Download or read book Boolean Networks as Predictive Models of Emergent Biological Behaviors written by Jordan C. Rozum and published by Cambridge University Press. This book was released on 2024-03-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Element shows interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions and the often-incomplete state of system knowledge. Boolean networks have emerged as a powerful tool for modeling these systems. The authors provide a methodological overview of Boolean network models of biological systems. After a brief introduction, they describe the process of building, analyzing, and validating a Boolean model. The authors then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.

Download or read book Boolean Networks as Predictive Models of Emergent Biological Behaviors written by Jordan C. Rozum and published by Cambridge University Press. This book was released on 2024-03-28 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions – from molecules in gene regulatory networks to species in ecological networks – and the often-incomplete state of system knowledge, such as the unknown values of kinetic parameters for biochemical reactions. Boolean networks have emerged as a powerful tool for modeling these systems. This Element provides a methodological overview of Boolean network models of biological systems. After a brief introduction, the authors describe the process of building, analyzing, and validating a Boolean model. They then present the use of the model to make predictions about the system's response to perturbations and about how to control its behavior. The Element emphasizes the interplay between structural and dynamical properties of Boolean networks and illustrates them in three case studies from disparate levels of biological organization.

Download or read book Attractor Identification and Control in Boolean Models of Plant pollinator Networks written by Fatemehsadat Fateminasrollahi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecological and biological systems consist of numerous interlinked components that interact and exchange information; such interactions give rise to emergent, collective behaviors that are of interest for ecologists and life scientists. The study of the relationship between the interactions and dynamics of individual components and the emergent dynamics of the system is important because it can lead to the development of control methods to manipulate the collective dynamics. In turn, these control methods can be used for ecological community management or restoration, or for therapeutic medical applications. One promising method to gain a deeper insight into such complex systems is to model the interactions among elements using a network and couple it with a predictive dynamical model. The analysis of such dynamical models provides us with a platform to advance our knowledge of the intricate behaviors exhibited by ecological and biological networks, and it has wide-ranging implications across various domains, spanning conservation efforts, the development of community management strategies, and drug target identification in the context of drug design. The innate challenge that arises when analyzing these models is the large size of the system and the non-linearity of the dynamical processes. Recently, a new approach has been developed by Jorge Gómez Tejeda Zañudo and collaborators that focuses on the stable motifs in the network; stable motifs are minimal positive feedback loops that maintain a specific state regardless of the state of the rest of the components in the system. By characterizing the stable motifs and the conditions that lead to their lock-in, this method can identify the system's dynamic repertoire, predict the outcome of specific interventions and suggest management and control methods. In this dissertation, the main focus is on mutualistic plant-pollinator networks, and specifically on their description by a well-established predictive dynamical model developed by Colin Campbell and collaborators. The study of such systems is of ecological significance as pollinator species face considerable degradation across the world. The loss of pollinator species has a dramatic negative effect on crops as the majority of food crops require pollination to survive. The examination of the reliability and stability of these communities holds great significance for agricultural management and ecological preservation endeavors. There is a great need for measures and methods to predict the magnitude of any cascading effects of species extinction, and for prevention and restoration strategies to maintain the communities. I contribute to this field of study by making it possible for the first time to apply stable motif analysis to plant-pollinator communities. I transform the equations of the existing model by changing threshold functions into suitable logical functions of plant-pollinator networks so that stable motif analysis can be applied to it. I then extend the classical stable motif analysis and introduce a novel method based on stable motifs that determines the stable communities of large plant-pollinator systems efficiently. This method relies on a new concept called the network of functional relationships among stable motifs; I show that these relationships can be leveraged to identify stable communities and accelerate the process significantly. Put into the ecological context, stable motifs can be intuitively interpreted as small groups of species in which species can maintain a specific survival state. I show how such groups of species and the relationships of these groups determine the final community outcomes in plant-pollinator networks. Once the stable communities are characterized, I study their reaction to perturbation and analyze the behavior of the system in the case of species extinction. I extend Boolean modeling concepts, so far only defined for functions of a specific logical form, to the plant-pollinator Boolean threshold functions and introduce a new algorithm to measure the cascading effects caused by species extinction. I then use the information gained from stable motifs to first identify the species whose extinction leads to massive catastrophe in the community and next suggest restoration measures that can be incorporated in ecological sciences. In chapters 1 and 2, I introduce the mutualistic plant-pollinator networks, the Campbell et al. Boolean model of community formation, and the key concepts of Boolean modeling respectively. In chapter 3 I present my contributions to the methodological advancements in the field of Boolean modeling and computational ecology. The methods in this chapter are presented in the context of plant-pollinator networks, but are general and can be implemented in other types of Boolean networks. Chapter 4 describes the properties of the alternative stable states available to the same group of species. Chapter 5 describes the response of plant-pollinator communities to the extinction of a species; specifically, whether there will be cascading effects. This chapter also proposed multiple damage prevention and community restoration measures. The analysis results in these two chapters rely heavily on the concept of stable motifs and the methods introduced in chapter 3. I demonstrate that stable motifs successfully pinpoint the crucial species and this method outperforms the previous well-established measures. Finally, in chapter 6 I study network control in Boolean networks that have a modular structure. In general network control means that by externally fixing the state or the dynamics of a group of nodes, the system as a whole will converge into a desired state or attractor. In this analysis, I aim to identify methods that identify control targets, relying solely on the properties of the network. Taking advantage of the fact that many ecological and biological networks are composed of smaller densely connected modules, I propose a novel module-based method to localize the search for control targets - nodes that if externally controlled, the system will converge into a desired dynamical outcome (e.g., a rich and bio-diverse stable community) or move away from the unwanted dynamical outcome (i.e., full collapse of the community). In this analysis, I study a large ensemble of biologically inspired synthetic Boolean networks to capture the properties of these systems across different levels of modularity. I show that it is considerably more efficient and advantageous to localize the search for control targets in networks with clear modular structure. Chapter 7 presents conclusions and possible future research directions.

