Image analysis and statistical modeling for applications in cytometry and bioprocess control

Today, signal processing has a central role in many of the advancements in systems biology. Modern signal processing is required to provide efficient computational solutions to unravel complex problems that are either arduous or impossible to obtain using conventional approaches. For example, imaging-based high-throughput experiments enable cells to be examined at even subcellular level yielding huge amount of image data. Cytometry is an integral part of such experiments and involves measurement of different cell parameters which requires extraction of quantitative experimental values from cell microscopy images. In order to do that for such large number of images, fast and accurate automated image analysis methods are required. In another example, modeling of bioprocesses and their scale-up is a challenging task where different scales have different parameters and often there are more variables than the available number of observations thus requiring special methodology. In many biomedical cell microscopy studies, it is necessary to analyze the images at single cell or even subcellular level since owing to the heterogeneity of cell populations the population-averaged measurements are often inconclusive. Moreover, the emergence of imaging-based high-content screening experiments, especially for drug design, has put single cell analysis at the forefront since it is required to study the dynamics of single-cell gene expressions for tracking and quantification of cell phenotypic variations. The ability to perform single cell analysis depends on the accuracy of image segmentation in detecting individual cells from images. However, clumping of cells at both nuclei and cytoplasm level hinders accurate cell image segmentation. Part of this thesis work concentrates on developing accurate automated methods for segmentation of bright field as well as multichannel fluorescence microscopy images of cells with an emphasis on clump splitting so that cells are separated from each other as well as from background. The complexity in bioprocess development and control crave for the usage of computational modeling and data analysis approaches for process optimization and scale-up. This is also asserted by the fact that obtaining a priori knowledge needed for the development of traditional scale-up criteria may at times be difficult. Moreover, employment of efficient process modeling may provide the added advantage of automatic identification of influential control parameters. Determination of the values of the identified parameters and the ability to predict them at different scales help in process control and in achieving their scale-up. Bioprocess modeling and control can also benefit from single cell analysis where the latter could add a new dimension to the former once imaging-based in-line sensors allow for monitoring of key variables governing the processes. In this thesis we exploited signal processing techniques for statistical modeling of bioprocess and its scale-up as well as for development of fully automated methods for biomedical cell microscopy image segmentation beginning from image pre-processing and initial segmentation to clump splitting and image post-processing with the goal to facilitate the high-throughput analysis. In order to highlight the contribution of this work, we present three application case studies where we applied the developed methods to solve the problems of cell image segmentation and bioprocess modeling and scale-up

Bioprocess optimization using machine learning methods

In bioprocess development, the need for optimization is to achieve improvements in the productivity as well as in the quality of the product. This involves acquiring an overview of dataset associated with different process runs, identifying primary control parameters, and determining a useful control direction. Hence, the use of several data analysis approaches to explore optimization possibilities can be very valuable in bioprocess development. In this thesis, multiple linear regression, Lasso regression, and artificial neural networks were used for modeling a bioprocess dataset. As a case study, we used the data obtained from a statistical culture media optimization experiment for microbial hydrogen production. Apart from the linear models, dataset were transformed to build the quadratic multiple linear regression and Lasso models. In addition, two-layer and three-layer artificial neural networks models were also developed. In order to predict the maximum achievable hydrogen production yield, a genetic algorithm was used to optimize the parameters of the developed models. The prediction accuracy and the maximum achievable hydrogen yield by Lasso and artificial neural networks models were benchmarked against those of the multiple linear regression. All the three methods were capable in providing a significant model for the culture media optimization. However, the performance of the quadratic multiple linear regression to fit the examined data was not adequate. In this case, the correlation between the observed and predicted yield was 0.37. The modeling was still successful with the quadratic Lasso model (0.82). The performances of two artificial neural network models outperformed the others. According to artificial neural networks, the correlations between the observed and predicted yield were 0.92 for two-layer and 0.91 for three-layer models. With the help of genetic algorithm, the maximum achievable hydrogen yield was 2.24 mol-H 2 /mol-glycerol consumed for the linear multiple linear regression model. On the other hand, the results obtained from the Lasso and artificial neural networks models were closer to the highest experimental observation. Thus, we found that both lasso regression and artificial neural networks were pertinent to this kind of bioprocess data

Relationship Between Risk Identification, Risk Response, and Project Success

AbstractProjects are used to implement the organization\u27s strategic goals, but high failure rates reduce projects\u27 effectiveness in successfully achieving goals. High failure rates reduce project managers’ effectiveness of projects in successfully achieving goals. Senior leaders and project managers are unable to deliver successful projects due to unmanaged risks. Grounded in expected utility theory, the purpose of this quantitative correlational study was to examine the relationship between risk identification, risk responses, and project success. A survey was created in SurveyMonkey® and distributed on LinkedIn. Survey responses were analyzed from 71 project managers with at least five years of experience in Washington, DC. The results of the standard multiple linear regression indicated the model was able to significantly predict project success, F(2, 70) = 7.260, p \u3c .001, R2 = .18. However, risk identification (t = 3.262, p \u3c .002) was the only statistically significant predictor. A key recommendation is for project managers to identify and mitigate any risks that could negatively impact a project. The implications for positive social change include the potential for project managers to understand how risk identification and risk response can lead to successful projects that achieve organizations\u27 goals and create opportunities for innovative products and services that deliver value to stakeholders

Relationship Between Intrinsic Job Satisfaction, Extrinsic Job Satisfaction, and Turnover Intentions Among Internal Auditors

In the auditing profession, many business owners are unable to retain auditing staff. The cost to replace an auditor can cost a company as much as 150% of the auditors\u27 annual salary. Perpetuating this problem is that some auditing business owners do not know the relationship between internal auditors\u27 intrinsic job satisfaction, extrinsic job satisfaction, and auditors\u27 turnover intention. Grounded in Herzberg\u27s 2- factor theory, the purpose of this correlational study was to examine the relationship between intrinsic job satisfaction, extrinsic job satisfaction, and auditors\u27 turnover intention. Participants included 96 members of the Central Florida Institute of Internal Auditors. Data were collected using the Minnesota Satisfaction Questionnaire and the Michigan Organizational Assessment Questionnaire. Results of the multiple regression analysis indicated the model as a whole was able to significantly predict auditors\u27 turnover intentions, F(2, 93) = 47.635, p \u3c .001, R2 = .506. Extrinsic job satisfaction was the only significant predictor (t = -6.515, p \u3c .001). Implications for social change include the potential for leaders to better understand predictors of involuntary turnover and the potential to save money on recruitment and training. Business owners may become more profitable through better employee retention strategies; these findings may also add to the body of knowledge for stable employment opportunities. Business owners can develop strategies to enhance the level of intrinsic and extrinsic job satisfaction of internal auditors. Job satisfaction of internal auditors is essential and a fundamental determinant of growth, service, and quality within an organization

Linking high throughput cell culture, multivariate analysis and economics for more effective process integration

As the antibody sector has matured, it has seen significant increases in cell culture titres. However, it is hard to predict the consequences of titre increase on impurity levels and downstream processing (DSP) performance. Hence it is critical to have systematic methods to explore such interactions. This project explored the potential of high throughput cell culture linked to multivariate analysis, uncertainty analysis and bioprocess economics to characterise cell culture processes, not only in terms of growth and productivity but also host cell protein (HCP) levels, robustness and costs. A Quality by Design (QbD) approach to cell culture process development is presented. Using this QbD framework it was shown that there is scope for cell culture processes in which the ratio of mAb to HCP can be increased and the association of mAb titre to HCP reduced. It is therefore feasible to identify conditions whereby it is possible to increase antibody titre with little impact on HCP levels and hence subsequent DSP operations. (36.5 oC, 313 mOsm kg-1 media osmolality, 1 × 106 cells mL-1 seeding density, pH 6.8 and low cell generation number in this case). The impact of cell culture factors on protease activity (problematic HCP species) was assessed. Culture temperature was found to have a significant impact on protease activity, with a decrease in temperature resulting in lower protease activity. The relationship between HCP levels and protease activity was also examined and it was shown that an increase in total HCP levels at harvest did not result in a concomitant increase in protease activity. Multivariate data analysis based on regression was used to derive statistical cause-and-effect correlations able to link mAb titre and HCP levels to key cell culture factors. The resulting cell culture predictive correlations were then integrated into a whole bioprocess economics and optimisation framework. This allowed the identification of the most cost effective cell culture strategies as well as the impact of uncertainty in cell culture parameters on outputs (product output (kg) and HCP final (ng/mg)) and the likelihood of these falling out of specification. The work in this thesis highlights the benefits of a systematic approach to providing enhanced process understanding of the impact of cell culture strategies on downstream processes. This can be used to facilitate effective process integration and enable continuous improvements