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Flipons, Repeat RNAs, and IDRs Enable the Rapid 2-D Search of Large Genomes Through the Holographic Hashing of Condensate Surfaces

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03 October 2026

Posted:

07 October 2026

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Abstract
Questions arise about how well early bacterial models of gene regulation generalize to eukaryotes and whether metazoan repetitive sequences are merely genomic spandrels. The repeat RNAs play multiple roles in the cell through sequence-specific interactions with proteins and other RNAs. Other repeat subsets, called flipons, adopt non-B-DNA conformations under physiological conditions without any sequence change or backbone cleavage. Other low-complexity sequences encode intrinsically disordered regions (IDRs) of proteins, promoting the formation of membrane-less condensates with specific cellular functions. Here, I propose that metazoan repeats enable a fast readout of genetic information by facilitating massively parallel searches of large genomes, far exceeding what diffusion alone can achieve. The locally transcribed repeats and flipon subsets seed condensates that display information about local DNA microstates on their surfaces, creating a 2-dimensional holographic hash of their contents. The hash enables transcription factors to rapidly discover critical cognate binding sites, facilitating rapid cellular responses to perturbation. The information displayed on condensate surfaces is constantly updated by both outward- and inward-directed processes. The rewrite further optimizes responses and increases the phenotypic variability on which natural selection acts. Applying the Principle of Least Action to the Lagrangian demonstrates that this strategy minimizes search times across arbitrary volumes.
Keywords: 
genome repeats
;  
flipons
;  
condensates
;  
Z-DNA
;  
G-quadruplexes
;  
triplexes
;  
search
;  
Shannon entropy
;  
least action
;  
Lagrangian
Subject: 
Biology and Life Sciences  -   Biophysics

Introduction

A fundamental question is how cells retrieve information from the genome not only in real time but also in constant time, to ensure rapid responses to perturbations. Do they use strategies equivalent to those used to optimize information retrieval from large computational databases? These constant-time strategies are based on search width or depth and differ widely in their implementation. They rely on keys that assign a unique identifier to each location that is then associated with a particular feature of the data. Various strategies generate unique keys to retrieve data. The Bloom filter, for example, ignores spaces that lack a particular key [1]. The elastic search strategy generates keys that optimize the size of data clusters, reducing the search time [2]. Other searches are hierarchical and use non-overlapping key matches to progress through the tree, reducing the search space at each step [3]. Here I discuss how cells can implement near-constant-time search strategies enabled by surface hashing and the layering of biological condensates. I will trace the development of these ideas from a historical perspective, as our understanding has changed dramatically as we progressed from the first studies of prokaryotes to the remarkable discovery that the large genomes of eukaryotes were richly riddled with self-replicating repeat elements. Contrary to expectations, the repetitive elements, the flipons they encode, and the epigenetic modifications they engender actually speed genomic search. The condensates they enable create surface hashes, providing a holographic representation of their contents. The hash enables a 2-dimensional search of 3D cellular space, enabling constant-time searches of cellular contents and facilitating rapid responses to perturbations.

Definitions

The paper synthesizes discoveries from quite divergent fields, each with terminology that is unique and unfamiliar to outsiders. Here, a hash function processes a ‘key’ to output a fixed-size hash value that indexes the location of a particular set of information. This fixed-size value enables efficient indexing regardless of input size. A hash table maps the ‘keys’ to the index for the underlying data. Searching the hash table to locate an object is faster than searching the entire database. A 2D holographic hash displays compressed genetic information on the surface of cellular condensates, allowing transcription factors and other proteins to rapidly search and navigate the genome [4]. Constant time search refers to a process that always takes the same amount of time to find an item, regardless of how large the dataset becomes (referred to in computer science and mathematics as an operation with O(1) complexity). Here, it refers to the time to locate a particular key in a condensate, which a parallel search strategy can minimize. The term biological condensate is less well defined in the community, but it generally refers to a membraneless cellular compartment enriched for a subset of cellular proteins and nucleic acids. The controversies center on the mechanism behind condensate formation, whether this is a liquid-liquid phase transition such as those easily studied in vitro, or due to the interaction of intrinsically disordered regions (IDRs), or the scaffolds formed by well-folded protein domains, or seeded by non-protein polymers such as s nucleic acids (see [5] for discussion of this controversy). This discussion focuses on condensate surfaces and only requires that each condensate is porous enough to allow small-molecule messengers that initiate internal updates. A flipon is a nucleic acid sequence that can adopt alternative nucleic acid conformations [6,7]. The transition between the structures they form occurs without requiring any sequence modification or strand scission.

Early Days

The early perceptions of genome structure were shaped by the organisms most amenable to laboratory study. Early studies focused on species that could be grown cheaply, proliferated rapidly, and were easy to mutagenize. Bacteria and their viruses yielded valuable insights [8]. Their small genomes enabled mapping DNA to proteins to pathways. Studies of lactose-induced gene regulation, through lac repressor binding to its cognate operator DNA sequence, set the stage. This work gave rise to the operon concept, with regulatory elements controlling gene expression in ways responsive to environmental changes [9]. Around 92% of bacterial transcription factors (TFs) engage binding sites (TFBS) of length 8-20 bp, with less than 1% with BS of 4-7 bp [10], with a mean of 15.9 (standard deviation (SD) =6.0) [11]. These findings led to the notion of one TFBS for one TF in one genome [12], a concept that was later generalized to genetic studies of multicellular organisms.

Bacterial and Metazoan Transcription Factors

Still, even with the small bacterial genomes studied, there were surprises. For example, the search by the lac repressor for its cognate binding site was two orders of magnitude faster than a simple 3-dimensional diffusion rate would predict [13], and the residence time at the lac operator was far shorter than expected based on the measured in vitro off-rate. Models explaining this “facilitated” diffusion were proposed that invoked two different processes. In one, non-specific electrostatic binding of the operator to DNA initiated a 1D search, with the TF sliding along the DNA helix until it engaged the high-affinity site. In the other, the TF repeatedly hopped from the current low-affinity binding site to another nearby DNA surface until it found the cognate binding site. This mechanism relied on the writhes of the circular DNA to approximate DNA segments that otherwise are hundreds of base pairs apart [14,15]. This regulatory scheme relies on the elaboration of sequence-specific TFs. It is also DNA-centric, as the major grooves of B-DNA can accommodate a protein α-helix capable of contacting base-specific residues, and the minor groove is shallow enough to reveal base-dependent variations in its contours. Double-stranded A-RNA cannot be sensed in either of these ways.
Applying these bacterial concepts to larger genomes of multicellular organisms was not straightforward. In contrast to bacteria, TFBS tend to be shorter, with a mean length of 9.9 bps (SD = 4.2), despite a genome that is 2-3 orders of magnitude larger [11]. Even allowing for the masking of many potential TFBS by heterochromatin, regulatory sites are still greatly outnumbered by the spurious TFBS in open chromatin. Further, a single TF is expected to bind to ~25% of regulatory regions by chance alone. Although the interactions are specific, they are non-functional [12]. A model requiring a specific combination of TFs to regulate gene expression was then invoked. The right combination of 6-8 nucleotide-binding sites produces a unique 30-base array for each promoter, or for a set of coordinately expressed promoters. For such a complex to bind specifically to a single locus in the human genome requires a cluster of between 10-15 TFs bound in a 500-1Kb window. This model requires high expression of each TF to saturate the multitude of other available nuclear targets that are non-functional [12].

Repeat Sequences and Selection

A further challenge to generalizing the concepts developed using bacterial genetics was the composition of metazoan genomes. Although much larger than bacterial genomes, whole-genome sequencing (WGS) did not reveal the expected expansion of protein-coding genes. Other explanations were needed to explain the additional phenotypic complexity. The discovery of RNA splicing and base-specific editing suggested a way to increase the protein repertoire [16,17]. Further, one splice could be conditioned on the splicing of another gene, as exemplified by the splicing cascade that determines sex in Drosophila species [18]. The splices made could also vary with epigenetic marks on histones within open chromatin regions that affect splice-site choice, or on nascent RNAs that alter their folding or processing [19]. Post-transcriptionally, noncoding RNAs such as miRNAs could also alter the available proteome by controlling protein production, either by affecting the levels of a particular RNA or its translation [20,21].
The use of noncoding RNA to control phenotype was proposed earlier, based on in vitro studies of the reannealing time of denatured DNA in several eukaryotic species. This work demonstrated that a large percentage of multicellular genomes were derived from repeat sequences of various types [22,23]. Later, WGS of the human genome revealed that at least 50% of the genome is derived from endogenous retroelements (EREs), which can copy their RNA back into the genome at other locations. The exact percentage depends on how ERE length and sequence identity are defined [24]. EREs require a reverse transcriptase (RT) to spread. The enzyme is encoded by long interspersed nuclear elements (LINEs), long terminal repeat (LTR) elements, and endogenous retrovirus (ERV) EREs, but not by the human genome [25]. A different class of EREs hijacks the RT to transpose; short interspersed nuclear elements (SINEs) are an example. SINES contain no protein-coding sequence. Instead, they abstract the control sequences necessary to drive their forward and reverse transcription [26]. In this process, they also incorporate adjacent sequences that contain TFBS motifs [27]. Given the frequency of SINEs, with over 1 million copies in the human genome, they initially appeared to have little informational value. They were termed either junk DNA, which accumulated over time, or selfish DNA, which existed only to replicate itself [28,29,30]. Such elements were selected against in bacterial genomes where rapid proliferation furthers survival, with advantageous mutations rapidly increasing in population frequency.

