Stuart Kauffman: Cellular metabolism is controlled by a dynamic network, where the activity of some genes and molecules affects the activity of other constituents of the network. It is constructed from unreliable parts and is subjected to mutations, yet it behaves in a robust and reliable manner.
Is this order and stability the result of natural selection? Kauffman: not entirely. There is a statistical tendency toward order and self-organization. Natural selection acts on self-organizing systems, rather than creating them. Without an innate tendency toward order, almost all mutations would be fatal.
MODELING CELLULAR METABOLISM AS A BOOLEAN NETWORK Gate ≡ Gene or other metabolic element State ≡ Active/Inactive (1 or 0) Inputs ≡ Regulators
DYNAMICS At any time Each gate is in state 0 or 1. Then, each gate reads states of its inputs, say and its state at time becomes.
STATE SPACE State of network at time is the vector of states of all gates.
The limit cycle describes the long-term behavior of the genomic network. In a multicellular organism, it corresponds to the cell type after differentiation.
KAUFFMAN’S EXPERIMENTS WITH RANDOM NETWORKS Number of inputs to each gate Number of gates For each gate: 1.Choose its function from the Boolean functions of arguments uniformly at random (u.a.r) 2.Choose its inputs u.a.r. 3.Choose its starting state u.a.r. Run the network deterministically
CLASSIFICATION OF BEHAVIOR Ordered: 1.Most gates stabilize (stop changing state) quickly. 2.Most gates can be perturbed without affecting the limit cycle entered. 3.Limit cycle is small. Chaotic: 1.Many unstable gates. 2.Sensitivity to initial conditions. 3.Large limit cycle.
Ordered behavior is characteristic of genomic and metabolic networks: they quickly settle down into periodic patterns of activity that resist disturbance. Chaotic behavior is characteristic of many non-biological complex systems: sensitivity to initial conditions, long transients, and very large limit cycles (strange attractors).
RESULTS OF KAUFFMAN’S SIMULATIONS When, the network behaved chaotically. When, the network exhibited stable behavior. Specifically, limit cycle sizes were when and when. This is analogous to a phase transition in dynamical systems.
Was the ordered behavior of networks due to the high proportion of constant Boolean functions? A Boolean function is constant if it ignores its inputs and always outputs the same value, i.e., or Two out of the two argument Boolean functions are constant, so about 1/8 of the gates will be assigned constant Boolean functions.
Kauffman also ran simulations of networks without constant gates: for each gate, choose its function from the 14 non- constant Boolean functions of two arguments. Simulations indicated that behavior was similar to random Boolean networks that used all 16 Boolean functions of two arguments.
Can these results be taken as evidence that: Biological systems exist at the edge of chaos? Self-organization occurs spontaneously in living systems? Other researchers have made similar claims: –Bak (self-organized criticality) –Langton –Packard –Wolfram
MATHEMATICS OF RANDOM BOOLEAN NETWORKS The behavior of random networks when or was already known: networks consist of disjoint cycles of 1- input gates (identity, negation, and constant gates). They are very stable in all three senses. networks behave like random functions on elements. They are very unstable in all three senses. Average state cycle size is
MORE RECENT RESULTS We define a very general class of random Boolean networks that includes the networks as special cases. We obtain partial results about the three measures of order on networks in this class. Some of our results corroborate Kauffman’s simulations, but some do not.
DEFINITION OF RANDOM BOOLEAN NETWORK Let be an ordering of all finite Boolean functions. For each, let be a probability, and let be the number of arguments of. We need some symmetry conditions: whenever and are the same functions, but with re-ordered arguments, or. Also and (mean and variance of the number of arguments is finite).
CONSTRUCTING A RANDOM BOOLEAN NETWORK WITH GATES For each gate, 1.Assign a Boolean function to, where is the probability that is assigned to. 2.Choose the inputs to uniformly at random. 3.Choose the initial state of uniformly at random.
This kind of random Boolean network includes as special cases: Kauffman’s random networks Networks with classical random graph topology (Erdős and Rényi) and edge probability Networks with power law degree distribution,, or smallworld topology
DEFINITIONS A gate is forced to in steps if its state at any time is, regardless of the initial state of the network.
Specifically, let be the Boolean function assigned to. is forced to in steps if is the constant function. Recursively, is forced to in steps if, letting be its inputs, there is a set such that for every, is forced to in steps, and for every satisfying for all,.
For example: Note that is forced in steps implies stabilizes within steps. Λ Forced to 1 in t steps Forced to 1 in t+1 steps
Let be a gate in a Boolean network with gates. Let be the initial state of the network, and. We say that is -weak on input if the state of the network at time is not affected by changing the state of at time. Note that is -weak on input implies that perturbing on input will not affect the limit cycle.
