Computational tools for whole-cell simulation Cara Haney (Plant Science)

1. Computational tools for whole-cell simulation Cara Haney (Plant Science) E-CELL: software environment for whole-cell simulation Tomita et al. 1999. Bioinformatics 15(1): 72-84 Mathematical simulation and analysis of cellular metabolism and regulation Goryanin et al. 1999. Bioinformatics 15(9): 749-758

2. Questions addressed in E-CELL • Can gene expression, signaling and metabolism be simulated in a manner that will allow one to make predications about a cell? • In simplifying a cell, what functions can be sacrificed? • What is the minimal gene set?

3. Overview • Simple cell based on Mycoplasma genitalium • User can define interactions between proteins, DNA and RNA within the cell, etc. as sets of (first order) reaction rules • User can observe changes in proteins, etc. M. Genitalium www.nature.come/nsu/010222/010222-17.html

4. Running the Program • Lists loaded at runtime: • Substances • Rule list • System List • Calculates change in concentration of substrates over a user-specified time interval • User can select either first-order Euler [error is O(Δt2)] or fourth-order Runge-Kutta [O(Δt5)] integration methods for each compartment

5. Cell Model • Hypothetical minimal cell from M. genitalium • Only genes essential for metabolism • Cell can take up glucose from environment and generates ATP by turning glucose into lactate via glycolysis and fermentation. Lactate is exported from the cell • Transcription and translation modeled by including transcription factors, rRNA, tRNA • Cell takes up glycerol and fatty acids in order to maintain membrane structure • Cell does not replicate

6. Metabolism in the model cell • Includes glycolysis, phospholipid biosynthesis, and transcription and translation metabolisms • Does not include machinery for replication (DNA replication, cell cycle), amino acid/nucleotide synthesis

7. Classes of Objects • Substance • all molecular species within the cell • Genes • Modeled as class GenomicElements with coding sequences, protein binding sites and intergenic spacers • Gene class includes transcribed GenomicElements • 120 (out of 507) M. genetalium. 7 from other organisms. • includes enzymes to recycle nucleotides and amino acids

8. Genes in the cell

9. 6-phosphofructasokinase ATPADP + H+ C0085 + C00002 C00354 + C00008 + C00080 [EC 2.7.1.11] Classes of Objects cont. • Reaction Rules • One substance turned into another via an enzyme D-fructose 6 phosphate D-fructose 1-6 bisphosphate • Can also represent formation of complexes and movement of substances within the cell • No repressors/enhancers (genes are never turned on or off) although user can specify gene regulation • Each protein and mRNA contain equal proportions of aa’s and nucleotides

10. Reaction Kinetics Reactions are modeled from EcoCyc and KEGG Non-enzymatic reactions: v = k • Π [Si]vi Enzymatic Reactions (Mechaelis-Menton): Vmax• [S] [S] + Km Also works for a number of substrates and products or formation/degredation of molecular complexes J-1 i v =

11. Virtual Experiments ATP initially increases ‘Starve’ cell by decreasing glucose Level of ATP plummets: cell dies

12. Changes in mRNA levels upon drop of ATP due to Glucose Deprivation

13. Applications • Optimization of culture systems • Minimal gene set • Discover new gene functions • Model more complex organisms • Genetic engineering • Drugs

14. The good and the bad • As is, can it tell us anything about the cell? • No repressors/enhancers (genes are not turned on or off) • Cell cannot replicate • No aa/nucleotide biosynthesis • Even modified, can it really tell us anything new?

15. Mathematical simulation and analysis of cellular metabolism and regulation • Interface for dealing with systems of differential equations. • Enter a matrix of equations, has ODE (ordinary differential equation) solver • In order to use this for biological applications: • Assumes genome has been sequenced, have gene networks and differential equations of how one gene influences another over time. • Need array of equations specifying how gene A changes with respect to gene B

16. Features • Evaluates over long period of time until steady state is reached within the ‘cell’ • Determine relative levels of proteins within a cell • Explicit solver • If it is known how much energy is being consumed from these genes undergoing given reactions • Implicit solver • If gene X doubles expression, how are all other genes affected? • Can plot change in GeneY as GeneX changes

17. More Features • Bifurcation Analysis • Chaos, multiple steady states may exist. • Bifurcation points—points where a slight shift in one substance may cause drastic change in steady state • Experimental data • Fit your model to experimental data to try and find the best steady state.

18. Problems • “It is now feasible to generate a complete metabolic model where complete genome data are available” hmm… • Data available is not there at whole cell level. • Even if all data is available, can we solve a 6,000 x 6,000 matrix? • Just using isolated pathways is this useful?

19. Comparison between two systems • Similarities • Both use similar approaches to looking at the dynamics of a cell. • Both make it possible to ‘knock out’ genes • Can make plots to observe changes • Differences • E-CELL starts from the ground up; builds cell as things are discovered. Math. Sim. Assumes information is there • E-CELL only useful for M. genetalium; Can use Math. Sim for any organism and adjust based on experimental data.