Perancangan Aplikasi Absensi Online Dengan Menggunakan Bahasa Pemrograman Kotlin

Pada saat ini banyak perusahaan yang masih menggunakan absensi manual khususnya di perusahaan atau instansi kecil, tentunya sangat disayangkan apabila di era digital ini tidak memanfaatkan teknologi yang makin berkembang, dalam hal ini banyak karyawan masih melakukan kecurangan dalam memanipulasi data absensi manual yang merugikan perusahaan sehingga menghambat kinerja kemajuan perusahaan, selain menimbulkan banyak resiko dan kecurangan yang dapat dilakukan karyawan, menggunakan metode manual pada sistem absensi juga menghabiskan banyak waktu karena harus mencatat satu per satu absensi setiap karyawan tentu saja hal ini tidak efektif. Oleh karena itu, penulis membuat suatu sistem perangkat android untuk menentukan posisi karyawan. Manajer dapat memantau posisi karyawan menggunakan absensi berbasis kotlin yang terhubung satelit dengan menggunakan metode Agile. Dengan adanya sistem ini membuat disiplin karyawan, mengurangi potensi kecurangan, meningkatkan efesiensi dan akurasi, dapat memantau karyawan yang sering absen, dapat memantau karyawan dalam penugasan, mampu menciptakan lingkungan kerja yang produktif, mengetahui posisi karyawan yang sedang bekerja.Kata Kunci: Absensi, Agile, Android, Digitalisasi, Kotli

Visual Analytics of Cascaded Bottlenecks in Planar Flow Networks

Finding bottlenecks and eliminating them to increase the overall flow of a network often appears in real world applications, such as production planning, factory layout, flow related physical approaches, and even cyber security. In many cases, several edges can form a bottleneck (cascaded bottlenecks). This work presents a visual analytics methodology to analyze these cascaded bottlenecks. The methodology consists of multiple steps: identification of bottlenecks, identification of potential improvements, communication of bottlenecks, interactive adaption of bottlenecks, and a feedback loop that allows users to adapt flow networks and their resulting bottlenecks until they are satisfied with the flow network configuration. To achieve this, the definition of a minimal cut is extended to identify network edges that form a (cascaded) bottleneck. To show the effectiveness of the presented approach, we applied the methodology to two flow network setups and show how the overall flow of these networks can be improved

Identifying model error in metabolic flux analysis - a generalized least squares approach

Background: The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. Results: In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). Conclusions: The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations. © 2016 The Author(s)

Predictive macroscopic modeling of Chinese hamster ovary cells in fed-batch processes

