Biology subjects

Brenner, N.

Publications and source records attributed to Brenner, N..

14 recordsLinked to original sources

bioRxiv2026

Evolution on degenerate fitness landscapes is not neutral: curvature drives directional bias

Degeneracy - the multiplicity of phenotypes with equivalent fitness - is a prevalent feature of biological systems. Such degeneracy is often associated with neutral evolution, under the assumption that adaptive dynamics on degenerate fitness manifolds lacks direction. Here we show that this is not generally the case. Using a minimal model of evolutionary dynamics on smooth degenerate fitness landscapes, we demonstrate that stochastic mutation-selection dynamics induce a directional drift on manifolds of optimal fitness toward regions of reduced curvature. This drift arises from an interaction between population variability and landscape curvature: curvature shapes phenotypic variation, which in turn biases evolutionary exploration even when fitness gradients vanish. As a result evolution exhibits an implicit bias, preferentially selecting flat and robust regions of the degenerate fitness manifolds, without explicit optimization for these properties. Interestingly, similar flatness-seeking implicit biases have been discovered in other stochastic optimization algorithms; here we reveal their different underlying mechanisms despite similar outcome, highlighting the unique properties of evolutionary dynamics. Our results highlight a general mechanism by which degeneracy shapes long-term evolutionary outcomes, affecting our interpretation of phenotypic variability, robustness, and neutrality in high-dimensional biological systems.

systems biology↗

bioRxiv2026

Likelihood-Based Identification of Cell Division Mechanisms

Cell-division control in bacteria has been studied for many years, but gaps in understanding its logic still remain. Simple candidate models of cell division control have been studied, but are assessed with heuristic analysis of single-cell data that does not provide a quantitative scale for comparison. We recast division control mechanism identification as a likelihood-based inference problem, defining an explicit metric for model comparison: among candidate mechanisms, the model with higher likelihood provides the better explanation of the data. We demonstrate that within a broad class of models, discrimination depends only on the conditional distribution of cell-cycle durations. Under mild independence assumptions, variability in growth rates and division asymmetry do not contribute to the likelihood-based comparison, effectively separating the problem of understanding growth from that of identifying division control. Applying the likelihood framework to simulations and experimentally measured long-term single-cell lineages of Escherichia coli, we find that candidate simple models are statistically identifiable in principle, but only weakly separated in practice. Sizer, adder, and related mechanisms all achieve comparable and generally high likelihoods, but no single one consistently outperforms others: their relative likelihood depends on the dataset and growth condition. In contrast, a flexible model trained directly on the data always achieves significantly higher likelihood, revealing structure not captured by any one of the simplified rules; surprisingly, it can be well fit by a low-dimensional variable combination. More broadly, this work establishes a general likelihood-based framework for identifying stochastic regulatory mechanisms and for comparing future mechanistic models on a quantitative scale.

cell biology↗

bioRxiv2025

Emergent Homeostasis and Degeneracy from multi-Dimensional Attractors

Biological systems maintain homeostasis, ensuring stability in the face of internal and external perturbations and counteracting stochastic noise. Traditionally, this is understood through the lens of control mechanisms designed to offset deviations and maintain certain quantities near functionally desired set-points. Here, we propose an alternative perspective to understand homeostasis: the dynamic perspective, in which homeostasis emerges from high-dimensional interactions creating stable attractors in the phase space. These multi-dimensional attractor manifolds can constrain all components collectively, eliminating the need for explicit control of individual variables. The presence of null directions on an attractor allows for degenerate states that can add flexibility while preserving functionality. Using single cell growth and division homeostasis as a test case, we develop and support our perspective by models and meta-analysis of numerous single-cell datasets across organisms and conditions. Importantly, we do not reject the control-theory perspective but rather suggest that control circuit models can be seen as low-dimensional projections of a more complex, multi-dimensional system.

