The development of a mathematical model is critical to the understanding of complex biological pro- cesses because it codi es current understanding so that it can be tested against existing data. A good model with sucient detail can be used to identify potential points of intervention (for example, drug targets) at which an undesired outcome (for example, e ects of disease) of the process might be altered. Model develop- ment proceeds through a cycle of model building, simulation of the model under numerous conditions, and comparison to experimental data. The cycle is repeated and often augmented by new experiments to capture additional data, until the resulting model can plausibly explain the data. Tremendous amounts of time and e ort must be devoted to nding and/or developing tools to analyze the model and compare it to the data, t the parameters and assess the e ects of typically large amounts of uncertainty in both the data and the parameters, simulate the model and analyze the simulation data, re ne the model to better capture our increased understanding at each stage of the process, decide which additional experiments would add most to our understanding, etc. Our objective in the proposed work is to facilitate and accelerate the modeling process by providing state of the art, well-integrated tools to report complete and informative results at each stage, enabling the modeler and the experimentalist to focus on what they do best: scienti c discovery. This is a renewal proposal that builds on the capabilities and infrastructure developed in the current project. In that work we developed StochSS, a novel Software-as-a-Service o ering for quantitative modeling of biochemical networks capable of seamless deployment in public cloud environments. StochSS does an excellent job of supporting two of the major steps of the modeling process: Model Building { taking your model description and putting it into a form that the StochSS simulation engines can work with, and Simulation { performing the simulations to produce the results. The proposed project has three complementary Aims. The rst is to further develop StochSS's core capabilities and to take the steps that will ensure its long-term sustainability; the second is to develop a Model Development Toolkit, and the third is to develop a Model Exploration Toolkit. Both of these toolkits will be integrated into our existing StochSS Model Building and Simulation environment and will leverage our existing software infrastructure for cloud computing.
Aim 1. Core Capabilities and Long-Term Sustainability This aim has three sub-aims: (1) instituting practices that will help ensure community involvement and better long-term sustainability of StochSS beyond NIH funding, (2) extending core StochSS functional capabilities, and (3) improving compatibility with other software via support for standard data formats.
Aim 2. Model Development Toolkit Develop and integrate tools to facilitate and accelerate the process of Model Development: the iterations of (modeling, simulation, experiment) that are typically required to converge on the most plausible model that can explain the data. The Model Development Toolkit will address parameter estimation and quanti cation of uncertainty, generation and evaluation of the set of plausible models, and optimal design of experiments.
Aim 3. Model Exploration Toolkit Develop and integrate tools for Model Exploration: the process of exploring the parameter space to ensure that the model is robust to variations in uncertain and/or undetermined parameters, to nd the regions of parameter space in which the model is capable of yielding a given behavior, and to discover all of the qualitatively distinct behaviors that the model is capable of within the space of uncertain and/or undetermined parameters. 1

Public Health Relevance

The development of a mathematical model is critical to the understanding of complex biological pro- cesses because it codi es current understanding so that it can be tested against existing data. A good model with sucient detail can be used to identify potential points of intervention (for example, drug targets) at which an undesired outcome (for example, e ects of disease) of the process might be altered, however its development requires a long sequence of steps and decisions, often including new experiments to provide additional data. This project proposes to develop an integrated set of tools to support the modeling process, to accelerate scienti c discovery. 1

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
2R01EB014877-04A1
Application #
9449211
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Peng, Grace
Project Start
2012-05-15
Project End
2022-06-30
Budget Start
2018-09-30
Budget End
2019-06-30
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of California Santa Barbara
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Meinecke, Lina (2017) Multiscale Modeling of Diffusion in a Crowded Environment. Bull Math Biol 79:2672-2695
Golkaram, Mahdi; Jang, Jiwon; Hellander, Stefan et al. (2017) The Role of Chromatin Density in Cell Population Heterogeneity during Stem Cell Differentiation. Sci Rep 7:13307
Hellander, Stefan; Hellander, Andreas; Petzold, Linda (2017) Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning. J Chem Phys 147:234101
Hellander, Stefan; Petzold, Linda (2017) Reaction rates for reaction-diffusion kinetics on unstructured meshes. J Chem Phys 146:064101
Kaucka, Marketa; Zikmund, Tomas; Tesarova, Marketa et al. (2017) Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage. Elife 6:
Meinecke, Lina; Engblom, Stefan; Hellander, Andreas et al. (2016) ANALYSIS AND DESIGN OF JUMP COEFFICIENTS IN DISCRETE STOCHASTIC DIFFUSION MODELS. SIAM J Sci Comput 38:A55-A83
Drawert, Brian; Hellander, Stefan; Trogdon, Michael et al. (2016) A framework for discrete stochastic simulation on 3D moving boundary domains. J Chem Phys 145:184113
Hellander, Stefan; Petzold, Linda (2016) Reaction rates for a generalized reaction-diffusion master equation. Phys Rev E 93:013307
Kaucka, Marketa; Ivashkin, Evgeny; Gyllborg, Daniel et al. (2016) Analysis of neural crest-derived clones reveals novel aspects of facial development. Sci Adv 2:e1600060
Golkaram, Mahdi; Hellander, Stefan; Drawert, Brian et al. (2016) Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion. PLoS Comput Biol 12:e1005122

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