Repeats and the Proteome

Surprisingly, the evidence for positive selection of repeat elements in metazoans has accumulated [31,32,33,34]. Some repeats were required to protect chromosomal ends and scaffold attachment sites, ensuring proper chromosomal segregation during division [35,36]. Others produced proteins essential to the evolution of placentas [37]. A further set provided promoters and enhancers to enable tissue-specific gene expression [38]. Many were expressed during early development and helped scaffold the nucleus and position nucleosomes, thereby separating active chromosomal regions from heterochromatin. This arrangement left DNA segments open and available for sequence-specific transcription factors to bind. Their transcripts can fold into stable tetraloops and pseudoknots, enabling sequence- and structure- specific recognition by proteins, just like the RNAs from protein-coding genes [39].
Computational analyses of the genome revealed that some repeat sequences were distributed non-randomly through protein-coding regions, with CG nucleotides enriched in promoters and 5ˊ-untranslated regions (UTRs) [40] to TA-rich sequences in 3ˊ-UTRs, and polypyrimidine tracts near 3ˊ splice sites [41,42]. Many of the transcripts from these elements are bound by a diverse set of sequence-specific proteins called heteronuclear ribonucleoproteins (hnRNPs) [43] that are found in nuclear bodies or granules that represent regions of liquid-liquid phase separation (often referred to as condensates) (reviewed in [5,44]). These proteins are abundant in the cell, with many at concentrations up to 100 million copies per cell, and a subset is expressed in a cell-specific manner [43,45]. Other work reported the genome-wide role of noncoding RNA clusters in organizing chromatin territories [46,47]. The condensates they form with hnRNP and other RNA-binding proteins (RBPs) represent one way to index a large genome. Their surface features capture information about their content, enabling a TF to search for and locate a TFBS faster than by diffusion.

Repeats and Flipons

The idea that nucleic acid repeats also seed condensates by forming alternative DNA and RNA scaffolds and structures is only recent [6,48]. These insights do not come from the recent discovery of these structures. Historically, as the Watson-Crick model of B-DNA was gaining widespread acceptance, a variety of other DNA conformations were experimentally revealed, including three-stranded triplexes and four-stranded quadruplexes (Figure 1A) [49,50]. Then R-Loops were reported, in which an RNA displaces a DNA strand with the same sequence from a B-DNA helix [51]. Later, left-handed Z-DNA turned up in the first synthetic DNA crystal ever solved (Figure 1B) [52] and then a four-stranded structure in which cytosine base pairs intercalate, called an i-motif, was described [53]. Each structure has a canonical repeat motif that favors its formation. Z-DNA is associated with alternating pyrimidine/purine repeats, with guanine-based repeats most favorable. GQ are formed from G-tetrads that stack in a number of different ways to form a quadruplex [54,55,56]. While triplexes are formed by the docking of a third strand to a homopurine repeat, i-motifs require a long cytosine strand.
Like the sequence repeats recognized by hnRNP, flipon motifs are not randomly distributed. For example, potential Z-DNA and GQ motifs were found in promoters and 5ˊ-UTRs [57,58,59], with around 300,000 of each mapped throughout the genome [60,61]. At the time of discovery, none of these alternative conformations had an obvious biological role, and many were formed at the bench only under non-physiological conditions. Rather, these repeats were associated with genomic instability. G-quadruplexes (GQ) and triplexes (TRX) initially attracted attention for their proposed roles in Mendelian and repeat-expansion diseases, particularly those affecting the neuromuscular system [62,63,64,65]. They were of interest in their own right but lacked experimental evidence of cellular function.

Biological Functions of Flipons

Over time, researchers identified conditions that favor the transition of these repeat elements, called flipons, from B-DNA to an alternative structure. For Z-DNA, unwinding of the B-DNA helix produced by negative supercoiling of plasmids produced sufficient strain to force the flip [69,70]. The energy required to initiate Z-DNA formation and to propagate the flip to adjacent segments was then determined using closed circular plasmids with different flipon inserts [71]. This work revealed that the energetic cost of forming Z-DNA depended on base composition and motif length. Base modifications, such as 5-methyl cytosine and 8-oxo-guanosine, were also found to promote Z-DNA formation [72,73].
Over the past 5 years, numerous papers have linked Z-DNA and Z-RNA to various infectious, inflammatory, neurological, and malignant disease outcomes. These events involve a highly specific Z-DNA and Z-RNA (collectively called ZNA) binding domain, called Zα, present in only two proteins in the human genome, ADAR and ZBP1 [74,75,76,77]. Their role, with ADAR negatively regulating interferon responses and ZBP1inducing various inflammatory and cell death pathways, is now well validated and the subject of numerous recent papers [78,79,80,81,82,83,84]. Evidence is also accumulating that other proteins can bind ZNA, TRX, and GQ, playing many different roles in the cell. These flipon conformations are often bound by proteins known to bind B-DNA in a sequence-specific manner, usually through the same binding domain [85,86,87].
Protein recognition of alternative flipon structures is widespread in eukaryotes. One example is provided by the two crystal structures of the yeast RAP1 bound either to its cognate binding site in B-DNA or to a GQ [88,89]. The interactions involve different faces of the same helix. Similarly, the ability of the large zinc-finger transcription family to dock to alternative flipon conformations is now recognized [87]. Binding affinity (KD) for Z-DNA and G-quadruplexes can be nanomolar, but on- and off-rates can vary, allowing both stable docking when the off-rate is low and rapid scanning when the off-rate is fast. For example, both the Zα domain that binds ZNA and the helicase domain of SMARCA4 that binds a wide range of GQs that stably bind to alternative flipon conformations have extremely slow off-rates [74,90].

