Biology subjects

Si, F.

Publications and source records attributed to Si, F..

11 recordsLinked to original sources

bioRxiv2026

Emergent, cost-free surplus of core biosynthesis governs bacterial fitness

Cells often express essential components in excess of the levels required to sustain steady-state growth. The concept of protein reserve or surplus has recently emerged as a strategy for long-term cell fitness in bacteria, typically assumed to reduce steady-state growth as a trade-off for faster adaptation to a new environment. However, the origin and physiological consequences of such surplus remain unclear, particularly for core cellular processes including transcription, translation, and central metabolism. Here, we establish a unified operational definition of surplus and quantify it for selected key components of transcription, translation, and central metabolism machinery in Escherichia coli at the single-cell level. By combining microfluidics, quantitative fluorescence imaging, and CRISPR interference-based gene knockdown, we demonstrate that substantial fractions of core biosynthetic components can be removed without affecting steady-state growth under nutrient-limited conditions. Unexpectedly, this surplus imposes no cost on steady-state growth, in contrast to prevailing trade-off models. Instead, we show that surplus emerges as a passive consequence of substrate limitation using a simple theoretical model, which is based on the universal autocatalytic-network structure of bacterial cells. Perturbation experiments confirm model predictions that surplus of transcription and translation machinery accelerates growth adaptation to nutrient-rich conditions, without affecting steady-state growth. Moreover, under slow-growth conditions, surplus suppresses cell death by reducing the risk of stochastic collapse of biosynthetic cycles, also in accord with our theoretical model prediction. Altogether, our results identify surplus as an intrinsic property of core biosynthesis and a key determinant of long-term bacterial fitness in fluctuating environments.

biophysics↗

bioRxiv2025

Power-law memory governs bacterial adaptation and learning in fluctuating environments

How do single-celled organisms adapt and learn to survive in dynamic environments without a nervous system? Here, we provide experimental evidence and a theoretical model demon-strating learning-like behavior by single bacterial cells in fluctuating environments. Using a custom microfluidic platform, we tracked individual E. coli cells in dynamic nutrient environments and found that bacteria adapt on multiple timescales, tuning their growth control behavior based on prior environmental experience. Motivated by our observation that cellular adaptation dynamics are approximately scale-free, we built a theoretical framework for bacterial growth control with dynamic power-law memory to explain how bacteria integrate environmental information over a range of timescales to enable growth rate adaptation. We show how this behavior arises naturally from heterogeneous ribosomal relaxation dynamics within a bacterial cell. Using this model, we identify an inherent trade-off between growth rate maximization and adaptation speed, which we validate experimentally in pulsatile nutrient environments. Finally, we connect our mechanistic reaction-network model to descriptions of artificial recurrent neural networks, identifying a minimal network architecture capable of exhibiting adaptation and learning at the single-cell level.

systems biology↗

bioRxiv2023

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

systems biology↗

bioRxiv2021

Mitogen-activated protein kinases associated sites of tobacco REPRESSION OF SHOOT GROWTH regulates its localization in plant cells

Plant defense and growth rely on multiple transcriptional factors (TFs). REPRESSION OF SHOOT GROWTH (RSG) is known as one of the important TFs in tobacco (Nicotiana tabacum) with a basic leucine zipper domain. RSG was involved in plant gibberellin feedback regulation by inducing the expression of key genes. The tobacco calcium-dependent protein kinase, CDPK1 was reported to interact with RSG and manipulate its intracellular localization by phosphorylating Ser-114 of RSG. Here, we identified tobacco mitogen-activated protein kinase 3 (NtMPK3) as a RSG interacted protein kinase. Mutation of predicted MAPK-associated phosphorylation site of RSG (Thr-30, Ser-74 and Thr-135) significantly altered the intracellular localization of NtMPK3-RSG interaction complex. Nuclear transport of RSG and its amino acids mutants (T30A and S74A) were observed after treated with plant defense elicitor peptide flg22 in 5 min, while the two mutated RSG swiftly relocalized in tobacco cytoplasm in 30 min. Moreover, triple points mutation of RSG (T30A/S74A/T135A) mimics constant unphosphorylated status, and predominantly localized in tobacco cytoplasm. RSG (T30A/S74A/T135A) showed no relocalization effect under the treatments of either flg22, B. cereus AR156 or GA3, and was impaired in its role as TFs. Our results suggest that MAPK associated phosphorylation sites of RSG regulate its localization in tobacco and constant unphosphorylation of RSG in Thr-30, Ser-74 and Thr-135 keeps RSG predominantly localized in cytoplasm.

