Interaction and fusion dynamics between cellular blebs

Membrane blebbing, as a mechanism for cells to regulate their internal pressure and membrane tension, is believed to play important roles in processes such as cell migration, spreading and apoptosis. However, the fundamental question of how different blebs interact with each other during their life cycles remains largely unclear. Here, we report a combined theoretical and experimental investigation to examine how the growth and retraction of a cellular bleb are influenced by neighboring blebs as well as the fusion dynamics between them. Specifically, a boundary integral model was developed to describe the shape evolution of cell membrane during the blebbing/retracting process. We showed that a drop in the intracellular pressure will be induced by the formation of a bleb whose retraction then restores the pressure level. Consequently, the volume that a second bleb can reach was predicted to heavily depend on its initial weakened size and the time lag with respect to the first bleb, all in quantitative agreement with our experimental observations. In addition, it was found that as the strength of membrane-cortex adhesion increases, the possible coalescence of two neighboring blebs changes from smooth fusion to abrupt coalescence and eventually to no fusion at all. Phase diagrams summarizing the dependence of such transition on key physical factors, such as the intracellular pressure and bleb separation, were also obtained.     

Whole-cell modeling framework in which biochemical dynamics impact aspects of cellular geometry

A mathematical framework for modeling biological cells from a physicochemical perspective is described. Cells modeled within this framework consist of at least two regions, including a cytosolic volume encapsulated by a membrane surface. The cytosol is viewed as a well-stirred chemical reactor capable of changing volume while the membrane is assumed to be an oriented 2-D surface capable of changing surface area. Two physical properties of the cell, namely volume and surface area, are determined by (and determine) the reaction dynamics generated from a set of chemical reactions designed to be occurring in the cell. This framework allows the modeling of complex cellular behaviors, including self-replication. This capability is illustrated by constructing two self-replicating prototypical whole-cell models. One protocell was designed to be of minimal complexity; the other to incorporate a previously reported well-known mechanism of the eukaryotic cell cycle. In both cases, self-replicative behavior was achieved by seeking stable physically possible oscillations in concentrations and surface-to-volume ratio, and by synchronizing the period of such oscillations to the doubling of cytosolic volume and membrane surface area. Rather than being enforced externally or artificially, growth and division occur naturally as a consequence of the assumed chemical mechanism operating within the framework.     

Exploring the cellular network of metabolic flexibility in the adipose tissue

Background

Metabolic flexibility is the ability of cells to change substrates for energy production based on the nutrient availability and energy requirement. It has been shown that metabolic flexibility is impaired in obesity and chronic diseases such as type 2 diabetes mellitus, cardiovascular diseases, and metabolic syndrome, although, whether it is a cause or an effect of these conditions remains to be elucidated.

Main body

In this paper, we have reviewed the literature on metabolic flexibility and curated pathways and processes resulting in a network resource to investigate the interplay between these processes in the subcutaneous adipose tissue. The adipose tissue has been shown to be responsible, not only for energy storage but also for maintaining energy homeostasis through oxidation of glucose and fatty acids. We highlight the role of pyruvate dehydrogenase complex–pyruvate dehydrogenase kinase (PDC-PDK) interaction as a regulatory switch which is primarily responsible for changing substrates in energy metabolism from glucose to fatty acids and back. Baseline gene expression of the subcutaneous adipose tissue, along with a publicly available obesity data set, are visualised on the cellular network of metabolic flexibility to highlight the genes that are expressed and which are differentially affected in obesity.

Conclusion

We have constructed an abstracted network covering glucose and fatty acid oxidation, as well as the PDC-PDK regulatory switch. In addition, we have shown how the network can be used for data visualisation and as a resource for follow-up studies.

     

Effects of gibberellin on the cellular dynamics of dwarf pea internode development

