---
_id: '15020'
abstract:
- lang: eng
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."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
citation:
ama: Hledik M. Genetic information and biological optimization. 2024. doi:10.15479/at:ista:15020
apa: Hledik, M. (2024). Genetic information and biological optimization.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:15020
chicago: Hledik, Michal. “Genetic Information and Biological Optimization.” Institute
of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:15020.
ieee: M. Hledik, “Genetic information and biological optimization,” Institute of
Science and Technology Austria, 2024.
ista: Hledik M. 2024. Genetic information and biological optimization. Institute
of Science and Technology Austria.
mla: Hledik, Michal. Genetic Information and Biological Optimization. Institute
of Science and Technology Austria, 2024, doi:10.15479/at:ista:15020.
short: M. Hledik, Genetic Information and Biological Optimization, Institute of
Science and Technology Austria, 2024.
date_created: 2024-02-23T14:02:04Z
date_published: 2024-02-23T00:00:00Z
date_updated: 2025-06-30T13:21:09Z
day: '23'
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- _id: GaTk
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keyword:
- Theoretical biology
- Optimality
- Evolution
- Information
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '158'
project:
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call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
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grant_number: '101055327'
name: Understanding the evolution of continuous genomes
publication_identifier:
issn:
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publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
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status: public
status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
title: Genetic information and biological optimization
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '12081'
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.'
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.”
article_number: e2123152119
article_processing_charge: No
article_type: original
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: '1'
citation:
ama: Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information
in evolution. Proceedings of the National Academy of Sciences. 2022;119(36).
doi:10.1073/pnas.2123152119
apa: Hledik, M., Barton, N. H., & Tkačik, G. (2022). Accumulation and maintenance
of information in evolution. Proceedings of the National Academy of Sciences.
Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2123152119
chicago: Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and
Maintenance of Information in Evolution.” Proceedings of the National Academy
of Sciences. Proceedings of the National Academy of Sciences, 2022. https://doi.org/10.1073/pnas.2123152119.
ieee: M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information
in evolution,” Proceedings of the National Academy of Sciences, vol. 119,
no. 36. Proceedings of the National Academy of Sciences, 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.
mla: Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.”
Proceedings of the National Academy of Sciences, vol. 119, no. 36, e2123152119,
Proceedings of the National Academy of Sciences, 2022, doi:10.1073/pnas.2123152119.
short: M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of
Sciences 119 (2022).
date_created: 2022-09-11T22:01:55Z
date_published: 2022-08-29T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '29'
ddc:
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department:
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- _id: GaTk
doi: 10.1073/pnas.2123152119
ec_funded: 1
external_id:
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month: '08'
oa: 1
oa_version: Published Version
pmid: 1
project:
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call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
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title: Accumulation and maintenance of information in evolution
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---
_id: '9000'
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.'
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.
article_processing_charge: No
article_type: original
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Benjamin
full_name: Zoller, Benjamin
last_name: Zoller
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Grah R, Zoller B, Tkačik G. Nonequilibrium models of optimal enhancer function.
PNAS. 2020;117(50):31614-31622. doi:10.1073/pnas.2006731117
apa: Grah, R., Zoller, B., & Tkačik, G. (2020). Nonequilibrium models of optimal
enhancer function. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.2006731117
chicago: Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Nonequilibrium Models of
Optimal Enhancer Function.” PNAS. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.2006731117.
ieee: R. Grah, B. Zoller, and G. Tkačik, “Nonequilibrium models of optimal enhancer
function,” PNAS, vol. 117, no. 50. National Academy of Sciences, pp. 31614–31622,
2020.
ista: Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer
function. PNAS. 117(50), 31614–31622.
mla: Grah, Rok, et al. “Nonequilibrium Models of Optimal Enhancer Function.” PNAS,
vol. 117, no. 50, National Academy of Sciences, 2020, pp. 31614–22, doi:10.1073/pnas.2006731117.
short: R. Grah, B. Zoller, G. Tkačik, PNAS 117 (2020) 31614–31622.
date_created: 2021-01-10T23:01:17Z
date_published: 2020-12-15T00:00:00Z
date_updated: 2023-08-24T11:10:22Z
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ddc:
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doi: 10.1073/pnas.2006731117
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grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
- _id: 267C84F4-B435-11E9-9278-68D0E5697425
name: Biophysically realistic genotype-phenotype maps for regulatory networks
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publication_identifier:
eissn:
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issn:
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publisher: National Academy of Sciences
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relation: press_release
url: https://ist.ac.at/en/news/new-compact-model-for-gene-regulation-in-higher-organisms/
scopus_import: '1'
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title: Nonequilibrium models of optimal enhancer function
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...
---
_id: '7675'
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.'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Benjamin
full_name: Zoller, Benjamin
last_name: Zoller
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Grah R, Zoller B, Tkačik G. Normative models of enhancer function. bioRxiv.
2020. doi:10.1101/2020.04.08.029405
apa: Grah, R., Zoller, B., & Tkačik, G. (2020). Normative models of enhancer
function. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.04.08.029405
chicago: Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Normative Models of Enhancer
Function.” BioRxiv. Cold Spring Harbor Laboratory, 2020. https://doi.org/10.1101/2020.04.08.029405.
ieee: R. Grah, B. Zoller, and G. Tkačik, “Normative models of enhancer function,”
bioRxiv. Cold Spring Harbor Laboratory, 2020.
ista: Grah R, Zoller B, Tkačik G. 2020. Normative models of enhancer function. bioRxiv,
10.1101/2020.04.08.029405.
mla: Grah, Rok, et al. “Normative Models of Enhancer Function.” BioRxiv,
Cold Spring Harbor Laboratory, 2020, doi:10.1101/2020.04.08.029405.
short: R. Grah, B. Zoller, G. Tkačik, BioRxiv (2020).
date_created: 2020-04-23T10:12:51Z
date_published: 2020-04-09T00:00:00Z
date_updated: 2023-09-07T13:13:26Z
day: '09'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1101/2020.04.08.029405
language:
- iso: eng
main_file_link:
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url: 'https://doi.org/10.1101/2020.04.08.029405 '
month: '04'
oa: 1
oa_version: Preprint
project:
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grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
- _id: 267C84F4-B435-11E9-9278-68D0E5697425
name: Biophysically realistic genotype-phenotype maps for regulatory networks
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
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title: Normative models of enhancer function
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year: '2020'
...