Calculating life? Duelling discourses in interdisciplinary systems biology

After the successful structural analysis of the human and other organisms’ genomes the last decade witnessed a fundamental shift in the area of research in molecular biology: the move into Omics. It produced a plethora of data that require methodological and conceptual approaches to systematize, integrate, and interpret data which go beyond a linear understanding of biological processes and systems. The promise of the rapidly developing field of systems biology is to extend—if not overcome—the methodological and theoretical limits set by previous research undertaken in molecular biology. Taking this contemporary development seriously, this chapter investigates the framing of basic epistemic concepts (life, system, reductionism, holism, and model) by scientists working in systems biology. Based on a corpus of written evidence and interviews conducted with system biologists in Germany, we analyze the metaphorical frameworks underlying their conceptualization to tackle implicit meanings and the practical relevance ascribed to them. It becomes apparent that (to some extent) different professional backgrounds bear an impact on the framing of different concepts and heterogeneous interpretation prevails. The results underline the need for theoretical clarification of basic epistemic concepts in systems biology and the implementation of a science philosophy curriculum as a basic ingredient of university education. Both aspects are important to avoid methodological and theoretical fallacies that restrict the innovative potential of systems biology.

In this article I address the interactions between biological knowledge and ideas about the kinds of entity that are suited to appropriation. I start by arguing that commodification and reductionism are closely linked, and that patenting suits entities that are discrete and isolable, such as those that are the focus of molecular biology. I then turn to the new field of systems biology, which recognizes that traditional reductionist approaches to biology are no longer adequate and attempts to provide a more integrative understanding of biological systems. In doing this, systems biology has to deal with emergent phenomena. But patenting does not suit the dynamic and interactive complexity that is the object of study in systems biology. If systems biology rejects reductionism where does that leave commodification? I examine attempts to commodify predictive computational models in systems biology. I then turn to systems biology's sister discipline, synthetic biology, which deals with emergence by reducing the complexity of biological systems. By factoring out messy contingencies, synthetic biology is, in theory, well suited to commodification. Drawing on both these examples I explore how ideas about appropriation, including open source, are influencing the nature and course of research in biology.

