Academic literature on the topic 'Biochemical Science'

<p> Recent advances in biology have shown that proteins and genes often interact probabilistically. The resulting effects that arise from these stochastic dynamics differ significantly than traditional deterministic formulations, and have biologically significant ramifications. This has led to the development of computational models of the discrete stochastic biochemical pathways found in living organisms. These include spatial stochastic models, where the physical extent of the domain plays an important role; analogous to traditional partial differential equations. </p><p> Simulation of spatial stochastic models is a computationally intensive task. We have developed a new algorithm, the Diffusive Finite State Projection (DFSP) method for the efficient and accurate simulation of stochastic spatially inhomogeneous biochemical systems. DFSP makes use of a novel formulation of Finite State Projection (FSP) to simulate diffusion, while reactions are handled by the Stochastic Simulation Algorithm (SSA). Further, we adapt DFSP to three dimensional, unstructured, tetrahedral meshes in inclusion in the mature and widely usable systems biology modeling software URDME, enabling simulation of the complex geometries found in biological systems. Additionally, we extend DFSP with adaptive error control and a highly efficient parallel implementation for the graphics processing units (GPU). </p><p> In an effort to understand biological processes that exhibit stochastic dynamics, we have developed a spatial stochastic model of cellular polarization. Specifically we investigate the ability of yeast cells to sense a spatial gradient of mating pheromone and respond by forming a projection in the direction of the mating partner. Our results demonstrates that higher levels of stochastic noise results in increased robustness, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function. This also highlights the importance of spatial stochastic modeling to reproduce experimental observations.</p>

Stokesia laevis (J. Hill) Greene is a herbaceous perennial with blue, lavender, violet, albescent, pale yellow or pale pink flowers. All cultivars are diploid (2n=2x=14) except for ?Omega Skyrocket, a tetraploid (2n=4x=28) cultivar selected from a wild population. Anthocyanidin and copigment aglycones extracted from floral tissue were characterized using high-performance liquid chromatography. Results indicated that blue, lavender, violet and albescent flowers contained petunidin, though albescent flowers contained a substantially smaller amount. Pale pink flowers were found to contain only cyanidin. Anthocyanidins and carotenoids were not present in pale yellow flowers. All flowers contained the flavone luteolin. Genetic analyses suggested that at least three genes (A, P, Y) each with two alleles control flower color: A permits normal synthesis of anthocyanins and other flavonoids, a reduces synthesis and/or prevents the accumulation of anthocyanins and other flavonoids; Y permits normal synthesis of anthocyanins, y completely blocks synthesis of anthocyanins; P produces petunidin, p produces cyanidin. All three genes are completely dominant, and yy is epistatic to A and P. We provide a model for flavonoid biosynthesis in Stokesia. Study of karyotypes and meiotic behavior of diploid cultivars and ?Omega Skyrocket? suggest that ?Omega Skyrocket? is an autotetraploid form of Stokesia. The karyotype of ?Omega Skyrocket? was almost indistinguishable from the average diploid karyotype. Meiotic pairing in diploids was normal (i.e. 100% bivalents); no meiotic irregularities such as laggards/bridges were observed and disjunction was equal (7:7). Meiotic pairing in ?Omega Skyrocket? demonstrated a high frequency (60%) of quadrivalent formation, though later stages of meiosis were regular with balanced disjunction (14:14). Meiosis in synthetic autotetraploids and triploids from crosses of diploid cultivars × ?Omega Skyrocket? included univalents, bivalents, trivalents, quadrivalents, 5-valents; abnormalities (i.e. laggards, unequal and/or premature disjunction, chromosome bridging, chromosome stickiness) were observed. Nuclear 2C DNA content for diploids and synthetic tetraploids was 20.3 pg and 39.9 pg. Nuclear 2C DNA content for ?Omega Skyrocket? was 37.3 pg (i.e. 8.2% less than twice the 2C DNA content of diploids), indicating that genomic downsizing has likely occurred in this cultivar. Differences in chromosome symmetry between the diploid and tetraploid karyotypes and the reduction in nuclear DNA content observed in ?Omega Skyrocket? both suggest that some divergence has occurred between ?Omega Skyrocket? and its diploid progenitor. A crossability study was conducted to determine the ploidy level and the frequency of progeny produced by interploid and intraploid crosses of Stokesia. A high percentage (70%) of progeny were aneuploids (i.e. 2x-1 to 6x+3) with the total percentage of aneuploids ranging from 92% to 94% in 2x × 3x, 3x × 2x, 3x × 3x, 3x × 4x and 4x × 3x crosses. Progeny (94%) from 2x × 2x crosses were diploids, and progeny (81%) from 2x × 4x and 4x × 2x crosses were triploids and 3x±1 aneuploids. Progeny from crosses of synthetic tetraploids were mostly tetraploids (16%) and tetraploid aneuploids (69%). Unreduced gamete production was estimated to be 0.7% to 1.4%. Reciprocal crosses of identical 2x and 4x parents produced viable progeny, demonstrating that a triploid block is not present in this species. Triploid fertility was higher than expected; crosses using triploids produced seed 38% of the time with an average seed set of ~2 seeds/inflorescence. Fertility of synthetic tetraploids was reduced relative to their progenitor diploids; pollen viability was reduced by 36% and the percentage of inflorescences producing seed and average seed set/inflorescence were reduced by ~50%. Pollen size was positively correlated with ploidy level (i.e. DNA content).

