Is it possible to equilibrate the different “levels” of an imbalanced biological system by acting upon one of them only? Example of the agonistic antagonistic networks

To answer the question in the title, we take as an example the model for the regulation of agonistic antagonistic couples (MRAAC). It is a model that associates 4 non-linear differential equations and allows to simulate balance, imbalance between two state variables, and control, if necessary, by two control variables of the same nature as the state variables: this control is defined as a bilateral strategy (bipolar therapy in the medical field). The super model for the regulation of agonism antagonistic couples (SMRAAC), that may be also considered as an agonistic antagonistic network (AAN), associates several elementary MRAAC's, but the interconnection structure falls equally under the category of the agonistic antagonism (AA). First we indicate what may be the results of the interconnection, favourable or not. In the last case, a control can be performed, by acting upon a sole couple of the SMRAAC, even if other couples were imbalanced (with two AA control variables only). Simulation examples are given. We bring also a new result with the SMRAAC, i.e. the appearance of strange auractors with this model. The general models belonging to the AA phylum should put on a growing importance in the future, because they could be the sole models enable to control some complex systems by avoiding the perverse effects of the unilateral strategies.     

Defining and measuring trophic role similarity in food webs using regular equivalence

We present a graph theoretic model of analysing food web structure called regular equivalence. Regular equivalence is a method for partitioning the species in a food web into "isotrophic classes" that play the same structural roles, even if they are not directly consuming the same prey or if they do not share the same predators. We contrast regular equivalence models, in which two species are members of the same trophic group if they have trophic links to the same set of other trophic groups, with structural equivalence models, in which species are equivalent if they are connected to the exact same other species. Here, the regular equivalence approach is applied to two published food webs: (1) a topological web (Malaysian pitcher plant insect food web) and (2) a carbon-flow web (St. Marks, Florida seagrass ecosystem food web). Regular equivalence produced a more satisfactory set of classes than did the structural approach, grouping basal taxa with other basal taxa and not with top predators. Regular equivalence models provide a way to mathematically formalize trophic position, trophic group and trophic niche. These models are part of a family of models that includes structural models used extensively by ecologists now. Regular equivalence models uncover similarities in trophic roles at a higher level of organization than do the structural models. The approach outlined is useful for measuring the trophic roles of species in food web models, measuring similarity in trophic relations of two or more species, comparing food webs over time and across geographic regions, and aggregating taxa into trophic groups that reduce the complexity of ecosystem feeding relations without obscuring network relationships. In addition, we hope the approach will prove useful in predicting the outcome of predator-prey interactions in experimental studies.     

Computer animations stimulate contagious yawning in chimpanzees

People empathize with fictional displays of behaviour, including those of cartoons and computer animations, even though the stimuli are obviously artificial. However, the extent to which other animals also may respond empathetically to animations has yet to be determined. Animations provide a potentially useful tool for exploring non-human behaviour, cognition and empathy because computer-generated stimuli offer complete control over variables and the ability to program stimuli that could not be captured on video. Establishing computer animations as a viable tool requires that non-human subjects identify with and respond to animations in a way similar to the way they do to images of actual conspecifics. Contagious yawning has been linked to empathy and poses a good test of involuntary identification and motor mimicry. We presented 24 chimpanzees with three-dimensional computer-animated chimpanzees yawning or displaying control mouth movements. The apes yawned significantly more in response to the yawn animations than to the controls, implying identification with the animations. These results support the phenomenon of contagious yawning in chimpanzees and suggest an empathic response to animations. Understanding how chimpanzees connect with animations, to both empathize and imitate, may help us to understand how humans do the same.     

Identification of homologous core structures

Using a large database of protein structure-structure alignments, we test a new method for distinguishing homologous and "analogous" structural neighbors. The homologous neighbors included in the test set show no detectable sequence similarity, but they may be well superimposed and show functional similarity or other evidence of evolutionary relationship. Analogous neighbors also show no sequence similarity and may be well superimposed, but they have different functions and their structural similarity may be the result of convergent evolution. Confirming results of other analyses, we find that remote homologs and analogs are not well distinguished by measures of pairwise structural similarity, including the percentage of identical residues and root-mean-square (RMS) superposition residual. We show, however, that with structure-structure alignments of analogous neighbors rarely superimpose the particular substructure that is shared among homologous neighbors. We call this characteristic substructure the homologous core structure (HCS), and we show that a cross-validated test for presence of the HCS correctly identifies 75% of remote homologs with a false-positive rate of 16% analogs, significantly better than discrimination by RMS or other measures of pairwise similarity. The HCS describes conservation of spatial structure within a protein family in much the way that a sequence motif describes sequence conservation. We suggest that it may be used in the same way, to identify homologous neighbors at greater evolutionary distance than is possible by pairwise comparison.     

