Optimization-based framework for inferring and testing hypothesized metabolic objective functions

An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions. Specifically, we examine whether the maximization of a weighted combination of fluxes can explain a set of observed experimental data. Coefficients of importance (CoIs) are identified that quantify the fraction of the additive contribution of a given flux to a fitness (objective) function with an optimization that can explain the experimental flux data. A high CoI value implies that the experimental flux data are consistent with the hypothesis that the corresponding flux is maximized by the network, whereas a low value implies the converse. This framework (i.e., ObjFind) is applied to both an aerobic and anaerobic set of Escherichia coli flux data derived from isotopomer analysis. Results reveal that the CoIs for both growth conditions are strikingly similar, even though the flux distributions for the two cases are quite different, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases. Interestingly, the CoI associated with a biomass production flux, complete with energy and reducing power requirements, assumes a value 9 and 15 times higher than the next largest coefficient for the aerobic and anaerobic cases, respectively.     

Toward objective selection of representative microscope images

Scientists wishing to communicate the essential characteristics of a pattern (such as an immunofluorescence distribution) currently must make a subjective choice of one or two images to publish. We therefore developed methods for objectively choosing a typical image from a set, with emphasis on images from cell biology. The methods involve calculation of numerical features to describe each image, calculation of similarity between images as a distance in feature space, and ranking of images by distance from the center of the feature distribution. Two types of features were explored, image texture measures and Zernike polynomial moments, and various distance measures were utilized. Criteria for evaluating methods for assigning typicality were proposed and applied to sets of images containing more than one pattern. The results indicate the importance of using distance measures that are insensitive to the presence of outliers. For collections of images of the distributions of a lysosomal protein, a Golgi protein, and nuclear DNA, the images chosen as most typical were in good agreement with the conventional understanding of organelle morphologies. The methods described here have been implemented in a web server (http://murphylab.web.cmu.edu/services/TyplC).     

The objective structured interview for medical student selection

An objective structured interview is an integral part of the process of selecting and admitting applicants to study medicine at this university. During the nine years (to the end of 1986) that the interview has been used 1600 candidates were interviewed out of roughly 13 000 applicants, and from these, 584 students were admitted to the course. Analysis of the interview data was carried out based on two aspects of student progress: graduation with honours and failure to complete the course of study.The interview as a whole, and especially some of the subscales, appears to identify students who may fail to complete the course: it may also help to predict which students are likely to graduate with honours.     

Conserved metabolic regulatory functions of sirtuins

Silent information regulator 2 (Sir2) proteins, or sirtuins, are protein deacetylases/mono-ADP-ribosyltransferases found in organisms ranging from bacteria to humans. Their dependence on nicotinamide adenine dinucleotide (NAD+) links their activity to cellular metabolic status. In bacteria, the sirtuin CobB regulates the metabolic enzyme acetyl-coenzyme A (acetyl-CoA) synthetase. The earliest function of sirtuins therefore may have been regulation of cellular metabolism in response to nutrient availability. Recent findings support the idea that sirtuins play a pivotal role in metabolic control in higher organisms, including mammals. This review surveys evidence for an emerging role of sirtuins as regulators of metabolism in mammals.     

Physiological and metabolic functions of melatonin

Melatonin is a lipophilic hormone, mainly produced and secreted at night by the pineal gland. Melatonin synthesis is under the control of postganglionic sympathetic fibers that innervates the pineal gland. Melatonin acts via high affinity G protein-coupled membrane receptors. To date, three different receptor subtypes have been identified in mammals: MT1 (Mel 1a) and MT2 (Mel 1b) and a putative binding site called MT3. The chronobiotic properties of the hormone for resynchronization of sleep and circadian rhythms disturbances has been demonstrated both in animal models or in clinical trials. Several other physiological effects of melatonin in different peripheral tissues have been described in the past years. In this way, it has been demonstrated that the hormone is involved in the regulation of seasonal reproduction, body weight and energy balance. This contribution has been focused to review some of the physiological functions of melatonin as well as the role of the hormone in the regulation of energy balance and its possible involvement in the development of obesity.     

Scale for resource selection functions

Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.     

Fluctuating selection on basal metabolic rate

BMR (Basal metabolic rate) is an important trait in animal life history as it represents a significant part of animal energy budgets. BMR has also been shown to be positively related to sustainable work rate and maximal thermoregulatory capacity. To this date, most of the studies have focused on the causes of interspecific and intraspecific variation in BMR, and fairly little is known about the fitness consequences of different metabolic strategies. In this study, we show that winter BMR affects local survival in a population of wild blue tits (Cyanistes caeruleus), but that the selection direction differs between years. We argue that this fluctuating selection is probably a consequence of varying winter climate with a positive relation between survival and BMR during cold and harsh conditions, but a negative relation during mild winters. This fluctuating selection can not only explain the pronounced variation in BMR in wild populations, but will also give us new insights into how energy turnover rates can shape the life‐history strategies of animals. Furthermore, the study shows that the process of global warming may cause directional selection for a general reduction in BMR, affecting the general life‐history strategy on the population level.     

