Computational neuroscience, phototransduction, stochastic problems in cellular and molecular biology, statistical analysis and modeling of genome-scale data
Professor of Biology, Mathematics and Neural Science
Computational neuroscience, phototransduction, stochastic problems in cellular and molecular biology, statistical analysis and modeling of genome-scale data
My research in computational biology and neuroscience integrates data from experimental studies, theoretical analysis, mathematical modeling, and computer simulation.
One of my ongoing projects, in vertebrate phototransduction, focuses on light adaptation in rods and cones, and on the reproducibility of rod responses to single photons. Electrophysiological and biochemical data provide a basis for developing mathematical models that account for light adaptation and statistical properties of the singe-photon response in terms of the molecular mechanisms underlying phototransduction.
Another ongoing project involves the development of "statistical mechanical" methods for modeling large neural networks. These methods employ the theory of the probability (population) density function. Factors that make conventional methods unwieldy or intractable - thousands of neurons and millions of synapses ¬are used to great advantage in these methods. Similar neurons are lumped together into populations, and one tracks the distribution of neurons over state space in each population. The state of a neuron is determined by the dynamic variables in the underlying single-neuron model. The population-firing rate is given by the flux of probability across a particular surface in state space. Neurons are coupled via stochastic synapses; the rate of excitatory/inhibitory input events for a target neuron is determined by the rate of action potentials in each of the presynaptic populations and by the average number of synapses the postsynaptic neuron receives from each of these populations.
In an ongoing collaboration with Arjun Raj at the University of Pennsylvania, I am analyzing and modeling stochastic gene expression. The models include gene switching, synthesis and degradation of mRNA and proteins, and cell divisions. A goal is to understand sources of large fluctuations in the number of transcripts from genes that are expressed at low levels, the consequences for protein levels, and the implications for cell fate and cell physiology in general. Future modeling will focus on gene networks.
Finally, I am collaborating with Professor Gloria Coruzzi and her lab on projects involving the identification and modeling of gene regulatory networks underlying nitrogen assimilation and metabolism in Arabidopsis. Statistical analysis of microarray, RNA-Seq and ChIP-Seq data plays a central role in these projects.
I teach both in the Department of Biology and the Department of Mathematics.
Graduate Biology courses include:
Graduate Mathematics courses include:
I received my Ph.D. in 1981 from the Rockefeller University, where I studied physical chemistry, membrane biophysics, neurophysiology, and mathematics. My dissertation was an analysis of information processing in the outer retina of the turtle. My postdoctoral research was a combined experimental and theoretical study of light adaptation -- a process by which the retina adjusts its sensitivity according to lighting conditions. In 1984, I joined the faculty of New York University with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology.
Recovery of sparse transformation-invariant signals with continuous basis pursuit.
IEEE Transactions on Signal Processing, 59, 4735—4744
Ekanadham, C., Tranchina, D., and Simoncelli, E.P. (2011)
Kinetics of Inhibitory Feedback from Horizontal Cells to Photoreceptors: Implications for an Ephaptic Mechanism.
J Neurosci (2016 Sep 28) PMC5039255 free full-text archive
Warren TJ, Van Hook MJ, Tranchina D, Thoreson WB
An alternating renewal process describes the buildup of perceptual segregation.
Front Comput Neurosci (2014) PMC4286718 free full-text archive
Steele SA, Tranchina D, Rinzel J
A unified framework and method for automatic neural spike identification.
J Neurosci Methods (2014 Jan 30) PMC4075282 free full-text archive
Ekanadham C, Tranchina D, Simoncelli EP
Plasticity regulators modulate specific root traits in discrete nitrogen environments.
PLoS Genet (2013) PMC3764102 free full-text archive
Gifford ML, Banta JA, Katari MS, Hulsmans J, Chen L, Ristova D, Tranchina D, Purugganan MD, Coruzzi GM, Birnbaum KD
Modulation of mouse rod response decay by rhodopsin kinase and recoverin.
