Transcript Special topics in electrical and systems engineering

ESE 680-003
Special topics in electrical and systems engineering:
Systems Biology
Pappas Kumar Rubin Julius Halász
Roadmap to Systems
Biology
What next?
• Cellular processes come down to
molecular interactions
– Rate laws
– Kinetic constants
– Differential equations
• … so all we need to do is get all the
reactions ,rate laws, constants, put them
into a computer  virtual cell
What next?
• Easier said than done:
– Processes not typically known in detail
– Kinetic constants
• Not measurable/Not measurable in vivo
• Meaningless
– High dimensional, nonlinear systems
• Yet often simple behavior: emergence
– Even if individual processes can be studied,
the cost of going through all of them is
prohibitive
What next?
• Biologists have “told us so”:
– Reductionism doesn’t work
– There are exceptions to all “laws”
– Qualitative descriptions are more meaningful
• Source of limitations
– Experimental input
– Lack of fundamental understanding of
processes
– Lack of appropriate mathematical “language”
What next?
• Systems/quantitative biology today:
– No mathematically expressed principles
– Several qualitative principles
• Robustness
• Redundancy
– Driven by experimental data
– Certain clusters of modeling activity
– Physics, circa 1670 (before Newton)
• Incremental progress on many fronts
• Best approach is to try to be useful to biology
Some of the fronts
•
•
•
•
•
•
•
Genetic network identification
Metabolic networks
Signaling
Cycles (cell, circadian)
Mesoscopic / stochastic phenomena
Synthetic biology
Software tools
Genetic network identification
• Microarrays
– One of the most spectacular advances in
experimental technique
– Typical of “high-throughput” approach
– Made possible by
• Genome sequencing projects of the 1990’s
• Semiconductor, microchip technology
Genetic network identification
• Microarrays
– Chips with a grid of RNA* microprobes
– Each probe has a different sequence*
– Probes represent genes
– Probes hybridize to mRNA from a sample
– Optical (fluorescence) readout
• Parallel measurement of gene expression
– Commercially available for several organisms
• Affymetrix – “the Microsoft of biotechnology”
Gene network identification
• What can we learn from high throughput,
semi-quantitative, perhaps time resolved,
gene expression data?
• Identification of transcription networks
– Ignore all details of interactions
– Focus on the existence of an influence of
Gene A onto Gene B
– Various levels of abstraction, from on/off to
Hill coefficients
Gene network identification
•
•
•
•
Next lecture
Papers by Collins, Liao
A whole industry has been spawned
Lots of room for new ideas coming from
computer science/hybrid systems
• Challenge: connect with biological
knowledge
Metabolic networks
• Another “breadth-first” approach
• Made possible by arduous work of many
postdocs, PubMed, and other databases
• Metabolic reactions curated into
comprehensive databases
• Stoichiometric information on hundreds of
concurrent chemical reactions
• The workings of the chemical factory
Metabolic networks
• The state of the system is the vector of all metabolite
concentrations c.
• Each reaction is represented by an integer vector:
A + B  3X [-1, -1, 3, 0]
2A + B  Y [-2, -1, 0, 1]
• Stoichiometric matrix S
• Vector of reaction rates v
• External fluxes of metabolites f
c  S  v  f
Metabolic networks
• At steady state, c is constant
• The state of the metabolic network is v
• Many possible solutions
– Feasiblity cone
– Which state is picked by nature?
– Determined by unknown kinetic details
• Models postulate optimization principles
Metabolic networks
• Many papers:
– Palsson, Church
• Lecture by Marcin Imielinski (?)
• Lots of linear algebra
Signaling
• Multi-cellular organisms are similar to
highly organized societies
– Every cell has the same genetic information
– Yet they are highly specialized/differentiated
– Widely different phenotypes, functions
– The organism works because each cell does
what it is supposed to
Signaling ensures that cells act properly
Signaling
• In cancer, the signaling machinery breaks
down
– Wrong signals and/or wrong interpretation
– Cells differentiate into the wrong type
– They grow when they are not supposed to
– Stop listening to the system commands
– Take a life of their own (tumors)
Signaling
• Signaling tells cells to do everything
– Lack of certain signals triggers cell suicide (apoptosis)
• Signals are carried by special molecules in the organism
– Hormones, growth factors
• There are specialized receptors on the cell surface
• Receptors transduce signals (binding of their ligand) into
the cytosol (the inside of the cell)
• Signaling cascades originate in the initial binding event
• Complicated networks of multistep phosphorylation
reactions
• Eventually they control gene expression
Signaling
• Signaling malfunctions result from small
mutations
– Lack of signaling
– Uninduced signals
– Over/under- amplification
• A few well studied networks
– EGF Erb/Her
• A few well studied cell lines
Cell signaling
• Huge literature
• Lecture: Avi Ghosh (Drexel)
Mesoscopic phenomena
• Face the reality of small molecule
numbers
• Stochastic nature of reactions
• Well established simulation methods
• Often ignored, wrongly
Mesoscopic phenomena
• A few important results
– Lambda phage (Arkin)
– Lac system (van Oudenaarden)
– Competence (Elowitz)
• Relevant experimental results
– Well delimited, controlled, yet live system
• Lecture by Mustafa Khammash
Cycles
• Complicated control systems
• Make sure that actions are taken in the
correct sequence
• Cell cycle
– Papers by Tyson
• Circadian cycle
– Papers by Doyle
Synthetic biology
• From simple genetic switches
• To tumor killing bacteria
• In between: synthesis of artemisin
(Keasling)
Software
•
•
•
•
•
Large industry
Lots of potential for new work
Largely ten years behind in modeling
Focus on languages standardization,..
Still very important