Choosing an appropriate modelling framework for analysing multispecies co-culture cell biology experiments

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insuffcient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Spectral analysis of pair-correlation bandwidth: Application to cell biology images

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.

Experiments on the induced breeding of milkfish, Chanos chanos (Forsskal) in 1978

Results indicated that a female having eggs with an average diameter of < 0.7 mm did not respond well to the hormone injections.

Distinguishing between mean-field, moment dynamics and stochastic descriptions of birth-death-movement processes

Mathematical descriptions of birth–death–movement processes are often calibrated to measurements from cell biology experiments to quantify tissue growth rates. Here we describe and analyze a discrete model of a birth–death-movement process applied to a typical two–dimensional cell biology experiment. We present three different descriptions of the system: (i) a standard mean–field description which neglects correlation effects and clustering; (ii) a moment dynamics description which approximately incorporates correlation and clustering effects, and; (iii) averaged data from repeated discrete simulations which directly incorporates correlation and clustering effects. Comparing these three descriptions indicates that the mean–field and moment dynamics approaches are valid only for certain parameter regimes, and that both these descriptions fail to make accurate predictions of the system for sufficiently fast birth and death rates where the effects of spatial correlations and clustering are sufficiently strong. Without any method to distinguish between the parameter regimes where these three descriptions are valid, it is possible that either the mean–field or moment dynamics model could be calibrated to experimental data under inappropriate conditions, leading to errors in parameter estimation. In this work we demonstrate that a simple measurement of agent clustering and correlation, based on coordination number data, provides an indirect measure of agent correlation and clustering effects, and can therefore be used to make a distinction between the validity of the different descriptions of the birth–death–movement process.

Modelling the movement of interacting cell populations: A moment dynamics approach

Mathematical models describing the movement of multiple interacting subpopulations are relevant to many biological and ecological processes. Standard mean-field partial differential equation descriptions of these processes suffer from the limitation that they implicitly neglect to incorporate the impact of spatial correlations and clustering. To overcome this, we derive a moment dynamics description of a discrete stochastic process which describes the spreading of distinct interacting subpopulations. In particular, we motivate our model by mimicking the geometry of two typical cell biology experiments. Comparing the performance of the moment dynamics model with a traditional mean-field model confirms that the moment dynamics approach always outperforms the traditional mean-field approach. To provide more general insight we summarise the performance of the moment dynamics model and the traditional mean-field model over a wide range of parameter regimes. These results help distinguish between those situations where spatial correlation effects are sufficiently strong, such that a moment dynamics model is required, from other situations where spatial correlation effects are sufficiently weak, such that a traditional mean-field model is adequate.

Introducció al disseny experimental: pràctiques de laboratori i de camp per a biòleg [versió html]

Aquest llibre pretén ser una guia útil per al disseny d'experiments científics en el camp de la biologia, tant pel que fa a les pràctiques de laboratori com al treball de camp. El llibre s'estructura en tres grans apartats: Mètode científic i disseny experimental, Estudi manipulatiu, i Estudi observacional o mesuratiu

Introducció al disseny experimental: pràctiques de laboratori i de camp per a biòlegs [versió pdf]

Aquest llibre pretén ser una guia útil per al disseny d'experiments científics en el camp de la biologia, tant pel que fa a les pràctiques de laboratori com al treball de camp. El llibre s'estructura en tres grans apartats: Mètode científic i disseny experimental, Estudi manipulatiu, i Estudi observacional o mesuratiu

Biologia de Acromyrmex balzani Emery, 1890 (Hymenoptera, Formicidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Experiments into biology-technology interaction

In this paper a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is the use of electrode technology, where either a connection is made directly with the cerebral cortex and/or nervous system or where implants into the human body are involved. The paper also considers robots that have biological brains in which human neurons can be employed as the sole thinking machine for a real world robot body.

Elementary experiments in radiation biology /

"Biology and medicine (TID-4500), 21st ed.

Problems, projects, and experiments in biology,

Contains "References."

Sampling Methods for Exploring Between Subject Variability in Cardiac Electrophysiology Experiments

Between-subject and within-subject variability is ubiquitous in biology and physiology and understanding and dealing with this is one of the biggest challenges in medicine. At the same time it is difficult to investigate this variability by experiments alone. A recent modelling and simulation approach, known as population of models (POM), allows this exploration to take place by building a mathematical model consisting of multiple parameter sets calibrated against experimental data. However, finding such sets within a high-dimensional parameter space of complex electrophysiological models is computationally challenging. By placing the POM approach within a statistical framework, we develop a novel and efficient algorithm based on sequential Monte Carlo (SMC). We compare the SMC approach with Latin hypercube sampling (LHS), a method commonly adopted in the literature for obtaining the POM, in terms of efficiency and output variability in the presence of a drug block through an in-depth investigation via the Beeler-Reuter cardiac electrophysiological model. We show improved efficiency via SMC and that it produces similar responses to LHS when making out-of-sample predictions in the presence of a simulated drug block.

