1. Gene regulatory code Alexander Kel BIOBASE GmbH Wolfenbüttel, Germany Beverly, USA Bangalor, India

3. George Gamow Vadim Ratner

4. + Frame-shift mutations + connectivity of the codon series +

5. organ, tissue, cell stage of development cell cycle phase extracellular signals Where ? When ? With whom? How ? gherllojunomd-bype Alexander fasltoiw

6. … cis trans

7. Insulin pathway TRANSPATH ®

8. TRANSPATH Professional: MOLECULE table

9. TRANSPATH: TNF-alpha – 1 step downstream TNF-alpha

10. TRANSPATH: TNF-alpha – 2 step downstream TNF-alpha

11. TRANSPATH: TNF-alpha – 3 step downstream

12. TNF-alpha TRANSPATH: TNF-alpha – 4 step downstream

19. Picture of WT mouse with hetero- and homozygous Sma1 mice. Heterozygous Sma1 mice show 33% reduction of the body weight, whereas homozygous mice exhibit a 56-58% reduction in body weight. Example2: Growth hormone-deficient mice (Sma1)

20. 0.0983 * V$TCF11MAFG_01(0.821) 0.0471 * V$FOXO4_01(0.961) 0.0301 * V$IPF1_Q4(0.852) 0.0410 * V$AR_01(0.851) 0.0766 * V$GR_Q6(0.971) 0.0482 * V$STAT1_02(0.995) 0.0508 * V$CEBPB_01(0.98) 0.0281 * V$STAT5A_02(0.826) 0.1040 * V$CETS1P54_02(0.949) -50- V$TCF4_Q5(0.908) 0.0751 * V$TCF1P_Q6(0.726) -50- V$STAT6_01(0.861) 0.0728 * V$SF1_Q6(0.684) -50- V$SMAD3_Q6(0.833) 0.0419 * V$ELK1_02(0.862) -50- V$GRE_C(0.842) Composite module found in promoters of differentially expressed genes in liver of growth hormone-deficient mice (Sma1). differentially expressed genes Non-changed genes

21. Results of the ArrayAnalyzer ™ search upstream TFs Identifying growth hormone (GH) and receptor tyrosine kinases (RTK) as potential key molecules involved in differential expression of the genes in liver of growth hormone- deficient mice (Sma1).

22. Data Sourse Background Mice were infected by leukemia viruses, either by neurovirulent FrCasE or by non- neurovirulent Fr75E; Aim was to find specific changes resulting from infection of microglia cells; Comparison of gene expression in FrCasE-infected versus Fr75E-infected microglia cells is done in the following example.

23. A View of loaded data set Current dataset is highlighted by black in the project tree

24. C Match output: single putative TF binding sites Match outputs on the project tree, with different profiles applied YES set: in this example genes upregulated 2- fold and more NO set: in this example genes downregulated 2-fold and more frequency of matches for given matrices in the YES and NO sets Ratio of frequencies YES/NO matrices P-value for the calculated ratio

25. Promoter model based on nerve-specific TFs Increase of Fitness function with number of iterationsComposition of the promoter model Sequences of YES and NO sets are well separated by the selected promoter model

26. Vizualization of the promoter models for particular genes

27. E Create a subset of TFs involved in the models Subset of TFs involved in the selected promoter models on the project tree, under the corresponding models

28. FSearching key nodes upstream of the selected TFs Score of the suggested key nodes Key node analysis can be done at the fixed number of steps upstream of the selected TFs, for example we can go one step upstream, or two,...steps upstream and suggest molecules (kinases, adaptors, receptors, ligands) that could provide coordinated regulation of the selected TFs. To create a subset of selected key nodes or of all molecules under the selected keynodes

29. FVizualization of the suggested key nodes Suggested key node, adaptor protein Hgs

30. Suggested key node Hgs is a known biomarker for neurofibromatosis

31. FVizualization of the suggested key nodes Suggested key node, adaptor protein TRAF2 Vizualization maps can be saved on the project tree

32. Suggested key node TRAF2 is important for the induction of apoptosis

33. TNF receptor associated factor 6 disease: osteopetrosis Example: human disease - Pseudoxanthoma Elasticum Elastic fibers calcification Mutations in ABCC6 transporter ELA2: human elastase 2 gene

34. Promoter evolution

35. TGAgTCA AP-1 TGAGTCA Human collagenase (-2013) ******* TGTGTAA ** ** * Mouse IL-2 (-143) TTTCTCC * ** Mouse TNF-alpha (-82) Consensus:

36. human TNF  promoter mast cells T-cells + ? dendritic cells T-cells -107-74 NFAT AP-1 NF-kB C/EBP AP-1 VDR

38. Size of zip file = complexity Time

40. „Molecular surrealism of promoters“

45. coding multiple regulatory messages in the same DNA sequence. A,B,C and D,E,F – two sets of TF; 1,2 – two sites in DNA; BC – basal complex. Fuzzy puzzle hypothesis of the multipurpose structure of the eukaryotic promoters

46. gherllojunomd-bype Alexander fasltoiw Several regulatory messages could be written in the same sequence. Reading of the messages depends on the cellular context 1) gherllojunomd-bype Alexander fasltoiw 2) gherllojunomd-bype Alexander fasltoiw 3) gherllojunomd-bype Alexander fasltoiw

47. SHMALGAUSEN Ivan Ivanovich Born on 23.04.1884. Died on 07.10.1963. Evolutional morphology. Academician of the Division of Mathematical and Natural Sciences since 01.06.1935. Evolution of mechanisms of evolution

48. Cybernetics: Cybernetics studies organization, communication and control in complex systems by focusing on circular (feedback) mechanisms. Control or regulation is most fundamentally formulated as a reduction of variety: perturbations with high variety affect the system's internal state, which should be kept as close as possible to the goal state, and therefore exhibit a low variety.

49. For appropriate regulation the variety in the regulator must be equal to or greater than the variety in the system being regulated. Or, the greater the variety within a system, the greater its ability to reduce variety in its environment through regulation. Only variety (in the regulator) can destroy variety (in the system being regulated). The law was formulated by Ross Ashby (1962). LAW OF REQUISITE VARIETY The Growth of Structural and Functional Complexity during Evolution Cybernetics:

50. Fundamental evolutional limitations Error catastrophe ( Eigen M., 1971; Ratner V. and Samin V., 1982 ) Haldane‘s Dilemma ( Haldane J., 1957; Crow J. and Kimura M, 1970 ) Population cannot evolve quickly in many genes simultaneously because losses are not redressed by fertility. „... there has not been enough time for evolution to have occurred - not even for human evolution...“ Losses due to Genetic Load Fitness of population: Solution:Neutrality (Kimura M. ) Sequence length: - replication errors

51. Stepwise breaking of the evolutional limitations in the course of progressive evolution to multicellular eukaryotic organisms Single-celled

52. Gradual evolution by fixation of multiple substitutions (Protein functional centres) Edited bipolymer by fixation of a small number of substitutions (Protein folding) Evolution at once by fixation of single substitutions (Regulatory regions of eukaryotic genes) Three mechanisms of biopolymer evolution

53. gherllojunomd-bype Alexander fasltoiw Even some messages which were not written gherllojunomd-bype Alexander fasltoiw gherllojunomd-zype Alexander fasltoiw b

54. Examples of anti-footprint (human/chimp) (minimized FP)

55. Diabetes mellitus, without diabetic complications Polycystic ovary syndrome Diabetes mellitus

56. Promoter is a white square www.biobase-international.com

57. BIOBASE explains biology

58. Sampling of BIOBASE Customers