Integrative analysis of metabolic models - from structure to dynamics

A detailed map of coupled circadian clock and cell cycle with qualitative dynamics validation

Background

The temporal coordination of biological processes by the circadian clock is an important mechanism, and its disruption has negative health outcomes, including cancer. Experimental and theoretical evidence suggests that the oscillators driving the circadian clock and the cell cycle are coupled through phase locking.

Results

We present a detailed and documented map of known mechanisms related to the regulation of the circadian clock, and its coupling with an existing cell cycle map which includes main interactions of the mammalian cell cycle. The coherence of the merged map has been validated with a qualitative dynamics analysis. We verified that the coupled circadian clock and cell cycle maps reproduce the observed sequence of phase markers. Moreover, we predicted mutations that contribute to regulating checkpoints of the two oscillators.

Conclusions

Our approach underlined the potential key role of the core clock protein NR1D1 in regulating cell cycle progression. We predicted that its activity influences negatively the progression of the cell cycle from phase G2 to M. This is consistent with the earlier experimental finding that pharmacological activation of NR1D1 inhibits tumour cell proliferation and shows that our approach can identify biologically relevant species in the context of large and complex networks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04158-9.

Qualitative dynamics semantics for SBGN process description

Background

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far.

Results

We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F.

Conclusion

The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0285-0) contains supplementary material, which is available to authorized users.

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants

Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.

Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis

The early stages of carcinogenesis are linked to defects in the cell cycle. A series of cell cycle checkpoints are involved in this process. The G1/S checkpoint that serves to integrate the control of cell proliferation and differentiation is linked to carcinogenesis and the mitotic spindle checkpoint is associated with the development of chromosomal instability. This paper presents the outcome of systems biology studies designed to evaluate if networks of covariate cell cycle gene transcripts exist in proliferative mammalian tissues including mice, rats, and humans. The GeneNetwork website that contains numerous gene expression datasets from different species, sexes, and tissues represents the foundational resource for these studies (www.genenetwork.org). In addition, WebGestalt, a gene ontology tool, facilitated the identification of expression networks of genes that co-vary with key cell cycle targets, especially Cdc20 and Plk1 (www.bioinfo.vanderbilt.edu/webgestalt). Cell cycle expression networks of such covariate mRNAs exist in multiple proliferative tissues including liver, lung, pituitary, adipose, and lymphoid tissues among others but not in brain or retina that have low proliferative potential. Sixty-three covariate cell cycle gene transcripts (mRNAs) compose the average cell cycle network with P = e(-13) to e(-36) . Cell cycle expression networks show species, sex and tissue variability, and they are enriched in mRNA transcripts associated with mitosis, many of which are associated with chromosomal instability.