biophysics↗

bioRxiv2024

Single-cell Growth Rate Variability in Balanced Exponential Growth

Exponential accumulation of cell size and highly expressed proteins is observed at the single-cell level in many bacterial species. While the exponential rates fluctuate from cycle to cycle, they remain stable on average over time and strongly correlated across different proteins and cell size. In this study, we investigate growth-rate variability at this state of balanced biosyn-thesis, and present a theoretical framework to explain its properties through the emergence of a high-dimensional collective dynamic attractor. This stable attractor arises from recurrent interactions among multiple cellular components, driving them to converge to the same expo-nential growth rate, thereby sustaining the balanced state. The convergence of growth rates induces a decay in instantaneous growth rate noise throughout the cell cycle, with a faster decay for higher average growth rates. Notably, our analysis identifies random deviations from symmetric division as the primary source of growth rate variability. The theory offers a coherent set of predictions for many observations, validated by extensive experimental single-cell data. The spontaneous emergence of homeostasis through dynamic interactions suggests that specific control mechanisms to compensate deviations from a target may not be necessary to maintain homeostasis in a balanced state.

biophysics↗

bioRxiv2022

Identifying Regulation with Adversarial Surrogate

Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system, and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar surrogate data. We test the algorithm on two synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.

bioengineering↗

bioRxiv2022

Cell-division time statistics from stochastic exponential threshold-crossing

Some phenotypic properties in bacteria exhibit universal statistics, with distributions collapsing under scaling. The extent and origins of such universality are not well understood. Using phenomenological modeling of growth and division, we identify compound "shape-factors" that describe the distributions throughout a large set of single-cell data. We find that the emergence of universal distributions is associated with the robustness of shape-factors across conditions, explaining the universality of cell size and highly expressed protein content and the non-universality of times between consecutive divisions. A wide range of experimental data sets support our theory quantitatively.

biophysics↗

bioRxiv2022

Race to survival during antibiotic breakdown determines the minimal surviving population size

A common strategy used by bacteria to resist antibiotics is enzymatic degradation or modification. Such a collective mechanism also enhances the survival of nearby cells, an effect that increases with the number of bacteria that are present. Collective resistance is of clinical significance, yet a quantitative understanding at the population level is lacking. Here we develop a general theoretical framework of collective resistance under antibiotic degradation. Our modeling reveals that population survival crucially depends on the ratio of timescales of two processes: the rates of population death and antibiotic removal. However, it is insensitive to molecular, biological and kinetic details of the underlying processes that give rise to these timescales. Another important aspect for this race to survival is the degree of cooperativity, which is related to the permeability of the cell wall for antibiotics and enzymes. These observations motivate a coarse-grained, phenomenological model and simple experimental assay to measure the dose-dependent minimal surviving population size. From this model, two dimensionless parameters can be estimated, representing the populations race to survival and single-cell resistance. Our simple model may serve as reference for more complex situations, such as heterogeneous bacterial communities.

microbiology↗

bioRxiv2022

Exit from lag phase in populations of Pseudomonas fluorescens is determined by strong interactions among cells

The relationship between the number of cells colonizing a new environment and time for resumption of growth is a subject of long-standing interest. In microbiology this is known as the "inoculum effect". Its mechanistic basis is unclear with possible explanations ranging from the independent actions of individual cells, to collective actions of populations of cells. Progress requires precise measurement of lag time distributions while at the same time, experimentally controlling inoculum size. Here we use a millifluidic droplet device in which the growth dynamics of hundreds of populations founded by different numbers of Pseudomonas fluorescens cells, ranging from a single cell, to one thousand cells, were followed in real time. Our data show that lag phase decreases with inoculum size. The average decrease, variance across droplets, and distribution shapes, follow predictions of extreme value theory, where the inoculum lag time is determined by the minimum value sampled from the single-cell distribution. Our experimental results show that exit from lag phase depends on strong interactions among cells, consistent with a "leader-cell" triggering end of lag phase for the entire population.

microbiology↗

bioRxiv2021

Transfer of Knowledge from Model Organisms to Evolutionarily Distant Non-Model Organisms: The Coral Pocillopora damicornis Membrane Signaling Receptome

