FamNet: A Framework to Identify Multiplied Modules Driving Pathway Expansion in Plants.

Gene duplications generate new genes that can acquire similar but often diversified functions. Recent studies of gene coexpression networks have indicated that, not only genes, but also pathways can be multiplied and diversified to perform related functions in different parts of an organism. Identification of such diversified pathways, or modules, is needed to expand our knowledge of biological processes in plants and to understand how biological functions evolve. However, systematic explorations of modules remain scarce, and no user-friendly platform to identify them exists. We have established a statistical framework to identify modules and show that approximately one-third of the genes of a plant's genome participate in hundreds of multiplied modules. Using this framework as a basis, we implemented a platform that can explore and visualize multiplied modules in coexpression networks of eight plant species. To validate the usefulness of the platform, we identified and functionally characterized pollen- and root-specific cell wall modules that multiplied to confer tip growth in pollen tubes and root hairs, respectively. Furthermore, we identified multiplied modules involved in secondary metabolite synthesis and corroborated them by metabolite profiling of tobacco (Nicotiana tabacum) tissues. The interactive platform, referred to as FamNet, is available at http://www.gene2function.de/famnet.html.

Decreased Nucleotide and Expression Diversity and Modified Coexpression Patterns Characterize Domestication in the Common Bean.

Using RNA sequencing technology and de novo transcriptome assembly, we compared representative sets of wild and domesticated accessions of common bean (Phaseolus vulgaris) from Mesoamerica. RNA was extracted at the first true-leaf stage, and de novo assembly was used to develop a reference transcriptome; the final data set consists of ∼190,000 single nucleotide polymorphisms from 27,243 contigs in expressed genomic regions. A drastic reduction in nucleotide diversity (∼60%) is evident for the domesticated form, compared with the wild form, and almost 50% of the contigs that are polymorphic were brought to fixation by domestication. In parallel, the effects of domestication decreased the diversity of gene expression (18%). While the coexpression networks for the wild and domesticated accessions demonstrate similar seminal network properties, they show distinct community structures that are enriched for different molecular functions. After simulating the demographic dynamics during domestication, we found that 9% of the genes were actively selected during domestication. We also show that selection induced a further reduction in the diversity of gene expression (26%) and was associated with 5-fold enrichment of differentially expressed genes. While there is substantial evidence of positive selection associated with domestication, in a few cases, this selection has increased the nucleotide diversity in the domesticated pool at target loci associated with abiotic stress responses, flowering time, and morphology.

Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment.

Flux phenotypes predicted by constraint-based methods can be refined by the inclusion of heterogeneous data. While recent advances facilitate the integration of transcriptomics and proteomics data, purely stoichiometry-based approaches for the prediction of flux phenotypes by considering metabolomics data are lacking. Here we propose a constraint-based method, termed TREM-Flux, for integrating time-resolved metabolomics and transcriptomics data. We demonstrate the applicability of TREM-Flux in the dissection of the metabolic response of Chlamydomonas reinhardtii to rapamycin treatment by integrating the expression levels of 982 genes and the content of 45 metabolites obtained from two growth conditions. The findings pinpoint cysteine and methionine metabolism to be most affected by the rapamycin treatment. Our study shows that the integration of time-resolved unlabeled metabolomics data in addition to transcriptomics data can specify the metabolic pathways involved in the system's response to a studied treatment.

Temporal kinetics of the transcriptional response to carbon depletion and sucrose readdition in Arabidopsis seedlings.

To investigate whether the transcriptional response to carbon (C) depletion and sucrose resupply depends on the duration and severity of the C depletion, Arabidopsis seedlings were grown in liquid culture and harvested 3, 6, 12, 24, 48 and 72 h after removing sucrose from the medium and 30 min after resupplying sucrose at each time. Expression profiling revealed early transcriptional inhibition of cell wall synthesis and remodelling of signalling, followed by induction of C recycling and photosynthesis and general inhibition of growth. The temporal sequence differed from the published response to progressive exhaustion of C during a night and extended night in vegetatively growing plants. The response to sucrose readdition was conserved across the C-depletion time course. Intriguingly, the vast majority of rapidly responding transcripts decreased rather than increased. The majority of transcripts that respond rapidly to sucrose and many transcripts that respond during C depletion also decrease after treating seedlings with the transcriptional inhibitor cordycepin A. Comparison with published responses to overexpression of otsA, AKIN10 and bZIP11 revealed that many genes that respond to C depletion, and especially sucrose resupply, respond to one or more of these C-signalling components. Thus, multiple factors contribute to C responsiveness, including many signalling components, transcriptional regulation and transcript turnover.