Comparison and analysis of objective functions in flux balance analysis

Flux balance analysis (FBA) is currently one of the most important and used techniques for estimation of metabolic reaction rates (fluxes). This mathematical approach utilizes an optimization criterion in order to select a distribution of fluxes from the feasible space delimited by the metabolic reactions and some restrictions imposed over them, assuming that cellular metabolism is in steady state. Therefore, the obtained flux distribution depends on the specific objective function used. Multiple studies have been aimed to compare distinct objective functions at given conditions, in order to determine which of those functions produces values of fluxes closer to real data when used as objective in the FBA; in other words, what is the best objective function for modeling cell metabolism at a determined environmental condition. However, these comparative studies have been designed in very dissimilar ways, and in general, several factors that can change the ideal objective function in a cellular condition have not been adequately considered. Additionally, most of them have used only one dataset for representing one condition of cell growth, and different measuring techniques have been used. For these reasons, a rigorous study on the effect of factors such as the quantity of used data, the number and type of fluxes utilized as input data, and the selected classification of growth conditions, are required in order to obtain useful conclusions for these comparative studies, allowing limiting clearly the application range on any of those results. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:985–991, 2014     

Peroxisomal beta-oxidation--a metabolic pathway with multiple functions

Fatty acid degradation in most organisms occurs primarily via the beta-oxidation cycle. In mammals, beta-oxidation occurs in both mitochondria and peroxisomes, whereas plants and most fungi harbor the beta-oxidation cycle only in the peroxisomes. Although several of the enzymes participating in this pathway in both organelles are similar, some distinct physiological roles have been uncovered. Recent advances in the structural elucidation of numerous mammalian and yeast enzymes involved in beta-oxidation have shed light on the basis of the substrate specificity for several of them. Of particular interest is the structural organization and function of the type 1 and 2 multifunctional enzyme (MFE-1 and MFE-2), two enzymes evolutionarily distant yet catalyzing the same overall enzymatic reactions but via opposite stereochemistry. New data on the physiological roles of the various enzymes participating in beta-oxidation have been gathered through the analysis of knockout mutants in plants, yeast and animals, as well as by the use of polyhydroxyalkanoate synthesis from beta-oxidation intermediates as a tool to study carbon flux through the pathway. In plants, both forward and reverse genetics performed on the model plant Arabidopsis thaliana have revealed novel roles for beta-oxidation in the germination process that is independent of the generation of carbohydrates for growth, as well as in embryo and flower development, and the generation of the phytohormone indole-3-acetic acid and the signal molecule jasmonic acid.     

Observing metabolic functions at the genome scale

Background  

High-throughput techniques have multiplied the amount and the types of available biological data, and for the first time achieving a global comprehension of the physiology of biological cells has become an achievable goal. This aim requires the integration of large amounts of heterogeneous data at different scales. It is notably necessary to extend the traditional focus on genomic data towards a truly functional focus, where the activity of cells is described in terms of actual metabolic processes performing the functions necessary for cells to live.     

Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome‐scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence‐level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.     

Exploring novel objective functions for simulating muscle coactivation in the neck

Musculoskeletal modeling allows for analysis of individual muscles in various situations. However, current techniques to realistically simulate muscle response when significant amounts of intentional coactivation is required are inadequate. This would include stiffening the neck or spine through muscle coactivation in preparation for perturbations or impacts. Muscle coactivation has been modeled previously in the neck and spine using optimization techniques that seek to maximize the joint stiffness by maximizing total muscle activation or muscle force. These approaches have not sought to replicate human response, but rather to explore the possible effects of active muscle. Coactivation remains a challenging feature to include in musculoskeletal models, and may be improved by extracting optimization objective functions from experimental data. However, the components of such an objective function must be known before fitting to experimental data. This study explores the effect of components in several objective functions, in order to recommend components to be used for fitting to experimental data. Four novel approaches to modeling coactivation through optimization techniques are presented, two of which produce greater levels of stiffness than previous techniques. Simulations were performed using OpenSim and MATLAB cooperatively. Results show that maximizing the moment generated by a particular muscle appears analogous to maximizing joint stiffness. The approach of optimizing for maximum moment generated by individual muscles may be a good candidate for developing objective functions that accurately simulate muscle coactivation in complex joints. This new approach will be the focus of future studies with human subjects.     

Considering ecological dynamics in resource selection functions

1.  Describing distribution and abundance is requisite to exploring interactions between organisms and their environment. Recently, the resource selection function (RSF) has emerged to replace many of the statistical procedures used to quantify resource selection by animals.
2.  A RSF is defined by characteristics measured on resource units such that its value for a unit is proportional to the probability of that unit being used by an organism. It is solved using a variety of techniques, particularly the binomial generalized linear model.
3.  Observing dynamics in a RSF – obtaining substantially different functions at different times or places for the same species – alerts us to the varying ecological processes that underlie resource selection.
4.  We believe that there is a need for us to reacquaint ourselves with ecological theory when interpreting RSF models. We outline a suite of factors likely to govern ecologically based variation in a RSF. In particular, we draw attention to competition and density-dependent habitat selection, the role of predation, longitudinal changes in resource availability and functional responses in resource use.
5.  How best to incorporate governing factors in a RSF is currently in a state of development; however, we see promise in the inclusion of random as well as fixed effects in resource selection models, and matched case–control logistic regression.
6.  Investigating the basis of ecological dynamics in a RSF will allow us to develop more robust models when applied to forecasting the spatial distribution of animals. It may also further our understanding of the relative importance of ecological interactions on the distribution and abundance of species.     

