Biological Compu-ng Ideas. Eric Mjolsness Computer Science Department UC Irvine

Size: px

Start display at page:

Download "Biological Compu-ng Ideas. Eric Mjolsness Computer Science Department UC Irvine"

Griffin Webb
5 years ago
Views:

1 Biological Compu-ng Ideas Eric Mjolsness Computer Science Department UC Irvine Calit2 June 2009

2 Principles shared in biology and compu-ng with focus on architecture Principle of regenera-on Mul-level network dynamics Computa-onal networks Biological networks Fixed vs. variable structure networks Evolu-on

3 Principle of regenera-on At all scales, regenera-on of subunits confers robustness against coevolved challenges. Regenera-on in turn implies an informa-on bonleneck. Biological examples Shark teeth, herbivore teeth. Stem cell niches, eg. shoot meristem. Immune system soma-c evolu-on. Sequestra-on of the germ line. Mul-cellular reproduc-on through zygote. BoNlenecks: genotype, cultural transmission, Computa-onal examples OS reinstalla-on. Network security. Nightly souware rebuild & retest. SoUware design refactoring.

4 Mul-level networks : Gene networks/neural networks/stochas-c opt. RGNN: Eg. scalable con-nuous codes => Gray codes, etc. Eg. evolu-on of reproducing metazoan lineage tree neural networks/cogni-ve networks

Transcrip-onal Gene Regula-on Networks Gene Regulation Network (GRN) model E.g. Drosophila A-P axis: v T Extracellular communication τ i v i = g ( j T ij v j + h ) Drosophila eve stripe expression in model (right) and data i λ i v (left).

5 Transcrip-onal Gene Regula-on Networks Gene Regulation Network (GRN) model E.g. Drosophila A-P axis: v T Extracellular communication τ i v i = g ( j T ij v j + h ) Drosophila eve stripe expression in model (right) and data i λ i v (left). Green: eve expression, red: kni expression. i [Mjolsness et al. J. Theor. Biol. 152: , 1991] From [Reinitz and Sharp, Mech. of Devel., 49: , 1995 ]. Cf. [Jaeger et al 2004]

6 Single slot organism Calit2 June 2009

7 Frameville cogni-ve/neural network architecture Main problem: crossbar communica-ons

8 Mul-level networks (cont) 2000?: biological realism ==> variable structure network dynamics metabolism/gene regula-on+signaling/ mul-cellular/spa-al network models large scale dynamics stochas-c dynamics morphodynamics

9 Amino Acid Syntheses G l u c o s e G l y c o l y s i s PDHC T C A c y c l e A l a t R N A - A l a L e u t R N A - L e u A s p TDA P y r α K B V a l I l e t R N A - V a l t R N A - I l e T h r + L y s M e t t R N A - T h r Kmech and (Val, Leu, Ile) biosynthesis: [Yang, Shapiro, Hung, Mjolsness, and Hatfield, Journal of Biological Chemistry, 280(12): , 2005 ] [Yang, Shapiro, Hung, Mjolsness Bioinformatics 21: , 2005.] Thr biosynthesis from Asp: [Najdi, Shapiro, Hatfield, and Mjolsness, Journal of Bioinformatics and Computational Biology, 4: , 2006.]

10 ES cell switch Pan and Thomson Cell Research (2007) 17: Chickarmane et al 2009: CSB 2009

11 Wall spring model Calit2 June 2009 Henrik Jönsson 2008

Cell complex framework: Plant cell mechanical model (a) (b) (c) (a) 3D polyhedral model of plant cell.

(b) FEM simulation of model cell deformation.

Resulting shape and mesh shown in red. Original cell given as the green outline.

12 Cell complex framework: Plant cell mechanical model (a) (b) (c) (a) 3D polyhedral model of plant cell. Expanded view shows separate walls (yellow) and cytoplasm (green). (b) FEM simulation of model cell deformation. Original cell was held at the bottom, stretched and twisted by 30 o. Resulting shape and mesh shown in red. Original cell given as the green outline. (c) The same cell expanding under uniform turgor pressure. Result of simulation shown in red; original cell in green. (d): Arabidipsis embryo FEM grid. [Figures courtesy Pawel Krupinski, UCI/Lund, Computable Plant project. ICSB 2007] Calit2 June 2009

13 Mul-level networks (cont) 2010? + Metadynamics evolu-on of dynamics by dynamics networks of rule like process models (eg. Dynamical Grammars) that specify dynamics, and also evolve by self applica-on Evolu-on and evolvability

14 Dynamical Grammar / Epithelium Guy Yosiphon, UCI Calit2 June 2009

15 DG Self applicability for Metadynamics Eg. gene-c algorithm in DG s muta-on, crossover processes development, selec-on processes alterna-vely: differen-al evolu-on, etc. Arrow muta-on operator Arrow reversal graph grammar exercise Machine learning by sta-s-cal inference e.g. hierarchical clustering (reported)? Equilibrium reac-on networks for MRF s Calit2 June 2009

16 What could be done jointly? Ar-ficial life simula-ons Herbivorous vision Realis-c plant growth and imagery Insect brain simula-on mul-level network dynamics General model language Scale up metadynamics eg. via dynamical grammars Anza Borrego sensor network, a la UCR James Reserve

Progress in modeling biological development as it bears on SBML

Progress in modeling biological development as it bears on SBML Eric Mjolsness UC Irvine representing the Computable Plant project www.computableplant.org Three relevant examples Meristem maintenance by