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Sarojini Attili Kimberly Taylor Sample D 2 Sample A Sample D Sample B Sample E Sample C 3 Sample A Sample D Sample B Sample E Sample C 4 Sample A Sample D Sample B Sample E Sample C 5 Sample A Sample D Sample B Sample E Sample C Computational model of the human body that integrates all the different whole cell models Computational model of the pathogen 6 Sample A Sample D Sample B Sample E Sample C Details of mutation/s and Phenotypic data Pathogen specific data Computational model of the human body that integrates all the different whole cell models Computational model of the pathogen 7 Sample A Sample D Sample B Sample E Sample C Details of mutation/s and Phenotypic data Pathogen specific data Computational model of the human body that integrates all the different whole cell models Computational model of the pathogen 8 Sample A Sample D Sample B Sample E Sample C Whole cell modeling for personalized medicine Details of mutation/s and Phenotypic data Pathogen specific data Computational model of the human body that integrates all the different whole cell models Computational model of the pathogen 9 Modeling Biological Systems • Significant task of systems biology and mathematical biology • Computational systems biology aims to develop and use Algorithms Data structures Visualization Communication tools • Goal: Perform computer modeling of biological systems. • It involves the use of computer simulations of biological systems to both analyze and visualize the complex connections of these cellular processes. 10 Whole cell modeling Developing a complete model for a specific cell that includes all pathways, processes and functionality. The whole cell model explains the entire lifecycle of the cell. The authors of this paper have modeled the life cycle of individual Mycoplasma genitalium cells. Whole cell modeling although challenging, is very important for the future of medicine. 11 Why whole cell modeling? • Whole cell models comprehensively predict how • • • • phenotypes emerge from genotypes. Whole-cell modeling could enable rational bioengineering and precision medicine. Whole cell models could also enable clinicians to individualize therapy. Combined with genome synthesis and transplantation, whole-cell models could enable bioengineers to produce biofuels. Overall, whole-cell models could be powerful scientific tools. 12 History of whole cell modeling • Beginning in the late 1970s, researchers began modeling cell physiology, primarily using ordinary differential equations, creating increasingly detailed models over the next three decades. • Later on, other groups introduced frameworks that require fewer parameters than ODE systems including constraintbased and Boolean methods. • Combining these approaches the authors of this paper developed a hybrid methodology to model the life cycle of individual Mycoplasma genitalium cells – Individual biological processes were modeled, each with its own mathematical representation and individual outputs were merged to compute the overall state of the cell. 13 Some existing computational models Cardiac system models – The first model developed for the heart was the Hodgkin–Huxley model, today we have more sophisticated models Computational models for different cellular processes or parts of the cell such as: The dynamics of Ca2+ wave propagation during xenopus oocyte maturation Dynamics of calcium sparks and calcium leak in the heart Metabolic model of the mitochondrion 14 Outline Core principles of whole-cell modeling Model construction process outline Example of a whole cell model Challenges to achieving complete models 15 The principles of whole-cell modeling 16 Single cellularity: Whole cell models should represent individual cells. Single cell models can account for how temporal dynamics and stochastic variation affect behavior. Single cells are also tractable because they are independent and directly result from molecular biochemistry. 17 Functional closure: Behavior is determined by interacting pathways and genes. Consequently, whole-cell models should represent every known cellular and gene function. Models which represent every known function are powerful tools. For example, genome-scale metabolic models which represent every known metabolic reaction and enzyme have been used to identify missing reactions and enzymes. 18 Molecular closure: Whole-cell models should represent the cell and its environment as a closed system. Models should explicitly account for exchanges among pathways and the environment and not have arbitrary sources and sinks. Temporal closure: Whole-cell models should also represent the entire cell cycle. This ensures that models account for how cells regulate pathways in time to coordinate their life cycle. For example, models should account for how the dynamics of DNA replication affect dNTP concentrations and metabolism. 