Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

Background  

Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.     

A unified stoichiometric model for oxidative and oxidoreductive growth of yeasts

Yeasts degrade glucose through different metabolic pathways, where the choice of the pathway is dependent on the nature of the limitation in the various substrates. When oxygen is limiting in addition to glucose, yeasts often grow according to a mixture of oxidative and reductive metabolism. Oxygen may be limiting either by supply or by inherent biological restrictions such as the respiratory bottleneck in Saccharomyces cerevisiae or by both. A unified model incorporating both supply and biological limitations is proposed for the quantitative prediction of growth rates, consumption and production rates, as well as key metabolite concentrations during mixed oxidoreductive metabolism occuring as a result of such oxygen limitations. This simple unstructured model can be applied to different yeast strains while at the same time requiring a minimum number of measured parameters. "Estimators" are utilized in order to predict the presence of supply-side or biological limitations. The values of these estimators also characterize the relative importance of oxidative to total metabolism. Results from the aerobic and oxygen-limited chemostat cultures were used to corroborate the model predictions. During these experiments, the heat released by the yeast cultures was also monitored on-line. The model correctly predicted the overall stoichiometry, steady-state concentrations, and rates including heat dissipation rates measured in the various situations of oxygen limitations. Direct continuous measurements such as heat can be used in conjunction with the unified model for on-line proces control. (c) 1992 John Wiley & Sons, Inc.     

JCoDA: a tool for detecting evolutionary selection

Background  

The incorporation of annotated sequence information from multiple related species in commonly used databases (Ensembl, Flybase, Saccharomyces Genome Database, Wormbase, etc.) has increased dramatically over the last few years. This influx of information has provided a considerable amount of raw material for evaluation of evolutionary relationships. To aid in the process, we have developed JCoDA (Java Codon Delimited Alignment) as a simple-to-use visualization tool for the detection of site specific and regional positive/negative evolutionary selection amongst homologous coding sequences.     

Tests and model selection for the general growth curve model

The model considered here is a generalized multivariate analysis of variance model useful especially for many types of growth curve problems including biological growth and technology substitution. It is defined as Yp x N = Xp x m tau m x r Ar x N + epsilon p x N, where tau is unknown, and X and A are known design matrices of ranks m less than p and r less than N, respectively. Furthermore, the columns of epsilon are independent p-variate normal with mean vector 0 and common covariance matrix sigma. In general, p is the number of time (or spatial) points observed on each of the N cases, (m - 1) is the degree of polynomial in time, and r is the number of groups. The main focus of this paper is the selection of models for the general growth curve model with regard to the covariance matrix sigma. Likelihood ratio tests and selection procedures based on sample reuse and predictions are proposed. Special emphasis is on the serial covariance structure for sigma, which has been shown to be quite important in the prediction of biological data and technology substitution data. One-population and K-population problems are considered. Some of the results are illustrated with two sets of biological data.     

Analysis of a continuum model for root growth

We give an analysis by asymptotic expansions of a model for root growth. The model assumes growth is due entirely to mechanical forces created by the flux of water from the surrounding soil. We attempt to suggest ways in which the model could account for experimentally observed patterns of root growth. The model predicts that the rate of growth of the root is independent of the extensibility of the root wall.     

Scanning for a unified model for translational repression by microRNAs

MicroRNAs (miRNAs) silence target mRNAs by inhibiting translation and subsequently initiating mRNA decay. The mechanism by which miRNAs silence translation is still poorly understood, with a number of competing models proposed. In this issue of The EMBO Journal, Kuzuo?lu‐Öztürk et al ( 2016 ) investigated miRNA silencing in human and insect cells. Their data support a model whereby miRNAs inhibit translation initiation. However, in contrast to several recent reports, their data suggest that translational inhibition is independent of 43S ribosomal subunit scanning, eIF4A translation factor activity, and 5′UTR secondary structure.     

Modeling invasive species spread in Lake Champlain via evolutionary computations

We use a reaction diffusion equation, together with a genetic algorithm approach for model selection to develop a general modeling framework for biological invasions. The diffusion component of the reaction diffusion model is generalized to include dispersal and advection. The reaction component is generalized to include both linear and non-linear density dependence, and Allee effect. A combination of the reaction diffusion and genetic algorithm is able to evolve the most parsimonious model for invasive species spread. Zebra mussel data obtained from Lake Champlain, which demarcates the states of New York and Vermont, is used to test the appropriateness of the model. We estimate the minimum wave spread rate of Zebra mussels to be 22.5 km/year. In particular, the evolved models predict an average northward advection rate of 60.6 km/year (SD ± 1.9), which compares very well with the rate calculated from the known hydrologic residence time of 60 km/year. A combination of a reaction diffusion model and a genetic algorithm is, therefore, able to adequately describe some of the hydrodynamic features of Lake Champlain and the spread of a typical invasive species—Zebra mussels within the lake.     

