scholarly journals Exponential trajectories, cell size fluctuations and the adder property in bacteria follow from simple chemical dynamics and division control

2018 ◽  
Author(s):  
Parth Pratim Pandey ◽  
Harshant Singh ◽  
Sanjay Jain
Keyword(s):  
Growth Rate ◽  
Cell Size ◽  
Bacterial Growth ◽  
Mass Action ◽  
System Level ◽  
Simple Explanation ◽  
Chemical Dynamics ◽  
Bacterial Cells ◽  
Mass Action Kinetics ◽  
Balanced Growth

Experiments on steady state bacterial cultures have uncovered several quantitative regularities at the system level. These include, first, the exponential growth of cell size with time and the balanced growth of intracellular chemicals between cell birth and division, which are puzzling given the nonlinear and decentralized chemical dynamics in the cell. We model a cell as a set of chemical populations undergoing nonlinear mass action kinetics in a container whose volume is a linear function of the chemical populations. This turns out to be a special class of dynamical system that generically has attractors in which all populations grow exponentially with time at the same rate. This explains exponential balanced growth of bacterial cells without invoking any regulatory mechanisms and suggests that this could be a robust property of protocells as well. Second, we consider the hypothesis that cells commit themselves to division when a certain internal chemical population reaches a threshold of N molecules. We show that this hypothesis leads to a simple explanation of some of the variability observed across cells in a bacterial culture. In particular it reproduces the adder property of cell size fluctuations observed recently inE. coli, the observed correlations between interdivision time, birth volume and added volume in a generation, and the observed scale of the fluctuations (CV ~ 10-30%) when N lies between 10 and 100. Third, upon including a suitable regulatory mechanism that optimizes the growth rate of the cell, the model reproduces the observed bacterial growth laws including the dependence of the growth rate and ribosomal protein fraction on the medium. Thus, the models provide a framework for unifying diverse aspects of bacterial growth physiology under one roof. They also suggest new questions for experimental and theoretical enquiry.

scholarly journals Bioremediation of Diazinon, Pirimicarb and Atrazine Pesticides Using Bacterial Species

2021 ◽  
pp. 14-20
Keyword(s):  
Growth Rate ◽  
Bacterial Growth ◽  
Bacterial Species ◽  
Serial Dilution ◽  
Bacterial Cells ◽  
E Coli ◽  
Highly Efficient

Bacterial species such as E.coli, S. aureus and Sa. bongori were isolated from soil by using serial dilution. Bioremediation results showed the S. aureus was highly efficient on Diazinon removal by 62%, 63.2% and 68.6%, Pirimicarb removal was 44%, 52.4% and 53.8%, and Atrazine removal was 61%, 65.6% and 70.6%. and the efficiency of E.coli removal on Diazinon was 59%, 60.8% and 63.8%; on Pirimicarb was 44%, 52.4% and 53.8%; and for Atrazine 57%, 60.8% and 64.4%. Sa. bongori efficiency on Diazinon was 49%, 51.2% and 55.8%; on Pirimicarb removal was 61%, 63.2% and 68.4%; Also, in Atrazine removal 48%, 50.4% and 57.2%. When comparing the growth rate of bacterial cells. The bacterial cells before treatment with S. aureus was 22.01×10^4, Results after treatment showed Diazinon of 35.58×10^4. The Pirimicarb 32.41×10^4 and Atrazine was 38.45 ×10^4. Either E. coli Its bacterial growth was before treatment 17.09×10^4 To show the results of growth on diazinon 30.43×10^4, Pirimicarb 27.71×10^4 and Atrazine 24.34 ×10^4. While the growth was in Sa. bongori Before treatment 10.09×10^4 While recorded a growth rate on Diazinon 18.82×10^4, Pirimicarb 19.98×10^4 and Atrazine 17.08 ×10^4.These bacterial species efficiencies on bioremediation of these three pesticides proved to be promising It can be used safely in the process of removing pesticides, yet more research on safety, mechanisms and kinetics needs to be further investigated.

scholarly journals MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

2021 ◽  
Vol 17 (1) ◽  
pp. e1008208
Author(s):  
Zachary B. Haiman ◽  
Daniel C. Zielinski ◽  
Yuko Koike ◽  
James T. Yurkovich ◽  
Bernhard O. Palsson
Keyword(s):  
Dynamic Simulation ◽  
Dynamic Modeling ◽  
Metabolic Networks ◽  
Dynamic Models ◽  
Mass Action ◽  
System Level ◽  
Unified Framework ◽  
Modeling Tools ◽  
Mass Action Kinetics ◽  
Simulation Engine