19 Biophysics: In addition, whole-cell models should represent cellular biochemistry and biophysics, including mass conservation, thermodynamics, and spatial organization. Some of the methods capable of representing cellular biophysics are - molecular dynamics, Brownian dynamics, latticebased models. Below are some representations of space: 20 Dynamics: S+E In particular, whole-cell models should be constructed from differential descriptions of molecular biochemistry and predict the emergence of cellular-scale dynamics. Emergent dynamics are valuable opportunities for experimental validation and discovery. k1 k-1 C k2 P+E ds k 1c k1se dt de (k 1 k 2 )c k1se dt dc k1se (k 2 k1 )c dt dp k2c dt 21 Stochasticity: Furthermore, whole-cell models should be discrete and stochastic. Stochastic models naturally predict the emergence of cellular variation. For example, stochastic models can account for how stochastic transcription initiation creates variation in gene expression and growth. This variation is another valuable opportunity for experimental validation. Species specificity: Whole-cell models must be evaluated by comparison to experimental data. Consequently, whole-cell models should represent specific genomes. This constrains the space of training data. 22 Parsimony: Despite the explosion in experimental data, limited data is available. For example, there is little data about non-coding RNA. Consequently, models should be parsimonious. This minimizes the need to identify unmeasured parameters. Modularity: Like other large engineered systems, whole-cell models are best developed by combining multiple pathway submodels. This enables submodels to be developed and tested independently by different investigators using different representations. 23 Reproducibility: Finally, whole-cell models should be transparent, well-annotated, and reproducible. Researchers should be able to reproduce models from their primary sources, as well as reproduce simulations using multiple simulators. Models should also be described using transparent languages, this is essential for collaborative modeling. Example: SBML 24 Model construction Experimental curation Mathematical formulation Submodel integration Parameter estimation Model refinement and validation Visualization and analysis 25 Experimental curation: The first step to constructing a model is to choose an organism and assess the feasibility of modeling it by assembling the available experimental knowledge. Some examples of experimental data sources include organism database tools such as Pathway Tools, WholeCellKB, BioMart and Intermine. 26 Mathematical formulation: • A mathematical description of how cells evolve over time must be constructed. • Describing the cell as thoroughly as possible using existing knowledge avoids unknown parameters and expensive computations. • One model can be used for many scientific questions. • Individual submodels must be implemented and/or constructed from experimental data. • Databases like BioModels and CellML contain many existing pathway models. • Rule-based modeling is a powerful and scalable approach for assembling genome-scale models. Some of the tools that can be used to generate mathematical models include: • BioNetGen • BioUML • CellDesigner • CobraPy • COPASI • E-Cell • iBioSim 27 Submodel Integration: • The individual submodels that were developed as part of the mathematical formulation must be combined. • Homogeneous submodels can be merged analytically. • Heterogeneous submodels must be combined in a multistep approach; hybrid simulators have been developed recently which are capable of integrating heterogeneous submodels. 28 Parameter Estimation: Once the model's structure has been implemented, the model's parameters must be identified by matching the model's predictions to experimental data. 29 Model refinement and validation: Next, an important step to constructing a whole-cell model is to iteratively evaluate the model's predictions and refine the model. Predicted phenotypes of genetic perturbations should be evaluated. Methods used to automate model refinement: Robotic and microfluidic experimentation Computational gap filling 30 Visualization and analysis: The last step in whole-cell modeling is to simulate the model, analyze simulation results to construct new hypotheses, and conduct experiments to test those hypotheses. 