A Novel Model of Chronic Wounds: Importance of Redox Imbalance and Biofilm-Forming Bacteria for Establishment of Chronicity

Chronic wounds have a large impact on health, affecting ∼6.5 M people and costing ∼$25B/year in the US alone [1]. We previously discovered that a genetically modified mouse model displays impaired healing similar to problematic wounds in humans and that sometimes the wounds become chronic. Here we show how and why these impaired wounds become chronic, describe a way whereby we can drive impaired wounds to chronicity at will and propose that the same processes are involved in chronic wound development in humans. We hypothesize that exacerbated levels of oxidative stress are critical for initiation of chronicity. We show that, very early after injury, wounds with impaired healing contain elevated levels of reactive oxygen and nitrogen species and, much like in humans, these levels increase with age. Moreover, the activity of anti-oxidant enzymes is not elevated, leading to buildup of oxidative stress in the wound environment. To induce chronicity, we exacerbated the redox imbalance by further inhibiting the antioxidant enzymes and by infecting the wounds with biofilm-forming bacteria isolated from the chronic wounds that developed naturally in these mice. These wounds do not re-epithelialize, the granulation tissue lacks vascularization and interstitial collagen fibers, they contain an antibiotic-resistant mixed bioflora with biofilm-forming capacity, and they stay open for several weeks. These findings are highly significant because they show for the first time that chronic wounds can be generated in an animal model effectively and consistently. The availability of such a model will significantly propel the field forward because it can be used to develop strategies to regain redox balance that may result in inhibition of biofilm formation and result in restoration of healthy wound tissue. Furthermore, the model can lead to the understanding of other fundamental mechanisms of chronic wound development that can potentially lead to novel therapies.     

SINBAD: A neocortical mechanism for discovering environmental variables and regularities hidden in sensory input

We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one since their existence is only indirectly suggested by the sensory input patterns the cortex receives – these variables are hidden. We present a candidate computational strategy for (i) discovering regularity-simplifying environmental variables, (ii) learning the regularities, and (iii) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated, functions of any kind over nonoverlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is to (i) discover and represent one of the regularity-simplifying environmental variables and (ii) learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells – each cell just attending to representation of its variable – can function as a sophisticated and useful inferential model of the outside world.     

Self-organization in the ontogeny of multicellular organisms: a computer simulation

The progress in understanding the patterns of evolution of ontogeny is hindered by the fact that many features of ontogeny are counterintuitive (as well as the features of other processes related to self-organization, self-assembly, and spontaneous increase in complexity). The basic principle of ontogeny of multicellular organisms is that it is the process of self-assembly of ordered multicellular structures by means of coordinated behavior of many individual modules (cells), each of which follows the same set of"rules" encoded in the genome. These rules are based on the genetic regulatory networks. We hypothesize that many specific features of ontogeny that seem nontrivial or enigmatic are, in fact, the inevitable consequences of this basic principle. If so, they do not need special explanations. In order to verify this hypothesis, we developed the computer program "Evo-Devo" based on the above principle. The program is designed to model the self-assembly of ordered multicellular structures from an aggregation of dividing cells that originate from a single original cell (zygote). Each cell follows a set of rules of behavior ("genotype") that can be specified arbitrarily by the experimenter, and is the same for all cells in the embryo (each cell is programmed in exactly the same way as all other cells). It is not allowed to specify rules for groups of cells or for the whole embryo: only local rules that should be followed at the level of a single cell are possible. The analysis of phenotypic implementation of different genotypes revealed several features which are present in the ontogeny of real organisms and are regularly reproduced in the model. These include: inherent stochasticity; inescapable necessity of development of stabilizing adaptations based on negative feedback in order to decrease this stochasticity; equifinality (noise resistance) resulting from these adaptations; the ability of ontogeny to respond to major perturbations by generating new morphological structures that differ from the "normal" ones, but have similar level of complexity; the similarity of phenotypic manifestations of different mutations; channeling of possible evolutionary transformations of ontogeny; Waddington's creodes; high probability of destabilization of ontogeny (e.g., because of mutations); the possibility of a new morphological character to appear initially as a rare anomaly (low penetrance of many mutations); pleiotropy of mutations affecting ontogeny; spontaneous emergence of morphogenetic correlations; integrity of the developing organism. The fact that these features are regularly reproduced in the model implies that they are probably the inevitable consequences of the basic principle of ontogeny of multicellular organisms formulated above.     