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.

scholarly journals Comparative activity of Ceftriaxone, Ciprofloxacin and Gentamicin as a function of bacterial growth rate probed by Escherichia coli chromosome replication in the mouse peritonitis model

2018 ◽  
Author(s):  
Maria Schei Haugan ◽  
Anders Løbner-Olesen ◽  
Niels Frimodt-Møller
Keyword(s):  
Growth Rate ◽  
Bacterial Growth ◽  
Growth Dynamics ◽  
Chromosome Replication ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
Pre Treatment

AbstractCommonly used antibiotics exert their effect predominantly on rapidly growing bacterial cells, yet growth dynamics taking place during infection in a complex host environment remain largely unknown. Hence, means to measure in situ bacterial growth rate is essential to predict the outcome of antibacterial treatment. We have recently validated chromosome replication as readout for in situ bacterial growth rate during Escherichia coli infection in the mouse peritonitis model. By the use of two complementary methods (qPCR and fluorescence microscopy) for differential genome origin and terminus copy number quantification, we demonstrated the ability to track bacterial growth rate, both on a population average and on a single-cell level; from one single biological specimen. Here, we asked whether the in situ growth rate could predict antibiotic treatment effect during infection in the same model. Parallel in vitro growth experiments were conducted as proof-of-concept. Our data demonstrate that the activity of commonly used antibiotics Ceftriaxone and Gentamicin correlated with pre-treatment bacterial growth rate; both drugs performing better during rapid growth than during slow growth. Conversely, Ciprofloxacin was less sensitive to bacterial growth rate, both in a homogenous in vitro bacterial population and in a more heterogeneous in vivo bacterial population. The method serves as a platform to test any antibiotic’s dependency upon active in situ bacterial growth. Improved insight into this relationship in vivo could ultimately prove helpful in evaluating future antibacterial strategies.ImportanceMost antibiotics in clinical use exert their effect predominantly on rapidly growing bacterial cells, yet there is a lack of insight into bacterial growth dynamics taking place during infection in vivo. We have applied inexpensive and easily accessible methods for extraction of in situ bacterial growth rate from bacterial chromosome replication during experimental murine infection. This approach not only allows for a better understanding of bacterial growth dynamics taking place during the course of infection, but also serves as a platform to test the activity of different antibiotics as a function of pre-treatment in situ growth rate. The method has the advantage that bacterial growth rate can be probed from a single biological sample, with the potential for extension into clinical use in pre-treatment infected biological specimens. A better understanding of commonly used antibiotics’ level of dependency upon bacterial growth, combined with measurements of in situ bacterial growth rate in infected clinical specimens, could prove helpful in evaluating future antibacterial treatment regimens.

scholarly journals Integrated biphasic growth rate, gene expression, and cell-size homeostasis behaviour of singleB. subtiliscells

2019 ◽  
Author(s):  
Niclas Nordholt ◽  
Johan H. van Heerden ◽  
Frank J. Bruggeman
Keyword(s):  
Cell Cycle ◽  
Growth Rate ◽  
Cell Division ◽  
Protein Expression ◽  
Cell Size ◽  
Cell Cycle Progression ◽  
Bacterial Cells ◽  
Cycle Progression ◽  
Average Cell ◽  
Cycle Dependent

ABSTRACTThe growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these disturbances. Here we quantified growth and constitutive protein expression dynamics of singleBacillus subtiliscells, as a function of cell-cycle-progression. Variation in birth size and growth rate, resulting from unequal cell division, is largely compensated for when cells divide again. We analysed the cell-cycle-dynamics of these compensations and found that both growth and protein expression exhibited biphasic behaviour. During a first phase of variable duration, the absolute rates were approximately constant and cells behaved as sizers. In the second phase, rates increased and growth behaviour exhibited characteristics of a timer-strategy. This work shows how cell-cycle-dependent rate adjustments of biosynthesis and growth are integrated to compensate for physio-logical disturbances caused by cell division.IMPORTANCEUnder constant conditions, bacterial populations can maintain a fixed average cell size and constant exponential growth rate. At the single cell-level, however, cell-division can cause significant physiological perturbations, requiring compensatory mechanisms to restore the growth-related characteristics of individual cells toward that of the average cell. Currently, there is still a major gap in our understanding of the dynamics of these mechanisms, i.e. how adjustments in growth, metabolism and biosynthesis are integrated during the bacterial cell-cycle to compensate the disturbances caused by cell division. Here we quantify growth and constitutive protein expression in individual bacterial cells at sub-cell-cycle resolution. Significantly, both growth and protein production rates display structured and coordinated cell-cycle-dependent dynamics. These patterns reveal the dynamics of growth rate and size compensations during cell-cycle progression. Our findings provide a dynamic cell-cycle perspective that offers novel avenues for the interpretation of physiological processes that underlie cellular homeostasis in bacteria.

