Logic-based models in systems biology: a predictive and parameter-free network analysis method

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofía D.; Schnell, Santiago

doi:10.1039/c2ib20193c

Cited by 111 publications

(93 citation statements)

References 82 publications

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“…the concentration of a substance or the potential across the plasma membrane is continuous in reality); there is, however, ample evidence of nonlinear regulation wherein not the concentration but rather its relationship with certain thresholds matters. Network-based discrete dynamic modelling has been successfully applied in a great variety of biological systems (reviewed in [89]–[92]). These models enabled the understanding of the systems and generated insightful predictions that were subsequently validated experimentally; recent examples include [24], [93]–[97].…”

Section: Discussionmentioning

confidence: 99%

Multi-level Modeling of Light-Induced Stomatal Opening Offers New Insights into Its Regulation by Drought

et al. 2014

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Plant guard cells gate CO2 uptake and transpirational water loss through stomatal pores. As a result of decades of experimental investigation, there is an abundance of information on the involvement of specific proteins and secondary messengers in the regulation of stomatal movements and on the pairwise relationships between guard cell components. We constructed a multi-level dynamic model of guard cell signal transduction during light-induced stomatal opening and of the effect of the plant hormone abscisic acid (ABA) on this process. The model integrates into a coherent network the direct and indirect biological evidence regarding the regulation of seventy components implicated in stomatal opening. Analysis of this signal transduction network identified robust cross-talk between blue light and ABA, in which [Ca2+]c plays a key role, and indicated an absence of cross-talk between red light and ABA. The dynamic model captured more than 1031 distinct states for the system and yielded outcomes that were in qualitative agreement with a wide variety of previous experimental results. We obtained novel model predictions by simulating single component knockout phenotypes. We found that under white light or blue light, over 60%, and under red light, over 90% of all simulated knockouts had similar opening responses as wild type, showing that the system is robust against single node loss. The model revealed an open question concerning the effect of ABA on red light-induced stomatal opening. We experimentally showed that ABA is able to inhibit red light-induced stomatal opening, and our model offers possible hypotheses for the underlying mechanism, which point to potential future experiments. Our modelling methodology combines simplicity and flexibility with dynamic richness, making it well suited for a wide class of biological regulatory systems.

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Section: Discussionmentioning

confidence: 99%

Multi-level Modeling of Light-Induced Stomatal Opening Offers New Insights into Its Regulation by Drought

et al. 2014

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“…The applied rules are represented by AND, OR and NOT interactions illustrated in Figure 1B. These interactions were obtained by a systemic analysis of the literature following the approach proposed in (29). Since there are nearly 300,000 possible states, randomly generating initial conditions will lead to a lot of wasted effort and reproduction of states.…”

Section: Resultsmentioning

confidence: 99%

“…Based on these data, we constructed a biological signaling network of developmental genes (involved in sympathetic neurogenesis and neuroblastoma) and adapted the approach used in (29) to convert the molecular interactions into logical functions using known outcomes. That is, developmental genes were represented as either active or inactive and the dynamics of the system evolved from downstream interactions of components (e.g., feed forward, feedback) that resulted in a particular outcome(s) (e.g., proliferation, apoptosis, differentiation, vasculogenesis).…”

Section: Methodsmentioning

confidence: 99%

Predicting neuroblastoma using developmental signals and a logic-based model

Kasemeier-Kulesa

Schnell

Woolley

et al. 2018

Biophysical Chemistry

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SUMMARY Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression.

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“…Integrative and predictive systems biology approaches explain biological phenomenon not on a gene by gene or trait basis but through the net interactions in cell or whole systems for all cellular and biochemical components (Wynn et al, 2012). These advanced complementary approaches, which must ultimately be integrated to understand the complex plant systems, are already having an impact on the crop improvement programme.…”

Section: Predictive Systems Biologymentioning

confidence: 99%

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

Kumar¹,

Pathak²,

Gupta

et al. 2015

OMICS: A Journal of Integrative Biology

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In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use wellorganized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

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Logic-based models in systems biology: a predictive and parameter-free network analysis method

Cited by 111 publications

References 82 publications

Multi-level Modeling of Light-Induced Stomatal Opening Offers New Insights into Its Regulation by Drought

Multi-level Modeling of Light-Induced Stomatal Opening Offers New Insights into Its Regulation by Drought

Predicting neuroblastoma using developmental signals and a logic-based model

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

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