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Trading Bits in the Readout from a Genetic Network Marianne Bauer, Mariela Petkova, Thomas Gregor, Eric Wieschaus, William Bialek

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Trading Bits in the Readout from a Genetic Network Marianne Bauer, Mariela Petkova, Thomas Gregor, Eric Wieschaus, William Bialek Trading bits in the readout from a genetic network Marianne Bauer, Mariela Petkova, Thomas Gregor, Eric Wieschaus, William Bialek To cite this version: Marianne Bauer, Mariela Petkova, Thomas Gregor, Eric Wieschaus, William Bialek. Trading bits in the readout from a genetic network. 2020. pasteur-03216308 HAL Id: pasteur-03216308 https://hal-pasteur.archives-ouvertes.fr/pasteur-03216308 Preprint submitted on 3 May 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Trading bits in the readout from a genetic network Marianne Bauer,1−4 Mariela D. Petkova,5 Thomas Gregor,1;2;6 Eric F. Wieschaus,2−4 and William Bialek1;2;7 1Joseph Henry Laboratories of Physics, 2Lewis{Sigler Institute for Integrative Genomics, 3Department of Molecular Biology, and 4Howard Hughes Medical Institute, Princeton University, Princeton, NJ 08544 USA 5Program in Biophysics, Harvard University, Cambridge, MA 02138, USA 6Department of Developmental and Stem Cell Biology, UMR3738, Institut Pasteur, 75015 Paris, France 7Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, 365 Fifth Ave, New York, NY 10016 In genetic networks, information of relevance to the organism is represented by the concentrations of transcription factor molecules. In order to extract this information the cell must effectively \measure" these concentrations, but there are physical limits to the precision of these measurements. We explore this trading between bits of precision in measuring concentration and bits of relevant information that can be extracted, using the gap gene network in the early fly embryo as an example. We argue that cells in the embryo can extract all the available information about their position, but only if the concentration measurements approach the physical limits to information capacity. These limits necessitate the observed proliferation of enhancer elements with sensitivities to combinations of transcription factors, but fine tuning of the parameters of these multiple enhancers is not required. Cells control the concentrations of proteins in part [15, 16]. The local concentrations of the gap gene proteins by controlling the transcription of messenger RNA. This provide just enough information to support this level of control is effected by the binding of transcription factor precision [15], and the algorithm that achieves optimal (TF) proteins to specific sites along the genome. Tran- readout of this positional information predicts, quantita- scription factors thus can regulate the synthesis of other tively, the distortions of the pair-rule stripes in mutant TFs, forming a genetic network. The dynamical vari- flies where individual maternal inputs are deleted [17]. ables at the nodes of this network are the TF concen- The gap gene network offers us the chance to ask how trations, which represent information of relevance to the accurately the transcription factor concentrations need cell. What must the cell do in order to extract and use to be measured to retain crucial biological information: this information? the information that the gap genes convey about position along the anterior{posterior axis is what allows nuclei to We usually think of transcription factors as control- make distinct cell fate decisions required for development. ling the level of gene expression, but we can also view We start with more traditional views of how information the expression level as being the cell's measurement of is represented in TF concentrations, and then argue for the TF concentration [1, 2]. As outside observers of the a more abstract formulation of the problem. cell, we can measure the concentration of transcription The classical view of the gap genes is that they are ex- factors with considerable accuracy [3]. However the cell's pressed in domains [14]. Implicitly this suggests that fine \measurement" of TF concentration is based on the ar- scale variations in the concentration of these molecules is rival of these molecules at their binding sites, and this is a not important; rather all that matters whether expres- noisy process, because TF concentrations are low, in the sion is on or off. The quantitative version of this idea is nanoMolar range [4{8]. Physical limits to the measure- that subsequent events are sensitive to whether expres- ment of such low concentrations were first explored in the sion levels are above or below a threshold, corresponding context of bacterial chemotaxis [9], but have