Baianu, I C Costescu, D Hofmann, N E Korban, S S Lozano, P You, T Near Infrared Microspectroscopy, Fluorescence Microspectroscopy, Infrared Chemical Imaging and High Resolution Nuclear Magnetic Resonance Analysis of Soybean Seeds, Somatic Embryos and Single Cells Novel methodologies are currently being developed and established for the chemical analysis of soybean seeds, embryos and single cells by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR) Microspectroscopy, Fluorescence and High-Resolution NMR (HR-NMR). The first FT-NIR chemical images of biological systems approaching one micron resolution are presented here. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as two cubic microns. Related, HR-NMR analyses of oil contents in somatic embryos are also presented here with nanoliter precision. Such 400 MHz 1H NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. ~20%) compared to non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monitored by FT-NIR with a precision approaching the picogram level. Indeed, detailed chemical analyses of oils and phytochemicals are now becoming possible by FT-NIR Chemical Imaging/ Microspectroscopy of single cells. The cost, speed and analytical requirements of plant breeding and genetic selection programs are fully satisfied by FT-NIR spectroscopy and Microspectroscopy for soybeans and soybean embryos. FT-NIR Microspectroscopy and Chemical Imaging are also shown to be potentially important in functional Genomics and Proteomics research through the rapid and accurate detection of high-content microarrays (HCMA). Multi-photon (MP), pulsed femtosecond laser NIR Fluorescence Excitation techniques were shown to be capable of Single Molecule Detection (SMD). Therefore, such powerful techniques allow for the most sensitive and reliable quantitative analyses to be carried out both in vitro and in vivo. Thus, MP NIR excitation for Fluorescence Correlation Spectroscopy (FCS) allows not only single molecule detection, but also molecular dynamics and high resolution, submicron imaging of femtoliter volumes inside living cells and tissues. These novel, ultra-sensitive and rapid NIR/FCS analyses have numerous applications in important research areas, such as: agricultural biotechnology, food safety, pharmacology, medical research and clinical diagnosis of viral diseases and cancers. KEYWORDS: Single Cancer Cell Detection by FT-NIR and FT-IR Instruments; applications of FCS/NIR;Single Virus (HIV, HBV, HPV-16) Particle Detection by FCS/FCCS; Agricultural biotechnology; IR Chemical Imaging and NMR, Microspectroscopy; DNA/RNA Micro-array analysis by NIR; High resolution and super-resolution FT-NIR/IR, IR Chemical Imaging by FPAW, Spotlight 300 Microspectrometer; Two photon NIR excitation for FCS, Single Cell and sister; single-molecule dynamics, FCS of molecules, single cells, Soybean oil, protein and moisture analysis, FT-NIR and FT-IR, high-resolution NMR of soybean oil in seeds and somatic embryos; chemical mutagenesis of soybean embryos; picomole FT-NIR and femtomole FCS-NIR analysis of single cells; phytochemicals detection in soybean seeds and cells by FT-NIR; high-power, femtosecond Ti:Sapphire NIR excitation for FCS; FCS/PCR, Nucleic acid hybridization; FT-IR and FT-NIR Images of Soybeans and Embryos, FT-IR and NIR Chemical Imaging Tests; One-Micron Spatial Resolution Tests; FT-NIR Micro-Imaging, FT-NIR Images of Soybeans and Embryos, FT-IR Reflectance Chemical Images, Somatic Embryo; NIR Reflectance Chemical Image of a Red Coat Azuki Red Bean; FT-NIR Chemical Imaging by Difference Spectroscopy (CIDS); High Resolution NMR Analysis of Soybean Oil in Somatic Embryos, HR NMR,H-1 NMR Spectrum of the somatic embryogenic culture of a soybean sample; TEM Micrograph of a Suspension of Soybean Somatic Embryos in Culture; Single Molecule Detection; two-photon excitation, one-photon excitation, three-photon-excitation; Fluorescence Correlation Spectroscopy (FCS); Fluorescence Resonance Energy Transfer (FRET); Fluorescence Lifetime Imaging Microscopy (FLIM); Fluorescence Recovery After Photobleaching (FRAP); Single Photon Confocal Fluorescence Correlation Spectroscopy, Inverted Epi-fluorescence Microscope; FCS auto-correlation, Fluorescence Fluctuations, Fluorescence Intensity; Fluorescence Correlation Spectroscopy and Imaging Experiments in Solutions and Plant Cell Suspensions; Pulsed, Two-Photon NIR Laser Excitation/ Multi-photon (MPE) NIR excitation; FCS Alba Spectrometer Microspectrometer System; FCCS Cross-Correlation with Two Fluorescent Labels; FCCS Applications to DNA Hybridization. eng 2002

