TY - JOUR A1 - Haase, Tobias A1 - Sunkara, Vikram A1 - Kohl, Benjamin A1 - Meier, Carola A1 - Bußmann, Patricia A1 - Becker, Jessica A1 - Jagielski, Michal A1 - von Kleist, Max A1 - Ertel, Wolfgang T1 - Discerning the spatio-temporal disease patterns of surgically induced OA mouse models JF - PLOS One N2 - Osteoarthritis (OA) is the most common cause of disability in ageing societies, with no effective therapies available to date. Two preclinical models are widely used to validate novel OA interventions (MCL-MM and DMM). Our aim is to discern disease dynamics in these models to provide a clear timeline in which various pathological changes occur. OA was surgically induced in mice by destabilisation of the medial meniscus. Analysis of OA progression revealed that the intensity and duration of chondrocyte loss and cartilage lesion formation were significantly different in MCL-MM vs DMM. Firstly, apoptosis was seen prior to week two and was narrowly restricted to the weight bearing area. Four weeks post injury the magnitude of apoptosis led to a 40–60% reduction of chondrocytes in the non-calcified zone. Secondly, the progression of cell loss preceded the structural changes of the cartilage spatio-temporally. Lastly, while proteoglycan loss was similar in both models, collagen type II degradation only occurred more prominently in MCL-MM. Dynamics of chondrocyte loss and lesion formation in preclinical models has important implications for validating new therapeutic strategies. Our work could be helpful in assessing the feasibility and expected response of the DMM- and the MCL-MM models to chondrocyte mediated therapies. Y1 - 2019 U6 - https://doi.org/10.1371/journal.pone.0213734 VL - 14 IS - 4 PB - PLOS One ER - TY - JOUR A1 - Sunkara, Vikram T1 - On the Properties of the Reaction Counts Chemical Master Equation JF - Entropy N2 - The reaction counts chemical master equation (CME) is a high-dimensional variant of the classical population counts CME. In the reaction counts CME setting, we count the reactions which have fired over time rather than monitoring the population state over time. Since a reaction either fires or not, the reaction counts CME transitions are only forward stepping. Typically there are more reactions in a system than species, this results in the reaction counts CME being higher in dimension, but simpler in dynamics. In this work, we revisit the reaction counts CME framework and its key theoretical results. Then we will extend the theory by exploiting the reactions counts’ forward stepping feature, by decomposing the state space into independent continuous-time Markov chains (CTMC). We extend the reaction counts CME theory to derive analytical forms and estimates for the CTMC decomposition of the CME. This new theory gives new insights into solving hitting times-, rare events-, and a priori domain construction problems. Y1 - 2019 U6 - https://doi.org/10.3390/e21060607 VL - 21 IS - 6 SP - 607 ER - TY - JOUR A1 - Temp, Julia A1 - Labuz, Dominika A1 - Negrete, Roger A1 - Sunkara, Vikram A1 - Machelska, Halina T1 - Pain and knee damage in male and female mice in the medial meniscal transection-induced osteoarthritis JF - Osteoarthritis and Cartilage Y1 - 2019 U6 - https://doi.org/10.1016/j.joca.2019.11.003 ER - TY - JOUR A1 - Sunkara, Vikram A1 - Heinz, Gitta A. A1 - Heinrich, Frederik F. A1 - Durek, Pawel A1 - Mobasheri, Ali A1 - Mashreghi, Mir-Farzin A1 - Lang, Annemarie T1 - Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint JF - Osteoarthritis and Cartilage Y1 - 2021 U6 - https://doi.org/10.1016/j.joca.2021.03.007 VL - 29 IS - 6 SP - 905 EP - 914 ER - TY - JOUR A1 - Laydon, Daniel J. A1 - Sunkara, Vikram A1 - Boelen, Lies A1 - Bangham, Charles R. M. A1 - Asquith, Becca T1 - The relative contributions of infectious and mitotic spread to HTLV-1 persistence JF - PLOS Computational Biology Y1 - 2020 U6 - https://doi.org/10.1371/journal.pcbi.1007470 ER - TY - JOUR A1 - Sunkara, Vikram A1 - Heinz, Gitta A. A1 - Heinrich, Frederik F. A1 - Durek, Pawel A1 - Mobasheri, Ali A1 - Mashreghi, Mir-Farzin A1 - Lang, Annemarie T1 - Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint JF - bioarxiv (Accepted in Osteoarthr. Cartil.) Y1 - 2020 U6 - https://doi.org/10.1101/2020.06.13.148056 ER - TY - JOUR A1 - Rettig, Anika A1 - Haase, Tobias A1 - Pletnyov, Alexandr A1 - Kohl, Benjamin A1 - Ertel, Wolfgang A1 - von Kleist, Max A1 - Sunkara, Vikram T1 - SLCV – A Supervised Learning - Computer Vision combined strategy for automated muscle fibre detection in cross sectional images JF - PeerJ N2 - Muscle fibre cross sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross sections to quantify CSA due to challenges posed by variation of bright- ness and noise in the staining images. In this paper, we introduce SLCV, a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross sectional images of varying staining quality suggest that combining SL and CV performs signifi- cantly better than both SL based and CV based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice Similarity Coefficient (DSC) of 0.9778. T3 - ZIB-Report - 19-10 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72639 PB - PeerJ CY - PeerJ ER - TY - JOUR A1 - Sunkara, Vikram T1 - Algebraic Expressions of Conditional Expectations in Gene Regulatory Networks JF - Journal of Mathematical Biology N2 - Gene Regulatory Networks are powerful models for describing the mechanisms and dynamics inside a cell. These networks are generally large in dimension and seldom yield analytical formulations. It was shown that studying the conditional expectations between dimensions (vertices or species) of a network could lead to drastic dimension reduction. These conditional expectations were classically given by solving equations of motions derived from the Chemical Master Equation. In this paper we deviate from this convention and take an Algebraic approach instead. That is, we explore the consequences of conditional expectations being described by a polynomial function. There are two main results in this work. Firstly: if the conditional expectation can be described by a polynomial function, then coefficients of this polynomial function can be reconstructed using the classical moments. And secondly: there are dimensions in Gene Regulatory Networks which inherently have conditional expectations with algebraic forms. We demonstrate through examples, that the theory derived in this work can be used to develop new and effective numerical schemes for forward simulation and parameter inference. The algebraic line of investigation of conditional expectations has considerable scope to be applied to many different aspects of Gene Regulatory Networks; this paper serves as a preliminary commentary in this direction. T3 - ZIB-Report - 18-52 KW - Markov Chains KW - Chemical Master Equations KW - Dimension Reduction Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-70526 SN - 1438-0064 IS - 79 SP - 1779 EP - 1829 PB - Journal of Mathematical Biology ER - TY - JOUR A1 - Möller, Jan A1 - Isbilir, Ali A1 - Sungkaworn, Titiwat A1 - Osberg, Brenda A1 - Karathanasis, Christos A1 - Sunkara, Vikram A1 - Grushevsky, Eugene O A1 - Bock, Andreas A1 - Annibale, Paolo A1 - Heilemann, Mike A1 - Schütte, Christof A1 - Lohse, Martin J. T1 - Single molecule mu-opioid receptor membrane-dynamics reveal agonist-specific dimer formation with super-resolved precision JF - Nature Chemical Biology Y1 - 2020 U6 - https://doi.org/10.1038/s41589-020-0566-1 VL - 16 SP - 946 EP - 954 ER - TY - JOUR A1 - Lang, Annemarie A1 - Vester, Antja A1 - Ferreira-Gomes, M. A1 - Guerra, G.M. A1 - Heinrich, Fredrich R. A1 - Götzke, C.C. A1 - Kurmies, Sebastian A1 - Sunkara, Vikram A1 - Durek, Pawel A1 - Boerckel, Joel D. A1 - Mashreghi, Mir-Farzin T1 - PIPELINE FOR SINGLE CELL SEQUENCING OF HUMAN CHONDROCYTE PELLET CULTURES TO DELINEATE IL-1β MODULATED CHANGES IN CELL HETEROGENEITY JF - Osteoarthritis and Cartilage Y1 - 2022 U6 - https://doi.org/10.1016/j.joca.2022.02.112 VL - 30 IS - Supplement 1 SP - S90 ER -