Two Challenges of Systems Biology

Hlavacek, William S.

doi:10.1002/9781119970606.ch1

Cited by 4 publications

(7 citation statements)

References 103 publications

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“…Increasing the scope of modeling efforts (without degrading model reliability) should enable deeper insights into cellular regulation and predictions of greater health significance. Rule‐based modeling approaches, which are scalable with respect to both specification and simulation, may offer solutions to some of the problems posed by this challenge …”

Section: Resultsmentioning

confidence: 99%

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Chylek

Harris

Tung

et al. 2013

WIREs Mechanisms of Disease

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109

146

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Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

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Section: Resultsmentioning

confidence: 99%

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Chylek

Harris

Tung

et al. 2013

WIREs Mechanisms of Disease

Self Cite

109

146

View full text Add to dashboard Cite

show abstract

“…One of the biologists said, “I think biologists need to drive systems biology, because if it’s driven by computation or engineers, without a depth of training in biology, they lose that sense, they tend to treat molecules as nodes and edges without a sense of how they’re performing their functions” [ 37 ]. Physicists and computer scientists might also agree with that, because often those who want to model a biological system sometimes have difficulty find a biological expert to help them to link the model to specific biological problems [ 38 ]. Along with the findings of the interviews, a systems biologist, Sui Huang, claims that biologists have become active players in systems biology because what they need to understand now is not a single gene or a protein, but networks of genes or proteins, and systems biology approach can help address these needs [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013

Zou

Laubichler

2018

PLoS ONE

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Systems biology is a discipline that studies biological systems from a holistic and interdisciplinary perspective. It brings together biologists, mathematicians, computer scientists, physicists, and engineers, so it has both biology-oriented components and systems-oriented components. We applied several computational tools to analyze the bibliographic information of published articles in systems biology to answer the question: Did the research topics of systems biology become more biology-oriented or more systems-oriented from 1992 to 2013? We analyzed the metadata of 9923 articles on systems biology from the Web of Science database. We identified the most highly cited 330 references using computational tools and through close reading we divided them into nine categories of research types in systems biology. Interestingly, we found that articles in one category, namely, systems biology’s applications in medical research, increased tremendously. This finding was corroborated by computational analysis of the abstracts, which also suggested that the percentages of topics on vaccines, diseases, drugs and cancers increased over time. In addition, we analyzed the institutional backgrounds of the corresponding authors of those 9923 articles and identified the most highly cited 330 authors over time. We found that before the mid-1990s, systems-oriented scientists had made the most referenced contributions. However, in recent years, researchers from biology-oriented institutions not only represented a huge percentage of the total number of researchers, but also had made the most referenced contributions. Notably, interdisciplinary institutions only produced a small percentage of researchers, but had made disproportionate contributions to this field.

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“…Based on this result, and the observations involving negative scalar curvature when inferring σ, it might seem that positive scalar curvature produces geodesics that extend beyond corresponding confidence regions, whereas negative scalar curvature has the opposite effect. However, repeating the analysis with different synthetic data sets-generated from a different random seed-suggests that in some cases the geodesics will extend beyond the confidence regions, and in some Parameter estimation is wrought with challenges relating to the availability and quality of experimental or field data [28,36,76,81]. This prompts a strong consideration of uncertainty quantification to support point-estimation of model parameters [20].…”

Section: Sir Epidemic Modelmentioning

confidence: 99%

“…For example, models need to be sufficiently sophisticated to adequately reflect the behaviour of the underlying system, while ideally admitting identifiable parameters that carry a physical interpretation, that can be estimated from available or obtainable data [29,46]. Further, available data can be limited and often is not collected for the express purpose of parameter estimation; data may be noisy, incomplete, or may not provide the level of detail or sample size required to obtain precise parameter estimates [28,36,76,81].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation and uncertainty quantification using information geometry

Sharp¹,

Browning²,

Burrage³

et al. 2021

Preprint

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In this work we (1) review likelihood-based inference for parameter estimation and the construction of confidence regions, and (2) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions.

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Two Challenges of Systems Biology

Cited by 4 publications

References 103 publications

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013

Parameter estimation and uncertainty quantification using information geometry

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