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-068; http://cds.cern.ch/record/768089/files/FCSMicrospectroscopy_SingleVirusFCCSDetection_SingleMoleculeDetection.doc; http://cds.cern.ch/record/768089/files/ext-2004-068.pdf;

Imported from Invenio. Baianu, I C Complex Systems Analysis of Cell Cycling Models in Carcinogenesis:II. Cell Genome and Interactome, Neoplastic Non-random Transformation Models in Topoi with Lukasiewicz-Logic and MV Algebras Quantitative Biology, abstract q-bio.OT/0406045 From: I.C. Baianu Dr. [view email] Date (v1): Thu, 24 Jun 2004 02:45:13 GMT (164kb) Date (revised v2): Fri, 2 Jul 2004 00:58:06 GMT (160kb) Complex Systems Analysis of Cell Cycling Models in Carcinogenesis: II. Authors: I.C. Baianu Comments: 23 pages, 1 Figure Report-no: CC04 Subj-class: Other Carcinogenesis is a complex process that involves dynamically inter-connected modular sub-networks that evolve under the influence of micro-environmentally induced perturbations, in non-random, pseudo-Markov chain processes. An appropriate n-stage model of carcinogenesis involves therefore n-valued Logic treatments of nonlinear dynamic transformations of complex functional genomes and cell interactomes. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous, Boolean or "fuzzy", logic models of genetic activities in vivo. Such models are then applied to cell transformations during carcinogenesis based on very extensive genomic transcription and translation data from the CGAP databases supported by NCI. Such models are represented in a Lukasiewicz-Topos with an n-valued Lukasiewicz Algebraic Logics subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in carcinogeness. Specific models for different types of cancer are then derived from representations of the dynamic state-space of LT non-random, pseudo-Markov chain process, network models in terms of cDNA and proteomic, high throughput analyses by ultra-sensitive techniques. This novel theoretical analysis is based on extensive CGAP genomic data for human tumors, as well as recently published studies of cyclin signaling. Several such specific models suggest novel clinical trials and rational therapies of cancer through re-establishment of cell cycling inhibition in stage III cancers. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-065; http://cds.cern.ch/record/749753/files/ANeuralGenNetworkLuknTopos_oknu4.pdf; http://cds.cern.ch/record/749753/files/GenLuknComplxNu3OK.doc; http://cds.cern.ch/record/749753/files/LUKasiewiczAlgebras_MVAlgebs.pdf; http://cds.cern.ch/record/749753/files/MolecBIOTransformation_Categories2.pdf; http://cds.cern.ch/record/749753/files/NaturalTransfMolecModelsnu6.pdf; http://cds.cern.ch/record/749753/files/ext-2004-065.pdf; http://cds.cern.ch/record/749753/files/q-bioArXiv_0406045.pdf;

Imported from Invenio. Baianu, I C Computer Models and Automata Theory in Biology and Medicine The applications of computers to biological and biomedical problem solving goes back to the very beginnings of computer science, automata theory [1], and mathematical biology [2]. With the advent of more versatile and powerful computers, biological and biomedical applications of computers have proliferated so rapidly that it would be virtually impossible to compile a comprehensive review of all developments in this field. Limitations of computer simulations in biology have also come under close scrutiny, and claims have been made that biological systems have limited information processing power [3]. Such general conjectures do not, however, deter biologists and biomedical researchers from developing new computer applications in biology and medicine. Microprocessors are being widely employed in biological laboratories both for automatic data acquisition/processing and modeling; one particular area, which is of great biomedical interest, involves fast digital image processing and is already established for routine clinical examinations in radiological and nuclear medicine centers, Powerful techniques for biological research are routinely employing dedicated, on-line microprocessors or array processors; among such techniques are: Fourier-transform nuclear magnetic resonance (NMR), NMR imaging (or tomography), x-ray tomography, x-ray diffraction, high performance liquid chromatography, differential scanning calorimetry and mass spectrometry. Networking of laboratory microprocessors linked to a central, large memory computer is the next logical step in laboratory automation. Previously unapproachable