A generic approach to behaviour-driven biochemical model construction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features

An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems

The authors would like to thank the support on this research by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD

Vitamin D receptor gene polymorphisms and prognosis of breast cancer among African-American and Hispanic women.

BackgroundVitamin D plays a role in cancer development and acts through the vitamin D receptor (VDR). Although African-Americans have the lowest levels of serum vitamin D, there is a dearth of information on VDR gene polymorphisms and breast cancer among African-Americans and Hispanics. This study examines whether VDR gene polymorphisms are associated with breast cancer in these cohorts.MethodsBlood was collected from 232 breast cancer patients (Cases) and 349 non-cancer subjects (Controls). Genotyping for four polymorphic variants of VDR (FokI, BsmI, TaqI and ApaI) was performed using the PCR-RFLP method.ResultsAn increased association of the VDR-Fok1 f allele with breast cancer was observed in African-Americans (OR = 1.9, p = 0.07). Furthermore, the FbTA, FbtA and fbtA haplotypes were associated with breast cancer among African-Americans (p<0.05). Latinas were more likely to have the VDR-ApaI alleles (Aa or aa) (p = 0.008). The VDR-ApaI aa genotype was significantly associated with poorly-differentiated breast tumors (p = 0.04) in combined Cases. Kaplan-Meier survival analysis showed decreased 5-year disease-free-survival (DFS) in breast cancer patients who had the VDR-Fok1 FF genotype (p<0.05). The Cox regression with multivariate analysis revealed the independent predictor value of the VDR-FokI polymorphism for DFS. The other three variants of VDR (BsmI, TaqI and ApaI) were not associated with disease outcome.ConclusionsVDR haplotypes are associated with breast cancer in African-Americans, but not in Hispanic/Latinas. The VDR-FokI FF genotype is linked with poor prognosis in African-American women with breast cancer

Computational models for inferring biochemical networks

Biochemical networks are of great practical importance. The interaction of biological compounds in cells has been enforced to a proper understanding by the numerous bioinformatics projects, which contributed to a vast amount of biological information. The construction of biochemical systems (systems of chemical reactions), which include both topology and kinetic constants of the chemical reactions, is NP-hard and is a well-studied system biology problem. In this paper, we propose a hybrid architecture, which combines genetic programming and simulated annealing in order to generate and optimize both the topology (the network) and the reaction rates of a biochemical system. Simulations and analysis of an artificial model and three real models (two models and the noisy version of one of them) show promising results for the proposed method.The Romanian National Authority for Scientific Research, CNDI–UEFISCDI, Project No. PN-II-PT-PCCA-2011-3.2-0917

Large Second-Harmonic Response and Giant Birefringence of CeF2(SO4) Induced by Highly Polarizable Polyhedra

Second-harmonic generation (SHG) response and birefringence are two critically important properties of nonlinear optical (NLO) materials. However, the simultaneous optimization of these two key properties remains a major challenge because of their contrasting microstructure requirements. Herein, we report the first tetravalent rare-earth metal fluorinated sulfate, CeF2(SO4). Its structure features novel net-like layers constructed by highly distorted [CeO4F4] polyhedra, which are further interconnected by [SO4] tetrahedra to form a three-dimensional structure. CeF2(SO4) exhibits the strongest SHG effect (8 times that of KH2PO4) and the largest birefringence for sulfate-based NLO materials, the latter exceeding the birefringent limit for oxides. Theoretical calculations and crystal structure analysis reveal that the unusually large SHG response and giant birefringence can be attributed to the introduction of the highly polarizable fluorinated [CeO4F4] polyhedra as well as the favorable alignment of [CeO4F4] polyhedra and [SO4] tetrahedra. This research affords a new paradigm for the designed synthesis of high-performance NLO materials.This research was financially supported by the National Natural Science Foundation of China (no. 51432006), the Ministry of Education of China for the Changjiang Innovation Research Team (no. IRT14R23), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (no. B13025), the Innovation Program of Shanghai Municipal Education Commission, and the National and Shanghai Postdoctoral Program for Innovative Talents (nos. BX201800216 and 2018192). M.G.H. thanks the Australian Research Council for support (DP170100411)

Giant Optical Anisotropy in the UV-Transparent 2D Nonlinear Optical Material Sc(IO3 )2 (NO3 )

Birefringence is a fundamental optical property for linear and nonlinear optical (NLO) materials. Thus far, it has proved to be very difficult to engineer large birefringence in optical crystals functioning in the UV region. Herein, we report the first 2D rare-earth iodate-nitrate crystal Sc(IO3)2 (NO3) (SINO), which is shown to exhibit giant optical anisotropy. Air-stable SINO possesses a short UV absorption edge (298 nm), a strong NLO response (4.0 times that of benchmark KH2 PO4) for the nitrate family, and the largest birefringence (Δn=0.348 at 546 nm) of inorganic oxide optical crystals. The unusually large birefringence and NLO response can be attributed to an optimized 2D layered structure, combined with highly polarizable and anisotropic building units [IO3]- and [NO3]-. These findings will facilitate the development of UV linear and NLO materials with giant optical anisotropy and promote their potential application in optoelectronic devices.This research was financially supported by the National Natural Science Foundation of China (no. 51432006), the Ministry of Education of China for the Changjiang Innovation Research Team (no. IRT14R23), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (no. B13025), and the Innovation Program of Shanghai Municipal Education Commission. C.W. thanks the National and Shanghai Postdoctoral Program for Innovative Talents (nos. BX201800216 and 2018192). M.G.H. thanks the Australian Research Council for support (DP170100411)