Principal Investigator

Benjamin L. Miller, Ph.D. University of Rochester work Box 697 601 Elmwood Ave Rochester NY 14642 office: MC 6-6820 p 585-275-9805

Contact

Benjamin Miller Lab University of Rochester work MC 5-6818 601 Elmwood Ave Rochester NY 14642 p 585-275-9805

Affiliations

Molecular Recognition & Biosensing

Research in the Miller group focuses on two fundamental areas: the control of biomolecular interactions through the synthesis of new small-molecule probes, and the observation of biomolecular interactions through the development of novel optical sensing technologies. In the area of control, we are particularly interested in the sequence-selective recognition of RNA. New RNA sequences with important functions in basic biology and human health and disease are being discovered at an ever-increasing rate, and yet our ability to target these sequences specifically is still at a rudimentary stage. To address this gap, we are applying techniques of molecular design and a novel combinatorial method of small-molecule evolution called Dynamic Combinatorial Chemistry, which allows us to rapidly prototype sequence-selective RNA binding molecules. Thus far we have used this methodology to RNA targets important in Myotonic Dystrophy and HIV. Protein-targeted small-molecule discovery projects are also of interest, and current projects include the mechanism of tight junction formation and the transport of beta-amyloid across the blood-brain barrier.

To the end of achieving better methods of observing biomolecular interactions, our group has a longstanding program in the use of the optical properties of nanostructured materials as the basis for new biosensors and diagnostic tools. Two examples of current efforts include Arrayed Imaging Reflectometry (AIR) and sensors based on two-dimensional photonic crystals (2-D PhC). AIR relies on the creation of a near-perfect antireflection coating on the surface of a silicon chip; binding of a biomolecular target destroys this antireflective condition and is visible by a change in reflected light. This allows for highly multiplexed (10's to 1000's of targets) and quantitative detection. Photonic crystal sensors, on the other hand, offer the possibility of ultrasensitive detection: for example, a major long-term goal of our work is the production of sensors that can effectively detect one virus in a blood sample.

Recent Publications

    1. Naj AC
    2. Jun G
    3. Reitz C
    4. Kunkle BW
    5. Perry W
    6. Park YS
    7. Beecham GW
    8. Rajbhandary RA
    9. Hamilton-Nelson KL
    10. Wang LS
    11. Kauwe JS
    12. Huentelman MJ
    13. Myers AJ
    14. Bird TD
    15. Boeve BF
    16. Baldwin CT
    17. Jarvik GP
    18. Crane PK
    19. Rogaeva E
    20. Barmada MM
    21. Demirci FY
    22. Cruchaga C
    23. Kramer PL
    24. Ertekin-Taner N
    25. Hardy J
    26. Graff-Radford NR
    27. Green RC
    28. Larson EB
    29. St George-Hyslop PH
    30. Buxbaum JD
    31. Evans DA
    32. Schneider JA
    33. Lunetta KL
    34. Kamboh MI
    35. Saykin AJ
    36. Reiman EM
    37. De Jager PL
    38. Bennett DA
    39. Morris JC
    40. Montine TJ
    41. Goate AM
    42. Blacker D
    43. Tsuang DW
    44. Hakonarson H
    45. Kukull WA
    46. Foroud TM
    47. Martin ER
    48. Haines JL
    49. Mayeux RP
    50. Farrer LA
    51. Schellenberg GD
    52. Pericak-Vance MA
    53. Alzheimer Disease Genetics Consortium
    54. Albert MS
    55. Albin RL
    56. Apostolova LG
    57. Arnold SE
    58. Barber R
    59. Barnes LL
    60. Beach TG
    61. Becker JT
    62. Beekly D
    63. Bigio EH
    64. Bowen JD
    65. Boxer A
    66. Burke JR
    67. Cairns NJ
    68. Cantwell LB
    69. Cao C
    70. Carlson CS
    71. Carney RM
    72. Carrasquillo MM
    73. Carroll SL
    74. Chui HC
    75. Clark DG
    76. Corneveaux J
    77. Cribbs DH
    78. Crocco EA
    79. DeCarli C
    80. DeKosky ST
    81. Dick M
    82. Dickson DW
    83. Duara R
    84. Faber KM
    85. Fallon KB
    86. Farlow MR
    87. Ferris S
    88. Frosch MP
    89. Galasko DR
    90. Ganguli M
    91. Gearing M
    92. Geschwind DH
    93. Ghetti B
    94. Gilbert JR
    95. Glass JD
    96. Growdon JH
    97. Hamilton RL
    98. Harrell LE
    99. Head E
    100. Honig LS
    101. Hulette CM
    102. Hyman BT
    103. Jicha GA
    104. Jin LW
    105. Karydas A
    106. Kaye JA
    107. Kim R
    108. Koo EH
    109. Kowall NW
    110. Kramer JH
    111. LaFerla FM
    112. Lah JJ
    113. Leverenz JB
    114. Levey AI
    115. Li G
    116. Lieberman AP
    117. Lin CF
    118. Lopez OL
    119. Lyketsos CG
    120. Mack WJ
    121. Martiniuk F
    122. Mash DC
    123. Masliah E
    124. McCormick WC
    125. McCurry SM
    126. McDavid AN
    127. McKee AC
    128. Mesulam M
    129. Miller BL
    130. Miller CA
    131. Miller JW
    132. Murrell JR
    133. Olichney JM
    134. Pankratz VS
    135. Parisi JE
    136. Paulson HL
    137. Peskind E
    138. Petersen RC
    139. Pierce A
    140. Poon WW
    141. Potter H
    142. Quinn JF
    143. Raj A
    144. Raskind M
    145. Reisberg B
    146. Ringman JM
    147. Roberson ED
    148. Rosen HJ
    149. Rosenberg RN
    150. Sano M
    151. Schneider LS
    152. Seeley WW
    153. Smith AG
    154. Sonnen JA
    155. Spina S
    156. Stern RA
    157. Tanzi RE
    158. Thornton-Wells TA
    159. Trojanowski JQ
    160. Troncoso JC
    161. Valladares O
    162. Van Deerlin VM
    163. Van Eldik LJ
    164. Vardarajan BN
    165. Vinters HV
    166. Vonsattel JP
    167. Weintraub S
    168. Welsh-Bohmer KA
    169. Williamson J
    170. Wishnek S
    171. Woltjer RL
    172. Wright CB
    173. Younkin SG
    174. Yu CE
    175. Yu L
    (2014 Nov 01). Effects of multiple genetic Loci on age at onset in late-onset Alzheimer disease: a genome-wide association study. JAMA Neurol. 71, 1394-404.
    1. Kapogiannis D
    2. Boxer A
    3. Schwartz JB
    4. Abner EL
    5. Biragyn A
    6. Masharani U
    7. Frassetto L
    8. Petersen RC
    9. Miller BL
    10. Goetzl EJ
    (2014 Oct 23). Dysfunctionally phosphorylated type 1 insulin receptor substrate in neural-derived blood exosomes of preclinical Alzheimer's disease. FASEB J. In press.
    1. Le NT
    2. Takei Y
    3. Izawa-Ishizawa Y
    4. Heo KS
    5. Lee H
    6. Smrcka AV
    7. Miller BL
    8. Ko KA
    9. Ture S
    10. Morrell C
    11. Fujiwara K
    12. Akaike M
    13. Abe J
    (2014 Oct 01). Identification of activators of ERK5 transcriptional activity by high-throughput screening and the role of endothelial ERK5 in vasoprotective effects induced by statins and antimalarial agents. J Immunol. 193, 3803-15.