Bioinformatics for the genomic sciences and towards systems biology. Japanese activities in the post-genome era

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.

WO terms now in use should awaken electrical engineers and those studying signal processing to the major role they have to play in the life sciences: systems biology and systems medicine. Systems biology involves understanding the manner in which the parts of an organism interact in complex networks, and systems medicine is the application of systems biology to medicine. Electrical engineers analyze and synthesize systems. Stimulated by the success of the Human Genome Project, genomics is a key driver of systems biology. It involves the study of large sets of genes and proteins, with the goal of understanding systems, not simply components. Translational genomics refers to the translation of scientific genomic knowledge into medicine. It constitutes a major effort in systems medicine. Electrical engineers have the requisite mathematical and statistical experience to play a central role in the new medicine that will come out of the systems movement. The current exponential expansion in systems biology is being made possible by the existence of high-throughput genomic and proteomic measurement technologies. These technologies are stimulating the growth of genomics, proteomics, metabolomics, immunomics, and a host of other “omics.” Each of these has a translational aspect in systems medicine. Engineering is the translational discipline of science. Just as the new science of Galileo and Newton stimulated engineering translation of that science into the industrial revolution, the new biology of systems is stimulating engineering translation of this science into medicine (and technology). Now, in addition to classical engineering disciplines such as mechanical, electrical, and chemical engineering, one can add genomic engineering, i.e., translational genomics. When one looks at the subject areas already contributing to translational genomics, one finds pattern recognition, image processing, signal processing, information theory, communication theory, dynamical systems, and control theory. Translational genomics may be nascent, but the contributing engineering factors to its trajectory are clear. Genomic signal processing (GSP) has been defined as the analysis, processing, and use of genomic signals for gaining biological knowledge and the translation of that knowledge into systems-based applications. A major goal of translational genomics is to discover families of genes whose products (mes

