An efficient procedure to assist in the re-parametrization of structurally unidentifiable models
Joubert, D. ; Stigter, J.D. ; Molenaar, J. - \ 2020
Mathematical Biosciences 323 (2020). - ISSN 0025-5564
Correlated parameter sets - Re-parametrization - State transformation - Structural identifiability - Systems biology
An efficient method that assists in the re-parametrization of structurally unidentifiable models is introduced. It significantly reduces computational demand by combining numerical and symbolic identifiability calculations. This hybrid approach facilitates the re-parametrization of large unidentifiable ordinary differential equation models, including models where state transformations are required. A model is first assessed numerically, to discover potential structurally unidentifiable parameters. We then use symbolic calculations to confirm the numerical results, after which we describe the algebraic relationships between the unidentifiable parameters. Finally, the unidentifiable parameters are substituted with new parameters and simplification ensures that all the unidentifiable parameters are eliminated from the original model structure. The novelty of this method is its utilisation of numerical results, which notably reduces the number of symbolic calculations required. We illustrate our procedure and the detailed re-parametrization process in 5 examples: (1) an immunological model, (2) a microbial growth model, (3) a lung cancer model, (4) a JAK/STAT model, and (5) a small linear model with a non-scalable re-parametrization.
SyNDI : Synchronous network data integration framework
Lindfors, Erno ; Dam, Jesse C.J. van; Lam, Carolyn Ming Chi ; Zondervan, Niels A. ; Martins dos Santos, Vitor A.P. ; Suarez-Diez, Maria - \ 2018
BMC Bioinformatics 19 (2018)1. - ISSN 1471-2105
Cytoscape - Galaxy - Mycobacterium tuberculosis - Network biology - Staphylococcus aureus - Synchronous network visualization - Systems biology - Workflow
Background: Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights. Results: In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified. Conclusions: Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.
Omics and systems biology : Integration of production and omics data in systems biology
Hettinga, Kasper ; Zhang, Lina - \ 2018
In: Proteomics in Domestic Animals Springer International Publishing - ISBN 9783319696812 - p. 463 - 485.
Biochemistry - Computation biology - Farm animal - Genomics - Interactomics - Metabolomics - Milk - Proteomics - Systems biology - Transcriptomics
Omics technologies have become of mainstream use in the study of farm animals, to better understand the physiology of the animal and the quality of the products produced by those animals. Such studies can be done at the level of genes, transcripts, proteins and/or metabolites. An important aspect of doing such omics studies is understanding of variation. For example, in relation to parity, lactation, feeding status and animal health, variation can happen in transcripts, proteins or metabolites found in farm animals and the products produced. This variation can help in better understanding the physiology of the animal. Also variation between individual animals exists, which may assist in better understanding of the animal's physiology. One limitation of the majority of the studies in this area is that they are performed using one specific omics technology. Integrating omics data captured using multiple omics technologies, using a systems biology approach, can shed more light on the biochemistry of the farm animal's physiology. At the end of this chapter, the outlook on such studies and the (software) developments that would be needed for optimal integration of omics data is discussed.
Studying microbial functionality within the gut ecosystem by systems biology
Hornung, Bastian ; Martins dos Santos, Vitor A.P. ; Smidt, Hauke ; Schaap, Peter J. - \ 2018
Genes & Nutrition 13 (2018)1. - ISSN 1555-8932
Community interactions - Genome scale metabolic model - Gut - Metagenome - Metatranscriptome - Microbial ecology - Microbiome - Modelling - NGS - Systems biology
Humans are not autonomous entities. We are all living in a complex environment, interacting not only with our peers, but as true holobionts; we are also very much in interaction with our coexisting microbial ecosystems living on and especially within us, in the intestine. Intestinal microorganisms, often collectively referred to as intestinal microbiota, contribute significantly to our daily energy uptake by breaking down complex carbohydrates into simple sugars, which are fermented to short-chain fatty acids and subsequently absorbed by human cells. They also have an impact on our immune system, by suppressing or enhancing the growth of malevolent and beneficial microbes. Our lifestyle can have a large influence on this ecosystem. What and how much we consume can tip the ecological balance in the intestine. A "western diet" containing mainly processed food will have a different effect on our health than a balanced diet fortified with pre- and probiotics. In recent years, new technologies have emerged, which made a more detailed understanding of microbial communities and ecosystems feasible. This includes progress in the sequencing of PCR-amplified phylogenetic marker genes as well as the collective microbial metagenome and metatranscriptome, allowing us to determine with an increasing level of detail, which microbial species are in the microbiota, understand what these microorganisms do and how they respond to changes in lifestyle and diet. These new technologies also include the use of synthetic and in vitro systems, which allow us to study the impact of substrates and addition of specific microbes to microbial communities at a high level of detail, and enable us to gather quantitative data for modelling purposes. Here, we will review the current state of microbiome research, summarizing the computational methodologies in this area and highlighting possible outcomes for personalized nutrition and medicine.
Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections
Vos, Marjon G.J. de; Zagorski, Marcin ; McNally, Alan ; Bollenbach, Tobias - \ 2017
Proceedings of the National Academy of Sciences of the United States of America 114 (2017)40. - ISSN 0027-8424 - p. 10666 - 10671.
Antibiotics - Infection - Microbiology - Systems biology
Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience.
Multi-level integration of environmentally perturbed internal phenotypes reveals key points of connectivity between them
Benis, Nirupama ; Kar, Soumya K. ; Martins dos Santos, Vitor A.P. ; Smits, Mari A. ; Schokker, Dirkjan ; Suarez-Diez, Maria - \ 2017
Frontiers in Physiology 8 (2017). - ISSN 1664-042X - 11 p.
Data integration - Gastrointestinal tract - Internal phenotype - Metabolomics - Microbiota - Proteomics - Systems biology - Transcriptomics
The genotype and external phenotype of organisms are linked by so-called internal phenotypes which are influenced by environmental conditions. In this study, we used five existing -omics datasets representing five different layers of internal phenotypes, which were simultaneously measured in dietarily perturbed mice. We performed 10 pair-wise correlation analyses verified with a null model built from randomized data. Subsequently, the inferred networks were merged and literature mined for co-occurrences of identified linked nodes. Densely connected internal phenotypes emerged. Forty-five nodes have links with all other data-types and we denote them "connectivity hubs." In literature, we found proof of 6% of the 577 connections, suggesting a biological meaning for the observed correlations. The observed connectivities between metabolite and cytokines hubs showed higher numbers of literature hits as compared to the number of literature hits on the connectivities between the microbiota and gene expression internal phenotypes. We conclude that multi-level integrated networks may help to generate hypotheses and to design experiments aiming to further close the gap between genotype and phenotype. We describe and/or hypothesize on the biological relevance of four identified multi-level connectivity hubs.
Theoretical approaches to understanding root vascular patterning : A consensus between recent models
Mellor, Nathan ; Adibi, Milad ; El-Showk, Sedeer ; Rybel, Bert De; King, John ; Mähönen, Ari Pekka ; Weijers, Dolf ; Bishopp, Anthony ; Etchells, Peter - \ 2017
Journal of Experimental Botany 68 (2017)1. - ISSN 0022-0957 - p. 5 - 16.
Auxin - Cytokinin - Mathematical modeling - Organ patterning - Systems biology - Vascular development
The root vascular tissues provide an excellent system for studying organ patterning, as the specification of these tissues signals a transition from radial symmetry to bisymmetric patterns. The patterning process is controlled by the combined action of hormonal signaling/transport pathways, transcription factors, and miRNA that operate through a series of non-linear pathways to drive pattern formation collectively. With the discovery of multiple components and feedback loops controlling patterning, it has become increasingly difficult to understand how these interactions act in unison to determine pattern formation in multicellular tissues. Three independent mathematical models of root vascular patterning have been formulated in the last few years, providing an excellent example of how theoretical approaches can complement experimental studies to provide new insights into complex systems. In many aspects these models support each other; however, each study also provides its own novel findings and unique viewpoints. Here we reconcile these models by identifying the commonalities and exploring the differences between them by testing how transferable findings are between models. New simulations herein support the hypothesis that an asymmetry in auxin input can direct the formation of vascular pattern. We show that the xylem axis can act as a sole source of cytokinin and specify the correct pattern, but also that broader patterns of cytokinin production are also able to pattern the root. By comparing the three modeling approaches, we gain further insight into vascular patterning and identify several key areas for experimental investigation.
The importance of endophenotypes to evaluate the relationship between genotype and external phenotype
Pas, Marinus F.W. te; Madsen, Ole ; Calus, Mario P.L. ; Smits, Mari A. - \ 2017
International Journal of Molecular Sciences 18 (2017)2. - ISSN 1661-6596
Bioinformatics - Genomic variation and environment - Integration - Livestock science - Metabolome - Methylome - Phenome - Proteome - Systems biology - Transcriptome
With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal’s transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal’s genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals’ microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.