The high mobility group protein HMGA2 is an architectural transcription factor, which is expressed during embryogenesis. Aberrant expression causes benign and malignant tumor formation. The protein possesses three "AT hook" domains and an acidic Cterminal. HMGA2 is natively unstructured, however it forms a homodimer. In this study site-directed mutagenesis was used to create single methionine mutants, HMGA2Q37M, HMGA2I71M and HMGA2Q85M. These mutants were cross-linked using EDC and then cleaved using CNBr to determine which domains are involved in homodimer formation. Our results indicate that the second "AT hook" domain may interact with the C-terminal. We then labeled a peptide containing the C-terminal (CTP) with tetramethylrhodamine-5- maleimide (TRM). We found that the CTP-TMR binds to HMGA2Α95-108, which lacks the C-terminal. These results suggest that the C-terminal is required for homodimer formation. The techniques used within this study can be applied to forensics and with further research HMGA2 may have a forensic application.

Three aspects of chemical and biochemical reactions were investigated. 1. The relative reactivities of pyrophosphate (phosphorus(V)) and pyro-di-H-phosphonate (phosphorus(III)) and its derivatives have been analysed at various pHs. The hydrolysis rate of pyro-di-H-phosphonate (PP(III)) was found to be higher than pyrophosphate at all pHs. Using ITC and NMR, pyrophosphate showed metal-ion complexing abilities whereas pyro-di-H-phosphonate showed weak or no complexing to metal-ions, although the rate of hydrolysis at pH 7 slightly increased compared to the spontaneous hydrolysis of PP(III). The enzymatic hydrolysis of pyrophosphate, which is thought to occur via MgPP(V)2-, occurs efficiently and is close to being diffusion controlled. Pyro-di-H-phosphonate on the other hand does not act as a substrate or as an inhibitor of pyrophosphatase. 2. Dichloromethane (DCM) is an alkylating agent for pyridine, producing methylene bis-pyridinium dication (MDP) upon refluxing the solution. The kinetics and mechanism of hydrolysis of methylene bis-pyridinium dication have been studied. Below pH 7 MDP is extremely stable and hydrolysis is first-order in hydroxide-ion. Above pH 9 an unusual intermediate is formed on hydrolysis which has a chromophore at 366 nm in water and its formation is second-order in hydroxide-ion. The carbon acidity of the central methylene group was also investigated kinetically using H/D exchange and the pKa was surprisingly high at 21.2 at 25oC (I = 1.0 M). 3. Isothermal titration calorimetry (ITC) is a technique mainly used by biochemists to obtain a range of physical and thermodynamic properties of a reaction. Analysing the data can become difficult when investigating complex reactions involving more than one step, for instance metal-ions binding to an enzyme. In this work models have been developed to simulate sequential reactions. These were used to simulate experimental ITC data for metal-ions: Zn2+, Co2+ and Cd2+ complexing to the active sites of BcII, a metallo β-lactamase responsible for antibiotic resistance, providing additional information on the mechanism by which this enzyme acts to deactivate β-lactam antibiotics. The simulations suggest that BcII has two very similar binding affinities to metal-ions which are filled sequentially.