physiology↗

bioRxiv2021

Quantitative examination of five stochastic cell-cycle and cell-size control models for Escherichia coli and Bacillus subtilis

We examine five quantitative models of the cell-cycle and cell-size control in Escherichia coli and Bacillus subtilis that have been proposed over the last decade to explain single-cell experimental data generated with high-throughput methods. After presenting the statistical properties of these models, we test their predictions against experimental data. Based on simple calculations of the defining correlations in each model, we first dismiss the stochastic Helmstetter-Cooper model and the Initiation Adder model, and show that both the Replication Double Adder and the Independent Double Adder model are more consistent with the data than the other models. We then apply a recently proposed statistical analysis method and obtain that the Independent Double Adder model is the most likely model of the cell cycle. By showing that the Replication Double Adder model is fundamentally inconsistent with size convergence by the adder principle, we conclude that the Independent Double Adder model is most consistent with the data and the biology of bacterial cell-cycle and cell-size control. Mechanistically, the Independent Adder Model is equivalent to two biological principles: (i) balanced biosynthesis of the cell-cycle proteins, and (ii) their accumulation to a respective threshold number to trigger initiation and division.

systems biology↗

bioRxiv2020

Single-cell data and correlation analysis support the independent double adder model in both Escherichia coli and Bacillus subtilis

The reference point for cell-size control in the cell cycle is a fundamental biological question. We previously reported that we were unable to reproduce the conclusions of Witz et al.s eLife paper (Witz, van Nimwegen, and Julou 2019) entitled, "Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism", despite extensive efforts. In this replication double adder (RDA) model, both replication and division cycles are determined via replication initiation as the sole implementation point of size control. Witz et al. justified the RDA model using a type of correlation analysis (the "I-value analysis") that they developed. By contrast, we previously showed that, in both Escherichia coli and Bacillus subtilis, replication initiation and cell division are determined by balanced biosynthesis of key cell cycle proteins (e.g., DnaA for initiation and FtsZ for cell division) and their accumulation to their respective threshold numbers, which Witz et al. coined the independent double adder (IDA) model. The adder phenotype is a natural quantitative consequence of these mechanistic principles. In a recent bioRxiv response to our report, Witz and colleagues explicitly confirmed two important limitations of the I-value analysis: (1) it is only applicable to non-overlapping cell cycles, wherein E. coli is known to deviate from the adder principle, and (2) it is only applicable to select biological models and, for example, cannot evaluate the IDA model. These limitations of the I-value analysis were not explained in the original eLife paper and were overlooked during the review process. In this report, we show using data analysis, mathematical modeling, and experiments why the I-value analysis - in its current implementation - cannot compare different biological models. Furthermore, the RDA model is incompatible with the adder principle and is not broadly supported by experimental data. For completeness, we also provide a detailed point-by-point response to Witz et al.s response (Witz, Julou, and van Nimwegen 2020) in the Supplemental Information.

systems biology↗

bioRxiv2020

Comment on 'Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism'

Witz et al. recently performed single-cell mother machine experiments to track growth and the replication cycle in E. coli. They analyzed the correlation structure of selected parameters using both their data and published data, and concluded that E. coli cell-size control is implemented at replication initiation, which challenged the newly emerged division-centric mechanism of cell-size control in bacteria. We repeated Witz et al.s analysis, and performed additional experiments and analytical calculations. These results explain Witz et al.s observation and in fact support the division-centric model.