Since its dawn, systems-and-control theory has been strongly and deeply interlaced with the life sciences, as is emphasized by the work of some of its greatest pioneers. Norbert Wiener presented Cybernetics in his book as Control and Communication in the Animal and the Machine, thus stressing the remarkable similarities between engineering mechanisms and regulation in living beings. Ludwig von Bertalanffy, the founder of General Systems Theory, advocated the use of mathematical approaches and system-level thinking in the study of biology, and was a biologist himself. The approaches developed within the realm of systems-and-control theory can be successfully employed to better understand the functioning of systems in the life sciences at all scales, from biochemical reaction networks to biological systems, from biomedical systems to ecological networks and epidemic phenomena. Novel rigorous methods can guide the control, optimization, and bottom-up design of systems in synthetic biology, as well as the development of approaches for biomedicine and of strategies for the control of epidemics. Systems in the life sciences are characterized by a high level of complexity, steep nonlinearities, and largely uncertain dynamics: their features pose many challenges that can inspire the development of new theory in robust and nonlinear control. This special issue has collected recent advances and new exciting ideas at the frontiers between systems-and-control theory and the life sciences, presented by renowned research teams worldwide. The scope of the special issue includes several complementary aspects, ranging from systems biology and synthetic biology (modeling, analysis, control, and design of biochemical and biological systems at all scales) to the analysis and management of ecological networks, from the study of biomedical systems (including disease management and the design and analysis of novel treatments) to the analysis and the control of epidemiological systems (embracing the study of their dynamics and approaches to suppress or mitigate the spread of contagion). Attention is devoted to modeling, identification, parameter estimation, as well as to analysis, control, and optimization. The special issue embraces both work with novel theoretical approaches and work that advances theoretical contributions with meaningful applications. The COVID-19 pandemic has highlighted the importance of mathematical and systems-and-control approaches to understand, manage, and control health emergencies, to predict the trend of epidemics and to plan effective strategies that curb the contagion. Indeed, several contributions of the special issue revolve around models for epidemics and infectious diseases and tackle their analysis and their control to suppress the contagion and eradicate infections, resorting to nonpharmaceutical interventions as well as vaccination. Eduardo D. Sontag proposes “An explicit formula for minimizing the infected peak in an SIR epidemic model when using a fixed number of complete lockdowns”; his work deals with the careful timing of nonpharmaceutical interventions, such as physical distancing, with the aim of minimizing the peak in the number of infected individuals, and provides an insightful, explicit, and easily computable rule for the timing of a fixed number of complete lockdowns of prespecified lengths. In their work “Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID-19,” Rijad Alisic, Philip E. Paré, and Henrik Sandberg analyze the detectability of sudden parameter changes for nonlinear dynamical systems in the presence of measurement noise, so as to assess the impact of individual nonpharmaceutical interventions or viral mutations on the epidemic spread, and look for the most informative data sample allowing to discriminate at best between different output trajectories, revealing, for example, that monitoring the number of infected (instead of recovered/deceased) is preferable. “Semi-Markov models of epidemics over networks with time delays” are proposed by Mohammad Ghousein, Emmanuel Moulay, and Patrick Coirault, who modify Markov models for epidemics by including minimum viral incubation period and recovery period as time delays affecting the states, leading to a more general class of systems that loses the Markovian property; sufficient conditions for the global exponential stability of the resulting time-delay probabilistic dynamical models are offered via Lyapunov theory. “A time-varying network model for sexually transmitted infections accounting for behavior and control actions” is proposed by Kathinka Frieswijk, Lorenzo Zino, and Ming Cao, whose stochastic network model for the spread of sexually transmitted infections includes asymptomatic infections, as well as behavioral choices related to the use of protective measures and their time evolution depending on the perceived risk and on persuasion due to exchange of opinions; different control actions (awareness campaigns, routine screening, and partner notification) are investigated along with their effectiveness. Francesco Parino, Lorenzo Zino, Giuseppe C. Calafiore, and Alessandro Rizzo, in their article “A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy,” face the challenge of optimally planning massive two-dose vaccination rollouts by leveraging a nonlinear model predictive control strategy that accounts for both healthcare and socioeconomic costs, which they apply to the 2021 COVID-19 vaccination campaign in Italy by considering a tailored epidemic model with parameters inferred from real data. Moving from the between-host scale of epidemiological models that describe contagion spreading in a population to the in-host treatment of diseases, the paper “Scheduling collateral sensitivity-based cycling therapies toward eradication of drug-resistant infections,” by Josephine N. A. Tetteh, Sorin