Successful fertility experiments with cryopreserved spermatozoa of barramundi, Lates calcarifer (Bloch), using dimethylsulfoxide and glycerol as cryoprotectants

The fertility of cryopreserved Lates calcarifer sperm was studied to increase the availability of semen for routine fertilization of stripped eggs and to provide a tool for selective breeding. Semen diluted (1:4 v/v) and frozen (-196 degrees C) with 5% dimethylsulfoxide (DMSO) or 10% glycerol (final concentration) as cryoprotectants was used to inseminate freshly stripped ova. Frozen-thawed sperm were motile for about 4 min after being mixed with seawater. In the DMSO medium, post-thaw sperm activation was immediate after dilution with seawater, but in the glycerol medium maximum motility intensity was delayed for up to 1 min. When eggs and sperm were mixed before the addition of seawater, semen frozen with DMSO as cryoprotectant gave a mean hatch rate (84.1%) no different (P > 0.05) from that of unfrozen semen diluted with Ringer's solution (80.7%) or with DMSO (83.7%), but higher (P < 0.05) than that of semen frozen with glycerol (60.9%). Adding sperm to seawater 30 s before mixing with eggs did not improve the fertility of sperm cryopreserved with glycerol. Eggs inseminated with glycerol-cryoprotected sperm showed higher mortality during incubation than those inseminated with DMSO-cryoprotected sperm. Sperm held in liquid nitrogen for 90 days with DMSO as cryoprotectant yielded acceptable fertilization and hatching rates with semen-to-ova ratios of up to 1:100 (v/v) , and produced fish with no apparent abnormalities over a 29-day period after hatch. These results show that cryopreservation of L. calcarifer sperm is feasible and well suited to a variety of hatchery purposes.

Heat stress effects on grain sorghum productivity-biology and modelling

Heat stress can cause sterility in sorghum and the anticipated increased frequency of high temperature events implies increasing risk to sorghum productivity in Australia. Here we summarise our research on specific varietal attributes associated with heat stress tolerance in sorghum and evaluate how they might affect yield outcomes in production environments by a crop simulation analysis. We have recently conducted a range of controlled environment and field experiments to study the physiology and genetics of high temperature effects on growth and development of sorghum. Sorghum seed set was reduced by high temperature effects (>36-38oC) on pollen germination around flowering, but genotypes differed in their tolerance to high temperature stress. Effects were quantified in a manner that enabled their incorporation into the APSIM sorghum crop model. Simulation analysis indicated that risk of high temperature damage and yield loss depended on sowing date, and variety. While climate trends will exacerbate high temperature effects, avoidance by crop management and genetic tolerance seems possible.

Developmental biology and prey preference of Diomus notescens Blackburn (Coleoptera: Coccinellidae): A predator of Aphis gossypii Glover (Hemiptera: Aphididae)

The minute two-spotted ladybeetle, Diomus notescens Blackburn is a common predator of aphids and other pests in Australian agricultural crops, however little is known about the biology of D. notescens. The aim of this study was to provide information on the life cycle of this predator and improve our understanding of its biological control potential, particularly against one of the major pests of cotton, Aphis gossypii Glover. In laboratory experiments, juvenile development, prey consumption, as well as adult lifespan and fecundity were studied. Results from this study revealed that D. notescens could successfully complete development on A. gossypii, which at 25 °C required 21 days and during this period they each consume 129 ± 5.2 aphids. At 25 °C adult lifespan was 77 ± 9.6 days, with a mean daily prey consumption of 28 ± 1.8 aphids and a mean daily fecundity of 8 ± 0.5 eggs. Net reproductive rate was estimated as 187 ± 25.1 females and the intrinsic rate of increase was estimated as 0.14. Juvenile development was recorded at four constant temperatures (15, 21, 26 and 27 °C) and using a linear model, the lower threshold for D. notescens development was estimated to be 10 ± 0.6 °C with 285 ± 4.7 degree days required to complete development. A prey choice experiment studying predation rates revealed a strong preference for A. gossypii nymphs compared to Bemisia tabaci Gennadius eggs.