We will give estimates on the number of gates that are forced in steps and –weak gates, where, based on the distribution. In the cases where almost all gates are forced in steps and are –weak, this implies two forms of ordered behavior: most gates will stabilize and most gates can be perturbed without affecting the limit cycle,. In some cases we also have estimates on the limit cycle size.
A PROPERTY OF BOOLEAN FUNCTIONS Let be a Boolean function with arguments. Let be a sequence of ’s and ’s. For we say that argument affects on input if where when and.
EXAMPLES Argument 2 affects on input but not on input. Argument 1 affects on all inputs.
Let is the average number of arguments that affect on a random input. Then is the average number of arguments that affect a random Boolean function on a random input.
IS A THRESHOLD FOR FORCED AND WEAK GATES There is a constant determined by the distribution, such that If, then with high probability almost all gates are forced in steps and are -weak. If, then with high probability there are at least gates that are not forced in steps and at least gates that are not -weak, where is a constant determined by the distribution.
APPLICATIONS TO NETWORKS When all 16 Boolean functions of two arguments are equally likely, most gates stabilize and are weak. When only the 14 non-constant Boolean functions of two arguments are used, instability and sensitivity to initial conditions in the first steps.
RESULTS ON LIMIT CYCLES IN NETWORKS When, with high probability, the limit cycle size is bounded by a constant. But when, with high probability, the limit cycle is larger than any polynomial in.
COMPARISON TO KAUFFMAN’S SIMULATIONS When all 16 2-argument Boolean functions are equally likely,. –Agreement regarding sensitivity to initial conditions and stable gates. –Disagreement over size of limit cycles: superpolynomial vs.. When only the 14 non-constant functions are used,. –Our results show disorder in the first steps. –But simulations behaved like the case with all 16 Boolean functions. –This is not necessarily disagreement: the network may settle down into ordered behavior after steps.
SOME PROOF IDEAS I. FORCED GATES The in-neighborhood of radius of almost all gates is a tree. AN IN-NEIGHBORHOOD OF RADIUS 3
Let be the in-neighborhood of a gate. Assuming is a tree, we extend the notion of “affects” to gates in : Gate affects itself if its Boolean function is not a constant. Recursively, assume the notion of “affects” has been extended to all gates in that are a distance from. Let be such a gate, be its Boolean function, and its inputs. For, affects if its corresponding argument affects.
Still assuming is a tree, is forced in steps there does not exist a gate in that affects.
The recursive definition of “affects” defines a branching process: –For each gate in that affects, its children are its inputs that affect. The expected number of children is.
By branching process theory: –If then with probability 1 the process becomes extinct with high probability, almost all gates are not affected by any gate in their in-neighborhood they are forced in steps. –If then with probability 1 the process will not become extinct with high probability, approximately gates are affected by some gate in their in-neighborhood they are not forced in steps.
II. WEAK GATES The out-neighborhood of radius of almost all gates is a tree. This means that the effect of most perturbations is approximated by a branching process.
Assume that the out-neighborhood of a gate is a tree. The probability that perturbing a gate in this out-neighborhood affects of its children is the expected number of children affected by the gate is.
Again by branching process theory: –If then with probability 1 the process becomes extinct with high probability, for almost all gates, the effect of the perturbation disappears. –If then with probability the process will not become extinct with high probability, for approximately gates, the effect of a perturbation will persist for at least steps.
OPEN PROBLEMS Do many perturbations of the initial state cause permanent changes in the state when ? What is the limit cycle size when ? Networks with external inputs and outputs: –What kinds of functions can be computed? –Is a region where complex functions are computed?
More general kinds of networks: –Real-valued states of gates –Asynchronous dynamics –Continuous dynamics –Probabilistic dynamics
SUMMARY Simulations of complex systems may not be reliable. If possible, they should be verified with analytic results. Dynamical systems approach needs to be applied to more realistic, detailed models. –In particular, the notion of “complexity at the edge of chaos” should be tested against specific systems, such as models of self-assembling membranes or other cellular organelles.
More generally, we’ve presented a form of Individual-Based Model, where the individuals are gates, the population is described by the functions of the gates and their connections, and we are interested in statistical properties of its dynamics.
Individual-Based Models are now being used to model populations at all scales in biology: –Bray & Firth: StochSim stochastic simulator of molecular reactions –Romey: fish & whirligigs Can a theory of the dynamics of Individual- Based Models be developed?
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