Con Sheahan

Metabolic engineering of algae for fourth generation biofuels production

The ecological footprint and economic performance of the current suite of biofuel production methods make them insufficient to displace fossil fuels and reduce their impact on the inventory of Green House Gas (GHG) in the global atmosphere. Algae metabolic engineering forms the basis for 4th generation biofuel production which can meet this need. The first generation biofuels are known to be made from agricultural products such as corn or sugarcane. The second generation biofuels use all forms of (lingo)cellulosic biomass. The third and fourth generation of biofuel production involves “algae-to-biofuels” technology: the former is basically processing of algae biomass for biofuel production, while the latter is about metabolic engineering of algae for producing biofuels from oxygenic photosynthetic microorganisms. Our review focuses on the research achievement of metabolic engineering of algae for biofuel production. It is concluded that 4th generation biofuel production has introduced the “cell factory” concept in this field, and shifted the research paradigm. There still exists several technical bottlenecks in algae biofuel research and development, which can only be solved by the use of post-genome tools on these photosynthetic organisms.

From enterprise models to scheduling models: bridging the gap

Enterprise models cover all aspects of modern enterprises, from accounting, through management of custom orders and invoicing, to operational data such as records on machines and workers. In other words, all data necessary for running the company are available in enterprise models. However, these data are not in the proper format for some tasks such as scheduling and optimization. Namely, the concepts and terminology used in enterprise models are different from what is traditionally used in scheduling and optimization software. This paper deals with the automated translation of data from the enterprise model to a scheduling model and back. In particular, we describe how to extract data from the enterprise model for solving the scheduling problem using constraint-based solvers.

Workflow Optimization with FlowOpt: On Modelling, Optimizing, Visualizing, and Analysing Production Workflows

Workflow optimization is an important aspect of many problems including project management and manufacturing. In this paper we describe an innovative general tool supporting a complete workflow optimization process in small and medium manufacturing enterprises. The tool supports interactive modeling of workflows with nested structure and additional logical and synchronization constraints. Then the tool automatically schedules workflows to available resources while optimizing on-time-in-full performance (just in time scheduling). Obtained schedules are visualized in the form of Gantt charts where the user can arbitrarily modify the schedule. Finally, the schedules can be analyzed and the tool suggests how to modify the enterprise, for example by buying new resources, to obtain better quality schedules. By supporting workflows with alternative processes the tool realizes integrated planning and scheduling.

Utilising the native plasmid, pCA2.4, from the cyanobacterium Synechocystis sp. strain PCC6803 as a cloning site for enhanced product production.

BackgroundThe use of photosynthetic autotrophs and in particular the model organism Synechocystis PCC6803 is receiving much attention for the production of sustainable biofuels and other economically useful products through metabolic engineering. Optimisation of metabolic-engineered organisms for high-level sustained production of product is a key element in the manipulation of this organism. A limitation to the utilisation of metabolically-engineered Synechocystis PCC6803 is the availability of strong controllable promoters and stable gene dosage methods for maximising gene expression and subsequent product formation following genetic manipulation.ResultsA native Synechocystis PCC6803 small plasmid, pCA2.4, is consistently maintained at a copy level of up to 7 times that of the polyploid chromosome. As this plasmid is stable during cell division, it is potentially an ideal candidate for maximising gene dosage levels within the organism. Here, we describe the construction of a novel expression vector generated from the native plasmid, pCA2.4. To investigate the feasibility of this new expression system, a yellow fluorescent protein (YFP) encoding gene was cloned downstream of the strong Ptrc promoter and integrated into a predicted neutral site within the pCA2.4 plasmid. The stability of the integrated construct was monitored over time compared to a control strain containing an identical YFP-expressing construct integrated at a known neutral site in a chromosomal location.ConclusionsA significantly higher fluorescence level of the yellow fluorescent protein was observed when its encoded gene was integrated into the pCA2.4 native plasmid when compared to the isogenic chromosomally integrated control strain. On average, a minimum of 20-fold higher fluorescence level could be achieved from integration into the native plasmid. Fluorescence was also monitored as a function of culture time and demonstrated to be stable over multiple sub-cultures even after the removal of selective pressure. Therefore, the native small plasmid, pCA2.4 may be utilised to stably increase gene expression levels in Synechocystis PCC6803. With the complementary utilisation of an inducible promoter system, rapid generation of commodity-producing Synechocystis PCC6803 strains having high level, controlled expression may be more achievable.

