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• Classic-Fiction

MacRae, Norman, 125

macrophage, 9

macrostate, 49–51, 54, 101, 307

Macy foundation meetings, 295–297

majority classification task, 160–161

cellular automaton evolved for, 162–164, 171

Malthus, Thomas, 76

Mandelbrot, Benoit, 103, 271–272

master genes, 278–281

Mathematica, 154, 158

Matthew, Patrick, 78

Maturana, Humberto, 298

Maxwell, James Clerk, 20, 43–47

Maxwell’s demon, 43–47, 169

as example of idea model, 211

Maxwell’s equations, 43, 210

May, Robert, 28, 33, 219–220, 223

Mayr, Ernst, 87

McCulloch, Warren, 296–297

McShea, Daniel, 110, 288

Mead, Margaret, 296–297

meaning (in complex systems), 171, 184, 208

mechanics, classical, 19, 48

meiosis, 88–89

Mendel, Gregor, 79–81

ideas considered as opposed to Darwin’s, 81–82

Mendelian inheritance, 79–81, 89, 276

messenger RNA, 90–93, 122, 275

metabolic pathways, 178–179, 249

feedback in, 181–182

metabolic networks, 110, 229, 249–250, 254

metabolic rate, 258–262, 265–267

scaling of (see metabolic scaling theory)

metabolic scaling theory, 264–266

controversy about, 267–269

as example of common principles in complex systems, 294–295

scope of, 266–267

metabolism, 79, 110, 116, 178–184, 249,

information processing (or computation) in, 178–185

rate of, 258–262, 265–267

as requisite for life, 116

scaling of (see metabolic scaling theory)

metanorms model, 219, 222–224

Metropolis, Nicholas, 28, 35–36

Michelson, Albert, ix

microstate, 49–51, 54, 307

microworld, 191

letter-string, 191–193

Milgram, Stanley, 227–229

Millay, Edna St. Vincent, 289

Miller, George, 272

Miller, John, 94

Minsky, Marvin, 187

MIT (Massachusetts Institute of Technology) Artificial Intelligence Lab, 190

mitosis, 88–8, 92

mobile genetic elements, 275

models, 209–210

computer (see computer models)

idea (see idea models)

mathematical, 25

Modern Synthesis, 81–84

challenges to, 84–87

molecular revolution, 274

Moravec, Hans, 123

Morgan, Thomas Hunt, 89

Mott, Keith, 168

mRNA, 90–93, 122, 275

Mukherji, Arijit, 223

mutations

in DNA, 89, 93

in genetic algorithms, 129

in the immune system, 9, 174–175, 181

via “jumping genes,” 275

knockout, 140

role in Evo-Devo, 280

role in Modern Synthesis, 83

mutation theory, 81

Myrberg, P. J., 35

natural selection

challenges to primacy of, 85–87, 285–288, 300

in Darwinian evolution, 72, 77–79

in immune system, 9, 175

in Modern Synthesis, 83

relation to meaning, 184

versus random genetic drift, 82–83

near-decomposability, 109–110

negative selection, 176

networks

clustering in, 235–236, 238–240, 245, 252, 255

definition of, 234

degree distribution of, 235

examples of, 229–230, 234–236, 247–251

hubs in, 236, 240, 245, 248, 250, 252

information spreading in, 255–258

path-length in, 237–239, 245, 257, 318

regular, 236–239

resilience in, 245–246

See also genetic regulatory networks

metabolic networks

random Boolean networks

scale-free networks

scientific citation networks

social networks

small-world networks

neurons, 6–7, 15, 189

information processing with, 161, 168

McCulloch and Pitts model of, 297

as network nodes, 229, 238, 247–248

neutral evolution (theory of), 86

New Energy Finance, 222

New Kind of Science, A (Stephen Wolfram), 156–159

Newman, Max, 60

Newton, Isaac, ix, 17–19

invention of calculus, 302

lack of definition of force, 95

law of gravity, 209–210, 269

laws, 19

Newton’s laws, 19

New York Stock Exchange, 11

Nicolis, Grégoire, 298

Nirenberg, Marshall, 93

noncoding regions, 96. See also junk DNA; genetic switches

noncoding RNA, 276, 279

noncomputable problem (or process), 157–158. See also uncomputability

nonlinearity, 22–27, 300

of genes, 276–277

non-von-Neumann-style architecture, 149, 151, 171

normal (or Gaussian) distribution, 243–244, 269

norms, social, 218–219

norms model, 218–219, 222

Galan and Izquierdo