Data integration through proximity-based networks provides biological principles of organization across scales.

Plant behaviors across levels of cellular organization, from biochemical components to tissues and organs, relate and reflect growth habitats. Quantification of the relationship between behaviors captured in various phenotypic characteristics and growth habitats can help reveal molecular mechanisms of plant adaptation. The aim of this article is to introduce the power of using statistics originally developed in the field of geographic variability analysis together with prominent network models in elucidating principles of biological organization. We provide a critical systematic review of the existing statistical and network-based approaches that can be employed to determine patterns of covariation from both uni- and multivariate phenotypic characteristics in plants. We demonstrate that parameter-independent network-based approaches result in robust insights about phenotypic covariation. These insights can be quantified and tested by applying well-established statistics combining the network structure with the phenotypic characteristics. We show that the reviewed network-based approaches are applicable from the level of genes to the study of individuals in a population of Arabidopsis thaliana. Finally, we demonstrate that the patterns of covariation can be generalized to quantifiable biological principles of organization. Therefore, these network-based approaches facilitate not only interpretation of large-scale data sets, but also prediction of biochemical and biological behaviors based on measurable characteristics.

Conserved changes in the dynamics of metabolic processes during fruit development and ripening across species.

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [Prunus persica] and two tomato [Solanum lycopersicum] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [Fragaria × ananassa] and pepper [Capsicum chilense]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.

An integrated genomic and metabolomic framework for cell wall biology in rice.

BACKGROUND: Plant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences. RESULTS: In this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification. CONCLUSIONS: Here, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.

PlaNet: combined sequence and expression comparisons across plant networks derived from seven species.

The model organism Arabidopsis thaliana is readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer acquired knowledge from Arabidopsis to crop species. However, the identification of functional equivalents of well-characterized Arabidopsis genes in other plants is a nontrivial task. It is well documented that transcriptionally coordinated genes tend to be functionally related and that such relationships may be conserved across different species and even kingdoms. To exploit such relationships, we constructed whole-genome coexpression networks for Arabidopsis and six important plant crop species. The interactive networks, clustered using the HCCA algorithm, are provided under the banner PlaNet (http://aranet.mpimp-golm.mpg.de). We implemented a comparative network algorithm that estimates similarities between network structures. Thus, the platform can be used to swiftly infer similar coexpressed network vicinities within and across species and can predict the identity of functional homologs. We exemplify this using the PSA-D and chalcone synthase-related gene networks. Finally, we assessed how ontology terms are transcriptionally connected in the seven species and provide the corresponding MapMan term coexpression networks. The data support the contention that this platform will considerably improve transfer of knowledge generated in Arabidopsis to valuable crop species.

Co-ordination and divergence of cell-specific transcription and translation of genes in arabidopsis root cells.

BACKGROUND AND AIMS: A key challenge in biology is to systematically investigate and integrate the different levels of information available at the global and single-cell level. Recent studies have elucidated spatiotemporal expression patterns of root cell types in Arabidopsis thaliana, and genome-wide quantification of polysome-associated mRNA levels, i.e. the translatome, has also been obtained for corresponding cell types. Translational control has been increasingly recognized as an important regulatory step in protein synthesis. The aim of this study was to investigate coupled transcription and translation by use of publicly available root datasets. METHODS: Using cell-type-specific datasets of the root transcriptome and translatome of arabidopsis, a systematic assessment was made of the degree of co-ordination and divergence between these two levels of cellular organization. The computational analysis considered correlation and variation of expression across cell types at both system levels, and also provided insights into the degree of co-regulatory relationships that are preserved between the two processes. KEY RESULTS: The overall correlation of expression and translation levels of genes resemble an almost bimodal distribution (mean/median value of 0·08/0·12), with a second, less strongly pronounced 'mode' for negative Pearson's correlation coefficient values. The analysis conducted also confirms that previously identified key transcriptional activators of secondary cell wall development display highly conserved patterns of transcription and translation across the investigated cell types. Moreover, the biological processes that display conserved and divergent patterns based on the cell-type-specific expression and translation levels were identified. CONCLUSIONS: In agreement with previous studies in animal cells, a large degree of uncoupling was found between the transcriptome and translatome. However, components and processes were also identified that are under co-ordinated transcriptional and translational control in plant root cells.