Local factors control the community composition of cyanobacteria in lakes while heterotrophic bacteria follow a neutral model

1. Neutral community models are derived from the proposition that basic probabilities of species loss (extinction, emigration) and gain (immigration, speciation) explain biological community structure, such that species with many individuals are very likely to be widespread. Niche models on the other hand assume that interactions between species and differential resource use mediate species coexistence, thus invoking environmental factors to explain community patterns. 2. In this study, we compared neutral and niche models to test how much of the spatial variability of assemblages of heterotrophic bacteria and phytoplankton in 13 lakes they could explain. Analysis of phytoplankton was restricted to cyanobacteria, so that they could be studied with the same molecular fingerprinting method, automated ribosomal intergenic spaces analysis (ARISA), as heterotrophic bacteria. We determined local biotic and abiotic lake variables as well as lake age, glacial history and distance between sites. 3. The neutral community model had a good fit to the community composition of heterotrophic bacteria (R² = 0.69), whereas it could not produce a significant model for the community composition of cyanobacteria. 4. The community composition of cyanobacteria was instead correlated to environmental variables. The best model, a combination of total organic carbon, biomass of eukaryotic phytoplankton, pH and conductivity, could explain 8% of the variation. In contrast, variation in the community composition of heterotrophic bacteria was not predicted by any of the environmental variables. Historical and spatial variables were not correlated to the community composition of either group. 5. The pattern found for heterotrophic bacteria suggests that stochastic processes are important. The correlation of cyanobacteria with local environmental variables alone is consistent with the niche model. We suggest that cyanobacteria, a group of organisms containing bloom‐forming species, may be less likely to fit a neutral community model, since these blooms are usually triggered by a particular combination of environmental conditions.     

Variability order of the latent and the infectious periods in a deterministic SEIR epidemic model and evaluation of control effectiveness

We use distribution theory and ordering of non-negative random variables to study the Susceptible-Exposed-Infectious-Removed (SEIR) model with two control measures, quarantine and isolation, to reduce the spread of an infectious disease. We identify that the probability distributions of the latent period and the infectious period are primary features of the SEIR model to formulate the epidemic threshold and to evaluate the effectiveness of the intervention measures. If the primary features are changed, the conclusions will be altered in an importantly different way. For the latent and infectious periods with known mean values, it is the dilation, a generalization of variance, of their distributions that ranks the effectiveness of these control measures. We further propose ways to set quarantine and isolation targets to reduce the controlled reproduction number below the threshold using observed initial growth rate from outbreak data. If both quarantine and isolation are 100% effective, one can directly use the observed growth rate for setting control targets. If they are not 100% effective, some further knowledge of the distributions is required.     

Adjusting for selection bias in retrospective, case-control studies

Retrospective case–control studies are more susceptibleto selection bias than other epidemiologic studies as by designthey require that both cases and controls are representativeof the same population. However, as cases and control recruitmentprocesses are often different, it is not always obvious thatthe necessary exchangeability conditions hold. Selection biastypically arises when the selection criteria are associatedwith the risk factor under investigation. We develop a methodwhich produces bias-adjusted estimates for the odds ratio. Ourmethod hinges on 2 conditions. The first is that a variablethat separates the risk factor from the selection criteria canbe identified. This is termed the "bias breaking" variable.The second condition is that data can be found such that a bias-correctedestimate of the distribution of the bias breaking variable canbe obtained. We show by means of a set of examples that suchbias breaking variables are not uncommon in epidemiologic settings.We demonstrate using simulations that the estimates of the oddsratios produced by our method are consistently closer to thetrue odds ratio than standard odds ratio estimates using logisticregression. Further, by applying it to a case–controlstudy, we show that our method can help to determine whetherselection bias is present and thus confirm the validity of studyconclusions when no evidence of selection bias can be found.     

The social cognition of social foraging: partner selection by underlying valuation

Humans and other animals have a variety of psychological abilities tailored to the demands of asocial foraging, that is, foraging without coordination or competition with other conspecifics. Human foraging, however, also includes a unique element: the creation of resource pooling systems. In this type of social foraging, people contribute when they have excess resources and receive provisioning when in need. Is this behavior produced by the same psychology as asocial foraging? If so, foraging partners should be judged by the same criteria used to judge asocial patches of resources: the net energetic benefits they provide. The logic of resource pooling speaks against this. Maintaining such a system requires the ability to judge others not on their short-term returns, but on the psychological variables that guide their behavior over the long term. We test this idea in a series of five studies using an implicit measure of categorization. Results showed that (a) others are judged by the costs they incur (a variable not relevant to asocial foraging), whereas (b) others are not judged by the benefits they provide when benefits provided are unrevealing of underlying psychological variables (despite this variable being relevant to asocial foraging). These results are suggestive of a complex psychology designed for both social and asocial foraging.     