With the ease of gene sequencing and the technology available to study and manipulate non-model organisms, the need to translate our understanding of model organisms to non-model organisms has become an urgent problem. For example, mining of large coral and their symbiont sequence data is a challenge, but also provides an opportunity for understanding functionality and evolution of these and other non-model organisms. Much more information than for any other eukaryotic species is available for humans, especially related to signal transduction and diseases. However, the coral cnidarian host and human have diverged over 700 million years ago and homologies between proteins are therefore often in the gray zone or undetectable with traditional BLAST searches. We introduce a two-stage approach to identifying putative coral homologues of human proteins. First, through remote homology detection using Hidden Markov Models, we identify candidate human homologues in the cnidarian genome. However, for many proteins, the human genome alone contains multiple family members with similar or even more divergence in sequence. In the second stage, therefore, we filter the remote homology results based on the functional and structural plausibility of each coral candidate, shortlisting the coral proteins likely to be true human homologues. We demonstrate our approach with a pipeline for mapping membrane receptors in humans to membrane receptors in corals, with specific focus on the stony coral, P. damicornis. More than 1000 human membrane receptors mapped to 335 coral receptors, including 151 G protein coupled receptors (GPCRs). To validate specific sub-families, we chose opsin proteins, representative GPCRs that confer light sensitivity, and Toll-like receptors, representative non-GPCRs, which function in the immune response, and their ability to communicate with microorganisms. Through detailed structure-function analysis of their ligand-binding pockets and downstream signaling cascades, we selected those candidate remote homologues likely to carry out related functions in the corals. This pipeline may prove generally useful for other non-model organisms, such as to support the growing field of synthetic biology.

bioinformatics↗

bioRxiv2021

Multiple timescales in bacterial growth homeostasis

In balanced exponential growth, bacterial cells maintain the stability of multiple properties simultaneously: cell size, growth rate, cycle time and more. These are not independent but strongly coupled variables; it is not a-priori clear which are under direct regulation and which are stabilized as a by-product of interactions. Here, we address this problem by separating different timescales in bacterial single-cell dynamics. Disentangling homeostatic set-points from fluctuations around them, we find that some properties have flexible set-points that highly sensitive to environment - defining "sloppy" variables, while other set-points are buffered and held tightly controlled - "stiff" variables. These control variables are combinations of sloppy ones that compensate one another over long times, creating a hierarchical buffering that protects them from environmental perturbations. This is manifested geometrically as a control manifold in the space of growth and division variables, whose in-plane directions span sloppy variables, while out-of-plane deviations are highly constrained. Cell size is found to be a sloppy variable, which is coupled to growth and division only on the short, single-cycle timescale. Our results show that cellular homeostasis involves multi-level regulation operating on multiple timescales. More generally, our work offers a data-driven approach for identifying control variables in a multi-dimensional system that can be applicable also in other contexts.

biophysics↗

bioRxiv2020

Cephalosporins target quorum sensing and suppress virulence of Pseudomonas aeruginosa in Caenorhabditis elegans infection model

Pseudomonas aeruginosa utilizes a chemical social networking system referred to as quorum sensing (QS) to strategically co-ordinate the expression of virulence factors and biofilm formation. Virulence attributes damage the host cells, impair the host immune system, and protect bacterial cells from antibiotic attack. Thus, anti-QS agents may act as novel anti-infective therapeutics to treat P. aeruginosa infections. The present study was performed to evaluate the anti-QS, anti-biofilm, and anti-virulence activity of {beta}-lactam antibiotics (carbapenems and cephalosporins) against P. aeruginosa. The anti-QS activity was quantified using Chromobacterium violaceum CV026 as a QS reporter strain. Our results showed that cephalosporins including cefepime (CP), ceftazidime (CF), and ceftriaxone (CT) exhibited potent anti-QS and anti-virulence activities against P. aeruginosa PAO1. These antibiotics significantly impaired motility phenotypes, decreased pyocyanin production, and reduced the biofilm formation by P. aeruginosa PAO1. In the present study, we studied isogenic QS mutants of PAO1: {Delta}LasR, {Delta}RhlR, {Delta}PqsA, and {Delta}PqsR and found that the levels of virulence factors of antibiotic-treated PAO1 were comparable to QS mutant strains. Molecular docking predicted high binding affinities of cephalosporins for the ligand-binding pocket of QS receptors (CviR, LasR, and PqsR). In addition, our results showed that the anti-microbial activity of aminoglycosides increased in the presence of sub-inhibitory concentrations (sub-MICs) of CP against P. aeruginosa PAO1. Further, utilizing Caenorhabditis elegans as an animal model for the in vivo anti-virulence effects of antibiotics, cephalosporins showed a significant increase in C. elegans survival by suppressing virulence factor production in P. aeruginosa. Thus, our results indicate that cephalosporins might provide a viable anti-virulence therapy in the treatment of infections caused by multi-drug resistant P. aeruginosa.

microbiology↗

bioRxiv2019

Increased serological response against human herpesvirus 6A is associated with risk for multiple sclerosis

Human herpesvirus (HHV)-6A or HHV-6B involvement in multiple sclerosis (MS) etiology has remained controversial mainly due to the lack of serological methods that can distinguish the two viruses.\n\nA novel multiplex serological assay measuring IgG reactivity against the immediate-early protein 1 from HHV-6A (IE1A) and HHV-6B (IE1B) was used in a MS cohort (8742 persons with MS and 7215 matched controls), and a pre-MS cohort (478 individuals and 476 matched controls) to investigate this further.\n\nThe IgG response against IE1A was positively associated with MS (OR = 1.55, p = 9x10-22), and increased risk of future MS (OR = 2.22, p = 2x10-5). An interaction was observed between IE1A and Epstein-Barr virus (EBV) antibody responses for MS risk (attributable proportion = 0.24, p = 6x10-6). In contrast, the IgG response against IE1B was negatively associated with MS (OR = 0.74, p = 6x10-11). The association did not differ between MS subtypes or vary with severity of disease. The genetic control of HHV-6A/B antibody responses were located to the Human Leukocyte Antigen (HLA) region and the strongest association for IE1A was the DRB1*13:01-DQA1*01:03-DQB1*06:03 haplotype while the main association for IE1B was DRB1*13:02-DQA1*01:02-DQB1*06:04.\n\nIn conclusion a role for HHV-6A in MS etiology is supported by an increased serological response against HHV-6A IE1 protein, an interaction with EBV, and an association to HLA genes.

immunology↗

bioRxiv2019

Scale free topology as an effective feedback system

Biological networks are often heterogeneous in their connectivity pattern, with degree distributions featuring a heavy tail of highly connected hubs. The implications of this heterogeneity on dynamical properties are a topic of much interest. Here we introduce a novel approach to analyze such networks the lumped hub approximation. Based on the observation that in finite networks a small number of hubs have a disproportionate effect on the entire system, we construct an approximation by lumping these nodes into a single effective hub, and replacing the rest by a homogeneous bulk. We use this approximation to study dynamics of networks with scale-free degree distributions, focusing on their probability of convergence to fixed points. We find that the approximation preserves convergence statistics over a wide range of settings. Our mapping provides a parametrization of scale free topology which is predictive at the ensemble level and also retains properties of individual realizations. Specifically for outgoing scale-free distributions, the role of the effective hub on the network can be elucidated by feedback analysis. We show that outgoing hubs have an organizing role that can drive the network to convergence, in analogy to suppression of chaos by an external drive. In contrast, incoming hubs have no such property, resulting in a marked difference between the behavior of networks with outgoing vs. incoming scale free degree distribution. Combining feedback analysis with mean field theory predicts a transition between convergent and divergent dynamics which is corroborated by numerical simulations. Our results show how interpreting topology as a feedback circuit can provide novel insights on dynamics. Furthermore, we highlight the effect of a handful of outlying hubs, rather than of the connectivity distribution law as a whole, on network dynamics.

biophysics↗

bioRxiv2018

Coexistence and cooperation in structured habitats

Many natural habitats are structured, which imposes certain environmental conditions on extant populations. Which conditions are important for coexistence of diverse communities, and how social traits in such populations stabilize, have been important ecological and evolutionary questions. We investigate a minimal ecological model of microbial population dynamics, that exhibits crucial features to show coexistence: Populations are repeatedly separated into compartmentalized habitats on a timescale typically longer than growth. In this framework, we consider several scenarios for possible interactions between different strains and their environments, which includes sharing a common nutrient source or expression of public goods that potentially increase population size. Examples for these public good dynamics are collective resistance against antibiotics, and enhanced iron-availability due to pyoverdine. We show that the two features of a long mixing timescale and spatial compartmentalization are already enough to enable coexisting strains. In the case of public goods, stable coexistence immediately entails cooperation.

evolutionary biology↗