This thesis focuses on developing a systematic modeling method that can capture the essential features for prediction of cell metabolism, growth and monoclonal antibody (mAb) production in Chinese Hamster Ovary (CHO) cells. In a first step all specific consumption rates are calculated based on time courses of extracellular metabolites, viable cell density and mAb. Then the metabolic phases within which the metabolic pseudo-steady state approximation is verified are identified. In a third step, all metabolic rates are expressed as a function of the specific growth rate within each metabolic phase. We have applied this method to a set of small bioreactor data and have shown that the model obtained can predict specific conversion rates both small and also at large scale. In the second part of this thesis, a kinetic model of the cell growth has been developed. Together with previously described methodology, this kinetic model results in a predictive metabolic model for each experimental cell growth data are not required. The kinetic model is based on Monod kinetics with a few modifications such as a varying the maximum specific growth rate as a function of the integral viable cell density. The full kinetic model can be used off line to design optimal feeding profiles. The results of this thesis demonstrate that rich knowledge can be derived from macroscopic data that can then be used to predict new production conditions in an industrial environment at small and large scale.Der Schwerpunkt dieser Dissertation liegt auf der systematischen Entwicklung Modellen für die Vorhersage des zellulären Stoffwechsels, des Wachstums und der Produktion von monoklonalen Antikörpern (mAb) in Kulturen von Chinesischen Hamster-Ovarzellen (CHO). Zunächst wurden mit segmentierter linearer Regression metabolischer Phasen identifiziert. Diese Identifizierung beruht auf der Annahme eines pseudo-stationären Zustands und somit, dass in einer Phase alle Raten linear miteinander korreliert waren. Die spezifischen Raten wurden aus den Zeitverläufen der Konzentrationen der Metabolite und des mAb sowie der Lebendzellzahl bestimmt. Durch die Korrelation konnten alle Raten über die Wachstumsrate im 2 L und im 2000 L Maßstab berechnet werden. Danach wurde ein kinetisches Modell des Wachstums der Zellen etabliert, was die Vorhersage aller Raten auch in fed-batch Kulturen erlaubt. Die Kinetik basiert auf der Monod-Kinetik modifiziert mit einer variablen maximalen spezifischen Wachstumsrate. Das kinetische Modell erlaubt eine rechnerische Optimierung der Substratzuführung für eine maximale Produktion. Damit wurde gezeigt, dass aus makroskopischen Daten, d.h. ohne intrazelluläre Messungen, wesentliche Informationen erhalten werden können, mit denen neue Experimente in einem industriellen Umfeld vorhergesagt werden können. Diese innovative und systematische Vorgangsweise eröffnet neue Perspektiven für die Reduzierung von Kosten und für eine Beschleunigung der Prozessentwicklung

\u3ci\u3eIn silico\u3c/i\u3e Driven Metabolic Engineering Towards Enhancing Biofuel and Biochemical Production

The development of a secure and sustainable energy economy is likely to require the production of fuels and commodity chemicals in a renewable manner. There has been renewed interest in biological commodity chemical production recently, in particular focusing on non-edible feedstocks. The fields of metabolic engineering and synthetic biology have arisen in the past 20 years to address the challenge of chemical production from biological feedstocks. Metabolic modeling is a powerful tool for studying the metabolism of an organism and predicting the effects of metabolic engineering strategies. Various techniques have been developed for modeling cellular metabolism, with the underlying principle of mass balance driving the analysis. In this dissertation, two industrially relevant organisms were examined for their potential to produce biofuels. First, Saccharomyces cerevisiae was used to create biodiesel in the form of fatty acid ethyl esters (FAEEs) through expression of a heterologous acyl-transferase enzyme. Several genetic manipulations of lipid metabolic and / or degradation pathways were rationally chosen to enhance FAEE production, and then culture conditions were modified to enhance FAEE production further. The results were used to identify the rate-limiting step in FAEE production, and provide insight to further optimization of FAEE production. Next, Clostridium thermocellum, a cellulolytic thermophile with great potential for consolidated bioprocessing but a weakly understood metabolism, was investigated for enhanced ethanol production. To accomplish the analysis, two models were created for C. thermocellum metabolism. The core metabolic model was used with extensive fermentation data to elucidate kinetic bottlenecks hindering ethanol production. The genome scale metabolic model was constructed and tuned using extensive fermentation data as well, and the refined model was used to investigate complex cellular phenotypes with Flux Balance Analysis. The work presented within provide a platform for continued study of S. cerevisiae and C. thermocellum for the production of valuable biofuels and biochemicals

Methods in Computational Biology

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen

From Metabolite Concentration to Flux – A Systematic Assessment of Error in Cell Culture Metabolomics

The growing availability of genomic, transcriptomic, and metabolomic data has opened the door to the synthesis of multiple levels of information in biological research. As a consequence, there has been a push to analyze biological systems in a comprehensive manner through the integration of their interactions into mathematical models, with the process frequently referred to as “systems biology”. Despite the potential for this approach to greatly improve our knowledge of biological systems, the definition of mathematical relationships between different levels of information opens the door to diverse sources of error, requiring precise, unbiased quantification as well as robust validation methods. Failure to account for differences in uncertainty across multiple levels of data analysis may cause errors to drown out any useful outcomes of the synthesis. The application of a systems biology approach has been particularly important in metabolic modeling. There has been a concentrated effort to build models directly from genomic data and to incorporate as much of the metabolome as possible in the analysis. Metabolomic data collection has been expanded through the recent use of hydrogen Nuclear Magnetic Resonance (1H-NMR) spectroscopy for cell culture monitoring. However, the combination of uncertainty from model construction and measurement error from NMR (or other means of metabolomic) analysis complicates data interpretation. This thesis establishes the precision and accuracy of NMR spectroscopy in the context of cell cultivation while developing a methodology for assessing model error in Metabolic Flux Analysis (MFA). The analysis of cell culture media via NMR has been made possible by the development of specialized software for the “deconvolution” of complex spectra, however, the process is semi-qualitative. A human “profiler” is required to manually fit idealized peaks from a compound library to an observed spectra, where the quality of fit is often subject to considerable interpretation. Work presented in this thesis establishes baseline accuracy as approximately 2%-10% of the theoretical mean, with a relative standard deviation of 1.5% to 3%. Higher variabilities were associated primarily with profiling error, while lower variabilities were due in part to tube insertion (and the steps leading up to spectra acquisition). Although a human profiler contributed to overall uncertainty, the net impact did not make the deconvolution process prohibitively imprecise. Analysis was then expanded to consider solutions that are more representative of cell culture supernatant. The combination of metabolites at different concentration levels was efficiently represented by a Plackett-Burman experiment. The orthogonality of this design ensured that every level of metabolite concentration was combined with an equal number of high and low concentrations of all other variable metabolites, providing a worst-case scenario for variance estimation. Analysis of media-like mixtures revealed a median error and standard deviation to be approximately 10%, although estimating low metabolite concentrations resulted in a considerable loss of accuracy and precision in the presence of resonance overlap. Furthermore, an iterative regression process identified a number of cases where an increase in the concentration of one metabolite resulted in increased quantification error of another. More importantly, the analysis established a general methodology for estimating the quantification variability of media-specific metabolite concentrations. Subsequent application of NMR analysis to time-course data from cell cultivation revealed correlated deviations from calculated trends. Similar deviations were observed for multiple (chemically) unrelated metabolites, amounting to approximately 1%-10% of the metabolite’s concentration. The nature of these deviations suggested the cause to be inaccuracies in internal standard addition or quantification, resulting in a skew of all quantified metabolite concentrations within a sample by the same relative amount. Error magnitude was estimated by calculating the median relative deviation from a smoothing fit for all compounds at a give timepoint. A metabolite time-course simulation was developed to determine the frequency and magnitude of such deviations arising from typical measurement error (without added bias from incorrect internal standard addition). Multiple smoothing functions were tested on simulated time-courses and cubic spline regression was found to minimize the median relative deviation from measurement noise to approximately 2.5%. Based on these results, an iterative smoothing correction method was implemented to identify and correct median deviations greater than 2.5%, with both simulation and correction code released as the “metcourse” package for the R programming language. Finally, a t-test validation method was developed to assess the impact of measurement and model error on MFA, with a Chinese hamster ovary (CHO) cell model chosen as a case study. The standard MFA formulation was recast as a generalized least squares (GLS) problem, with calculated fluxes subject to a t-significance test. NMR data was collected for a CHO cell bioreactor run, with another set of data simulated directly from the model and perturbed by observed measurement error. The frequency of rejected fluxes in the simulated data (free of model error) was attributed to measurement uncertainty alone. The rejection of fluxes calculated from observed data as non-significant that were not rejected in the simulated data was attributed to a lack of model fit i.e. model error. Applying this method to the observed data revealed a considerable level of error that was not identified by traditional χ2 validation. Further simulation was carried out to assess the impact of measurement error and model structure, both of which were found to have a dramatic impact on statistical significance and calculation error that has yet to be addressed in the context of MFA