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SysBio Class Major papers to cover ReiSharp Davidson/Yuh/Bolouri, Endo16 Yeast microarrays: Spellman et al. Ferrell Ideker et al (w. Hood, yeast) Cellerator Yeast ChIP-chip Drosophila, DV modeling: Barkai ? von Dassow et al. Something on scale-free networks Barabasi 48 species paper – Pierre New paper in Science – hierarchies Science, Nature summaries – click through library Repressilator - Liebler Sporulation switch in b. subtilus – Hidde de Jong Qualitative modeling, ODE’s mixed. Java models D. Holloway S. J. Gould book : evodevo thought; also plant evodevo book (reviewed in ~19 Dec 02 Science mag) papers still needed: A. Levchenko, NFkB; Ferrell MAPK; repressilator; von Dassow; Major topics to cover Classification of bio processes Networks: mechanical networks, reaction networks, regulatory networks, dynamic computational networks, (software transformation networks,) … data sources: Pathway databases, p:p interactions, microarray, images, … Arrow translation: intro to mathematical model-building Problem: obtaining the rate constants Schema: reactions, reactants (protein state modifications), knowledge sources, models. Scientific inference systems perspective Observation; hypothesis generation; prediction; experiment Enzyme kinetics schemata MWC Ping-pong reaction schema Gene Ontology Markov Chain modeling Detailed balance Relation to reaction schema Bayes nets, Petri nets, other network formalisms & learning algorithms Developmental modeling Plant development details Evo-devo modeling Major software elements to supply Graphical/numerical environments Matlab or Mathematica or PyNumeric or R or … Cellerator ML/ANN environment SIGMOID Pathway database (OJB or Pymerase or squashed sigmoid or …) distributed computing backbone : CORBA, Java 2, .net, … SBML/SBW Extensions to handle development Data sources to cover Microarray / yeast GRID p:p interactions (K. Lin spreadsheets?) Downloaded KEGG / yeast Final projects list Cellerator I/O Pathway GUI - connect to Sigmoid pathway modeling db, other sources Optimization: connect to Lam Delosme- annealer Translate KEGG into Cellerator and/or Sigmoid Translate GRID into Cellerator and/or Sigmoid circuit inference algorithms (scalable?) probabilistic circuit inference/clustering project ChIP/Chip project: yeast network inference Fly data fitting Plant data fitting Particular pathways to investigate Synapse formation – Kennedy and Sejnowski … PKC, Ca oscillations, PI3K, AKT/cancer metabolism ICS280 Winter 2003 Mjolsness Lecture 1 What is systems biology? Network properties Amplification – sigmoidal I/O curves Feedback-dependent behaviors – oscillation, adaptation, … Nonobvious behaviors from circuit diagram alone Computable models of cell behavior Multiscale modeling What are the objects and relationships to be modeled? DB schema: reactants, processes/reactions, knowledge sources, models, behaviors (observable, selectable phenotypes) Reactants Central dogma: DNA, RNA, protein cells, organelles, membranes, … protein/DNA modifications: phosphorylation, methylation, acetylation, ubiquitination, .. localization to compartments, membranes and regions binding sites: DNA, protein, … Processes and Reactions Metabolic network steps Allosteric enzymes - cooperativity Protein-protein regulatory interactions Transcriptional regulation, with feedback Diffusion, transport, and signaling Processes (we have models and literature) Basic issues: resources, information, replication metabolism Cell cycle – e.g. budding yeast Signal transduction pathways e.g. yeast pheromone response, stress response, … Multicellular development Gene/Signal Regulatory Network e.g. plant growth, Drosophila blastoderm, many others. Knowledge sources Gene expression images Microarray expression data p:p interactions sequence data textual information retrieval on scientific literature map to: Biology mathematics algorithms software biology loop, scientific method automated assistance. Examples Cellerator Demo1 StochCeller SGD SWI4 drill down to sequence, p:p interactions, … STE5 Suitable mathematical modeling formalisms Stochastic vs. deterministic network models Dilute solutions vs. binding sites vs. small# compartments vs. machines Coarse grained: Artificial neural nets Bayes nets Detailed: ODE systems diffusion Particle simulations Langevin equations Software architecture to integrate data sources database model generator simulator optimizer hypothesis formulation e.g. SBML, Cellerator, KEGG, … Issue: How to span the gap between small- and genomic-scale data? Database (of reactions and pathway modeling information) Knowledge representation schema Populate with best available datasets SIGMOID and populating it Model generation: reactions to mathematical models Connection from database Algorithms Simulation ODE solvers Stochastic simulation algorithms Optimization Stability analysis Machine learning Pattern recognition/hypothesis formulation Possibilities for coarse-grained or multiscale models and model reduction Papers and their topic categories reviews, yeast_cell_cycle, pathways, software, networks, development, other; evolution, … Figures used: Central dogma Compartments: nucleus, cytosol, membrane DNA and protein strings with binding sites Phosphorylation reaction 3-element regulatory loop with – feedback (autopilot, thermostat, chemotaxing cell) or + feedback (flip-flop circuit, cell type decision in multicellular org) central dogma with TF feedback Even-skipped GRN figures from my review paper: real, simulated expression patterns equations of motion with state variables, compared to Newtonian gravity Yeast cell cycle microarray expression cluster trajectories – shows noise Plant Shoot Apical Meristem expression imagery – real, and synthetic Lecture 2: How to do it Use of Cellerator Yeast MAPK signaling + feedback Yeast cell cycle Drosophila AP axis Lecture 3: Mathematical models Mass action Homodimeric transcriptional regulation example Michaelis-Menten kinetics Complexes and explosion of states Effects of scaffolding Allosteric enzymes Small scale vs. large scale network models Lecture 4: Stochastic Master Equation 0 B example solve master equation get Poisson distribution stochastic dynamics: deterministic, Langevin/F-P, random # of molecules / compartment, random positions and momenta Lecture 5 Indexing : IP3 receptor; MAPK cascade; static compartmental models; (dynamic) Optimization Bisection search, brents method, etc via simulated annealing Newton’s method, conjugate gradients, BFGS, … Software support for optimization: code generation and SBML approach. Collect solutions and make them public Homework 1 Run Cellerator on your example HW2 Optimize a parameter in your example, to give “ground truth” trajectory data analogous to microarray data create an optimization “engine” for 1 or more params scoring function move generator search algorithm HW3 Create a GUI for model generation Read in pathway data Display and edit it Output in Cellerator form Optional: Run the model Optimize it HW4 Large scale pathway data ingestion HW5??? Cellerator with indices Molecular scale + aggregation Developmental models