Probabilistic automata as a model for epigenesis of cellular networks

Probabilistic automata are compared with deterministic ones in simulations of growing networks made of dividing interconnected cells. On examples of chains, wheels and tree-like structures made of large numbers of cells it is shown that the number of necessary states in the initial generating cell automaton is reduced drastically when the automaton is probabilistic rather than deterministic. Since the price being paid is a decrease in the accuracy of the generated network, conditions under which reasonable compromises can be achieved are studied. They depend on the degree of redundancy of the final network (defined from the complexity of a deterministic automaton capable of generating it with maximum accuracy), on the "entropy" of the generating probabilistic automaton, and on the effects of different inputs on its transition probabilities (as measured by its "'capacity" in the sense of Shannon's information theory). The results are used to discuss and make more precise the notion of biological specificity. It is suggested that the weak metaphor of a genetic program, classically used to account for the role of DNA in specific genetic determinations, is replaced by that of inputs to biochemical probabilistic automata.     

Enzymes as molecular automata: a reflection on some numerical and philosophical aspects of the hypothesis.

Enzymes, by means of their properties of specific recognition and allosteric modulation, are able to integrate many separate processes into systemic units with coherent functions; in a sense, they have to be considered as the true organizers of the cytoplasmic processes. In this respect, the present article describes a simple model, based on binary variables and automata theory, which simulates the basic regulatory performance of the modulated enzyme. The model admits a variety of modifications and improvements; it also suggests some original lines of thought on which to reflect about the organization and collective phenomena of the networks of enzymes. In discussing the connection of this 'molecular automata' hypothesis with other areas of present-day theoretical biology, a fertile panorama of initiatives appear. A special partnership between Information Science (computation) and Biology is developing.     

A cellular automata model of tumor-immune system interactions

We present a hybrid cellular automata-partial differential equation model of moderate complexity to describe the interactions between a growing tumor next to a nutrient source and the immune system of the host organism. The model allows both temporal and two-dimensional spatial evolution of the system under investigation and is comprised of biological cell metabolism rules derived from both the experimental and mathematical modeling literature. We present numerical simulations that display behaviors which are qualitatively similar to those exhibited in tumor-immune system interaction experiments. These include spherical tumor growth, stable and unstable oscillatory tumor growth, satellitosis and tumor infiltration by immune cells. Finally, the relationship between these different growth regimes and key system parameters is discussed.     

A cellular automata model of ligand passage over a protein hydrodynamic landscape

The subject of ligand passage to an active site on a protein is addressed. Current views on the mechanism and the possible role of surface water are discussed. A theory is presented in which the pattern of hydropathic states of protein surface amino acid side chains is invoked as the influence on the relative hydrophobic effects of nearby water. The theory describes a ligand passage through the hydrodynamic near-surface water which exhibits temporary organized cavities resembling the chreodes introduced by Waddington. The passage of the ligand to the active site is facilitated by this dynamic mechanism. Cellular automata models of preferential directional diffusion through these chreodes support the theory. The theory may be invoked to explain a number of ligand-active site observations and serves as an idea for further studies.     

The association of glycolytic enzymes with cellular and model membranes

This article deals with the binding of glycolytic enzymes with membranous or protein subcellular structures. The representative papers of the last three decades dealing with this matter are reviewed. The studies evidencing the binding of some glycolytic enzymes to insoluble subcellular proteins and membranous structures are presented. It is currently generally accepted that the glycolytic enzymes work in some organisation. Such organisation undoubtedly plays a marked role, although still poorly known, in the regulation processes of glycolysis. From this review, the conclusion emerges that the regulatory ability of the binding of glycolytic enzymes to cellular membranes should be added to the list of well-known mechanisms of post-translational regulation of the glycolytic enzymes. Some of the results presented are the background for the hypothesis that planar phospholipid domains in/on the membrane surface are capable of functioning as binding sites for these enzymes. Such binding can modify the conformation state of the enzymes, which results in changes in their kinetic properties; thus, it may function as a regulator of catalytic activity     

Towards a genome-scale kinetic model of cellular metabolism

Background  

Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.     

