Protein biosynthesis in mitochondria

Translation, that is biosynthesis of polypeptides in accordance with information encoded in the genome, is one of the most important processes in the living cell, and it has been in the spotlight of international research for many years. The mechanisms of protein biosynthesis in bacteria and in the eukaryotic cytoplasm are now understood in great detail. However, significantly less is known about translation in eukaryotic mitochondria, which is characterized by a number of unusual features. In this review, we summarize current knowledge about mitochondrial translation in different organisms while paying special attention to the aspects of this process that differ from cytoplasmic protein biosynthesis.     

Error propagation in intracellular information transfer

The translation of genetic information from polynucleotides to proteins is mediated by proteins themselves. The cyclic nature of this process admits the possibility of a feedback of errors which may become lethal to the cell. During ageing, it is known that cells in some organisms show increased levels of altered or defective protein, and it has been suggested that the propagation of macromolecular errors may play a causative role in the progressive loss of homeostasis with increasing age. Experimental studies of this hypothesis have so far been inconclusive, and it is shown that theoretical models of intracellular error propagation throw important light on the determinants of stability within the translation apparatus and can improve the design of future experiments, as well as aid in their interpretation.Critical features of any model are its assumptions about the amino acid sequence changes required for a component of the translation apparatus to become error-prone and about the magnitude of any resultant change in activity. Existing models, which differ in these respects, are critically compared, and one is shown to be more flexible than the rest. In common with others, this model predicts that a normally stable translation apparatus has a threshold error level above which stability cannot be regained. The risk of crossing onto an irreversible path to cell death is determined by the distance between the stable and threshold error levels, and experiments to estimate this “safety margin” are suggested. Evolutionary modification of translational stability is also discussed.     

An outcome-based model for predicting recovery pathways in restored ecosystems: The Recovery Cascade Model

Restoration is increasingly the focus of ecosystem management. Few conceptual models exist for predicting the consequences of restoration, especially those that predict the stages of recovery following restoration. Existing models focus either on defining endpoints for recovery or on defining ecosystem processes, but often do not identify barriers to recovery or potential negative effects of restoration. We describe a conceptual model that identifies the outcomes of the recovery pathways following flow restoration in rivers: the Recovery Cascade Model. The model identifies six general aspects of recovery following restoration: physical ecosystem change; creation of, or improvement in habitat condition; reconnection of the restored area to adjacent ecosystems; recolonization of the restored area; resumption of ecological processes; re-establishment of biotic interactions and reproduction by colonists in the restored area. These aspects may occur in sequence, such that recovery is blocked by a single barrier. The model accommodates feedback loops and includes strong connections between physical processes and ecosystem processes, but also identifies factors that are important in achieving endpoints such as potential barriers to further recovery. Identification of barriers to recovery enables improved planning to maximise the positive effects of restoration. By focussing on outcomes, the model provides a planning tool for managers that can be adapted for different ecosystems and restoration methods and which can be used to identify the amenities that an ecosystem will deliver at different stages of recovery. Ecosystem recovery is as much about overcoming barriers as it is about restorative actions.     

Graphical modeling for gene set analysis: A critical appraisal

Current demand for understanding the behavior of groups of related genes, combined with the greater availability of data, has led to an increased focus on statistical methods in gene set analysis. In this paper, we aim to perform a critical appraisal of the methodology based on graphical models developed in Massa et al. ( 2010 ) that uses pathway signaling networks as a starting point to develop statistically sound procedures for gene set analysis. We pay attention to the potential of the methodology with respect to the organizational aspects of dealing with such complex but highly informative starting structures, that is pathways. We focus on three themes: the translation of a biological pathway into a graph suitable for modeling, the role of shrinkage when more genes than samples are obtained, the evaluation of respondence of the statistical models to the biological expectations. To study the impact of shrinkage, two simulation studies will be run. To evaluate the biological expectation we will use data from a network with known behavior that offer the possibility of carrying out a realistic check of respondence of the model to changes in the experimental conditions.     

The possible role of reaction-diffusion in leaf shape

We consider mechanisms that may determine certain simple leaf shapes. Compared with other aspects of plant morphogenesis, such as phyllotaxis or spiral leaf arrangement, rather little is known about leaf-shape-determining mechanisms. We develop mathematical models for the gross pattern of leaf shape based on reaction diffusion systems. These models are consistent with what is known about factors that might determine leaf shape. They show that diverse leaf shapes may be obtained from a single reaction diffusion system. This has implications in terms of both convergent and divergent evolution. The models make predictions that can be tested experimentally. We predict the form of pre-patterns of growth promoters in leaf primordia of different sizes when the morphogens either diffuse into the primordia or are produced locally. We also predict the effects on leaf shape of removing parts of primordia at different times. The models can also predict the effects on leaf shape of the topical application of activators and inhibitors to leaf primordia.     