proven to be to whether a cell is inside or outside an expression do- arXiv:2012.15817v1 [q-bio.MN] 31 Dec 2020 much more general [1, 10{12]. What will be important main. We know that such simple thresholding omits a lot for our discussion is not the precise values of these limits, of the information that gap gene expression levels carry but rather that the limits exist and are significant on the about position along the anterior-posterior axis [15]. scale of biological function. In Figure 1A and B we use the gap gene hunchback We focus on the example of the gap genes that are (Hb) to illustrate this information loss. At each point x crucial in the early events of embryonic development in there is an expression level g, drawn from a probability fruit flies [13, 14]. These four genes form a network with distribution P (g x); looking at many embryos we have inputs from primary maternal morphogen molecules, and samples out of thisj distribution. If cells are sensitive outputs in the striped patterns of pair-rule gene expres- only to whether expression levels are above or below a sion. These stripes are positioned with an accuracy of threshold θ, then the variable which matters is 1% along the long (anterior{posterior) axis of the em- ± σ = H(g θ); (1) bryo, and this is the accuracy of subsequent developmen- − tal events such as the formation of the cephalic furrow where H is the Heaviside step function, H(y > 0) = 1 2 A B E C D FIG. 1: (A) Hb expression level vs position along the anterior{posterior axis embryo. Mean (line) ± one standard deviation (shading) across Nem = 38 embryos in a five minute window (40{44 min) in nuclear cycle 14 [17]. (B) Positional information vs threshold, from Eq (4). (C) Positional information with two thresholds, Iθ1θ2 (σ1; σ2; x) (bits). (D) Eigenvalues fλig of the Hessian matrix χ, from Eq (8). The number of eigenvalues is the number of thresholds, one less than the number of resolvable expression levels. Shaded bands are ± one standard error in our estimates. (E) Positional information captured with i = 1; ··· ;K thresholds, as a function of the number of resolvable levels K + 1. Error bars (red) are mean ± one standard error of our estimate of the maximum. Circles (grey) are 300 values of I(fσig; x) at random settings of the K thresholds fθig. The black dashed line indicates the positional information I(g; x), available from the expression level if measured precisely, and gray dashed lines are ± one standard error in our estimate of this information. and H(y < 0) = 0. Then we can estimate the distri- One path to recovering the information that was lost bution Pθ(σ x), where the subscript reminds us that the by thresholding is to imagine that the cell can resolve result dependsj on our choice of threshold, more details, perhaps distinguishing reliably among three or four different expression levels rather than just two. Z This is equivalent to imagining that we have multiple Pθ(σ = 1 x) = dg H(g θ)P (g x) (2) j − j readout mechanisms, each of which can only distinguish Pθ(σ = 0 x) = 1 Pθ(σ = 1 x): (3) on/off, but the different mechanisms can have different j − j thresholds, in the spirit of the \French flag” model [18]. Finally we compute the amount of information that the Because we can always put the thresholds in order, hav- discrete variable σ provides about position, ing K binary switches is the same as distinguishing K +1 Z different expression levels. It can be useful to think of X Pθ(σ x) I (σ; x) = dx P (x)P (σ x) log j bits; thresholding as being implemented at individual binding θ θ 2 P (σ) σ j θ sites for the TFs, or perhaps at cooperative arrays of (4) binding sites in enhancers, but it is important that our where a priori all positions along the length of the embryo arguments are independent of these microscopic details. are equally likely, P (x) = 1=L, and If we have two different elements, each of which re- Z ports on whether the expression level is above or below Pθ(σ) = dx P (x)Pθ(σ x): (5) a threshold, then the relevant variables are j σ1 = H(g θ1) (6) It is important that in exploring the impact of threshold- − σ2 = H(g θ2): (7) ing we allow for the best possible choice of the threshold − θ, which in this example proves to be at θ∗ 1=3 of the We see in Fig 1C that there is a broad optimum in maximal mean expression level. ∼ the positional information that these variables capture, If the expression level is represented only by the on/off Iθ1θ2 ( σ1; σ2 ; x), when the two thresholds are quite dif- f g ∗ ∗ or binary variable σ, then it can provide at most one bit ferent from one another, θ1 = 0:1 and θ2 = 0:58; of information (about anything), and we see that the in- these bracket the optimal single threshold θ = 0:34.
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