Modeling and data analysis of emergent properties in different biological systems

Mandwal, Ayush Kumar

Modeling and data analysis of emergent properties in different biological systems

dc.contributor.advisor	Davidsen, Joern
dc.contributor.advisor	Lewis, Ian A.
dc.contributor.author	Mandwal, Ayush Kumar
dc.contributor.committeemember	Orlandi, Javier
dc.contributor.committeemember	Simon, Christoph
dc.contributor.committeemember	Wieser, Micheal E.
dc.contributor.committeemember	Broeckling, Corey D.
dc.date	2024-02
dc.date.accessioned	2024-01-17T21:51:51Z
dc.date.available	2024-01-17T21:51:51Z
dc.date.issued	2024-01-12
dc.description.abstract	Biological systems exhibit a wide array of rich and diverse emergent phenomena, resulting from the intricate modulation of interactions between their components. This modulation can involve changes in both the strength and nature of interactions, leading to complex behaviors and properties. To gain insights into these complex phenomena, various computational and modeling approaches are employed to decipher experimental observations and allow for a deeper understanding of the underlying mechanisms at play. This thesis aims to explore emergent properties in Neuroscience and Microbiology. In Neuroscience, connections between neurons are known to store memories, although it is not clear how. To investigate how memory forms and changes with learning locally between neurons, I proposed a novel molecular mechanism of temporal learning in the Cerebellum part of the brain and validated it using mathematical models. My investigations aimed to shed light on the fundamental processes that lie at the core of learning and cognitive function, providing a deeper understanding of how our brains acquire and learn from experiences. In Microbiology, the behavior of microbes is closely linked to their metabolism, which is often constructed from the genome sequence using algorithmic genetic annotation pipelines. However, these methods can leave gaps in the metabolic network, leading to incorrect interpretations of a pathogen’s metabolic capabilities and hindering therapeutic interventions. To address this, I developed the MINNO network analysis web application, which allows one to identify gaps and explore potential targets for drugs. As microorganisms continue to develop resistance to existing drugs, the need for novel drugs and therapeutic targets becomes increasingly pressing. Moreover, determining the targets of these novel drugs is crucial yet challenging. I addressed this issue using metabolic fingerprinting, which can classify known antibiotics based on the metabolic phenotype they induce in Escherichia coli. Additionally, I formulated an extension of kinetic flux profiling for perturbed cases to gain insights into cellular responses to antibiotics or other stresses in general. Due to their versatility and general applicability, both the MINNO and perturbed kinetic flux profiling are anticipated to enable novel insights into microbial metabolism and various complex biological phenomena observed in nature.
dc.identifier.citation	Mandwal, A. K. (2024). Modeling and data analysis of emergent properties in different biological systems (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117984
dc.identifier.uri	https://doi.org/10.11575/PRISM/42828
dc.language.iso	en
dc.publisher.faculty	Science
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Purkinje cells
dc.subject	Memory
dc.subject	Genetic divergent species
dc.subject	Antibiotics
dc.subject	Network visualization
dc.subject.classification	Microbiology
dc.subject.classification	Physics
dc.subject.classification	Biophysics
dc.subject.classification	Physics--Theory
dc.title	Modeling and data analysis of emergent properties in different biological systems
dc.type	doctoral thesis
thesis.degree.discipline	Physics & Astronomy
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.