A comparative method for studying adaptation to a randomly evolving environment

Most phylogenetic comparative methods used for testing adaptive hypotheses make evolutionary assumptions that are not compatible with evolution toward an optimal state. As a consequence they do not correct for maladaptation. The "evolutionary regression" that is returned is more shallow than the optimal relationship between the trait and environment. We show how both evolutionary and optimal regressions, as well as phylogenetic inertia, can be estimated jointly by a comparative method built around an Ornstein-Uhlenbeck model of adaptive evolution. The method considers a single trait adapting to an optimum that is influenced by one or more continuous, randomly changing predictor variables.     

Models to predict lake annual mean total phosphorus

A lake is a product of processes in its watershed, and these relationships should be empirically quantifiable. Yet few studies have made that attempt. This study quantifies and ranks variables of significance to predict annual mean values of total phosphorus (TP) in small glacial lakes. Several new empirical models based on water chemistry variables, on map parameters of the lake and its catchment, and combinations of such variables are presented. Each variable provides only a limited (statistical) explanation of the variation in annual mean values of TP among lakes. The models are markedly improved by accounting for the distribution of the characteristics (e.g., the mires) in the watershed. The most important map parameters were the proportion of the watershed lying close to the lake covered by rocks and open land (as determined with the drainage area zonation method), relief of the drainage area, lake area and mean depth. These empirical models can be used to predict annual mean TP but only for lakes of the same type. The model based on map parameters (r ²=0.56) appears stable. The effects of other factors/variables not accounted for in the model (like redox-induced internal loading and anthropogenic sources) on the variation in annual mean TP may then be estimated quantitatively by residual analysis. A new mixed model (which combines a dynamic mass-balance approach with empirical knowledge) was also developed. The basic objective was to put the empirical results into a dynamic framework, thereby increasing predictive accuracy. Sensitivity tests of the mixed model indicate that it works as intended. However, comparisons against independent data for annual mean TP show that the predictive power of the mixed model is low, likely because crucial model variables, like sedimentation rate, runoff rate, diffusion rate and precipitation factor, cannot be accurately predicted. These model variables vary among lakes, but this mixed model, like most dynamic models, assumed that they are constants.     

Effects of sex, body size, temperature, and location on the antipredator tactics of free-ranging gartersnakes (Thamnophis sirtalis, Colubridae)

Gartersnakes (Thamnophis sirtalis parietalis) in southern Manitoba are subject to intense predation (primarily by crows) duringtheir spring breeding season. The huge numbers of snakes providea unique opportunity to quantify behavioral traits. We simulatedpredator attacks by "pecking" more than 500 free-ranging snakes,to explore the determinants of snake response. Snakes respondedto a human finger in the same way as they did to a more realisticstimulus (a model crow). A snake's response to attack dependedon several factors, which interacted in complex ways. The primaryinfluences on response were body temperature (warmer snakes tended to flee, whereas colder snakes remained cryptic or flattenedand/or gaped and struck) and sex (males were more likely toflee). Responses also depended on microhabitat (i.e., insidethe winter den versus in adjacent grassland) and on the snake'sprior activity (e.g., courting snakes often ignored our closeapproach). These factors interacted in significant ways; for example, snakes outside the den were smaller and warmer thanthose inside, male snakes were smaller and warmer than females,and mean body temperatures were higher in larger snakes withineach sex. Thus, a snake's body size and its location affectedits defensive response indirectly (via their influence on bodytemperature). Our results differ from those of previous studiesand suggest that antipredator responses in these animals dependin a flexible and complex way upon biotic and abiotic variables.Interactions among these variables also must be consideredbefore we can identify underlying causal processes.     

Summation theorems for flux and concentration control coefficients of dynamic systems

Metabolic control analysis (MCA) was developed to quantify how system variables are affected by parameter variations in a system. In addition, MCA can express the global properties of a system in terms of the individual catalytic steps, using connectivity and summation theorems to link the control coefficients to the elasticity coefficients. MCA was originally developed for steady-state analysis and not all summation theorems have been derived for dynamic systems. A method to determine time-dependent flux and concentration control coefficients for dynamic systems by expressing the time domain as a function of percentage progression through any arbitrary fixed interval of time is reported. Time-dependent flux and concentration control coefficients of dynamic systems, provided that they are evaluated in this novel way, obey the same summation theorems as steady-state flux and concentration control coefficients, respectively.     

