A variational approach to assess reaction coordinates for two-step crystallization.

Molecule- and particle-based simulations provide the tools to test, in microscopic detail, the validity of classical nucleation theory. In this endeavor, determining nucleation mechanisms and rates for phase separation requires an appropriately defined reaction coordinate to describe the transformation of an out-of-equilibrium parent phase for which myriad options are available to the simulator. In this article, we describe the application of the variational approach to Markov processes to quantify the suitability of reaction coordinates to study crystallization from supersaturated colloid suspensions. Our analysis indicates that collective variables (CVs) that correlate with the number of particles in the condensed phase, the system potential energy, and approximate configurational entropy often feature as the most appropriate order parameters to quantitatively describe the crystallization process. We apply time-lagged independent component analysis to reduce high-dimensional reaction coordinates constructed from these CVs to build Markov State Models (MSMs), which indicate that two barriers separate a supersaturated fluid phase from crystals in the simulated environment. The MSMs provide consistent estimates for crystal nucleation rates, regardless of the dimensionality of the order parameter space adopted; however, the two-step mechanism is only consistently evident from spectral clustering of the MSMs in higher dimensions. As the method is general and easily transferable, the variational approach we adopt could provide a useful framework to study controls for crystal nucleation.

Implementing core NICE guidelines for osteoarthritis in primary care with a model consultation (MOSAICS): a cluster randomised controlled trial.

OBJECTIVE: To determine the effectiveness of a model osteoarthritis consultation, compared with usual care, on physical function and uptake of National Institute for Health and Care Excellence (NICE) osteoarthritis recommendations, in adults ≥45 years consulting with peripheral joint pain in UK general practice. METHOD: Two-arm cluster-randomised controlled trial with baseline health survey. Eight general practices in England. PARTICIPANTS: 525 adults ≥45 years consulting for peripheral joint pain, amongst 28,443 population survey recipients. Four intervention practices delivered the model osteoarthritis consultation to patients consulting with peripheral joint pain; four control practices continued usual care. The primary clinical outcome of the trial was the SF-12 physical component score (PCS) at 6 months; the main secondary outcome was uptake of NICE core recommendations by 6 months, measured by osteoarthritis quality indicators. A Linear Mixed Model was used to analyse clinical outcome data (SF-12 PCS). Differences in quality indicator outcomes were assessed using logistic regression. RESULTS: 525 eligible participants were enrolled (mean age 67.3 years, SD 10.5; 59.6% female): 288 from intervention and 237 from control practices. There were no statistically significant differences in SF-12 PCS: mean difference at the 6-month primary endpoint was -0.37 (95% CI -2.32, 1.57). Uptake of core NICE recommendations by 6 months was statistically significantly higher in the intervention arm compared with control: e.g., increased written exercise information, 20.5% (7.9, 28.3). CONCLUSION: Whilst uptake of core NICE recommendations was increased, there was no evidence of benefit of this intervention, as delivered in this pragmatic randomised trial, on the primary outcome of physical functioning at 6 months. TRIAL REGISTRATION: ISRCTN06984617.

Isolation and characterization of human antibodies targeting human aspartyl (asparaginyl) beta-hydroxylase.

Over-expression of the enzyme human aspartyl (asparaginyl) beta-hydroxylase (HAAH) has been detected in a variety of cancers. It is proposed that upon cellular transformation, HAAH is overexpressed and translocated to the tumor cell surface, rendering it a specific surface antigen for tumor cells. In this work, twelve human single-chain Fv fragments (scFv) against HAAH were isolated from a human non-immune scFv library displayed on the surface of yeast. Five of the twelve were reformatted as human IgG1. Two of the five IgGs, 6-22 and 6-23, showed significant binding to recombinant HAAH in ELISA, tumor cell lines, and tumor tissues. The apparent dissociation constants of 6-22 and 6-23 IgG were 1.0 +/- 0.2 nM and 20 +/- 10 nM respectively. These two antibodies were shown to target different domains of HAAH, with 6-22 targeting the catalytic domain of HAAH and 6-23 targeting the N-terminal non-catalytic domain of HAAH. 6-22 IgG was further characterized, as it had high affinity and targeted the catalytic domain. 6-22 IgG alone does not exhibit significant cytotoxicity toward the tumor cells. However, 6-22 internalizes into tumor cells and can therefore be employed to deliver cytotoxic moieties. A goat anti-human IgG-saporin conjugate was delivered into tumor cells by 6-22 IgG and hence elicited cytotoxicity toward the tumor cells in vitro. These tumor-binding human antibodies can potentially be used in both diagnosis and immunotherapy targeting HAAH-expressing tumor cells.

Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project.

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

Systems biology markup language: Level 2 and beyond.

The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with the modelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology.

Researchers in computational biology today make use of a large number of different software packages for modeling, analysis, and data manipulation and visualization. In this paper, we describe the ERATO Systems Biology Workbench (SBW), a software framework that allows these heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' data and algorithmic capabilities. Our goal is to create a simple, open-source software infrastructure which is effective, easy to implement and easy to understand. SBW uses a broker-based architecture and enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe the SBW architecture and the current set of modules, as well as alternative implementation technologies.

Measurement and prediction of the short-term response of soft tissue sarcomas to chemotherapy.

BACKGROUND: Preoperative chemotherapy has been advocated for the treatment of soft tissue sarcomas, yet there is little information about how these tumors respond pathologically to therapy or whether tumor response can be predicted from a pretreatment biopsy. METHODS: Biopsy was done of 25 intermediate- or high-grade soft tissue sarcomas before they were treated with three cycles of doxorubicin and cisplatin and resected. The authors compared the pathologic features of the treated tumors with clinical and radiologic evidence of response to identify the pathologic features that best reflected chemotherapeutic effect. They analyzed the pretreatment biopsy specimens by light microscopic study and flow cytometry to identify parameters that predict short-term response to chemotherapy. RESULTS: In the treated tumors, the clearest indicator of early chemotherapeutic effect was the percentage of the resected mass composed of viable neoplasm. Eleven of 25 resection specimens contained less than 15% viable neoplasm (patients with pathologic response); 9 of these had 5% or less. Flow cytometric estimates of the proliferative rate in the initial biopsy specimen predicted early chemotherapeutic effect; 7 of 10 tumors with S-phase fractions (SPF) greater than 6% responded pathologically, whereas only 3 of 12 tumors with lower SPF responded (P = 0.041). Initial tumor grade, cell type, percent tumor necrosis, mitotic rate, cellularity, and ploidy did not predict chemotherapy response. CONCLUSION: These results indicate that soft tissue sarcomas often respond dramatically to chemotherapy, that the amount of residual viable sarcoma is an indicator of short-term effect, and that flow cytometric estimates of cell proliferation predict early response to chemotherapy.