How a meristem cell senses its own size

Despite many advances made in the field of cell biology, the molecular mechanism for cell size regulation remains a subject of intense study across biological kingdoms. In this study, I used plant meristematic cells as a model to address this question and proposed a mechanism for cell size control that acts at the G1/S transition and uses DNA as an internal metric. To test this hypothesis, I developed a novel live imaging technique that allowed me to follow growth and cell cycle progression in meristem cells for long periods of time and high time resolution. I also showed how total protein and nuclear size scale with cell size and analysed the cellular behaviour of inhibitors of the S-phase transition, identifying KIP RELATED 4 (KRP4) as a candidate regulator of cell size during the G1-S transition. KRP4 bound to chromatin during mitosis, suggesting a possible mechanism that uses chromosome segregation as a mean for equal inheritance, followed by dilution of KRP4 to a threshold that triggers S-phase entry. The protein F-BOX LIKE 17 (FBL17) was identified as a component used for targeted proteolysis of excess KRP4, ensuring that production of the latter matched chromatin content. To better understand the dynamics of KRP4 production and dilution, a mathematical model was produced, which predicted the behaviour of various mutants, solidifying the understanding of cell size control using DNA as an internal metric. Additionally, data for a possible size regulatory machinery acting during G2 are presented, with the hope to guide future research in this topic, suggesting a different mechanism that utilises microtubules as the measuring structure. Finally, I discuss the broader implications of this study, suggesting ways in which it could be implemented by plants during their development and consequences for the evolutionary history of cell size control in this kingdom of life

Prediction of Ubiquitination Sites Using UbiNets

Ubiquitination controls the activity of various proteins and belongs to posttranslational modification. Various machine learning techniques are taken for prediction of ubiquitination sites in protein sequences. The paper proposes a new MLP architecture, named UbiNets, which is based on Densely Connected Convolutional Neural Networks (DenseNet). Computational machine learning techniques, such as Random Forest Classifier, Gradient Boosting Machines, and Multilayer Perceptrons (MLP), are taken for analysis. The main target of this paper is to explore the significance of deep learning techniques for the prediction of ubiquitination sites in protein sequences. Furthermore, the results obtained show that the newly proposed model provides significant accuracy. Satisfactory experimental results show the efficiency of proposed method for the prediction of ubiquitination sites in protein sequences. Further, it has been recommended that this method can be used to sort out real time problems in concerned domain