J Neurosci (2012 Nov 07) PMC3501282 free full-text archive
Chen CK, Woodruff ML, Chen FS, Chen Y, Cilluffo MC, Tranchina D, Fain GL
Population density approach for discrete mRNA distributions in generalized switching models for stochastic gene expression.
Phys Rev E Stat Nonlin Soft Matter Phys (2012 Jun) PMID: 23005139
Stinchcombe AR, Peskin CS, Tranchina D
Recovery of sparse translation-invariant signals with continuous basis pursuit.
IEEE Trans Signal Process (2011 Oct 01) PMC3860587 free full-text archive
Ekanadham C, Tranchina D, Simoncelli E
Channel modulation and the mechanism of light adaptation in mouse rods.
J Neurosci (2010 Dec 01) PMC3010974 free full-text archive
Chen J, Woodruff ML, Wang T, Concepcion FA, Tranchina D, Fain GL
In silico evaluation of predicted regulatory interactions in Arabidopsis thaliana.
BMC Bioinformatics (2009 Dec 21) PMC2803859 free full-text archive
Nero D, Katari MS, Kelfer J, Tranchina D, Coruzzi GM
A system biology approach highlights a hormonal enhancer effect on regulation of genes in a nitrate responsive "biomodule".
BMC Syst Biol (2009 Jun 06) PMC2702358 free full-text archive
Nero D, Krouk G, Tranchina D, Coruzzi GM
Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.
Neural Comput (2009 Feb) PMID: 19431264
Ly C, Tranchina D
A systems approach uncovers restrictions for signal interactions regulating genome-wide responses to nutritional cues in Arabidopsis.
PLoS Comput Biol (2009 Mar) PMC2652106 free full-text archive
Krouk G, Tranchina D, Lejay L, Cruikshank AA, Shasha D, Coruzzi GM, Gutierrez RA
Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling.
Neural Comput (2007 Aug) PMID: 17571938
Ly C, Tranchina D
Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods.
Network (2006 Dec) PMID: 17162461
Apfaltrer F, Ly C, Tranchina D
Stochastic mRNA synthesis in mammalian cells.
PLoS Biol (2006 Oct) PMC1563489 free full-text archive
Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S
A comparison of release kinetics and glutamate receptor properties in shaping rod-cone differences in EPSC kinetics in the salamander retina.
J Physiol (2005 Dec 15) PMC1383429 free full-text archive
Cadetti L, Tranchina D, Thoreson WB
Toward a unified model of vertebrate rod phototransduction.
Vis Neurosci (2005 Jul-Aug) PMC1482458 free full-text archive
Hamer RD, Nicholas SC, Tranchina D, Lamb TD, Jarvinen JL
Kinetics of synaptic transfer from rods and cones to horizontal cells in the salamander retina.
Neuroscience (2003) PMID: 14622921
Thoreson WB, Tranchina D, Witkovsky P
Multiple steps of phosphorylation of activated rhodopsin can account for the reproducibility of vertebrate rod single-photon responses.
J Gen Physiol (2003 Oct) PMC1480412 free full-text archive
Hamer RD, Nicholas SC, Tranchina D, Liebman PA, Lamb TD
Modeling corticofugal feedback and the sensitivity of lateral geniculate neurons to orientation discontinuity.
Vis Neurosci (2001 Nov-Dec) PMID: 12020077
Hayot F, Tranchina D
Hypermutation in shark immunoglobulin light chain genes results in contiguous substitutions.
Immunity (2002 Apr) PMID: 11970880
Lee SS, Tranchina D, Ohta Y, Flajnik MF, Hsu E
Transmission at the photoreceptor synapse.
Prog Brain Res (2001) PMID: 11420937
Witkovsky P, Thoreson W, Tranchina D
Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size.
Network (2001 May) PMID: 11405420
Haskell E, Nykamp DQ, Tranchina D
A population density approach that facilitates large-scale modeling of neural networks: extension to slow inhibitory synapses.
Neural Comput (2001 Mar) PMID: 11244554
Nykamp DQ, Tranchina D
A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning.