Movement patterns, social dynamics, and the evolution of cooperation

The structure of social interactions influences many aspects of social life, including the spread of information and behavior, and the evolution of social phenotypes. After dispersal, organisms move around throughout their lives, and the patterns of their movement influence their social encounters over the course of their lifespan. Though both space and mobility are known to influence social evolution, there is little analysis of the influence of specific movement patterns on evolutionary dynamics. We explored the effects of random movement strategies on the evolution of cooperation using an agent-based prisoner’s dilemma model with mobile agents. This is the first systematic analysis of a model in which cooperators and defectors can use different random movement strategies, which we chose to fall on a spectrum between highly exploratory and highly restricted in their search tendencies. Because limited dispersal and restrictions to local neighborhood size are known to influence the ability of cooperators to effectively assort, we also assessed the robustness of our findings with respect to dispersal and local capacity constraints. We show that differences in patterns of movement can dramatically influence the likelihood of cooperator success, and that the effects of different movement patterns are sensitive to environmental assumptions about offspring dispersal and local space constraints. Since local interactions implicitly generate dynamic social interaction networks, we also measured the average number of unique and total interactions over a lifetime and considered how these emergent network dynamics helped explain the results. This work extends what is known about mobility and the evolution of cooperation, and also has general implications for social models with randomly moving agents.     

Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways – particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile ‘plug and play’ set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.     

The use of in vitro systems to assess cancer mechanisms and the carcinogenic potential of chemicals

Carcinogenesis is a highly complex, multi-stage process that can occur over a relatively long period before its clinical manifestation. While the sequence in which a cancer cell acquires the necessary traits for tumour formation can vary, there are a number of mechanisms that are common to most, if not all, cancers across the spectrum of possible causes. Many aspects of carcinogenesis can be modelled in vitro. This has led to the development of a number of mechanistically driven, cell-based assays to assess the pro-carcinogenic and anti-carcinogenic potential of chemicals. A review is presented of the current in vitro models that can be used to study carcinogenesis, with examples of cigarette smoke testing in some of these models, in order to illustrate their potential applications. We present an overview of the assays used in regulatory genotoxicity testing, as well as those designed to model other aspects that are considered to be hallmarks of cancer. The latter assays are described with a view to demonstrating the recent advances in these areas, to a point where they should now be considered for inclusion in an overall testing strategy for chemical carcinogens.     

The neuronal ceroid lipofuscinoses: the same, but different?

The NCLs (neuronal ceroid lipofuscinoses) (also known as Batten disease) are a group of at least ten fatal inherited storage disorders. Despite the identification of many of the disease-causing genes, very little is known about the underlying disease mechanisms. However, now that we have mouse or large-animal models for most forms of NCL, we can investigate pathogenesis and compare what happens in the brain in different types of the disease. Broadly similar neuropathological themes have emerged, including the highly selective nature of neuron loss, early effects upon the presynaptic compartment, together with an early and localized glial activation. These events are especially pronounced within the thalamocortical system, but it is clear that where and when they occur varies markedly between different forms of NCL. It is now becoming apparent that, despite having pathological endpoints that resemble one another, these are reached by a sequence of events that is specific to each subtype of NCL.     

Genetic modifiers of muscular dystrophy: implications for therapy

The genetic understanding of the muscular dystrophies has advanced considerably in the last two decades. Over 25 different individual genes are now known to produce muscular dystrophy, and many different "private" mutations have been described for each individual muscular dystrophy gene. For the more common forms of muscular dystrophy, phenotypic variability can be explained by precise mutations. However, for many genetic mutations, the presence of the identical mutation is associated with marked phenotypic range that affects muscle function as well as cardiac function. The explanation for phenotype variability in the muscular dystrophies is only now being explored. The availability of genetically engineered animal models has allowed the generation of single mutations on the background of highly inbred strain. Phenotypic variation that is altered by genetic background argues for the presence of genetic modifier loci that can ameliorate or enhance aspects of the dystrophic phenotype. A number of individual genes have been implicated as modifiers of muscular dystrophy by studies in genetically engineered mouse models of muscular dystrophy. The value of these genes and products is that the pathways identified through these experiments may be exploited for therapy.     

