SysBioMed report: advancing systems biology for medical applications

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

In silico modeling for tumor growth visualization

BACKGROUND

Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research.

RESULTS

We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ .

CONCLUSION

In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.

A model qualification method for mechanistic physiological QSP models to support model-informed drug development

Mechanistic physiological modeling is a scientific method that combines available data with scientific knowledge and engineering approaches to facilitate better understanding of biological systems, improve decision‐making, reduce risk, and increase efficiency in drug discovery and development. It is a type of quantitative systems pharmacology (QSP) approach that places drug‐specific properties in the context of disease biology. This tutorial provides a broadly applicable model qualification method (MQM) to ensure that mechanistic physiological models are fit for their intended purposes.

The search for organizing principles as a cure against reductionism in systems medicine

Biological complexity has forced scientists to develop highly reductive approaches, with an ever-increasing degree of specialization. As a consequence, research projects have become fragmented, and their results strongly dependent on the experimental context. The general research question, that originally motivated these projects, appears to have been forgotten in many highly specialized research programmes. We here investigate the prospects for use of an old regulative ideal from systems theory to describe the organization of cellular systems 'in general' by identifying key concepts, challenges and strategies to pursue the search for organizing principles. We argue that there is no tension between the complexity of biological systems and the search for organizing principles. On the contrary, it is the complexity of organisms and the current level of techniques and knowledge that urge us to renew the search for organizing principles in order to meet the challenges that are arise from reductive approaches in systems medicine. Reductive approaches, as important and inevitable as they are, should be complemented by an integrative strategy that de-contextualizes through abstractions, and thereby generalizes results.

Modelling the regulation of telomere length: the effects of telomerase and G-quadruplex stabilising drugs

Telomeres are guanine-rich sequences at the end of chromosomes which shorten during each replication event and trigger cell cycle arrest and/or controlled death (apoptosis) when reaching a threshold length. The enzyme telomerase replenishes the ends of telomeres and thus prolongs the life span of cells, but also causes cellular immortalisation in human cancer. G-quadruplex (G4) stabilising drugs are a potential anticancer treatment which work by changing the molecular structure of telomeres to inhibit the activity of telomerase. We investigate the dynamics of telomere length in different conformational states, namely t-loops, G-quadruplex structures and those being elongated by telomerase. By formulating deterministic differential equation models we study the effects of various levels of both telomerase and concentrations of a G4-stabilising drug on the distribution of telomere lengths, and analyse how these effects evolve over large numbers of cell generations. As well as calculating numerical solutions, we use quasicontinuum methods to approximate the behaviour of the system over time, and predict the shape of the telomere length distribution. We find those telomerase and G4-concentrations where telomere length maintenance is successfully regulated. Excessively high levels of telomerase lead to continuous telomere lengthening, whereas large concentrations of the drug lead to progressive telomere erosion. Furthermore, our models predict a positively skewed distribution of telomere lengths, that is, telomeres accumulate over lengths shorter than the mean telomere length at equilibrium. Our model results for telomere length distributions of telomerase-positive cells in drug-free assays are in good agreement with the limited amount of experimental data available.

The road from systems biology to systems medicine

As research institutions prepare roadmaps for "systems medicine," we ask how this differs from applications of systems biology approaches in medicine and what we (should) have learned from about one decade of funding in systems biology. After surveying the area, we conclude that systems medicine is the logical next step and necessary extension of systems biology, and we focus on clinically relevant applications. We specifically discuss three related notions. First, more interdisciplinary collaborations are needed to face the challenges of integrating basic research and clinical practice: integration, analysis, and interpretation of clinical and nonclinical data for diagnosis, prognosis, and therapy require advanced statistical, computational, and mathematical tools. Second, strategies are required to (i) develop and maintain computational platforms for the integration of clinical and nonclinical data, (ii) further develop technologies for quantitative and time-resolved tracking of changes in gene expression, cell signaling, and metabolism in relation to environmental and lifestyle influences, and (iii) develop methodologies for mathematical and statistical analyses of integrated data sets and multilevel models. Third, interdisciplinary collaborations represent a major challenge and are difficult to implement. For an efficient and successful initiation of interdisciplinary systems medicine programs, we argue that epistemological, ontological, and sociological aspects require attention.