The necessity of light for plants to sustain their autotrophic lifestyle has made the optimization of growth to maximize light capture a crucial strategy for survival in light-limiting environments. Increases in light capture can be achieved through alterations in plant architecture, such as modifications to leaf position and stem length. Responses to the light environment are mediated by a network of photoreceptor proteins, which sense specific wavelengths of light and respond to light excitation by initiating signaling. Higher plants respond to red and far-red light through the phytochrome family, blue light through cryptochromes, the zeitlupe family, and phototropins, and UV-B light through the UV RESISTANCE LOCUS 8 photoreceptor. Of these photoreceptor proteins, the phototropins (phots) are perhaps the most closely tied to photosynthetic efficiency. Higher plant phots, phot1 and phot2, mediate leaf expansion to maximize the surface area available for light capture as well as control movement and positioning responses, such as petiole inclination, movement towards more favorable light conditions through phototropism, and, at a cellular level, chloroplast movement. Furthering the role of phots in optimizing responses upstream of photosynthesis, phot1 and phot2 also control stomatal opening in response to blue light, allowing the uptake of carbon dioxide into the leaf for fixation into sugars. In general, these responses are redundantly coordinated by both phot1 and phot2, with phot1 acting as the primary sensor due to its greater sensitivity. Because of the profound effect phots have on photosynthetic competence, the studies presented here examine phot1 with the goal of understanding the physiological role of phot1 sensitivity in plants and explore the possibility that enhancing phot1 sensitivity could increase plant growth. Phots consist of two N-terminal light sensing LOV (Light, Oxygen or Voltage) domains, LOV1 and LOV2, coupled to a serine/threonine kinase domain at the C-terminus. Each of the LOV domains bind a flavin mononucleotide (FMN) chromophore that allows these domains to perceive blue light. In darkness, FMN is non-covalently bound within each of the LOV domains, which repress the activity of the kinase domain. When FMN is excited by blue light, a covalent bond is formed between a conserved cysteine residue present within each LOV domain and FMN. LOV2 specifically is coupled to the kinase domain through two alpha helices, Jα and A’α, which become disordered following the formation of the covalent photoadduct. The unfolding of these alpha helices relieves repression of the kinase domain, initiating signaling. The onset of phot1 signaling is characterized by phot1 autophosphorylation and the dephosphorylation of the phot1 signaling partner NON-PHOTOTROPIC HYPOCOTYL 3 (NPH3). Over time, the covalent photoadduct decays and phot1 returns to its inactive dark state, completing the photocycle. The chemistry of the phot1 photocycle in vitro is understood in detail, but its downstream signaling following activation remains relatively elusive, with only a handful of signaling partners and phosphorylation substrates identified. For the sensitivity of phot1 to be thoroughly explored, how the phot1 photocycle affects plant growth as well as how phot1 activity is modulated by signaling partners needed to be addressed. Therefore, a biochemical approach was used to introduce mutations within LOV2 to slow its dark reversion to prolong signaling and investigate how this modulates phot1 sensitivity in vitro and in planta, and, secondly, a genetic strategy was employed to uncover whether any signaling processes can modulate phot1 sensitivity in plants. Compared to other photoreceptors that receive blue light through LOV domains, dark reversion of phot1 following a light stimulus is relatively fast, with the lit state lasting only approximately 15 minutes, while other LOV domains remain activated for many hours. To generate slow photocycle mutants of phot1, previous characterizations of slow photocycling LOV domains were exploited to engineer the phot1 photocycle to have a slower dark reversion by introducing mutations into LOV2. To study the photocycle in vitro, the phot1 light-sensing module consisting of the LOV1 and LOV2 domains (LOV1+LOV2) was heterologously expressed and purified from E. coli and the photocycle was measured spectrophotometrically. Using this approach, 13 LOV2 variants were generated and examined to identify slow photocycle mutants. Three mutations in LOV2, N476L, V478I, and L558I, were found to slow the LOV1+LOV2 photocycle in vitro. Following identification, these mutations were introduced into full-length phot1 expressed heterologously in insect cells to verify the autophosphorylation activity of each mutant. Following the characterization of the candidate slow photocycle mutants in vitro, each phot1 photocycle mutant was examined in planta in a phot1phot2 double mutant background to see whether possession of a slow photocycle increased phot1 sensitivity. Of the three candidate mutations, V478I and L558I were verified as possessing a slow dark reversion through the phosphorylation status of NPH3. NPH3 is dephosphorylated in a phot1-dependent manner following light treatment; it was found that in the presence of wild-type phot1, the phosphorylated form of NPH3 is recovered around one hour following a return to darkness after phot1 stimulation by blue light. By contrast, the dephosphorylated state of NPH3 was sustained in phot1-V478I and -L558I for a substantially longer period of time, consistent with a slow phot1 photocycle and prolonged phot1 activation in these mutants. Surprisingly, it was found that these mutants were less sensitive than wild-type phot1 for phototropism in response to low intensity light treatments. Furthermore, biomass accumulation was not increased in the phot1-L558I mutant under growth conditions consisting of very low light. While the photocycle mutants did not exhibit increased sensitivity or growth in response to continuous light treatments, evidence from collaborators indicated that phot1-L558I is more efficient than wild-type phot1 for the chloroplast accumulation response following brief pulses of blue light. While the role of the phot1 photocycle under continuous irradiation remained unclear, this enhanced chloroplast accumulation response implies that the phot1 photocycle is important for its sensitivity to brief irradiations. Unlike