Flipons and Genetic Encoding

Flipons within the repeat genome offer an alternative way to encode genetic information, affecting our understanding of how cells work and how organisms evolve. As B-DNA, flipons have little informational value because repeat motifs occur so frequently. However, this is not true for all flipons. A small flipon subset marks active regions of the genome where sufficient free energy is available to power the transition. They are positioned in promoters and other regulatory segments. Depending on their structure, flipons can interact with different sets of proteins, changing the readout of genetic information and altering cellular programming. Flipons can also act mechanically, operating as actuators. In this role, they act as topological switches that modulate transcription, affecting both elongation and reinitiation [91]. In such cases, Z-DNA formation is driven by the negative supercoiling generated by RNA polymerase as it plows through the helix [92]. Recent work suggests that the general transcription factor IIE subunit 1 (encoded by GTF2E1) can bind to Z-DNA and reinitiate the reassembly of pre-initiation complexes [93]. ZNAs also modulate innate immunity and cell death pathways, with the single Zα domain on ADAR1 negatively regulating interferon responses and the two Zα domains on ZBP1 initiating cell death pathways. GQs also facilitate a number of outcomes. They can localize TFs to promoters and splice sites, and also regulate translation by engaging initiation factors [54,62,94,95,96,97]. Triplex formation can occur locally in DNA direct repeats that fold back to form the third strand, or with non-coding transcripts, either in cis or trans, and with small RNAs [98,99,100,101,102,103,104]

Condensate Biophysics

Targeting non-B-DNA flipon conformations reduces the search space for TFs to find a cognate binding site. They help maintain an open chromatin region because histones optimally wrap around right-handed DNA conformers and poorly around alternative DNA structures. GQs in particular provide a docking site from which TF can initiate scanning of the local region to find a cognate TFBS, while TRX provides a different way to nucleate condensates associated with DNA damage. The transcripts from active genomic regions help scaffold condensates by engaging sequence-specific hnRNPs. These condensates capture the composition gradient between GC-rich promoters, ERE-rich enhancers, polypyrimidine-rich spliceosome sites, and AT-rich transcript termination sites. Merging of condensates formed at these various sites modulates RNA production, processing, and quality control. These interactions are transient, forming and dissolving within seconds to minutes rather than hours or days [105,106,107]. Measurements performed using a variety of experimental approaches across a range of organisms confirm their dynamic nature and their regulation by suppressive factors [108].
Condensate assembly can occur cotranscriptionally with proteins translated at different times and locations in the cell [21]. Other interactions depend on IDRs, where charge properties rather than exact sequence determine whether a protein is included or excluded. The interactions are transient with fast on- and off-rates. Context-specific protein modifications, such as phosphorylation, ubiquitylation, cysteine modification, or acylation, can modify these outcomes. The adducts and condensates produced reflect cell state. Condensates can grow or shrink by varying the rates at which components associate or dissociate. At equilibrium, the rates are the same and the chemical potentials of free and bound components are equal. However, the concentration of an unbound component may be much lower, with the gradient maintained by interactions with other constituents in the condensate [109].

