Project description:Utilizing kinetic models of biological systems commonly require computational approaches to estimate parameters, posing a variety of challenges due to their highly non-linear and dynamic nature, which is further complicated by the issue of non-identifiability. We propose a novel parameter estimation framework by combining approaches for solving identifiability with a recently introduced filtering technique that can uniquely estimate parameters where conventional methods fail. This framework first conducts a thorough analysis to identify and classify the non-identifiable parameters and provides a guideline for solving them. If no feasible solution can be found, the framework instead initializes the filtering technique with informed prior to yield a unique solution.This framework has been applied to uniquely estimate parameter values for the sucrose accumulation model in sugarcane culm tissue and a gene regulatory network. In the first experiment the results show the progression of improvement in reliable and unique parameter estimation through the use of each tool to reduce and remove non-identifiability. The latter experiment illustrates the common situation where no further measurement data is available to solve the non-identifiability. These results show the successful application of the informed prior as well as the ease with which parallel data sources may be utilized without increasing the model complexity.The proposed unified framework is distinct from other approaches by providing a robust and complete solution which yields reliable and unique parameter estimation even in the face of non-identifiability.
Project description:Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.
Project description:Allopolyploidisation merges evolutionarily distinct parental genomes (subgenomes) into a single nucleus. A frequent observation is that one subgenome is 'dominant' over the other subgenome, often being more highly expressed. Here, we 'replayed the evolutionary tape' with six isogenic resynthesised Brassica napus allopolyploid lines and investigated subgenome dominance patterns over the first 10 generations postpolyploidisation. We found that the same subgenome was consistently more dominantly expressed in all lines and generations and that >70% of biased gene pairs showed the same dominance patterns across all lines and an in silico hybrid of the parents. Gene network analyses indicated an enrichment for network interactions and several biological functions for the Brassica oleracea subgenome biased pairs, but no enrichment was identified for Brassica rapa subgenome biased pairs. Furthermore, DNA methylation differences between subgenomes mirrored the observed gene expression bias towards the dominant subgenome in all lines and generations. Many of these differences in gene expression and methylation were also found when comparing the progenitor genomes, suggesting that subgenome dominance is partly related to parental genome differences rather than just a byproduct of allopolyploidisation. These findings demonstrate that 'replaying the evolutionary tape' in an allopolyploid results in largely repeatable and predictable subgenome expression dominance patterns.
Project description:Society values landscapes that reliably provide many ecosystem functions. As the study of ecosystem functioning expands to include more locations, time spans, and functions, the functional importance of individual species is becoming more apparent. However, the functional importance of individual species does not necessarily translate to the functional importance of biodiversity measured in whole communities of interacting species. Furthermore, ecological diversity at scales larger than neighborhood species richness could also influence the provision of multiple functions over extended time scales. We created experimental landscapes based on whole communities from the world's longest running biodiversity-functioning field experiment to investigate how local species richness (α diversity), distinctness among communities (β diversity), and larger scale species richness (γ diversity) affected eight ecosystem functions over 10 y. Using both threshold-based and unique multifunctionality metrics, we found that α diversity had strong positive effects on most individual functions and multifunctionality, and that positive effects of β and γ diversity emerged only when multiple functions were considered simultaneously. Higher β diversity also reduced the variability in multifunctionality. Thus, in addition to conserving important species, maintaining ecosystem multifunctionality will require diverse landscape mosaics of diverse communities.
