Physical Biology

The dynamics of gene regulatory networks are governed by intrinsic noise, stemming from the random nature of biochemical reactions, and by extrinsic noise, arising from fluctuations in cellular components and environmental conditions. Together, these sources can compromise the reliability of predictive computational models if not properly accounted for, and capturing both effects within a single framework remains a non-trivial task in computational biology. In this work, we propose an uncertainty quantification framework that addresses these two contributions jointly: intrinsic stochasticity is described through a partial integro-differential equation (PIDE) for the protein probability density function, whereas extrinsic noise is represented as parametric uncertainty in the kinetic parameters. The propagation of the uncertainty is carried out via an intrusive polynomial chaos expansion (PCE), in which the PCE coefficients are obtained from a stochastic Galerkin projection of the PIDE, yielding a coupled deterministic system that is solved with standard numerical methods. We illustrate the approach on a positive autoregulatory gene network with one and two uncertain kinetic parameters. The proposed approach accurately reproduces the mean, variance, and full protein probability density function, including the bimodal distributions, at a substantially lower computational cost.

Focal adhesions maintain force-bearing attachment between cells and the extracellular matrix but can also undergo dynamic remodeling. Their assembly and actomyosin tension are coupled through mechanochemical feedback. The processes underlying this feedback are not instantaneous and therefore involve a time delay. However, how this delayed feedback gives rise to stable adhesion maintenance or dynamic remodeling remains unclear. Here, paired time-lapse measurements of vinculin fluorescence and traction stress revealed distinct local adhesion-force dynamics, including low-fluctuation and recurrent fluctuation patterns. To examine how these patterns could arise, we formulated a minimal mechanochemical model coupling focal adhesion assembly and actomyosin force through delayed reciprocal feedback. The model exhibited stable and oscillatory modes depending on feedback strength, the balance of opposing feedback effects, and the effective feedback delay. Bistability and hysteretic switching also occurred in a subset of parameter space, and the oscillation period followed a power-law relation with the delay. These results suggest that stable adhesion maintenance and dynamic remodeling can emerge from a common mechanochemical feedback architecture.

Thermodynamic models quantitatively describe interactions between transcription machinery and bacterial promoters. Contrary to conventional understanding, model analysis by Parisutham et al. (2025) attributes transcriptional inhibition by repressors to overstabilization of the RNA polymerase-promoter complex rather than prevention of its formation. Moreover, it suggests an inverse scaling relationship between basal promoter strength and transcriptional fold change, applicable to both repressor- and activator-mediated regulation. To reevaluate findings from this study, we systematically analyze empirical data and compare its framework with conventional thermodynamic models. In contrast to the inverse scaling relationship, data across multiple sources exhibit a peaked tradeoff between basal promoter strength and fold change, underscoring the importance of broad data coverage in revealing the full pattern required for reliable model inference. Furthermore, we identify the model assumption responsible for the apparent inverse scaling and misinterpretation of regulatory mechanisms. Relaxing this assumption enables the model to capture the peaked tradeoff and yield inferences consistent with established mechanisms of transcriptional repression and activation. We further derive a mathematical solution that connects basal expression to fold change for both repressor- and activator-regulated promoters. Our results underscore the importance of broad data coverage to avoid a blind-men-and-elephant interpretation and establish basal promoter strength as a key design parameter governing transcriptional regulation.

Neutron scattering is a powerful technique to investigate atomic structures and molecular dynamics of proteins at the nano-scale. When it comes to dynamics, incoherent and coherent scattering respectively provide information on the single and collective dynamics of nuclei. In proteins, hydrogen has the highest incoherent cross-section, and it is common practice to overlook the contribution of coherent terms stemming from all nuclei. However, the fast collective dynamics of heavier nuclei could also be studied if coherent scattering and incoherent scattering were experimentally separated. The recent advent of polarized neutron spectroscopy with sufficient flux and energy resolution has made it possible, and opens new perspectives to investigate the relative importance of coherent scattering and the information it provides on biological samples. The present study reports on the use of polarized quasi-elastic neutron scattering (QENS) and the application of a minimalistic model adapted to both individual and collective dynamics. Using a perdeuterated green fluorescent protein as a model globular protein, the study provides an interpretation of the dynamical parameters obtained with QENS, and a comparative study of the Elastic Coherent and Incoherent Scattering Factor. Based on both experiments and calculations, we discuss the relative importance of distinct and self components of coherent scattering, which is often wrongly assumed to be representative of collective dynamics only. The results highlight the current impediments rendering complicated a straightforward analysis of fast collective dynamics in hydrated protein samples.

