Identification of Nonlinear State-Space Systems from Heterogeneous Datasets

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through (a) replicate measurements from the same biological system or (b) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed, heterogeneous data. To enable scale up to large number of features, the resulting non-convex optimisation problem is relaxed to a re-weighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalised eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres

Remnants of an ancient metabolism without phosphate

Phosphate is essential for all living systems, serving as a building block of genetic and metabolic machinery. However, it is unclear how phosphate could have assumed these central roles on primordial Earth, given its poor geochemical accessibility. We used systems biology approaches to explore the alternative hypothesis that a protometabolism could have emerged prior to the incorporation of phosphate. Surprisingly, we identified a cryptic phosphate-independent core metabolism producible from simple prebiotic compounds. This network is predicted to support the biosynthesis of a broad category of key biomolecules. Its enrichment for enzymes utilizing iron-sulfur clusters, and the fact that thermodynamic bottlenecks are more readily overcome by thioester rather than phosphate couplings, suggest that this network may constitute a "metabolic fossil" of an early phosphate-free nonenzymatic biochemistry. Our results corroborate and expand previous proposals that a putative thioester-based metabolism could have predated the incorporation of phosphate and an RNA-based genetic system. PAPERCLIP

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process

Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis