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In eukaryotes, the enormously long DNA molecules need to be packaged together with histone proteins into nucleosomes and further into compact chromatin structures to fit it into the nucleus. This nuclear organisation interferes with all phases of transcription that require the polymerase to bind to DNA. During transcription – the process in which the hereditary information stored in DNA is transferred to many transportable RNA molecules - nucleosomes form a physical obstacle for polymerase progression. Thus, transcription is usually accompanied by processes mediating nucleosome destabilisation, including post-translational histone modifications (PTMs) or exchange of canonical histones by their variant forms. To the best of our knowledge, acetylation of histones has the highest capability to induce chromatin opening. The lysine modification can destabilise histone-DNA interactions within a nucleosome and can serve as a binding site for various chromatin remodelers that can modify the nucleosome composition. For example, H4 acetylation can impede chromatin folding and can stimulate the exchange of canonical H2A histone by its variant form H2A.Z at transcription start sites (TSSs) in many eukaryotes, including humans. As histone H4, H2A.Z can be post-translationally acetylated and as acetylated H4, acetylated H2A.Z is enriched at TSSs suggested to be critical for transcription. However, thus far, it has been difficult to study the cause and consequence of H2A.Z acetylation.
Even though, genome-wide chromatin profiling studies such as ChIP-seq have already revealed the genomic localisation of many histone PTMs and variant proteins, they can only be used to study individual chromatin marks and not to identify all factors important for establishing a distinct chromatin structure. This would require a comprehensive understanding of all marks associated to a specific genomic locus. However, thus far, such analyses of locus-specific chromatin have only been successful for repetitive regions, such as telomeres.
In my doctoral thesis, I used the unicellular parasite Trypanosoma brucei as a model system for chromatin biology and took advantage of its chromatin landscape with TSSs comprising already 7% of the total T. brucei genome (humans: 0.00000156%). Atypical for a eukaryote, the protein-coding genes are arranged in long polycistronic transcription units (PTUs). Each PTU is controlled by its own ~10 kb-wide TSS, that lies upstream of the PTU. As observed in other eukaryotes, TSSs are enriched with nucleosomes containing acetylated histones and the histone variant H2A.Z. This is why I used T. brucei to particularly investigate the TSS-specific chromatin structures and to identify factors involved in H2A.Z deposition and transcription regulation in eukaryotes. To this end, I established an approach for locus-specific chromatin isolation that would allow me to identify the TSSs- and non-TSS-specific chromatin marks. Later, combining the approach with a method for quantifying lysine-specific histone acetylation levels, I found H2A.Z and H4 acetylation enriched in TSSs-nucleosomes and mediated by the histone acetyltransferases HAT1 and HAT2. Depletion of HAT2 reduced the levels of TSS-specific H4 acetylation, affected targeted H2A.Z deposition and shifted the sites of transcription initiation. Whereas HAT1 depletion had only a minor effect on H2A.Z deposition, it had a strong effect on H2A.Z acetylation and transcription levels. My findings demonstrate a clear link between histone acetylation, H2A.Z deposition and transcription initiation in the early diverged unicellular parasite T. brucei, which was thus far not possible to determine in other eukaryotes. Overall, my study highlights the usefulness of T. brucei as a model system for studying chromatin biology. My findings allow the conclusion that H2A.Z regardless of its modification state defines sites of transcription initiation, whereas H2A.Z acetylation is essential co-factor for transcription initiation. Altogether, my data suggest that TSS-specific chromatin establishment is one of the earliest developed mechanisms to control transcription initiation in eukaryotes.
Transcription describes the process of converting the information contained in DNA into RNA. Although, tremendous progress has been made in recent decades to uncover this complex mechanism, it is still not fully understood. Given the advances and reduction in cost of high-throughput sequencing experiments, more and more data have been generated to help elucidating this complex process. Importantly, these sequencing experiments produce massive amounts of data that are incomprehensible in their raw form for humans. Further, sequencing techniques are not always 100% accurate and are subject to a certain degree of variability and, in special cases, they might introduce technical artifacts. Thus, computational and statistical methods are indispensable to uncover the information buried in these datasets.
In this thesis, I worked with multiple high throughput datasets from herpes simplex virus 1 (HSV-1) and human cytomegalovirus (HCMV) infections. During the last decade, it has became clear that a gene might not have a single, but multiple sites at which transcription initiates. These multiple transcription start sites (TiSS) demonstrated to have regulatory effects on the gene itself depending on which TiSS is used. Specialized experimental approaches were developed to help identify TiSS (TiSS-profiling). In order to facilitate the identification of all potential TiSS that are used for cell type- and condition-specific transcription, I developed the tool iTiSS. By using a new general enrichment-based approach to predict TiSS, iTiSS proved to be applicable in integrated studies and made it less prone to false positives compared to other TiSS-calling tools. Another improvement in recent years was made in metabolic labeling experiments such as SLAM-seq. Here, they removed the time consuming and laborious step of physically separating new from old RNA in the samples. This was achieved by inducing specific nucleotide conversions in newly synthesized RNA that are later visible in the data. Consequently, the separation of new and old RNA is now done computationally and, hence, tools are needed that accurately quantify these fold-changes. My second tool that I developed, called GRAND-SLAM proved to be capable to accomplish this task and outperform competing programs. As both of my tools, iTiSS and GRAND-SLAM are not specifically tailored to my own goals, but could also facilitate the research of other groups in this field, I made them publicly available on GitHub.