This study aimed to (1) develop an elasticity framework for the sprint force-velocity (F-V) relationship and (2) examine how maximal force (F_{0}), maximal velocity (v_{0}), and sprint distance modulate the four derived elasticity metrics, and (3) explore these elasticity metrics' interrelation. After modelling the F-V relationship differential equation, four elasticity metrics were defined as force elasticity (F_{e}), the elasticity of sprint time to F_{0}; velocity elasticity (v_{e}), the elasticity of sprint time to v_{0}; the force-velocity elasticity norm {(\mathrm{F}-\mathrm{V}}_{\mathrm{EN}}=\sqrt{F_{e}^{2}+v_{e}^{2}}), capturing the combined sprint time sensitivity to proportional changes in F_{0} and v_{0}; and the force-velocity elasticity ratio {(\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=F_{e}{\div v}_{e}), indicating which variable dominates the sprint time response. Model simulations showed that F_{e} decreased with rising F_{0} and increased with rising v_{0}, while v_{e} showed the opposite pattern. With increasing sprint distance, F_{e} decreased and v_{e} increased. Given its negligible effect on sprint time, ignoring air resistance yields a conservation law (2F_{e}+v_{e}\equiv 1), indicating that a gain in one elasticity metric necessarily diminishes the other in a fixed proportion. This framework also identifies a valley distance (d_{valley}) at {\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=2, where {\mathrm{F}-\mathrm{V}}_{\mathrm{EN}} is minimized (\sqrt{0.2}) and sprint time is least responsive to changes in F-V relationship variables. Empirical data confirmed that the two theoretical laws still hold approximately when air resistance is considered. By linking changes in F_{0} and v_{0} to sprint time across different distances, the elasticity framework provides a quantitative basis for estimating the theoretical sprint time response to documented changes in F-V relationship variables.

Modern medicine implicitly assumes that physiological responses to intervention are predictably determined by administered treatments. However, physiological systems containing intrinsic delays between the detection of a stimulus and the biological response may violate this assumption. We investigate the human glucose-insulin system as described by the Ultradian model and mathematically demonstrate that clinically relevant forcing protocols-such as pulsatile insulin delivery and step-wise glucose infusion, both commonly used in intensive care units (ICUs)-can induce sustained temporal chaos that may hamper accurate prediction of the physiological response. If not accounted for, these chaotic dynamics could create difficulties in achieving optimal dosing and timing when administering glucose and insulin in clinical or home care settings. This phenomenon, termed delay-induced uncertainty (DIU), arises from the interaction between physiological delay, intrinsic shear near a limit cycle, and external forcing. Using the Ultradian glucose-insulin model, we compute top Lyapunov exponents to quantify predictability. Across a range of pulsatile and step-wise forcing regimes, including stochastic amplitudes drawn from Markov processes, we observe positive Lyapunov exponents, indicating sustained chaos. Our results suggest that delayed endocrine regulation may fundamentally limit the predictive value of the models used to develop glycemic management strategies, with implications for clinical protocols in the ICU.

Mechanistic mathematical models have been used extensively to provide a deeper understanding of biological mechanisms, including unveiling the regulation of tumour growth and its response to various treatments. However, given the breadth of biological regulatory mechanisms, these models are frequently large and thus prone to potential issues with parameter identifiability. Statistical metrics like the Akaike and Bayesian information criteria can help identify a parsimonious model by balancing goodness of fit against model complexity. Yet simple models may fail to provide sufficient biological insight if they do not adequately capture known physiological processes or mechanisms. A modeller must therefore balance hypothesis generation and biological learning with model tractability. Here, we illustrate this balance using models of ovarian cancer growth and treatment response to cisplatin and immune checkpoint blockade in homologous recombination (HR)-deficient and HR-proficient immunocompetent mouse models. We develop a hierarchy of mathematical models of increasing complexity to describe tumour growth, treatment response, and immune dynamics. Our results highlight the limits of relying purely on statistical metrics for model selection, particularly when the goal is to obtain biological insight and underscore the importance of balancing model complexity to avoid overfitting and parameter unidentifiability.