Olaru, Hans Crauel, and Esteban A. Hernandez-Vargas, explores the control-theoretic aspects and implications of facing drug-resistant pathogens through the sequential use of drugs where resistance to the former drug induces sensitivity to the next (collateral sensitivity); a switched system formulation allows to optimally tailor the order and time of cycling between drugs to the pathogen population present in the host. Systems-and-control theory offers powerful tools to gain insight into the behavior of biological systems, including their response to perturbations, their stability properties, and their ability to give rise to sustained and synchronized oscillations. In his paper “Sign-sensitivity of metabolic networks: Which structures determine the sign of the responses,” Nicola Vassena follows a structural approach to investigate the sign of the responses of metabolic networks to external perturbations, affecting both reaction rates and metabolite concentrations at the equilibrium, exclusively based on the network stoichiometry, without any information about the parameter values; subnetworks that have a crucial role in determining the sign sensitivity are identified and shown to be associated with kernel vectors of the stoichiometric matrix and thus independent of the chosen kinetics. Nicolas Augier, Madalena Chaves, and Jean-Luc Gouzé deal with “Weak synchronization and convergence in coupled genetic regulatory networks: Applications to damped oscillators and multistable circuits”: they consider a general model of genetic networks, compare the equilibria and stability properties of different interconnections based on either homogeneous or heterogeneous coupling, and find conditions for weak synchronization, applied to the synchronization of damped oscillators and to the control of multistable systems. Control-theoretic approaches allow us to govern, enhance, and improve biological phenomena at various scales, ranging from gene regulation and metabolism to the control of cell populations and their growth, and enables the design of biological mechanisms with the desired behavior. In a biological context, control strategies can aim at optimality, but most often need to face huge uncertainty and environmental variability, which requires fundamental robustness properties. Virginia Fusco, Davide Salzano, Davide Fiore, and Mario di Bernardo present a feedback strategy for the “Embedded control of cell growth using tuneable genetic systems”: they control the density of a microbial population, where cells self-regulate their growth rate, by tuning the production of a growth inhibitor protein based on the information on the population density obtained from a quorum sensing mechanism, they show that the set-point can be flexibly changed online, and they test the robustness of the designed controller with respect to disturbances and parameter variations due, for example, to cell-to-cell variability. “Self-regulation in a stochastic model of chemical self-replication” is investigated by Alessandro Borri, Massimiliano d'Angelo, and Pasquale Palumbo, who analyze burst noise propagation in the context of gene expression and investigate the impact of feedback on noise attenuation, both within a computational numerical framework based on a stochastic simulation algorithm and within an approximated analytical framework that replaces nonlinear terms with linear ones to enable closed-loop solutions for first- and second-order moments. Debojyoti Biswas, Sayak Bhattacharya, and Pablo A. Iglesias investigate “Enhanced chemotaxis through spatially regulated absolute concentration robustness” and introduce a control mechanism to enhance the efficiency of chemotaxis (the directional motility of cells in response to spatial chemical gradients) in amoeboid cells, whose movement is enabled by protrusions driven by the stochastic threshold crossing of an underlying excitable system, by suppressing undesirable protrusions in the wrong directions; the control action couples an absolute concentration robustness mechanism to the cellular signaling machinery. The work “Optimal control of probabilistic Boolean control networks: A scalable infinite horizon approach” by Sonam Kharade, Sarang Sutavani, Sushama Wagh, Amol Yerudkar, Carmen Del Vecchio, and Navdeep Singh deals with the optimal control of large-scale gene regulatory networks, modeled as probabilistic Boolean control networks based on Markov decision processes, with the aim of developing therapeutic intervention strategies that alter gene regulation dynamics so as to avoid diseased states. Nicolas Augier and Agustín G. Yabo deal with the “Time-optimal control of piecewise affine bistable gene-regulatory networks,” where the goal is to minimize the time that a piecewise affine bistable genetic toggle switch needs to transition between its two stable steady states, and show that time-optimal transitions pass through an undifferentiated state, which has fundamental importance in cell biology in relation to fate differentiation of cells and has applications in synthetic biology, biotechnology, and gene therapy. The paper “Optimal periodic resource allocation in reactive dynamical systems: application to Microalgal production” by Olivier Bernard, Liu-Di Lu, and Julien Salomon focuses on a periodic resource allocation problem applied to a dynamical system, which is inspired by the optimization of a mixing strategy to enhance the growth rate in a microalgal raceway system; the control is represented by a permutation applied on the system to reallocate the resources to the different activities. Estimating the state of a biological system based on experimentally measured quantities is fundamental. Alex dos Reis de Souza, Denis Efimov, Andrey Polyakov, Jean-Luc Gouzé, and Eugenio Cinquemani discuss “State observation in microbial consortia: A case study on a synthetic