Linear selection indices for non-linear profit functions

Summary Two methods of deriving linear selection indices for non-linear profit functions have been proposed. One is by linear approximation of profit, and another is the graphical method of Moav and Hill (1966). When profit is defined as the function of population means, the graphical method is optimal. In this paper, profit is defined as the function of the phenotypic values of individual animals; it is then shown that the graphical method is not generally optimal. We propose new methods for constructing selection indices. First, a numerical method equivalent to the graphical method is proposed. Furthermore, we propose two other methods using quadratic approximation of profit: one is based on Taylor series about means before selection, and the other is based on Tayler series about means after selection. Among these different methods, it is shown that the method using quadratic approximation based on Taylor series about means after selection is the most efficient.     

Accuracy of resource selection functions across spatial scales

Resource selection functions (RSFs) can be used to map suitable habitat of a species based on predicted probability of use. The spatial scale may affect accuracy of such predictions. To provide guidance as to which spatial extent or grain is appropriate and most accurate for animals, we used the concept of hierarchical selection orders to dictate extent and grain. We conducted a meta-analysis from 123 RSF studies of 886 species to identify differences in prediction success that might be expected for five selection orders. Many studies do not constrain spatial extent to the grain of the next broader selection order in the hierarchy, mixing scaling effects. Thus, we also compared accuracy of single- vs. multiple-grain RSFs developed at the unconstrained extent of an entire study area. Results suggested that the geographical range of a species was the easiest to predict of the selection orders. At smaller scales within the geographical range, use of a site was easier to predict when environmental variables were measured at a grain equivalent to the home-range size or a microhabitat feature required for reproduction or resting. Selection of patches within home ranges and locations of populations was often more difficult to predict. Multiple-grain RSFs were more predictive than single-grain RSFs when the entire study area was considered available. Models with variables measured at both small and large (> 100 ha) grains were usually most predictive, even for many species with small home ranges. Multiple-grain models may be particularly important for species with moderate dispersal abilities in habitat fragments surrounded by an unsuitable matrix. We recommend studies should no longer address only one grain to map animal species distributions.     

布朗葡萄藻的培养基选择及其产物代谢规律

通过对布朗葡萄藻分别在Chu13、Chu13×2和BG-11培养基中培养结果的比较,发现在气升式光照生物反应器中Chu13培养基最有利于布朗葡萄藻的生长和烃的合成,培养15d后,其生物量和粗烃质量分数分别为1.82g/L和58.7%;棕榈酸、油酸和亚麻酸是布朗葡萄藻的主要脂肪酸组成,Chu13培养获得的藻体不饱和脂肪酸比例最高。Chu13培养基中布朗葡萄藻代谢规律的研究表明:粗烃含量随着生物量的增加而逐渐增大,15d后粗烃产量达到最大值1.07g/L,不同生长周期烃的组成保持一致,布朗葡萄藻的烃主要由C33H56和C34H58组成;在布朗葡萄藻生长周期中,不饱和脂肪酸的比例显著上升,培养15d达到64%以上。     

Single cell mutant selection for metabolic engineering of actinomycetes

Actinomycetes are important producers of pharmaceuticals and industrial enzymes. However, wild type strains require laborious development prior to industrial usage. Here we present a generally applicable reporter-guided metabolic engineering tool based on random mutagenesis, selective pressure, and single-cell sorting. We developed fluorescence-activated cell sorting (FACS) methodology capable of reproducibly identifying high-performing individual cells from a mutant population directly from liquid cultures. Actinomycetes are an important source of catabolic enzymes, where product yields determine industrial viability. We demonstrate 5-fold yield improvement with an industrial cholesterol oxidase ChoD producer Streptomyces lavendulae to 20.4 U g⁻¹ in three rounds. Strain development is traditionally followed by production medium optimization, which is a time-consuming multi-parameter problem that may require hard to source ingredients. Ultra-high throughput screening allowed us to circumvent medium optimization and we identified high ChoD yield production strains directly from mutant libraries grown under preset culture conditions. Genome-mining based drug discovery is a promising source of bioactive compounds, which is complicated by the observation that target metabolic pathways may be silent under laboratory conditions. We demonstrate our technology for drug discovery by activating a silent mutaxanthene metabolic pathway in Amycolatopsis. We apply the method for industrial strain development and increase mutaxanthene yields 9-fold to 99 mg l⁻¹ in a second round of mutant selection. In summary, the ability to screen tens of millions of mutants in a single cell format offers broad applicability for metabolic engineering of actinomycetes for activation of silent metabolic pathways and to increase yields of proteins and natural products.