Giant membrane vesicles as a model to study cellular substrate uptake dissected from metabolism

In order to use giant vesicles for substrate uptake studies in metabolically important tissues, we characterized giant vesicles isolated from heart, liver, skeletal muscle and adipose tissue. We investigated which cell types and which plasma membrane regions are involved in giant vesicle formation and we examined the presence of transporters for metabolic substrates. Analysis of giant vesicles with markers specific for distinct cell types and distinct domains of the plasma membrane reveals that the plasma membrane of parenchymal cells, but not endothelial cells, are the source of the vesicle membranes. In addition, plasma membrane regions enriched in caveolae and involved in docking of recycling vesicles from the endosomal compartment are retained in giant vesicles, indicating that KCl-induced alterations in recycling processes are involved in giant vesicle formation. Giant vesicles contain vesicular lumen consisting of the soluble constituents of the cytoplasm including, fatty-acid binding proteins. Furthermore, giant vesicles isolated from heart, liver, skeletal muscle and adipose tissue are similar in size (10–15 m) and shape and do not contain subcellular organelles, providing the advantage that substrate fluxes in the different organs can be studied independently of the surface/volume ratio but most importantly in the absence of intracellular metabolism.     

Enzymes of carbohydrate metabolism as potential drug targets

The potential for chemotherapeutic exploitation of carbohydrate metabolism in the Trypanosomatidae is reviewed. This review is based largely on discussions held at a meeting of the COST B9 Action, entitled 'Bioenergetics of Protozoan Parasites'. The major questions posed were: which enzymes are the best to target; what further information is required to allow their use for rational drug development; what compounds would constitute the best inhibitors and which of the enzymes of the pentose-phosphate pathway are present inside the glycosomes, as well? Only partial answers could be obtained in many cases, but the interactive discussion between the multidisciplinary group of participants, comprising chemists, biochemists and molecular biologists, provided thought-provoking ideas and will help direct future research.     

A hybrid cellular automaton model of clonal evolution in cancer: the emergence of the glycolytic phenotype

We present a cellular automaton model of clonal evolution in cancer aimed at investigating the emergence of the glycolytic phenotype. In the model each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. This implies that cells might react differently to the environment and when space and nutrients are limited only the fittest cells will survive. With this model we have investigated the impact of the environment on the growth dynamics of the tumour. In particular, we have analysed the influence of the tissue oxygen concentration and extra-cellular matrix density on the dynamics of the model. We found that the environment influences both the growth and the evolutionary dynamics of the tumour. For low oxygen concentration we observe tumours with a fingered morphology, while increasing the matrix density gives rise to more compact tumours with wider fingers. The distribution of phenotypes in the tumour is also affected, and we observe that the glycolytic phenotype is most likely to emerge in a poorly oxygenated tissue with a high matrix density. Our results suggest that it is the combined effect of the oxygen concentration and matrix density that creates an environment where the glycolytic phenotype has a growth advantage and consequently is most likely to appear.     

BorealFireSim: A GIS-based cellular automata model of wildfires for the boreal forest of Quebec in a climate change paradigm

Wildfires are the main cause of forest disturbance in the boreal forest of Canada. Climate change studies forecast important changes in fire cycles, such as increases in fire intensity, severity, and occurrence. The geographical information system (GIS) based cellular automata model, BorealFireSim, serves as a tool to identify future fire patterns in the boreal forest of Quebec, Canada. The model was calibrated using 1950–2010 climate data for the present baseline and forecasts of burning probability up to 2100 were calculated using two RCP scenarios of climate change. Results show that, with every scenario, the mean area burned will likely increase on a provincial scale, while some areas might expect decreases with a low emission scenario. Comparison with other models shows that areas forecasted to have an increase in fire likelihood, overlap with predicted areas of higher vegetation productivity. The results presented in this research aid identifying key areas for fire-dependent species in the near future.     

A stochastic evolutionary model of molecular sequences.

A stochastic evolutionary model of molecular sequences is proposed. The basic forces in evolution are supposed to be mutation and selection. The concept is somewhat similar to Kauffman-Levin's concept of adaptive walks and corresponding analytical expressions have been developed. The selective force is divided into two parts: a slowly-varying part and a rapidly-changing fluctuation. The latter influences the distribution of sequences and results in an equation of motion along the flow line. The former plays a more important role in the emergence of evolutionary order. It is demonstrated that the asymmetry of selective forces would lead to a definite order of the system.     