problems, such as molecular dynamics of solutions, many-body interaction calculations and statistical mechanics of biological processes are all likely to benefit from the increasing access to the new generation of "supercomputers". In view of the large number, diversity and complexity of computer applications in biology and medicine, we could not review in any degree of detail all computer applications in these fields; instead, we shall be selective and focus our discussion on suggestive computer models of biological systems and those fundamental aspects of computer applications that are likely to continue to make an impact on biological and biomedical research. Thus, we shall consider unifying trends in mathematics, mathematical logics and computer science that are relevant to computer modeling of biological and biomedical systems. The latter are pitched at a more formal, abstract level than the applications and, therefore, encompass a number of concepts drawn from the abstract theory of sets and relations, network theory, automata theory, Boolean and n-valued logics, abstract algebra, topology and category theory. The purpose of these theoretical sections is to provide the ans for approaching a number of basic biological questions: (1) What are the essential characteristics of a biological organism as opposed to an automaton? (2) Are biological systems recursively computable? (3) What is the structure of the simplest (primordial) organism? (4) What are the basic structures of neural and genetic networks? (5) What are the common properties of classes of biological organisms? (6) Which system representations are adequate for biodynamics? (7) What is the optimal strategy for modifying an organism through genetic engineering? (8) What is the optimal simulation of a biological system with a digital or analog computer? (9) What is life? The present analysis of relational theories in biology and computer simulation has also inspired a number of new results which are presented here as "Conjectures" since their proofs are too lengthy and too technical to be included in this review. In order to maintain a self-contained presentation-the definitions of the main concepts are given, with the exception of a minimum of simple mathematical concepts. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-060; http://cds.cern.ch/record/746672/files/CopmputerAutomataBiolMedicine3oK.pdf; http://cds.cern.ch/record/746672/files/ext-2004-060.pdf;

Imported from Invenio. Prisecaru, V I Baianu, I C Complex Biological Systems Analysis of Cell Cycling Models in Carcinogenesis: I. The essential roles of modifications in the c-Myc, TP53/p53, p27 and hTERT modules in Cancer Initiation and Progression A new approach to the integration of results from a modular, complex biological systems analysis of nonlinear dynamics in cell cycling network transformations that are leading to carcinogenesis is proposed. Carcinogenesis is a complex process that involves dynamically inter-connected biomolecules in the intercellular, membrane, cytosolic, nuclear and nucleolar compartments that form numerous inter-related pathways referred to as networks. One such network module contains the cell cyclins whose functions are essential to cell cycling and division. Cyclins are proteins that also link to several critical pro-apoptotic and other cell cycling/division components, such as: c-Myc, p27, the tumor suppressor gene TP53 and its product-- the p53 protein with key roles in controlling DNA repair, inducing apoptosis and activating p21 (which can depress cell cyclins if activated), mdm2(with its biosynthesis activated by p53 and also, in its turn, inhibiting p53), p21, the Thomsen-Friedenreich antigen(T- antigen),Rb,Bax, Bad and Bcl-2. These key network components were reported to play major roles in the carcinogenesis of many types of cancer. Our novel theoretical analysis is based on recently published studies of intra-cellular cyclin signaling, with special emphasis being placed on the roles of cyclins D1 and E. This novel analysis suggests strategies for successful clinical trials and rational therapies of cancer through re-establishment of the cell cycling inhibition in metastatic cancer cells. Cyclins are proteins that are often found to be over-expressed in cancerous cells (Dobashi et al., 2004). Cyclin-dependent kinases (CDK), their respective cyclins, and inhibitors of CDKs (CKIs) were identified as central, highly-connected components of the cell cycle-regulating machinery. In mammalian cells the complexes of cyclins