Download or read book Probabilistic Boolean Networks written by Ilya Shmulevich and published by SIAM. This book was released on 2010-01-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.

Download or read book Predictive Network Modeling And Experimentation In Complex Biological Systems written by Steven Steinway and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology is incredibly complex -- at the molecular, cellular, tissue, and population level, there exists a tremendous number of discrete interacting components tightly regulating the processes that sustain life. Biological systems have traditionally been viewed in a reductionist manner often literally (and metaphorically) through a magnifying glass, leading to insight into how the individual parts work. Network theory, on the other hand, can be used to put the pieces together, to understand how complex and emergent behaviors arise from the totality of interactions in complex systems, such as those seen in biology. Network theory is the study of systems of discrete interacting components and provides a framework for understanding complex systems. A network-focused investigation of a complex biological system allows for the understanding of the system's emergent properties, for example its function and dynamics. Network dynamics are of particular interest biologically because biological systems are not static but are constantly changing in response to perturbations and environmental stimuli in space and time. Systems level biological analysis has been aided by the recent explosion of high throughput data. This has led to an abundance of quantitative and qualitative information related to the activation of biological systems, but frequently there is still a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of biological systems by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system (i.e. how it changes over time), both in normal and in perturbation (e.g. disease) scenarios. In this dissertation we present the application of network theory and discrete dynamic modeling integrated with experimental laboratory analysis to understand biological diseases in three contexts. The first is the construction of a network model of epidermal derived growth factor receptor (EGFR) signaling in cancer. We translate this model into two types of discrete models: a Boolean model and a three-state model. We show how the effects of an EGFR inhibitor (such as the drug gefitinib) can suppress tumor growth, and we model how genomic variants can augment the effect of EGFR inhibition in tumor growth. Importantly, we compare discrete modeling outcomes to an alternative modeling framework, which relies on detailed kinetic information, called ordinary differential equation (ODE) modeling and show that both models achieve similar findings. Our results demonstrate that discrete dynamic model can accurately model biomedical systems and make important predictions about the effect a drug will have on a disease (e.g. tumor growth) in the context of various perturbations. Importantly, discrete dynamic models can be employed in the absence of kinetic parameters, making this modeling approach suitable for the many biological systems in which detailed kinetic information is not available. Second, we construct a network model of epithelial-to-mesenchymal transition (EMT), a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. We demonstrate that the EMT network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We confirm the cross-talk between TGF[beta], Wnt and SHH signaling in vitro in multiple human liver cancer cell lines and tumor samples. Next, we use the EMT network model to systematically explore perturbations that suppress EMT, with the ultimate goal of identifying therapeutic interventions that suppress tumor invasion. We computationally explore close to half a million individual and combination perturbations to the EMT network and identify that only a dozen suppress EMT. We test these interventions experimentally and our findings suggest that many predicted interventions suppress the EMT process. Lastly, we construct a model of the enormous ecological community of bacteria that live in our intestines, collectively called the gut microbiome. This model is used to understand the effect of antibiotic treatment and opportunistic C. difficile infection (a devastating and highly prevalent disease entity) on the native microbiome and predict therapeutic probiotic interventions to suppress C. difficile infection. We integrate this modeling with another type of modeling, genome scale metabolic network reconstructions, to understand metabolic differences between community members and to identify the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of my computational model, that Barnesiella intestinihominis can in fact suppress C. difficile growth. This novel result suggests that Barnesiella could potentially be used as a probiotic to suppress C. difficile growth.Taken together, the studies presented in this thesis demonstrate the tremendous capacity of network modeling to elucidate biomedical systems. We build networks, construct mathematical models, study network dynamics, and use network-directed insight to guide experiments in critical biomedical areas. The ultimate goal of this work has been to translate network-directed insight into actionable biomedical findings that lead to improved understanding of human disease, enhanced patient care, and a betterment of the human condition.

