Aidan MacNamara

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

BackgroundOptimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools.ResultsWe present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods.ConclusionsMEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

State–time spectrum of signal transduction logic models

Despite the current wealth of high-throughput data, our understanding of signal transduction is still incomplete. Mathematical modeling can be a tool to gain an insight into such processes. Detailed biochemical modeling provides deep understanding, but does not scale well above relatively a few proteins. In contrast, logic modeling can be used where the biochemical knowledge of the system is sparse and, because it is parameter free (or, at most, uses relatively a few parameters), it scales well to large networks that can be derived by manual curation or retrieved from public databases. Here, we present an overview of logic modeling formalisms in the context of training logic models to data, and specifically the different approaches to modeling qualitative to quantitative data (state) and dynamics (time) of signal transduction. We use a toy model of signal transduction to illustrate how different logic formalisms (Boolean, fuzzy logic and differential equations) treat state and time. Different formalisms allow for different features of the data to be captured, at the cost of extra requirements in terms of computational power and data quality and quantity. Through this demonstration, the assumptions behind each formalism are discussed, as well as their advantages and disadvantages and possible future developments.

A rapidly reversible chemical dimerizer system to study lipid signaling in living cells

Chemical dimerizers are powerful tools for non-invasive manipulation of enzyme activities in intact cells. Here we introduce the first rapidly reversible small-molecule-based dimerization system and demonstrate a sufficiently fast switch-off to determine kinetics of lipid metabolizing enzymes in living cells. We applied this new method to induce and stop phosphatidylinositol 3-kinase (PI3K) activity, allowing us to quantitatively measure the turnover of phosphatidylinositol 3,4,5-trisphosphate (PIP3) and its downstream effectors by confocal fluorescence microscopy as well as standard biochemical methods.

PIP3 Induces the Recycling of Receptor Tyrosine Kinases

EGFR is recycled to the cell surface in response to the phosphoinositide PIP3. Recycling Receptors The epidermal growth factor receptor (EGFR) promotes cellular proliferation. Activation of EGFRs by ligand binding typically leads to receptor internalization and then degradation of the receptor, thereby terminating signaling downstream of the receptor. Laketa et al. found that high concentrations of the phosphoinositide PIP3 (phosphatidylinositol 3,4,5-trisphosphate) triggered the internalization of EGFRs and their recycling to the cell surface. Because high concentrations of PIP3 can be generated both physiologically and pathophysiologically, this mechanism could prevent activated EGFRs from degradation, diverting them back to the surface to sustain the cell’s response to EGF. Down-regulation of receptor tyrosine kinases such as the epidermal growth factor receptor (EGFR) is achieved by endocytosis of the receptor followed by degradation or recycling. We demonstrated that in the absence of ligand, increased phosphatidylinositol 3,4,5-trisphosphate (PIP3) concentrations induced clathrin- and dynamin-mediated endocytosis of EGFR but not that of transferrin or G protein (heterotrimeric guanine nucleotide–binding protein)–coupled receptors. Endocytosis of the receptor in response to binding of EGF resulted in a decrease in the abundance of the EGFR, but PIP3-induced internalization decreased receptor ubiquitination and phosphorylation and resulted in recycling of the receptor to the plasma membrane. An RNA interference (RNAi) screen directed against lipid-binding domain–containing proteins identified polarity complex proteins, including PARD3 (partitioning defective 3), as essential for PIP3-induced receptor tyrosine kinase recycling. Thus, PIP3 and polarity complex proteins regulate receptor tyrosine kinase trafficking, which may enhance cellular responsiveness to growth factors.

Modeling Signaling Networks with Different Formalisms: A Preview

In the last 30 years, many of the mechanisms behind signal transduction, the process by which the cell takes extracellular signals as an input and converts them to a specific cellular phenotype, have been experimentally determined. With these discoveries, however, has come the realization that the architecture of signal transduction, the signaling network, is incredibly complex. Although the main pathways between receptor and output are well-known, there is a complex net of regulatory features that include crosstalk between different pathways, spatial and temporal effects, and positive and negative feedbacks. Hence, modeling approaches have been used to try and unravel some of these complexities. We use the mitogen-activated protein kinase cascade to illustrate chemical kinetic and logic approaches to modeling signaling networks. By using a common well-known model, we illustrate here the assumptions and level of detail behind each modeling approach, which serves as an introduction to the more detailed discussions of each in the accompanying chapters in this book.

A single-cell model of PIP3 dynamics using chemical dimerization.

Most cellular processes are driven by simple biochemical mechanisms such as protein and lipid phosphorylation, but the sum of all these conversions is exceedingly complex. Hence, intuition alone is not enough to discern the underlying mechanisms in the light of experimental data. Toward this end, mathematical models provide a conceptual and numerical framework to formally evaluate the plausibility of biochemical processes. To illustrate the use of these models, here we built a mechanistic computational model of PI3K (phosphatidylinositol 3-kinase) activity, to determine the kinetics of lipid metabolizing enzymes in single cells. The model is trained to data generated upon perturbation with a reversible small-molecule based chemical dimerization system that allows for the very rapid manipulation of the PIP3 (phosphatidylinositol 3,4,5-trisphosphate) signaling pathway, and monitored with live-cell microscopy. We find that the rapid relaxation system used in this work decreased the uncertainty of estimating kinetic parameters compared to methods based on in vitro assays. We also examined the use of Bayesian parameter inference and how the use of such a probabilistic method gives information on the kinetics of PI3K and PTEN activity.