Special Issue "Biological Networks"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Systems".

Deadline for manuscript submissions: closed (31 January 2018)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Prof. Dr. Rudiyanto Gunawan

Department Chemical & Biological Engineering, University of Buffalo, 335 Bell Hall, Buffalo, NY 14260, USA
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Interests: systems biology, network inference, systems analysis, dynamical modeling, model identification, parameter estimation, design of experiments.
Guest Editor
Dr. Neda Bagheri

Chemical & Biological Engineering, McCormick School of Engineering Northwestern University, Evanston, IL 60208, USA
Website | E-Mail
Interests: The Bagheri Lab operates at the evolving interface between engineering and biology, promoting a diverse, creative research environment consisting of engineers and basic scientists that share the common mission of advancing medicine and biology. Through this collective effort, the lab aim to identify design principles that underlie complex biological function, and modulate extrinsic factors to optimize therapeutic interventions

Special Issue Information

Dear Colleagues,

Biological networks describe systems of interacting components that provide context-specific responses. Networks pervade biology, governing biological processes at all scales, from subcellular protein signaling to entire ecosystems. An underlying characteristic of biological networks is the inherently robust response to stimuli/perturbations that are “greater than the sum of its parts”. In other words, these characteristics represent emergent and often non-intuitive behaviors. Quantitative approaches—including the mathematical modeling and systems analysis of biological networks—have played a critical role in uncovering regulatory network motifs that drive system performance, and predicting emergent phenomena. Earlier applications have focused on small to medium sized biological systems and have improved our basic understanding of countless biological processes, from enzyme kinetics to electrophysiology to predator-prey relationships.

Recent advances in high-throughput cell measurement technology, in combination with the exponential drop in the cost of next-generation sequencing, have stimulated further interest and enabled broad systematic interrogation of large, whole-cell networks. Investigation of biological processes with such breadth and resolution is unprecedented. This special issue on “Biological Networks” aims to curate novel advances in the development and application of network modeling and systems analysis to address longstanding challenges in biology. Topics include, but are not limited to:

  1. Development of new tools to interrogate biological networks
  2. Prediction of emergent biological phenomena through mathematical modeling
  3. Analysis of robust biological responses to uncover underlying design principles

Prof. Dr. Rudiyanto Gunawan
Dr. Neda Bagheri
Guest Editors

Manuscript Submission Information

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Published Papers (11 papers)

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Editorial

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Open AccessEditorial Special Issue on “Biological Networks”
Processes 2018, 6(12), 242; https://doi.org/10.3390/pr6120242
Received: 20 November 2018 / Accepted: 20 November 2018 / Published: 27 November 2018
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Abstract
Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells, and from years (e.g., the life cycle of periodical cicadas) to milliseconds (e.g., allosteric enzyme
[...] Read more.
Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells, and from years (e.g., the life cycle of periodical cicadas) to milliseconds (e.g., allosteric enzyme
regulation[...] Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available

