Complexity and Systems in Medicine

Question 1

Properties of complex systems

Answer 1

complex collective behaviour
- large network of individual components
- simple rules defie behaviour, no control or leader
signalling and information processing
adaptation
- change in behaviour

Question 2

defnition of complex systems

Answer 2

• a system in which large networks of components with no central control and simple rules give rise to complex collecitve behaviour, sophisticated information processing and adaptation via learning and evolution
• a system that exhibits nontrivial emergent and self-organizing behaviours

Question 3

notions of emergence

Answer 3

• much coming form little

- the whole is more than its parts

Question 4

Emergence and complexity

Answer 4

small number of rules can generate systems of suprising complexity
→ generate complexity, which lead to perpetual novelity and emergence

Question 5

Hallmarks of Emergence

Answer 5

Mechanism & perpetual novelty
dynamics & regularities
hierarchical organizaiton

Question 6

order at the edge of chaos

Answer 6

adpatations → “poised” state near boundary btw order and chaos
- this state optimizes complexity of the task & the system’s evolvability

Question 7

simple rules generate complexity

Answer 7

simple local rules describing behaviour of individual components → enough to generate extremely complex strucutres/ behaviour

Question 8

Modelling of complex systems

Answer 8

• use methods that take their characteristics into account

- modelling of individual components, their interaction btw each other and with the environment

Question 9

cellular automata

Answer 9

Discrete lattice of cells: L∈Z^d with d≥1
Finite set of cell states: S={0,1,…,k-1}⊆Z_k with k≥2 integers
Interaction neighbourhood of cells: N(ci )={c(i-1),c(i+1) } for L∈Z^1 and r=1
State-transition function of cells: ai^(t+1)=ϕ[a(i-1),ai,a(i+1)]

Question 10

boundary conditions

Answer 10

• topic/domain related to boundary condition
• periodic condition → what happens at boarder
• dirichlet condition → speficies solution values at boundaries
• neuman condition → specifies derivatives of solutions

Question 11

Simple 1D cellular automation

Answer 11

class 1: stable
class 2: oscillating → repetition of regular patterns
class 3: random → produce decent randomness
class 4: complex → some regularities but unclear when they appear

Question 12

Systems biology

Answer 12

making sense of complex observations/ experimental & clinical datasets to improve understanding of diseases & their treatments w/o putting aside context in which they appear/develop

Question 13

systems medicine

Answer 13

measureable imporvement of patient health though system-based approaches & practice

Question 14

goals of systems medicien

Answer 14

practical goal: related to diagnosis, prognosis or treatments
theoretical goal: discovering mechanisms

Question 15

systems medicine: differences to systems biology

Answer 15

data more divers → includes sub-cellular molecular data, higher level clinical variables & environmental features

Question 16

Typical characteristics of systems medicine

Answer 16

Individualized: precise personalizes → group patients & develop new treatments
Integrative: bring all data & levels together → objective & subjective, dynamic & snapshot
Predicitve: try to make predictions via model which arise form integrative step → forecasting models & physiological models
Participatory: empowerment of the patient

Question 17

SIR model

Answer 17

S(t) = susceptible but not yet infected
I(t) = number of infectious individuals
R(t) = indivuals recovered form disease with immunity