Condensate Surfaces and Optimized Search Strategies

Because of self-assembly, condensate surfaces index their contents and provide a simple search mechanism akin to the lateral diffusion models proposed for the LacI repressor in bacteria [15,110]. Figure 2A shows a simple intuition: a cubic lattice formed from well-ordered colored spheres. This close packing enables a massively parallelized Cartesian-grid search of all sphere surfaces to locate a subset of properties associated with a particular sphere color. Searching for a feature on a sphere’s surface is more efficient than searching the entire volume of the cube that contains the sphere. The search space is decreased by a factor of π/2r (the surface area of a sphere of radius r divided by the volume of a cube with sides of length 2r). In this example, color serves as the key. Increasing the number of keys k on the surface generates a hash that uniquely identifies each sphere. Shannon entropy quantifies the information displayed, with the key density δ given by:
Δ =   − ∑ k p i log p i 4 π r 2
where pi represents the probability of finding the ith key on a particular surface in a cell with n keys, where ∑ p i = 1   . The nature and number of available search keys n may vary by cell and tissue types, potentially providing a way to classify their state.
The formula can be easily adjusted for irregular surfaces. The parameter δ is tunable by modifying the surface informational molecules and varying the condensate size, thereby altering key density. Surfaces with only a single copy of a key require searching the entire surface and will be confounded by noise arising from the protein’s association with unrelated condensates. When search time is limited, such low-density keys will be disfavored, setting a signal-to-noise threshold.
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Figure 2. Search based on the Surface Hash of Condensates. A. The optimal packing of equal-sized spheres within a cubic lattice occupies over 50% of the volume. Surface features, in this case, colors, enable rapid searching to locate a subset of colored spheres that contain the required information. B. The uncondensed elements of the pink spheres occupy more volume and act as a barrier that impedes diffusion of other molecules through that space. C. A cellular implementation of sphere packing produced discrete clusters that optimize information storage and retrieval. The condensates that form spherical structures have porous surfaces that allow free diffusion of the small molecules enzymes require. These outward-directed processes dynamically rehash their surfaces by changing their contents. Fewer spheres increase free space for folding and unfolding of the protein’s intrinsically disordered regions, shown as wavy lines. Fewer spheres also increase the diffusion volume. The surface hash on each condensate allows rapid searches by excluding certain genomic regions, analogous to a Bloom filter, while targeting transcription factors and complexes to others. Environmentally triggered inward-directed processes can modify the hash. Images drawn with nanobanana2.
Figure 2. Search based on the Surface Hash of Condensates. A. The optimal packing of equal-sized spheres within a cubic lattice occupies over 50% of the volume. Surface features, in this case, colors, enable rapid searching to locate a subset of colored spheres that contain the required information. B. The uncondensed elements of the pink spheres occupy more volume and act as a barrier that impedes diffusion of other molecules through that space. C. A cellular implementation of sphere packing produced discrete clusters that optimize information storage and retrieval. The condensates that form spherical structures have porous surfaces that allow free diffusion of the small molecules enzymes require. These outward-directed processes dynamically rehash their surfaces by changing their contents. Fewer spheres increase free space for folding and unfolding of the protein’s intrinsically disordered regions, shown as wavy lines. Fewer spheres also increase the diffusion volume. The surface hash on each condensate allows rapid searches by excluding certain genomic regions, analogous to a Bloom filter, while targeting transcription factors and complexes to others. Environmentally triggered inward-directed processes can modify the hash. Images drawn with nanobanana2.
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Search speed varies with the number and size of spheres. These features define the unoccupied (free) space in the lattice. In Figure 2A, ~48% (1 - π/6) of the lattice volume lies outside the spheres, allowing free diffusion of molecules small enough to fit into the gaps, such as ions, nucleotides, or other secondary messengers. Other variations can also affect the search volume. For example, each sphere can be spatially addressed to a particular lattice location, much as zip codes direct RNA delivery [111] and signal peptides target proteins [112]. The search can then be restricted by addressing keys to these particular spaces.

An example of Search Optimization Based on the Principle of Least Action

The principle of least action, first proposed by Maupertuis in 1744, captures how holographic hashing can optimize search within a cell. The principle describes the way a physical system naturally chooses a path between two points that minimizes a specific quantity. A widely used approach described by Lagrange in 1788 determines the optimal path. Figure 3A shows a Lagrangian (boxed in blue) defined by two parameters, α and β, for calculating the minimum time required to search an arbitrary 3D volume using surface-bounded operations. For each combination of α and β, the number of spheres and their radius are varied to capture different outcomes. The plots in Figure 3B–D show how the cost surface behaves for a surface constraint value K = 500, with the red line indicating the feasible path and a red arrow pointing to the minimum reachable search time. These examples reveal that the optimization landscape is highly sensitive to hardware constraints, with the minimum search time varying by approximately 20-fold (1.59 s versus 31.71s) depending on the specific combination of α and β.
In this system, α represents the time required to read a unit of surface data. If the time spent processing the surfaces, α, is high, the system reduces dwell time by shrinking the sphere size and increasing the number of readers. β corresponds to the time between surfaces, representing the non-productive time spent traversing empty space. The system optimizes by using fewer large spheres, increasing efficiency by searching larger continuous surfaces. Counterintuitively, large volumes with small C and small volumes with large C have the same K and the same α and β optimizations. Here, lower C corresponds to a more efficient packing scheme. Lowering the volume does not increase search efficiency, because many redundant checks are performed on each surface. In these cases, search time remains near constant because the effects offset each other.

