Biochemical Reaction Rules with Constraints

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

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.

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.

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.

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control.

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.

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 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.

Adding big data to biology is changing the way we describe how cells work, which lets us make more complete and accurate models of biological systems. With the rise of high-throughput technologies like genetics, transcriptomics, proteomics, and metabolomics, huge amounts of biological data are being produced, which gives scientists a chance to study how cells work like never before. This essay looks at how big data can help us better understand how cells work by focussing on how machine learning, network analysis, and systems biology can be used. Patterns can be found in multi-omic datasets using machine learning algorithms, such as guided and uncontrolled learning. These algorithms help us understand gene regulation, protein interactions, and cellular reactions. Network-based methods improve cellular process modelling even more by building complex networks of molecule interactions that mirror the complicated control systems inside cells. Systems biology and computer simulations provide dynamic models that duplicate how cells behave, which makes it easier to study how diseases work, how drugs work, and how to treat them. Even with these improvements, there are still problems with integrating, scaling, and interpreting data because living systems are very complicated and data is not all the same. The study also talks about how artificial intelligence (AI) can help solve these problems, make predictions more accurate, and make cellular models more useful. Using big data modelling in disease research, like studying cancer and brain illnesses, shows that it can help find signs, make treatment plans better, and move personalised medicine forward. In the end, combining big data with bioinformatics has the huge potential to change how we think about how cells work, opening up new areas for medical study and drug creation.

Bacteriochlorophylls (BChls) c, d, and e are the major chlorophylls in chlorosomes, which are the largest and one of the most efficient antennae produced by chlorophototrophic organisms. In the biosynthesis of these three BChls, a C-13(2)-methylcarboxyl group found in all other chlorophylls (Chls) must be removed. This reaction is postulated to be the first committed step in the synthesis of these BChls. Analyses of gene neighborhoods of (B)Chl biosynthesis genes and distribution patterns in organisms producing chlorosomes helped to identify a gene (bciC) that appeared to be a good candidate to produce the enzyme involved in this biochemical reaction. To confirm that this was the case, a deletion mutant of an open reading frame orthologous to bciC, CT1077, was constructed in Chlorobaculum tepidum, a genetically tractible green sulfur bacterium. The CT1077 deletion mutant was unable to synthesize BChl c but still synthesized BChl a and Chl a. The deletion mutant accumulated large amounts of various (bacterio)pheophorbides, all of which still had C-13(2)-methylcarboxyl groups. A C. tepidum strain in which CT1077 was replaced by an orthologous gene, Cabther_B0081 [corrected] from "Candidatus Chloracidobacterium thermophilum" was constructed. Although the product of Cabther_B0081 [corrected] was only 28% identical to the product of CT1077, this strain synthesized BChl c, BChl a, and Chl a in amounts similar to wild-type C. tepidum cells. To indicate their roles in the first committed step of BChl c, d, and e biosynthesis, open reading frames CT1077 and Cabther_B0081 [corrected] have been redesignated bciC. The potential mechanism by which BciC removes the C-13(2)-methylcarboxyl moiety of chlorophyllide a is discussed.

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.

Chemical reactions are rearrangements of chemical bonds. Each atom in an educt molecule thus appears again in a specific position of one of the reaction products. This bijection between educt and product atoms is not reported by chemical reaction databases, however, so that the “Atom Mapping Problem” of finding this bijection is left as an important computational task for many practical applications in computational chemistry and systems biology. Elementary chemical reactions feature a cyclic imaginary transition state (ITS) that imposes additional restrictions on the bijection between educt and product atoms that are not taken into account by previous approaches. We demonstrate that Constraint Programming is well-suited to solving the Atom Mapping Problem in this setting. The performance of our approach is evaluated for a manually curated subset of chemical reactions from the KEGG database featuring various ITS cycle layouts and reaction mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-014-0023-3) contains supplementary material, which is available to authorized users.

Stochastic modelling and simulations play a major role in Systems Biology because, at molecular level, biological systems exhibit noise coming both from within the cell (intrinsic) and from the environment (extrinsic). Stochastic modelling takes into account the effects of noise over the system dynamics, that can strongly affect the behavior of the system in conditions of relatively low amounts of molecular species. Stochastic simulations provide an effective way to describe the system dynamics, and can be applied on systems where specified chemical species are processed by a set of biochemical reactions, each one characterized by a stochastic constant. In the context of stochastic modelling, Parameter Sweep Applications (PSAs) can be a useful way to explore the huge spaces generated by the combinations of variables and parameters values in order to test their effects on systems dynamics. PSAs are common in the scientific community and are structured as sets of instances, each one characterized by a distinct parametrisation. A PSA that aims to sample such large spaces must involve a large number of instances and hence the problem becomes very time consuming. However, the independence of each instance of a particular PSA makes the distributed computing paradigm a very useful solution for large scale PSAs. In this work we present a grid based version of a multi-volume stochastic simulator, tau-DPP, implemented on the EGEE project platform. The aim of the proposed work is to exploit this platform for testing the reliability of PSAs over the grid, pointing out critical factors, bottlenecks and scalability by providing data about our experience in this kind of biological modelling and simulations. As a case study, we present a number of PSAs for a stochastic model of bacterial chemotaxis composed of 59 reactions and 31 chemical species.

Handbook of Constraint Programming

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).