FlowOpt: bridging the gap between optimization technology and manufacturing planners

FlowOpt is an integrated collection of tools for workflow optimization in production environments. It was developed as a demonstration of advancements in the areas of modeling and optimization with the focus on simplifying the usage of the technology for end customers. The system consists of several interconnected modules. First, the user visually models a workflow describing the production of some item. Then the user specifies which items and how many of them should be produced (order management) and the system automatically generates a production schedule. This schedule is then visualized in the form of a Gantt chart where the user can arbitrarily modify the schedule. Finally, the system can analyze the schedule and suggest some improvements such as buying a new machine. Constraint satisfaction technology is the solving engine behind these modules.

A novel metric for determining the constraining effect of resources in manufacturing via simulation

This paper provides a novel method for determining the constraining effect of resources in a manufacturing system using discrete event simulation. Traditionally manufacturing systems are constrained by one or more bottlenecks. Eliminating or mitigating the bottleneck will speed up the system throughput. However, bottlenecking resources generally only refer to machines, and primarily focus on flow-shops not job-shops. One important resource we believe that is often overlooked is workers and their associated skills, and we propose that a particular skill could be flagged as a bottleneck resource. We define new metrics known as resource constraint metrics (RCM) for measuring the constraining effect of a resource on the entire manufacturing system. These metrics are flexible and differentiate between the constraining effects of machines and their requested skills. The metrics can also deal with complex workflows with alternative routing, alternative resources, calendars (a necessary consideration when dealing with workers), worker performance, and multiple modes of operation of machines (e.g. run, setup, and maintenance). The use of RCMs in simulation aids in real-world decision-making, by determining which resource should be focussed on and improved to reduce the overall system feeling constrained. This will have the effect of increasing throughput or at least providing the capacity for increased throughput.

Taxonomy of a decision: ranking operational decisions within the SME

Classical biological taxonomy comprises of three distinctive processes: the identification, classification and nomenclature of a given entity. This research applies the taxonomic process to operational decisions within the SME, allowing one such decision to be rated over another. The methodology was then applied to a case study company, before and after the introduction of decision support software (DSS), for a key decision-making process within that organisation. The value of this research lies in the ability to empirically measure the effect DSS (or a training program) has on a particular operational decision.

A Methodology for Improving Enterprise Performance by Analysing Worker Capabilities via Simulation

In this paper we outline a methodology for improving the overall performance of small to medium sized enterprises (SMEs) by analysing worker capabilities through simulation and modelling. We firstly examine key performance indicators (KPIs) of the SME in its as-is state. The primary KPIs we examine are the resource constraint metrics (RCMs) and customer misery index (CMI). The RCMs help to identify the skill that is the biggest contributor to the overall system constrainedness. The CMI is a measure of customer demand satisfaction. By increasing the supply of the most heavily constrained skill we should increase the flow of work orders through the system, which will in turn result in a reduced CMI, or at least provide a potential for more work orders to flow through the system. We run a set of experiments on data from a real factory, which upgrades the skill sets of workers with the most heavily constrained skill, and then we look at the system improvement. The overall impact of this experimental methodology is that it can make recommendations to an organisation about which worker to upgrade with which skill, and how the training should be implemented, to yield the optimal improvement to the enterprise.

Evaluation of the ethanol tolerance for wild and mutant Synechocystis strains by flow cytometry

Highlights • Flow cytometry was used to evaluate the effect of ethanol on Synechocystis strains.• The three Synechocystis strains behaved differently in the presence of ethanol.• UL 004 and UL 030 were more tolerant to the presence of ethanol than the WT strain.• The most efficient ethanol producer (UL030) was also the most tolerant to ethanol.

An investigation into heuristics for alternative worker selection in discrete event simulation

In this paper, we analyse four different heuristics for qualified worker selection for machines in discrete event simulation. Conventional simulators simply select a capable worker randomly or from the top-of-the-stack (TOS) of candidates that are qualified to operate a machine, without considering the impact of removing that worker from the current available qualification pool (qPool). To investigate the efficacy of this approach, we compare these random and TOS approaches with two other worker selection rules: least number of qualifications (LENQ), and a heuristic that selects a worker with the lowest impact factor on the qualification pool (LIMP). LIMP ranks workers based on their contribution to the qPool and the constrainedness of each of their qualifications. We apply LENQ to a simulation model of a real company, and compared with the Random heuristic we observe a 44% reduction in the qualification resource constraint metric (RCMq) and a 2% reduction in the total lateness in sales-order satisfaction. For the LIMP heuristic, the RCMq reduction is 77%. However, LIMP yields no significant improvement in sales-order lateness over the simpler LENQ approach. The LENQ and LIMP heuristics also have the benefit of more closely modelling what happens in reality, as they are based on intuition that would be used in practice, rather than using a random or simple TOS approach followed in conventional simulation.