systems biology↗

bioRxiv2019

Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

systems biology↗

bioRxiv2018

Mechanistic origin of cell-size control and homeostasis in bacteria

Evolutionarily divergent bacteria share a common phenomenological strategy for cell-size homeostasis under steady-state conditions. In the presence of inherent physiological stochasticity, cells following this \"adder\" principle gradually return to their steady-state size by adding a constant volume between birth and division regardless of their size at birth. However, the mechanism of the adder has been unknown despite intense efforts. In this work, we show that the adder is a direct consequence of two general processes in biology: (1) threshold -- accumulation of initiators and precursors required for cell division to a respective fixed number, and (2) balanced biosynthesis -- maintenance of their production proportional to volume growth. This mechanism is naturally robust to static growth inhibition, but also allows us to \"reprogram\" cell-size homeostasis in a quantitatively predictive manner in both Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. By generating dynamic oscillations in the concentration of the division protein FtsZ, we were able to oscillate cell size at division and systematically break the adder. In contrast, periodic induction of replication initiator protein DnaA caused oscillations in cell size at initiation, but did not alter division size or the adder. Finally, we were able to restore the adder phenotype in slow growing E. coli, the only known steady-state growth condition wherein E. coli significantly deviates from the adder, by repressing active degradation of division proteins. Together these results show that division and replication are independently controlled, and that division processes exclusively drive cell-size homeostasis in bacteria.

systems biology↗

bioRxiv2017

Role of Membrane-tension Gated Ca Flux in Cell Mechanosensation

Eukaryotic cells are sensitive to mechanical forces that they experience from the environment. The process of mechanosensation is complex, and involves elements such as the cytoskeleton and active contraction from myosin motors. Ultimately, mechanosensation is connected to changes in gene expression in the cell, or mechanotransduction. While the involvement of the cytoskeleton in mechanosensation is known, processes upstream to cytoskeletal changes is unclear. In this paper, using a microfluidic device that mechanically compresses live cells, we demonstrate that calcium currents and membrane tension-sensitive ion channels directly signals to the Rho GTPase and myosin contraction. In response to membrane tension changes, cell actively regulates cortical myosin contraction to balance external forces. The process is captured by a mechanochemical model where membrane tension, myosin contraction and the osmotic pressure difference between the cytoplasm and extracellular environment are connected by mechanical force-balance. Finally, to complete the picture of mechanotransduction, we find that the tension-sensitive transcription factor YAP translocates from the nucleus to the cytoplasm in response to mechanical compression.

cell biology↗

bioRxiv2016

Deconstructing cell size control into physiological modules in Escherichia coli

It is generally assumed that the allocation and synthesis of total cellular resources in microorganisms are uniquely determined by the growth conditions. Adaptation to a new physiological state leads to a change in cell size via reallocation of cellular resources. However, it has not been understood how cell size is coordinated with biosynthesis and robustly adapts to physiological states. We show that cell size in Escherichia coli can be predicted for any steady-state condition by projecting all biosynthesis into three measurable variables representing replication initiation, replication-division cycle, and the global biosynthesis rate. These variables can be decoupled by selectively controlling their respective core biosynthesis using CRISPR interference and antibiotics, verifying our predictions that different physiological states can result in the same cell size. We performed extensive growth inhibition experiments, and discovered that cell size at replication initiation per origin, namely the initiation mass or \"unit cell,\" is remarkably invariant under perturbations targeting transcription, translation, ribosome content, replication kinetics, fatty acid and cell-wall synthesis, cell division, and cell shape. Based on this invariance and balanced resource allocation, we explain why the total cell size is the sum of all unit cells. These results provide an overarching framework with quantitative predictive power over cell size in bacteria.

microbiology↗