SUMMARY How cells control their size and maintain size homeostasis is a fundamental open question. Cell-size homeostasis has been discussed in the context of two major paradigms: sizer, in which the cell actively monitors its size and triggers the cell cycle once it reaches a critical size, and timer, in which the cell attempts to grow for a specific amount of time before division. These paradigms, in conjunction with the “growth law” [1] and the quantitative bacterial cell cycle model [2], inspired numerous theoretical models [3-9] and experimental investigations from growth [10,11] to cell cycle and size control [12–15]. However, experimental evidence involved difficult-to-verify assumptions or population-averaged data, which allowed different interpretations [1–5,16–20] or limited conclusions [4–9]. In particular, population-averaged data and correlations are inconclusive as the averaging process masks causal effects at the cellular level. In this work, we extended a microfluidic “mother machine” [21] and monitored hundreds of thousands of Gram-negative Escherichia coli and Gram-positive Bacillus subtilis cells under a wide range of steady-state growth conditions. Our combined experimental results and quantitative analysis demonstrate that cells add a constant volume each generation irrespective of their newborn sizes, conclusively supporting the so-called constant Δ model. This model was introduced for E. coli [6,7] and recently revisited [9], but experimental evidence was limited to correlations. This “adder” principle quantitatively explains experimental data at both the population and single-cell levels, including the origin and the hierarchy of variability in the size-control mechanisms, and how cells maintain size homeostasis.

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods. ]); 61 | The MUST sets are the sets of reactions that must increase or decrease their flux in order to achieve the desired phenotype in the mutant strain. As shown in Figure 6, the first order MUST sets are MustU and MustL while second order MUST sets are denoted as MustUU, MustLL, and MustUL. After parameters and constraints are defined, the functions findMustL and findMustU are run to determine the mustU and mustL sets, respectively. Define an ID of the run with:Each time the MUST sets are determined, folders are generated to read inputs and store outputs, i.e., reports. These folders are located in the directory defined by the uniquely defined runID.62 | In order to find the first order MUST sets, constraints should be defined: >> constrOpt = struct('rxnList', {{'EX_gluc', 'R75', 'EX_suc'}}, 'values', [-100; 0; 155.5]); 63 | The first order MUST set MustL is determined by running: >> [mustLSet, pos_mustL] = findMustL(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);If runID is set to 'TestoptForceL', a folder TestoptForceL is created, in which two additional folders InputsMustL and OutputsMustL are created. The InputsMustL folder contains all the inputs required to run the function findMustL, while the OutputsMustL folder contains the mustL set found and a report that summarises all the inputs and outputs. In order to maintain a chronological order of computational experiments, the report is timestamped.64 | Display the reactions that belong to the mustL set using: >> disp(mustLSet) 65 | The first order MUST set MustU is determined by running: >> [mustUSet, pos_mustU] = findMustU(model, minFluxesW, maxFluxesW, ... 'constrOpt', constrOpt, 'runID', runID);...

Summary 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.

Highlightsd The adder requires accumulation of division proteins to a threshold for division d The adder requires constant production of division proteins during cell elongation d In E. coli and B. subtilis, initiation and division are independently controlled d In E. coli and B. subtilis, cell division exclusively drives size homeostasis

No abstract

Cell size control and homeostasis is a long-standing subject in biology. Recent experimental work provides extensive evidence for a simple, quantitative size homeostasis principle coined adder (as opposed to sizer or timer). The adder principle provides unexpected insights into how bacteria maintain their size without employing a feedback mechanism. We review the genesis of adder and recent cell size homeostasis study on evolutionarily divergent bacterial organisms and beyond. We propose new coarse-grained approaches to understand the underlying mechanisms of cell size control at the whole cell level.

The ecology of microbes in the gut has been shown to play important roles in the health of the host. To better understand microbial growth and population dynamics in the proximal colon, the primary region of bacterial growth in the gut, we built and applied a fluidic channel that we call the "minigut." This is a channel with an array of membrane valves along its length, which allows mimicking active contractions of the colonic wall. Repeated contraction is shown to be crucial in maintaining a steady-state bacterial population in the device despite strong flow along the channel that would otherwise cause bacterial washout. Depending on the flow rate and the frequency of contractions, the bacterial density profile exhibits varying spatial dependencies. For a synthetic cross-feeding community, the species abundance ratio is also strongly affected by mixing and flow along the length of the device. Complex mixing dynamics due to contractions is described well by an effective diffusion term. Bacterial dynamics is captured by a simple reaction-diffusion model without adjustable parameters. Our results suggest that flow and mixing play a major role in shaping the microbiota of the colon.

Multicomponent efflux complexes remove antibiotics and other toxins from Gram-negative bacteria and thereby contribute to antibiotic resistance. Multicomponent efflux complexes can shift dynamically between an assembled functional state and a disassembled non-functional state. We have previously shown that mechanical stress promotes disassembly of CusCBA, an RND family efflux complex that is responsible for removing toxic Cu þ and Ag þ from Escherichia coli. It is unclear if mechanical stress also influences other efflux complexes. Here, we examine the effect of mechanical stress on the disassembly of MacAB-TolC, an efflux complex from the ABC family that contributes to resistance to macrolide antibiotics. Mechanical stress was applied to Escherichia coli using a microfluidic device that uses fluid pressure to force bacteria into narrow tapered channels where they experience mechanical stress and deformation. The assembly of the MacAB-TolC efflux complex was examined by measuring the motion of the inner membrane pump protein MacB (fast moving MacB was not assembled) in more than 600 cells at fluid pressure levels ranging from 0 to 30 kPa. At greater magnitudes of mechanical stress, the proportion of disassembled MacAB-TolC complexes increased (p=1.7*10 À5 ). MacB diffusivity decreased at greater levels of mechanical stress (p=1.9*10 À14 ). Our findings show that mechanical stress not only influences RND family efflux complexes but also ABC family efflux complexes that enable resistance to clinically relevant antibiotics.