D1, D2, D3, A and E with CDKs are considered to be the 'motors that drive' cells to enter and pass through the G2 to the S and M phases of the cell cycle. Specific cell cycle regulation mechanisms determine both the cell division and tissue proliferation; thus the cell cycle was reported to be finely regulated by the interactions of cyclins with CDKs and CKIs, among other molecules (Morgan et al., 1995), such as p27 and p21 that when activated are capable of deppressing or completely inhibiting the cell cycling. The Genetic database I (DSI, Paris, France) sequence for Cyclin-D1 can be found and downloaded at the PDB website:=http://www.dsi.univ-paris5.fr/genatlas/fiche.php?symbol=CCND1. The signaling pathways in cells that are thought to be involved in Carcinogenesis in humans and mice are extensively, but not completely, documented at the CGAP website supported by the National Cancer Institute(NCI)of the NIH in the USA. Extensive tumor genomics data sets are also available at this CGAP web site and also at several other web sites supported in the USA by NCI, such as those of the existing National Integrative Complex Systems Cancer Biology Program and the Fred Hutchinson Cancer Center. Urgent progress with Proteomics and Cell Interactomics high-quality, high throughput/ high-speed data acquisition for separated, human tumor cell lines is needed in order to design and implement rational, individualized cancer therapy strategies and successful clinical trials at Cancer Centers. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-057; http://cds.cern.ch/record/746295/files/A_GENOMELukNTOPOS_OKnu2.doc; http://cds.cern.ch/record/746295/files/ApplBiotechnology8NU_kitfinalokjune23.pdf; http://cds.cern.ch/record/746295/files/Cancersignaling_ICBval.pdf; http://cds.cern.ch/record/746295/files/QuantumGeneCompAutoOK.doc; http://cds.cern.ch/record/746295/files/ext-2004-057.pdf;

Imported from Invenio. Baianu,I C Quantum Genetics in terms of Quantum Reversible Automata and Quantum Computation of Genetic Codes and Reverse Transcription The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In previous publications (Baianu,1971a, b) the formal concept of quantum automaton and quantum computation, respectively, were introduced and their possible implications for genetic processes and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b; 1977; 1987; 2004; Baianu et al, 2004). The notions of topological semigroup, quantum automaton, or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Lukasiewicz Logic Algebras that showed significant dissimilarities (Baianu, 1977; 2004a; Baianu et al, 2004b) from Boolean models of human neural networks (McCullough and Pitts, 1943). Molecular models in terms of categories, functors and natural transformations were then formulated for uni-molecular chemical transformations, multi-molecular chemical and biochemical transformations (Baianu, 1983, 1987, 2004a). Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, oncogenesis and medicine were extensively reviewed and several important conclusions were reached regarding both the potential and limitations of the computation-assisted modeling of biological systems, and especially complex organisms such as Homo sapiens sapiens (Baianu,1987). Novel approaches to solving the realization problems of Relational Biology models in Complex System Biology are introduced in terms of natural transformations between functors of such molecular categories. Several applications of such natural transformations of functors were then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Topoi of Lukasiewicz Logic Algebras and Intuitionistic Logic (Heyting) Algebras are being considered for modeling nonlinear dynamics and cognitive processes in complex neural networks that are present in the human brain, as well as stochastic modeling of genetic networks in Lukasiewicz Logic Algebras. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-058; http://cds.cern.ch/record/746662/files/NaturalTransfMolecModelsnu6.pdf; http://cds.cern.ch/record/746662/files/QuantumAutnu3_ICB.pdf; http://cds.cern.ch/record/746662/files/ext-2004-058.pdf;

Imported from Invenio. Baianu, I C Lukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models A categorical and Lukasiewicz-Topos framework for Lukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes is proposed. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Lukasiewicz Topos with an n-valued Lukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-059; http://cds.cern.ch/record/746663/files/A_GENOMELukNTOPOS_OKnu4.doc; http://cds.cern.ch/record/746663/files/COMPUTERMODELSAUTOMATA_THEORYCOGNITIVEBIOLOGYMEDminOK.doc; http://cds.cern.ch/record/746663/files/COMPUTER_MODEL_AND_AUTOMATA_THEORY_IN_BIOLOGY2p.pdf; http://cds.cern.ch/record/746663/files/ORGANISMIC_SUPERCATEGORIES_AND_QUALITATIVE_DYNAMICS_OF_SYSTEMS2.doc; http://cds.cern.ch/record/746663/files/Organismic_SupercategoriesVnuOK4.doc; http://cds.cern.ch/record/746663/files/ext-2004-059.pdf;

Imported from Invenio. Baianu, I C Molecular Models of Genetic and Organismic Structures In recent studies we showed that the earlier relational theories of organismic sets (Rashevsky,1967), Metabolic-Replication (M,R)-systems (Rosen,1958)and molecular sets (Bartholomay,1968) share a joint foundation that can be studied within a unified categorical framework of functional organismic structures (Baianu,1980. This is possible because all relational theories have a biomolecular basis, that is, complex structures such as genomes, cells,organs and biological organisms are mathematically represented in terms of biomolecular properties and entities,(that are often implicit in their representation axioms. The definition of organismic sets, for example, requires that certain essential quantities be determined from experiment: these are specified by special sets of values of general observables that are derived from physicochemical measurements(Baianu,1970; Baianu,1980; Baianu et al, 2004a.)Such observables are context-dependent and lead directly to natural transformations in categories and Topoi, that are again encountered in (M,R) -systems genralized theories and their biomolecular representations, as well as in molecular set theory (as demonstrated in Baianu, 1980 and Baianu, 2004). Since the problem of uniquely decomposing such organismic structures into their functional(or active) components appears to be an unsolvable one (cf. R.Rosen, 1998, 2001), we--and more recently several other authors- have adopted the complementary procedure of building up functional biomolecular models of specific, genetic and organismic structures (Baianu,1971;1972,Baianu and Marinescu,1974; Baianu,1977,1980; Baianu, 2004; Baianu et al., 2004b. Then, we have examined their genral and specific properties in terms of their mathematical representations and of the generating formalisms (Baianu,1970; 1977;1980; Baianu et al, 2004). In this context, it is interesting that energetic considerations in categories of biological systems(Baianu,1972)considered as open systems with partial ordering of charges and protein dipoles are consistent with the metastable, multi-states postulated by us (Baianu, 1970), as well as by other authors (Frohlich et al,1998). A detailed investigation of natural transformations of biological organism structures is, therefore, essential to enable the understanding of genetic network dynamics and of cell interactomics in terms of relational theories in biology. Such studies are focused on realizability conditions, observability of molecular processes and their uniqueness of representation in categories of diagrames, functors and natural transformations of biological organismic structures. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-067; http://cds.cern.ch/record/768088/files/MolecularModelsICB3.doc; http://cds.cern.ch/record/768088/files/ext-2004-067.pdf;

Imported from Invenio. Baianu, I C Prisecaru, V I Lozano, P Lin, H C Novel Techniques and Their Wide Applications to Health Foods, Medical and Agricultural Biotechnology in Relation to Policy Making on Genetically Modified Crops and Foods Selected applications of novel techniques in Agricultural Biotechnology, Health Food formulations and Medical Biotechnology are being reviewed with the aim of unraveling future developments and policy changes that are likely to open new markets for Biotechnology and prevent the shrinking or closing of existing ones. Amongst the selected novel techniques with applications in both Agricultural and Medical Biotechnology are: immobilized bacterial cells and enzymes, microencapsulation and liposome production, genetic manipulation of microorganisms, development of novel vaccines from plants, epigenomics of mammalian cells and organisms, and biocomputational tools for molecular modeling related to disease and Bioinformatics. Both fundamental and applied aspects of the emerging new techniques are being discussed in relation to their anticipated, marked impact on future markets and present policy changes that are needed for success in either Agricultural