This paper presents and critically reviews the results of original and some literature based experimental studies conducted by the authors last years on the structural organization of DNA in dormant (starvation stress), anabiotic dormant (4 HR treatment) E. coli cells, as well as the K12 {Delta}dps strain, which lacks the Dps protein (Dps null E. coli). The experimental data includes small-angle synchrotron radiation diffraction (SAXS) and transmission electron microscopy (TEM) data. Synchrotron radiation diffraction experiments on K12{Delta}dps cells allowed us to conclude that peaks at 44.3, 22.1, and 14.8 angstrom resolutions are associated exclusively with ordered DNA organization. Peaks at 44.3, 22.1, and 14.8 angstrom resolutions are also observed for samples of dormant (starvation stress) cells and anabiotically dormant cells. Therefore, this ordered DNA organization also applies to samples of dormant and anabiotically dormant cells. A model is proposed that considers the ordered DNA organization in the cell as a cholesteric liquid crystal. The powder diffraction pattern calculated based on this model is compared with experimental small angle X ray scattering (SAXS) data obtained on Dps-null cell samples. The model completely reproduces the key features of the experimental diffraction pattern from Dps-null cell samples. Accordingly, the cholesteric liquid crystal model corresponds to DNA packaging in dormant and anabiotically dormant cells. Cholesteric liquid crystal ordering should be further considered in all models of cellular DNA packaging. To address the question of which structural organization of DNA predominates in the cell: the cholesteric liquid crystal or nanocrystalline or whether they coexist and fully manifest themselves under different external conditions, it is necessary to utilize the latest methodological advances in structural analysis.

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and severe evolutionary bottlenecks. While classical phenomenological models, such as the Linear-Quadratic (LQ) framework, reliably predict macroscopic population survival, they obscure the intrinsic single-cell stochasticity that governs critical rare events like tumor recurrence or the emergence of radioresistant persisters. To bridge this divide, we develop a mathematically exact stochastic differential equation (SDE) framework that models continuous DSB induction and repair as a Feller square-root process. By deriving exact closed-form expressions for the foci moments, we establish a highly efficient Maximum Likelihood Estimation (MLE) pipeline that circumvents computationally exhaustive Monte Carlo simulations, allowing the direct extraction of deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. Integrating this kinetic model with a cumulative damage hazard via the Feynman-Kac formalism, our framework seamlessly recovers the classic macroscopic LQ survival topology from microscopic first principles. Furthermore, systematic sensitivity analysis uncovers a fundamental evolutionary duality: while initial physical damage operates additively, ultimate cellular fate is driven by a nonlinear survival response governed by the trade-off between the damage hazard rate and intrinsic molecular noise strength. Crucially, we demonstrate that this molecular noise inherently enhances population survival. Governed by Jensen's inequality, stochastic variance acts as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with transiently low damage loads. Ultimately, this exact stochastic framework bridges microscopic biophysics and macroscopic demographics, offering deep mechanistic insights into the evolutionary roots of radioresistance.

In linear polysomes, excluded-volume interactions among ribosomes can induce dimensional reduction of mRNA. Yet linear architectures allow steric stress to relax at open ends-- limiting how strongly crowding can remodel the mRNA's structure and dynamics. Using coarse-grained molecular-dynamics simulations, we compare circular and linear polysomes over a range of ribosome densities. Circular closure selects a predominantly quasi-planar global conformational ensemble, as indicated by a shape dimensionality dshape {approx} 2 over a range of ribosome densities. Crucially, circular topology and ribosome crowding act cooperatively to suppress structural fluctuations. While closure alone or linear crowding reduces relative global size fluctuations ({Delta}Rg/Rg) only to {approx} 0.16, their combined effect drives this fluctuation down to {approx} 0.07. Within this stabilized architecture, increasing ribosome density drives a distinct in-plane reorganization: the ring becomes more isotropic, global size fluctuations are strongly suppressed, and the scaling exponent increases toward {nu} [~=] 0.74 - 0.77, consistent with two-dimensional self-avoiding walk-like value over the accessible finite-size window, 1000 [≤] N [≤] 4969. Closure shortens the radius-of-gyration decorrelation time of circular polysomes by 40-fold relative to matched linear systems, reflecting the topological elimination of free ends. Within this closureselected ensemble, ribosome crowding further reduces the decorrelation time by up to 20% at the highest density. A fluctuation-informed crossover model links the density dependence of the global scaling exponent to inter-ribosomal subchain statistics. These results distinguish the geometric role of circular closure from the density-dependent steric response that it enables, revealing a confined yet dynamically responsive conformational regime for circular polysomes.