This study analysed the dynamics of cell production and extension, and how these were affected by applied gibberellic acid (GA₃), during internode development in dwarf peas (Pisum sativum L. cv. Meteor). Image analysis was used to obtain cell number and length data for entire cell columns along the epidermis, the two outermost cortical layers, and the pith, from internode 7, over a time period covering the whole of the internode's growth phase. For a few days following the inception of an internode at the shoot apex, little further growth occurred, and there was no significant effect of GA₃ on cell division or cell extension. The subsequent growth of the internode was stimulated more than fourfold by GA₃ as a result of the production of more than twice the number of cells, which were twice as long. At least 96.5% of the cells of the mature internode were actually formed within the internode itself during this period of growth, demonstrating that the internode cells themselves represent the morphogenetic site of response to GA₃. Mitoses and cell extension occurred along the full length of the internode throughout its development. The daily changes in cell numbers were modelled by the Richards function, and manipulations of the fitted functions to reveal time trends of absolute and specific cell production rates were performed for each stem tissue. The increase in cell numbers in the +GA₃ plants was brought about by an increase in the rate of cell production, over a shorter time interval; specific cell production rates declined continuously from initial rapid rates in the +GA₃ epidermis and pith, but declined more slowly in the cortex. The control (−GA₃) epidermis and cortex cells exhibited a constant specific cell production rate (i.e. purely exponential) for several days. Cell extension rates were calculated so as to compensate for the size-reduction effects of concurrent cell division. These calculations confirmed that `real' cell extension rates were higher in the +GA₃ internodes. Models of the cellular controls of internode growth, based on the estimated dynamics of cell division and extension, are discussed. Received: 1 July 1997 / Accepted: 30 July 1997     

On the identifiability of metabolic network models

A major problem for the identification of metabolic network models is parameter identifiability, that is, the possibility to unambiguously infer the parameter values from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models. Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use singular value decomposition to detect identifiability problems and reduce the model to an identifiable approximation by a principal component analysis approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only $4$ out of $31$ reactions, and $37$ out of $100$ parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models, such as generalized mass-action (power-law) models. Moreover, it provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.     

The use of social network analysis to describe the effect of immune activation on group dynamics in pigs

The immune system can influence social motivation with potentially dire consequences for group-housed production animals, such as pigs. The aim of this study was to test the effect of a controlled immune activation in group-housed pigs, through an injection with lipopolysaccharide (LPS) and an intervention with ketoprofen on centrality parameters at the individual level. In addition, we wanted to test the effect of time relative to the injection on general network parameters in order to get a better understanding of changes in social network structures at the group level. 52 female pigs (11–12 weeks) were allocated to four treatments, comprising two injections: ketoprofen-LPS (KL), ketoprofen-saline (KS), saline-LPS (SL) and saline-saline (SS). Social behaviour with a focus on damaging behaviour was observed continuously in 10 × 15 min bouts between 0800 am and 1700 pm 1 day before (baseline) and two subsequent days after injection. Activity was scan-sampled every 5 min for 6 h after the last injection in the pen. Saliva samples were taken for cortisol analysis at baseline and at 4, 24, 48, 72 h after the injections. A controlled immune activation affected centrality parameters for ear manipulation networks at the individual level. Lipopolysaccharide-injected pigs had a lower in-degree centrality, thus, received less interactions, 2 days after the challenge. Treatment effects on tail manipulation and fighting networks were not observed at the individual level. For networks of manipulation of other body parts, in-degree centrality was positively correlated with cortisol response at 4 h and lying behaviour in the first 6 h after the challenge in LPS-injected pigs. Thus, the stronger the pigs reacted to the LPS, the more interactions they received in the subsequent days. The time in relation to injection affected general network parameters for ear manipulation and fighting networks at the group level. For ear manipulation networks, in-degree centralisation was higher on the days following injection, thus, certain individuals in the pen received more interactions than the rest of the group compared to baseline. For fighting networks, betweenness decreased on the first day after injection compared to baseline, indicating that network connectivity increased after the challenge. Networks of tail manipulation and manipulation of other body parts did not change on the days after injection at the group level. Social network analysis is a method that can potentially provide important insights into the effects of sickness on social behaviour in group-housed pigs.     

Understanding the dynamics of cellular responsiveness to modifications of metabolic substrates in perifusion

A novel microperifusion system with capabilities for continuous, real-time, potentiometric monitoring of extracellular hydrogen ion concentration has been used to define the response of HeLa cells to abrupt changes in extracellular energy sources or introduction of an inhibitor of glycolysis. Glycolytic inhibition, induced by removal of glucose or introduction of iodoacetate, each led to a rapid, continuous decrease in acid release. The response to iodoacetate took longer than removal of glucose, perhaps due to the time required for binding and activation. Once inhibition began, however, the rate of change was greater than following glucose removal. Conversely, recovery time following iodoacetate inhibition was much slower than with glucose removal. Unlike the response to short-term glucose depletion, a second pulse of iodoacetate resulted in a faster response followed by an even longer recovery time. The response to switching between glucose and glutamine began almost without evident delay. The response patterns revealed that HeLa cells prefer glutamine to glucose, but, in the presence of both energy sources, some glucose continues to be used. In summary, these results indicate that continuous, real-time monitoring of the kinetics of hydrogen-ion release can be used to gain new insights into the dynamics of cellular response to perturbations of extracellular energy sources. © 1994 Wiley-Liss, Inc.     

