Heuristic approaches to models and modeling in systems biology

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.

Calcific aortic valve disease (CAVD) arises from coupled interactions between blood flow, tissue mechanics, and cellular signaling. Hemodynamic forces influence endothelial and interstitial cell behavior, while the resulting tissue remodeling alters valve motion and flow patterns. Capturing this two-way feedback requires models that integrate fluid-structure mechanics with biochemical regulation, yet such multiscale coupling remains technically challenging. Previous computational models have focused on isolated aspects of the disease: fluid-structure interaction (FSI) simulations reproduce valve deformation and flow, and systems biology (SB) models describe molecular signaling that drives fibrosis and calcification. However, without coupling, these approaches cannot predict how mechanical dysfunction initiates biochemical remodeling or how biochemical changes feed back on mechanics. Here, we present a multi-physics computational framework that couples three-dimensional FSI simulations of aortic valve dynamics with a mechanistic SB model of calcification signaling. The FSI module resolves pulsatile blood flow and leaflet deformation, yielding local wall shear stresses and tissue strains throughout the cardiac cycle. These mechanical quantities are used as inputs to the SB module, which represents key biochemical pathways governing inflammation, TGF- β /SMAD signaling, and nitric-oxide (NO)-mediated inhibition within valvular cells. Simulations predict long-term calcification trajectories for valves of varying thickness, showing that fibrosis-induced stiffening lowers shear stress, reduces NO synthesis, and enhances TGF- β activation, thereby accelerating calcification. The framework is modular and extensible, allowing future incorporation of additional pathways, as well as implementation of full two-way coupling between the FSI and SB models. By integrating physics-based hemodynamics with systems-level biochemistry, this study provides a proof-of-principle demonstration of a next-generation, multi-scale modeling platform for studying cardiovascular disease that unites blood flow dynamics and biochemical signaling.

A First Course in Systems Biology is intended to serve as the textbook for a semester-long course in systems biology directed toward advanced undergraduate or graduate students. The book meets this goal admirably. Eberhard O. Voit writes in a style that is both interesting to read and highly informational. A First Course in Systems Biology manages to combine the fields of mathematics, engineering, and biology in a textbook that could be read and assimilated (with varying degrees of difficulty) by students of all three disciplines. The text is broken into three main sections: mathematical modeling, “molecular inventories” of the requisite biological systems, and case studies. Despite the extensive mathematical component, A First Course in Systems Biology is not set up like a traditional mathematics textbook. Instead of long proofs and stark equations, concepts are first introduced intuitively before the equations are formalized, and readers are walked through a concrete example. A number of plots and descriptive images accompany the math, ensuring that even a novice number-cruncher can grasp the basics of Boolean network models, stability analysis, and parameter estimation. The middle third of the text covers the most pertinent biological details required for studying gene regulatory systems, protein interaction networks, metabolic pathways, and cell signaling cascades from a systems biology perspective. The book closes with a series of case studies that focus on the proper selection and application of biological models. For example, in Chapter 12, Voit demonstrates the type of experimental data and computational simulation that would be required for modeling several aspects of heart function, from a physiological model of blood flow through the atria and ventricles to one that examines oscillations of calcium signaling. Throughout, Voit provides clear and concise explanations of the most important and basic modeling and biological concepts without resorting to oversimplification, though explanations can sometimes be vague. Further, some topics, particularly that of epigenetics, are disappointingly terse. However, the brevity is always compensated for with extensive references for the curious reader. Indeed, despite the approachability of the text, this book requires great concentration and interaction on the part of the reader. Frequently, Voit urges that “the reader should confirm” the mathematical conclusions stated in the text. Extensive and open-ended exercises are given at the end of each chapter. These problems are not for the faint of heart and often require additional research, computer coding, or essay writing to complete. While A First Course in Systems Biology does not teach students precisely how to code mathematical models or provide protocols for biological experiments, the book does grant students an entry into the proper mindset of systems biologists. The continual emphasis on determining how to choose an appropriate model for a given problem and understanding what type of information disparate models could provide prepares readers well for generating systems biology questions and models of their own.