Additional role of O-acetylserine as a sulfur status-independent regulator during plant growth.

O-acetylserine (OAS) is one of the most prominent metabolites whose levels are altered upon sulfur starvation. However, its putative role as a signaling molecule in higher plants is controversial. This paper provides further evidence that OAS is a signaling molecule, based on computational analysis of time-series experiments and on studies of transgenic plants conditionally displaying increased OAS levels. Transcripts whose levels correlated with the transient and specific increase in OAS levels observed in leaves of Arabidopsis thaliana plants 5-10 min after transfer to darkness and with diurnal oscillation of the OAS content, showing a characteristic peak during the night, were identified. Induction of a serine-O-acetyltransferase gene (SERAT) in transgenic A. thaliana plants expressing the genes under the control of an inducible promoter resulted in a specific time-dependent increase in OAS levels. Monitoring the transcriptome response at time points at which no changes in sulfur-related metabolites except OAS were observed and correlating this with the light/dark transition and diurnal experiments resulted in identification of six genes whose expression was highly correlated with that of OAS (adenosine-5'-phosphosulfate reductase 3, sulfur-deficiency-induced 1, sulfur-deficiency-induced 2, low-sulfur-induced 1, serine hydroxymethyltransferase 7 and ChaC-like protein). These data suggest that OAS displays a signalling function leading to changes in transcript levels of a specific gene set irrespective of the sulfur status of the plant. Additionally, a role for OAS in a specific part of the sulfate response can be deduced.

High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions.

The time-resolved response of Arabidopsis thaliana towards changing light and/or temperature at the transcriptome and metabolome level is presented. Plants grown at 21°C with a light intensity of 150 µE m⁻² sec⁻¹ were either kept at this condition or transferred into seven different environments (4°C, darkness; 21°C, darkness; 32°C, darkness; 4°C, 85 µE m⁻² sec⁻¹; 21 °C, 75 µE m⁻² sec⁻¹; 21°C, 300 µE m⁻² sec⁻¹ ; 32°C, 150 µE m⁻² sec⁻¹). Samples were taken before (0 min) and at 22 time points after transfer resulting in (8×) 22 time points covering both a linear and a logarithmic time series totaling 177 states. Hierarchical cluster analysis shows that individual conditions (defined by temperature and light) diverge into distinct trajectories at condition-dependent times and that the metabolome follows different kinetics from the transcriptome. The metabolic responses are initially relatively faster when compared with the transcriptional responses. Gene Ontology over-representation analysis identifies a common response for all changed conditions at the transcriptome level during the early response phase (5-60 min). Metabolic networks reconstructed via metabolite-metabolite correlations reveal extensive environment-specific rewiring. Detailed analysis identifies conditional connections between amino acids and intermediates of the tricarboxylic acid cycle. Parallel analysis of transcriptional changes strongly support a model where in the absence of photosynthesis at normal/high temperatures protein degradation occurs rapidly and subsequent amino acid catabolism serves as the main cellular energy supply. These results thus demonstrate the engagement of the electron transfer flavoprotein system under short-term environmental perturbations.

Metabolomic and transcriptomic stress response of Escherichia coli.

Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.

PRIDE: new developments and new datasets.

The PRIDE (http://www.ebi.ac.uk/pride) database of protein and peptide identifications was previously described in the NAR Database Special Edition in 2006. Since this publication, the volume of public data in the PRIDE relational database has increased by more than an order of magnitude. Several significant public datasets have been added, including identifications and processed mass spectra generated by the HUPO Brain Proteome Project and the HUPO Liver Proteome Project. The PRIDE software development team has made several significant changes and additions to the user interface and tool set associated with PRIDE. The focus of these changes has been to facilitate the submission process and to improve the mechanisms by which PRIDE can be queried. The PRIDE team has developed a Microsoft Excel workbook that allows the required data to be collated in a series of relatively simple spreadsheets, with automatic generation of PRIDE XML at the end of the process. The ability to query PRIDE has been augmented by the addition of a BioMart interface allowing complex queries to be constructed. Collaboration with groups outside the EBI has been fruitful in extending PRIDE, including an approach to encode iTRAQ quantitative data in PRIDE XML.