Condensate Surfaces and Excluded Volume

Using surfaces for 2D hashing therefore lets cells optimize response times by varying both the number and size of condensates across cytoplasmic and nuclear compartments. Condensate packing density also impacts how quickly a particular condensate turns over. Available space, as measured by excluded volume, plays a critical role. In contrast to globular states, which have a radius of gyration (rg) that scales as L1/3 (where L is their length), fully unfolded polymers have a rg that scales as L3/5, reflecting their increased entropy [113]. Figure 2B illustrates the reduced free volume when spheres are replaced with their unfolded constituents. The physical barriers created by unfolded proteins negatively impact search efficiency. However, disordered proteins will diffuse more easily through crowded cellular spaces than rigid globular spheres [114]. Unfolding will also expose previously buried charged residues, altering the protein interactions with condensate surfaces [115]. Both folding kinetics and diffusion limit the rate of dynamic rehashing.
Studies reveal that in cells, the excluded volume is much larger than for close-packed spheres, with estimates centered within the range of approximately 20-30% [116]. The lower concentration of proteins in solution than in the condensates allows diffusion to proceed without aggregation [109]. As with many other processes, small changes in the cell state can trigger condensate formation. For example, modifications altering protein charge can affect their inclusion into or exclusion from condensates [115].
Once a condensate forms, access to its interior depends on mesh size and component size. In biological systems, inert particles larger than 4 nm are excluded [117]; a globular 30-kd protein is about that size. Large proteins, such as antibodies and ribosomes, instead partition into condensates when charge interactions overcome the entropic barriers involved [118]. Polymeric modifications based on ubiquitin or ubiquitin-like proteins, such as SUMO [119], PARylation, and lipidation, can tune exit and entry [120]. With larger structures like transport vesicles, the cytoskeleton guides them past barriers created by condensates. Here, an ice-breaker model may operate, in which components at the bow divert, dissolve, or derivatize condensates and other obstacles ahead of the complex to ensure passage.
Tethering of condensate components to actin filaments and microtubules enables these movements. These interactions also affect the condensate’s internal structure, reflecting the tensegrity arising from actin-generated strain and the compressive resistance of tubulin structures (Figure 1D). These forces, along with helicases and nucleases, impact the dsRNA, GQ, TRX, and Z-RNAs formed by the nucleic acids within the condensate. By tensioning the RNA scaffold, forces prevent pore collapse. They also counter the formation of dsRNA from hybridization of unconstrained repeat elements [121,122]. When the tension is released, as occurs during dissolution, the now-relaxed single-stranded RNA can form different structures: they can fold into GQs and hybridize to form dsRNA and Z-RNA-containing tangles. These structures then engage structure- and sequence-specific proteins to nucleate a new condensate, whose nature depends on the cell state. If unresolved, the different nucleic acid folds can scaffold immune responses.

Condensate and Surface Rehashing

Condensate surfaces can also update dynamically. These may arise from inward-directed processes that reflect changes in cell state or outward-directed processes driven by internal events within the condensates. Over time, both processes can add or remove keys, or modify existing ones, changing the hash table. For example, a condensate with acetylated surface proteins functions differently than one with methylated surface proteins. In some cases, the same proteins may internally scaffold a range of condensates, yet produce different outcomes because they present different keys on the surface. One set of keys may promote gene expression, while another could suppress transcription. Knocking out a core component common to each condensate type may occasionally produce variable effects and contradictory experimental results.
Cells in various states of differentiation or from other tissues may use the same key, but the hash table indexes a different response. Phenotypes change as condensate contents vary even though the keys remain the same. TF may, for example, both activate and repress transcription, manifesting what has been called “duality” [123]. The threshold for the switch from positive to negative regulation can vary by locus, depending on the number and positioning of TFBS. Natural selection shapes such outcomes, repurposing keys without altering their existing use to reprogram pathways and generate novel cell types.
Hash table updates can have effects outside the condensate. Changes to surface keys can initiate or restrict interactions with other condensates. For example, efficiently disposing of unneeded cytoplasmic RNAs can require merging and material transfer from stress granules to processing bodies (P-bodies) [124,125]. In other cases, promoter and spliceosome condensates may interact only after each has assembled correctly and rewritten its surface. If these transactions fail, transcription terminates prematurely [126]. A related strategy is to bridge distinct condensate types using key-specific, cellular adaptor proteins. Cargo adaptor proteins involved in condensate protein quality control provide an example [127]. Here, surface keys direct the assembly of a production line for sequential RNA processing. Separate factories for each gene help ensure the fidelity of the products they produce.