The control of multi-muscle systems: human jaw and hyoid movements

A model is presented of sagittal plane jaw and hyoid motion based on the model of motor control. The model, which is implemented as a computer simulation, includes central neural control signals, position- and velocity-dependent reflexes, reflex delays, and muscle properties such as the dependence of force on muscle length and velocity. The model has seven muscles (or muscle groups) attached to the jaw and hyoid as well as separate jaw and hyoid bone dynamics. According to the model, movements result from changes in neurophysiological control variables which shift the equilibrium state of the motor system. One such control variable is an independent change in the membrane potential of -motoneurons (MNs); this variable establishes a threshold muscle length () at which MN recruitment begins. Motor functions may be specified by various combinations of s. One combination of s is associated with the level of coactivation of muscles. Others are associated with motions in specific degrees of freedom. Using the model, we study the mapping between control variables specified at the level of degrees of freedom and control variables corresponding to individual muscles. We demonstrate that commands can be defined involving linear combinations of change which produce essentially independent movements in each of the four kinematic degrees of freedom represented in the model (jaw orientation, jaw position, vertical and horizontal hyoid position). These linear combinations are represented by vectors in space which may be scaled in magnitude. The vector directions are constant over the jaw/hyoid workspace and result in essentially the same motion from any workspace position. The demonstration that it is not necessary to adjust control signals to produce the same movements in different parts of the workspace supports the idea that the nervous system need not take explicit account of musculo-skeletal geometry in planning movements.This article was processed by the author using the LAT_EX style file pljour2 from Springer-Verlag.     

Determination of genotypes of human aldehyde dehydrogenase ALDH2 locus

Virtually all Caucasians have two major aldehyde dehydrogenase isozymes, ALDH1 and ALDH2, in their livers, while approximately 50% of Japanese and other Orientals are "atypical" in that they have only ALDH1 and are missing ALDH2. We previously demonstrated the existence of an enzymatically inactive but immunologically cross-reactive material (CRM) in atypical Japanese livers. Among 10 Japanese livers examined, five had ALDH1 but not ALDH2 isozyme. These are considered to be homozygous atypical at the ALDH2 locus. Four had both ALDH1 and ALDH2 components detected by starch gel electrophoresis, that is, they are apparently usual. However, biochemical and immunological studies revealed that three of these four livers contained CRM. These three livers should be heterozygous atypical in the ALDH2 locus, that is, genotype ALDH2(1)/ALDH2(2). A Japanese liver, as well as control Caucasian livers, had no CRM, and they must be homozygous usual ALDH2(1)/ALDH2(1). Although the number of liver specimens examined is limited, the frequencies of three genotypes determined in this study are compatible with the values calculated based on the genetic model that two common alleles ALDH2(1) and ALDH2(2) for the same locus are codominantly expressed in Orientals. The remaining liver had only ALDH2 isozyme and was missing ALDH1. This type was not previously found in Caucasians and Orientals. The two-dimensional crossed immunoelectrophoresis revealed the existence of a CRM corresponding to ALDH1 in this liver. The abnormality can be considered to be due to structural mutation at the ALDH1 locus producing a defective ALDH1 molecule, although other possibilities such as post-translational modifications are not ruled out.     

A connectionist model of development.

We present a phenomenological modeling framework for development. Our purpose is to provide a systematic method for discovering and expressing correlations in experimental data on gene expression and other developmental processes. The modeling framework is based on a connectionist or "neural net" dynamics for biochemical regulators, coupled to "grammatical rules" which describe certain features of the birth, growth, and death of cells, synapses and other biological entities. We outline how spatial geometry can be included, although this part of the model is not complete. As an example of the application of our results to a specific biological system, we show in detail how to derive a rigorously testable model of the network of segmentation genes operating in the blastoderm of Drosophila. To further illustrate our methods, we sketch how they could be applied to two other important developmental processes: cell cycle control and cell-cell induction. We also present a simple biochemical model leading to our assumed connectionist dynamics which shows that the dynamics used is at least compatible with known chemical mechanisms.     

Integrating protein structures and precomputed genealogies in the Magnum database: Examples with cellular retinoid binding proteins

Background

Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.

Results

We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF.

Conclusion

We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.