Aclonifen targets solanesyl diphosphate synthase, representing a novel mode of action for herbicides.

BACKGROUND: Aclonifen is a unique diphenyl ether herbicide. Despite its structural similarities to known inhibitors of the protoporphyrinogen oxidase (e.g. acifluorfen, bifenox or oxadiazon), which result in leaf necrosis, aclonifen causes a different phenotype that is described as bleaching. This also is reflected by the Herbicide Resistance Action Committee (HRAC) classification that categorizes aclonifen as an inhibitor of pigment biosynthesis with an unknown target. RESULTS: A comprehensive Arabidopsis thaliana RNAseq dataset comprising 49 different inhibitor treatments and covering 40 known target pathways was used to predict the aclonifen mode of action (MoA) by a random forest classifier. The classifier predicts for aclonifen a MoA within the carotenoid biosynthesis pathway similar to the reference compound norflurazon that inhibits the phytoene desaturase. Upon aclonifen treatment, the phytoene desaturation reaction is disturbed, resulting in a characteristic phytoene accumulation in vivo. However, direct enzyme inhibition by the herbicide was excluded for known herbicidal targets such as phytoene desaturase, 4-hydroxyphenylpyruvate dioxygenase and homogentisate solanesyltransferase. Eventually, the solanesyl diphosphate synthase (SPS), providing one of the two homogentisate solanesyltransferase substrate molecules, could be identified as the molecular target of aclonifen. Inhibition was confirmed using biochemical activity assays for the A. thaliana SPSs 1 and 2. Furthermore, a Chlamydomonas reinhardtii homolog was used for co-crystallization of the enzyme-inhibitor complex, showing that one inhibitor molecule binds at the interface between two protein monomers. CONCLUSION: Solanesyl diphosphate synthase was identified as the target of aclonifen, representing a novel mode of action for herbicides. © 2020 Society of Chemical Industry.

Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects.

Glucocorticoids are indispensable anti-inflammatory and decongestant drugs with high prevalence of use at (~)0.9% of the adult population. Better holistic insights into glucocorticoid-induced changes are crucial for effective use as concurrent medication and management of adverse effects. The profiles of 214 metabolites from plasma of 20 male healthy volunteers were recorded prior to and after ingestion of a single dose of 4 mg dexamethasone (+20 mg pantoprazole). Samples were drawn at three predefined time points per day: seven untreated (day 1 midday - day 3 midday) and four treated (day 3 evening - day 4 evening) per volunteer. Statistical analysis revealed tremendous impact of dexamethasone on the metabolome with 150 of 214 metabolites being significantly deregulated on at least one time point after treatment (ANOVA, Benjamini-Hochberg corrected, q < 0.05). Inter-person variability was high and remained uninfluenced by treatment. The clearly visible circadian rhythm prior to treatment was almost completely suppressed and deregulated by dexamethasone. The results draw a holistic picture of the severe metabolic deregulation induced by single-dose, short-term glucocorticoid application. The observed metabolic changes suggest a potential for early detection of severe side effects, raising hope for personalized early countermeasures increasing quality of life and reducing health care costs.

Differential metabolic and coexpression networks of plant metabolism.

Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.

Biological cluster evaluation for gene function prediction.

Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.

Concurrent conditional clustering of multiple networks: COCONETS.

The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components). We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities) which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by existing experimental evidence. Therefore, the comparison of multiple networks based on concurrent conditional clustering offers a novel venue for detection and investigation of preserved network substructures.

Principal components analysis.

Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored. The visualization and statistical analysis of these new variables, the principal components, can help to find similarities and differences between samples. Important original variables that are the major contributors to the first few components can be discovered as well.This chapter seeks to deliver a conceptual understanding of PCA as well as a mathematical description. We describe how PCA can be used to analyze different datasets, and we include practical code examples. Possible shortcomings of the methodology and ways to overcome these problems are also discussed.

Network-based segmentation of biological multivariate time series.

Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into [Formula: see text] segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.