or Medical Biotechnology. The novel techniques are illustrated with applications that attempt to convey-- albeit in a simplified manner-- the most important features of representative and powerful tools that are currently being developed for both immediate, and long, term applications in Agriculture, Health Food formulation and production, pharmaceuticals and Medicine. The research aspects are naturally emphasized in our review as they are key to further developments in Biotechnology. On the other hand, the course adopted for the implementation of biotechnological applications, and the policies associated with biotechnological applications in medicine,new drug development and genetically-modified crop growing are clearly the determining factors for future Biotechnology successes in the world markets, be they pharmaceutical, medical or agricultural. KEY WORDS: Applications in Agricultural and Medical Biotechnology; immobilized bacterial cells and enzymes; microencapsulation and liposome production; genetic manipulation of microorganisms;genetically modified crops; genetic therapy in medicine; development of Novel Vaccines from Plants; epigenomics of mammalian cells and organisms; biocomputational tools for molecular modeling related to disease; Bioinformatics; Suggested policy changes for future and immediate successes of Biotechnology in the world markets; Health Food Applications;Medical Applications of Lectins for diagnostics and treatments; High-Resolution Nuclear Magnetic Resonance (NMR), MRI/ MRM and FT-NIR Hyperspectral Imaging in Medicine, Biology, Foods and Agriculture; Fluorescence Correlation Spectroscopy(FCS/FCCS) and applications to Single Molecule Detection, Single Virus Particle Detection by FCCS; Single virus particle, HIV, HPV, Hepatitis B and C, detection for early, successful treatments; Novel, Ultra-sensitive, Selective Detection Methods for Early and Relaible Diagnostics of Cancers. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-066; http://cds.cern.ch/record/768087/files/ApplBiotechnology8NU.doc; http://cds.cern.ch/record/768087/files/FCSNIRHRNMR_Microspectroscopy_HIVHPVSingleVirusParticleDetection_Single_Molecule_Detepdf; http://cds.cern.ch/record/768087/files/FCS_NIRMicrospectroscopy_SingleVirusFCCSDetection_SingleMoleculeDetection.doc; http://cds.cern.ch/record/768087/files/ext-2004-066.pdf;

Imported from Invenio. Baianu,I C Costescu, D Hofmann, N E Korban, S S Lozano, P You, T Fourier Transform Near Infrared Microspectroscopy, Infrared Chemical Imaging, High-Resolution Nuclear Magnetic Resonance and Fluorescence Microspectroscopy Detection of Single Cancer Cells and Single Viral Particles Single Cancer Cells from Human tumors are being detected and imaged by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR)Hyperspectral Imaging and Fluorescence Correlation Microspectroscopy. The first FT-NIR chemical, microscopic images of biological systems approaching one micron resolution are here reported. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are also presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as two cubic microns. Related, HR-NMR analyses of oil contents in somatic embryos as well as 99% accurate calibrations are also presented here with nanoliter precision. Such high-resolution, 400 MHz H-1 NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. >~20%) compared to the average levels in non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monitored by FT-NIR with a precision approaching the picogram level. Tenfold, or hundred-fold, sensitivity increases are predicted for in vivo FT-NIR Fluorescence measurements. Therefore, detailed chemical analyses of oils and phytochemicals are now becoming possible by FT-NIR Chemical Imaging/ Microspectroscopy of single cells. Signalling pathways and key interactions controlling the cell division in normal and transformed cells may be thus monitored in vivo, noninvasively, in human patients with very high sensitivity, accuracy and remarkable speed. In other, agricultural applications, such as the genetic selection and breeding programs, the lower cost, speed and analytical requirements of such genetic selection programs are fully satisfied by FT-NIR spectroscopy and Microspectroscopy, as in the case of whole soybean seeds and soybean embryos that we have investigated extensively and also validated with over 50,000 different soybean samples. FT-NIR Microspectroscopy and Chemical Imaging are also shown to be potentially important in functional Genomics and Proteomics research through the