A redesigned genetic code for selective labeling in protein NMR

The outline of a universal cell-free translation system capable of site-specific insertion of any types of labeled amino acids is presented. The system could be an invaluable tool for NMR spectroscopy by making the exclusive and exact labeling of the segments of interest possible. Although the development of such a system requires considerable efforts and can not be expected to be available in the next few years, we argue that recent findings concerning the translation apparatus provide clues for overcoming the major difficulties that might arise. We propose a genetic code and a reactor expected to fulfill the specific requirements. Importantly, incomplete systems could also be useful to study selected functional aspects of a number of proteins, examples of which are also given.     

The hydraulic system of trees: theoretical framework and numerical simulation

Empirical studies pose the problem of the physiological integration of the tree organism, which is also important on the scale of ecosystems. Recently, spatially distributed models emerged, which approach this problem by reflecting the close linkage between physiological processes and the structures of trees and tree stands. In the case of water flow, the tree organism can be regarded as hydraulic system and the branched tree architecture as hydraulic network. Previous models of the hydraulic system either did not take into account the network structure, or they had shortcomings regarding the translation of the underlying physiological assumptions by the discrete computation method. We have developed a theoretical framework which takes the form of a numerical simulation model of tree water flow. A discrete initial boundary value problem (IBVP) combines the phenomena of Darcy flow, water storage and conductivity losses in the hydraulic network. The software HYDRA computes the solution of the IBVP. The theoretical derivation and model tests corroborate the consistent translation of the physiological assumptions by the computational method. Simulation studies enabled us to formulate hypotheses on the following points: (1) differences in the hydraulic segmentation between Picea abies and Thuja occidentalis, (2) responses of the hydraulic system to rapid transpiration changes and to a scenario of drought stress, and (3) how these responses depend on architectural quantities of the trees. The simulation studies demonstrated our possibilities of deriving theoretically well-founded hypotheses about the functioning of the hydraulic system and its relation to system structure. The numerical simulation model is designed as a tool for structure-function studies, which is able to treat tree architecture as independent variable. The model supports the integration of data on tree level, and it can be used for computer experiments which quantify the dynamics of the hydraulic system according to the concepts of system theory. Copyright 1999 Academic Press.     

The response of cortical neurons to in vivo-like input current: theory and experiment

The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In particular, under such an assumption, it is possible to determine and study all the equilibrium points of the network dynamics when the neuronal response to noisy, in vivo-like, synaptic currents is known. The response function can be computed analytically for simple integrate-and-fire neuron models and it can be measured directly in experiments in vitro. Here we review theoretical and experimental results about the neural response to noisy inputs with stationary statistics. These response functions are important to characterize the collective neural dynamics that are proposed to be the neural substrate of working memory, decision making and other cognitive functions. Applications to the case of time-varying inputs are reviewed in a companion paper (Giugliano et al. in Biol Cybern, 2008). We conclude that modified integrate-and-fire neuron models are good enough to reproduce faithfully many of the relevant dynamical aspects of the neuronal response measured in experiments on real neurons in vitro.     

“Essentially,all models are wrong,but some are useful”<Emphasis Type="Italic">—</Emphasis>a cross-disciplinary agenda for building useful models in cell biology and biophysics

Intuition alone often fails to decipher the mechanisms underlying the experimental data in Cell Biology and Biophysics, and mathematical modeling has become a critical tool in these fields. However, mathematical modeling is not as widespread as it could be, because experimentalists and modelers often have difficulties communicating with each other, and are not always on the same page about what a model can or should achieve. Here, we present a framework to develop models that increase the understanding of the mechanisms underlying one’s favorite biological system. Development of the most insightful models starts with identifying a good biological question in light of what is known and unknown in the field, and determining the proper level of details that are sufficient to address this question. The model should aim not only to explain already available data, but also to make predictions that can be experimentally tested. We hope that both experimentalists and modelers who are driven by mechanistic questions will find these guidelines useful to develop models with maximum impact in their field.     

A PDMP model of the epithelial cell turn-over in the intestinal crypt including microbiota-derived regulations

Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. That is, whether parameters can be uniquely determined from perfect or realistic data in theory and practice. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.