Download or read book Comprehensive Biotechnology written by and published by Elsevier. This book was released on 2019-07-17 with total page 4876 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Biotechnology, Third Edition, Six Volume Set unifies, in a single source, a huge amount of information in this growing field. The book covers scientific fundamentals, along with engineering considerations and applications in industry, agriculture, medicine, the environment and socio-economics, including the related government regulatory overviews. This new edition builds on the solid basis provided by previous editions, incorporating all recent advances in the field since the second edition was published in 2011. Offers researchers a one-stop shop for information on the subject of biotechnology Provides in-depth treatment of relevant topics from recognized authorities, including the contributions of a Nobel laureate Presents the perspective of researchers in different fields, such as biochemistry, agriculture, engineering, biomedicine and environmental science

Download or read book Mathematical Models for Biological Networks and Machine Learning with Applications written by Yushan Qiu and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Mathematical Models for Biological Networks and Machine Learning With Applications" by Yushan, Qiu, 邱育珊, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Systems biology studies complex systems which involve a large number of interacting entities such that their dynamics follow systematical regulations for transition. To develop computational models becomes an urgent need for studying and manipulating biologically relevant systems. The properties and behaviors of complex biological systems can be analyzed and studied by using computational biological network models. In this thesis, construction and computation methods are proposed for studying biological networks. Modeling Genetic Regulatory Networks (GRNs) is an important topic in genomic research. A number of promising formalisms have been developed in capturing the behavior of gene regulations in biological systems. Boolean Network (BN) has received sustainable attentions. Furthermore, it is possible to control one or more genes in a network so as to avoid the network entering into undesired states. Many works have been done on the control policy for a single randomly generated BN, little light has been shed on the analysis of attractor control problem for multiple BNs. An efficient algorithm was developed to study the attractor control problem for multiple BNs. However, one should note that a BN is a deterministic model, a stochastic model is more preferable in practice. Probabilistic Boolean Network (PBN), was proposed to better describe the behavior of genetic process. A PBN can be considered as a Markov chain process and the construction of a PBN is an inverse problem which is computationally challenging. Given a positive stationary distribution, the problem of constructing a sparse PBN was discussed. For the related inverse problems, an efficient algorithm was developed based on entropy approach to estimate the model parameters. The metabolite biomarker discovery problem is a hot topic in bioinformatics. Biomarker identification plays a vital role in the study of biochemical reactions and signalling networks. The lack of essential metabolites may result in triggering human diseases. An effective computational approach is proposed to identify metabolic biomarkers by integrating available biomedical data and disease-specific gene expression data. Pancreatic cancer prediction problem is another hot topic. Pancreatic cancer is known to be difficult to diagnose in the early stage, and early research mainly focused on predicting the survival rate of pancreatic cancer patients. The correct prediction of various disease states can greatly benefit patients and also assist in design of effective and personalized therapeutics. The issue of how to integrating the available laboratory data with classification techniques is an important and challenging issue. An effective approach was suggested to construct a feature space which serves as a significant predictor for classification. Furthermore, a novel method for identifying the outliers was proposed for improving the classification performance. Using our preoperative clinical laboratory data and histologically confirmed pancreatic cancer samples, computational experiments are conducted successfully with the use of Support Vector Machine (SVM) to predict the status of patients. Subjects: Biomathematics Biology - Mathematical models