Project description:ObjectivesLatent class trajectory modelling (LCTM) is a relatively new methodology in epidemiology to describe life-course exposures, which simplifies heterogeneous populations into homogeneous patterns or classes. However, for a given dataset, it is possible to derive scores of different models based on number of classes, model structure and trajectory property. Here, we rationalise a systematic framework to derive a 'core' favoured model.MethodsWe developed an eight-step framework: step 1: a scoping model; step 2: refining the number of classes; step 3: refining model structure (from fixed-effects through to a flexible random-effect specification); step 4: model adequacy assessment; step 5: graphical presentations; step 6: use of additional discrimination tools ('degree of separation'; Elsensohn's envelope of residual plots); step 7: clinical characterisation and plausibility; and step 8: sensitivity analysis. We illustrated these steps using data from the NIH-AARP cohort of repeated determinations of body mass index (BMI) at baseline (mean age: 62.5 years), and BMI derived by weight recall at ages 18, 35 and 50 years.ResultsFrom 288 993 participants, we derived a five-class model for each gender (men: 177 455; women: 111 538). From seven model structures, the favoured model was a proportional random quadratic structure (model F). Favourable properties were also noted for the unrestricted random quadratic structure (model G). However, class proportions varied considerably by model structure-concordance between models F and G were moderate (Cohen κ: men, 0.57; women, 0.65) but poor with other models. Model adequacy assessments, evaluations using discrimination tools, clinical plausibility and sensitivity analyses supported our model selection.ConclusionWe propose a framework to construct and select a 'core' LCTM, which will facilitate generalisability of results in future studies.
Project description:Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.
Project description:Although diversity-stability relationships have been extensively studied in local ecosystems, the global biodiversity crisis calls for an improved understanding of these relationships in a spatial context. Here, we use a dynamical model of competitive metacommunities to study the relationships between species diversity and ecosystem variability across scales. We derive analytic relationships under a limiting case; these results are extended to more general cases with numerical simulations. Our model shows that, while alpha diversity decreases local ecosystem variability, beta diversity generally contributes to increasing spatial asynchrony among local ecosystems. Consequently, both alpha and beta diversity provide stabilising effects for regional ecosystems, through local and spatial insurance effects respectively. We further show that at the regional scale, the stabilising effect of biodiversity increases as spatial environmental correlation increases. Our findings have important implications for understanding the interactive effects of global environmental changes (e.g. environmental homogenisation) and biodiversity loss on ecosystem sustainability at large scales.
Project description:Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco-evolutionary dynamics.
Project description:The importance and applications of polyploidy have long been recognized, from shaping the evolutionary success of flowering plants to improving agricultural productivity. Recent studies have shown that one of the parental subgenomes in ancient polyploids is generally more dominant - having both retained more genes and being more highly expressed - a phenomenon termed subgenome dominance. How quickly one subgenome dominates within a newly formed polyploid, if immediate or after millions of years, and the genomic features that determine which genome dominates remain poorly understood. To investigate the rate of subgenome dominance emergence, we examined gene expression, gene methylation, and transposable element (TE) methylation in a natural less than 140 year old allopolyploid (Mimulus peregrinus), a resynthesized interspecies triploid hybrid (M. robertsii), a resynthesized allopolyploid (M. peregrinus), and diploid progenitors (M. guttatus and M. luteus). We show that subgenome expression dominance occurs instantly following the hybridization of two divergent genomes and that subgenome expression dominance significantly increases over generations. Additionally, CHH methylation levels are significantly reduced in regions near genes and within transposons in the first generation hybrid, intermediate in the resynthesized allopolyploid, and are repatterned differently between the dominant and submissive subgenomes in the natural allopolyploid. Our analyses reveal that the subgenome differences in levels of TE methylation mirror the increase in expression bias observed over the generations following the hybridization. These findings not only provide important insights into genomic and epigenomic shock that occurs following hybridization and polyploid events, but may also contribute to uncovering the mechanistic basis of heterosis and subgenomic dominance.
Project description:SummaryWe present a set of software packages that provide uniform access to diverse biological vocabulary resources that are instrumental for current biocuration efforts and tools. The Unified Biological Dictionaries (UniBioDicts or UBDs) provide a single query-interface for accessing the online API services of leading biological data providers. Given a search string, UBDs return a list of matching term, identifier and metadata units from databases (e.g. UniProt), controlled vocabularies (e.g. PSI-MI) and ontologies (e.g. GO, via BioPortal). This functionality can be connected to input fields (user-interface components) that offer autocomplete lookup for these dictionaries. UBDs create a unified gateway for accessing life science concepts, helping curators find annotation terms across resources (based on descriptive metadata and unambiguous identifiers), and helping data users search and retrieve the right query terms.Availability and implementationThe UBDs are available through npm and the code is available in the GitHub organisation UniBioDicts (https://github.com/UniBioDicts) under the Affero GPL license.Supplementary informationSupplementary data are available at Bioinformatics online.