producer-cleaner consortium”; they frame an observability analysis and propose state estimators for each component of a system describing a two-strain microbial consortium that involves both producers and cleaners; they deal with observer design and they assess the advantage of measuring a fluorescent reporter in addition to the total biomass, even though measuring total biomass alone is enough to make the system detectable. System identification and the generation of reduced order models are of crucial importance in systems and synthetic biology. Francesco Montefusco, Anna Procopio, Declan G. Bates, Francesco Amato, and Carlo Cosentino tackle the “Scalable reverse-engineering of gene regulatory networks from time-course measurements”: they propose a computationally efficient method for topological inference of biological interaction networks from experimental data, which combines system identification based on instrumental variables with a regularization strategy to deal with over-parameterized systems, and can simultaneously exploit multiple time series from multiple experiments and be applied to large-scale networks with thousands of nodes. Gemma Massonis, Julio R. Banga, and Alejandro F. Villaverde propose “AutoRepar: a method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights”; their approach automatically re-parameterizes nonlinear ODE models to guarantee their structural identifiability and observability, so that the values of the model parameters and state variables can be inferred from data without ambiguities and the models can provide reliable predictions and insight. “Robustness guarantees for structured model reduction of dynamical systems with applications to biomolecular models” are offered by Ayush Pandey and Richard M. Murray, who focus on the robustness properties of the error in model reduction in the presence of uncertainties; they compute robustness guarantee metrics for a class of reduced models of engineered biological systems in uncertain environments, and propose an automated model reduction method to find the best possible reduced model, which is applied to gene expression systems with limited resources and to circuits for bacterial population control. Developing parameter identification, state estimation, and control approaches for biomedical systems is key to provide robust and theoretically grounded support to healthcare workers in the clinical practice. “Parameter identification and state estimation for a diabetic glucose-insulin model via an adaptive observer” are considered by Roberto Franco, Héctor Ríos, Alejandra Ferreira de Loza, Louis Cassany, David Gucik-Derigny, Jérôme Cieslak, and David Henry, who design an LMI-based adaptive observer for patients with type 1 diabetes mellitus, using Bergman's minimal model, to simultaneously estimate states and parameters corresponding to the insulin-dependent glucose disappearance rate in the presence of parameter uncertainties and food intake regarded as an external disturbance; the estimates are based on intravenous glucose measurements and the approach is validated in the UVA/Padova metabolic simulator. Luca Ranghetti, Daniel E. Rivera, Penghong Guo, Antonio Visioli, Jennifer Savage Williams, and Danielle Symons Downs discuss “A control-based observer approach for estimating energy intake during pregnancy” aimed at regulation of gestational weight so as to prevent risks for both the mother and her unborn child; an energy balance model predicts gestational weight based on physical activity and unmeasured disturbances associated with energy intake, and two different control-based observer formulations, relying on internal model control and model predictive control, are proposed and tested on real data. “Nonlinear dynamic modelling and model predictive control of thrombin generation to treat trauma-induced coagulopathy” are investigated by Damon E. Ghetmiri and Amor A. Menezes: they propose a nonlinear dynamic coagulation model including the complex biochemical reactions of clotting and demonstrate the effectiveness of a model predictive control scheme that administers blood proteins as system inputs to robustly automate clinical interventions for trauma-induced coagulopathy, even in the presence of experimentally observed uncertainties involving nonlinearities and delays. Two contributions deal with decision-making in populations and with the control of ecological systems. Leonardo Stella and Dario Bauso investigate “The impact of irrational behaviors in the optional prisoner's dilemma with game-environment feedback”, where players can choose to cooperate, defect or abstain, their behavior can be irrational according to prospect theory (capturing, e.g., reference dependence or loss aversion), and environmental effects exert a feedback over the population dynamics; the system is analyzed in the context of evolutionary games, with applications to opinion dynamics in society (elections) and collective decision-making in honeybee swarms. “Cyclic control equilibria for switched systems with applications to ecological systems” are discussed by Alejandro L. Anderson, Pablo Abuin, Antonio Ferramosca, Esteban A. Hernandez-Vargas, and Alejandro H. Gonzalez, who investigate permanence regions for switched systems under arbitrary waiting-time constraints and their (un)suitability as control targets, and propose a case study concerning population control and the stabilization of communities in an ecological system. We are deeply grateful to all the authors who contributed to this special issue by entrusting their valuable work and offering a rich and varied range of perspectives on “Control-Theoretic Approaches to Systems in the Life Sciences.” We also have heartful thanks for all the anonymous reviewers for their thorough assessment of submissions and for the insightful suggestions and comments they offered.