Upgrading of low-boiling fraction of bio-oil in supercritical methanol and reaction network

In this work, the low-boiling fraction (LBF) of bio-oil was used as feed stock. LBF is a very complex mixture, and the three groups in LBF: acids, aldehydes and phenols, are primarily responsible for deterioration in the quality. The upgrading reactions were carried out over Pt/Al₂(SiO₃)₃, Pt/C or Pt/MgO in supercritical methanol. It is demonstrated that supercritical condition can greatly facilitate the esterification process, and after 6 h reaction, all the acids can be converted into esters even without adding any catalyst. The total amount of the three groups left in products was much less exhibited on Pt supported on active carbon and MgO in the presence of hydrogen. By investigating the model reactions, the relations between the representative compounds and major products were identified, and the conversion scheme of the upgrading reactions is proposed.     

Neuronal network analyses: premises, promises and uncertainties

Neuronal networks assemble the cellular components needed for sensory, motor and cognitive functions. Any rational intervention in the nervous system will thus require an understanding of network function. Obtaining this understanding is widely considered to be one of the major tasks facing neuroscience today. Network analyses have been performed for some years in relatively simple systems. In addition to the direct insights these systems have provided, they also illustrate some of the difficulties of understanding network function. Nevertheless, in more complex systems (including human), claims are made that the cellular bases of behaviour are, or will shortly be, understood. While the discussion is necessarily limited, this issue will examine these claims and highlight some traditional and novel aspects of network analyses and their difficulties. This introduction discusses the criteria that need to be satisfied for network understanding, and how they relate to traditional and novel approaches being applied to addressing network function.     

Control by cytokinins of the cellular behavior in the plate meristem of zucchini cotyledons

The temporal and spatial effects of exogenous cytokinins on both cell expansion and division activity in the plate meristem of cultured zucchini cotyledons were studied. N ⁶-benzylaminopurine (1–100 μM) and N-(2-chloro-4pyridyl)-N′-phenylurea (4PU-30) (0.1–100 μM) greatly stimulated the cell growth and division. They provoked multiple cell cycles, formation of larger clusters of daughter cells and an increase of the final number of cells. Both cytokinins led to earlier achievement of final cotyledon size and shortened the cell doubling time. By contrast to the purine cytokinin, phenylurea cytokinin 4PU-30 enlarged the cotyledon predominantly in length. Zeatin and kinetin were less effective, particularly in stimulating cell expansion. In low concentrations, all cytokinins were more effective in stimulating division activity rather than expansion. The cells in the cotyledon margins displayed a higher division activity, especially when treated with exogenous cytokinins. The final cotyledon and cluster areas were not of the strict proportional dependence upon the number of their cells. These results provide a novel example where stimulated cell division fails to evoke a respective increase in the final organ size.     

The dynamics of production and destruction: Analytic insight into complex behavior

This paper analytically explores the properties of simple differential-difference equations that represent dynamic processes with feedback dependent on prior states of the system. Systems with pure negative and positive feedback are examined, as well as those with mixed (positive/negative) feedback characteristics. Very complex time dependent behaviors may arise from these processes. Indeed, the same mechanism may, depending on system parameters and initial conditions, produce simple, regular, repetitive patterns and completely irregular random-like fluctuations.For the differential-delay equations considered here we prove the existence of: (i) stable and unstable limit cycles, where the stable cycles may have an arbitrary number of extrema per period; and (ii) chaos, meaning the presence of infinitely many periodic solutions of different period and of infinitely many irregular and mixing solutions.     

Tetraspanins interweave EV secretion,endosomal network dynamics and cellular metabolism

Tetraspanin proteins organize membrane nanodomains related to cell adhesion and migration. An essential feature conserved along the superfamily is their cone-shaped tertiary structure, which allows tetraspanins to be enriched in highly curved membrane structures. Their conical shape, together with their ability to associate to transmembrane receptors and to bind to cystoskeletal and signaling scaffolds, are key in their ability to regulate endosomal network dynamics and Extracellular Vesicle biogenesis and cargo selection. Recent evidence suggests that tetraspanins have a relevant impact in mitochondria turnover and regulation of cellular metabolism. In this review we highlight those reports that point to tetraspanins as key regulators in the communication between the endosomal network, EVs and the cellular metabolism.     