Towards a unified model of neuroendocrine-immune interaction

Although the neuroendocrine system has immunomodulating potential, studies examining the relationship between stress, immunity and infection have, until recently, largely been the preserve of behavioural psychologists. Over the last decade, however, immunologists have begun to increasingly appreciate that neuroendocrine-immune interactions hold the key to understanding the complex behaviour of the immune system in vivo. The nervous, endocrine and immune systems communicate bidirectionally via shared messenger molecules variously called neurotransmitters, cytokines or hormones. Their classification as neurotransmitters, cytokines or hormones is more serendipity than a true reflection of their sphere of influence. Rather than these systems being discrete entities we would propose that they constitute, in reality, a single higher-order entity. This paper reviews current knowledge of neuroendocrine-immune interaction and uses the example of T-cell subset differentiation to show the previously under-appreciated importance of neuroendocrine influences in the regulation of immune function and, in particular, Th1/Th2 balance and diurnal variation there of.     

Global dynamics of a selection model for the growth of a population with genotypic fertility differences

A population growth model is considered for a one locus two allele problem with selection based entirely on fertility differences. A local stability analysis is carried out for the critical points — which include possible polymorphic states — of the resulting nonlinear differential equations. The methods of dynamical systems theory are applied to obtain limiting genotypic proportions for every initial state. Thus the results are global and there are no periodic solutions.Research for this paper was partially supported by the National Science and Engineering Research Council of Canada Grant NSERC A-8130Research for this paper was partially supported by the National Science and Engineering Research Council of Canada Grant NSERC A-4823Research supported by NSF Grant MCS 7901069. A portion of the work was carried out while the author was a Visiting Professor at the University of Utah, Salt Lake City, Utah     

A unified model for budburst of trees

Accurate plant phenology (seasonal plant activity driven by environmental factors) models are vital tools for ecosystem simulation models and for predicting the response of ecosystems to climate change. Since the early 1970s, efforts have concentrated on predicting phenology of the temperate and boreal forests because they represent one-third of the carbon captured in plant ecosystems and they are the principal ecosystems with seasonal patterns of growth on Earth (one-fifth of the plant ecosystems area). Numerous phenological models have been developed to predict the growth timing of temperate or boreal trees. They are in general empirical, nonlinear and non-nested. For these reasons they are particularly difficult to fit, to test and to compare with each other. The methodological difficulties as well as the diversity of models used have greatly slowed down their improvement. The aim of this study was to show that the most widely used models simulating vegetative or reproductive phenology of trees are particular cases of a more general model. This unified model has three main advantages. First, it allows for a direct estimation of (i) the response of bud growth to either chilling or forcing temperatures and (ii) the periods when these temperatures affect the bud growth. Second, it can be simplified according to standard statistical tests for any particular species. Third, it provides a standardized framework for phenological models, which is essential for comparative studies as well as for robust model identification.     

The replicator dynamics for multilevel selection in evolutionary games

Journal of Mathematical Biology - We consider a stochastic model for evolution of group-structured populations in which interactions between group members correspond to the Prisoner’s Dilemma...     

Analysis of a mathematical model for the growth of tumors

 In this paper we study a recently proposed model for the growth of a nonnecrotic, vascularized tumor. The model is in the form of a free-boundary problem whereby the tumor grows (or shrinks) due to cell proliferation or death according to the level of a diffusing nutrient concentration. The tumor is assumed to be spherically symmetric, and its boundary is an unknown function r=s(t). We concentrate on the case where at the boundary of the tumor the birth rate of cells exceeds their death rate, a necessary condition for the existence of a unique stationary solution with radius r=R ₀ (which depends on the various parameters of the problem). Denoting by c the quotient of the diffusion time scale to the tumor doubling time scale, so that c is small, we rigorously prove that (i) lim inf _t→∞ s(t)>0, i.e. once engendered, tumors persist in time. Indeed, we further show that (ii) If c is sufficiently small then s(t)→R ₀ exponentially fast as t→∞, i.e. the steady state solution is globally asymptotically stable. Further, (iii) If c is not “sufficiently small” but is smaller than some constant γ determined explicitly by the parameters of the problem, then lim sup _t→∞ s(t)<∞; if however c is “somewhat” larger than γ then generally s(t) does not remain bounded and, in fact, s(t)→∞ exponentially fast as t→∞. Received: 25 February 1998 / Revised version: 30 April 1998     