This volume contains the proceedings of the Second International Workshop Hybrid Systems and Biology (HSB 2013) held in Taormina (Italy), on September 2th, 2013. The workshop is affiliated to the 12th European Conference on Artificial Life (ECAL 2013). Systems biology aims at providing a system-level understanding of biological systems by unveiling their structure, dynamics and control methods. Due to the intrinsic multi-scale nature of these systems in space, in organization levels and in time, it is extremely difficult to model them in a uniform way, e.g., by means of differential equations or discrete stochastic processes. Furthermore, such models are often not easily amenable to formal analysis, and their simulations at the organ or even at the cell levels are frequently impractical. Indeed, an important open problem is finding appropriate computational models that scale well for both simulation and formal analysis of biological processes. Hybrid modeling techniques, combining discrete and continuous processes, are gaining more and more attention in such a context, and they have been successfully applied to capture the behavior of many biological complex systems, ranging from genetic networks, biochemical reactions, signaling pathways, cardiac tissues electro-physiology, and tumor genesis. This workshop aims at bringing together researchers in computer science, mathematics, and life sciences, interested in the opportunities and the challenges of hybrid modeling applied to systems biology. The workshop programme included the keynote presentation of Alessandro Astolfi (Imperial College of London, UK) on Immune response enhancement via hybrid control. Furthermore, 8 papers were selected out of 13 submissions by the Program Committee of HSB 2013. The papers in this volume address the hybrid modeling of a number important biological processes (iron homeostasis network, mammalian cell cycle, vascular endothelial growth factor (VEGF), genetic regulatory network in mammalian sclera) and, the formalisms and techniques for specifying and validating properties of biological systems (such as, robustness, oscillations).