Download or read book Bio inspired Information and Communication Technologies written by Adriana Compagnoni and published by Springer. This book was released on 2019-07-23 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 11th International Conference on Bio-Inspired Information and Communications Technologies, held in Pittsburgh, PA, USA, in March 2019. The 13 revised full papers and 2 short papers were selected from 29 submissions. Past iterations of the conference have attracted contributions in Direct Bioinspiration (physical biological materials and systems used within technology) as well as Indirect Bioinspiration (biological principles, processes and mechanisms used within the design and application of technology). This year, the scope has expanded to include a third thrust: Foundational Bioinspiration (bioinspired aspects of game theory, evolution, information theory, and philosophy of science).

Download or read book Reconstructing Networks written by Giulio Cimini and published by Cambridge University Press. This book was released on 2021-09-09 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.

Download or read book Comprehensive Medicinal Chemistry III written by and published by Elsevier. This book was released on 2017-06-03 with total page 4609 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Medicinal Chemistry III, Eight Volume Set provides a contemporary and forward-looking critical analysis and summary of recent developments, emerging trends, and recently identified new areas where medicinal chemistry is having an impact. The discipline of medicinal chemistry continues to evolve as it adapts to new opportunities and strives to solve new challenges. These include drug targeting, biomolecular therapeutics, development of chemical biology tools, data collection and analysis, in silico models as predictors for biological properties, identification and validation of new targets, approaches to quantify target engagement, new methods for synthesis of drug candidates such as green chemistry, development of novel scaffolds for drug discovery, and the role of regulatory agencies in drug discovery. Reviews the strategies, technologies, principles, and applications of modern medicinal chemistry Provides a global and current perspective of today's drug discovery process and discusses the major therapeutic classes and targets Includes a unique collection of case studies and personal assays reviewing the discovery and development of key drugs

Download or read book Algebraic and Discrete Mathematical Methods for Modern Biology written by Raina Robeva and published by Academic Press. This book was released on 2015-05-09 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by experts in both mathematics and biology, Algebraic and Discrete Mathematical Methods for Modern Biology offers a bridge between math and biology, providing a framework for simulating, analyzing, predicting, and modulating the behavior of complex biological systems. Each chapter begins with a question from modern biology, followed by the description of certain mathematical methods and theory appropriate in the search of answers. Every topic provides a fast-track pathway through the problem by presenting the biological foundation, covering the relevant mathematical theory, and highlighting connections between them. Many of the projects and exercises embedded in each chapter utilize specialized software, providing students with much-needed familiarity and experience with computing applications, critical components of the "modern biology" skill set. This book is appropriate for mathematics courses such as finite mathematics, discrete structures, linear algebra, abstract/modern algebra, graph theory, probability, bioinformatics, statistics, biostatistics, and modeling, as well as for biology courses such as genetics, cell and molecular biology, biochemistry, ecology, and evolution. Examines significant questions in modern biology and their mathematical treatments Presents important mathematical concepts and tools in the context of essential biology Features material of interest to students in both mathematics and biology Presents chapters in modular format so coverage need not follow the Table of Contents Introduces projects appropriate for undergraduate research Utilizes freely accessible software for visualization, simulation, and analysis in modern biology Requires no calculus as a prerequisite Provides a complete Solutions Manual Features a companion website with supplementary resources

Download or read book Bioinformatics for Immunomics written by Darren D.R. Flower and published by Springer. This book was released on 2009-10-03 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Like many words, the term “immunomics” equates to different ideas contingent on context. For a brief span, immunomics meant the study of the Immunome, of which there were, in turn, several different definitions. A now largely defunct meaning rendered the Immunome as the set of antigenic peptides or immunogenic proteins within a single microorganism – be that virus, bacteria, fungus, or parasite – or microbial population, or antigenic or allergenic proteins and peptides derived from the environment as a whole, containing also proteins from eukaryotic sources. However, times have changed and the meaning of immunomics has also changed. Other newer definitions of the Immunome have come to focus on the plethora of immunological receptors and accessory molecules that comprise the host immune arsenal. Today, Immunomics or immunogenomics is now most often used as a synonym for high-throughput genome-based immunology. This is the study of aspects of the immune system using high-throughput techniques within a conc- tual landscape borne of both clinical and biophysical thinking.

