SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks

Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI

Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular interactions, unknown parameters need to be estimated. Most of biological data are expressed in relative or arbitrary units, raising the question of how to compare model simulations with data. It has recently been shown that for models with large number of unknown parameters, fitting algorithms using a data-driven normalization of the simulations (DNS) performs best in terms of the convergence time and parameter identifiability. DNS approach compares model simulations and corresponding data both normalized by the same normalization procedure, without requiring additional parameters to be estimated, as necessary for widely used scaling factor-based methods. However, currently there is no parameter estimation software that directly supports DNS. In this chapter, we show how to apply DNS to dynamic models of systems and synthetic biology using PEPSSBI (Parameter Estimation Pipeline for Systems and Synthetic Biology). PEPSSBI is the first software that supports DNS, through algorithmically supported data normalization and objective function construction. PEPSSBI also supports model import using SBML and repeated parameter estimation runs executed in parallel either on a personal computer or a multi-CPU cluster.

ATG13 dynamics in nonselective autophagy and mitophagy: insights from live imaging studies and mathematical modeling

During macroautophagy/autophagy, the ULK complex nucleates autophagic precursors, which give rise to autophagosomes. We analyzed, by live imaging and mathematical modeling, the translocation of ATG13 (part of the ULK complex) to the autophagic puncta in starvation-induced autophagy and ivermectin-induced mitophagy. In nonselective autophagy, the intensity and duration of ATG13 translocation approximated a normal distribution, whereas wortmannin reduced this effect and shifted to a log-normal distribution. During mitophagy, multiple translocations of ATG13 with increasing time between peaks were observed. We hypothesized that these multiple translocations arise because the engulfment of mitochondrial fragments required successive nucleation of phagophores on the same target, and a mathematical model based on this idea reproduced the oscillatory behavior. Significantly, model and experimental data were also in agreement that the number of ATG13 translocations is directly proportional to the diameter of the targeted mitochondrial fragments. Thus, our data provide novel insights into the early dynamics of selective and nonselective autophagy.Abbreviations: ATG: autophagy related 13; CFP: cyan fluorescent protein; dsRED: Discosoma red fluorescent protein; GABARAP: GABA type A receptor-associated protein; GFP: green fluorescent protein; IVM: ivermectin; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; MTORC1: mechanistic target of rapamycin kinase complex 1; PIK3C3/VPS34: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3P: PtdIns-3-phosphate; ULK: unc-51 like autophagy activating kinase.

Neurogranin stimulates Ca2+/calmodulin-dependent kinase II by suppressing calcineurin activity at specific calcium spike frequencies

Calmodulin sits at the center of molecular mechanisms underlying learning and memory. Its complex and sometimes opposite influences, mediated via the binding to various proteins, are yet to be fully understood. Calcium/calmodulin-dependent protein kinase II (CaMKII) and calcineurin (CaN) both bind open calmodulin, favoring Long-Term Potentiation (LTP) or Depression (LTD) respectively. Neurogranin binds to the closed conformation of calmodulin and its impact on synaptic plasticity is less clear. We set up a mechanistic computational model based on allosteric principles to simulate calmodulin state transitions and its interactions with calcium ions and the three binding partners mentioned above. We simulated calcium spikes at various frequencies and show that neurogranin regulates synaptic plasticity along three modalities. At low spike frequencies, neurogranin inhibits the onset of LTD by limiting CaN activation. At intermediate frequencies, neurogranin facilitates LTD, but limits LTP by precluding binding of CaMKII with calmodulin. Finally, at high spike frequencies, neurogranin promotes LTP by enhancing CaMKII autophosphorylation. While neurogranin might act as a calmodulin buffer, it does not significantly preclude the calmodulin opening by calcium. On the contrary, neurogranin synchronizes the opening of calmodulin's two lobes and promotes their activation at specific frequencies. Neurogranin suppresses basal CaN activity, thus increasing the chance of CaMKII trans-autophosphorylation at high-frequency calcium spikes. Taken together, our study reveals dynamic regulatory roles played by neurogranin on synaptic plasticity, which provide mechanistic explanations for opposing experimental findings.

PyCoTools: a Python toolbox for COPASI

Motivation

COPASI is an open source software package for constructing, simulating and analyzing dynamic models of biochemical networks. COPASI is primarily intended to be used with a graphical user interface but often it is desirable to be able to access COPASI features programmatically, with a high level interface.

Results

PyCoTools is a Python package aimed at providing a high level interface to COPASI tasks with an emphasis on model calibration. PyCoTools enables the construction of COPASI models and the execution of a subset of COPASI tasks including time courses, parameter scans and parameter estimations. Additional ‘composite’ tasks which use COPASI tasks as building blocks are available for increasing parameter estimation throughput, performing identifiability analysis and performing model selection. PyCoTools supports exploratory data analysis on parameter estimation data to assist with troubleshooting model calibrations. We demonstrate PyCoTools by posing a model selection problem designed to show case PyCoTools within a realistic scenario. The aim of the model selection problem is to test the feasibility of three alternative hypotheses in explaining experimental data derived from neonatal dermal fibroblasts in response to TGF-β over time. PyCoTools is used to critically analyze the parameter estimations and propose strategies for model improvement.

Availability and implementation

PyCoTools can be downloaded from the Python Package Index (PyPI) using the command ’pip install pycotools’ or directly from GitHub (https://github.com/CiaranWelsh/pycotools). Documentation at http://pycotools.readthedocs.io.

Supplementary information

Supplementary data are available at Bioinformatics online.

PTEN Regulates PI(3,4)P2 Signaling Downstream of Class I PI3K

The PI3K signaling pathway regulates cell growth and movement and is heavily mutated in cancer. Class I PI3Ks synthesize the lipid messenger PI(3,4,5)P₃. PI(3,4,5)P₃ can be dephosphorylated by 3- or 5-phosphatases, the latter producing PI(3,4)P₂. The PTEN tumor suppressor is thought to function primarily as a PI(3,4,5)P₃ 3-phosphatase, limiting activation of this pathway. Here we show that PTEN also functions as a PI(3,4)P₂ 3-phosphatase, both in vitro and in vivo. PTEN is a major PI(3,4)P₂ phosphatase in Mcf10a cytosol, and loss of PTEN and INPP4B, a known PI(3,4)P₂ 4-phosphatase, leads to synergistic accumulation of PI(3,4)P₂, which correlated with increased invadopodia in epidermal growth factor (EGF)-stimulated cells. PTEN deletion increased PI(3,4)P₂ levels in a mouse model of prostate cancer, and it inversely correlated with PI(3,4)P₂ levels across several EGF-stimulated prostate and breast cancer lines. These results point to a role for PI(3,4)P₂ in the phenotype caused by loss-of-function mutations or deletions in PTEN.

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PTEN is a PI(3,4)P₂ 3-phosphatase

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PTEN and INPP4B regulate PI(3,4)P₂ accumulation downstream of class I PI3K

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PTEN regulates PI(3,4)P₂-dependent activation of Akt and formation of invadopodia

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PI(3,4)P₂ signaling may play a role in the tumor suppressor function of PTEN

Reciprocal regulation of ARPP-16 by PKA and MAST3 kinases provides a cAMP-regulated switch in protein phosphatase 2A inhibition

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

DOI:http://dx.doi.org/10.7554/eLife.24998.001