The evolutionary forces maintaining a wild polymorphism of Littorina saxatilis: model selection by computer simulations

Two rocky shore ecotypes of Littorina saxatilis from north-west Spain live at different shore levels and habitats and have developed an incomplete reproductive isolation through size assortative mating. The system is regarded as an example of sympatric ecological speciation. Several experiments have indicated that different evolutionary forces (migration, assortative mating and habitat-dependent selection) play a role in maintaining the polymorphism. However, an assessment of the combined contributions of these forces supporting the observed pattern in the wild is absent. A model selection procedure using computer simulations was used to investigate the contribution of the different evolutionary forces towards the maintenance of the polymorphism. The agreement between alternative models and experimental estimates for a number of parameters was quantified by a least square method. The results of the analysis show that the fittest evolutionary model for the observed polymorphism is characterized by a high gene flow, intermediate-high reproductive isolation between ecotypes, and a moderate to strong selection against the nonresident ecotypes on each shore level. In addition, a substantial number of additive loci contributing to the selected trait and a narrow hybrid definition with respect to the phenotype are scenarios that better explain the polymorphism, whereas the ecotype fitnesses at the mid-shore, the level of phenotypic plasticity, and environmental effects are not key parameters.     

Modeling aggregation and growth processes in an algal population model: analysis and computations

 Aggregation, the formation of large particles through multiple collision of smaller ones is a highly visible phenomena in oceanic waters which can control material flux to the deep sea. Oceanic aggregates more than 1 cm in diameter have been observed and are frequently described to consist of phytoplankton cells as well as other organic matter such as fecel pellets and mucus nets from pteropods. Division of live phytoplankton cells within an aggregate can also increase the size of aggregate (assuming some daughter cells stay in the aggregate) and hence could be a significant factor in speeding up the formation process of larger aggregate. Due to the difficulty of modeling cell division within aggregates, few efforts have been made in this direction. In this paper, we propose a size structured approach that includes growth of aggregate size due to both cell division and aggregation. We first examine some basic mathematical issues associated with the development of a numerical simulation of the resulting algal aggregation model. The numerical algorithm is then used to examine the basic model behavior and present a comparison between aggregate distribution with and without division in aggregates. Results indicate that the inclusion of a growth term in aggregates, due to cell division, results in higher densities of larger aggregates; hence it has the impact to speed clearance of organic matter from the surface layer of the ocean. Received 1 July 1994; received in revised form 23 February 1996     

An evolutionary hybrid cellular automaton model of solid tumour growth

We propose a cellular automaton model of solid tumour growth, in which each cell is equipped with a micro-environment response network. This network is modelled using a feed-forward artificial neural network, that takes environmental variables as an input and from these determines the cellular behaviour as the output. The response of the network is determined by connection weights and thresholds in the network, which are subject to mutations when the cells divide. As both available space and nutrients are limited resources for the tumour, this gives rise to clonal evolution where only the fittest cells survive. Using this approach we have investigated the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. The results show that the oxygen concentration affects the selection pressure, cell population diversity and morphology of the tumour. A low oxygen concentration in the tissue gives rise to a tumour with a fingered morphology that contains aggressive phenotypes with a small apoptotic potential, while a high oxygen concentration in the tissue gives rise to a tumour with a round morphology containing less evolved phenotypes. The tissue oxygen concentration thus affects the tumour at both the morphological level and on the phenotype level.     

A unified model for apical caspase activation

Apoptosis is orchestrated by the concerted action of caspases, activated in a minimal two-step proteolytic cascade. Existing data suggests that apical caspases are activated by adaptor-mediated clustering of inactive zymogens. However, the mechanism by which apical caspases achieve catalytic competence in their recruitment/activation complexes remains unresolved. We explain that proximity-induced activation of apical caspases is attributable to dimerization. Internal proteolysis does not activate these apical caspases but is a secondary event resulting in partial stabilization of activated dimers. Activation of caspases-8 and -9 occurs by dimerization that is fully recapitulated in vitro by kosmotropes, salts with the ability to stabilize the structure of proteins. Further, single amino acid substitutions at the dimer interface abrogate the activity of caspases-8 and -9 introduced into recipient mammalian cells. We propose a unified caspase activation hypothesis whereby apical caspases are activated by dimerization of monomeric zymogens.