phot1, further work with phot2 later indicated that introducing a slow photocycle mutation to phot2 LOV2 can significantly increase growth in a phot1phot2 mutant background under continuous low light. To investigate other factors that may affect phot1 sensitivity, a genetic screen was undertaken in an attempt to identify suppressors of phot1 activity. The LOV2Kinase (L2K) transgenic line, which expresses a truncated version of phot1 in a phot1phot2 double mutant background, was previously found to be unable to respond to low-intensity blue light, though it can mediate phot1 responses when the light intensity is increased. Because L2K possesses this conditional phenotype, random mutations were introduced into the genome of L2K-expressing plants and a screen was established to identify mutants that were able to respond to low-intensity light with the hypothesis that those mutations could lie within suppressors of phot1 activity, allowing L2K to signal under circumstances where it ordinarily could not. Using this approach, three independent candidate suppressor mutants were identified that had increased sensitivity for the petiole positioning response under low light. One suppressor mutant was identified as a novel allele of the phytochrome B red light receptor, the second is likely to be a mutant of a transcription factor, and the identity of the third candidate suppressor is still not known, though it overexpressed the L2K protein. These candidate suppressors may represent novel modulators of phot1 activity and possible mechanisms for how these candidate suppressors may act on phot1 activity are discussed. In summary, both the biochemical and genetic approaches yielded mutants with increased sensitivity for phot1-mediated responses, enabling a more detailed understanding of how phot1 sensitivity influences its activity and plant growth.<br>This lays the groundwork for extending the increased sensitivity observed in response to pulses in the photocycle mutants to responses other phot1-mediated responses, and for integrating new models of suppression of phot1 activity into our framework for phot1 activation and signaling.

Information visualization is a field of computer science that deals with the computerized visualization of complex information in a form that is easier for human beings to comprehend. Information visualization has applications in many domains, including business, science, and medicine. Visualization of biochemical pathways, as graphs of nodes representing biochemical entities and arcs representing the relationships between entities, is one such application. This thesis begins by reviewing work that has been done on the usability of information visualization techniques, and in particular these that apply to biochemical pathways. Then, the thesis presents three different usability evaluation techniques that are used to gather information about existing biochemical pathway visualization tools. These are (1) conducting videotaped evaluation sessions of existing biochemical visualization tools, (2) collecting questionnaires, and (3) conducting a brainstorming session. The results from these studies are used to define the requirements, design, and build a biochemical pathway visualization tool, taking into account conclusions drawn from both literature and user studies. The tool is then tested and compared to existing tools. Results show that the developed tool has more relevant features to biochemical pathway visualization than existing tools, accomplishes certain tasks faster than other tools, and is intuitive and easy to use. In addition, positive feedback from users is documented. At the end of the thesis, we make some generalizations to the area of information visualization and we then present areas for further research.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 169-181).<br>The large-scale simulation of biochemical reaction networks in cells is important in pathway discovery in medicine, in analyzing complex cell function in systems biology, and in the design of synthetic biological circuits in living cells. However, cells can undergo many trillions of reactions over just an hour with multi-scale interacting feedback loops that manifest complex dynamics; their pathways exhibit non-modular behavior or loading; they exhibit high levels of stochasticity (noise) that require ex- pensive Gillespie algorithms and random-number generation for accurate simulations; and, they routinely operate with nonlinear statics and dynamics. Hence, such simulations are extremely computationally intensive and have remained an important bottleneck in computational biology over decades. By exploiting common mathematical laws between electronics and chemistry, this thesis demonstrates that digitally programmable analog integrated-circuit 'cytomorphic' chips can efficiently run stochastic simulations of complex molecular reaction networks in cells. In a proof-of-concept demonstration, we show that 0.35 [mu]m BiC- MOS cytomorphic gene and protein chips that interact via molecular data packets with FPGAs (Field Programmable Gate Arrays) to simulate networks involving up to 1,400 biochemical reactions can achieve a 700x speedup over COPASI, an efficient bio- chemical network simulator. They can also achieve a 30,000x speedup over MATLAB. The cytomorphic chips operate over five orders of magnitude of input concentration; they enable low-copy-number stochastic simulations by amplifying analog thermal noise that is consistent with Gillespie simulations; they represent non-modular load- ing effects and complex dynamics; and, they simulate zeroth, first, and second-order linear and nonlinear gene-protein networks with arbitrary parameters and network connectivity that can be flexibly digitally programmed. We demonstrate successful stochastic simulation of a p53 cancer pathway and glycolytic oscillations that are consistent with results obtained from conventional digital computer simulations, which are based on experimental data. We show that unlike conventional digital solutions, an increase in network scale or molecular population size does not compromise the simulation speed and accuracy of our completely parallel cytomorphic system. Thus, commonly used circuit improvements to future chips in our digital-to-analog converters, noise generators, and biasing circuits can enable further orders of magnitude of speedup, estimated to be a million fold for large-scale networks.<br>by Sung Sik Woo.<br>Ph. D.

