Mutual inactivation of Notch receptors and ligands facilitates developmental patterning

Developmental patterning requires juxtacrine signaling in order to tightly coordinate the fates of neighboring cells. Recent work has shown that Notch and Delta, the canonical metazoan juxtacrine signaling receptor and ligand, mutually inactivate each other in the same cell. This cis-interaction generates mutually exclusive sending and receiving states in individual cells. It generally remains unclear, however, how this mutual inactivation and the resulting switching behavior can impact developmental patterning circuits. Here we address this question using mathematical modeling in the context of two canonical pattern formation processes: boundary formation and lateral inhibition. For boundary formation, in a model motivated by Drosophila wing vein patterning, we find that mutual inactivation allows sharp boundary formation across a broader range of parameters than models lacking mutual inactivation. This model with mutual inactivation also exhibits robustness to correlated gene expression perturbations. For lateral inhibition, we find that mutual inactivation speeds up patterning dynamics, relieves the need for cooperative regulatory interactions, and expands the range of parameter values that permit pattern formation, compared to canonical models. Furthermore, mutual inactivation enables a simple lateral inhibition circuit architecture which requires only a single downstream regulatory step. Both model systems show how mutual inactivation can facilitate robust fine-grained patterning processes that would be difficult to implement without it, by encoding a difference-promoting feedback within the signaling system itself. Together, these results provide a framework for analysis of more complex Notch-dependent developmental systems.     

Molding the business end of neurotoxins by diversifying evolution

A diverse range of organisms utilize neurotoxins that target specific ion channels and modulate their activity. Typically, toxins are clustered into several multigene families, providing an organism with the upper hand in the never-ending predator-prey arms race. Several gene families, including those encoding certain neurotoxins, have been subject to diversifying selection forces, resulting in rapid gene evolution. Here we sought a spatial pattern in the distribution of both diversifying and purifying selection forces common to neurotoxin gene families. Utilizing the mechanistic empirical combination model, we analyzed various toxin families from different phyla affecting various receptors and relying on diverse modes of action. Through this approach, we were able to detect clear correlations between the pharmacological surface of a toxin and rapidly evolving domains, rich in positively selected residues. On the other hand, patches of negatively selected residues were restricted to the nontoxic face of the molecule and most likely help in stabilizing the tertiary structure of the toxin. We thus propose a mutual evolutionary strategy of venomous animals in which adaptive molecular evolution is directed toward the toxin active surface. Furthermore, we propose that the binding domains of unstudied toxins could be readily predicted using evolutionary considerations.     

On latent-variable model misspecification in structural measurement error models for binary response

Summary .  We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.     

Is predator-prey coevolutlon an arms race?

Biologists have often used simple analogies to help them think about complex processes in evolution. The mutual evolution of predator and prey has often been conceived of as an arms race. An increase in the armaments of one contestant in the race simply causes the other contestant to increase armaments in response. This analogy implies that the evolution in the predator population of improved abilities to capture prey should result in an evolutionary response in the prey that improves its abilities to avoid capture. Conversely, the evolution of improved escape abilities should result in increased capture abilities. The general applicability of this arms race analogy has not been supported by mathematical models of predatorprey interactions.     

The program of Founding Research Centers for Emerging and Reemerging Infectious Diseases: the present status and future prospects

The program of Founding Research Centers for Emerging and Reemerging Infectious Diseases was commenced in 2005 with an outline for Japanese universities and research institutions to establish bilateral collaboration research bases in countries where emerging and reemerging infections are breaking out or will likely break out. So far, six universities and two institutions are participating in the program and ten collaboration bases have been established in six countries (five in Asia and one in Africa). Each research base aims to contribute to the security and safety of the partner and own countries by facilitating better understanding of infectious diseases, technology innovation in diagnosis, therapy and prevention, and human resources development. The experiences of the Reseau International des Instituts Pasteur (RIIP), France, and the Wellcome Trust Southeast Asian Tropical Medicine Research Units (Oxford Network), United Kingdom, which appear to share similar missions, suggest that infectious diseases research that is based on overseas research bases can produce first-time results through the building of long-term mutual trust with the counterparts. By referring to these networks as models, Japan's program should be implemented over the long run but not be based on a short-time perspective. Thus, secure funding is a major issue.     

