The conceptual approach to quantitative modeling of guard cells

Much of the 70% of global water usage associated with agriculture passes through stomatal pores of plant leaves. The guard cells, which regulate these pores, thus have a profound influence on photosynthetic carbon assimilation and water use efficiency of plants. We recently demonstrated how quantitative mathematical modeling of guard cells with the OnGuard modeling software yields detail sufficient to guide phenotypic and mutational analysis. This advance represents an all-important step toward applications in directing “reverse-engineering” of guard cell function for improved water use efficiency and carbon assimilation. OnGuard is nonetheless challenging for those unfamiliar with a modeler’s way of thinking. In practice, each model construct represents a hypothesis under test, to be discarded, validated or refined by comparisons between model predictions and experimental results. The few guidelines set out here summarize the standard and logical starting points for users of the OnGuard software.     

Computational model for autophagic vesicle dynamics in single cells

Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically-induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.     

Southern right whales show no behavioral response to low noise levels from a nearby unmanned aerial vehicle

Unmanned aerial vehicles (UAVs) are increasingly used for wildlife research and monitoring, but little information exists on their potential effect on marine mammals. We assessed the effects of a UAV on the behavior of southern right whales (Eubalaena australis) in Australia. Focal follows of ten right whale mother-calf pairs were conducted using a theodolite. Control data were recorded for 30 min, and then a DJI Inspire 1 Pro was flown above the whales for 10 min at 5 m altitude. Potential changes to horizontal behavior (swim speed and turning angle) and surfacing pattern (interbreath intervals) were investigated by comparing mother-calf behavior before and during UAV approaches. Changes in respiration rate were used to quantify energetic effects. We also explored acoustic cue perceptibility of the UAV at 5, 10, and 30 m altitude, by measuring the received UAV underwater noise level on whales equipped with acoustic tags (DTAGs). The received noise levels were 86.0 ± 3.9 dB re 1 μPa, while the measured ambient noise was 80.7 ± 7.3 dB re 1 μPa in the same frequency band (100–1,500 Hz). No behavioral response to the UAV was observed. This provides support for UAVs as a noninvasive tool to study baleen whale behavior and ecophysiology.     

Novel nucleic acid detection strategies based on CRISPR-Cas systems: From construction to application

Beyond their widespread application as genome-editing and regulatory tools, clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) systems also play a critical role in nucleic acid detection due to their high sensitivity and specificity. Recently developed Cas family effectors have opened the door to the development of new strategies for detecting different types of nucleic acids for a variety of purposes. Precise and efficient nucleic acid detection using CRISPR-Cas systems has the potential to advance both basic and applied biological research. In this review, we summarize the CRISPR-Cas systems used for the recognition and detection of specific nucleic acids for different purposes, including the detection of genomic DNA, nongenomic DNA, RNA, and pathogenic microbe genomes. Current challenges and further applications of CRISPR-based detection methods will be discussed according to the most recent developments.     

Converting differential-equation models of biological systems to membrane computing

This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand–receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.     

Exploring the concept of interaction computing through the discrete algebraic analysis of the Belousov–Zhabotinsky reaction

Interaction computing (IC) aims to map the properties of integrable low-dimensional non-linear dynamical systems to the discrete domain of finite-state automata in an attempt to reproduce in software the self-organizing and dynamically stable properties of sub-cellular biochemical systems. As the work reported in this paper is still at the early stages of theory development it focuses on the analysis of a particularly simple chemical oscillator, the Belousov–Zhabotinsky (BZ) reaction. After retracing the rationale for IC developed over the past several years from the physical, biological, mathematical, and computer science points of view, the paper presents an elementary discussion of the Krohn–Rhodes decomposition of finite-state automata, including the holonomy decomposition of a simple automaton, and of its interpretation as an abstract positional number system. The method is then applied to the analysis of the algebraic properties of discrete finite-state automata derived from a simplified Petri net model of the BZ reaction. In the simplest possible and symmetrical case the corresponding automaton is, not surprisingly, found to contain exclusively cyclic groups. In a second, asymmetrical case, the decomposition is much more complex and includes five different simple non-abelian groups whose potential relevance arises from their ability to encode functionally complete algebras. The possible computational relevance of these findings is discussed and possible conclusions are drawn.     

Fitting dynamic growth models of biological phenomena from sample observations through Gaussian diffusion processes

This paper addresses the building of stochastic models that adequately describe dynamic phenomena and, in particular, those that occur in the Biosciences. In this context, the empirical fitting of a Gaussian diffusion process from sample data of a dynamic growth phenomenon is considered. In order to do this, a methodology based on approximations to its mean and variance functions is presented. Finally, several applications based on simulated and real data have been carried out.     

Inflorescences: concepts,function, development and evolution

Background

Inflorescences are complex structures with many functions. At anthesis they present the flowers in ways that allow for the transfer of pollen and optimization of the plant''s reproductive success. During flower and fruit development they provide nutrients to the developing flowers and fruits. At fruit maturity they support the fruits prior to dispersal, and facilitate effective fruit and seed dispersal. From a structural point of view, inflorescences have played important roles in systematic and phylogenetic studies. As functional units they facilitate reproduction, and are largely shaped by natural selection.

Scope

The papers in this Special Issue bridge the gap between structural and functional approaches to inflorescence evolution. They include a literature review of inflorescence function, an experimental study of inflorescences as essential contributors to the display of flowers, and two papers that present new methods and concepts for understanding inflorescence diversity and for dealing with terminological problems. The transient model of inflorescence development is evaluated in an ontogenetic study, and partially supported. Four papers present morphological and ontogenetic studies of inflorescence development in monophyletic groups, and two of these evaluate the usefulness of Hofmeister''s Rule and inhibitory fields to predict inflorescence structure. In the final two papers, Bayesian and Monte-Carlo methods are used to elucidate inflorescence evolution in the Panicoid grasses, and a candidate gene approach is used in an attempt to understand the evolutionary genetics of inflorescence evolution in the genus Cornus (Cornaceae). Taken as a whole, the papers in this issue provide a glimpse of contemporary approaches to the study of the structure, development, and evolution of inflorescences, and suggest fruitful new directions for research.     

Evaluation of some fungal diseases and yield of groundnut in groundnut-based cropping systems

A study on the evaluation of some fungal diseases and yield of groundnut in groundnut-based cropping systems was conducted in 2002 and 2003 planting seasons. Analysis of variance indicated that intercropping was highly significant on leaf spot disease severity 0.76; 0.75, rust 2.75; 2.69, as well as percentage defoliation 78.42%; 78.10% in 2002 and 2003, respectively. Plant population was significant on leaf spot severity 4.52, 4.60 rust 2.76; 366 and defoliation 226.5; 441.1 fungal as well as yield, while interactions were not significant on the fungal diseases and yield. Sole groundnut recorded significantly high severity of the fungal diseases investigated but low yield, when groundnut was intercropped with maize and melon and recorded the lowest yield in 2002 and 2003 respectively. 250,000 plants/ha recorded the lowest severity of the diseases investigated, while 444,444 plants/ha recorded the highest. Sole groundnut also recorded the highest percentage defoliation 79.37%, 79.25% when groundnut was intercropped with maize recorded the lowest 77.06%; 77.60%. 250,000 plants/ha had the lowest defoliation when 444,444 plants/ha had the highest 80.75% 82.13% in 2002 and 2003, respectively. Sole groundnut recorded the lowest in the majority of yields when intercropped with maize and with melon recorded a high yield and yield components in 2002 and 2003, respectively. The microorganisms identified were Cercospora spp., Aspergillus linked and Blastomyces.