www.cs.nott.ac.uk/~nxk ECSB II, Sant Feliu de Guixols, Spain /94 Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science and Information Technology Centre for Integrative Systems Biology School of Biology Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham A Gentle Introduction to Executable Biology 1
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www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Natalio KrasnogorASAP - Interdisciplinary Optimisation LaboratorySchool of Computer Science and Information Technology
Centre for Integrative Systems BiologySchool of Biology
Centre for Healthcare Associated InfectionsInstitute of Infection, Immunity & Inflammation
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
InfoBioticswww.infobiotic.net
The utilisation of cutting-edge information processing techniques for biological modelling and synthesis
The understanding of life itself as multi-scale (Spatial/Temporal) information processing systems
Composed of 3 key components: Executable Biology (or other modeling techniques) Automated Model and Parameter Estimation Model Checking (and other formal analysis)
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
There are good reasons to think that information processing is an enabling viewpoint when modeling living systems
Life as we know is:• coded in discrete units (DNA, RNA, Proteins)• combinatorially assembles interactions (DNA-RNA, DNA-Proteins,RNA-Proteins , etc) through evolution and self-organisation• Life emerges from these interacting parts• Information is:
• transported in time (heredity, memory e.g. neural, immune system, etc)• transported in space (molecular transport processes, channels, pumps, etc)
• Transport in time = storage/memory a computational process• Transport in space = communication a computational process• Signal Transduction = processing a computational process
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Cells
Colonies
Modeling in Systems & Synthetic Biology
Networks
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Systems Biology Synthetic Biology
• Understanding• Integration• Prediction• Life as it is
•Control• Design• Engineering•Life as it could be
Computational modelling toelucidate and characterisemodular patterns exhibitingrobustness, signal filtering,amplification, adaption, error correction, etc.
Computational modelling toengineer and evaluate possible cellular designsexhibiting a desiredbehaviour by combining well studied and characterised cellular modules
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
It is a hard process to design suitable models in systems/synthetic biology where one has to consider the choice of the model structure and model parameters at different points repeatedly.
Some use of computer simulation has been mainly focused on the computation of the corresponding dynamics for a given model structure and model parameters.
Ultimate goal: for a new biological system (spec) one would like to estimate the model structure and model parameters (that match reality/constructible) simultaneously and automatically.
Models should be clear & understandable to the biologist
Set of equations showing relationships between molecular quantities and how they change over time.They are approximated numerically. (I.e. Ordinary Differential Equations, PDEs, etc)
Operational Semantics Models:Algorithm (list of instructions) executable by an abstract machine whose computation resembles the behaviour of the system under study.(I.e. Finite State Machine)
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Jasmin Fisher and Thomas Henzinger. Executable cell biology. Nature Biotechnology, 25, 11, 1239-1249 (2008)
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Stochasticity in Cellular Systems Most commonly recognised sources of noise in cellular system are low
number of molecules and slow molecular interactions.
Over 80% of genes in E. coli express fewer than a hundred proteins per cell.
Mesoscopic, discrete and stochastic approaches are more suitable: Only relevant molecules are taken into account. Focus on the statistics of the molecular interactions and how often they
take place.
Mads Karn et al. Stochasticity in Gene Expression: From Theories to Phenotypes. Nature Reviews, 6, 451-464 (2005)Purnananda Guptasarma. Does replication-induced transcription regulate synthesis of the myriad low copy number poteins of E. Coli. BioEssays, 17, 11, 987-997
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Molecular Interactions Comprehensive and relevant rule-based schema
for the most common molecular interactions taking place in living cells.
Transformation/Degradation Complex Formation and Dissociation Diffusion in / out Binding and Debinding Recruitment and Releasing Transcription Factor Binding/Debinding Transcription/Translation
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Stochastic P Systems Gillespie Algorithm (SSA) generates trajectories of a stochastic
system consisting of modified for multiple compartments/volumes:
1) A stochastic constant is associated with each rule.2) A propensity is computed for each rule by multiplying the
stochastic constant by the number of distinct possible combinations of the elements on the left hand side of the rule.
3) The rule to apply j0 and the waiting time τ for its application are computed by generating two random numbers r1,r2 ~ U(0,1) and using the formulas:
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F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Modularity in Gene Regulatory Networks
According to E. Davidson functional cis-regulatory modules are nonrandom clusters of target binding sites for transcription factors regulating the same gene or operon.
A library of modules corresponding to promoters of well studied genes. The activity of these promoters have been modelled mechanistically in terms of rewriting rules representing TF binding and debinding and transcription initiation.
E. Davidson, The Regulatory Genome, Gene Regulatory Networks in Development and Evolution, Elsevier.
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Representing transcriptional fusions and synthetic gene regulatory networks
Variables in our modules can be instantiated with the name of specific genes to represent a construct where the gene is fused to the promoter or cluster of TF binding sites modelled by the module.
These genes can in turn codify other TFs that can interact with other modules producing a synthetic gene regulatory network.
F. J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, and N. Krasnogor. Modular assembly of cell systems biology models using p systems. International Journal of Foundations of Computer Science, 2009
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Multi-component negative-feedback oscillator
Mathematical model− Xc = [mRNA in cytosol]− Yc = [protein in cytosol]− Xn = [mRNA in nucleus]− Yn = [protein in nucleus]− E = [total protease]− p = “integer indicating
whether Y binds to DNA as a monomer, trimer, or so on”
Executable Biology makes this more obvious:
we can vary the value of p and the sequence of binding...
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Model Checking on the Pulse Generator
The simulation of the Pulse Generator show some interesting properties that were subsequently analysed using model checking.
Due to the complexity of the system (state space explosion) we perform approximate model checking with a precision of 0.01 and a confidence of 0.001 which needed to run 100000 simulations.
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Model Checking on the Pulse Generator
The simulations show that although the number of signals reaches eventually the same level in all the cells in the lattice those cells that are far from the sending cells produce fewer number of GFP molecules.
The difference between cells close to and far from the sending cells is the rate of increase of the signal AHL.
We study the effect of the rate of increase of the signal AHL in the number of GFP produced.
S. Basu, R. Mehreja, et al. Spatiotemporal control of gene expression with pulse generating networks, PNAS, 101, 6355-6360
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Finally, assuming that for a cell to be fluorescence it needs to have a given number of GFP for an appreciable period of time we studied the expected amount of time a cell have more than 50 GFP molecules during the first 60 minutes after the signals arrive to the cell.
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Towards a synthetic cell from the bottom up
Biocompatible vesicles as long-circulating carriers Polymer self-assembly into higher-order structures Cell-mimics with hydrophobic ‘cell-wall’ and glycosylated
surfaces Potential for cross-talk with biological cells
Pasparakis, G. Angew Chem Int Ed. 2008 47 (26), 4847-4850
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Dissipative Particle Dynamics First introduced by Hoogerbrugge and Koelmann in 1992. Statistical mechanics of the model derived by espanol and warren in
1995. A coarse graining approach is used so that one simulation particle
represents a number of real molecules of a given type. Since the timescale at which interactions occur is longer than in MD,
fewer time-steps are required to simulation the same period of real time. The short force cut-off radius enables optimisation of the force calculation
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Case Study One: Vesicle Diffusion
Polar heads
Non polar tails
Pores
J. Smaldon, J. Blake, D. Lancet, and N. Krasnogor. A multi-scaled approach to artificial life simulation with p systems and dissipative particle dynamics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Publisher, 2008.
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Case Study One: Vesicle DiffusionTagged solvent particles were placed within the liposome inner volume, the change in concentration due to diffusion of solvent through the membrane pores was measures
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Case Study Two: Liposome LogicOR gate results for different inputs: (¬X,¬Y) (¬X,Y) (X,¬Y) (X,Y)
J. Smaldon, N. Krasnogor, M. Gheorghe, and A. Cameron. Liposome logic. In Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), 2009
www.cs.nott.ac.uk/~nxkECSB II, Sant Feliu de Guixols, Spain /94
Computational models can thus be executed (quite a few tools out there, lots still missing)
Quantitative VS qualitative modelling: computational models can be very useful even when not every detail about a system is known.
Missing Parameters/model structures can sometimes be fitted with of-the-shelf optimisation strategies (e.g. COPASI, GAs, etc)
Computational models can be analysed by model checking: thus they can be used for testing hypothesis and expanding experimental data in a principled way