Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Systematic reconstruction of TRANSPATH data into Cell System Markup Language

<p>Abstract</p> <p>Background</p> <p>Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level.</p> <p>Results</p> <p>We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models.</p> <p>Conclusion</p> <p>By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.</p

Design and Basic Verification of a Discrete Event Simulator for Glucose Metabolism in Human Beings

This thesis describes the design and basic verification of a discrete event simulator for glucose metabolism in human beings. The simulator implements the glucose metabolism related behavior of various organs in the human body and tracks the blood plasma glucose level as the human body goes through a sequence of diet and exercise events. The simulator can mimic insulin resistance in various organs as well as the loss of insulin production in the pancreas and the adverse impact of these changes on the metabolic behavior of various organs. Thus, the simulator can serve as a model for people with diabetes. Such a model can be immensely useful to study the impact of specific life style changes on a person with diabetes. This thesis describes the simulator design as well as the results of simulations that verify the basic correctness of the simulator. This simulator represents the result of a multi-year collaborative effort involving the author, her MS thesis advisor and several other students working with the thesis advisor

CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&amp;D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&amp;S) methods. We suggest strategies and scenarios intended to enable new M&amp;S use cases that directly engage R&amp;D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&amp;S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&amp;S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&amp;S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

<p>Abstract</p> <p>Background</p> <p>The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system.</p> <p>Results</p> <p>In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular <it>Mg</it>²⁺ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the <it>Mg</it>²⁺ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system.</p> <p>Conclusions</p> <p>Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.</p

Toward community standards and software for whole-cell modeling

Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate, comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in SBML. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance:We anticipate that these new standards and software will enable more comprehensive models

Visual Support for the Modeling and Simulation of Cell Biological Processes

This dissertation aims at bringing information visualization closer to the demands of analytical problem solving for the specific domain of modeling and simulating cell biological systems. To this end, main segments of visual support in the domain are identified. For one of these segments, the visual analysis of simulation data, new concepts are developed. First, this includes the visualization of simulation data in the context of data generation. Second, new multiple view techniques for large and complex simulation data are introduced.Diese Arbeit verfolgt das Ziel, Informationsvisualisierung näher an die Anforderungen des Analyseprozesses heranzuführen, mit Blick auf die konkrete Anwendung der Modellierung und Simulation zellbiologischer Systeme. Dazu werden wesentliche Teilbereiche der visuellen Unterstützung identifiziert. Für den Teilbereich der visuellen Analyse von Simulationsdaten werden neue Konzepte entwickelt. Dies beinhaltet zum einen die Visualisierung von Simulationsdaten im Kontext der Datengenerierung. Zum anderen werden neue Multiple-View-Techniken für große und komplexe Simulationsdaten vorgestellt