Research

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Open AccessFeature PaperArticle Genome-Scale In Silico Analysis for Enhanced Production of Succinic Acid in Zymomonas mobilis
Processes 2018, 6(4), 30; https://doi.org/10.3390/pr6040030
Received: 18 January 2018 / Revised: 12 March 2018 / Accepted: 26 March 2018 / Published: 1 April 2018
Cited by 1 | PDF Full-text (17850 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Presented herein is a model-driven strategy for characterizing the production capability of expression host and subsequently identifying targets for strain improvement by resorting to network structural comparison with reference strain and in silico analysis of genome-scale metabolic model. The applicability of the strategy [...] Read more.
Presented herein is a model-driven strategy for characterizing the production capability of expression host and subsequently identifying targets for strain improvement by resorting to network structural comparison with reference strain and in silico analysis of genome-scale metabolic model. The applicability of the strategy was demonstrated by exploring the capability of Zymomonas mobilis, as a succinic acid producer. Initially, the central metabolism of Z. mobilis was compared with reference producer, Mannheimia succiniciproducens, in order to identify gene deletion targets. It was followed by combinatorial gene deletion analysis. Remarkably, resultant in silico strains suggested that knocking out pdc, ldh, and pfl genes encoding pyruvate-consuming reactions as well as the cl gene leads to fifteen-fold increase in succinic acid molar yield. The current exploratory work could be a promising support to wet experiments by providing guidance for metabolic engineering strategies and lowering the number of trials and errors. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Mathematical Modeling and Parameter Estimation of Intracellular Signaling Pathway: Application to LPS-induced NFκB Activation and TNFα Production in Macrophages
Processes 2018, 6(3), 21; https://doi.org/10.3390/pr6030021
Received: 28 January 2018 / Revised: 10 February 2018 / Accepted: 21 February 2018 / Published: 25 February 2018
Cited by 5 | PDF Full-text (1240 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Due to the intrinsic stochasticity, the signaling dynamics in a clonal population of cells exhibit cell-to-cell variability at the single-cell level, which is distinct from the population-average dynamics. Frequently, flow cytometry is widely used to acquire the single-cell level measurements by blocking cytokine [...] Read more.
Due to the intrinsic stochasticity, the signaling dynamics in a clonal population of cells exhibit cell-to-cell variability at the single-cell level, which is distinct from the population-average dynamics. Frequently, flow cytometry is widely used to acquire the single-cell level measurements by blocking cytokine secretion with reagents such as Golgiplug. However, Golgiplug can alter the signaling dynamics, causing measurements to be misleading. Hence, we developed a mathematical model to infer the average single-cell dynamics based on the flow cytometry measurements in the presence of Golgiplug with lipopolysaccharide (LPS)-induced NF κ B signaling as an example. First, a mathematical model was developed based on the prior knowledge. Then, average single-cell dynamics of two key molecules (TNF α and I κ B α ) in the NF κ B signaling pathway were measured through flow cytometry in the presence of Golgiplug to validate the model and maximize its prediction accuracy. Specifically, a parameter selection and estimation scheme selected key model parameters and estimated their values. Unsatisfactory results from the parameter estimation guided subsequent experiments and appropriate model improvements, and the refined model was calibrated again through the parameter estimation. The inferred model was able to make predictions that were consistent with the experimental measurements, which will be used to construct a semi-stochastic model in the future. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Elucidating Cellular Population Dynamics by Molecular Density Function Perturbations
Processes 2018, 6(2), 9; https://doi.org/10.3390/pr6020009
Received: 21 December 2017 / Revised: 16 January 2018 / Accepted: 18 January 2018 / Published: 23 January 2018
Cited by 1 | PDF Full-text (3698 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In [...] Read more.
Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of a signal transduction pathway involving TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis shows that caspase-8 activation regulates the timing of the switch-like increase of cPARP (cleaved poly(ADP-ribose) polymerase), an indicator of apoptosis. Meanwhile, the cell-to-cell variability in the commitment to apoptosis depends on mitochondrial outer membrane permeabilization (MOMP) and events following MOMP, including the release of Smac (second mitochondria-derived activator of caspases) and cytochrome c from mitochondria, the inhibition of XIAP (X-linked inhibitor of apoptosis) by Smac, and the formation of the apoptosome. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Mathematical Modeling of Tuberculosis Granuloma Activation
Processes 2017, 5(4), 79; https://doi.org/10.3390/pr5040079
Received: 23 October 2017 / Revised: 28 November 2017 / Accepted: 4 December 2017 / Published: 11 December 2017
Cited by 1 | PDF Full-text (2022 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread [...] Read more.
Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood. Animal and human studies currently cannot probe the dynamics of activation, so a computational model is developed to fill this gap. This dynamic mathematical model focuses specifically on the latent to active transition after the initial immune response has successfully formed a granuloma. Bacterial leakage from latent granulomas is successfully simulated in response to the MMP-1 dynamics under several scenarios for granuloma activation. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix
Processes 2017, 5(4), 63; https://doi.org/10.3390/pr5040063
Received: 14 September 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 1 November 2017
Cited by 2 | PDF Full-text (351 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum [...] Read more.
The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher information matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs, as the FIM only accounts for the linear variation in the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, for which the model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of baker’s yeast. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model
Processes 2017, 5(3), 49; https://doi.org/10.3390/pr5030049
Received: 16 June 2017 / Revised: 25 July 2017 / Accepted: 22 August 2017 / Published: 1 September 2017
Cited by 4 | PDF Full-text (1442 KB) | HTML Full-text | XML Full-text
Abstract
IL-6 signaling plays an important role in inflammatory processes in the body. While a number of models for IL-6 signaling are available, the parameters associated with these models vary from case to case as they are non-trivial to determine. In this study, optimal [...] Read more.
IL-6 signaling plays an important role in inflammatory processes in the body. While a number of models for IL-6 signaling are available, the parameters associated with these models vary from case to case as they are non-trivial to determine. In this study, optimal experimental design is utilized to reduce the parameter uncertainty of an IL-6 signaling model consisting of ordinary differential equations, thereby increasing the accuracy of the estimated parameter values and, potentially, the model itself. The D-optimality criterion, operating on the Fisher information matrix and, separately, on a sensitivity matrix computed from the Morris method, was used as the objective function for the optimal experimental design problem. Optimal input functions for model parameter estimation were identified by solving the optimal experimental design problem, and the resulting input functions were shown to significantly decrease parameter uncertainty in simulated experiments. Interestingly, the determined optimal input functions took on the shape of PRBS signals even though there were no restrictions on their nature. Future work should corroborate these findings by applying the determined optimal experimental design on a real experiment. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach
Processes 2017, 5(3), 47; https://doi.org/10.3390/pr5030047
Received: 29 June 2017 / Revised: 5 August 2017 / Accepted: 13 August 2017 / Published: 17 August 2017
Cited by 1 | PDF Full-text (1741 KB) | HTML Full-text | XML Full-text
Abstract
Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease [...] Read more.
Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E ε4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Open AccessFeature PaperArticle Structural Properties of Dynamic Systems Biology Models: Identifiability, Reachability, and Initial Conditions
Processes 2017, 5(2), 29; https://doi.org/10.3390/pr5020029
Received: 12 May 2017 / Revised: 26 May 2017 / Accepted: 31 May 2017 / Published: 2 June 2017
Cited by 5 | PDF Full-text (982 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: Dynamic modelling is a powerful tool for studying biological networks. Reachability (controllability), observability, and structural identifiability are classical system-theoretic properties of dynamical models. A model is structurally identifiable if the values of its parameters can in principle be determined from observations of [...] Read more.
Abstract: Dynamic modelling is a powerful tool for studying biological networks. Reachability (controllability), observability, and structural identifiability are classical system-theoretic properties of dynamical models. A model is structurally identifiable if the values of its parameters can in principle be determined from observations of its outputs. If model parameters are considered as constant state variables, structural identifiability can be studied as a generalization of observability. Thus, it is possible to assess the identifiability of a nonlinear model by checking the rank of its augmented observability matrix. When such rank test is performed symbolically, the result is of general validity for almost all numerical values of the variables. However, for special cases, such as specific values of the initial conditions, the result of such test can be misleading—that is, a structurally unidentifiable model may be classified as identifiable. An augmented observability rank test that specializes the symbolic states to particular numerical values can give hints of the existence of this problem. Sometimes it is possible to find such problematic values analytically, or via optimization. This manuscript proposes procedures for performing these tasks and discusses the relation between loss of identifiability and loss of reachability, using several case studies of biochemical networks. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Review