Condensate Layering

In biological systems, the simple search design implemented with condensates can be further modified by layering condensates. This strategy creates another level of regulation (Figure 1C). The layers control entry into the condensate’s inner space and regulate its output. These distinct spaces enable optimization of chemistries in each layer. Nucleoli provide one example, with ribosomal RNAs maturing stepwise before assembly into ribosomes. The inner fibrillar layer forms through nucleolin interactions with G-quadruplexes, while rRNA processing, cleavage, and modification occur in the central layer and ribosome assembly in the outer granular shell (Figure 1A) [66,128]. Another example is paraspeckles, where the layers are based on the block copolymer structure of nuclear paraspeckle assembly transcript 1 (NEAT1) [129]. This arrangement allows the sequential, ordered assembly of protein components as the NEAT1 gene is transcribed [130]. Variations in the RNA components used to assemble components also enable optimized search over time
Protein modifications that affect charge, such as phosphorylation or acylation, can affect a protein’s location within a condensate. The layering may restrict physical access to internal spaces, further filtering what reaches the core. The layers also filter what emerges from those spaces. Such filters play an essential role in preventing the release of defectively processed RNAs. In the simplest of such schemes, epigenetic marks are added cotranscriptionally to introns. If splicing does not remove these marks, the RNA undergoes triage rather than transfer to the next layer. Such layering has been modeled using DNA structures called nanostars that vary in the number of arms and in the sequence specificity of their single-stranded ends. Pairing these ends with other nanostars creates three-dimensional forms that vary with each nanostar’s number and geometry. Nanostars can be assembled into layers, with the outer layer controlling the assembly’s stability and its propensity to merge with other complexes [131]. Each layer can be designed to optimize particular chemistries.

Condensates and Flipons

Overall, these diverse findings help resolve the dilemma posed by genomes that grow larger through the accrual of noncoding and repetitive sequences. How do TFs with short recognition motifs rapidly localize to functional sites? A potential solution lies in the interplay between condensates, flipons, and transcription factors.
Condensates undergo rapid updates within the nucleus. This process potentially enables TFs to rapidly search large genomes to find their critical TFBS. In the simplest model, condensates seed at actively transcribed loci. The DNA at these sites is open and negatively supercoiled. The energy is sufficient to power the transition of flipons to an alternative conformation. The exact structure formed depends on the flipon motif. As described above, many TFs may scan the genome for such structures to localize sequence-specific B-DNA binding sites in active genes [87]. Similarly, the locally produced RNA transcripts may contain repeat motifs to which sequence-specific hnRNPs bind [45,132]. The flipon and sequence motifs jointly specify the condensate character by the components that they engage. The condensate grows through domain- or IDR-mediated protein interactions, updating the hash table as the surface changes. The holographic hash created indexes the underlying genomic structure. This design enables parallel, key-based searches in near constant-time (O(1)), speeding responses. In contrast, other genomic repeat classes will promote DNA packaging into heterochromatin. These include EREs whose masking is promoted by small, sequence-specific RNAs, such as piRNAs. This negative hashing protects the genome by suppressing the retrotransposition of these EREs to other loci. These condensates reduce the search space because they lack keys associated with active chromatin.
Condensate turnover enables updating of the TFs within as the cell state changes. In addition to their B-DNA cognate binding sites, the newly incorporated TFs can also recognize alternative flipon conformations [87,88,89]. Indeed, one surprise from the ENCODE project was the identification of condensates at “hot” loci that engage a large number of TF, but lack their TFBSs [133]. Over repeated cycles, TFs that bind B-DNA sequences within the region are retained, increasing the local concentration and leading to the formation of higher-affinity transcriptional or repressive complexes. Condensates can play a slightly different role in assembling TF dimers. Dimers can exchange monomers to generate higher-affinity combinations. One potential example involves the assembly of highly specific heterodimeric helix-loop-helix (HLH) and leucine zipper (LZ) TFs. The partner exchange is enabled by the rapid dissociation of HTH and LZ dimers [134,135,136]. Structure-specific binding within the condensate of existing dimers to alternative flipon conformations, such as ZNA, may promote reassociation by bringing different sets into close proximity. After the monomer exchange, the newly formed partners will likely recognize different B-DNA sequences than the original dimers. Binding to a nearby TFBS then will stabilize these dimers. Over time, their local concentration of these higher-affinity dimers will also increase. This flipon-based strategy does not require metazoa to encode TFs specific for unique motifs in the genome. Instead, the dimers assemble and are selected dynamically based on their affinity for sequences present at the locus. This scheme contrasts with the genetic encoding of bacterial TFs, where it is necessary to have long binding motifs that ensure recognition of a specific operon [10,11,12,136].

