Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Computational methods for integrated analysis of omics and pathway data

One of the key tenets of bioinformatics is to find ways to enable the interoperability of heterogeneous data sources and improve the integration of various biological data. High-throughput experimental methods continue to improve and become more easily accessible. This allows researchers to measure not just their specific gene or protein of interest, but the entirety of the biological machinery inside the cell. These measurements are referred to as omics , such as genomics, transcriptomics, proteomics, metabolomics, and fluxomics. Omics data is highly interrelated at the systems-level, as each type of molecule (DNA, RNA, protein, etc.) can interact with and have an impact on the other types. These interactions may be direct, such as the central dogma of biology that information flows from DNA to RNA to protein. They may also be indirect, such as the regulation of gene expression or metabolic feedback loops. Regardless, it is becoming apparent that multiple levels of omics data must be analyzed and understood simultaneously if we are to advance our understanding of systems-level biology. Much of our current biological knowledge is stored in public databases, most of which specialize in a particular type of omics or a specific organism. Despite efforts to improve consistency between databases, there are many challenges which can impede efforts to meaningfully compare or combine these resources. At a basic level, differences in naming and internal database ID assignments prevent simple mapping between objects in these databases. More fundamentally, though, is the lack of a standardized way to define equivalency between two functionally identical biological entities. One benefit of improving database interoperability is that targeted high quality data from one database can be used to improve another database. Comparison between MaizeCyc and CornCyc identified many manually curated GO annotations present in MaizeCyc but not in CornCyc. CycTools facilitates the transfer of high-quality annotation data from one database to another by automatically mapping equivalent objects in both databases. This java-based tool has a graphical user interface which guides users through the transfer process. A case study which uses two independent Zea Mays pathway databases, CornCyc and MaizeCyc, illustrates the challenges of comparing the content of even closely related resources. This example highlights the downstream implications that the choice of initial computational enzymatic function assignment pipelines and subsequent manual curation had on the overall scope and quality of the content of each database. We compare the prediction accuracy of the protein EC assignments for 177 maize enzymes between these resources and find that while MaizeCyc covers a broader scope of enzyme predictions, CornCyc predictions are more accurate. The advantage of high quality, integrated data resources must be realized through analysis methods which can account for multiple data types simultaneously. Due to the difficulty in obtaining systems-wide metabolic flux measurements, researchers have made several efforts to integrate transcriptional regulatory data with metabolic models in order to improve the accuracy of metabolic flux predictions. Transcriptional regulation involves the binding of transcription factors (i.e. proteins) to binding sites on the DNA in order to positively or negatively influence expression of the targeted gene. This has an indirect, downstream impact on the organism\u27s metabolism, as metabolic reactions depend on gene-derived enzymes in order to catalyze the reaction. A novel method is proposed which seeks to integrate transcriptional regulation and metabolic reactions data into a single model in order to investigate the interactions between metabolism and regulation. In contrast to existing methods which seek to use transcriptional regulation networks to limit the solution space of the constraint-based metabolic model, we seek to define a transcriptional regulatory space which can be associated with the metabolic distribution of interest. This allows us to make inferences about how changes in the regulatory network could lead to improved metabolic flux

Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

<div><p>There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach <i>Gene Expression and Metabolism Integrated for Network Inference</i> (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10⁻¹⁷²), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for <i>Saccharomyces cerevisiae</i> involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10⁻¹⁴) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at <a href="https://sourceforge.net/projects/gemini-data/" target="_blank">https://sourceforge.net/projects/gemini-data/</a></p></div

Metabolic characterization and viable delivery of Akkermansia muciniphila for its future application

The gut harbors a complex ecosystem in which many bacteria, both beneficial and pathogens, thrive. The potential importance of A. muciniphila as a member of the intestinal microbiota comes from the fact that A. muciniphila is reversely correlated with several diseases and reduce the fat mass gain of mice fed a high fat diet. We describe the use of genome-scale metabolic models to further understand the genetic and metabolic potential of microbiota members, as well as potential phenotypes and influence on the host. We emphasize the importance of culturing bacteria and provide an outline in which GEMs are used to aid in the development of minimal culture media. The use of GEMs for the development of minimal media was applied for A. muciniphila. We found that the essential components of A. muciniphila medium are L-threonine and either N-acetylglucosamine (GlcNAc) or N-acetylgalactosamine (GalNAc). The composition of the minimal medium was used to develop an animal component free medium. The addition of soy derived peptides increased the growth rate an yield, and the omission of animal components makes the cultured bacteria applicable in humans. We o analyzed the expression of the gene Amuc_1100, which was found to be involved in host signaling previously. There was no significant alteration in the expression of this genes, or genes in the associated gene cluster. In a subsequent experiment, we discovered that the anaerobic bacterium A. muciniphila is able to tolerate ambient . The addition of oxygen during growth increased the growth rate and yield, which was the result of cytochrome bd mediated oxygen reduction. To protect A. muciniphila during gastric passage, we encapsulated the cell in a water in oil in water double emulsion. We found a 100 fold higher survival of the encapsulated cells. We concluded that the double emulsion could be an effective matrix for the viable delivery of A. muciniphila. The final steps required for the application of A. muciniphila as therapeutic microbe are described shortly in the discussion, and are all within reach.</p