Institute for Systems Biology

About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.

Multiparameter analysis for predictive medicine

Abstract: The invention provides a method of determining a comparative expression profile in an individual by comparing the expression levels of a sample of molecules in a population of molecules in a specimen from the individual with a health-associated reference expression region of the sample of molecules, wherein expression levels within the health-associated reference expression region indicate a reference expression profile and wherein expression levels outside the health-associated reference expression region indicate a perturbed expression profile. The invention also provides methods of diagnosing a disease or a health state in an individual by comparing the expression level of a sample of molecules in a specimen from the individual with a health-associated reference expression region of the sample of molecules.

DNA methyltransferase-mediated transcriptional silencing in malignant glioma: a combined whole-genome microarray and promoter array analysis.

Abstract: Epigenetic inactivation of tumor suppressor genes is a common feature in human cancer. Promoter hypermethylation and histone deacetylation are reversible epigenetic mechanisms associated with transcriptional regulation. DNA methyltransferases (DNMT1 and DNMT3b) regulate and maintain promoter methylation and are overexpressed in human cancer. We performed whole-genome microarray analysis to identify genes with altered expression after RNAi-induced suppression of DNMT in a glioblastoma multiforme (GBM) cell line. We then identified genes with both decreased expression and evidence of promoter CpG island hypermethylation in GBM tissue samples using a combined whole-genome microarray transcriptome analysis in conjunction with a promoter array analysis after DNA immunoprecipitation with anti-5-methylcytidine. DNMT1 and 3b knockdown resulted in the restored expression of 308 genes that also contained promoter region hypermethylation. Of these, 43 were also found to be downregulated in GBM tissue samples. Three downregulated genes with hypermethylated promoters and restored expression in response to acute DNMT suppression were assayed for methylation changes using bisulfite sequence analysis of the promoter region after chronic DNMT suppression. Restoration of gene expression was not associated with changes in promoter region methylation, but rather with changes in histone methylation and chromatin conformation. Two of the identified genes exhibited growth suppressive activity in in vitro assays. Combining targeted genetic manipulations with comprehensive genomic and expression analyses provides a potentially powerful new approach for identifying epigenetically regulated genes in GBM.

The role of predictive modelling in rationally re-engineering biological systems

Abstract: Technologies to synthesize and transplant a complete genome into a cell have opened limitless potential to redesign organisms for complex, specialized tasks. However, large-scale re-engineering of a biological circuit will require systems-level optimization that will come from a deep understanding of operational relationships among all the constituent parts of a cell. The integrated framework necessary for conducting such complex bioengineering requires the convergence of systems and synthetic biology. Here, we review the status of these rapidly developing interdisciplinary fields of biology and provide a perspective on plausible venues for their merger.

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Abstract: Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model9s performance parameters, which supports informed model development and facilitates error detection. Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies

Abstract: Background: Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a "stay at home" order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. Methods: We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020, on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. Findings: We find that significant relaxation of social distancing measures on 7th May can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Conclusions: Our work supports the decision to apply stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. We provide strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.