Conclusions and Future Studies

Scientific advances lead to new concepts beyond what earlier generations imagined or even conceived as possible. Genetic encoding by flipon provides one example of how our understanding has changed, and how genome readout is regulated. Meanwhile, new tools have sparked renewed interest in the functions of the condensates observed many decades ago [137]. Together, these new and old findings suggest that condensates facilitate rapid searches of large genomes, enabling the fast responses crucial to surviving a perilous world. Their surface features provide a hash of their contents, directing effectors to their targets, transactions with other condensates, the chemistry performed within, and the transitions to other states. Here we apply the Principle of Least Action to the Lagrangian to show how holographic hashing minimizes total execution time while enabling near-constant-time search (O(1)).
The programmability of condensate surfaces by both inner- and outward-directed processes allows further enhancements over time. Key distribution and density across condensates can vary, generating phenotypic diversity on which natural selection can act. The central role of noncoding repeat sequences in metazoan condensate formation represents a different evolutionary strategy from bacteria with their tiny genomes. The efficient 2D searches and rapid responses in metazoa enabled by holographic hashing contrast with bacterial strategies where binding sites are discovered by sliding along, or hopping between, DNA surfaces. High-affinity metazoan TFs can then undergo local assembly in condensates rather than being fully specified by the genome as they are in bacteria.
Future studies aimed at understanding and modulating the surface hash of condensates will lead to therapeutic strategies to control condensate formation, size, and fate. Progress in this direction has already been made. Approaches include expressing RNAs in cells to program artificial condensates and targeting them with CRISPR guides [131,138,139,140]. This work could evolve into screening RNA scaffolds to create condensate surfaces for keys that localize specific TFs that induce or suppress local gene expression. Engineering these keys into cells would then enable TF-specific therapeutic applications. Screening could also include holographic imaging of the condensates [4]. Other RNA strategies to regulate condensate formation by targeting flipon conformation are also possible, with some hints or already that Nature implements such a strategy ([141] and references within). This work could also be coupled with biophysical measurements that relate condensate formation to response time. The Nuclear Factor Kappa B (NFκB) model system enables these types of studies, either in response to Tumour Necrosis Factor alpha (TNFα) or viral infection. With TNFα, NFκB nuclear translocation triggers gene expression and is modulated by an oscillatory feedback circuit. These outcomes can be measured in single cells. With viral infection, NFκB enhances IFNβ gene expression by binding to sites within AluSx and AluY repeats [142,143,144]. These noncoding SINEs encode different classes of flipons, and potentially help localize TFs to active regions of the genome [104,145,146,147,148].
The paper addresses limitations in generalizing from prokaryotes to eukaryotes, and from one species to another. Here, I provide a framework for how repetitive sequences are adapted to enable searches of large genomes. Important roles are invoked for noncoding sequences and flipons. The limitations of this perspective will also become apparent once better measures of condensates in both cultured cells and living organisms are possible. These experiments will inform on how holographic hashing enables cells to survive from one moment to the next.

Funding

This work was not externally funded.

Conflicts of Interest

The author is the founder of InsideOutBio and declares no competing interests.

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Figure 1. Condensates from the inside out. Flipons can nucleate condensates in active genomic regions. A. Nucleolin bound to a parallel-stranded G-quadruplex (based on protein database structure 9CB5 [66]). B. The Zα domain engages left-handed Z-DNA that forms within a B-DNA segment, as modeled with AlphaFold 3 with two B-Z junction shown [67]. Both images were rendered with ChimeraX [68]. C. The nucleolar condensate has layers. The unique components are labeled. D. Stress granules are modulated by attachment to microtubules and actin-myosin filaments of the cell’s cytoskeleton. Images drawn with nanobanana2.
Figure 1. Condensates from the inside out. Flipons can nucleate condensates in active genomic regions. A. Nucleolin bound to a parallel-stranded G-quadruplex (based on protein database structure 9CB5 [66]). B. The Zα domain engages left-handed Z-DNA that forms within a B-DNA segment, as modeled with AlphaFold 3 with two B-Z junction shown [67]. Both images were rendered with ChimeraX [68]. C. The nucleolar condensate has layers. The unique components are labeled. D. Stress granules are modulated by attachment to microtubules and actin-myosin filaments of the cell’s cytoskeleton. Images drawn with nanobanana2.
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Figure 3. Varying the number of search surfaces dramatically alters the reaction time. A. Lagrangian and parameters used to apply the principle of least action to determine the search time when the surface search (α) and 3D diffusion (β) parameters were varied to determine how search time varied with sphere surface area and number when spheres were optimally packed in an arbitrary volume. B, C, and D illustrate how search time can be minimized by leveraging 2D surface-area operations. The feasible path for the surface constraint K = 500 is mapped onto the cost surface for different combinations of α and β, with a red arrow indicating the optimal search time.
Figure 3. Varying the number of search surfaces dramatically alters the reaction time. A. Lagrangian and parameters used to apply the principle of least action to determine the search time when the surface search (α) and 3D diffusion (β) parameters were varied to determine how search time varied with sphere surface area and number when spheres were optimally packed in an arbitrary volume. B, C, and D illustrate how search time can be minimized by leveraging 2D surface-area operations. The feasible path for the surface constraint K = 500 is mapped onto the cost surface for different combinations of α and β, with a red arrow indicating the optimal search time.
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