How to model a complex trait. 2. Analysis with two disease loci

Complex traits are often governed by more than one trait locus. The first step towards an adequate model for such diseases is a linkage analysis with two trait loci. Such an analysis can be expected to have higher power to detect linkage than a standard single-trait-locus linkage analysis. However, it is crucial to accurately specify the parameters of the two-locus model. Here, we recapitulate the general two-locus model with and without genomic imprinting. We relate heterogeneity, multiplicative, and additive two-locus models to biological or pathophysiological mechanisms, and give the corresponding averaged ("best-fitting") single-trait-locus models for each of the two loci. Furthermore, we derive the two-locus penetrances from the averaged single-locus models, under the assumption of one of the three model classes mentioned above. Using these formulae, if the best-fitting single-locus models are available, investigators may perform a two-trait-locus linkage analysis under a realistic model. This procedure will maximize the power to detect linkage for traits which are governed by two or more loci, and lead to more accurate estimates of the disease-locus positions.     

Mathematical Models for Sleep-Wake Dynamics: Comparison of the Two-Process Model and a Mutual Inhibition Neuronal Model

Sleep is essential for the maintenance of the brain and the body, yet many features of sleep are poorly understood and mathematical models are an important tool for probing proposed biological mechanisms. The most well-known mathematical model of sleep regulation, the two-process model, models the sleep-wake cycle by two oscillators: a circadian oscillator and a homeostatic oscillator. An alternative, more recent, model considers the mutual inhibition of sleep promoting neurons and the ascending arousal system regulated by homeostatic and circadian processes. Here we show there are fundamental similarities between these two models. The implications are illustrated with two important sleep-wake phenomena. Firstly, we show that in the two-process model, transitions between different numbers of daily sleep episodes can be classified as grazing bifurcations. This provides the theoretical underpinning for numerical results showing that the sleep patterns of many mammals can be explained by the mutual inhibition model. Secondly, we show that when sleep deprivation disrupts the sleep-wake cycle, ostensibly different measures of sleepiness in the two models are closely related. The demonstration of the mathematical similarities of the two models is valuable because not only does it allow some features of the two-process model to be interpreted physiologically but it also means that knowledge gained from study of the two-process model can be used to inform understanding of the behaviour of the mutual inhibition model. This is important because the mutual inhibition model and its extensions are increasingly being used as a tool to understand a diverse range of sleep-wake phenomena such as the design of optimal shift-patterns, yet the values it uses for parameters associated with the circadian and homeostatic processes are very different from those that have been experimentally measured in the context of the two-process model.     

Modeling Mutual Exclusivity of Cancer Mutations

In large collections of tumor samples, it has been observed that sets of genes that are commonly involved in the same cancer pathways tend not to occur mutated together in the same patient. Such gene sets form mutually exclusive patterns of gene alterations in cancer genomic data. Computational approaches that detect mutually exclusive gene sets, rank and test candidate alteration patterns by rewarding the number of samples the pattern covers and by punishing its impurity, i.e., additional alterations that violate strict mutual exclusivity. However, the extant approaches do not account for possible observation errors. In practice, false negatives and especially false positives can severely bias evaluation and ranking of alteration patterns. To address these limitations, we develop a fully probabilistic, generative model of mutual exclusivity, explicitly taking coverage, impurity, as well as error rates into account, and devise efficient algorithms for parameter estimation and pattern ranking. Based on this model, we derive a statistical test of mutual exclusivity by comparing its likelihood to the null model that assumes independent gene alterations. Using extensive simulations, the new test is shown to be more powerful than a permutation test applied previously. When applied to detect mutual exclusivity patterns in glioblastoma and in pan-cancer data from twelve tumor types, we identify several significant patterns that are biologically relevant, most of which would not be detected by previous approaches. Our statistical modeling framework of mutual exclusivity provides increased flexibility and power to detect cancer pathways from genomic alteration data in the presence of noise. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

Comparing genetic ancestry and self-reported race/ethnicity in a multiethnic population in New York City

Self-reported race/ethnicity is frequently used in epidemiological studies to assess an individual’s background origin. However, in admixed populations such as Hispanic, self-reported race/ethnicity may not accurately represent them genetically because they are admixed with European, African and Native American ancestry. We estimated the proportions of genetic admixture in an ethnically diverse population of 396 mothers and 188 of their children with 35 ancestry informative markers (AIMs) using the STRUCTURE version 2.2 program. The majority of the markers showed significant deviation from Hardy-Weinberg equilibrium in our study population. In mothers self-identified as Black and White, the imputed ancestry proportions were 77.6% African and 75.1% European respectively, while the racial composition among self-identified Hispanics was 29.2% European, 26.0% African, and 44.8% Native American. We also investigated the utility of AIMs by showing the improved fitness of models in paraoxanase-1 genotype-phenotype associations after incorporating AIMs; however, the improvement was moderate at best. In summary, a minimal set of 35 AIMs is sufficient to detect population stratification and estimate the proportion of individual genetic admixture; however, the utility of these markers remains questionable.     

Coexistence in MacArthur-style consumer-resource models

The nature of and conditions for permanent coexistence of consumers and resources are characterized in a family of models that generalize MacArthur's consumer-resource model. The generalization is of the resource dynamics, which need not be of Lotka-Volterra form but are subject only to certain restrictions loose enough to admit many resource dynamics of biological interest. For any such model, (1) if there is an interior equilibrium, then it is globally attracting, else some boundary equilibrium is globally attracting-thus permanent coexistence is coexistence at a globally attracting equilibrium; (2) there is an interior equilibrium if and only if for any species, the equilibrium approached in the absence of that species and the presence of the others is invasible by that species--thus permanent coexistence is equivalent to mutual invasibility; (3) for resources without direct interactions, the conditions for permanent coexistence of the consumers admit an instructive formulation in terms of regression statistics. The significance and limitations of the models and results are discussed.     

EQUIL: Simulation and data analysis of binding reactions with arbitrary chemical models

We have developed an algorithm for simulation and analysis of arbitrary chemical systems in equilibrium, with emphasis on ligand binding reactions. The program EQUIL can treat reactions involving multiple ligands, multiple binding sites, ternary complex models, allosteric effectors, competitive and noncompetitive binding, conformational changes, cooperativity, and generally any scheme that can be represented as a set of chemical equations. EQUIL is based on a general thermodynamic model of chemical equilibria; it does not involve nonlinear transformation of experimental data, but it does require the user to define the model of interaction between ligands and receptors by writing down the appropriate chemical reactions. EQUIL contains features of particular importance to ligand binding experiments: variable binding capacities, nonspecific binding, and the ability to simultaneously analyze data from different types of experiments. Furthermore, the simulation feature of EQUIL allows the user to investigate the feasibility of experiments that could possibly distinguish between different reaction models. We illustrate the use of this program on personal computers to analyze and simulate simple and complicated interactions between ligands and receptors.     

A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields. Yet, existing models have failed to predict the diverse shapes of receptive fields that occur in nature. The existing models used a particular "soft" form of sparseness that limits average neuronal activity. Here we study models of efficient coding in a broader context by comparing soft and "bard" forms of neuronal sparseness. As a result of our analyses, we propose a novel network model for visual cortex. The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited. This form of hard sparseness also economises cortical resources like synaptic memory and metabolic energy. Furthermore, our model accurately predicts the distribution of receptive field shapes found in the primary visual cortex of cat and monkey.     

Likelihood methods for detecting temporal shifts in diversification rates

Maximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios indicate that the birth-death likelihood method has much greater power to detect variation in diversification rates when extinction is present. Furthermore, this method appears to be the only approach available that can distinguish between a temporal increase in diversification rates and a rate-constant model with nonzero extinction. I illustrate use of the method by analyzing a published phylogeny for Australian agamid lizards.     

Hemovigilance in developing countries

Hemovigilance like quality systems and audits has become an integral part of the Blood Transfusion Service (BTS) in the developed world and has contributed greatly to the development of the blood service. However developing countries are still grappling with donor recruitment and efforts towards sufficiency and safety of the blood supply. In these countries the BTS is generally fragmented and a national hemovigilance program would be difficult to implement. However a few developing countries have an effective and sustainable blood program that can deliver equitable, safe and sufficient blood supply to the nation. Different models of hemovigilance program have been introduced with variable success. There are deficiencies but the data collected provided important information that can be presented to the health authorities for effective interventions.Hemovigilance program modeled from developed countries require expertise and resources that are not available in many developing countries. Whatever resources that are available should be utilized to correct deficiencies that are already apparent and obvious. Besides there are other tools that can be used to monitor the blood program in the developing countries depending on the need and the resources available. More importantly the data collected should be accurate and are used and taken into consideration in formulating guidelines, standards and policies and to affect appropriate interventions. Any surveillance program should be introduced in a stepwise manner as the blood transfusion service develops.     

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.     

Moving towards the detection of frailty with biomarkers: A population health study

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: “Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study”) (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66–0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66–0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.     

基于Bootstrap方法的基因表达谱数据互信息估计研究

基因调控网络模型为深入理解生命本质提供了一个新的研究框架和平台。作为基因调控网络模型的其中一种,互信息关联网络模型使用熵和互信息描述基因和基因之间的关联。本文描述了用互信息度量基因表达相似性的方法,提出基于Bootstrap的互信息估计算法,并对产生的偏离现象提出了改进策略。实验结果表明,改进的互信息估计方法可以有效提高基因表达相似性估计的精确度。     

Consumer–resource matching in a food chain when both predators and prey are free to move

The classical theory of the ideal free distribution (IFD) predicts that the spatial distribution of consumers should follow the distribution of the resources they depend on. Here, we study consumer–resource matching in a community context. Our model for the community is a food chain with three levels. We study whether the primary consumers are able to match resources both under predation risk and in its absence. Both prey and predators have varying degrees of knowledge of the global and local resource distribution. We present two versions of the model. In the "resource maximising" model, the consumers consider the availability of their resource only. In the "balancing" model, individual consumers minimise predation risk per unit of resource that they can gain access to. We show that both models can lead to perfect matching of consumers on resources and predators on consumers, assuming that individuals have full knowledge of the whole environment. However, when the consumers' information and freedom of movement are greater than those of the predators, then the predators generally undermatch the consumers. In the opposite case, we observe overmatching and high consumer movement rates. Furthermore, undermatching of predators on consumers tends to induce overmatching of consumers on resources.     

A discrete-time model with vaccination for a measles epidemic.

A discrete-time, age-independent SIR-type epidemic model is formulated and analyzed. The effects of vaccination are also included in the model. Three mathematically important properties are verified for the model: solutions are nonnegative, the population size is time-invariant, and the epidemic concludes with all individuals either remaining susceptible or becoming immune (a property typical of SIR models). The model is applied to a measles epidemic on a university campus. The simulated results are in good agreement with the actual data if it is assumed that the population mixes nonhomogeneously. The results of the simulations indicate that a rate of immunity greater than 98% may be required to prevent an epidemic in a university population. The model has applications to other contagious diseases of SIR type. Furthermore, the simulated results of the model can easily be compared to data, and the effects of a vaccination program can be examined.     

Learning directed acyclic graphs from large-scale genomics data

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.