scholarly journals Fundamental limits on the rate of bacterial growth

2020 ◽  
Author(s):  
Nathan M. Belliveau ◽  
Grifin Chure ◽  
Christina L. Hueschen ◽  
Hernan G. Garcia ◽  
Jane Kondev ◽  
...  
Keyword(s):  
Growth Rate ◽  
Cell Size ◽  
Bacterial Growth ◽  
Ribosome Biogenesis ◽  
Cell Envelope ◽  
Growth Conditions ◽  
Bacterial Growth Rate ◽  
Proteomic Data ◽  
Primary Determinant ◽  
State Growth

AbstractRecent years have seen an experimental deluge interrogating the relationship between bacterial growth rate, cell size, and protein content, quantifying the abundance of proteins across growth conditions with unprecedented resolution. However, we still lack a rigorous understanding of what sets the scale of these quantities and when protein abundances should (or should not) depend on growth rate. Here, we seek to quantitatively understand this relationship across a collection of Escherichia coli proteomic data covering ≈ 4000 proteins and 36 growth rates. We estimate the basic requirements for steady-state growth by considering key processes in nutrient transport, cell envelope biogenesis, energy generation, and the central dogma. From these estimates, ribosome biogenesis emerges as a primary determinant of growth rate. We expand on this assessment by exploring a model of proteomic regulation as a function of the nutrient supply, revealing a mechanism that ties cell size and growth rate to ribosomal content.

scholarly journals MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

2020 ◽  
Author(s):  
Zachary B. Haiman ◽  
Daniel C. Zielinski ◽  
Yuko Koike ◽  
James T. Yurkovich ◽  
Bernhard O. Palsson
Keyword(s):  
Steady State ◽  
Dynamic Modeling ◽  
Metabolic Networks ◽  
Dynamic Models ◽  
Mass Action ◽  
System Level ◽  
Modeling Tools ◽  
Mass Action Kinetics ◽  
Simulation Engine ◽  
Genome Scale

AbstractMathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner; the Systems Biology Markup Language (SBML) simulation engine. Three case studies demonstrate how to use MASSpy: 1) to simulate dynamics of detailed mechanisms of enzyme regulation; 2) to generate an ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify uncertainty, and 3) to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenge that arise in dynamic modeling of metabolic networks, both at a small and large scale.Author SummaryGenome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

scholarly journals Simplified Approach to Predict Food Safety through the Maximum Specific Bacterial Growth Rate as Function of Extrinsic and Intrinsic Parameters

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Pedro D. Gaspar ◽  
Joel Alves ◽  
Pedro Pinto
Keyword(s):  
Growth Rate ◽  
Food Safety ◽  
Information And Communication Technologies ◽  
Bacterial Growth ◽  
Low Cost ◽  
Extrinsic Factors ◽  
Bacterial Growth Rate ◽  
Modeling Tools ◽  
Simplified Approach ◽  
Food Safety And Quality

Currently, we assist the emergence of sensors and low-cost information and communication technologies applied to food products, in order to improve food safety and quality along the food chain. Thus, it is relevant to implement predictive mathematical modeling tools in order to predict changes in the food quality and allow decision-making for expiration dates. To perform that, the Baranyi and Roberts model and the online tool Combined Database for Predictive Microbiology (Combase) were used to determine the factors that define the growth of different bacteria. These factors applied to the equation that determines the maximum specific growth rate establish a relation between the bacterial growth and the intrinsic and extrinsic factors that define the bacteria environment. These models may be programmed in low-cost wireless biochemical sensor devices applied to packaging and food supply chains to promote food safety and quality through real time traceability.

scholarly journals Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

2014 ◽  
Vol 11 (93) ◽  
pp. 20131100 ◽  
Author(s):  
Peter Banda ◽  
Christof Teuscher ◽  
Darko Stefanovic
Keyword(s):  
State Of The Art ◽  
Chemical Species ◽  
Mass Action ◽  
Mass Action Kinetics ◽  
Dna Strand Displacement ◽  
Autonomous Adaptation ◽  
Biochemical Systems ◽  
Dna Strand ◽  
Logic Functions ◽  
Application Specific

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.

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