Every year, the 12 scientific sections of the American Physiological Society each select a physiologist prominent in their scientific domain to deliver a distinguished, named lecture at the Society's annual meeting (Experimental Biology). In 2011, the Environmental and Exercise Physiology section chose Michael Joyner, MD, from the Mayo Clinic for this honour, named the Edward Adolph Lecture. Joyner's lecture appears in the Journal of Applied Physiology (Joyner, 2011) complementing his related narrative that recently appeared in The Journal of Physiology (Joyner & Pedersen, 2011). He used this occasion to continue to promote the importance of the discipline of physiology, not just as a discipline that has delivered huge advances in understanding, diagnosing, and treating human and other animal disease, but as the cornerstone of what is currently the major biomedical research push – translational research. Simply put, physiology and physiological research remain the essential links between genes and clinical care. Translational research just cannot be accomplished without physiology. Joyner provocatively contrasted the relative failure (to date at least) of the molecular revolution of the past 30 or so years to deliver on its own promises of cures, against the successes of physiology. Whether you buy Joyner's stance or not, there is no question that the genomic revolution has had a major effect on the discipline of physiology. In many ways it has been a double-edged sword. The enormous impact of two editorials in 1987 (by the then Director of the NHLBI, Dr Claude Lenfant, and Director of the NHBLI Lung Division, Dr Suzanne Hurd) in the American Review of Respiratory Disease (Hurd & Lenfant, 1987a,b), technically still promoting a balance between physiology and molecular biology, but clearly suggesting that investigators wishing for research support through NHLBI had better get molecular, was but one blow to physiology that has since led to the disappearance, reorganization, and/or renaming of many former departments of physiology around the world, especially in the USA and UK. We have come dangerously close to losing the foundations of physiology, as the masters who built those foundations have been lost. It was then ironic, to say the least, when Lenfant himself later complained that the molecular revolution had not so far produced adequate results, when he concluded (Lenfant, 2003): 'Enthusiasm for gene-centered medicine is contagious, and I am certainly not immune to it. In my view, however, the fundamental issue remains the same. Enormous amounts of new knowledge are barreling down the information highway, but they are not arriving at the doorsteps of our patients.' There is, however, light at the end of the physiological tunnel that is not from the headlamp of an onrushing train: The molecular revolutionaries have started to recognize the need for a partnership with physiology. Increasingly, they are coming to those few physiologists left and asking for help in studying the significance of their genetic and genomic discoveries. They have even invented a new discipline 'Systems Biology'– which of course is physiology. To this point, systems biology mostly addresses the interactions among genes to produce functional effects within cells. This will eventually build into larger and larger units of structure and function, and one day, we will proudly know that handshaking between molecular biology and physiology was the key research community transformation that advanced our ability to diagnose and treat disease. Physiology cannot do it alone, and molecular biology cannot do it alone. While there is light at the end of our tunnel, remaining passive about our discipline and waiting for reductionists to knock on our doors may not get us to the end of that tunnel. We think we are in a period of incredible opportunity for physiology, precisely because of the genomic revolution and the resulting push for translational research – but we have to get the word out beyond our own ranks. We just have to become individually and collectively much more active in explaining the importance of our discipline to the rest of the world. As we all know, that is better done with data than with table banging. This has been recognized for some time, and both the American Physiological Society in North America and the Physiological Society in the UK have been and will continue to be active in meeting with funding agencies and political decision-making bodies to explain the importance of the discipline through examples. Equally importantly, we have, and will continue to, put a lot of societal energy and resources into what we call the 'pipeline'– the physiologists of the future who are currently in primary school, secondary school, college, or university. They and their teachers need to be exposed to more physiology and thereby become excited by it. In this way, we can play a major role in ensuring the future of the discipline and, as a result, in translating basic discoveries into clinical care. We just cannot let this unique opportunity slip by and thank Dr Joyner for his provocative thoughts and timely encouragement. This should be a great springboard to a call for action to all physiologists, wherever you live and work. We need you to promote your discipline, not hide it. We urge you to speak to your local and national politicians and funding agencies about how physiology is essential to successful translation of molecular discoveries. We urge you to visit schools and colleges to promote and maybe even help teach physiology. We urge you to rebadge your Departments of Everything-But-Physiology back to Departments of Physiology. And most of all, we urge you to partner with those molecular biologists (and systems biologists) who are now in need of your scientific expertise, without which translation will not happen.

Simple SummaryThis review provides a roadmap to develop safe and effective OV-based future therapeutics by using several novel synthetic and system biology strategies. Integration of system and synthetic biology can improve the genetic design of OVs backbone, maintaining enough virulence-associated genes. Furthermore, specific computation pipelines can refine target discovery in several types of cancer, identifying the MHC-I and II-restricted peptide repertoire recognized by T-cells and reinforcing anticancer immune responses. Using these versatile approaches could encourage the development of a next-generation of OVs-based therapies, overcoming current challenges such as on-target, off-tumor effects, and variable clinical responses. The efficacy and safety profile of the current OVs-based regimens could be further enhanced with additional long-lasting clinical effects.Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

