The ABC of Reverse Engineering Biological Signalling Systems
Molecular BioSystems, Jan 1, 2009
Modelling biological systems would be straightforward if we knew the structure of the model and t... more Modelling biological systems would be straightforward if we knew the structure of the model and the parameters governing their dynamics. For the overwhelming majority of biological processes, however, such parameter values are unknown and often impossible to measure directly. This means that we have to estimate or infer these parameters from observed data. Here we argue that it is also important to appreciate the uncertainty inherent in these estimates. We discuss a statistical approach--approximate Bayesian computation (ABC)--which allows us to approximate the posterior distribution over parameters and show how this can add insights into our understanding of the system dynamics. We illustrate the application of this approach and how the resulting posterior distribution can be analyzed in the context of the mitogen-activated protein kinase phosphorylation cascade. Our analysis also highlights the added benefit of using the distribution of parameters rather than point estimates of parameter values when considering the notion of sloppy models in systems biology.
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Papers by Maria Secrier
Neuronal migration, the process by which neurons migrate from their place of origin to their final position in the brain, is a central process for normal brain development and function. Advances in experimental techniques have revealed much about many of the molecular components involved in this process. Notwithstanding these advances, how the molecular machinery works together to govern the migration process has yet to be fully understood. Here we present a computational model of neuronal migration, in which four key molecular entities, Lis1, DCX, Reelin and GABA, form a molecular program that mediates the migration process.
Results
The model simulated the dynamic migration process, consistent with in-vivo observations of morphological, cellular and population-level phenomena. Specifically, the model reproduced migration phases, cellular dynamics and population distributions that concur with experimental observations in normal neuronal development. We tested the model under reduced activity of Lis1 and DCX and found an aberrant development similar to observations in Lis1 and DCX silencing expression experiments. Analysis of the model gave rise to unforeseen insights that could guide future experimental study. Specifically: (1) the model revealed the possibility that under conditions of Lis1 reduced expression, neurons experience an oscillatory neuron-glial association prior to the multipolar stage; and (2) we hypothesized that observed morphology variations in rats and mice may be explained by a single difference in the way that Lis1 and DCX stimulate bipolar motility. From this we make the following predictions: (1) under reduced Lis1 and enhanced DCX expression, we predict a reduced bipolar migration in rats, and (2) under enhanced DCX expression in mice we predict a normal or a higher bipolar migration.
Conclusions
We present here a system-wide computational model of neuronal migration that integrates theory and data within a precise, testable framework. Our model accounts for a range of observable behaviors and affords a computational framework to study aspects of neuronal migration as a complex process that is driven by a relatively simple molecular program. Analysis of the model generated new hypotheses and yet unobserved phenomena that may guide future experimental studies. This paper thus reports a first step toward a comprehensive in-silico model of neuronal migration.