Adenylate kinase I does not affect cellular growth characteristics under normal and metabolic stress conditions

Adenylate kinase (AK)-catalyzed phosphotransfer is essential in the maintenance of cellular energetic economy in cells of fully differentiated tissues with highly variable energy demand, such as muscle and brain. To investigate if AK isoenzymes have a comparable function in the energy-demand management of proliferating cells, AK1 and AK1beta were expressed in mouse neuroblastoma N2a cells and in human colon carcinoma SW480 cells. Glucose deprivation, galactose feeding, and metabolic inhibitor tests revealed a differential energy dependency for these two cell lines. N2a cells showed a faster proliferation rate and strongest coupling to mitochondrial activity, SW480 proliferation was more dependent on glycolysis. Despite these differences, ectopic expression of AK1 or AK1beta did not affect their growth characteristics under normal conditions. Also, no differential effects were seen under metabolic stress upon treatment with mitochondrial and glycolytic inhibitors in in vitro culture or in solid tumors grown in vivo. Although many intimate connections have been revealed between cell death and metabolism, our results suggest that AK1- or AK1beta-mediated high-energy phosphoryl transfer is not a modulating factor in the survival of tumor cells during episodes of metabolic crisis.     

Dissipative particle dynamics simulation of the relaxation behaviour of a triblock copolymer supramolecular network

Since block copolymers self-assemble into various nanostructures and these are widely used in soft materials such as cosmetics and paints, these continue to be the subjects of fundamental studies that progress new technologies. ABA triblock copolymers self-organise into supramolecular networks in which crosslinked structures change upon heating and other external stimulation. Supramolecular networks that consist of ABA triblock copolymers have three components: bridges, loops and dangles. These supramolecular networks have structural regions (clusters) in which end blocks of polymer chains are aggregated and connected by the internal blocks of the polymer chains. Despite of the importance of relaxation behaviour during the measurement and control of polymer materials, the molecular-level behaviour of these systems has not been addressed. We observed pull-out phenomenon that the ends of these polymers detach from clusters, and estimated characteristic detachment times by counting the number of detachments and compared it with the time of longest relaxation in that system.     

On the dynamics of predation risk perception for a vigilant forager

There has been much recent interest in modelling epidemics on networks, particularly in the presence of substantial clustering. Here, we develop pairwise methods to answer questions that are often addressed using epidemic models, in particular: on the basis of potential observations early in an outbreak, what can be predicted about the epidemic outcomes and the levels of intervention necessary to control the epidemic? We find that while some results are independent of the level of clustering (early growth predicts the level of ‘leaky’ vaccine needed for control and peak time, while the basic reproductive ratio predicts the random vaccination threshold) the relationship between other quantities is very sensitive to clustering.     

Coherent and robust modulation of a metabolic network by cytoskeletal organization and dynamics

In order to investigate the influence of cytoskeletal organization and dynamics on cellular biochemistry, a mathematical model was formulated based on our own experimental evidence. The model couples microtubular protein (MTP) dynamics to the glycolytic pathway and its branches: the Krebs cycle, ethanolic fermentation, and the pentose phosphate (PP) pathway. Results show that the flux through glycolysis coherently and coordinately increases or decreases with increased or decreased levels of polymerized MTP, respectively. The rates of individual enzymatic steps and metabolite concentrations change with the polymeric status of MTP throughout the metabolic network. Negative control is exerted by the PP pathway on the glycolytic flux, and the extent of inhibition depends inversely on the polymerization state of MTP, i.e. a high degree of polymerization relieves the negative control. The stability of the model's steady state dynamics for a wide range of variation of metabolic parameters increased with the degree of polymerized MTP. The findings indicate that the organization of the cytoskeleton bestows coherence and robustness to the coordination of cellular metabolism.     

Chaotic dynamics may determine the effect of inter-patch migration on metapopulation survival

A simple, strategic model of a system of habitat fragments connected by conservation corridors is presented. The intrinsic dynamics of the population on each fragment are stochastic. In addition, at each generation there is a probability of a catastrophic event occurring which affects all the habitat fragments by greatly reducing the size of the population on each. Global extinction is considered to occur when all the populations simultaneously fall below a threshold value. If the intrinsic dynamics on each fragment are simple cycles or a stable equilibrium, then the addition of conservation corridors does not reduce the frequency of global extinction. This is because migration between fragments induces their populations to have values which are similar to each other. However, if the intrinsic population dynamics are chaotic then the probability of global extinction is greatly reduced by the introduction of conservation corridors. Although local extinction is likely, the chaos acts to oppose the synchronising effect of migration. Often a subset of the populations survive a catastrophe and can recolonize the other patches.     

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors emerge from interactions among individuals.Despite the importance of,and growing interest in,the temporal dynamics of social interactions,it is not clear how to quantify changes in interactions over time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems.