Modeling and Controlling of Multi-scale Biological Networks with Applications to Precision Medicine

2018-10-22T20:26:18Z (GMT) by Chieh Lo
Biological networks can provide new insights in understanding basic mechanisms controlling normal cellular processes and disease pathologies. In this thesis, we develop a computational framework for bacteria and microbiome dynamics by modeling their interactions and evolution through multi-scale biological networks; this newly developed framework is a step towards making<br>precision medicine a reality. The first part of this thesis focuses on engineering bacteria at population level. To this end, we first develop a cell-level full pathway model to describe bacteria movement (i.e., chemotaxis) and their communication mechanism (i.e., quorum sensing). Based on this model, we then propose an autonomous<br>and adaptive bacteria-based drug delivery system that integrates bacterial chemotaxis and quorum sensing to deliver drugs efficiently and precisely at<br>various locations in the human body. Further, we address the problem of antibiotic resistance. As new drug-resistant bacteria continue to appear, substantial<br>research efforts have shifted focus toward innovative therapies, such as quorum sensing inhibition (QSI) which aims at disabling bacteria molecular signaling channels. However, the excessive use of QSI may induce the selective<br>pressure among bacteria and make them resistant to QSI. To address this issue, we propose an autonomous biological controller that can adaptively generate QSIs and control the nutrient availability in the environment. In the second part of this thesis, we consider multiple species of bacteria<br>(microbiome) and investigate the formation and dynamics of microbiome interaction networks. Despite the role the microbiome plays in human health, models and algorithms that can qualitatively and quantitatively describe the<br>interactions among various microbes (i.e., how microbes interact with each other) and identify the microbiome community structure (i.e., how microbes<br>form different functional groups) have not yet been established. Once a general network model becomes available, we can not only develop strategies<br>to cure the diseases caused by changes in the microbiome status, but also provide valuable prophylactic information and analyze the impact of drugs or probiotics on human health; this is precisely what motivates our proposed<br>research on modeling and controlling the microbiome dynamics. Finally, we propose a computational model that can correctly identify human microbiome related disease. We further explore the possibility of using<br>our network analysis model to identify various microbial functional groups that can be viewed as potential therapeutic targets. The expected results would suggest the customized use of probiotics or drugs in clinical settings according<br>to patients’ specific condition in order to maximize drugs efficacy. Consequently, together with the inferred microbial interaction network, the proposed framework can be used to develop a personalized medical system with treatment<br>generation capabilities.