After "human genome project" has been accomplished, the life science comes to a new era, the post-genome era. In the post-genome era, the "big sciences" such as genomics, proteomics and metabolomics (so-called "omics") gradually become a new popular research methodology to provide global pictures of cells or organisms, although the classical experimental biology (small sciences) such as molecular biology or cell biology is still the mainstream in life sciences. The concept and strategy of omics is completely different from the classical experimental biology. The omics is called a "discovery science", of which the goal is to identify all the genes or proteins in the organisms, whereas the classical experimental biology such as molecular biology is called a "hypothesis-driven science", since the researches of these disciplines are initiated based on the scientific hypothesis and focus on studying the structure and functions of individual gene or protein. Systems biology is a newly born discipline in the post-genome era, which integrates the research strategy of classical experimental biology such as molecular biology with the new research strategy of "omics". Systems biology is also a new interdisciplinary frontier based mainly on the integration of the "wet" experiments such as molecular biology or "omics" with the "dry" experiments such as bioinformatics and computational biology. Technology of systems biology includes the "omics" platforms such as proteomics-platform and the theoretical platforms for computing and modeling. From these properties, Systems biology is defined as an integrating methodology for analyzing the components and dynamical behavior of biological systems as a whole. More importantly, these properties have made systems biology as a powerful analytical tool to reveal the complex diseases such as cancer and diabetes. Although the complex diseases have been extensively studied for a long term, it is far beyond understanding the mechanisms of the disease-process and curing these diseases. The difficulties for dealing with the complex diseases arise from the aspects of the complex diseases: 1) the causes of the initiation and development of the complex diseases involve multiple genetic factors, environment factors and the interaction of these two kinds of factors. 2) the different kinds of cells or tissues involve in the diseases. For example, the brain, pancreas, liver, skeletal muscle and adipose tissue mainly involve in the type 2 diabetes. 3) the molecular defects for the complex disease disrupt the normal behaviors of the complex molecular networks of genes and proteins. The classical bio-medicine based on molecular biology, cell biology, genetics and other experimental biology has made significant progress for against disease in general. However, the researchers on the bio-medicine area still face the great challenge for against the complex diseases such as cancer and diabetes since the methodology of the classical experimental biology is based on studying individual gene and protein and treat the organisms as a simple and linear system, which is not good enough to solve such problems of the complex diseases. Therefore, it is clear that the methodology and techniques of system biology must be applied for analyzing the molecular mechanisms of the complex diseases, and provide new solutions for preventing and curing the diseases.

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. Part of a special issue, Ontologies of Living Beings, guest-edited by A. M. Ferner and Thomas Pradeu

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

BiotecVisions 2012, April

Introduction to the Symposium on Synthetic Life Alan N. Schechter, Editor On behalf of the editors of Perspectives in Biology and Medicine, I am pleased to introduce the editors and venue of the Symposium on Synthetic Life: Scientific, Historical, and Ethical Perspectives, which occupies much of the current issue of our journal. We are very pleased that Professors Ute Deichmann, Michel Morange, and Anthony S. Travis planned, organized, and edited the proceedings of the workshop on this topic, held on March 5-6, 2012, at the Jacques Loeb Centre for the History and Philosophy of the Life Sciences and the NIBN at Ben-Gurion University of the Negev in Beer Sheva, Israel. Professor Deichmann has been the founding director of the Centre since it was created in 2007. Although she trained in biology in Germany, most of her career has been devoted to the history of fundamental life science research during the last century, especially in Europe and including the dreadful periods before and during the Second World War. The Centre (as described in a bit more detail below) has become one of the few institutions in the world devoted to a systematic study of the transformations during the last century of those aspects of biology that might in their entirety be termed molecular biology. On the most general level, these transformations occurred in the context of the transition from the vague colloid theories at the beginning of the last century to analyses of specific molecules during the last several decades. Indeed, to understand the origins of molecular biology, and thus much of the current excitement in the life sciences and even medicine, one must know the several threads of structural and genetic analyses that came together as molecular biology in the mid-1950s and that have flourished since. It is the thesis of the symposium presented in the papers in this issue of Perspectives that further philosophical syntheses are now underway, including with synthetic chemistry to create the fields of synthetic biology and even synthetic life. This is analogous to the syntheses occurring with physiology that have led to what is generally termed systems biology. Professor Morange is Professor of Biology and Director of the Centre Cavaillès for history and philosophy of science at the Ecole Normale Supérieure in [End Page 467] Paris, and was trained initially in molecular biology. In the last two decades he has published several very well received books on the history of molecular biology, especially of the concept of genes, and on the history of science more generally. Professor Travis is interested in the history of chemistry and technology and has long been associated with the Sidney M. Edelstein Center for the History and Philosophy of Science, Technology and Medicine at the Hebrew University of Jerusalem, the Jacques Loeb Centre, and the Leo Baeck Institute in London. Thus the three editors bring very important and complementary perspectives to these issues. The Jacques Loeb Centre (http://in.bgu.ac.il/en/loeb/Pages/default.aspx) honors in its name the German-American physiologist who lived from 1859 to 1924 and from whom Professor Deichmann and her colleagues rightly trace the concepts that were the fundamental basis of the application of physical-chemical ideas and methods to biology during the last century. This work led to the complete replacement of the vitalistic approaches with the mechanistic tradition that we now accept with few second thoughts in fields ranging from evolution to medicine. In her writings, and in the focus of the work of the Centre, Professor Deichmann has traced the specific questions and controversies that constitute this history, including in the work of other pioneers, especially during the first half of the last century. Her paper on the contributions of Leonor Michaelis during this period is published as a special addition to this issue of Perspectives. The workshop proceedings published here are the fifth in a yearly series at the Centre. The titles of the others will give an idea of the scope of their enterprise: Philosophies in Biology (2008); Darwinism and Functional Biology, Other Sciences, and the Humanities (2009); Evolutionary-Developmental Biology: The Evolution of the Fields and Prospects for Future Developments (2010...

