Concept: Malthusian growth model
Recent empirical research questions the validity of using Malthusian theory in preindustrial England. Using real wage and vital rate data for the years 1650-1881, I provide empirical estimates for a different region: Northern Italy. The empirical methodology is theoretically underpinned by a simple Malthusian model, in which population, real wages, and vital rates are determined endogenously. My findings strongly support the existence of a Malthusian economy wherein population growth decreased living standards, which in turn influenced vital rates. However, these results also demonstrate how the system is best characterized as one of weak homeostasis. Furthermore, there is no evidence of Boserupian effects given that increases in population failed to spur any sustained technological progress.
The maximum exponential growth rate, the Malthusian parameter (MP), is commonly used as a measure of fitness in experimental studies of adaptive evolution and of the effects of antibiotic resistance and other genes on the fitness of planktonic microbes. Thanks to automated, multi-well optical density plate readers and computers, with little hands-on effort investigators can readily obtain hundreds of estimates of MPs in less than a day. Here we compare estimates of the relative fitness of antibiotic susceptible and resistant strains of E. coli, Pseudomonas aeruginosa and Staphylococcus aureus based on MP data obtained with automated multi-well plate readers with the results from pairwise competition experiments. This leads us to question the reliability of estimates of MP obtained with these high throughput devices and the utility of these estimates of the maximum growth rates to detect fitness differences.
In this paper, we develop methods for inferring tumor growth rates from the observation of tumor volumes at two time points. We fit power law, exponential, Gompertz, and Spratt’s generalized logistic model to five data sets. Though the data sets are small and there are biases due to the way the samples were ascertained, there is a clear sign of exponential growth for the breast and liver cancers, and a 2/3’s power law (surface growth) for the two neurological cancers.
Cell proliferation assays are routinely used to explore how a low-density monolayer of cells grows with time. For a typical cell line with a doubling time of 12 h (or longer), a standard cell proliferation assay conducted over 24 h provides excellent information about the low-density exponential growth rate, but limited information about crowding effects that occur at higher densities. To explore how we can best detect and quantify crowding effects, we present a suite of in silico proliferation assays where cells proliferate according to a generalised logistic growth model. Using approximate Bayesian computation we show that data from a standard cell proliferation assay cannot reliably distinguish between classical logistic growth and more general non-logistic growth models. We then explore, and quantify, the trade-off between increasing the duration of the experiment and the associated decrease in uncertainty in the crowding mechanism.
Polymer blends of poly(vinylalcohol, PVA) and poly(methylvinylether-co-maleic anhydride, PMVE/MA) were formulated and their viscoelastic and mucoadhesive properties characterised. The viscoelastic and mucoadhesive properties were dependent on polymer concentration, molecular weight of PVA and PVA:PMVE/MA ratio. Alteration of these properties allowed platforms to be designed to offer defined rheological and mucoadhesive properties, properties that could not be achieved using monopolymeric platforms. A strong correlation was noted between the modulus of the polymeric blends and mucoadhesion. After storage, the polymeric blends underwent rheological structuring (ageing) with an attendant enhancement of mucoadhesion. In certain blends containing the highest molecular weight of PVA (146-186 kDa), storage ultimately resulted in an increase and then a significant decrease in the rheological and mucoadhesive properties, the latter phenomenon being accredited to polymer recrystallisation. Ageing of the rheological and mucoadhesive properties was modelled using an exponential growth model, allowing predictions of the storage period associated with the maxima in viscoelastic and mucoadhesive properties. These observations highlight the possible implications whenever interactive polymeric blends are employed in drug delivery. Caution is therefore urged whenever a formulation strategy based on interactive polymer blends is employed to ensure that ageing is fully understood and mathematically characterised. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci.
The study of population growth reveals that the behaviors that follow the power law appear in numerous biological, demographical, ecological, physical and other contexts. Parabolic models appear to be realistic approximations of real-life replicator systems, while hyperbolic models were successfully applied to problems of global demography and appear relevant in quasispecies and hypercycle modeling. Nevertheless, it is not always clear why non-exponential growth is observed empirically and what possible origins of the non-exponential models are. In this paper the power equation is considered within the frameworks of inhomogeneous population models; it is proven that any power equation describes the total population size of a frequency-dependent model with Gamma-distributed Malthusian parameter. Additionally, any super-exponential equation describes the dynamics of inhomogeneous Malthusian density-dependent population model. All statistical characteristics of the underlying inhomogeneous models are computed explicitly. The results of this analysis show that population heterogeneity can be a reasonable explanation for power law accurately describing total population growth.
We studied the appearance of fruit body primordia, the growth of individual fruit bodies and the development of the consecutive flushes of the crop. Relative growth, measured as cap expansion, was not constant. It started extremely rapidly, and slowed down to an exponential rate with diameter doubling of 1.7 d until fruit bodies showed maturation by veil breaking. Initially many outgrowing primordia were arrested, indicating nutritional competition. After reaching 10 mm diameter, no growth arrest occurred; all growing individuals, whether relatively large or small, showed an exponential increase of both cap diameter and biomass, until veil breaking. Biomass doubled in 0.8 d. Exponential growth indicates the absence of competition. Apparently there exist differential nutritional requirements for early growth and for later, continuing growth. Flushing was studied applying different picking sizes. An ordinary flushing pattern occurred at an immature picking size of 8 mm diameter (picking mushrooms once a day with a diameter above 8 mm). The smallest picking size yielded the highest number of mushrooms picked, confirming the competition and arrested growth of outgrowing primordia: competition seems less if outgrowing primordia are removed early. The flush duration (i.e. between the first and last picking moments) was not affected by picking size. At small picking size, the subsequent flushes were not fully separated in time but overlapped. Within 2 d after picking the first individuals of the first flush, primordia for the second flush started outgrowth. Our work supports the view that the acquisition of nutrients by the mycelium is demand rather than supply driven. For formation and early outgrowth of primordia, indications were found for an alternation of local and global control, at least in the casing layer. All these data combined, we postulate that flushing is the consequence of the depletion of some unknown specific nutrition required by outgrowing primordia.
In this article we shall trace the historical development of tumour growth laws, which in a quantitative fashion describe the increase in tumour mass/volume over time. These models are usually formulated in terms of differential equations that relate the growth rate of the tumour to its current state, and range from the simple one-parameter exponential growth model, to more advanced models that contain a large number of parameters. Understanding the assumptions and consequences of such models is important, since they often underpin more complex models of tumour growth. The conclusion of this brief survey is that although much improvement has occurred over the last century, more effort and new models are required if we are to understand the intricacies of tumour growth.