Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach

Bhowmick, Amiya Ranjan; Chattopadhyay, Goutami; Bhattacharya, Sabyasachi

doi:10.1007/s10867-013-9336-6

Cited by 23 publications

(32 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bacterial growth was monitored every 30 min at 600 nm for 18 h using a multimode plate reader (TECAN Infinite M200 Pro). The area under the curve (AUC) [50,51] was calculated with the R package Growth Curver as described by Sprouffske and Wagner [52].…”

Section: Growth Curvesmentioning

confidence: 99%

Understanding gold toxicity in aerobically-grown Escherichia coli

et al. 2020

View full text Add to dashboard Cite

Background: There is an emerging field to put into practice new strategies for developing molecules with antimicrobial properties. In this line, several metals and metalloids are currently being used for these purposes, although their cellular effect(s) or target(s) in a particular organism are still unknown. Here we aimed to investigate and analyze Au 3+ toxicity through a combination of biochemical and molecular approaches. Results: We found that Au 3+ triggers a major oxidative unbalance in Escherichia coli, characterized by decreased intracellular thiol levels, increased superoxide concentration, as well as by an augmented production of the antioxidant enzymes superoxide dismutase and catalase. Because ROS production is, in some cases, associated with metal reduction and the concomitant generation of gold-containing nanostructures (AuNS), this possibility was evaluated in vivo and in vitro. Conclusions: Au 3+ is toxic for E. coli because it triggers an unbalance of the bacterium's oxidative status. This was demonstrated by using oxidative stress dyes and antioxidant chemicals as well as gene reporters, RSH concentrations and AuNS generation.

show abstract

Section: Growth Curvesmentioning

confidence: 99%

Understanding gold toxicity in aerobically-grown Escherichia coli

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We use the routine lsqcurvefit of Matlab R , that solves nonlinear curvefitting (data-fitting) problems in least-squares sense, where the predicted values are obtained from the nonlinear equations of the models (8), (9) and (13). Once determined the parameters of the three models, in order to evaluate the attained approximations, we compare the growth curves by using the ISRP growth metric, introduced in Bhowmick et al [6]. For the three growth models, in Fig.…”

Section: Data Analytic Examplesmentioning

confidence: 99%

Analysis of a growth model inspired by Gompertz and Korf laws, and an analogous birth-death process

Crescenzo

Spina

2016

Mathematical Biosciences

View full text Add to dashboard Cite

We propose a new deterministic growth model which captures certain features of both the Gompertz and Korf laws. We investigate its main properties, with special attention to the correction factor, the relative growth rate, the inflection point, the maximum specific growth rate, the lag time and the threshold crossing problem. Some data analytic examples and their performance are also considered. Furthermore, we study a stochastic counterpart of the proposed model, that is a linear time-inhomogeneous birth-death process whose mean behaves as the deterministic one. We obtain the transition probabilities, the moments and the population ultimate extinction probability for this process. We finally treat the special case of a simple birth process, which better mimics the proposed growth model.

show abstract

“…These five parameterisations represent different categories of relative growth rate (RGR) (i.e. the body mass increase per unit mass per unit time) 77 Parameterisation of the Exponential model. When = A 1 relative growth rate is constant and growth is purely exponential, which yields the solution www.nature.com/scientificreports www.nature.com/scientificreports/ = − m m k t t exp( ( ))…”

Section: Methodsmentioning

confidence: 99%

A new framework for growth curve fitting based on the von Bertalanffy Growth Function

Lee

Atkinson

Hirst

et al. 2020

Sci Rep

View full text Add to dashboard Cite

All organisms grow. Numerous growth functions have been applied to a wide taxonomic range of organisms, yet some of these models have poor fits to empirical data and lack of flexibility in capturing variation in growth rate. We propose a new VBGF framework that broadens the applicability and increases flexibility of fitting growth curves. This framework offers a curve-fitting procedure for five parameterisations of the VBGF: these allow for different body-size scaling exponents for anabolism (biosynthesis potential), besides the commonly assumed 2/3 power scaling, and allow for supraexponential growth, which is at times observed. This procedure is applied to twelve species of diverse aquatic invertebrates, including both pelagic and benthic organisms. We reveal widespread variation in the body-size scaling of biosynthesis potential and consequently growth rate, ranging from isomorphic to supra-exponential growth. This curve-fitting methodology offers improved growth predictions and applies the VBGF to a wider range of taxa that exhibit variation in the scaling of biosynthesis potential. Applying this framework results in reliable growth predictions that are important for assessing individual growth, population production and ecosystem functioning, including in the assessment of sustainability of fisheries and aquaculture. Body size is a fundamental characteristic of all organisms. Body size has received much attention from biologists owing to its widespread covariation with a plethora of ecological and evolutionary functions and physiological traits 1-9. Understanding growth (i.e. the changes in body size over time) is fundamental to many areas of biology, as well as being crucial for industries based on animal and plant production. Accurate growth predictions are fundamental to aquaculture and production industries, for example, over-or underestimating species growth will result in unreliable predictions of production and hence revenue and profit for producers 10. For example, modelling the growth rates of farmed tiger prawns, Penaeus monodon, under varying environmental conditions including temperature and pond age, allows for predictions of production rates, and hence profitability, in new farming locations 11. Moreover, gaining knowledge of growth parameters can help to inform management plans, which are required for effective conservation management of target species in aquaculture or reducing pressure on natural populations 12. For example, growth models have predicted parameter values associated with slow growth and long lifespan in Stichopus vastus which has helped inform restrictions on catch quotas to allow natural populations to recover 13. In addition, understanding growth dynamics has been shown to be important for bivalve species in aquaculture and their use in mitigating eutrophication in coastal areas, for example, gaining accurate growth predictions of soft tissue can help the efficiency of mussel production that is required for eutrophic coastal waters 14. Methods for fitting growth curves to em...

show abstract

Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach

Cited by 23 publications

References 55 publications

Understanding gold toxicity in aerobically-grown Escherichia coli

Understanding gold toxicity in aerobically-grown Escherichia coli

Analysis of a growth model inspired by Gompertz and Korf laws, and an analogous birth-death process

A new framework for growth curve fitting based on the von Bertalanffy Growth Function

Contact Info

Product

Resources

About