Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

The nervous system is the most complex system in an organism. Functioning of the nervous system requires proper formation of neural cells, as well as accurate connectivity and signaling among them. While the major events that occur during these processes are known, the finer details are yet to be understood. Hence, an attempt was made to look for novel genes that could be involved in them. The focus of the present study is on CG9650, a gene that was uncovered in a misexpression screen, as a possible player in neuronal development in Drosophila melanogaster. The first chapter of the thesis reviews existing knowledge about neuronal development and function. The first section of this chapter explains in brief the formation and specification of neural stem cells, and their differentiation to neurons and glia. Sections 2 and 3 describe neuronal connectivity and signaling with respect to axon growth, synapse formation, function and plasticity. A comparison of invertebrate and vertebrate neuronal development is provided in section 4 of this chapter. This part also explains the use of Drosophila as a model for neuronal development and function. Chapter 2 describes the expression pattern of CG9650, which was characterized to gain insights into the possible role it plays during Drosophila neurogenesis.CG9650 is expressed in multiple cell types in the nervous system at the embryonic stage. Some of the cell sub-types have been identified from their morphology and position. Expression was restricted to neurons in the larval stage (except in the optic lobe, where it was expressed in precursors also), and continued in the pupal stage. No expression was seen in adults (except in the optic lobe). CG9650 has a putative DNA binding region, which bears homology to the mouse proteins CTIP1 and CTIP2, implying that CG9650 is possibly a transcription factor. In order to understand the function of CG9650, the protein was knocked down panneuronally. The resultant animals showed locomotor defects at both larval and adult stages, which have been described in chapter 3. Knock down larvae showed reduced displacement and speed of movement. The number of peristaltic cycles was also reduced in these animals but the cycle period was normal. In adults, movement was uncoordinated and righting reflex was lost, resulting in inability to walk, climb or fly. These results imply a defect in neuronal signaling. Sensory perception was unaffected in these animals. Stage specific knockdown of CG9650 indicated that the requirement for this protein is primarily during the larval stage. All CG9650-expressing neurons in the ventral nerve cord were glutamatergic, implying that its role in controlling locomotor activity is likely through glutamatergic circuits. Following up on these observations, signaling at the neuromuscular junction was assessed in CG9650 knock down animals. Chapter 4 discusses the signaling defects seen on CG9650 knock down, and the possible role of this protein. Electrophysiological recordings from Dorsal Longitudinal Muscles showed reduced and irregular neuronal firing in the knock down animals. These animals also had reduced bouton and active zone numbers. Moreover, overexpression of BRP, an active zone protein, rescued the locomotor defects caused by knock down of CG9650. Chapter 5 reports the effect of over expression of CG9650. Pan-neural over expression of CG9650 resulted in embryos with severe axon scaffolding defects, as well as aberrant neuronal and glial pattern. However, the incorrectly positioned glial cells in these embryos did not express CG9650, indicating that their aberrant positioning was probably due to incorrect signaling from the neurons. In conclusion, this study reports the requirement for CG9650, a hitherto unknown protein, in locomotor activity and signaling, thus ascribing for it a role in neuronal development and function of Drosophila melanogaster.