31 Modelling example Karr, JR et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389-401 One of the most complete and rigorous models to date 32 Modelling of Mycoplasma genitalium Small bacterium isolated from urethra in 1980 Causes urethritis (inflammation of the urethra) in both men and women Implicated in HIV transmission Genome is single circular dsDNA with 525 genes 33 Goals of M. genitalium modelling Describe the complete life cycle of a single cell on level of single molecules Replicate function of every known gene product Predict multiple cellular behaviors, including macromolecular synthesis and the complete cell cycle Models were based on 900+ publications and 1900+ observed parameters 34 How did they do it? Identified 28 modules covering cellular functions All modules independently built, parameterized and tested 35 How did they do it? Modules were assumed independent at time scales < 1 s 36 Model integration 37 Why modules? • Module-based modelling proposed in 1999 – 668 citations in PubMed • Model is based on experimental observations 1. Action potentials 2. Decision making in bacteriophage λ 38 Hartwell et al. (1999) From molecular to modular cell biology. Nature 402: C47-C52 Experimental validation 39 Exploring the cell cycle 40 Global energy analysis 41 Phenotype studies Disruption studies performed for all 525 genes 284 essential genes and 117 non-essential Model allows prediction of phenotype from known genotype 42 Challenges in whole-cell modelling Macklin, DN et al. (2014) The future of whole-cell modeling. Current Opinion in Biotechnology 28:111–115 43 Challenges in whole-cell modeling 44 Experimental interrogation M. genitalium was chosen because of its small genome, but there are relatively few papers on this pathogen Future research will focus on well-studied organisms such as E. coli, S. cerevisiae or Mycoplasma pneumoniae E. coli: 347,790 articles in PubMed (as of 11/12/2015) 4,288 protein-coding genes 45 Data curation Where and how do you store the data? Data for M. genitalium available at http://www.wholecellkb.org/ Automatic curation will be needed Human and machine involvement Separate database for each organism Question: how should the data be formatted? 46 WholeCellKB 47 http://www.wholecellkb.org/ WholeCellKB http://www.wholecellkb.org/ 48 Other considerations Mathematical models could use a Boolean “switch” for communication between modules and sub-modules Cell behavior cannot violate physical laws Model must be consistent with biological phenotypes 49 Accelerated computation ~10 hours were required for each simulation of M. genitalium Simulation of 525 single-gene disruptions required 5250 hours, or ~220 days In theory, a single simulation of E. coli would take 81 days! 50 Speeding up computation High performance parallel computing Custom hardware platforms Could this be an application for quantum computing? 51 Analysis and visualization Extensive analysis of raw data is needed to interpret results Machine learning Dynamic systems analysis How do you visualize large data sets? 52 Model validation Feedback between model and experimental results M. genitalium work was more intuitive than rigorous Quantitative metrics must be developed 53 Collaboration and community development Code base for the M. genitalium whole-cell model released under MIT license Whole cell modelling will proceed faster with collaboration between all researchers Will competing groups want to share their results before publication? Is a uniform format needed? 54 Conclusions Whole-cell modelling has been shown to be successful Validated by experimental results Phenotype predicted from genotype Potential for study of cell processes that cannot be addressed experimentally Improvements in analysis, visualization and technology are needed 55 Future directions Non-bacterial cells Cells with large genomes With current technology, simulation of single human cell (3 billion bp, ~25k genes) would take 476 hours or ~20 days! Cell-cell interactions Modelling of organs and organ systems Modelling of multicellar organisms 56 References Karr, JR et al. (2015) The principles of whole-cell modeling. Current Opinion in Microbiology 27: 18-24 http://www.sciencedirect.com/science/article/pii/S1369 527415000685 Karr, JR et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389-401 http://www.sciencedirect.com/science/article/pii/S0958 166914000251 Macklin, DN et al. (2014) The future of whole-cell modeling. Current Opinion in Biotechnology 28:111–115 http://www.biomedcentral.com/1471-2105/14/253 57 References http://biologypop.com/the-evolution-of-the-cells/ http://www.biomedcentral.com/1752 0509/2/72/figure/F4?highres=y http://oreillyscienceart.com/figures-for-publication/ http://www.elveflow.com/microfluidic-tutorials/cellbiology-imaging-reviews-and-tutorials/microfluidic-forcell-biology/concepts-and-methodologies/ https://openi.nlm.nih.gov/detailedresult.php?img=322438 2_1752-0509-5-155-1&req=4 http://www.biomedcentral.com/1471-2105/14/253 https://www.google.com/imghp 58 Thank you! 59