A Decade After the Genome, Bioinformatics Comes of Age

The ultimate goal of systems biology is the development of executable in silico models of cells and organisms. Systems biology attempts to provide an integrative methodology, which while able to cope with -on the one hand- the data deluge that is being generated through high throughput experimental technologies -and on the other hand- emerging technologies that produce scarce often noisy data, would allow to capture within human understandable models and simulations novel biological knowledge.

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.

At the time when “classical” bioinformatics developed further towards modern systems biology, the idea of a holistic view of a biological system was not completely new: the aim to provide a comprehensive picture, e.g. about the genes and their regulatory features encoded in a genome, was inherent in bioinformatics research from the very beginning. Also the attempt to come up with an integrative view across the different levels of organisation was at least conceptually implicit in the numerous approaches to integrate the rapidly growing information about biological objects into comprehensive knowledge bases. However, to transcend the research focus on static objects and to step forward to the computer-aided investigation of biological processes was significantly pushed ahead by the emerging field of systems biology. The new paradigm to formally represent the processes that make up a biological system is now the “network”. The term “process” implies dynamic events, changes, that we may wish to simulate with the aid of a computer in order to predict the behavior of a biological system under certain circumstances. Biochemistry provides the formal instruments to do so for defined (bio)chemical reactions, usually resulting in a set of ordinary differential equations (ODEs). Solving the large number of ODEs that are required to exactly describe the behavior of a complex biological system may be cumbersome, but computationally feasible as soon as we have at hand all necessary parameters such as the corresponding kinetic constants for all reactions involved. Even in those cases where these kinetics have been studied in vitro, it is still questionable whether the insights we gained from these experiments are applicable on specific in vivo conditions. Nevertheless, this approach has been proven to work for (parts of) the metabolic network of living cells, but regulatory events that depend on just a very low number of individual molecules per cell may require different approaches. Moreover, applying ODEs onto a large complex system may be mere overkill, and a (presumably) less exact approach might be of even more appropriate granularity, at least for the larger part of the network under consideration. Several years ago, Petri nets have been suggested to be well suited for modeling metabolic networks by overcoming some of the limitations outlined above [Reddy et al., 1993]. Since then, a lot of further conceptual work, technical tool implementations and applications onto biological problems have been reported and demonstrated the usefulness of this concept for what we know today as systems biology. Being intuitively understandable to scientists trained in life sciences, they also have a robust mathematical foundation and provide the required flexibility with regard to the models’ granularity. As a result, Petri net technology appears to be a very promising approach to modeling biological systems.

