A scalable method for parameter identification in kinetic models of metabolism using steady-state data.

Motivation: In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity.

Making Triage Decisions for the Acute Community Care Program: Paramedics Caring for Urgent Health Problems in Patients' Homes.

The Acute Community Care Program (ACCP) initiative sends specially trained paramedics to evaluate and treat patients with urgent care problems in their residences during evening hours. ACCP safety depends on making appropriate triage decisions from patients' reports during phone calls about whether paramedics could care for patients' urgent needs or whether they require emergency department (ED) services. Furthermore, after ACCP paramedics are on scene, patients may nonetheless need ED care if their urgent health problems are not adequately treated by the paramedic's interventions.

Early experiences with the Acute Community Care Program in eastern Massachusetts.

Objectives: Emergency departments (EDs) frequently provide care for nonemergent health conditions outside of usual physician office hours. A nonprofit, fully integrated health insurer/care delivery system that enrolls socioeconomically disadvantaged adults with complex health needs partnered with an ambulance service provider to offer after-hours urgent care by specially trained and equipped paramedics in patients' residences. The Massachusetts Department of Public Health gave this initiative, the Acute Community Care Program (ACCP), a Special Project Waiver.

Model-based design of bistable cell factories for metabolic engineering.

Motivation: Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks.

Constructing kinetic models of metabolism at genome-scales: A review.

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty.

Engineering metabolism through dynamic control.

Metabolic engineering has proven crucial for the microbial production of valuable chemicals. Due to the rapid development of tools in synthetic biology, there has been recent interest in the dynamic regulation of flux through metabolic pathways to overcome some of the issues arising from traditional strategies lacking dynamic control. There are many diverse implementations of dynamic control, with a range of metabolite sensors and inducers being used.

Characterizing metabolic pathway diversification in the context of perturbation size.

Cell metabolism is an important platform for sustainable biofuel, chemical and pharmaceutical production but its complexity presents a major challenge for scientists and engineers. Although in silico strains have been designed in the past with predicted performances near the theoretical maximum, real-world performance is often sub-optimal. Here, we simulate how strain performance is impacted when subjected to many randomly varying perturbations, including discrepancies between gene expression and in vivo flux, osmotic stress, and substrate uptake perturbations due to concentration gradients in bioreactors.

Analysis and design of a genetic circuit for dynamic metabolic engineering.

Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.

Metabolic model refinement using phenotypic microarray data.

Phenotypic microarray (PM) is a standardized, high-throughput technology for profiling phenotypes of microorganisms, which allows for characterization on around 2,000 different media conditions. The data generated using PM can be incorporated into genome-scale metabolic models to improve their predictive capability. In addition, a comparison of phenotypic profiles of wild-type and gene knockout mutants can give essential information about gene functions of unknown genes.

Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design.

Background: In recent years, constraint-based metabolic models have emerged as an important tool for metabolic engineering; a number of computational algorithms have been developed for identifying metabolic engineering strategies where the production of the desired chemical is coupled with the growth of the organism. A caveat of the existing algorithms is that they do not take the bioprocess into consideration; as a result, while the product yield can be optimized using these algorithms, the product titer and productivity cannot be optimized. In order to address this issue, we developed the Dynamic Strain Scanning Optimization (DySScO) strategy, which integrates the Dynamic Flux Balance Analysis (dFBA) method with existing strain algorithms.

EMILiO: a fast algorithm for genome-scale strain design.

Systems-level design of cell metabolism is becoming increasingly important for renewable production of fuels, chemicals, and drugs. Computational models are improving in the accuracy and scope of predictions, but are also growing in complexity. Consequently, efficient and scalable algorithms are increasingly important for strain design.

Dynamic metabolic engineering for increasing bioprocess productivity.

Yield and productivity are critical for the economics and viability of a bioprocess. In metabolic engineering, the main objective is the increase of a target metabolite production through genetic engineering. However, genetic manipulations usually result in lower productivity due to growth impairment.