rapid and accurate detection of high-content microarrays (HCMA). Multi-photon(MP), pulsed (~150) femtosecond laser NIR Fluorescence Excitation techniques were shown to be capable of Single Molecule Detection (SMD) and 0.25 micron resolution with turbid cell suspensions. Therefore, such powerful techniques allow for ultra-sensitive and reliable quantitative analyses to be carried out both in vitro and in vivo. Thus, MP NIR excitation for Fluorescence Correlation Spectroscopy (FCS) allows not only single molecule detection, but also molecular dynamics and high resolution, submicron imaging of sub-femtoliter volumes inside living cells and tissues. These novel, ultra-sensitive and rapid NIR/FCS analyses have numerous applications in important research areas, such as: medical/cancer research, pharmacology, early clinical diagnosis of viral diseases and cancers, agricultural biotechnology and food safety. *Corresponding Author: Professor I.C. Baianu KEYWORDS: FT-NIR and FT-IR Instruments, applications of FCS/NIR; Agricultural biotechnology; IR Chemical Imaging and NMR Microspectroscopy; DNA/RNA Micro-array analysis by NIR; High resolution and super-resolution FT-NIR/IR; IR Chemical Imaging by FPAW, Spotlight 300 Microspectrometer; Two photon NIR excitation for FCS; Single Cell and single molecule dynamics; FCS of molecules, single cells, Soybean oil, protein and moisture analysis; FT-NIR and FT-IR, high-resolution NMR of soybean oil in seeds and somatic embryos; chemical mutagenesis of soybean embryos; picomole FT-NIR and femtomole FCS-NIR analysis of single cells; phytochemicals detection in soybean seeds and cells by FT-NIR; high-power, femtosecond Ti:Sapphire NIR excitation for FCS, FCS/PCR; Nucleic acid hybridization, FT-IR and FT-NIR Images of Soybeans and Embryos; FT-IR and NIR Chemical Imaging Tests; Spatial Resolution Test in FT-NIR Micro-Imaging; FT-NIR Images of Soybeans and Embryos; FT-IR Reflectance Chemical Images; Somatic Embryos; NIR Reflectance Chemical Image of a Red Coat Azuki Red Bean; FT-NIR Chemical Imaging by Difference Spectroscopy (CIDS); High Resolution NMR Analysis of Soybean Oil in Somatic Embryos; HR NMR, H-1 NMR Spectrum of somatic soybean embryogenic cultures; TEM Micrograph of a Suspension of Soybean Somatic Embryos in Culture; Single Molecule Detection, two-photon excitation, one-photon excitation, three-photon-excitation, Fluorescence Correlation Spectroscopy (FCS); Fluorescence Resonance Energy Transfer (FRET); Fluorescence Lifetime Imaging Microscopy (FLIM); Fluorescence Recovery After Photobleaching (FRAP); Single Photon Confocal Fluorescence Correlation Spectroscopy; Inverted Epifluorescence Microscope; FCS auto-correlation, Fluorescence Fluctuations, Fluorescence Intensity, Fluorescence Correlation Spectroscopy and Imaging Experiments in Solutions and Plant Cell Suspensions; Pulsed, Two-Photon NIR Laser Excitation; Multi-photon (MPE) NIR excitation; FCS Alba Spectrometer Microspectrometer System; FCCS Cross-Correlation with Two Fluorescent Labels; FCCS Applications to DNA Hybridization. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-069; http://cds.cern.ch/record/768090/files/SinglecancerCellFTNIRFCSIR.doc; http://cds.cern.ch/record/768090/files/ext-2004-069.pdf;

Imported from Invenio. Baianu, I C Lin, H C Computer Simulation and Computabiblity of Biological Systems The ability to simulate a biological organism by employing a computer is related to the ability of the computer to calculate the behavior of such a dynamical system, or the "computability" of the system. However, the two questions of computability and simulation are not equivalent. Since the question of computability can be given a precise answer in terms of recursive functions, automata theory and dynamical systems, it will be appropriate to consider it first. The more elusive question of adequate simulation of biological systems by a computer will be then addressed and a possible connection between the two answers given will be considered as follows. A symbolic, algebraic-topological "quantum computer" (as introduced in Baianu, 1971b) is here suggested to provide one such potential means for adequate biological simulations based on QMV Quantum Logic and meta-Categorical Modeling as for example in a QMV-based, Quantum-Topos (Baianu and Glazebrook,2004. eng 2004

http://documents.cern.ch/cgi-bin/setlink?base=preprint&categ=ext&id=ext-2004-072; http://cds.cern.ch/record/771494/files/ext-2004-072.htm; http://cds.cern.ch/record/771494/files/ext-2004-072.pdf;

Imported from Invenio.