System approaches of Weiss and Bertalanffy and their relevance for systems biology today

System approaches in biology have a long history. We focus here on the thinking of Paul A. Weiss and Ludwig von Bertalanffy, who contributed a great deal towards making the system concept operable in biology in the early 20th century. To them, considering whole living systems, which includes their organisation or order, is equally important as the dynamics within systems and the interplay between different levels from molecules over cells to organisms. They also called for taking the intrinsic activity of living systems and the conservation of system states into account. We compare these notions with today's systems biology, which is often a bottom-up approach from molecular dynamics to cellular behaviour. We conclude that bringing together the early heuristics with recent formalisms and novel experimental set-ups can lead to fruitful results and understanding.

Hybrid models of tumor growth

Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentration or density fields, respectively. Each discrete cell can also be equipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individual-based models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor growth.

Interoperability of time series cytometric data: a cross platform approach for modeling tumor heterogeneity

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation-while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principal aim in cancer biology is to seek an understanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cell-based event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in-depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well-defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross-validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross-sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression

Personalized Medicine in Diabetes

BACKGROUND

Multiple genes that are associated with the risk of developing diabetes or the risk of diabetes complications have been identified by candidate gene analysis and genomewide scanning. These molecular markers, together with clinical data and findings from proteomics, metabolomics, pharmacogenetics, and other methods, lead to a consideration of the extent to which personalized approaches can be applied to the treatment of diabetes mellitus.

CONTENT

Known genes that cause monogenic subtypes of diabetes are reviewed, and several examples are discussed in which the genotype of an individual with diabetes can direct considerations of preferred choices for glycemic therapy. The extent of characterization of polygenic determinants of type 1 and type 2 diabetes is summarized, and the potential for using this information in personalized management of glycemia and complications in diabetes is discussed. The application and current limitations of proteomic and metabolomic methods in elucidating diabetes heterogeneity is reviewed.

SUMMARY

There is established heterogeneity in the determinants of diabetes and the risk of diabetes complications. Understanding the basis of this heterogeneity provides an opportunity for personalizing prevention and treatment strategies according to individual patient clinical and molecular characteristics. There is evidence-based support for benefits from a personalized approach to diabetes care in patients with certain monogenic forms of diabetes. It is anticipated that strategies for individualized treatment decisions in the more common forms of diabetes will emerge with expanding knowledge of polygenic factors and other molecular determinants of disease.

Predictive, preventive, personalized and participatory medicine: back to the future

The pioneering work of Jean Dausset on the HLA system established several principles that were later reflected in the Human Genome Project and contributed to the foundations of predictive, preventive, personalized and participatory (P4) medicine. To effectively develop systems medicine, we should take advantage of the lessons of the HLA saga, emphasizing the importance of exploring a fascinating but mysterious biology, now using systems principles, pioneering new technology developments and creating shared biological and information resources.

Thresholds in transient dynamics of signal transduction pathways

Transient dynamics of signal transduction pathways play an important role in many biological processes, including cell differentiation, apoptosis, metabolism and DNA damage response. Recent examples of quantitative methods to characterize transient signals include transient metabolic control coefficients and finite time Lyapunov exponents. In our work we compare these quantitative methods to characterize transient phenomena and specifically discuss their predictive power for three examples. We focus on the identification of thresholds that separate different transient dynamic behaviors. Our investigation leads to the following results: The spectrum of the finite-time Lyapunov exponents unambiguously and reliably identifies putative thresholds in transient dynamics. Metabolic control coefficients do not reliably detect all thresholds and suffer from false positives.