There is an old saying (at least in English) that what you don't know can't hurt you. In science it is the reverse. So how much are we thinking about the deep unknowns in the life sciences, compared with gathering data? The European Commission set the tone for research with its launch of the “Information Society,” and proclamation (made in 2000 and known as the “Lisbon Agenda”) to make Europe the world-leading knowledge-based economy by 2010. With the rapid technological advances in molecular biology (particularly genome sequencing), some of this knowledge was practically in the bag, so it seemed. It was a “known” entity with predictable growth prospects. Thinking about what we already know, and know how to do, enables us to build on knowledge in an evolutionary, incremental, sense. Thinking about what we don't know, and don't know how to do, on the other hand, raises the truly fascinating questions in biology that are likely to culminate in revolutions in understanding. But at a more practical level, carefully thinking about what we don't know can help us make the right decision as to which data to gather, how to interpret them, and which other advances we need to support this endeavor. It is not necessarily easy to get the concept across these days, because large sectors of the life sciences are concerned with massive data collecting and interpretation projects. It might very well have been more implicitly understood 50 years ago, for instance, when we had but a fraction of the biological data to ponder that we do today. How to sequence (this has been known, in principle, for a very long time) How to decode the sequence (this has also been known for a very long time) But the ambitious aim of understanding the data needed to be matched by ambitious thinking about the unknowns. What we did not know was how to embed the knowledge of the sequence into the model of gene interactions as complex networks with phenotypic outputs that tend to be unpredictable. However, network biology as a discipline had been around for quite some time already. Now we tend to call it systems biology, and systems biology has been a defined discipline for probably more than 40 years: Mihajlo Mesarovic defined it as a research field in 1966 with an international symposium “Systems Theory and Biology” (though Ludwig von Bertalanffy's book “General Systems Theory in Physics and Biology” was published in 1950). Concerted funding for systems biology only really started in the late 1990s. If discovery of information advances faster than the rate at which we are developing the means to understand that information, it is inevitable that we will start misunderstanding it, and – even worse – misapplying it. A hackneyed example is the concept that the human genome sequence would give us the “genes for X, Y, Z”. Unfortunately, this worked against a positive trend in perception that individual genes were not determinants of phenotype.

Bioinformatics is an extremely important field of biological science that also includes in-depth knowledge and skill in statistics and computer science. With the advance of new sequencing projects, bioinformatics helps to comprehend biological processes to primarily serve the agriculture and healthcare sectors with various spinoffs. To address the advances and awareness in bioinformatics to students and researchers this book will serve as a quick reference book on the subject. Bioinformatics is essential in all the fields of biotechnology and molecular biology that deal with molecular data. Thus the subject caters to divergent disciplines of biological science. The book on Concepts in Bioinformatics, Basics to Advances is a compilation of basic information on bioinformatics and also includes advanced areas that are required by students and professionals. The authors have great knowledge and experience in putting together updated information on animal biotechnology through eminent experts. The beginning of the book familiarizes the readers with the concept of bioinformatics and its history. Then some important concepts of basic bioinformatics have been discussed in the subsequent five chapters, that includes, databases, multiple sequence alignment, primer designing, and molecular phylogeny. This section is important to the postgraduate students of bioinformatics, biotechnology, and molecular biology. Chapters 9 to chapter 14 discusses BLAST and FASTA, protein structure prediction through homology modeling and molecular modeling, which are equally important for acquiring skills for in silico analysis. The last three chapters of the book discuss some advanced components of bioinformatics, namely, drug designing, systems biology, and synthetic biology. In general, the book is meant as a “short introduction” to bioinformatics and can be used as a sensitizer to study the subject. The book provides a comprehensive introduction to the field of bioinformatics, covering a range of topics from basic concepts to advanced techniques. The book is aimed at students, researchers, and educators in the field of biotechnology and bioinformatics, and is designed to be a valuable resource for those just starting out in the field. The topics covered in the book include molecular data analysis, multiple sequence alignment, primer design, phylogenomics, omics, molecular modeling, drug design, and synthetic biology. Overall, it seems like the book would be a useful tool for anyone looking to gain a solid foundation in the field of bioinformatics.

The onset of addiction is marked with drug induced positive experiences that keep being repeated. During that time, adaptation occurs and addiction is stabilized. Interruption of those processes induces polysymptomatic withdrawal syndromes. Abstinence is accompanied by risks of relapse. These features of addiction suggest adaptive brain dynamics with common pathways in complex neuronal networks. Addiction research has used animal models, where some of those phenomena could be reproduced, to find correlates of addictive behavior. The major thrust of those approaches has been on the involvement of genes and proteins. Recently, an enormous amount of data has been obtained by high throughput technologies in these fields. Therefore, (Computational) "Systems Biology" had to be implemented as a new approach in molecular biology and biochemistry. Conceptually, Systems Biology can be understood as a field of theoretical biology that tries to identify patterns in complex data sets and that reconstructs the cell and cellular networks as complex dynamic, self-organizing systems. This approach is embedded in systems science as an interdisciplinary effort to understand complex dynamical systems and belongs to the field of theoretical neuroscience (Computational Neuroscience). Systems biology, in a similar way as computational neuroscience is based on applied mathematics, computer-based computation and experimental simulation. In terms of addiction research, building up "computational molecular systems biology of the (addicted) neuron" could provide a better molecular biological understanding of addiction on the cellular and network level. Some key issues are addressed in this article.

Systems biology is an interdisciplinary field that applies engineering principles and computational modeling to understand complex biological systems at multiple scales. This paper examines how systems biology evolved from reductionist approaches to integrative, system-level methodologies that leverage high-throughput technologies and mathematical frameworks. Core topics include the history of systems thinking in biology, computational modeling of metabolic and signaling networks, data integration techniques, and applications in medicine, agriculture, and synthetic biology. With the advent of omics technologies and bioinformatics tools, researchers can now simulate, predict, and design biological functions with increasing precision. However, despite its promise, systems biology faces significant challenges, including data standardization, model validation, and ethical implications. By blending systems engineering with molecular biology, this field aims not only to advance fundamental science but also to offer innovative solutions to pressing societal needs such as disease treatment, food security, and environmental sustainability. Keywords: Systems Biology, Computational Modeling, Biological Networks, Data Integration, Synthetic Biology, Engineering Principles, Omics Technologies.

Carbonic anhydrase: chemistry, physiology, and inhibition.

Scientists have isolated cancer stem cells that lead to the growth of a type of cancer found in the kidneys of children. The researchers have used these cancer stem cells to test a new therapeutic approach that one day might be used for treatment. The results are published in EMBO Molecular Medicine. "In earlier studies, cancer stem cells were isolated from adult cancers [] but so far much less is known about stem cells in paediatric cancers," remarked Prof Benjamin Dekel. "Cancer stem cells contain the complete genetic machinery necessary to start, sustain and propagate tumor growth and they are often referred to as cancer-initiating cells. As such, they not only represent a useful system to study cancer development but they also serve as a way to study new drug targets and potential treatments designed to stop the growth and spread of different types of cancer."