Software that goes with the flow in systems biology

A recent article in BMC Bioinformatics describes new advances in workflow systems for computational modeling in systems biology. Such systems can accelerate, and improve the consistency of, modeling through automation not only at the simulation and results-production stages, but also at the model-generation stage. Their work is a harbinger of the next generation of more powerful software for systems biologists

Identification of Pigment Epithelium-Derived Factor Protein Forms with Distinct Activities on Tumor Cell Lines

Purpose. Pigment epithelium-derived factor (PEDF) is a multifunctional serpin. The purpose of this study is to identify PEDF protein forms and investigate their biological activities on tumor cell lines. Methods. Recombinant human PEDF proteins were purified by cation- and anion-exchange column chromatography. They were subjected to SDS-PAGE, IEF, deglycosylation, heparin affinity chromatography, and limited proteolysis. Cell viability, real-time electrical impedance of cells, and wound healing assays were performed using bladder and breast cancer cell lines, rat retinal R28, and human ARPE-19 cells. Results. Two PEDF protein peaks were identified after anion-exchange column chromatography: PEDF-1 eluting with lower ionic strength than PEDF-2. PEDF-1 had higher pI value and lower apparent molecular weight than PEDF-2. Both PEDF forms were glycosylated, bound to heparin, and had identical patterns by limited proteolysis. However, PEDF-2 emerged as being highly potent in lowering cell viability in all tumor cell lines tested, and in inhibiting tumor and ARPE-19 cell migration. In contrast, PEDF-1 minimally affected tumor cell viability and cell migration but protected R28 cells against death caused by serum starvation. Conclusion. Two distinct biochemical forms of PEDF varying in overall charge have distinct biological effects on tumor cell viability and migration. The existence of PEDF forms may explain the multifunctional modality of PEDF

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Orton et al.BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway. RESULTS: We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors. CONCLUSION: Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.This work was funded by the Department of Trade and Industry (DTI), under their Bioscience Beacon project programme. AG was funded by an industrial PhD studentship from Scottish Enterprise and Cyclacel

Evidence for concerted and mosaic brain evolution in dragon lizards

The brain plays a critical role in a wide variety of functions including behaviour, perception, motor control, and homeostatic maintenance. Each function can undergo different selective pressures over the course of evolution, and as selection acts on the outputs of brain function, it necessarily alters the structure of the brain. Two models have been proposed to explain the evolutionary patterns observed in brain morphology. The concerted brain evolution model posits that the brain evolves as a single unit and the evolution of different brain regions are coordinated. The mosaic brain evolution model posits that brain regions evolve independently of each other. It is now understood that both models are responsible for driving changes in brain morphology; however, which factors favour concerted or mosaic brain evolution is unclear. Here, we examined the volumes of the 6 major neural subdivisions across 14 species of the agamid lizard genus Ctenophorus (dragons). These species have diverged multiple times in behaviour, ecology, and body morphology, affording a unique opportunity to test neuroevolutionary models across species. We assigned each species to an ecomorph based on habitat use and refuge type, then used MRI to measure total and regional brain volume. We found evidence for both mosaic and concerted brain evolution in dragons: concerted brain evolution with respect to body size, and mosaic brain evolution with respect to ecomorph. Specifically, all brain subdivisions increase in volume relative to body size, yet the tectum and rhombencephalon also show opposite patterns of evolution with respect to ecomorph. Therefore, we find that both models of evolution are occurring simultaneously in the same structures in dragons, but are only detectable when examining particular drivers of selection. We show that the answer to the question of whether concerted or mosaic brain evolution is detected in a system can depend more on the type of selection measured than on the clade of animals studied. (C) 2017 S. Karger AG, Base

PlantSimLab - a modeling and simulation web tool for plant biologists.