The folding thermodynamics of proteins are dominated by two opposing forces, the loss in backbone entropy and the packing of hydrophobic groups. The same forces are major contributors to the extension thermodynamics of elastic proteins with the distinction that both processes act in concert, favoring the higher chain and solvent entropy of a relaxed conformation. The relative entropic contributions specify the recoil mechanism; human elastin recoil is primarily driven by hydrophobic forces, whereas fly resilin has a rubber-like mechanism driven by backbone entropy. Despite the importance of elastic proteins to tissue biomechanics, few have been identified, let alone characterized to the same extent as elastin and resilin. We develop a thermodynamic framework that maps proteins by sequence-derived estimates of extension-induced backbone and solvent entropy changes. Putative elastic proteins are proposed and classified by recoil mechanism based on estimated thermodynamic features. Proteins that map to elastic regions are overrepresented by the skin proteome. The set of predicted elastic domains is further extended by incorporating sequence context embedded in protein language models. Protein domains with distinct thermodynamic recoil mechanisms cluster on the latent space manifold. Some of these domains are anticipated to have roles within molecular machines, expanding the scope of elastic protein function beyond mechanical materials like elastin and resilin.

Maintaining a prescribed composition in engineered microbial consortia is difficult because small fitness differences can drive competitive exclusion. We study a two-strain consortium in continuous culture and develop a feedback architecture that regulates composition by selectively slowing the fast strain as a function of the population ratio. At the population level, we derive an idealized ratio-feedback law with a tunable positive coexistence equilibrium. We then propose a biomolecular realization using orthogonal quorum sensing, an sRNA-based ratiometric controller, and a ppGpp-mediated growth actuator. Exploiting the separation between slow population growth and faster intracellular controller dynamics, we use singular perturbation theory to show that, for sufficiently fast controller dynamics, the full implementation model inherits the coexistence equilibrium and its local stability properties from the reduced model. Numerical simulations validate the reduction and show how weaker timescale separation or loss of the assumed molecular regime degrades performance.

A key goal in HIV-1 cure research is to understand why some individuals control viral rebound after stopping antiretroviral therapy (ART). Recent human studies have identified responding CD8+ T cells expressing Ki-67 and the transcription factor TCF-1 as correlates of post-treatment control, but the mechanistic basis of this association remains unclear. Using the theoretical framework of Conway and Perelson, we fit mechanistic within-host models to viral load and CD8+ T cell data from 9 individuals in a combination immunotherapy trial following ART interruption. Although Ki-67 and TCF-1 measurements were not used for fitting, the inferred effector cell expansion sensitivity, i.e., the responsiveness of effector expansion to low antigen levels, shows a strong linear relationship with Ki-67 and TCF-1 levels at rebound (Pearsons r {approx} 0.8). Building on this, we show analytically that the post-rebound viral load set point is inversely proportional to the effector cell expansion sensitivity, and thus strongly correlates with cycling (Ki-67+) CD8+ T cells (r {approx} -0.8) at rebound, and a subset that expresses TCF-1 (r {approx} -0.9). In effect, individuals with a larger proportion of CD8+ T cells responding to viral rebound, and a greater representation of TCF-1 expressing cells within the responding subset, achieve markedly lower viral set points through a higher effector cell expansion sensitivity. This mechanism is consistent with prior modeling in a non-intervention ATI setting, suggesting it may generalize across more rebound contexts. Our results provide a mechanistic explanation why both Ki-67+ responding CD8+ T cells and their TCF-1-expressing subset predict post-treatment control, linking clinical correlation to its underlying cause and highlighting Ki-67 and TCF-1 as potential early biomarkers of HIV immunotherapy success.