J Comput Neurosci (2000 Jan-Feb) PMID: 10798498
Nykamp DQ, Tranchina D
Recovery of cable properties through active and passive modeling of subthreshold membrane responses from laterodorsal tegmental neurons.
J Neurophysiol (1998 Nov) PMID: 9819266
Surkis A, Peskin CS, Tranchina D, Leonard CS
The calculus of rod phototransduction.
J Gen Physiol (1998 Jan) PMC1887772 free full-text archive
Tranchina D
Gain of rod to horizontal cell synaptic transfer: relation to glutamate release and a dihydropyridine-sensitive calcium current.
J Neurosci (1997 Oct 01) PMID: 9295376
Witkovsky P, Schmitz Y, Akopian A, Krizaj D, Tranchina D
Background contrast modulates kinetics and lateral spread of responses to superimposed stimuli in outer retina.
Vis Neurosci (1995 Nov-Dec) PMID: 8962830
Reifsnider ES, Tranchina D
Modulation of synaptic transfer between retinal cones and horizontal cells by spatial contrast.
J Gen Physiol (1994 Sep) PMC2229224 free full-text archive
Cadenas ID, Reifsnider ES, Tranchina D
Origin of the apparent tissue conductivity in the molecular and granular layers of the in vitro turtle cerebellum and the interpretation of current source-density analysis.
J Neurophysiol (1994 Aug) PMID: 7983532
Okada YC, Huang JC, Rice ME, Tranchina D, Nicholson C
Membrane currents of horizontal cells isolated from turtle retina.
J Neurophysiol (1992 Aug) PMID: 1382117
Golard A, Witkovsky P, Tranchina D
Light adaptation in turtle cones. Testing and analysis of a model for phototransduction.
Biophys J (1991 Jul) PMC1260053 free full-text archive
Tranchina D, Sneyd J, Cadenas ID
Light adaptation in the primate retina: analysis of changes in gain and dynamics of monkey retinal ganglion cells.
Vis Neurosci (1990 Jan) PMID: 2176096
Purpura K, Tranchina D, Kaplan E, Shapley RM
Photoreceptor to horizontal cell synaptic transfer in the Xenopus retina: modulation by dopamine ligands and a circuit model for interactions of rod and cone inputs.
J Neurophysiol (1989 Oct) PMID: 2530319
Witkovsky P, Stone S, Tranchina D
Phototransduction in cones: an inverse problem in enzyme kinetics.
Bull Math Biol (1989) PMID: 2573396
Sneyd J, Tranchina D
Light adaptation in the turtle retina: embedding a parametric family of linear models in a single nonlinear model.
Vis Neurosci (1988) PMID: 3154803
Tranchina D, Peskin CS
A model for the polarization of neurons by extrinsically applied electric fields.
Biophys J (1986 Dec) PMC1329788 free full-text archive
Tranchina D, Nicholson C
Retinal light adaptation--evidence for a feedback mechanism.
Nature (1984 Jul 26-Aug 1) PMID: 6462216
Tranchina D, Gordon J, Shapley RM
The receptive field organization of X-cells in the cat: spatiotemporal coupling and asymmetry.
Vision Res (1984) PMID: 6740975
Dawis S, Shapley R, Kaplan E, Tranchina D
How to see in the dark: photon noise in vision and nuclear medicine.
Ann N Y Acad Sci (1984) PMID: 6598007
Peskin CS, Tranchina D, Hull DM
Spatial and temporal properties of luminosity horizontal cells in the turtle retina.
J Gen Physiol (1983 Nov) PMC2228714 free full-text archive
Tranchina D, Gordon J, Shapley R
Linear information processing in the retina: a study of horizontal cell responses.
Proc Natl Acad Sci U S A (1981 Oct) PMC349076 free full-text archive
Tranchina D, Gordon J, Shapley R, Toyoda J
An open system kinetic transport model for the Hodgkin-Huxley equations.
J Theor Biol (1976 Feb) PMID: 1271823
Starzak ME, Tranchina DA