Introduction to Systems Biology: Workbook for Flipped-classroom Teaching (PDF)

This book is an introduction to the language of systems biology, which is spoken among many disciplines, from biology to engineering. Authors Thomas Sauter and Marco Albrecht draw on a multidisciplinary background and evidence-based learning to facilitate the understanding of biochemical networks, metabolic modeling and system dynamics. Their pedagogic approach briefly highlights core ideas of concepts in a broader interdisciplinary framework to guide a more effective deep dive thereafter. The learning journey starts with the purity of mathematical concepts, reveals its power to connect biological entities in structure and time, and finally introduces physics concepts to tightly align abstraction with reality. This workbook is all about self-paced learning, supports the flipped-classroom concept, and kick-starts with scientific evidence on studying. Each chapter comes with links to external YouTube videos, learning checklists, and Integrated real-world examples to gain confidence in thinking across scientific perspectives. The result is an integrated approach that opens a line of communication between theory and application, enabling readers to actively learn as they read. This overview of capturing and analyzing the behavior of biological systems will interest adherers of systems biology and network analysis, as well as related fields such as bioinformatics, biology, cybernetics, and data science

A metabolic network with one blocked reaction (A↔B).

<p>Note that A appears with stoichiometric coefficient 2 in the boundary reaction →2A.</p

Mean urea/glutamine ratio in the extended liver model obtained by fastcore.

<p>Healthy (normal homozygote), partial (heterozygote) and full knock-out cases. See text for details.</p

Flowchart of the overall pipeline for generating consistent context-specific models.

<p>Flowchart of the overall pipeline for generating consistent context-specific models.</p

Data_Sheet_1_Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.XLSX

<p>Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.</p

Comparing fastcore to MBA [10] on liver model reconstruction from c-Recon1.

<p>number of intracellular reactions.</p><p>the reported time (in seconds), as well as the number of LPs, refer to a single pruning step of MBA, whereas and IR refer to the full MBA.</p

Comparing fastcore to an exact MILP solver on a small <i>E. coli</i> model [38].

<p>Shown are mean values of sizes of reconstructed models (over 50 repetitions for each core set; standard deviations were small and are omitted to avoid clutter) as a function of the size of the core set. fastcore computes near-optimal reconstructions, which improve with the size of the core set.</p

Comparing fastcc to fastFVA [30] and CMC [10] on four input models.

<p>in seconds.</p

Presentation_1_Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways.PDF

<p>Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.</p

Summary of the main characteristics of GIMME [8], MBA [10], iMAT [35], mCADRE [26], INIT [13], and fastcore (this paper) reconstruction algorithms.

<p>Summary of the main characteristics of GIMME <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003424#pcbi.1003424-Becker1" target="_blank">[8]</a>, MBA <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003424#pcbi.1003424-Jerby1" target="_blank">[10]</a>, iMAT <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003424#pcbi.1003424-Zur1" target="_blank">[35]</a>, mCADRE <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003424#pcbi.1003424-Wang1" target="_blank">[26]</a>, INIT <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003424#pcbi.1003424-Agren1" target="_blank">[13]</a>, and fastcore (this paper) reconstruction algorithms.</p