Bayesian Computation Methods for Inference in Stochastic Kinetic Models

<div>Posterior mean, <span class="nowrap"><svg height="14.7625pt" id="M340" style="vertical-align:-5.47417pt" version="1.1" viewbox="-0.0498162 -9.28833 68.9373 14.7625" width="68.9373pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,.157,0)"><path d="M470 557C393 591 314 624 246 666H224C156 624 76 591 0 557L12 532C86 550 163 573 235 596C309 572 384 550 458 532L470 557Z"></path></g><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M494 0V32C450 38 426 53 393 105L296 260C330 305 354 338 375 361C411 396 421 401 476 410V442H293V410C332 402 337 394 323 371C311 351 295 324 274 293L227 371C213 393 218 402 252 410V442H16V410C70 402 80 391 108 344L196 199C166 159 140 123 115 95C81 54 61 43 7 32V0H196V32C156 41 150 53 167 81C182 105 199 138 219 166L269 79C287 49 283 38 245 32V0H494Z"></path></g><g transform="matrix(.013,0,0,-0.013,10.078,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,21.341,0)"><path d="M593 650H142L136 622C219 616 224 611 209 531L130 120C114 39 108 34 23 28L17 0H501C518 32 557 130 568 158L540 169C519 132 495 93 469 70C439 43 409 35 332 35C272 35 241 37 225 50C211 61 211 89 222 149L253 320H350C441 320 443 312 441 239H471L510 434H481C455 366 448 358 360 358H260L303 572C311 613 312 615 348 615H431C509 615 522 603 532 581C544 555 546 531 545 491L574 493C583 555 592 631 593 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,30.458,2.392)"><path d="M490 563C409 598 330 631 258 673H233C161 631 81 597 0 563L14 533C89 551 168 576 245 598C320 575 400 551 476 533L490 563Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,29.284,3.132)"><path d="M573 302C573 402 527 451 414 451C386 451 343 446 313 437L330 513L320 522C295 508 261 484 243 463L230 415C194 400 159 383 126 359L131 330C159 344 187 357 222 368L109 -147C96 -204 80 -214 18 -223L13 -255L256 -244L259 -212L236 -210C184 -205 180 -195 191 -141L219 -1C240 -10 268 -12 284 -12C352 4 431 48 484 104C543 166 573 240 573 302ZM481 290C481 165 381 37 305 37C280 37 249 56 235 71L302 395C328 399 353 402 372 402C427 402 481 378 481 290Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,34.737,3.132)"><path d="M309 -142C211 -61 151 99 151 271S210 601 309 683L288 710C141 611 75 451 75 272V271C75 90 141 -72 288 -169L309 -142Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,37.995,3.132)"><path d="M504 0V39C460 43 432 59 396 115L305 260C341 307 363 339 385 361C421 395 430 399 486 407V445H300V407C336 400 341 393 327 371C316 353 300 326 279 297L233 369C219 392 225 400 257 407V445H17V407C71 400 83 390 111 344L198 202C167 161 142 127 116 98C84 60 61 48 8 39V0H200V39C161 46 159 58 174 84C187 105 205 138 224 164L273 83C291 54 287 44 249 39V0H504Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,42.582,3.132)"><path d="M167 -163V704H102V-163H167Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,45.029,3.132)"><path d="M492 445H306V408C355 399 365 387 354 356C337 306 301 199 279 149L206 365C196 394 203 400 246 408V445H-7V408C42 400 55 390 73 347C90 308 146 165 199 20C204 6 205 -9 200 -25C197 -37 191 -47 181 -65C164 -93 140 -112 101 -132C81 -145 73 -154 73 -172C73 -210 117 -238 147 -238C164 -238 178 -233 185 -211C204 -155 222 -94 291 67C350 203 384 289 408 339C433 390 443 400 492 408V445Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,49.507,3.132)"><path d="M283 271C283 451 220 610 70 710L48 683C146 603 207 443 207 271S149 -59 48 -142L70 -169C220 -67 283 90 283 271Z"></path></g><g transform="matrix(.013,0,0,-0.013,53.33,0)"><path d="M290 -163V-135C183 -126 181 -122 181 -44V583C181 662 184 666 290 675V703H120V-163H290Z"></path></g><g transform="matrix(.013,0,0,-0.013,57.815,0)"><path d="M494 0V32C450 38 426 53 393 105L296 260C330 305 354 338 375 361C411 396 421 401 476 410V442H293V410C332 402 337 394 323 371C311 351 295 324 274 293L227 371C213 393 218 402 252 410V442H16V410C70 402 80 391 108 344L196 199C166 159 140 123 115 95C81 54 61 43 7 32V0H196V32C156 41 150 53 167 81C182 105 199 138 219 166L269 79C287 49 283 38 245 32V0H494Z"></path></g><g transform="matrix(.013,0,0,-0.013,64.237,0)"><path d="M226 -163V703H56V676C162 667 165 662 165 584V-43C165 -122 162 -126 56 -136V-163H226Z"></path></g></svg>,</span> of the populations obtained in a single simulation run of pMCMC (<i>left</i>) and NPMC (<i>right</i>) in the PO scenario (only a linear combination of the proteins is observed, corrupted by noise).</div>

Complexity

fig4

Figure 4

Figure 4: Bayesian Computation Methods for Inference in Stochastic Kinetic Models 