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Biological Modeling in the Discovery and Validation of Cognitive Dysfunctions Biomarkers

  • François Iris
Chapter

Abstract

Cognitive disorders are highly heterogenous in terms of symptoms, clinical etiologies, disease progression and therapeutic responses. Furthermore, their potential biological causes remain largely unknown. Progress at these different levels is currently mired in a vicious circle.

The identification of coherent biomarkers, essential for clinical and therapeutic progress, requires an understanding of either the relevant pathogenic processes or, at the very least, of the parameters that need to be monitored. But, syndrome-dominated conceptual thinking has become a barrier to understanding the biological processes linked to diseases characterized by clinical and therapeutic heterogeneity. As a result, current biomarkers of cognitive disorders are much too numerous, too heterogenous, and too variable to serve useful purposes. This leads to an untenable situation that precludes coherent therapeutic developments since it effectively prevents defining what could constitute valid biological, clinical and therapeutic approaches. How to escape from this situation? The problem could be partly resolved by adopting the much wider views allowed by “system-wide” approaches: in effects, by constructing predictive theoretical models of what could constitute pathological cognitive processes. This, naturally, requires the integration of massive amounts of highly heterogenous and often conflicting information. This chapter aims to provide a necessarily brief overview of the concepts, the breadth of data and the variety of network dynamics that will have to be considered while proposing a functional, experimentally validated model-building approach that could be fruitfully utilized.

Keywords

Pyramidal Cell GABAA Receptor Blood Oxygenation Level Dependent Basket Cell Gamma Oscillation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ADHD

Attention-deficit/hyperactivity disorder

ACM

Astrocyte-conditioned medium

ADP

After-depolarization membrane potential

AHP

Ca2+-dependent, K+-mediated after-hyperpolarization

Akt

Protein kinase (for “rac-family serine/threonine-protein kinase homolog”)

Akt-P

Phosophorylated form of Akt

AMPA

Alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid

BDNF

Brain-derived neurotrphic factor

BDWG

Biomarkers Definitions Working Group

BOLD

Blood oxygenation level-dependent (neurophysiology)

BST

Biochemical systems theory

CA1/3

Cornu ammonis, field 1/3

CaMKII

Protein kinase, (for “Calmodulin kinase type II”)

Ca2+

Calcium ion

CCK

Cholecystokinin

Cdc42

GTP-binding protein/GTPase (for “cell division cycle protein 42”)

c-fos

Transcription factor (for “F-type osteosarcoma viral oncogene homolog”)

CNS

Central nervous system

Cx

Connexins (isoforms 26, 30, 43, etc.)

EC

Entorhinal cortex

EEG

Electroencephalogram

EPSC

Electric potential generated by spontaneous slow cation conductence

EPSP

Electric potential generated by spontaneous patterns of activity

ERM

Ezrin-Moesin-Radixin protein family

FD

Fascia dentata

FDA

Food & Drugs Administration (USA)

fra-2

Transcription factor (for “fos-related antigen 2”)

GABA

Gamma-aminobutyric acid

GAP-43

Memory-associated protein (for “growth & plasticity-associated protein 43”)

GAT1

GABA transporter, isoform I

GluR

Glutamate & AMPA receptors (isoforms 1 to 4)

GTPase

Guanydine triphosphate catabolic enzyme

Hz

Hertz (wave frequencies measurement unit)

5-HT4R

5-Hydroxytryptamine type 4 receptor

iEEG

Intra-cranial electroencephalogram

IPSPs

Inhibitory post-synaptic potentials

JunB

Transcription factor (for “J-type unnamed osteosarcoma oncogene B”)

LTD

Long-term depression (neurophysiology)

LTP

Long-term potentiation (neurophysiology)

M2

Muscarinic receptor type II

MAPK

Protein kinase (for “mitogen-activated protein kinase”)

MAPK-P

Phosphorylated form of MAPK

MEK

Protein kinase kinase (for “MAPK/Erk kinase”)

mIPSC

Miniature inhibitory post-synaptic conductivity

MMPs

Matrix metalloproteases

MRI

Magnetic resonance imaging

mRNA

Messenger ribonucleic acid

MS-DB

Medial septal-diagonal band nuclei

NGF

Nerve growth factor

NHE1

Na+/H+ exchanger type I

NHERF

Na+/H+ exchanger regulatory factor

NIH

National Institute of Health (USA)

NMDA

N-methyl-D-aspartate

NPY

Neuro-peptide Y

ORF

Open reading frame (coding genetic sequence)

PET

Positron emission tomography

PI3K

Phosphoinositol triphosphate kinase

PKC

Protein kinase C

PTM

Post-translational modification

PV

Parvalbumin

p75NTR

Neurotrophin receptor, (for “protein of 75 kilo-dalton in molecular mass)

Rac1

GTP-binding protein/GTPase (for “ras-related C3 botulinum toxin substrate 1”)

Raf

Serine/threonine-protein kinase ( for “ras-associated factor”)

Ras

GTP-binding protein/GTPase (for “rat sarcoma oncogene”)

RF

Reticular formation

RhoA/B

GTP-binding proteins/GTPases (for “ras homolog gene family, member A/B”)

RTCs

Receptor-laden transport carriers

Shc

Adaptor protein, (for “src homology 2 domain-containing protein”)

Tgfbr2

Transforming growth factor beta receptor II, isoform 2

TrkA/B

Neural receptor protein-tyrosine kinase, typeA/B

TTX

Tetrodotoxin

WHO

World Health Organization

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  1. 1.Bio-Modeling Systems26 Rue St LambertParisFrance

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