Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Spatial interaction models: from human geography to plant-herhivore interactions

The spatial pattern of defoliation by mammalian herbivores across vegetation mosaics bas been frequently discussed, but rarely spatially quantified. Here we considered the role of plant-herbivore interactions in determining the spatial distribution of shrub defoliation by a large mammalian herbivore across a grass-shrub mosaic.
We investigated the spatial pattern of heather defoliation by sheep in heather-grass mosaics. Heather-grass mosaics are two-phased vegetation mosaics, in which a spatially localized plant community (grass) fulfils nutritional needs, whilst a spatially extensive plant community (heather) meets energy requirements but is nutritionally marginal.
We used a spatial analysis method, originating from human geography, to show that heather defoliation was not spread across the mosaic homogeneously, but that the spatial pattern was determined by geometric characteristics of the mosaic, grazing intensity, and the contrast between preferred and less preferred communities.
The spatial analysis method proved to be a powerful tool to describe the spatial pattern of shrub defoliation. Applications of the method are explored and the implications of the spatial distribution of shrub defoliation are discussed.     

Evolution of biological interaction networks: from models to real data

We are beginning to uncover common mechanisms leading to the evolution of biological networks. The driving force behind these advances is the increasing availability of comparative data in several species.     

Network archaeology: uncovering ancient networks from present-day interactions

What proteins interacted in a long-extinct ancestor of yeast? How have different members of a protein complex assembled together over time? Our ability to answer such questions has been limited by the unavailability of ancestral protein-protein interaction (PPI) networks. To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. Our likelihood-based method finds a probable previous state of the graph by applying an assumed growth model backwards in time. This approach retains node identities so that the history of individual nodes can be tracked. Using this methodology, we estimate protein ages in the yeast PPI network that are in good agreement with sequence-based estimates of age and with structural features of protein complexes. Further, by comparing the quality of the inferred histories for several different growth models (duplication-mutation with complementarity, forest fire, and preferential attachment), we provide additional evidence that a duplication-based model captures many features of PPI network growth better than models designed to mimic social network growth. From the reconstructed history, we model the arrival time of extant and ancestral interactions and predict that complexes have significantly re-wired over time and that new edges tend to form within existing complexes. We also hypothesize a distribution of per-protein duplication rates, track the change of the network''s clustering coefficient, and predict paralogous relationships between extant proteins that are likely to be complementary to the relationships inferred using sequence alone. Finally, we infer plausible parameters for the model, thereby predicting the relative probability of various evolutionary events. The success of these algorithms indicates that parts of the history of the yeast PPI are encoded in its present-day form.     

Microbial interactions with germanium

Germanium is a semi-precious, widely-dispersed, and biologically non-essential metal with considerable potential for application in the fields of electronics, computer engineering and medicine. Research on the physiology and genetics of germanium toxicity and accumulation in microorganisms has received scant attention. An understanding of these aspects is important from both fundamental and applied points of view. This review will examine the diverse range of interactions between germanium and microbial species.     

Microbial interactions with aluminium

Although aluminium is the most abundant metal in the Earth's crust, it lacks biological functions and shows a low bioavailability. Acid rain, however, solubilizes aluminium to toxic levels. Most research on the biological effects of aluminium has been centred on the analysis of aluminium-tolerant plants as well as its possible relationship with neurological disorders in humans. Also, several studies have been reported concerning aluminium effects on microorganisms, with more interest directed to cyanobacteria, soil bacteria and mycorrhizal fungi. Competition with iron and magnesium, and binding to DNA, membranes or cell walls are considered the main toxic effects of aluminium in microbes.     

Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions

Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called "querying quantitative logic models" (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor.     

Microbial interactions in alcoholic beverages

International Microbiology - This review examines the different types of interactions between the microorganisms involved in the fermentation processes of alcoholic beverages produced all over the...     

Implications of flexible foraging for interspecific interactions: lessons from simple models

1.  Some types of flexible foraging behaviours were incorporated into ecological thought in the 1960s, but the population dynamical consequences of such behaviours are still poorly understood.
2.  Flexible foraging-related traits can be classified as shifts in general and specific foraging effort, and shifts in general and specific defense.
3.  Many flexible foraging behaviours suggested by theory have received very little empirical attention, and empirical techniques used to compare the magnitudes of behavioural and non-behavioural responses to predation are likely to have overestimated the behavioural components.
4.  Adaptively flexible foraging in theory causes significant changes in the forms of consumer functional responses and generates a variety of indirect interactions. These can alter fundamental ecological processes, such as co-existence of competitors, and top-down or bottom-up effects in food webs.
5.  Many aspects of flexible foraging are still largely unknown, including the issues of how to represent the dynamics of such phenotypically plastic traits, how flexible traits in multiple species interact, what types of adaptive movements occur in metacommunities, and how adaptive behaviours influence evolutionary change.
6.  Population dynamics in large food webs may be less dependent on behavioural flexibility than in small webs because species replacement may preempt some potential types of behavioural change within species.     

RMBNToolbox: random models for biochemical networks

Background  

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.     

Microbial synergists pathogenic to Lymantria dispar: Chitinolytic and fermentative bacterial interactions

A chitinolytic bacterium isolated from a healthy gypsy moth, Lymantria dispar, larva was shown to be pathogenic to larvae when administered per os after growth on chitin broth. However, the chitin hydrolase produced by this isolate had a pH optimum for activity of 5.5 and the high alkalinity in the mesenteron of L. dispar larvae severely limited chitinolysis of the midgut lining. Fermentative, nonpathogenic, acid-producing bacteria isolated from healthy gypsy moth larvae effectively lowered larval mesenteron pH when administered per os and the combination of fermentative isolates with a crude culture aliquot of the chitinolytic strain produced a synergistic increase in mortality over either dose administered by itself. Increased mortality was also observed for most fermentative strains when they were combined with crude supernatants of centrifuged cultures of the chitinolytic strain, although these combinations proved less effective than when fermenters were added to the whole-culture aliquots of the chitinolytic strain. In vitro studies showed that other bacteria isolated from environments foreign to that of the gypsy moth could ferment carbohydrates with acid production at an alkaline pH; however, in vivo studies demonstrated that these bacteria were incapable both of poising larval midgut pH and of enhancing mortality when added to chitinolytic bacteria.