Insilico Biotechnology (Germany)

Abstract Model‐based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model‐based approaches. Thanks to recent technological advancements in digitalization, machine‐learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model‐based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model‐based control and optimization is increasing rapidly. However, there are no easy‐to‐use, flexible, and freely available open‐source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy‐to‐use Python toolbox that allows to solve machine‐learning‐supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state‐of‐the‐art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO‐MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.

Abstract Parkinson’s disease (PD) is associated with abnormal b band oscillations (13-30 Hz) in the cortico-basal ganglia circuits.Abnormally increased striato-pallidal inhibition and strengthening the synaptic coupling between subthalamic nucleus (STN)and globus pallidus externa (GPe), due to the loss of dopamine, are accounted as the potential sources of b oscillations in thebasal ganglia. Deep brain stimulation (DBS) of the basal ganglia subregions is known as a way to reduce the pathological boscillations and motor deficits related to PD. Despite the success of the DBS, its underlying mechanism is poorly understoodand, there is controversy about the inhibitory or excitatory role of the DBS in the literature. Here, we utilized a computationalnetwork model of basal ganglia which consists STN, GPe, globus pallidus interna (GPi), and thalamus neuronal population.This model can capture healthy and pathological b oscillations as what has been observed in experimental studies. Using thismodel, we investigated the effect of DBS to understand whether its effect is excitatory or inhibitory. Our results show that theexcitatory DBS (EDBS) is able to quench the pathological synchrony and b oscillations, while, applying inhibitory DBS (IDBS)failed to quench the PD signs. In addition, the EDBS ameliorated the thalamic activity related to tremor in the model, while,the IDBS outperformed. However, with the help of the model results, we conclude that the effect of the DBS on its target isexcitatory

Cancer is a group of diseases that involves abnormal cell growth, resulting from genetic perturbations in signaling mechanisms.High resolution RNAseq and microarray assays enable the evaluation of the transcriptional activity of high number of signaling molecules.Furthermore, many signaling pathways are described in publically available databases.Today's challenge lies in the connection of signaling pathways and signaling data to produce predictive models which have the power to validate and identify targets in disease treatment.Curating networks manually can be exhaustive handiwork.We designed an ensemble approach of gene set enrichment on seven pathway databases.It generates a basic gene set mapping of the complex input data on comprehensive pathways.Using two publically available protein-protein interaction databases, the novel algorithm automatically reconstructs a comprehensive biological system representation from these mappings.The reconstruction was based on a newly shortest path algorithm.Using a microarray data set from hepatocellular cancer cells as input, a network with well-known cancer signaling mechanisms was derived.Furthermore, nodes accounting for hormone signaling were found as being modified in liver cancer that can be used as future research targets.Two recent publically available networks were adequately inferred when testing the method to reconstruct manually curated signaling networks.Finally, our method shows that integration of raw data and publically available knowledge expeditiously generates convenient and analyzable network views.

In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc.

Mathematical modelling is increasingly becoming an indispensable tool for the study of cellular processes, allowing their analysis in a systematic and comprehensive manner. In the vast majority of the cases, models focus on specific subsystems, and in particular describe either metabolism, gene expression or signal transduction. Integrated models that are able to span and interconnect these layers are, by contrast, rare as their construction and analysis face multiple challenges. Such methods, however, would represent extremely useful tools to understand cell behaviour, with application in distinct fields of biological and medical research. In particular, they could be useful tools to study genotype-phenotype mappings, and the way they are affected by specific conditions or perturbations. Here, we review existing computational approaches that integrate signalling, gene regulation and/or metabolism. We describe existing challenges, available methods and point at potentially useful strategies.

Metabolomics emerges to become an important profiling technique in bio(techno)logical systems. In addition to intracellular metabolite concentrations at (quasi) stationary conditions, stimulus-response experiments provide information on the dynamic behavior of metabolic pathways. These data are relevant for bioprocess analysis on the level of metabolism and for application of metabolic engineering principles aiming at a metabolic redesign of producer cells. However, even for the well-studied bacteria Escherichia coli only limited growth-rate dependent intracellular metabolite information is currently available, thereby impeding comprehensive metabolome analysis. Here, we present intracellular metabolite concentration data of representative glycolytic intermediates in E. coli cultivated in glucose-limited chemostats, (i) at systematic variation of growth-rate (D = 0.1, 0.2, 0.3, and 0.4 h(-1)) and (ii) at both steady-state and after a glucose pulse applying a recently introduced integrated sampling procedure and LC-MS analytical method. Whereas intracellular steady-state concentrations of upper part glycolytic intermediates FBP and DHAP+GAP increased 2.3-fold, respectively 2.8-fold, when specific growth-rate is raised from micro = 0.1 h(-1) to micro = 0.4 h(-1), the opposite trend was observed for 2PG+3PG and PEP pools with a decrease by a factor of 2.1, respectively 1.9. In glucose pulse experiments FBP and DHAP+GAP showed a 3.3 (1.8)-fold, respectively 2.8 (2.0)-fold, increase relative to the steady-state level at micro = 0.1 (0.4) h(-1). Also, the dynamics changed with growth-rate for these two metabolite pools. In contrast, 2PG+3PG and PEP were characterized by decreased concentrations in response to a glucose pulse and the relative changes related to steady-state values were significantly smaller compared with FBP and DHAP+GAP. The observed growth-rate dependency of our data clearly indicates the necessity for metabolome studies covering a broader range of physiological growth conditions.