Ciona intestinalis notochord as a new model to investigate the cellular and molecular mechanisms of tubulogenesis

Biological tubes are a prevalent structural design across living organisms. They provide essential functions during the development and adult life of an organism. Increasing progress has been made recently in delineating the cellular and molecular mechanisms underlying tubulogenesis. This review aims to introduce ascidian notochord morphogenesis as an interesting model system to study the cell biology of tube formation, to a wider cell and developmental biology community. We present fundamental morphological and cellular events involved in notochord morphogenesis, compare and contrast them with other more established tubulogenesis model systems, and point out some unique features, including bipolarity of the notochord cells, and using cell shape changes and cell rearrangement to connect lumens. We highlight some initial findings in the molecular mechanisms of notochord morphogenesis. Based on these findings, we present intriguing problems and put forth hypotheses that can be addressed in future studies.     

Consequences of landscape fragmentation on Lyme disease risk: a cellular automata approach

The abundance of infected Ixodid ticks is an important component of human risk of Lyme disease, and various empirical studies have shown that this is associated, at least in part, to landscape fragmentation. In this study, we aimed at exploring how varying woodland fragmentation patterns affect the risk of Lyme disease, through infected tick abundance. A cellular automata model was developed, incorporating a heterogeneous landscape with three interactive components: an age-structured tick population, a classical disease transmission function, and hosts. A set of simplifying assumptions were adopted with respect to the study objective and field data limitations. In the model, the landscape influences both tick survival and host movement. The validation of the model was performed with an empirical study. Scenarios of various landscape configurations (focusing on woodland fragmentation) were simulated and compared. Lyme disease risk indices (density and infection prevalence of nymphs) differed considerably between scenarios: (i) the risk could be higher in highly fragmented woodlands, which is supported by a number of recently published empirical studies, and (ii) grassland could reduce the risk in adjacent woodland, which suggests landscape fragmentation studies of zoonotic diseases should not focus on the patch-level woodland patterns only, but also on landscape-level adjacent land cover patterns. Further analysis of the simulation results indicated strong correlations between Lyme disease risk indices and the density, shape and aggregation level of woodland patches. These findings highlight the strong effect of the spatial patterns of local host population and movement on the spatial dynamics of Lyme disease risks, which can be shaped by woodland fragmentation. In conclusion, using a cellular automata approach is beneficial for modelling complex zoonotic transmission systems as it can be combined with either real world landscapes for exploring direct spatial effects or artificial representations for outlining possible empirical investigations.     

Chlamydial infection and spatial ascension of the female genital tract: a novel hybrid cellular automata and continuum mathematical model

Sexually transmitted chlamydial infection initially establishes in the endocervix in females, but if the infection ascends the genital tract, significant disease, including infertility, can result. Many of the mechanisms associated with chlamydial infection kinetics and disease ascension are unknown. We attempt to elucidate some of these processes by developing a novel mathematical model, using a cellular automata–partial differential equation model. We matched our model outputs to experimental data of chlamydial infection of the guinea-pig cervix and carried out sensitivity analyses to determine the relative influence of model parameters. We found that the rate of recruitment and action of innate immune cells to clear extracellular chlamydial particles and the rate of passive movement of chlamydial particles are the dominant factors in determining the early course of infection, magnitude of the peak chlamydial time course and the time of the peak. The rate of passive movement was found to be the most important factor in determining whether infection would ascend to the upper genital tract. This study highlights the importance of early innate immunity in the control of chlamydial infection and the significance of motility-diffusive properties and the adaptive immune response in the magnitude of infection and in its ascension.     

Regulation of carbohydrate metabolism in cultured mammalian cells: Energy provision in a glycolytic mutant

The metabolism of radioactively labelled D-glucose, L-glutamine, and L-glutamate has been determined in a glycolytic mutant of Chinese-hamster ovary cells, R1.1.7, and in its parent, CHO-K1. The complete oxidation of glucose via the TCA-cycle is negligible in both cell types, but there is significant oxidation of carbon-1. CHO-K1 cells derive most of their energy from glycolysis and are independent of respiration in the short term. R1.1.7 cells are respiration-dependent and are rapidly killed by respiratory inhibitors. Both cell types oxidize L-glutamine and L-glutamate, but the oxidation of these substrates does not appear sufficient to satisfy completely the energy requirements of R1.1.7 cells.