Andres Kriete

Recent advances in the modeling of the cell cycle through computer simulation demonstrate the power of systems biology. By definition, systems biology has the goal to connect a parts list, prioritized through experimental observation or high-throughput screens, by the topology of interactions defining intracellular networks to predict system function. Computer modeling of biological systems is often compared to a process of reverse engineering. Indeed, designed or engineered technical systems share many systems-level properties with biological systems; thus studying biological systems within an engineering framework has proven successful. Here we review some aspects of this process as it pertains to cell cycle modeling.

What makes the study of aging particularly challenging is the wide spectrum of phenotypical changes that can be observed during its progression. While initial attention was paid to damage accumulation, dysfunction, and failure, it is now realized that aging, and associated diseases including dementias, are influenced by a multitude of interacting factors. Proximal mechanisms beyond passive accumulation of damage include regulatory mechanisms, stress responses, changes in networks, as well as genetic and stochastic effects. The application of computational systems biology in aging, which is in line with other attempts to overcome the study of isolated or compartmentalized mechanisms, has made initial progress allowing us to simulate partial aspects of the aging dynamics and to make new hypotheses about how these aging mechanism shape disease progression. Here we provide examples for analysis of networks, regulatory mechanisms, and spatiotemporal effects in the study of proximal mechanisms of aging and Parkinson’s Disease. In addition, we introduce complexity theories that may contribute to explain the ultimate causes of aging with an evolutionary view.

Parkinson’s disease (PD), like many other dementias, is a disease of old age with neurological-pathological signs and underlying molecular mechanisms that precede cell death. Deciphering PD specifically from an aging perspective has many advantages. PD shares multiple mechanisms with aging, albeit in an accelerated fashion, including accumulation of damage and dysfunction, stress responses, and deficiencies in the maintenance of protein quality. Here, we review the foundations of a new hybrid, phenotypical model, which combines organelle phenotypes with molecular mechanisms associated with the long-term progression of normal aging. Subsequently, we adapt this model to PD and demonstrate the acceleration of dysfunction. On the level of molecular mechanisms, we specifically discuss two pathways that play a key role in the progression of both aging and PD: NF-κB and mTOR. The introduction of comprehensive modeling approaches is expected to make a significant contribution in deciphering the relationship between the different processes and risks factors. In particular, the tight relationship of aging and PD as discussed here sheds new light on future strategies for interventions.

An initial set of structural properties to model the bronchial tree of the mammalian lung is envisioned, which allows to study morphology and function. Three levels of structural organization are taken into account: 1) the well defined main bronchii of the lung supplying for the lung lobes, 2) the macroscopic, gas conductive segments of the bronchial tree and their random distribution forming the lobes and 3) the respiratory units (acini) at microscopic resolution. The final model combining these structural hierarchies can be used to study gas transport visualized by computer graphical tools.

This chapter reviews various approaches in basic biological research and medicine for generating quantitative, flow, and image-based data for a comprehensive profiling and structural state space analysis. The value of the single-cell or single-individual analysis concept resides in its clinical value for the individual patient as well as in the bio-parameter patterns being of interest for molecular reverse engineering by systems biology. Currently, the concept of cytomics profits from advances in areas such as location proteomics, flow and tissue cytometry, screening assays, and cell and tissue arrays. Such advances move us toward a broad, systematic collection of information for clustering and cataloging cells according to their molecular, organelle, and morphometric phenotypes. One of the most important outcomes of the Human Genome Project is the realization that there is considerably more biocomplexity in the genome and the proteome than previously appreciated. A substantial advantage over flow cytometry is that cells in adherent cell cultures and tissues can be analyzed without prior disintegration.

The aggregate of statistical bioinformatics tools for collecting, storing, retrieving, and analyzing complex biological data has repeatedly proven useful in biological decision support and discovery, a notable hallmark being the deciphering of the human genome as led by the Genome International Sequencing Consortium. An envisioned digital blueprint of complex diseases but also of biological development, aging, and immunity should not solely consist of descriptive charts as widely found in scientific literature or in genomic databases. They should instead be based on rigorously quantitative data-based mathematical models of metabolic pathways, signal transduction cascades, cell-to-cell communication, and so on. Data is not only generated by genomics sequencing and structural proteomics, but increasingly by image-based spatial and time-lapse microscopic observations. Modeling approaches can be divided into bottom-up and top-down. In the bottom-up approach, we use a reductionist approach and study basic components, and integrate these to find relevant patterns and functions, such as pathways. Time-discrete dynamic systems models have long been used in biology. Biologic computer simulations require careful consideration as to the level of details necessary for a representative model, because unnecessary details will lead to models so complex that detailed numerical study would become highly cumbersome or impossible.

This chapter provides an introduction into spatiotemporal (ST) systems biology. It is a testament to systems biology's recent coming of age that even this immense complexity belies the true nature of cells. A host of compartments—such as the mitochondria, endoplasmic reticulum (ER), nucleus, Golgi apparatus, lysosomes, and peroxisomes—play important and localized roles in cellular function. The nucleus serves as a repository for the genome and is the chief location of regulatory processes controlling gene expression as well as DNA and RNA synthesis. Defunct macromolecules are degraded in lysosomes. Specific oxidative reactions that would be harmful if occurring in the cytosol are confined within peroxisomes. Because biological processes involve a large number of molecules and protein species, the state space is too large for an exact solution of stochastic differential equations describing a reaction. CellSim will integrate the coupled system using both standard integrators as well as Rosenbrock methods. For standard integrators, the propagator is reasonably simple to define. One needs only to generate the appropriate equations and consider the extended system.

As permitted by the size limitations of this publication we will survey and describe a selection of image restoration algorithms. The main emphasis lies on the iterative group as they are based on rigorous mathematical foundations and provide for restorations with limited data. Common topics, like finding the optimal regularization parameter, the influence of data discretisation and the significance of an accurate point spread function (PSF) are dealt with accordingly. To control image quality for iterative deconvolution methods, an image quality measure is described. It is based on information theory. The method allows for a more accurate and objective description of the image quality in digital microscopy than previously possible, since it takes into account the signal distribution, the transfer characteristic of the system and an estimate of the noise. Computer simulations of fluorescent beads serve as an example.

The vast amount of biological information emerging from largescale high-volume genomics and proteomics has significantly changed biological research. The problem is, however, that we currently do not have the methods to analyze the enormous complexity of cells or cellular systems within reasonable time intervals using the traditional approach of hypothesis formulation followed by experimental verification. One challenge is that the data gained from observing a few cells is rarely suited for statistical evaluation. This limitation has stimulated development of new approaches to the comprehensive collection of information with more immediate biological importance at the cellular level (cytomics). This effort spans both basic biomedical research and drug development.