In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

The baculovirus expression vector system (BEVS) is a versatile and powerful platform for protein expression in insect cells. With the ability to approach similar post-translational modifications as in mammalian cells, the BEVS offers a number of advantages including high levels of expression as well as an inherent safety during manufacture and of the final product. Many BEVS products include proteins and protein complexes that require expression from more than one gene. This review examines the expression strategies that have been used to this end and focuses on the distinguishing features between those that make use of single polycistronic baculovirus (co-expression) and those that use multiple monocistronic baculoviruses (co-infection). Three major areas in which researchers have been able to take advantage of co-expression/co-infection are addressed, including compound structure-function studies, insect cell functionality augmentation, and VLP production. The core of the review discusses the parameters of interest for co-infection and co-expression with time of infection (TOI) and multiplicity of infection (MOI) highlighted for the former and the choice of promoter for the latter. In addition, an overview of modeling approaches is presented, with a suggested trajectory for future exploration. The review concludes with an examination of the gaps that still remain in co-expression/co-infection knowledge and practice.
Co-expression vs. co-infection using baculovirus expression vectors in insect cell culture: Benefits and drawbacks

Genetic and environmental factors will influence the growth of an RNA virus, but their relative contributions are challenging to resolve because standard culture methods mask how virus particles interact with individual host cells. Here, single particles of vesicular stomatitis virus, a prototype RNA virus, were used to infect individual BHK cells. Infected cells produced 50 to 8000 progeny virus particles, but these differences were lost upon subsequent culture, suggesting the diversity of yields reflected cell-to-cell differences rather than viral genetic variation. Cells infected at different phases of their cell cycle produced from 1400 (early S) to 8700 (G 2 M) infectious virus particles, coinciding with the middle-to-upper range of the observed distribution. Fluctuations in virus and cell compositions and noisy gene expression may also contribute to the broad distribution of virus yields. These findings take a step toward quantifying how environmental variation can impact the fitness distribution of an RNA virus.
Growth of an RNA virus in single cells reveals a broad fitness distribution

Hong Kong's bioinformatics sector is attaining new heights in combination with its economic boom and the predominance of the working-age group in its population. Factors such as a knowledge-based and free-market economy have contributed towards a prominent position on the world map of bioinformatics. In this review, we have considered the educational measures, landmark research activities and the achievements of bioinformatics companies and the role of the Hong Kong government in the establishment of bioinformatics as strength. However, several hurdles remain. New government policies will assist computational biologists to overcome these hurdles and further raise the profile of the field. There is a high expectation that bioinformatics in Hong Kong will be a promising area for the next generation. Algorithms and software tools on computational biology [89, 90] Prof. Tang Lei-Han Department of Physics, HKUST Working on unravelling the underlying mechanism of conformational transformations of biopolymers as well as modelling the metabolic network of a biological system [91, 92] Prof. , HKBU Hypothetical and computational methods of rebuild gene regulatory networks as well as transcriptional regulatory networks [45, 95, 96] Working on the algorithms and networks of computational molecular biology [97, 98] Interdiscip Sci Comput Life Sci (2017) 9:224-236 233

Rising Strengths Hong Kong SAR in Bioinformatics