The deformability of the red blood cell (RBC) is essential for microcirculatory flow and is profoundly altered in hemoglobinopathies and during infection with Plasmodium falciparum. While many mechanical tests have been developed to probe RBC-mechanics, the dynamics of cell shape recovery following large deformations remains poorly characterized. Here, we integrate microfluidic constriction assays, ultrafast imaging, and computer simulations to quantify time-resolved shape recovery of individual erythrocytes. We show that parasite infection is the primary determinant of RBC viscoelastic behavior. In wild-type (HbAA) erythrocytes, the relaxation time increases progressively from ring to trophozoite to schizont stages, consistent with parasite-induced membrane stiffening and enhanced membrane-cytoskeleton coupling. In contrast, sickle trait (HbAS) erythrocytes exhibit a distinct response: although deformation becomes increasingly irreversible during parasite maturation, the relaxation time after constriction remains largely unchanged. Analysis of a mutant parasite line with enlarged and sparsely distributed knobs revealed a significant increase in relaxation time, demonstrating that knob architecture modulates recovery kinetics. Together, these findings suggest that the coupling between membrane and cytoskeleton, which is strongly changed by the establishment of the knobs during an infection with Plasmodium falciparum, should have a strong detrimental effect on microcirculatory flow, which is however weakened by the sickle cell trait.

Biological aging is accompanied by systematic changes in epigenetic modifications and chromatin organization. The reversal of the effects of aging, rejuvenation, is experimentally achieved by the transient induction of factors that modify these marks in cells and organisms. Here, we show that key features of rejuvenation experiments emerge from the biophysical interplay between dynamic epigenetic marks and the three-dimensional conformation of chromatin. Using a minimal field theory and molecular dynamics simulations, we show that the system responds in three distinct temporal regimes. The intermediary regime fulfills necessary conditions for successful rejuvenation. In this regime, the system spends time near a separatrix, allowing for high epigenetic plasticity, while memory retained in the chromatin conformation enables restoration of the original epigenetic correlations. Analysis of sequencing data further supports the predicted coupling between chromatin compaction and epigenetic correlations. Our results provide a physical explanation for how rejuvenation may remodel age-associated epigenetic states without irreversibly erasing cellular identity. We identify a general mechanism by which memory stored in a slow structural variable permits reversible remodeling of a faster internal state.

A subset of untreated primary prostate cancer (PCa) contain substantial focal T-cell infiltrates, but whether these reflect antitumor responses that could potentially be enhanced by immune checkpoint blockade (ICB) remains unclear. We used immunohistochemistry, immunofluorescence, whole-slide spatial analysis, bulk RNA sequencing, and immune-cell deconvolution to characterize immune infiltrates in untreated primary PCa. Absolute CD8 T-cell density generally increased with total CD3 T-cell density, but the CD8/CD3 ratio decreased as overall T-cell density increased, indicating a preferential increase in CD4 T cells. Highly infiltrated tumors also had lower GZMB abundance relative to CD8 T-cell abundance. Multiplex analysis showed trends toward greater TIM3 and LAG3 expression among PD1CD8 T cells and increased regulatory T-cell features in highly infiltrated tumors. TIGIT cell density and the TIGIT/CD3 ratio increased with T-cell infiltration, whereas PD1/CD3 was not associated with overall CD3 T-cell density. Both TIGIT/CD3 and PD1/CD3 ratios were enriched within lymphoid aggregates compared with matched tumor and benign regions, consistent with these structures being checkpoint-rich immune niches. Transcriptomic analyses supported a shift in relative immune composition toward CD4 T cells and selective increases in immune checkpoints. Together these findings suggest that effective immune responses in a subset of primary PCa with increased T-cell infiltration are being repressed by several mechanisms and may respond to therapies targeting specific immunosuppressive mechanisms.

Carbapenems are the most potent {beta}-lactams, key antibiotics for healthcare-associated infections by Gram-negative bacteria and evade hydrolysis by most {beta}-lactamases, but are increasingly threatened by emergence of enzymes exhibiting hydrolytic activity towards them. Of the four recognised {beta}-lactamase subclasses, class A (active-site serine enzymes that hydrolyse {beta}-lactams via a covalent acylenzyme intermediate) is the most widely disseminated and, while the majority of such enzymes react with carbapenems to form long-lasting acylenzyme complexes, several possess carbapenem-hydrolyzing activity (carbapenemases). Here, we investigate the basis for these differences in a panel of class A {beta}-lactamases using molecular dynamics (MD) simulations of the respective acylenzyme complexes and tetrahedral intermediates (TI). The simulations reveal multiple features associated with catalytic activity across the spectrum of enzymes tested, including more extensive interactions of the carbapenem acylenzyme carbonyl and generally increased lifetimes of active site water molecules positioned for deacylation. Analysis of the dynamic trajectories shows carbapenemases to have reduced root mean-squared fluctuation (RMSF) differences between the acylenzyme and TI, that are not limited to the active site, indicating that the acylenzyme complex is pre-organised for reaction in carbapenemases but not in carbapenem-inhibited enzymes. Similarly, Principal Component Analysis (PCA) of acylenzyme and TI dynamics shows greater overlap between the two states in carbapenemases, providing further evidence for acylenzyme pre-organisation. Such simulations may represent an effective computational assay able to identify enzymes with carbapenemase activity at relatively modest computational cost.

