An Introduction to Rule-based Modeling of Immune Receptor Signaling

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$\varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at http://bionetgen.org.Comment: 5 figure

Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation

Rule-based modeling is a graph-based approach to specifying the kinetics of cell signaling systems. A reaction rule is a compact and explicit graph-based representation of a kinetic process, and it matches a class of reactions that involve identical sites and identical kinetics. Compact rule- based models have been used to generate large and combinatorially complex reaction networks, and rules have also been used to compile databases of kinetic interactions targeting specific cells and pathways. In this work, I address three technological challenges associated with rule-based modeling. First, I address the ability to generate an automated global visualization of a rule-based model as a network of signal flows. I showed how to analyze a reaction rule and extract a set of bipartite regulatory relationships, which can be aggregated across rules into a global network. I also provide a set of coarse-graining approaches to compress an automatically generated network into a compact pathway diagram, even for models with 100s of rules. Second, I resolved an incompatibility between two recent advances in rule-based modeling: network-free simulation (which enables simulation without generating a reaction network), and energy-based rule-based modeling (which enables specifying a model using cooperativity parameters and automated accounting of free energy). The incompatibility arose because calculating the reaction rate requires computing the reaction free energy in an energy-based model, and this requires knowledge of both reactants and products of the reaction, but the products are not available in a network-free simulation until after the reaction event has fired. This was resolved by expanding each energy- based rule into a number of normal reaction rules for which reaction free energies can be calculated unambiguously. Third, I demonstrated a particular type of modularization that is based on treating a set of rules as a module. This enables building models from combinations of modular hypotheses and supplements the other modularization strategies such as macros, types and energy-based compression

Comparative study of sequence-dependent hybridization kinetics in solution and on microspheres

Hybridization kinetics of DNA sequences with known secondary structures and random sequences designed with similar melting temperatures were studied in solution and when one strand was bound to 5 μm silica microspheres. The rates of hybridization followed second-order kinetics and were measured spectrophotometrically in solution and fluorometrically in the solid phase. In solution, the rate constants for the model sequences varied by almost two orders of magnitude, with a decrease in the rate constant with increasing amounts of secondary structure in the target sequence. The random sequences also showed over an order of magnitude difference in the rate constant. In contrast, the hybridization experiments in the solid phase with the same model sequences showed almost no change in the rate constant. Solid phase rate constants were approximately three orders of magnitude lower compared with the solution phase constants for sequences with little or no single-stranded structure. Sequences with a known secondary structure yielded solution phase rate constants as low as 3 × 10(3) M(−1) s(−1) with solid phase rate constants for the same sequences measured at 2.5 × 10(2) M(−1) s(−1). The results from these experiments indicate that (i) solid phase hybridization occurs three orders of magnitude slower than solution phase, (ii) trends observed in structure-dependent kinetics of solution phase hybridization may not be applicable to solid phase hybridization and (iii) model probes with known secondary structure decrease reaction rates; however, even random sequences with no known internal single-stranded structure can yield a broad range of reaction rates

Commercialisation of electric assist utility trailer

New technological innovations is currently the key aspect for sustainable development. There is a need for a more sustainable world due to factors like increase in carbon emissions (414 ppm) and transportation sector accounting for 28% of them. However, with the potential increase in electric vehicles fleet there also arises limitations such as towing trailers which consequently causes range anxiety. Thus, this paper focuses extensively to judge the market potential of a new technology conceptualised by the Electric Power and Engineering centre (EPECentre) titled ‘The Electric Assist Utility Trailer’. The main value proposition of this trailer is safety, sustainability and accessibility. This project also focuses on the feasibility to commercialise the technology in New Zealand market. New Zealand currently has 17,831 EVs most of which are Nissan Leafs. The penetration of EVs in New Zealand has grown from 0.13% to 2% by 2019. The EV fleet is expected to dominate in the next 10 years facilitated by government policies, infrastructure and rise in renewable energy sources. The target market here are the owners of light EVs especially Nissan Leaf. The increase in EV fleet with more number of light EVs, paves a potential opportunity for electric trailer. Furthermore, a market analysis for light utility trailers was performed using the registered fleet data from Ministry of transport. The analysis showed slow to medium increase in light trailers with an average annual rate of 3%. A cost analysis was performed based on various reliable references including direct and indirect costs for which the final selling price was estimated for nearly $21,000 NZD. However, a sensitivity analysis was performed to estimate the selling price based on the varying Li-ion battery prices for which the least selling price would be$11,429 NZD. The cost analysis was fed to estimate the target market which was forecasted to be 2.6 million in 2025. The commercialisation plan for the EAUT was hindered by legal and political factors. According to Land Transport Act 1998, the EAUT might qualify as motor vehicle and this perception may develop a potential barrier to operate the trailer on the New Zealand roads. However, a law change may be required to make it road legal but it depends on the dominant penetration of EV market in New Zealand. To conclude, the EAUT is not feasible enough to be commercialised in the current market but it is recommended that the technology could be implemented for the caravan market in New Zealand since there is no similar patent filed

Future Carbon Regulations and Current Investments in Alternative Coal-Fired Power Plant Designs

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).This paper assesses the role of uncertainty over future U.S. carbon regulations in shaping the current choice of which type of power plant to build. The pulverized coal technology (PC) still offer the lowest cost power— assuming there is no need to control emissions of carbon. The integrated coal gasification combined cycle technology (IGCC) may be cheaper if carbon must be captured. Since a plant built now will be operated for many years, and since carbon regulations may be instituted in the future, a U.S. electric utility must make the current investment decision in light of the uncertain future regulatory rules. This paper shows how this decision is to be made. We start by describing the economics of the two key coal-fired power plant technologies, PC and IGCC. We then analyze the potential costs of future carbon regulations, including the costs of retrofitting the plant with carbon capture technology and the potential cost of paying charges for emissions. We present the economics of each design in the form of a cash flow spreadsheet yielding the present value cost, and show the results for different scenarios of emissions regulation. We then discuss how to incorporate uncertainty about the future regulation of carbon emissions into the decision to build one plant design or the other. As an aid to decision making, we provide some useful benchmarks for possible future regulation and show how these benchmarks relate back to the relative costs of the two technologies and the optimal choice for the power plant investment. Few of the scenarios widely referenced in the public discussion warrant the choice of the IGCC technology. Instead, the PC technology remains the least costly. The level of future regulation required to justify a current investment in the IGCC technology appears to be very aggressive, if not out of the question. However, the current price placed on carbon emissions in the European Trading System, is higher than these benchmarks. If it is any guide to possible future penalties for emissions in the U.S., then current investment in the IGCC technology is warranted.This research was supported by the MIT Joint Program on the Science and Policy of Global Change and the MIT Carbon Sequestration Initiative. The MIT modeling facility used in this analysis was supported by the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937), the US Environmental Protection Agency (XA-83042801-0), the Electric Power Research Institute, and by a consortium of industry and foundation sponsors

BioNetGen 2.2: Advances in Rule-Based Modeling

BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation, and analysis of larger and more complex models than previously possible.Comment: 3 pages, 1 figure, 1 supplementary text file. Supplementary text includes a brief discussion of the RK-PLA along with a performance analysis, two tables listing all new actions/arguments added in BioNetGen 2.2, and the "BioNetGen Quick Reference Guide". Accepted for publication in Bioinformatic