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dc.contributor.authorNoorbakhsh, Javaden_US
dc.date.accessioned2016-04-08T15:39:36Z
dc.date.available2016-04-08T15:39:36Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/15645
dc.description.abstractRecent advances in biology have revolutionized our understanding of living systems. For the first time, it is possible to study the behavior of individual cells. This has led to the discovery of many amazing phenomena. For example, cells have developed intelligent mechanisms for foraging, communicating, and responding to environmental changes. These diverse functions in cells are controlled through biochemical networks consisting of many different proteins and signaling molecules. These molecules interact and affect gene expression, which in turn affects protein production. This results in a complex mesh of feedback and feedforward interactions. These complex networks are generally highly nonlinear and stochastic, making them difficult to study quantitatively. Recent studies have shown that biochemical networks are also highly modular, meaning that different parts of the network do not interact strongly with each other. These modules tend to be conserved across species and serve specific biological functions. However, detect- ing modules and identifying their function tends to be a very difficult task. To overcome some of these complexities, I present an alternative modeling approach that builds quantitative models using coarse-grained biological processes. These coarse-grained models are often stochastic (probabilistic) and highly non-linear. In this thesis, I focus on modeling biochemical networks in two distinct biological systems: Dictyostelium discoideum and microRNAs. Chapters 2 and 3 focus on cellular communication in the social amoebae Dictyostelium discoideum. Using universality, I propose a stochastic nonlinear model that describes the behavior of individual cells and cellular populations. In chapter 4 I study the interaction between messenger RNAs and noncoding RNAs, using Langevin equations.en_US
dc.language.isoen_US
dc.subjectPhysicsen_US
dc.subjectBiochemical networken_US
dc.subjectNonlinearityen_US
dc.subjectStochasticityen_US
dc.titleNonlinearity and stochasticity in biochemical networksen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-03-12T07:13:59Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplinePhysicsen_US
etd.degree.grantorBoston Universityen_US


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