BACKGROUND: At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists. RESULTS: This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers. CONCLUSIONS: Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise

MRI atlas of a lizard brain

Magnetic resonance imaging (MRI) is an established technique for neuroanatomical analysis, being particularly useful in the medical sciences. However, the application of MRI to evolutionary neuroscience is still in its infancy. Few magnetic resonance brain atlases exist outside the standard model organisms in neuroscience and no magnetic resonance atlas has been produced for any reptile brain. A detailed understanding of reptilian brain anatomy is necessary to elucidate the evolutionary origin of enigmatic brain structures such as the cerebral cortex. Here, we present a magnetic resonance atlas for the brain of a representative squamate reptile, the Australian tawny dragon (Agamidae: Ctenophorus decresii), which has been the subject of numerous ecological and behavioral studies. We used a high-field 11.74T magnet, a paramagnetic contrasting-enhancing agent and minimum-deformation modeling of the brains of thirteen adult male individuals. From this, we created a high-resolution three-dimensional model of a lizard brain. The 3D-MRI model can be freely downloaded and allows a better comprehension of brain areas, nuclei, and fiber tracts, facilitating comparison with other species and setting the basis for future comparative evolution imaging studies. The MRI model and atlas of a tawny dragon brain (Ctenophorus decresii) can be viewed online and downloaded using the Wiley Biolucida Server at wiley.biolucida.net.Government of Australia, Grant/Award Numbers: APA#31/2011, IPRS#1182/2010; National Science and Engineering Research Council of Canada, Grant/Award Number: PGSD3-415253-2012; Quebec Nature and Technology Research Fund, Grant/AwardNumber: 208332; National Imaging Facility of Australia; Spanish Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number:BFU2015-68537-

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/

The Vehicle, June 1960, Vol. 2 no. 3

Vol. 2, No. 3 To the ReaderRobert Mills Frenchpage 2 Blue-Nosed RobinThomas McPeakpage 3 Forest EtudeJames M. Jenkinsonpage 7 Chant For The MenJerry Whitepage 8 It\u27s OK Now, Chief J.B. Youngpage 9 Magic WordsKathleen Ferreepage 11 SpurnedRay Hoopspage 12 Danger!A. Seerpage 13 GenecideGeorge Fosterpage 14 To a Stern ParentC.E.S.page 14 ReservationNeil O. Parkerpage 14 The Worm and IRichard Blairpage 15 One Way -- Non-TransferableRobert Mills Frenchpage 15 NorthlightEDSpage 16https://thekeep.eiu.edu/vehicle/1007/thumbnail.jp

Examination of effects of GSK3β phosphorylation, β-catenin phosphorylation, and β-catenin degradation on kinetics of Wnt signaling pathway using computational method

<p>Abstract</p> <p>Background</p> <p>Recent experiments have explored effects of activities of kinases other than the well-studied GSK3β, in wnt pathway signaling, particularly at the level of β-catenin. It has also been found that the kinase PKA attenuates β-catenin degradation. However, the effects of these kinases on the level and degradation of β-catenin and the resulting downstream transcription activity remain to be clarified. Furthermore, the effect of GSK3β phosphorylation on the β-catenin level has not been examined computationally. In the present study, the effects of phosphorylation of GSK3β and of phosphorylations and degradation of β-catenin on the kinetics of the wnt signaling pathway were examined computationally.</p> <p>Methods</p> <p>The well-known computational Lee-Heinrich kinetic model of the wnt pathway was modified to include these effects. The rate laws of reactions in the modified model were solved numerically to examine these effects on β-catenin level.</p> <p>Results</p> <p>The computations showed that the β-catenin level is almost linearly proportional to the phosphorylation activity of GSK3β. The dependence of β-catenin level on the phosphorylation and degradation of free β-catenin and downstream TCF activity can be analyzed with an approximate, simple function of kinetic parameters for added reaction steps associated with effects examined, rationalizing the experimental results.</p> <p>Conclusion</p> <p>The phosphorylations of β-catenin by kinases other than GSK3β involve free unphorphorylated β-catenin rather than GSK3β-phosphorylated β-catenin*. In order to account for the observed enhancement of TCF activity, the β-catenin dephosphorylation step is essential, and the kinetic parameters of β-catenin phosphorylation and degradation need to meet a condition described in the main text. These findings should be useful for future experiments.</p

Exploring hypotheses of the actions of TGF-beta 1 in epidermal wound healing using a 3D computational multiscale model of the human epidermis

In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-beta 1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-beta 1 literature-derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units ( keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-beta 1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged ( by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-beta 1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-beta 1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing