Gabriele Gramelsberger

Simulation and System Understanding

Systems biology is based on a mathematized understanding of molecular biological processes. Because genetic networks are so complex, a system understanding is required that allows for the appropriate modelling of these complex networks and its products up to the whole-cell scale. Since 2000 standardizations in modelling and simulation techniques have been established to support the community-wide endeavors for whole-cell simulations. The development of the Systems Biology Markup Language (SBML), in particular, has helped systems biologists achieve their goal. This paper explores the current developments of modelling and simulation in systems biology. It discusses the question as to whether an appropriate system understanding has been developed yet, or whether advanced software machineries of whole-cell simulations can compensate for the lack of system understanding.

Generation of Evidence in Simulation Runs: Interlinking With Models for Predicting Weather and Climate Change

Meteorology has employed automatic computing machines since the early days of electronic computers. From the 1950s on, a large body of models used for “in silico” experiments (numerical simulation) has been built up, together with an international infrastructure of measuring, modeling, and testing. These outstanding developments— unique in science—led not only to an increasing standardization in developing and applying models but also to deepening the interlinking between modeling and generating evidence. The article explores needs and strategies for evaluating scientific results based on mass data output devices.

Philosophical perspectives on synthetic biology

Although the emerging field of synthetic biology looks back on barely a decade of development, the stakes are high. It is a multidisciplinary research field that aims at integrating the life sciences with engineering and the physical/chemical sciences. The common goal is to design and construct novel biological components, functions and systems in order to implement, in a controlled way, biological devices and production systems not necessarily found in nature. Among the many potential applications are novel drugs and pesticides, cancer treatments, biofuels, and new materials. According to the most optimistic visions, synthetic biology may thus lead to a biotechnological revolution by transforming microorganisms into ‘factories’ of sorts, which could eventually displace conventional industrial methods. Beyond the immediate interest of natural scientists and engineers, synthetic biology has also attracted the attention of social scientists, economists, and philosophers. As early as 2002, Evelyn Fox Keller, drawing on precursor notions such as Stéphane Leduc’s ‘biologie synthétique’, outlined the aims of synthetic biology in her book Making Sense of Life (2002). In 2006, the anthropologist Paul Rabinow became involved in the work of the Synthetic Biology Engineering Research Center (SynBERC), where he created the Center’s Human Practices division, which was itself conceived of as a contribution to ‘anthropological research on the contemporary’ (Rabinow & Bennett, 2009; see www.anthropos-lab.net). By 2007, both the economic potential of synthetic biology (Henkel & Maurer, 2007) and its property rights problems (Rai & Boyle, 2007) had already been explored; simultaneously, over a two-year period in 2007–2008, the European Union’s Synbiosafe project implemented a study on the safety and ethical aspects of the nascent discipline (Schmidt, 2009; Schmidt, Kelle, Ganguli-Mitra, & de Vriend, 2009; see www.synbiosafe.eu). In 2008, a group of researchers at the University of Exeter’s ESRC Centre for Genomics in Society (Egenis), which included philosopher Maureen O’Malley, sociologist Jane Calvert, and a pair of research students, characterized synthetic biology as a high-profile area of research, driven by the challenge of DNA-based device construction, genome-driven cell engineering and protocell creation (O’Malley, Powell, Davies,

Modelling the Climate System: An Overview

A Google search for the keyword ‘climate’ on a cold summer day in August 2010 delivered more than 150 million links in 0.23 s, and ‘climate change’ brought another 58 million. Obviously it is no problem to find floods of information about these topics on the net, yet understanding the scientific concept of climate and climate modelling is not so easy. The trouble with ‘climate’ starts when it is mixed up with the idea of weather, and when extreme weather events and short-term trends in temperature or precipitation are interpreted as effects of climate change. Usually, these interpretations are linked to an individual’s memory of experiences in childhood and other periods of life. But the trouble results not from this individual definition, which does not accord with the World Meteorological Organization’s official definition of climate as the statistics of weather. The trouble is raised by the scientific concept of climate as a mathematical construct that cannot be experienced directly. This problem is hitting science now that socio-political demands are coming into play. For responding to such demands, science has to break down its statistical and general concepts into individual and local conclusions, but this is—at the moment at least—not possible. The reason lies in the top-down approach of modern science, which uses globally valid equations to achieve increasingly higher resolution. The great challenge for meteorology during the next years and decades will be to translate statistical and general results into individual and local knowledge. Or in other words, science has to connect its global view with local circumstances. Regional modelling and downscaling are just the beginning, although these methods are still far removed from any particular individual or local view of a particular city or area. Of course, one can ask why humans do not simply get used to the scientific concept of climate. But when concrete environmental activities are required, individual needs and local effects play the main role, not the annual mean global temperature.

Continuous culture techniques as simulators for standard cells: Jacques Monod’s, Aron Novick’s and Leo Szilard’s quantitative approach to microbiology

Continuous culture techniques were developed in the early twentieth century to replace cumbersome studies of cell growth in batch cultures. In contrast to batch cultures, they constituted an open concept, as cells are forced to proliferate by adding new medium while cell suspension is constantly removed. During the 1940s and 1950s new devices have been designed—called “automatic syringe mechanism,” “turbidostat,” “chemostat,” “bactogen,” and “microbial auxanometer”—which allowed increasingly accurate quantitative measurements of bacterial growth. With these devices cell growth came under the external control of the experimenters and thus accessible for developing a mathematical theory of growth kinetics—developed mainly by Jacques Monod, Aron Novick and Leo Szilard in the early 1950s and still in use today. The paper explores the development of continuous culture devices and claims that these devices are simulators for standard cells following specific requirements, in particular involving mathematical constraints in the design and setting of the devices as well as experiments. These requirements have led to contemporary designs of continuous culture techniques realizing a specific event-based flow algorithm able to simulate directed evolution and produce artificial cells and microorganisms. This current development is seen as an alternative approach to today’s synthetic biology.

Problems in Mathematizing Systems Biology

“I think biology stands out as a discipline in which there has been a wild flowering of interdisciplinary approaches – bioinformatics, for example, and applications of informational visualization. This flowering has not kept pace with our understanding. For example, there is a small industry devoted to creating visualization tools for biologists. One of the prominent standards for assessing whether these tools perform well is whether using the tools facilitates insight; but there remains conceptual confusion within the info-vis community about just what counts as an insight, about how to measure or even count insights, and about whether insight is the proper standard for success as opposed to, say, some speed-related standard. This is just the sort of issue for which philosophers of science, and maybe even some epistemologists, are especially well equipped to address.”

Introduction to the Volume

In 1979 meteorologist Jule Charney and colleagues published a globally recognized report on Carbon Dioxide and Climate: A Scientific Assessment (Charney et al. 1979). They finished the report with the conclusions that “our best estimate is that changes in global temperature on the order of 3°C will occur and that these will be accompanied by significant changes in regional climatic patterns” (p. 17). The estimates of the so-called Charney report were based on two, at that time state-of-the art, general circulation models of the atmosphere that carried out numerical studies on the impact of doubling carbon dioxide on the global mean temperature. This measure, called climate sensitivity, was introduced by Charney et al. and were supposed to provide some insight into the ‘vast geophysical experiment’ mankind was about to conduct (Revelle and Suess 1957). A full two decades before the release of the Charney report, Charles D. Keeling had begun measurements of atmospheric carbon dioxide concentration at Mauna Loa Observatory in Hawaii in order “to make sure that man’s ‘vast geophysical experiment’ would be properly monitored and its results analyzed” (Keeling 1978, p. 38). The ‘Keeling Curve’, a time series of annual departures from 1958 on, clearly shows the increased CO2 concentration in the atmosphere. This curve has become one of the icons of man-induced climate change today. However, this kind of ‘vast geophysical experiment’ should be subject to a digital climate, not to nature. Therefore climate models are indispensable tools for the emerging climate change science. Rooted in simple barotropic models of the atmosphere, first computed by Charney and colleagues on ENIAC in 1950, these models have developed into complex Earth system models—incorporating knowledge from not only meteorology, but also oceanography, hydrology, biology, geochemistry, economy, and other fields. Within the last six decades, climate models have turned from purely meteorological into multidisciplinary objects of Earth science. Similarly, forecasts of changes in air pressure fields based on barotropic models have developed into projections of climate change and its impact on ecology using vast software machineries.