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.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

In this review, we describe technology and use of single-fluorophore imaging and detection in living cells with regard to application in systems biology and medicine. Because all biological reactions occur under aqueous conditions, the realization of single-fluorophore imaging using an optical microscope has led to the direct observation of biological molecules at work. Today, we can observe single molecules in individual living cells and even higher multicellular organisms. Using single-molecule imaging, we can determine the absolute values of kinetic and dynamic parameters of molecular reactions as a whole and during fluctuations and distribution. In addition, identification of the coordinate of single molecules has enabled super-localization techniques to virtually improve spatial resolution of optical microscopy. Single-molecule detection that depends on point detection instead of imaging is also useful in detecting concentrations, diffusive movements, and molecular interactions in living cells, especially in the cytoplasm. The precise and absolute values of positional, kinetic, and dynamic parameters that are determined by single-molecule imaging and detection in living cells constitute valuable data on unitary biological reactions, because they are obtained without destroying the integrity of complex cellular systems. Moreover, most parameters that are determined by single-molecule measurements can be substituted directly into equations that describe kinetic and dynamic models in systems biology and medicine.

An Application of Conditional Robust Calibration (CRC) to The Lotka-Volterra Predator-Prey model in computational systems biology: a comparison of two sampling strategies

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models' predictability: how accurately can the models' parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters' confidence intervals. Development of methods, which can accurately produce parameters' confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling. We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article. The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).

In this paper, we have overviewed deterministic and stochastic approaches for the modeling of bio-molecular reactions in systems biology. We have described and compared different versions of stochastic simulation approaches towards the modeling of bio-molecular reaction systems, the direct approach and the family of the tauleap methods. We also have illustrated differences between different approaches by providing numerical examples of computational analyses for selected models. Computational examples of application of stochastic simulation algorithms involved two systems of different interacting bio-molecular species and one model from the area of ecology, describing interactions between two animal populations. Apart from the direct approach and tau-leap family of methods, we have also overviewed the so called hybrid algorithm of stochastic simulations, proven to be very efficient when there are huge differences between reaction rates in the system.

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

A selection of papers prepared by participants of 7th Young Scientists School SBB’15 is presented in three Supplementary Issues to the journals of BMC series, namely, BMC Genomics, BMC Genetics and BMC Microbiology. SBB’15 took place June 22nd-25th in Novosibirsk, Russia (http://conf.nsc.ru/sbb2015). The series of Young Scientists Schools in Systems Biology and Bioinformatics (SBB) started several years ago, in 2008, as educational workshop associated with the large international conference series on bioinformatics known as BGRS\SB (Bioinformatics of Genome Regulation and Structure\Systems Biology) (http://conf.bionet.nsc.ru/bgrssb2016). After its inauguration year of 1998, BGRS/SBB held biannually at the Institute of Cytology and Genetics SB RAS in Novosibirsk. In a recent decade, it became a prime meeting venue for biologists, computer scientists, physicists, mathematicians and biochemists working in an interdisciplinary field of systems biology and computer genomics. Being the largest system biology meeting series in Russia, BGRS\SB-14 attracted participants from 27 countries. In the past BioMedCentral had published series of special issues based on best materials presented at the conference in BMC Genomics (http://www.biomedcentral.com/bmcgenomics/supplements/15/S12), BMC Evolutionary Biology (http://www.biomedcentral.com/bmcevolbiol/supplements/15/S1), BMC Genetics (http://www.biomedcentral.com/bmcgenet/supplements/16/S1) and BMC Systems biology (http://www.biomedcentral.com/bmcsystbiol/supplements/9/S2). In addition, the protein structure related studies presented by BGRS/SBB participants were collated as special issues in Journal of Bioinformatics and Computational Biology (http://www.worldscientific.com/toc/jbcb/4113/01; http://www.worldscientific.com/toc/jbcb/11/01). [1] In addition, the SBB School series has its own history to be proud of. The first generation of its participants already matured enough to substantially advance in the field. A majority of the participants graduated with PhDs, and now are contributing to the school as the lecturers and to a number of world universities as professors. For some students, the conference became instrumental in obtaining their post-doc or first independent position in academic world. In many cases, the informal atmosphere of the school helped to spark a collaboration that later led to publications in leading journals or the dramatic changes in their science career. The Program Committee of the conference and SBB Schools series includes data analysis, bioinformatics and computational biology professionals from different countries. For the last decade, the SBB conference was co-chaired by Academician Prof. Nikolay A. Kolchanov of the Institute of Cytology and Genetics SB RAS, Russia and Prof. Dr. Ralf Hofestadt of Bielefeld University, Germany. System biology and bioinformatics are rapidly developing and wide fields of science knowledge. For Young Scientists Schools, every year is different as it comprises the topics for the trainings that are the hottest at the time of the event. In 2015, the SBB School concentrated on the modern genomics and an analysis of high-throughput sequencing data. The materials presented in the current special issue provide examples of seamless integration of experimental studies performed in the lab and the computer-assisted inquiries into in the complex patterns of the organization and the functioning of biologic systems at the molecular, cell, tissue, organ, and body levels. The scientific topics discussed in 2015 were presented at the following sections: Next generation sequencing (NGS) and data analysis Evolutionary bioinformatics Systems biology and gene network modeling In SBB’15 Supplements to BMC journals Genomics, we collected the best studies presented at the conference. Due to the general breadth of the field of Systems Biology and Bioinformatic, the manuscripts were accepted into three separate tracks, thus forming three Supplementary Issues, BMC Genomics, BMC Genetics and BMC Microbiology. Below we will describe contents of these issues.

Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.

In the last few years, the issues related to reproducibility problems in life sciences and biomedical research are gaining a lot of interest and traction in the scientific community. These problems are not limited to laboratory procedures and protocols but are also related to computational analysis of experimental data. Results of computational findings often cannot be reproduced because of problems associated with software versioning, packaging, installation and execution, and because of lack of proper documentation. By enabling the reuse of research assets, research becomes considerably more efficient and economical. This can only be achieved reliably and efficiently if these are generated according to standards and Standard-Operating-Procedures (SOPs). Thus, standards represent important drivers in the life sciences because they guarantee that data become accessible, shareable and comparable along the value chain. Several initiatives launched the development and implementation of standards. Unfortunately these efforts remain fragmented and largely disconnected, therefore efficient exchange of data and results is often not possible or is at least hindered. The COST Action CHARME (harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research) is focused on the problems of standardization in systems biology and computational modelling. Currently 29 countries are involved in this non-commercial initiative, which makes it one of the most widely recognized actions among all COST initiatives. CHARME consists of five interactively working groups: (i) community/platform-building, (ii) innovation transfer, (iiI) design, implementation, and incorporation of software solutions, (iv) development of a strategic dissemination and (v) educational advertising. The main objective of CHARME is to create a common platform for sustainable and efficient cooperation on standardization. The CHARME aims to bridge and combine the fragmented areas of the various moves in the development of new norms and standards with a particular reference to systems biology and computational modelling. This will be done by identifying needs and gaps, and teaming up with other initiatives to avoid duplication and overlap of standardisation activities. The CHARME efforts are focused on increasing the awareness for the need of standards, promoting FAIR data, and data stewardship in scientific community. By active involvement of all stakeholders (from research, industry and policy), a common, long-term strategy will be developed to successfully assimilate standards into the daily workflow. Finally, CHARME will provide a common ground for researchers from academia, research institutes, SMEs and multinational organizations. Your contribution participation is welcome. Please contact us via our website http://cost-charme.eu/.

Bioactive materials present important micro-environmental cues that induce specific intracellular signaling responses which ultimately determine cell behavior. For example, vascular endothelial cells on a normal vessel wall resist inflammation and thrombosis, but the same cells seeded on an artificial vascular graft or stent do not. What makes these cells behave so differently when they are adhered to different materials? Intracellular signaling from integrins and other cell-surface receptors is an important part of the answer, but these signaling responses constitute a highly-branched, interconnected network of molecules. In order to perform rational design of biomaterials, one must understand how altering the properties of the material (micro-environment) causes changes in cell behavior, and this in turn requires understanding the complex signaling response. Systems biology and mathematical modeling aid analysis of the connectivity of this network. This review summarizes applicable systems biology and mathematical modeling techniques including ordinary differential equations-based models, principal component analysis, and Bayesian networks. Next covered is biomaterials research which studies the intracellular signaling responses generated by variation of biomaterial properties. Finally, the review details ways in which modeling has been or could be applied to better understand the link between biomaterial properties and intracellular signaling.

Over the last 10 years, 'Systems Biology' has focused on the integration of biology and medicine with information technology and computation. The current challenge is to use the discoveries of the last 20 years, such as genomics and proteomics, to develop targeted therapeutical strategies. These strategies are the result of understanding the aetiologies of complex diseases. Scientists predict the data will make personalized medicine rapidly available. However, the data need to be considered as a highly complex system comprising multiple inputs and feedback mechanisms. Translational medicine requires the functional and conceptual linkage of genetics to proteins, proteins to cells, cells to organs, organs to systems and systems to the organism. To help understand the complex integration of these systems, a mathematical model of the entire human body, which accurately links the functioning of all organs and systems together, could provide a framework for the development and testing of new hypotheses that will be important in clinical outcomes. There are several efforts to develop a 'Human Physiome', with the strengths and weaknesses of each being presented here. The development of a 'Human Model', with verification, documentation and validation of the underlying and integrative responses, is essential to provide a usable environment. Future development of a 'Human Model' requires integrative physiologists working in collaboration with other scientists, who have expertise in all areas of human biology, to develop the most accurate and usable human model.