Systems biology and its usage of mathematical modeling to analyse biological data is rapidly becoming an established approach to biology. A crucial advantage of this approach is that more information can be extracted from observations of intricate dynamics, which allows nontrivial complex explanations to be evaluated and compared. In this minireview we explain this process, and review some of the most central available analysis tools. The focus is on the evaluation and comparison of given explanations for a given set of experimental data and prior knowledge. Three types of methods are discussed: (a) for evaluation of whether a given model is sufficiently able to describe the given data to be nonrejectable; (b) for evaluation of whether a slightly superior model is significantly better; and (c) for a general evaluation and comparison of the biologically interesting features in a model. The most central methods are reviewed, both in terms of underlying assumptions, including references to more advanced literature for the theoretically oriented reader, and in terms of practical guidelines and examples, for the practically oriented reader. Many of the methods are based upon analysis tools from statistics and engineering, and we emphasize that the systems biology focus on acceptable explanations puts these methods in a nonstandard setting. We highlight some associated future improvements that will be essential for future developments of model based data analysis in biology.
Background: Mechanisms of insulin resistance in type 2 diabetes are not known. Results: A first dynamic mathematical model based on data from human adipocytes yields systems level understanding of insulin resistance. Conclusion: Attenuation of an mTORC1-derived feedback in diabetes explains reduced sensitivity and signal strength throughout the insulin-signaling network. Significance: Findings give a molecular basis for insulin resistance in the signaling network.
It is often predicted that model-based data analysis will revolutionize biology, just as it has physics and engineering. A widely used tool within such analysis is hypothesis testing, which focuses on model rejections. However, the fact that a systems biology model is non-rejected is often a relatively weak statement, as such models usually are highly over-parametrized with respect to the available data, and both parameters and predictions may therefore be arbitrarily uncertain. For this reason, we formally define and analyse the concept of a core prediction. A core prediction is a uniquely identified property that must be fulfilled if the given model structure is to explain the data, even if the individual parameters are non-uniquely identified. It is shown that such a prediction is as strong a conclusion as a rejection. Furthermore, a new method for core prediction analysis is introduced, which is beneficial for the uncertainty of specific model properties, as the method only characterizes the space of acceptable parameters in the relevant directions. This avoids the curse of dimensionality associated with the generic characterizations used by previously proposed methods. Analysis on examples shows that the new method is comparable to profile likelihood with regard to practical identifiability, and thus generalizes profile likelihood to the more general problem of observability. If used, the concepts and methods presented herein make it possible to distinguish between a conclusion and a mere suggestion, which hopefully will contribute to a more justified confidence in systems biology analyses.
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between coexisting submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, modelbased data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.Hormonal control of target cells involves signal transduction from ligand-activated receptors to control of rate-limiting enzymes or proteins that affect key steps in metabolism or other processes within the cell. The signal transduction is carried out by a network of interacting signal mediators. A high degree of complexity is due to the presence of feedback and feed-forward loops, both negative and positive, and the fact that the importance of different interactions changes over time and according to intracellular location. This, in combination with redundancy, shared signal mediators, shared signal paths, and ample cross-talk between signals, leads to a complexity that poses new challenges to progress in dissecting and understanding cellular control. Many diseases, such as cancer and insulin resistance and type 2 diabetes, arise from malfunctioning in signaling networks.Insulin controls target cells through binding to its receptor at the cell surface (1), which activates the intracellular domains of the insulin receptor (IR) 4 to trans-autophosphorylate at specific tyrosine residues. The receptor can then transduce the insulin signal into the cell and to its various effectuating systems, such as glucose uptake and antilipolysis. Foremost of the directly downstream signal-mediating proteins are members of the insulin receptor substrate (IRS) family, in particular IRS1, which is rapidly phosphorylated at specific tyrosine residues by...
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