Biomarkers are mostly correlated to a complex biomedical process such as disease propagation or drug action, which is affected by a multitude of biological mechanisms. Systems biology, focusing on the integrated approach to describe the behavior of complex biological systems, in contrast to the reductionist approach focusing on the system’s entities, may be the way toward more specific biomarkers. However, utilizing systems biology in order to develop biomarkers that are both sensitive and specific requires the identification and validation of quantitative models for the system behavior representing the complex interaction of its relevant entities. However, the identification of the relevant entities and their interactions suffers from the complexity of the multi-scale architecture of biological systems and requires the integration of processes on the molecular, cellular and multicellular up to the macroscopic scale such that quantitative models of full-scale biology are far beyond reality today. Still, the increasing availability of biomedical data repositories in combination with machine learning induces increasing interest in academia and industry in methods to tackle the biological complexity. Because a generic systems biology approach is not yet available, we will discuss possible routes toward systematic biomarker identification using systems biology for specific examples.

The 22nd Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) will be a world-class scientific meeting that brings together computational biologists and bioinformaticians of every career stage from diverse scientific disciplines. ISMB 2014 will convene at the John B. Hynes Memorial Convention Center in Boston, Massachusetts, on July 11–15, 2014. ISMB is the flagship conference of the International Society for Computational Biology (ISCB). This unique meeting draws scientists from a broad range of fields that use computational biology and bioinformatics, including sequence analysis, comparative genomics, proteomics, structural biology, data mining, and systems biology. ISMB 2014 is anchored by six keynote presentations from world-renowned scientists. Isaac “Zak” Kohane, the Director of the Children's Hospital Informatics Program and the Henderson Professor of Pediatrics and Health Sciences and Technology at Harvard Medical School, will be speaking on Sunday, July 13. Kohane's unique background in both pediatric endocrinology and computer science has enabled him to develop a research program that uses genomics to better understand the genetic basis of diseases, including autism and cancer. He has also developed computer systems that permit the use of information from electronic health records for genetic studies while maintaining patient privacy. Sunday, July 13, will also feature a keynote presentation by Eugene “Gene” Myers, the 2014 recipient of the ISCB Accomplishment by a Senior Scientist Award. This award honors luminaries in the fields of computational biology and bioinformatics who have made significant contributions to these areas through research, education, and service. Myers is the Director and Tschira Chair of Systems Biology at the Max Planck Institute of Molecular Biology and Genetics in Dresden, Germany. Myers is well known for his work on developing the BLAST algorithm for sequence comparison, as well as his work on using shotgun sequencing to sequence the human genome at Celera Genomics. His research is now focused on computational bioimaging. He has developed new microscopic devices and software that are used for building 3D biological models, and these tools are providing unparalleled insights into the inner workings of cells and systems. Michal Linial, a Professor of Biochemistry, Molecular Biology, and Bioinformatics at the Hebrew University of Jerusalem, Israel, will be a keynote speaker on Monday, July 14. Linial is the Director of the Sudarsky Center for Computational Biology and is the first female head of the Israel Institute for Advanced Studies. Her broad research activities encompass both “wet lab” projects and computational modeling, with particular interests in neuronal cell differentiation and synapse formation, proteomic analysis of membrane proteins, and functional genomics. The 2014 winner of the Overton Prize, Dana Pe'er, is featured as a keynote speaker on Monday, July 14, as well. The Overton Prize recognizes early- or mid-career scientists working in computational biology or bioinformatics who are rising leaders in these fields. Pe'er is an Associate Professor in the Department of Biological Sciences and Systems Biology at Columbia University. Her research focuses on understanding cellular and molecular networks at a holistic level by using computational approaches to analyze complex data sets. Robert Langer, a Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology, will give a keynote presentation on Tuesday, July 15. Langer is a prolific researcher who works on developing novel drug-delivery systems, with a particular interest in using polymers to deliver therapeutic molecules like DNA and genetically engineered proteins. Langer's innovative work was recognized most recently when he was selected as a recipient of the 2014 Breakthrough Prize in Fundamental Physics and Life Sciences. The last keynote presentation will be given on Tuesday, July 15, by Russ Altman, a Professor of Bioengineering, Genetics, and Medicine, and Computer Science. Altman has been selected as this year's ISCB Fellows Keynote Speaker. He works on building and applying new algorithms to explore diverse topics including RNA structure, how drug efficacy is impacted by genomics, and how to model motion and dynamics of biological structures. Beyond the keynote speakers, ISMB will be brimming with talks on cutting-edge discoveries across diverse areas. The Special Sessions track will run throughout the meeting and will feature hot topics that have not been featured in previous ISMB meetings. The Highlights and Proceedings tracks are also popular conference tracks that include oral presentations based on recently published papers selected through rigorous peer-review processes. The Proceedings papers are also published as an online-only open-access section of the Bioinformatics journal. The Technology track features presentations that showcase the use of novel software or hardware relevant to computational biologists. The Late Breaking Research track will also feature talks on a wide range of topics of significant interest to the bioinformatics community. A large poster session will provide an opportunity for trainees and scientists from every career stage to present their latest research findings in a collegial and collaborative atmosphere. “Birds of a Feather” sessions and workshops will be more informal sessions that encourage discussion and collaboration. These sessions will feature such themes as bioinformatics curriculum guidelines, personalized medicine, bioinformatics core facility management, trends in digital publishing, and data analysis. The exhibit hall will showcase a wide variety of organizations and companies that are developing tools and reagents relevant to computational biologists and bioinformaticians, and attendees will be able to see some of these items in action at exhibitor presentations. The ISCB Student Council will be organizing several high profile events throughout ISMB 2014. The annual Student Council Symposium will convene just prior to ISMB 2014 and will include talks by a keynote speaker and student presenters, as well as a poster session. Opportunities for career guidance and social events are also included. In addition, the ISCB Student Council will be coordinating an Art & Science Exhibition during the ISMB meeting that will feature images and videos of scientific material derived from research projects or artwork generated from scientific tools or methods. Saturday, July 12, and Sunday, July 13, will be filled with substantive one- and two-day specialized meetings that precede the main ISMB meeting. Special Interest Group (SIG) and Satellite meetings will be focused on a range of topics that include but are not limited to structural bioinformatics, mass spectrometry, and regulatory genomics. Two half-day tutorial sessions will also be held on July 12 and will feature (1) Computational Metagenomics and (2) Wikipedia: WikiProject Computational Biology. Several social events will balance out the program for ISMB 2014 and will create ample opportunities for attendees to gather together in informal settings. An opening reception is scheduled for the evening of Saturday, July 12, and poster viewing receptions are being held on both Sunday, July 13, and Monday, July 14. A World Cup viewing area will also be set up in the Exhibit Hall. As a long-standing hub of biological and computational research breakthroughs, Boston promises to be an excellent host to ISMB 2014. Both local Boston- and Cambridge-area scientists, as well as visitors from every corner of the globe, will be showcasing diverse topics that span from personalized medicine, to machine learning in systems biology, to open-source bioinformatics software development. This must-see event has something for everyone and is an excellent destination to start your next collaboration.

