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The Challenge of Understanding Specificity in Bio-molecular Networks

Explore the intricate world of bio-molecular networks and delve into the challenges of specificity. Uncover the mechanisms behind information flow, substrate recognition, and robustness in biological systems. Dive into the evolution of regulatory elements and the significance of quantitative models in understanding specificity. Discover the complex interplay of genetic networks, combinatorial controls, and commitment to cell fates. This journey combines biophysics, dynamical modeling, and bioinformatics to unravel the mysteries of specificity in molecular interactions.

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The Challenge of Understanding Specificity in Bio-molecular Networks

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  1. The Challenge of Understanding Specificity in Bio-molecular Networks Anirvan M. Sengupta Physics Dept./ BioMaPS Inst. Rutgers University

  2. A Corner of the Molecular Jungle

  3. Biology=Lots of Rube Golberg Machines Melded into One?

  4. Questions: Information Flow, Specificity and Robustness • How does such sytems function robustly? • How come crosstalk does not become interference? • Design principles or frozen accidents?

  5. Lock and Key Specificity • Specificity of substrate recognition built into enzymes. • Each pathway has dedicated extremely specific enzymes. • Often not true for eukaryotic systems.

  6. Things to Understand “Quantitatively” • Constraints on graded affinity • Network effects in the signaling system • Combinatorial control of promoters • Genetic networks with complex promoters • Commitment to cell fates

  7. Evolution of Regulatory Elements and Bioinformatics • Affinity is graded and number targets decided by concentration. • Evolutionary model says more pleiotropic ->less specific (Sengupta, Djordjevic, Shraiman, PNAS, 2002). • Improved bioinformatics for E. Coli TFs (Djordjevic, Sengupta, Shraiman, Genome Research, 2003, in press)

  8. Yeast Life Cycle

  9. Yeast MAPK Conundrum • Mating and filamentation governed by same pathway. • No scaffold found so far • Could the strength/pattern of excitation be the casue?

  10. Combinatorial Control of Yeast Mating Type Identity • Combinatorial control by three regulated factors (and a constitutive one) regulate cell type identity. • Detection of direct targets by combining sequence analysis and microarray data (O’Flanagan, Nagaraj, Mathias, Vershon, Sengupta, in preparation)

  11. Dorso-ventral Patterning in Fly Embryo • Morphogen gradient: Nuclearized dorsal. • Different cell fates along the axis.

  12. Dorsal Ventral Patterning Network • How does the gradient of dorsal get read into different cell fates? • Transcriptional cascade involving twist and snail.

  13. Dorsal High-affinity dorsal binding site Low-affinity dorsal binding site Twist Twist snail snail rhomboid rhomboid Gradients and Differential Expression High levels of dorsal TF Ventral Drosophila embryo cells Low levels of dorsal TF Lateral Drosophila embryo cells Low dorsal concentrations mean that only the high affinity sites in the rhomboid promoter are able to bind dorsal. Since neither Twist nor snail are expressed, rhomboid can be transcribed At high dorsal concentrations, low affinity binding sites permit transcription of Twist which in turn activates transcription of snail. Snail inhibits rhomboid transcription

  14. Conclusion • Understanding quantitative models of specificity is going to become more and more important. • We need to combine biophysics, dynamical modeling and bioinformatics together for that purpose.

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