Evolution of new regulatory functions on biophysically realistic fitness landscapes

Friedlander, Tamar; Prizak, Roshan; Barton, Nicholas H.; Tkačik, Gašper

doi:10.1038/s41467-017-00238-8

Cited by 32 publications

(30 citation statements)

References 75 publications

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“…Yet, crosstalk is not fully eliminated by selection. Despite the functional interference it causes in the short run, crosstalk is thought to promote evolvability in both gene regulatory and signaling networks in the long run [50][51][52][53][54]. However, the interplay between these two opposing effects of crosstalk, is still poorly understood.…”

Section: Discussionmentioning

confidence: 99%

The relation between crosstalk and gene regulation form revisited

Grah

Friedlander

2020

PLoS Comput Biol

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Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called "crosstalk", could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite 'idle' design, where the default unregulated state of genes is their frequently required activity state. We found, that 'idle' design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

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Section: Discussionmentioning

confidence: 99%

The relation between crosstalk and gene regulation form revisited

Grah

Friedlander

2020

PLoS Comput Biol

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“…One could also question whether the importance we ascribed to high specificity is really warranted. Evolutionarily, regulatory crosstalk due to lower specificity helps networks evolve during transient bouts of adaptation, even though it could be ultimately selected against [57]. Mechanistically, molecular mechanisms such as chromatin modification or the regulated 3D structure of DNA decrease the number of possible non-cognate targets that could trigger erroneous gene expression [58, 59], and thus alleviate the need for the high specificity of the transcriptional control.…”

Section: Discussionmentioning

confidence: 99%

Normative models of enhancer function

Grah

Zoller

Tkačik

2020

Preprint

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In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping 3 from promoter sequences to gene expression levels that is compatible with in vivo and in vitro bio-4 physical measurements. Such concordance has not been achieved for models of enhancer function 5 in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription 6 factor (TF) residence times on the DNA with the high specificity of regulation. In non-equilibrium 7 models, progress is difficult due to an explosion in the number of parameters. Here, we navigate 8 this complexity by looking for minimal non-equilibrium enhancer models that yield desired regula-9 tory phenotypes: low TF residence time, high specificity and tunable cooperativity. We find that a 10 single extra parameter, interpretable as the "linking rate" by which bound TFs interact with Medi-11 ator components, enables our models to escape equilibrium bounds and access optimal regulatory 12 phenotypes, while remaining consistent with the reported phenomenology and simple enough to 13 be inferred from upcoming experiments. We further find that high specificity in non-equilibrium 14 models is in a tradeoff with gene expression noise, predicting bursty dynamics -an experimentally-15 observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space to 16 a much smaller subspace that optimally realizes biological function prior to inference from data, our 17 normative approach holds promise for mathematical models in systems biology. 18 Keywords: transcriptional regulation | non-equilibrium models | noise in gene expression | enhancer function 73 somes are assembled in equilibrium [4]. Kinetic schemes 74 may be required to match the reported characteristics of 75 bursty gene expression (vi) [26], or realize high cooper-76 ativity (vii) [27]. Thermodynamic models undisputedly 77 have statistical power to predict expression from regula-78 tory sequence even in eukaryotes [28], yet this does not 79 resolve their biophysical inconsistencies or rule out non-80 equilibrium models. Unfortunately, mechanistically de-81 tailed non-equilibrium models entail an explosion in the 82 complexity of the corresponding reaction schemes and 83 the number of associated parameters: on the one hand, 84 such models are intractable to infer from data, while on 85 2the other, it is difficult to understand which details are 86 essential for the emergence of regulatory function. 87To deal with this complexity, we systematically sim-88 plify the space of enhancer models. We adopt the norma-89 tive approach, commonly encountered in the applications 90 of optimality ideas in neuroscience and elsewhere [29-91 31]: we theoretically identify those models for which var-92 ious performance measures of gene regulation, which we 93 call "regulatory phenotypes", are maximized. Such op-94 timal model classes are our candidates that could subse-95 quently be refined for particular biological systems and 96 confronted with data. Thus, rath...

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“…Likewise, several processes affecting the evolution of a molecular family (selection, duplication, transfer, and divergence in primary sequence) can be inferred by classic phylogenetic analyses or, as we proposed, by analyses of sequence similarity networks [ 157 ]. Such studies provide additional evolutionary colors (like quantitative measures: intensity of selection, rates of duplication, transfer, and percentage of divergence), which can be associated with nodes in ECNs [ 139 , 149 , 154 , 158 – 161 ]. Thus, ECNs contain both topological information, characteristic of the biological network under investigation, as well as evolutionary information: what node belongs to a family prone to duplication, divergence, or lateral transfer, as well as when this family arose.…”

Section: Concrete Strategies To Enhance Network-based Evolutionary Anmentioning

confidence: 99%

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

Bapteste

Huneman

2018

BMC Biol

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The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.

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Evolution of new regulatory functions on biophysically realistic fitness landscapes

Cited by 32 publications

References 75 publications

The relation between crosstalk and gene regulation form revisited

The relation between crosstalk and gene regulation form revisited

Normative models of enhancer function

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

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