Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development

Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.Comment: 24 pages, 6 figures, 1 tabl

An adaptive space-time boundary element method for impulsive wave propagation in elastodynamics

Wave propagation in natural or man-made bodies is an important problem in civil engineering, electronic engineering and ocean engineering etc. Common examples of wave problems include earthquake wave modeling, ocean wave modeling, soil- structure interaction, geological prospecting, and acoustic or radio wave diffraction. The Boundary Element Method (BEM) is a widely-used numerical method to solve such problems in both science and engineering fields. However, conventional BEM modeling of wave problems encounters many difficulties. Firstly, the method is expensive since influence matrices are computed at each time step and BEM solutions at every former time step have to be stored. Secondly, if large time steps are used, inaccuracies arise in BEM solutions; but if small time steps are used, computational costs become impractical. Thirdly, the dimensionless space-time ratio must be limited to a narrow range to produce a stable solution. In this thesis, we attack these problems by introducing adaptive schemes and mesh refinement. Instead of using uniform meshes and uniform time steps, error indicators are employed to locate high-gradient areas; then mesh refinement in space-time is used to improve the resolution in those areas only. Another strategy is to introduce the space-time concept to track moving wave fronts. In wave problems, wave fronts move in space-time, and high gradients arise both in space and in time. It is thus inadequate to refine the mesh in space only because there are high gradients in time as well. Hence, besides a locally mesh refinement scheme employed in space, local time stepping is also used to improve the accuracy and efficiency of the algorithm. This adaptive scheme is implemented in the C language and used to solve scalar and electrodynamic 2D and 3D wave propagation problems in a open and closed field. Gradient-based and resolution-based error indicators are employed to locate these moving high-gradient areas. A space mesh refinement scheme and the local time stepping is used to refine the area to achieve higher accuracy. The adaptive BEM solver is 1.4 ~ 1.8 times faster than the conventional BEM solver. It is also more stable than the conventional BEM. We also parallelize the BEM solver to further improve its efficiency. Compared with the non-parallel code, using a 8-processor Linux cluster, a speed-up factor of four is achieved. This suggests that substantial further gains can be obtained if a larger parallel computer is available. July 11. 2007

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

BACKGROUND: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. RESULTS: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. CONCLUSION: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression

Large Scale Growth and Magnetic Properties of Fe and Fe₃O₄ Nanowires

Fe and Fe3O4 nanowires have been synthesized by thermal decomposition of Fe(CO)5, followed by heat treatments. The Fe wires are formed through the aggregation of nanoparticles generated by decomposition of Fe(CO)5. A core-shell structure with an iron oxide shell and Fe core is observed for the as-prepared Fe wires. Annealing in air leads to the formation of Fe2O3/Fe3O4 wires, which after heat treatment in a N2/alcohol atmosphere form Fe3O4 wires with a sharp Verwey [Nature (London) 144, 327 (1939)] transition at 125 K. The Fe3O4 wires have coercivities of 261 and 735 Oe along the wire axis at RT and 5 K, respectively. The large increase of coercivity at 5 K as compared to RT is due to the increase of anisotropy resulting from the Verwey transition

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

Semi-supervised learning (SSL) is a promising machine learning paradigm to address the issue of label scarcity in medical imaging. SSL methods were originally developed in image classification. The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations. However, image level perturbations violate the cluster assumption in the setting of segmentation. Moreover, existing image level perturbations are hand-crafted which could be sub-optimal. Therefore, it is a not trivial to straightforwardly adapt existing SSL image classification methods in segmentation. In this paper, we propose MisMatch, a semi-supervised segmentation framework based on the consistency between paired predictions which are derived from two differently learnt morphological feature perturbations. MisMatch consists of an encoder and two decoders. One decoder learns positive attention for foreground on unlabelled data thereby generating dilated features of foreground. The other decoder learns negative attention for foreground on the same unlabelled data thereby generating eroded features of foreground. We first develop a 2D U-net based MisMatch framework and perform extensive cross-validation on a CT-based pulmonary vessel segmentation task and show that MisMatch statistically outperforms state-of-the-art semi-supervised methods when only 6.25\% of the total labels are used. In a second experiment, we show that U-net based MisMatch outperforms state-of-the-art methods on an MRI-based brain tumour segmentation task. In a third experiment, we show that a 3D MisMatch outperforms a previous method using input level augmentations, on a left atrium segmentation task. Lastly, we find that the performance improvement of MisMatch over the baseline might originate from its better calibration

CONFORMATIONALLY STABILIZED HIV ENVELOPE IMMUNOGENS

Isolated immunogens including a HIV-1 gp120 polypeptide or immunogenic fragment thereof stabilized in a CD4 bound confirmation by crosslinked cysteines, and methods of their use are disclosed. The immunogens are useful, for example, for generating an immune response to HIV-1 gp120 in a Subject

CONFORMATIONALLY STABILIZED HIV ENVELOPE IMMUNOGENS

Isolated immunogens including a HIV-1 gp120 polypeptide or immunogenic fragment thereof stabilized in a CD4 bound confirmation by crosslinked cysteines, and methods of their use are disclosed. The immunogens are useful, for example, for generating an immune response to HIV-1 gp120 in a Subject

In-situ solvothermal processing of polycaprolactone/hydroxyapatite nanocomposites with enhanced mechanical and biological performance for bone tissue engineering

The interest in biodegradable polymer-matrix nanocomposites with bone regeneration potential has been increasing in recent years. In the present work, a solvothermal process is introduced to prepare hydroxyapatite (HA) nanorod-reinforced polycaprolactone in-situ. A non-aqueous polymer solution containing calcium and phosphorous precursors is prepared and processed in a closed autoclave at different temperatures in the range of 60–150 °C. Hydroxyapatite nanorods with varying aspect ratios are formed depending on the processing temperature. X-ray diffraction analysis and field-emission scanning electron microscopy indicate that the HA nanorods are semi-crystalline. Energy-dispersive X-ray spectroscopy and Fourier transform infrared spectrometry determine that the ratio of calcium to phosphorous increases as the processing temperature increases. To evaluate the effect of in-situ processing on the mechanical properties of the nanocomposites, highly porous scaffolds (>90%) containing HA nanorods are prepared by employing freeze drying and salt leaching techniques. It is shown that the elastic modulus and strength of the nanocomposites prepared by the in-situ method is superior (∼15%) to those of the ex-situ samples (blended HA nanorods with the polymer solution). The enhanced bone regeneration potential of the nanocomposites is shown via an in vitro bioactivity assay in a saturated simulated body fluid. An improved cell viability and proliferation is also shown by employing (3-(4,5- dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide) (MTT) assay in human osteosarcoma cell lines. The prepared scaffolds with in vitro regeneration capacity could be potentially useful for orthopaedic applications and maxillofacial surgery