dimensionality of many biological channels. Sometimes, a lower bound on mutual information is computed by replacing the high-dimensional channel outputs with decodes (signal estimates). Our result provides a corresponding upper bound, provided that the decodes are the maximum posterior estimates of the signal.","lang":"eng"}],"day":"23","doi":"10.15479/at:ista:15020","ddc":["576","519"],"author":[{"first_name":"Michal","last_name":"Hledik","full_name":"Hledik, Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87"}],"_id":"15020","alternative_title":["ISTA Thesis"],"title":"Genetic information and biological optimization","date_created":"2024-02-23T14:02:04Z","article_processing_charge":"No","department":[{"_id":"GradSch"},{"_id":"NiBa"},{"_id":"GaTk"}],"publication_status":"published","file_date_updated":"2024-02-23T14:20:16Z","ec_funded":1,"page":"158","publisher":"Institute of Science and Technology Austria"},{"publisher":"Proceedings of the National Academy of Sciences","article_type":"original","ec_funded":1,"quality_controlled":"1","file_date_updated":"2022-09-12T08:08:12Z","publication_status":"published","article_processing_charge":"No","date_created":"2022-09-11T22:01:55Z","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"title":"Accumulation and maintenance of information in evolution","intvolume":"       119","_id":"12081","pmid":1,"scopus_import":"1","author":[{"id":"4171253A-F248-11E8-B48F-1D18A9856A87","full_name":"Hledik, Michal","first_name":"Michal","last_name":"Hledik"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","first_name":"Nicholas H","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"1","last_name":"Tkačik","first_name":"Gašper"}],"issue":"36","acknowledgement":"We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and two anonymous reviewers for discussions and comments on the manuscript. G.T. and M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018. N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”","volume":119,"ddc":["570"],"doi":"10.1073/pnas.2123152119","day":"29","abstract":[{"lang":"eng","text":"Selection accumulates information in the genome—it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright–Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap."}],"date_updated":"2025-06-30T13:21:05Z","citation":{"mla":"Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.” <i>Proceedings of the National Academy of Sciences</i>, vol. 119, no. 36, e2123152119, Proceedings of the National Academy of Sciences, 2022, doi:<a href=\"https://doi.org/10.1073/pnas.2123152119\">10.1073/pnas.2123152119</a>.","short":"M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of Sciences 119 (2022).","ista":"Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.","apa":"Hledik, M., Barton, N. H., &#38; Tkačik, G. (2022). Accumulation and maintenance of information in evolution. <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2123152119\">https://doi.org/10.1073/pnas.2123152119</a>","ama":"Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information in evolution. <i>Proceedings of the National Academy of Sciences</i>. 2022;119(36). doi:<a href=\"https://doi.org/10.1073/pnas.2123152119\">10.1073/pnas.2123152119</a>","ieee":"M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information in evolution,” <i>Proceedings of the National Academy of Sciences</i>, vol. 119, no. 36. Proceedings of the National Academy of Sciences, 2022.","chicago":"Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and Maintenance of Information in Evolution.” <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences, 2022. <a href=\"https://doi.org/10.1073/pnas.2123152119\">https://doi.org/10.1073/pnas.2123152119</a>."},"year":"2022","isi":1,"external_id":{"isi":["000889278400014"],"pmid":["36037343"]},"language":[{"iso":"eng"}],"oa_version":"Published Version","project":[{"call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152"},{"_id":"2665AAFE-B435-11E9-9278-68D0E5697425","grant_number":"RGP0034/2018","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?"}],"month":"08","article_number":"e2123152119","publication":"Proceedings of the National Academy of Sciences","has_accepted_license":"1","file":[{"date_updated":"2022-09-12T08:08:12Z","content_type":"application/pdf","file_name":"2022_PNAS_Hledik.pdf","date_created":"2022-09-12T08:08:12Z","file_size":2165752,"checksum":"6dec51f6567da9039982a571508a8e4d","file_id":"12091","creator":"dernst","success":1,"access_level":"open_access","relation":"main_file"}],"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"15020"}]},"publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2022-08-29T00:00:00Z","type":"journal_article"},{"title":"Nonequilibrium models of optimal enhancer function","intvolume":"       117","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2021-01-10T23:01:17Z","article_processing_charge":"No","author":[{"id":"483E70DE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2539-3560","full_name":"Grah, Rok","first_name":"Rok","last_name":"Grah"},{"full_name":"Zoller, Benjamin","first_name":"Benjamin","last_name":"Zoller"},{"last_name":"Tkačik","first_name":"Gašper","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"issue":"50","pmid":1,"_id":"9000","scopus_import":"1","article_type":"original","publisher":"National Academy of Sciences","file_date_updated":"2021-01-11T08:37:31Z","page":"31614-31622","quality_controlled":"1","abstract":[{"lang":"eng","text":"In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future."}],"doi":"10.1073/pnas.2006731117","day":"15","isi":1,"external_id":{"isi":["000600608300015"],"pmid":["33268497"]},"date_updated":"2023-08-24T11:10:22Z","year":"2020","citation":{"short":"R. Grah, B. Zoller, G. Tkačik, PNAS 117 (2020) 31614–31622.","mla":"Grah, Rok, et al. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>, vol. 117, no. 50, National Academy of Sciences, 2020, pp. 31614–22, doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>.","ista":"Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer function. PNAS. 117(50), 31614–31622.","apa":"Grah, R., Zoller, B., &#38; Tkačik, G. (2020). Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>","ama":"Grah R, Zoller B, Tkačik G. Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. 2020;117(50):31614-31622. doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>","ieee":"R. Grah, B. Zoller, and G. Tkačik, “Nonequilibrium models of optimal enhancer function,” <i>PNAS</i>, vol. 117, no. 50. National Academy of Sciences, pp. 31614–31622, 2020.","chicago":"Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>."},"ddc":["570"],"volume":117,"acknowledgement":"G.T. was supported by Human Frontiers Science Program Grant RGP0034/2018. R.G. was supported by the Austrian Academy of Sciences DOC Fellowship. R.G. thanks S. Avvakumov for helpful discussions.","month":"12","oa_version":"Published Version","project":[{"grant_number":"RGP0034/2018","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","_id":"2665AAFE-B435-11E9-9278-68D0E5697425"},{"_id":"267C84F4-B435-11E9-9278-68D0E5697425","name":"Biophysically realistic genotype-phenotype maps for regulatory networks"}],"publication":"PNAS","has_accepted_license":"1","language":[{"iso":"eng"}],"oa":1,"publication_identifier":{"eissn":["10916490"],"issn":["00278424"]},"date_published":"2020-12-15T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png"},"related_material":{"link":[{"url":"https://ist.ac.at/en/news/new-compact-model-for-gene-regulation-in-higher-organisms/","description":"News on IST Homepage","relation":"press_release"}]},"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file":[{"success":1,"access_level":"open_access","relation":"main_file","file_id":"9004","creator":"dernst","date_created":"2021-01-11T08:37:31Z","checksum":"69039cd402a571983aa6cb4815ffa863","file_size":1199247,"date_updated":"2021-01-11T08:37:31Z","content_type":"application/pdf","file_name":"2020_PNAS_Grah.pdf"}]},{"language":[{"iso":"eng"}],"publisher":"Cold Spring Harbor Laboratory","publication":"bioRxiv","_id":"7675","author":[{"id":"483E70DE-F248-11E8-B48F-1D18A9856A87","last_name":"Grah","first_name":"Rok","full_name":"Grah, Rok","orcid":"0000-0003-2539-3560"},{"last_name":"Zoller","first_name":"Benjamin","full_name":"Zoller, Benjamin"},{"first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2020-04-23T10:12:51Z","article_processing_charge":"No","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"project":[{"_id":"2665AAFE-B435-11E9-9278-68D0E5697425","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","grant_number":"RGP0034/2018"},{"name":"Biophysically realistic genotype-phenotype maps for regulatory networks","_id":"267C84F4-B435-11E9-9278-68D0E5697425"}],"oa_version":"Preprint","publication_status":"published","month":"04","title":"Normative models of enhancer function","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/2020.04.08.029405 "}],"related_material":{"record":[{"status":"public","id":"8155","relation":"dissertation_contains"}]},"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2020","citation":{"apa":"Grah, R., Zoller, B., &#38; Tkačik, G. (2020). Normative models of enhancer function. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.04.08.029405\">https://doi.org/10.1101/2020.04.08.029405</a>","ama":"Grah R, Zoller B, Tkačik G. Normative models of enhancer function. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.04.08.029405\">10.1101/2020.04.08.029405</a>","ieee":"R. Grah, B. Zoller, and G. Tkačik, “Normative models of enhancer function,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2020.","chicago":"Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Normative Models of Enhancer Function.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2020. <a href=\"https://doi.org/10.1101/2020.04.08.029405\">https://doi.org/10.1101/2020.04.08.029405</a>.","mla":"Grah, Rok, et al. “Normative Models of Enhancer Function.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020, doi:<a href=\"https://doi.org/10.1101/2020.04.08.029405\">10.1101/2020.04.08.029405</a>.","short":"R. Grah, B. Zoller, G. Tkačik, BioRxiv (2020).","ista":"Grah R, Zoller B, Tkačik G. 2020. Normative models of enhancer function. bioRxiv, <a href=\"https://doi.org/10.1101/2020.04.08.029405\">10.1101/2020.04.08.029405</a>."},"date_updated":"2023-09-07T13:13:26Z","type":"preprint","date_published":"2020-04-09T00:00:00Z","day":"09","doi":"10.1101/2020.04.08.029405","oa":1,"abstract":[{"lang":"eng","text":"In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene expression levels that is compatible with in vivo and in vitro bio-physical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In non-equilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal non-equilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in non-equilibrium models is in a tradeoff with gene expression noise, predicting bursty dynamics — an experimentally-observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space to a much smaller subspace that optimally realizes biological function prior to inference from data, our normative approach holds promise for mathematical models in systems biology."}]}]