Download or read book An Introduction to Transfer Entropy written by Terry Bossomaier and published by Springer. This book was released on 2016-11-15 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book considers a relatively new metric in complex systems, transfer entropy, derived from a series of measurements, usually a time series. After a qualitative introduction and a chapter that explains the key ideas from statistics required to understand the text, the authors then present information theory and transfer entropy in depth. A key feature of the approach is the authors' work to show the relationship between information flow and complexity. The later chapters demonstrate information transfer in canonical systems, and applications, for example in neuroscience and in finance. The book will be of value to advanced undergraduate and graduate students and researchers in the areas of computer science, neuroscience, physics, and engineering.

Download or read book Logical Modeling of Cellular Processes From Software Development to Network Dynamics written by Matteo Barberis and published by Frontiers Media SA. This book was released on 2019-08-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models have become invaluable tools for understanding the intricate dynamic behavior of complex biochemical and biological systems. Among computational strategies, logical modeling has been recently gaining interest as an alternative approach to address network dynamics. Due to its advantages, including scalability and independence of kinetic parameters, the logical modeling framework is becoming increasingly popular to study the dynamics of highly interconnected systems, such as cell cycle progression, T cell differentiation and gene regulation. Novel tools and standards have been developed to increase the interoperability of logical models, which can now be employ to respond a variety of biological questions. This Research Topic brings together the most recent and cutting-edge approaches in the area of logical modeling including, among others, novel biological applications, software development and model analysis techniques.

Download or read book Artificial Immune Systems written by Hugues Bersini and published by Springer Science & Business Media. This book was released on 2006-08-30 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Artificial Immune Systems, ICARIS 2006. The book presents 34 revised full papers, are organized in topical sections on computer simulation of classical immunology, computer simulation of idiotypic network, immunoinformatics conceptual papers, pattern recognition type of application, optimization type of application, control and time-series type of application, danger theory inspired application, and text mining application.

Download or read book Individual based Modeling and Ecology written by Volker Grimm and published by Princeton University Press. This book was released on 2013-11-28 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Individual-based models are an exciting and widely used new tool for ecology. These computational models allow scientists to explore the mechanisms through which population and ecosystem ecology arises from how individuals interact with each other and their environment. This book provides the first in-depth treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call "individual-based ecology.? Grimm and Railsback start with a general primer on modeling: how to design models that are as simple as possible while still allowing specific problems to be solved, and how to move efficiently through a cycle of pattern-oriented model design, implementation, and analysis. Next, they address the problems of theory and conceptual framework for individual-based ecology: What is "theory"? That is, how do we develop reusable models of how system dynamics arise from characteristics of individuals? What conceptual framework do we use when the classical differential equation framework no longer applies? An extensive review illustrates the ecological problems that have been addressed with individual-based models. The authors then identify how the mechanics of building and using individual-based models differ from those of traditional science, and provide guidance on formulating, programming, and analyzing models. This book will be helpful to ecologists interested in modeling, and to other scientists interested in agent-based modeling.

Download or read book Systems Immunology written by Jayajit Das and published by CRC Press. This book was released on 2018-09-03 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Taken together, the body of information contained in this book provides readers with a bird’s-eye view of different aspects of exciting work at the convergence of disciplines that will ultimately lead to a future where we understand how immunity is regulated, and how we can harness this knowledge toward practical ends that reduce human suffering. I commend the editors for putting this volume together." –Arup K. Chakraborty, Robert T. Haslam Professor of Chemical Engineering, and Professor of Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA New experimental techniques in immunology have produced large and complex data sets that require quantitative modeling for analysis. This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.