Top-Down / Bottom Up Modeling Approaches
Top Down Approach:
- From OMICS via statistics to prediction
Bottom-up Approach:
- From molecular interactions cell behavior
Holistic vs Reductionist Approach -> Criticism
Top- Down Approach:
* Violates individuality and locality
* How to gain biological knowledge?
Bottom-Up Approach:
* How to scale to large networks?
* How to infer cell/organ/tissue behavior?
* “Take the world apart without an idea how to put it back together again.”
Transnational Dilemma: How can Systems Biology be translated into actionable knowledge that can be used to aid mankind?
Systems Medicine
Approach to integrate disjoint concepts for personalized medicine
Combines Modeling (Molecular Function, Bioinformatics, Systems Biology), Big Data (Biostatistics, Biomathematics, Computer Science) and Medical Informatics (Medical Computer Science) together with Ethics and Economics
Why are biological systems so complex?
- Biological Systems have function and a cause
-> Survival and reproduction
->Adapt to environment - Physics:
-> cause and eect temporally distinct - Biology:
-> systems are teleonomic => adapted towards the future
-> cyclic feedback - Biological Systems have an irreducible complexity
-> emergence hinders interpolation between scales - Biological Systems need to be robust
-> N ways of perturbations => N+1 ways of control
-> ‘simplicity through complexity’
Complexity for Simplicity
- Human cell: 20,000 genes => all regulated => on/o * 220,000states: more than atoms in the universe.
- A cell can do only 4 things:
-> Grow/Divide
-> Migrate
-> Die
-> Differentiate
Emergence via Self-organization
- spontaneous organization of macroscopic order
- Local interactions lead to global patterns,
- no external guide (watchmaker)
- Emergence: system acquires new properties that cannot be understood by superposition of individual contributions
=> global behavior from local interactions
ORGANIZATION
each element acts well defined upon given orders to produce output
SELF-ORGANIZATION
each element acts without external orders, yet with mutual understanding
Simulating Complex Systems - Cellular Automata
- Discrete model to study complex behavior
- Regular grid with “nite # of states
- Temporal evolution via “nite # of update rules
- Update depends on neighborhood
- Can be universal, i.e. emulate any system
black -> yellow -> red
-> change if 1, 2, 3, 4 neighbors of same color
Conclusions on the idea of Systems Biology
- Systems Biology wants to make Biology and Medicine a quantitative science
- Many technical advances: sequencing, proteomics, imaging etc.
- Still translational gap from molecular interactions to medical advances
- Systems Theory might bridge the gap
-> “Simplicity through Complexity” - Idea:
-> Consider small chemical systems & derive properties
-> Scale up to large systems & deduce properties
-> Show how to deal with large “Omics” data
Cell chemistry
- Cells consist of 70% water: chemistry in aqueous reactions, carbon based
- Reactions between molecules
-> cluster of atoms held together by covalent bonds - Molecule Mass: 1 Da (Dalton) => approx. mass of 1 hydrogen atom
- Mole: amount of substance (a base unit)
- 1 M is NA = 6.022 1023 molecules/atoms
- Molar weight
molar mass = kg/mole - Molar concentration (molarity)
1 M = 1 mol/Liter
Our first Model - Reaction Kinetics
- Let S be a species of molecules
- Let pressure p, Volume V and Temperature T be constant
- Then: # of molecular collisions per unit time between any two molecules is constant (Kinetic theory of Gases, Boltzmann, 1877)
- Average number of molecules S = n
delta n ca n delta t - or delta n = k · n delta t
where k is the kinetic or rate constant - Rearranging and taking the limit
lim delta n/ delta t =dn/dt =k·n - Leads to Ordinary Dierential Equation (ODE) => our “rst “model”
The Law of Mass Action
- Reaction rate proportional to probability of reactant collisions
- Collision probability is proportional to reactant concentration to the power of the molecularity
Enzyme Dynamics
- Proteins that catalyze reactions
- Reduce activation energy
- Catalyst is not used up
- Works via reduction of electrical repulsion, breaking of bonds
- Highly speci”c, work on substrates
- Increase reaction rate by factor 107
- Enzyme concentration small => exception MAPK- Cascade
Activation / Inhibition of Reactions
- Catalyst: lowers activation energy of chemical reaction
- Enzyme: cellular catalyst, works via temporal binding to substrate
-> # of free enzyme + # of bound enzyme = constant - Effector molecules:
-> enzyme activity depends on temperature, pH or effector molecules
-> effectors bind to enzymes to inhibit/activate activity
-> Competitive inhibition: inhibitor and substrate fight for active site of enzyme - Non-competitive inhibition: inhibitor binds to allostertic site, enzyme inactive by structural change => allosteric control (can also enhance enzyme activity)
-> un-competitive inhibition: inhibitor binds substrate-enzyme complex
