Determining Interconnections in Chemical Reaction Networks

We present a methodology for robust determination of chemical reaction network interconnections. Given time series data that are collected from experiments and taking into account the measurement error, we minimize the 1-norm of the decision variables (reaction rates) keeping the data in close Euler-flt with a general model structure based on mass action kinetics which models the species' dynamics. We illustrate our methodology on a hypothetical chemical reaction network under various experimental scenarios

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Metabolic Networks Analysis using Convex Optimization

Metabolic networks map the biochemical reactions in a living cell to the flow of various chemical substances in the cell, which are called metabolites. A standard model of a metabolic network is given as a linear map from the reaction rates to the change in metabolites concentrations. We study two problems related to the analysis of metabolic networks, the minimal network problem and the minimal knockout problem

Optimization with Integrated Offline Parametric Optimization of Detailed Process Model of an Interceptor Unit for Water Network Synthesis and Retrofit Design

Petroleum refineries is a prime example of industrial plants that demand high quantities of water for process consumption and generate volumes of highly contaminated industrial eflluents and wastewaters. Scarcity of freshwater resources and increasingly stringent environmental regulations on industrial effluents have motivated refineries to develop water reuse technologies for sustainability of plant operations. The technology concept can be characterized into three (3) strategies: reuse, regeneration, and recycle (W3R). The major contribution of this work is to consider the design of alternative refinery water network structures that incorporate the detailed design of wastewater treatment technology (or interceptor) in an optimization-based modeling framework as an offline parameter optimization problem. For this purpose, a source-interceptor -sink superstructure representation is adopted that embeds many feasibly possible alternative water network configurations. A mixed-integer nonlinear programming (MINLP) optimization model is formulated based on the superstructure with the objective of minimizing freshwater import, wastewater generation, piping interconnections, and the total cost of installing and operating the treatment technology. The parametric optimization problem comprising of material balances and the detailed phenomena model for interceptor, specifically for a single-stage hollow fiber reverse osmosis (HFRO) membrane module, is incorporated in the overall MINLP framework. The modeling approach is developed in conjunction with its implementation into general algebraic modeling system (GAMS), using data of a real operating refinery situation. The model is solved iteratively by branch and reduce optimization navigator (BARON), resulting in freshwater consumption requirements to be 296.2 m3 /h at the optimal refinery water network structure and operating conditions, which accounts for nearly 61% of water recovery compared to current operating requirements (before the integration and retrofit initiatives based on W3R)

Identification of Nonlinear State-Space Systems from Heterogeneous Datasets

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through (a) replicate measurements from the same biological system or (b) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed, heterogeneous data. To enable scale up to large number of features, the resulting non-convex optimisation problem is relaxed to a re-weighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalised eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise

Superstructure optimisation of a water minimisation network with a embedded multicontaminant electrodialysis model

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2016The water-energy nexus considers the relationship between water and energy resources. Increases in environmental degradation and social pressures in recent years have necessitated the development of manufacturing processes that are conservative with respect to both these resources, while maintaining financial viability. This can be achieved by process integration (PI); a holistic approach to design which emphasises the unity of processes. Within the realm of PI, water network synthesis (WNS) explores avenues for reuse, recycle and regeneration of effluent in order to minimise freshwater consumption and wastewater production. When regeneration is required, membrane-based treatment processes may be employed. These processes are energy intensive and result in a trade-off between water and energy minimisation, thus creating an avenue for optimisation. Previous work in WNS employed a black box approach to represent regenerators in water minimisation problems. However, this misrepresents the cost of regeneration and underestimates the energy requirements of a system. The aim of the research presented in this dissertation is to develop an integrated water regeneration network synthesis model to simultaneously minimise water and energy in a water network. A novel MINLP model for the design of an electrodialysis (ED) unit that is capable of treating a binary mixture of simple salts was developed from first principles. This ED model was embedded into a water network superstructure optimisation model, where the objective was to minimise freshwater and energy consumption, wastewater productions, and associated costs. The model was applied to a pulp and paper case study, considering several scenarios. Global optimisation of the integrated water network and ED design model, with variable contaminant removal ratios, was found to yield the best results. A total of 38% savings in freshwater, 68% reduction in wastewater production and 55% overall cost reduction were observed when compared with the original design. This model also led to a 80% reduction in regeneration (energy) cost.GS201

Prediction of Formation Conditions of Gas Hydrates Using Machine Learning and Genetic Programming

The formation of gas hydrates in the pipelines of oil, gas, chemical, and other industries has been a significant problem for many years because the formation of gas hydrates may block the pipelines. Hence, the knowledge of the phase equilibrium conditions of gas hydrate became necessary for the economic and safe working of oil, gas, chemical industries. Various thermodynamic approaches with various mathematical techniques are available for the prediction of formation conditions of gas hydrates. In this chapter, the authors have discussed the least square support vector machine and artificial neural network models for the prediction of stability conditions of gas hydrates and the use of genetic programming (GP) and genetic algorithm (GA) to develop a generalized correlation for predicting equilibrium conditions of gas hydrates