Selecting Optimal Models Based on Efficiency and Robustness in Multi-valued Biological Networks
Abstract
In this paper, we propose an optimization algorithm for literature-derived model and parameter identification in multi-valued biological regulatory networks. Our approach is a multi-objective optimization method where the objectives are inspired from structural Efficiency, dynamical Robustness and biological selectivity of cells in their actions. Given an incomplete model derived from literature and partially instrumented clinical observations, our method identifies the optimal model parameterization by maximizing structural Efficiency, dynamical Robustness and Selectivity. As the parameterization space is super exponential, we implemented our method in a constraint satisfaction framework by defining logical equivalences of the dynamical features. The implemented framework is then solved with a lazy clause solver known as Chuffed. We apply our method on female Hypothalamic-Pituitary-Gonadal axis (HPG) and demonstrate how it is able to identify a model that reproduces the complex menstrual cycle. The algorithm found a structure and parameterization for the 5 node 14 edge (≈ 50% edge density) HPG model with a normalized length cost and robustness of 1.46 and 0.35 respectively in 713 seconds on an Intel core i7 machine.Our method discovered that there are at least 6 more regulatory interactions that must be added to the commonly accepted HPG basic model in order to reproduce the menstrual cycle efficiently and robustly. The discovery of additional interactions suggest that our algorithm provides new insight to the biological model identification by combining the information from literature, clinical measurements and dynamical parameters.
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A simple computational model of HPA function previously reported by the authors' group readily supported an alternate stable resting state characterized by persistently low levels of circulating cortisol.
“Indeed, a simple computational model of HPA function previously reported by our group readily supported an alternate stable resting state characterized by persistently low levels of circulating cortisol36.”
From Leveraging Prior Knowledge of Endocrine Immune Regulation in the Therapeutically-relevant Phenotyping of Women with Chronic Fatigue Syndrome (2019) · cites this paper as [36]
Cites
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Propagation via lazy clause generation
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Symbolic Reachability Analysis Based on SAT-Solvers
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Sedghamiz, H., Morris, M., Craddock, T. J. A., Whitley, D., & Broderick, G. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00048 · Cited by 25 · see citing works
“Inspired from the same ideas, in this section, we introduce several biologically relevant objective functions employed in our parameterization to rank the constraint satisfying models (Sedghamiz et al., 2017).”
BackgroundMethodInfluentialSelf-citationHigh-fidelity discrete modeling of the HPA axis: a study of regulatory plasticity in biology
Sedghamiz, H., Morris, M. C., Craddock, T. J. A., Whitley, D., & Broderick, G. (2018) · BMC Systems Biology · doi:10.1186/s12918-018-0599-1 · Cited by 23 · see citing works
Self-citationMorris, M. C., Cooney, K. E., Sedghamiz, H., Abreu, M., Collado, F., Balbin, E. G., Craddock, T. J. A., Klimas, N. G., Broderick, G., & Fletcher, M. A. (2019) · Clinical Therapeutics · doi:10.1016/j.clinthera.2019.03.002 · Cited by 14 · see citing works
Self-citationComputation of Robust Minimal Intervention Sets in Multi-Valued Biological Regulatory Networks
Sedghamiz, H., Morris, M. C., Whitley, D., Craddock, T. J. A., Pichichero, M. E., & Broderick, G. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00241 · Cited by 11 · see citing works
“Stochasticity and Time Updates As mentioned earlier, there are two common update schemes typically employed in logical modeling, namely synchronous and asynchronous updating (Sedghamiz et al., 2017).”
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“To accommodate often sparse and partially observed experimental data we redefined the conventional goal-directed search for parameter values to one where resting state and time course data were applied as constraints; combinations of parameter values were retained if they supported model predictions that complied with…”
BackgroundMethodSelf-citationA regulatory pathway model of neuropsychological disruption in Havana syndrome
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Self-citationA Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies
Page, J., Moore, N., & Broderick, G. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4200-9_14 · Cited by 1 · see citing works
Self-citationShinn, E. H., Busch, B. E., Jasemi, N., Lyman, C. A., Toole, J. T., Richman, S., Symmans, W. F., Chávez‐MacGregor, M., Peterson, S. K., & Broderick, G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.856813
Self-citationPage, J., Kelly, K. A., Michalovicz, L. T., O’Callahghan, J. P., Shen, S., Zhu, X., Qu, J., Boyd, J., & Broderick, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61569-6_7
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- Authors
- Sedghamiz, H., Chen, W., Rice, M., Whitley, D., & Broderick, G.
- Affiliations
- Rochester General Hospital (US)
- Colorado State University (US)
- Rochester Institute of Technology (US)
- Venue
- 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 200-205
- ISSN
- 2159-5410 (ISSN-L)
- OpenAlex
- W2783487549
- Cited by
- 11
- References
- 10
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 1 · 2020: 1 · 2019: 3 · 2018: 2
- FWCI
- 6.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Bioinformatics and Bioengineering (BIBE) (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- biological regulatory networks, model parameter identification, multi-objective optimization, structural efficiency, dynamical robustness, selectivity, constraint satisfaction, hypothalamic-pituitary-gonadal axis, menstrual cycle, regulatory interactions
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