Parameter estimation in tree graph metabolic networks
Astola, Laura ; Stigter, Hans ; Gomez Roldan, Maria Victoria ; Eeuwijk, Fred van; Hall, Robert D. ; Groenenboom, Marian ; Molenaar, Jaap J. - \ 2016
PeerJ 2016 (2016)9. - ISSN 2167-8359
Glycosylation - Kinetic models - Metabolic networks - Network inference - Solanum lycopersicum - Systems biology
We study the glycosylation processes that convert initially toxic substrates to nu- tritionally valuable metabolites in the flavonoid biosynthesis pathway of tomato (Solanum lycopersicum) seedlings. To estimate the reaction rates we use ordinary differential equations (ODEs) to model the enzyme kinetics. A popular choice is to use a system of linear ODEs with constant kinetic rates or to use Michaelis-Menten kinetics. In reality, the catalytic rates, which are affected among other factors by kinetic constants and enzyme concentrations, are changing in time and with the approaches just mentioned, this phenomenon cannot be described. Another problem is that, in general these kinetic coefficients are not always identifiable. A third problem is that, it is not precisely known which enzymes are catalyzing the observed glycosylation processes. With several hundred potential gene candidates, experimental validation using purified target proteins is expensive and time consuming. We aim at reducing this task via mathematical modeling to allow for the pre-selection of most potential gene candidates. In this article we discuss a fast and relatively simple approach to estimate time varying kinetic rates, with three favorable properties: firstly, it allows for identifiable estimation of time dependent parameters in networks with a tree- like structure. Secondly, it is relatively fast compared to usually applied methods that estimate the model derivatives together with the network parameters. Thirdly, by combining the metabolite concentration data with a corresponding microarray data, it can help in detecting the genes related to the enzymatic processes. By comparing the estimated time dynamics of the catalytic rates with time series gene expression data we may assess potential candidate genes behind enzymatic reactions. As an example, we show how to apply this method to select prominent glycosyltransferase genes in tomato seedlings.
Impact of Pathogen Population Heterogeneity and Stress-Resistant Variants on Food Safety
Abee, T. ; Koomen, J. ; Metselaar, K.I. ; Zwietering, M.H. ; Besten, H.M.W. Den - \ 2016
Annual Review of Food Science and Technology 7 (2016). - ISSN 1941-1413 - p. 439 - 456.
Food processing - Growth kinetics - Listeria monocytogenes - Systems biology
This review elucidates the state-of-the-art knowledge about pathogen population heterogeneity and describes the genotypic and phenotypic analyses of persister subpopulations and stress-resistant variants. The molecular mechanisms underlying the generation of persister phenotypes and genetic variants are identified. Zooming in on Listeria monocytogenes, a comparative whole-genome sequence analysis of wild types and variants that enabled the identification of mutations in variants obtained after a single exposure to lethal food-relevant stresses is described. Genotypic and phenotypic features are compared to those for persistent strains isolated from food processing environments. Inactivation kinetics, models used for fitting, and the concept of kinetic modeling-based schemes for detection of variants are presented. Furthermore, robustness and fitness parameters of L. monocytogenes wild type and variants are used to model their performance in food chains. Finally, the impact of stress-resistant variants and persistence in food processing environments on food safety is discussed.
Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
Megchelenbrink, Wout ; Katzir, Rotem ; Lu, Xiaowen ; Ruppin, Eytan ; Notebaart, Richard A. - \ 2015
Proceedings of the National Academy of Sciences of the United States of America 112 (2015)39. - ISSN 0027-8424 - p. 12217 - 12222.
Cancer - Genetic interactions - Human metabolism - Synthetic dosage lethality - Systems biology
Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.
Seasonal allergic rhinitis and systems biology-oriented biomarker discovery
Baars, E.W. ; Nierop, A.F.M. ; Savelkoul, H.F.J. - \ 2015
In: General Methods in Biomarker Research and their Applications Springer Netherlands - ISBN 9789400776968 - p. 1251 - 1275.
Biomarkers - Cytokines - Permuted stepwise regression - Seasonal allergic rhinitis - Systems biology
There is an increasing interest in science and medicine in the systems approach. Instead of the reductionist approach that focuses on the physical and chemical properties of the individual components, systems biology aims to describe, understand, and explain from the complex biological systems that are studied: All levels of structural and functional complexity, explicitly including the supracellular domain; their systems behavior or phenotypes; their networks with relationships that interact with the genome, the environment, and the phenotype; and their multifactorial processes involved in maintaining homeostasis and the breakdown of homeostasis within the system. This shift from a more reductionist to a more holistic approach on both the epistemological (theoretical) and the methodological level is also important for the conceptualization and the development of biomarkers. Based on the dataset of a randomized controlled trial on the effects of a treatment of seasonal allergic rhinitis, using five different methods of permuted stepwise regression, three systems biology-oriented immunological pattern variables (biomarkers) were developed that demonstrated larger CV correct values than the separate cytokines with regard to the classification of cytokine samples in baseline (before treatment) and post-baseline (after treatment). This example demonstrates that a systems biological approach in both the conceptualization and development of biomarkers is promising. However, more empirical studies with larger datasets are necessary to confirm the positive results of the presented study.