Mathematical models are a fundamental tool used in many branches of science and engineering to gain insights into the dynamics of systems. In Systems Biology [122], these models facilitate the analysis of complex biochemical networks that describe molecular interactions at different level in living organisms. In the last years, several efforts have been dedicated to the characterization of biological systems [98, 127, 128, 165, 209], specific interaction mechanisms [10, 15, 44, 45] and biological phenomena such as oscillations and bistability [80, 119, 147, 175]. As result, the use of mathematical models of biological processes have shown to be an useful asset in the development of several medical applications including drug design and target therapy [55, 110, 179, 197]. There are two main problems to solve when working with these types of models. The first one is the typically large computational cost required for their analysis, which is giving in part to the large number of configurations in which components such as proteins, or genes are present in the living cell. The second problem is related to the difficulty in calibrating large models, since they are generally associated with many parameters whose experimental estimation in living cells is notoriously difficult to make. In this dissertation we explore several approaches for the reduction of biological systems, paying special attention to the interpretability of the aggregated system. In general, we are interested in techniques that produce an exact reduction, i.e., algorithms that given an input chemical network, produce a smaller network (consisting of fewer species and reactions) that preserves the output dynamics of interest to the modeler, e.g. [137, 201]. We present a framework for the automatic analysis of largescale quantitative repositories of biological models, with special support for models written in the well-known SBML [103] specifications. For networks with stochastic dynamics, we provide equivalences at the level of the Markov chain that can be applied to several models including biological networks and epidemic processes on complex networks. In addition, we approach the simplification of biochemical models by focusing on their steady-state behaviour [2], a stable condition attained by the biological system when the influence of the initial condition can be disregarded. Here, we discuss a method for the computation of equilibrium points, with the guarantee of yielding the unique equilibrium of the ODE under defined graph-theoretical conditions. Overall, this dissertation provides a detailed analysis of techniques for the reduction of quantitative models of biochemical reaction networks, together with the interpretation of the biological and functional characteristics of the reductions obtained over a wide collection of case studies from the literature. Interestingly, the inspection of the obtained reductions has revealed reducible motifs, i.e., structures embedded in the structure of the network that are often compressed in the models analyzed. These findings provide the intuition that these techniques can abstract beyond the behavior of the system to capture structural/functional segments of the network that can be simplified with no (or little) effect in the dynamics of the model.

Gene and protein interaction networks have evolved to precisely specify cell fates and functions. Here, we analyse whether the architecture of these networks affects evolvability. We find evidence to suggest that in yeast these networks are mainly acyclic, and that evolutionary changes in these parts do not affect their global dynamic properties. In contrast, feedback loops strongly influence dynamic behaviour and are often evolutionarily conserved. Feedback loops are often found to reside in a clustered manner by means of coupling and nesting with each other in the molecular interaction network of yeast. In these clusters some feedback mechanisms are biologically vital for the operation of the module and some provide auxiliary functional assistance. We find that the biologically vital feedback mechanisms are highly conserved in both transcription regulation and protein interaction network of yeast. In particular, long feedback loops and oscillating modules in protein interaction networks are found to be biologically vital and hence highly conserved. These data suggest that biochemical networks evolve differentially depending on their structure with acyclic parts being permissive to evolution while cyclic parts tend to be conserved.