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Open AccessFeature PaperReview Improving Bioenergy Crops through Dynamic Metabolic Modeling
Processes 2017, 5(4), 61; https://doi.org/10.3390/pr5040061
Received: 4 September 2017 / Revised: 3 October 2017 / Accepted: 7 October 2017 / Published: 18 October 2017
Cited by 4 | PDF Full-text (3162 KB) | HTML Full-text | XML Full-text
Abstract
Enormous advances in genetics and metabolic engineering have made it possible, in principle, to create new plants and crops with improved yield through targeted molecular alterations. However, while the potential is beyond doubt, the actual implementation of envisioned new strains is often difficult, [...] Read more.
Enormous advances in genetics and metabolic engineering have made it possible, in principle, to create new plants and crops with improved yield through targeted molecular alterations. However, while the potential is beyond doubt, the actual implementation of envisioned new strains is often difficult, due to the diverse and complex nature of plants. Indeed, the intrinsic complexity of plants makes intuitive predictions difficult and often unreliable. The hope for overcoming this challenge is that methods of data mining and computational systems biology may become powerful enough that they could serve as beneficial tools for guiding future experimentation. In the first part of this article, we review the complexities of plants, as well as some of the mathematical and computational methods that have been used in the recent past to deepen our understanding of crops and their potential yield improvements. In the second part, we present a specific case study that indicates how robust models may be employed for crop improvements. This case study focuses on the biosynthesis of lignin in switchgrass (Panicum virgatum). Switchgrass is considered one of the most promising candidates for the second generation of bioenergy production, which does not use edible plant parts. Lignin is important in this context, because it impedes the use of cellulose in such inedible plant materials. The dynamic model offers a platform for investigating the pathway behavior in transgenic lines. In particular, it allows predictions of lignin content and composition in numerous genetic perturbation scenarios. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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Other

Open AccessFeature PaperOpinion On the Use of Multivariate Methods for Analysis of Data from Biological Networks
Processes 2017, 5(3), 36; https://doi.org/10.3390/pr5030036
Received: 9 June 2017 / Revised: 26 June 2017 / Accepted: 30 June 2017 / Published: 3 July 2017
Cited by 4 | PDF Full-text (2478 KB) | HTML Full-text | XML Full-text
Abstract
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper [...] Read more.
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate analysis. Full article
(This article belongs to the Special Issue Biological Networks) Printed Edition available
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