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

Highlights of the 1st Latin American Conference of Women in Bioinformatics and Data Science

Secondary metabolites are enigmatic in terms of costs and benefits for their producers and how their ontogenetic accumulation or timely induction affects the evolution of single species and co-evolution of interacting species. More than 50 years ago, Gottfried Fraenkel dispelled the notion of secondary metabolites as waste products: “Thus, the animal world which surrounds the plant is deeply influenced not only by their morphology, but also by their chemistry”. Today, in the post-genomic era, with huge amounts of data generated by functional genomic, metabolomic, and proteomic studies, the emerging picture of secondary metabolites is more complex. Also, the recognition has grown that one of the ultimate challenges in the life sciences is not just to understand component parts, but rather the systems comprised of these parts. This has led to the establishment of a new discipline - systems biology which advocates an integrative rather than reductionist approach. The relations between plants and the microbes and herbivores that colonize and eat them once were described as a “wobbling triangle” of interactions that occur both below- and above ground. In this scenario, a reliable and flexible signalling system is mandatory to coordinate gene expression in separate tissues accordingly. Present knowledge suggests that in plants, as in other organisms, co-ordinated redox chemical reactions between reactive oxygen species (ROS) and hormones represent the upstream part of this signalling system which, further downstream, is continued by MAP kinases, helping to maintain the homeodynamics of micro- and macromolecules required during ontogenesis (Mittler et al., 2011). One important recognized component is retrograde (organelle to nucleus) signalling. Abiotic and biotic stresses impair the functionality of electron transport chains in chloroplasts and mitochondria. As a consequence, instead of four electrons that are required to reduce molecular oxygen to water, incorrectly transferred single electrons lead more quicklytothe formation of superoxide anion radicals, O2 – , than upregulated enzyme expression, e.g. NADPH oxidase, which is involved in systemic ROS production (Kerchev et al., 2011). So far, metabolomic studies have revealed extensive reprogramming ofprimary metabolites in context with retrograde signalling. The fact that no single cytosolic component yet has beenidentified unequivocally as a retrograde signal induced Thomas Pfannschmidt (2010) to ask a heretical, albeit justified question: ”Maybe a single metabolite is not sufficient to work as a signal, but what about a metabolite signature?” .I n terms of a systems biological approach, we may also consider whether secondary metabolites function as ensemble components of a metabolite signature. Although GC–MS is far from ideal for detecting non-volatile

Informing Biological Design by Integration of Systems and Synthetic Biology

Bioinformatics, the application of computer science to biological problems, is a central feature of post-genomic science which grew rapidly during the 1990s and 2000s. Post-genomic science is often high-throughput, involving the mass production of inscriptions [Latour and Woolgar (1986), Laboratory Life: the Construction of Scientific Facts. Princeton, NJ: Princeton University Press]. In order to render these mass inscriptions comprehensible, bioinformatic techniques are employed, with bioinformaticians producing what we call secondary inscriptions. However, despite bioinformaticians being highly skilled and credentialed scientists, the field struggles to develop disciplinary coherence. This paper describes two tensions militating against disciplinary coherence. The first arises from the fact that bioinformaticians as producers of secondary inscriptions are often institutionally dependent, subordinate even, to biologists. With bioinformatics positioned as service, it cannot determine its own boundaries but has them imposed from the outside. The second tension is a result of the interdisciplinary origin of bioinformatics – computer science and biology are disciplines with very different cultures, values and products. The paper uses interview data from two different UK projects to describe and examine these tensions by commenting on Calvert's [(2010) “Systems Biology, Interdisciplinarity and Disciplinary Identity.” In Collaboration in the New Life Sciences, edited by J. N. Parker, N. Vermeulen and B. Penders, 201–219. Farnham: Ashgate] notion of individual and collaborative interdisciplinarity and McNally's [(2008) “Sociomics: CESAGen Multidisciplinary Workshop on the Transformation of Knowledge Production in the Biosciences, and its Consequences.” Proteomics 8: 222–224] distinction between “black box optimists” and “black box pessimists.”