Delamination within stratified epithelia like the skin epidermis describes the detachment and upward motion of cells originating from the basal layer. Despite its fundamental importance for tissue development, homeostatic regeneration and repair, the mechanisms that drive delamination remain a longstanding open question. Upward motion follows cell shape changes, which are inherently driven by physical forces, but their role is elusive. Here, we investigate delamination in stratifying keratinocytes by combining imaging, force measurements and theoretical modeling. We identify a local change in force balance between differentiating cells and their environment as the key step initiating delamination. Within a homogeneous cell layer with apically polarized contractility, differentiation leads to actomyosin remodeling, redistributing cellular force exertion to the basal side. Such mechanical heterogeneity then results in differentiating cells experiencing and inward basal and outward apical forces that manifest in the formation of a +1 force defect and promote shape changes culminating in upward motion. Simultaneously, delaminating cells actively pull on their underlying neighbors, generating convergent tissue flows which close the basal layer below. Together, we propose a general physical description of delamination initiation, which may act across various multilayered epithelia.

In vertebrates, top-down visual attention is a cognitive process where internal goals modulate the tuning of peripheral sensory systems. This leads to increased perceived contrast to both goal-relevant objects and areas of the visual field that are attended. Such a system would also be beneficial to bees, enabling them to detect and recognise the most profitable flowers in their environment. We tested whether bumblebees possess a top-down attentional system resembling that seen in vertebrates. We trained two groups of bees to collect rewards under high contrast targets. To potentially induce a difference in attention while searching for the targets, one group received a higher concentration of sucrose rewards compared to the other. During tests, the targets were presented with a series of lower contrasts to measure the contrast sensitivity curves of the bees induced by the different learnt reward levels. We predicted a stronger effect of any attention-like process on contrast sensitivity in the high reward group. We also repeated this experiment with the neonicotinoid pesticide imidacloprid dissolved in the sucrose rewards to test whether this affects bee attention. Across all test contrasts, higher rewards significantly increased bee accuracy when locating targets, lowered contrast thresholds and reduced the latency to make first choices. Imidacloprid reduced bee accuracy but did not influence first choice latency. These results suggest that learnt floral rewards can influence bee behavioural contrast sensitivity in a manner resembling vertebrate top-down attention and that imidacloprid may modulate this through effects on their nervous system.

We present the solution structures of HIV-1 proteins NC(p7)1-55 corresponding to the full-length NC(p7) and mature p6. The studies were carried in water and, to mimic the membrane, in micellar DPC (Dodecylphosphocholine) conditions. Our results unravel for the first time the structure adopted by the N-terminal amino acids of the free NC(p7)1-55, with the formation of a small helix spanning residues F6 to R10. Our NMR and Fluorescence Anisotropy data disclose an interaction between NC(p7)1-55 and p6 both in water and DPC, with respective Kd of 2.5mM and 370 mM at 23{degrees}C. The interaction is thus strengthened in lipidic conditions. Protein p6 stabilizes the N-terminus of NC(p7)1-55 while increasing at the same time the dynamic of the first zinc finger. Although the entire p6 sequence is involved in the interaction, we show that its C-terminal region is particularly sensitive to the presence of NC(p7)1-55, with a propensity of forming a a helix ranging from amino acids S111 to F116. This study brings experimental evidence of a direct protein-protein interaction between p6 and the N-terminal region of NC(p7)1-55. We further show that such interaction is readily accommodated within the NC(p15) framework and hypothesize that it may facilitate the selective assembly of assembly of the viral genomic RNA (gRNA) in the cell.