     

Animal models of Zygomycosis – Absidia,Rhizopus, Rhizomucor,and Cunninghamella

Infections caused by zygomycetes, which have been increasing in recent years, are known for their difficulty of diagnosis and treatment. Because little is known about this fungus and its infection, vigorous research is now in serious demand. As in many other systemic mycoses, animal model studies are essential in the investigation of zygomycosis, particularly for the study of pathogenesis, diagnosis and treatment. Unfortunately, such studies have been limited when compared with those of aspergillosis. To help investigating the disease, here in this review article, the profile of human zygomycosis is briefly described, followed by a review of the heretofore used animal models of zygomycosis. Among clinically important zygomycetes causing human infection, animal models are available for Absidia corymbifera,Rhizopus oryzae,R. microsporus var.rhizopodiformis, Rhizomucor pusillus and Cunninghamella bertholletiae. Mice are the most commonly used animals, but models using guinea pigs and rabbits are also available. Pretreatment of animals with cyclophosphamide, corticosteroid, alloxan or streptozocine is frequently done to create an immunocompromised state. Treatment with desferrioxamine, an iron chelator, is also used to make animal models. In terms of the route of infection, the airborne route is used for pathophysiological studies in pulmonary infection models, but sometimes intravenous injection is preferred, particularly for antifungal drug studies. When pathophysiological analysis is the purpose of the study, the animals must be cautiously examined both histopathologically and mycologically. For the most part, zygomycosis model studies can be performed in a similar manner to those of aspergillosis. However, Aspergillus spp. and zygomycetes are completely different fungi, and researchers should be aware of the specific, critical aspects when handling zygomycosis models, such as homogenization of infected organs and staining of pathological samples. This revised version was published online in August 2006 with corrections to the Cover Date.     

Structural identifiability of a model for the acetic acid fermentation process

Modelling has proved an essential tool for addressing research into biotechnological processes, particularly with a view to their optimization and control. Parameter estimation via optimization approaches is among the major steps in the development of biotechnology models. In fact, one of the first tasks in the development process is to determine whether the parameters concerned can be unambiguously determined and provide meaningful physical conclusions as a result. The analysis process is known as 'identifiability' and presents two different aspects: structural or theoretical identifiability and practical identifiability. While structural identifiability is concerned with model structure alone, practical identifiability takes into account both the quantity and quality of experimental data. In this work, we discuss the theoretical identifiability of a new model for the acetic acid fermentation process and review existing methods for this purpose.     

Translation initiation of cyanobacterial rbcS mRNAs requires the 38-kDa ribosomal protein S1 but not the Shine-Dalgarno sequence: development of a cyanobacterial in vitro translation system

Little is known about the biochemical mechanism of translation in cyanobacteria though substantial studies have been made on photosynthesis, nitrogen fixation, circadian rhythm, and genome structure. To analyze the mechanism of cyanobacterial translation, we have developed an in vitro translation system from Synechococcus cells using a psbAI-lacZ fusion mRNA as a model template. This in vitro system supports accurate translation from the authentic initiation site of a variety of Synechococcus mRNAs. In Synechococcus cells, rbcL and rbcS encoding the large and small subunits, respectively, of ribulose-1,5-bisphosphate carboxylase/oxygenase are co-transcribed as a dicistronic mRNA, and the downstream rbcS mRNA possesses two possible initiation codons separated by three nucleotides. Using this in vitro system and mutated mRNAs, we demonstrated that translation starts exclusively from the upstream AUG codon. Although there are Shine-Dalgarno-like sequences in positions similar to those of the functional Shine-Dalgarno elements in Escherichia coli, mutation analysis indicated that these sequences are not required for translation. Assays with deletions within the 5'-untranslated region showed that a pyrimidine-rich sequence in the -46 to -15 region is necessary for efficient translation. Synechococcus cells contain two ribosomal protein S1 homologues of 38 and 33 kDa in size. UV cross-linking and immunoprecipitation experiments suggested that the 38-kDa S1 is involved in efficient translation via associating with the pyrimidine-rich sequence. The present in vitro translation system will be a powerful tool to analyze the basic mechanism of translation in cyanobacteria.     

Seasonality in ecology: Progress and prospects in theory

Seasonality is an important feature of essentially all natural systems but the consequences of seasonality have been vastly underappreciated. Early work emphasized the role of seasonality in driving cyclic population dynamics, but the consequences of seasonality for ecological processes are far broader. Yet, seasonality is often not explicitly included in either empirical or theoretical studies. Many aspects of ecological dynamics can only be understood when seasonality is included, ranging from the oscillations in the incidence of childhood diseases to the coexistence of species. Through several case studies, we outline what is now known about seasonality in an ecological context and set the stage for future efforts. We discuss various approaches and tools for incorporating seasonality in mathematical models. We argue, however, that these tools are still limited in scope and more easily-accessible approaches need to be developed.