I applied my tools to datasets generated in our lab as well as to publicly available data sets from HSV-1 and HCMV, respectively. For HSV-1, I was able to predict and validate TiSS with nucleotide precision using iTiSS. This has lead to the most comprehensive annotation for HSV-1 to date, which now serves as the fundamental basis of any future transcriptomic research on HSV-1. By combining both my tools, I was further able to uncover parts of the highly complex gene kinetics in HCMV and to resolve the limitations caused by the densely packed genome of HCMV.
With the ever-increasing advances in sequencing techniques and their decrease in cost, the amounts of data produced will continue to rise massively in the future. Additionally, more and more specialized omics approaches are appearing, calling for new tools to leverage their full information potential. Consequently, it has become apparent that specialized computational tools such as iTiSS and GRAND-SLAM are needed and will become an essential and indispensable part of the analysis.
The holy grail of structural biology is to study a protein in situ, and this goal has been fast approaching since the resolution revolution and the achievement of atomic resolution. A cell's interior is not a dilute environment, and proteins have evolved to fold and function as needed in that environment; as such, an investigation of a cellular component should ideally include the full complexity of the cellular environment. Imaging whole cells in three dimensions using electron cryotomography is the best method to accomplish this goal, but it comes with a limitation on sample thickness and produces noisy data unamenable to direct analysis. This thesis establishes a novel workflow to systematically analyse whole-cell electron cryotomography data in three dimensions and to find and identify instances of protein complexes in the data to set up a determination of their structure and identity for success. Mycoplasma pneumoniae is a very small parasitic bacterium with fewer than 700 protein-coding genes, is thin enough and small enough to be imaged in large quantities by electron cryotomography, and can grow directly on the grids used for imaging, making it ideal for exploratory studies in structural proteomics. As part of the workflow, a methodology for training deep-learning-based particle-picking models is established.
As a proof of principle, a dataset of whole-cell Mycoplasma pneumoniae tomograms is used with this workflow to characterize a novel membrane-associated complex observed in the data. Ultimately, 25431 such particles are picked from 353 tomograms and refined to a density map with a resolution of 11 Å. Making good use of orthogonal datasets to filter search space and verify results, structures were predicted for candidate proteins and checked for suitable fit in the density map. In the end, with this approach, nine proteins were found to be part of the complex, which appears to be associated with chaperone activity and interact with translocon machinery.
Visual proteomics refers to the ultimate potential of in situ electron cryotomography: the comprehensive interpretation of tomograms. The workflow presented here is demonstrated to help in reaching that potential.
Adrenocortical carcinoma (ACC) is a rare, but highly aggressive endocrine malignancy. Tumor-related hypercortisolism is present in 60 % of patients and associated with worse outcome. While cancer immunotherapies have revolutionized the treatment of many cancer entities, the results of initial studies of different immune checkpoint inhibitors in ACC were heterogeneous. Up to now, five small clinical trials with a total of 121 patients have been published and demonstrated an objective response in only 17 patients. However, one of the studies, by Raj et al., reported a clinically meaningful disease control rate of 52 % and a median overall survival of almost 25 months suggesting that a subgroup of ACC patients may benefit from immunotherapeutic approaches. Following the hypothesis that some ACCs are characterized by a glucocorticoid-induced T lymphocytes depletion, several studies were performed as part of the presented thesis. First, the immune cell infiltration in a large cohort of 146 ACC specimens was investigated. It was demonstrated for the first time, and against the common assumption, that ACCs were infiltrated not only by FoxP3+ regulatory T cells (49.3 %), but also that a vast majority of tumor samples was infiltrated by CD4+ TH cells (74 %) and CD8+ cytotoxic T cells (84.3 %), albeit the immune cell number varied heterogeneously and was rather low (median: 7.7 CD3+ T cells / high power field, range: 0.1-376). Moreover, the presence of CD3+-, CD4+- and CD8+ ACC-infiltrating lymphocytes was associated with an improved recurrence-free (HR: 0.31 95 % CI 0.11-0.82) and overall survival (HR: 0.47 96 % CI 0.25-0.87). Particularly, patients with tumor-infiltrating CD4+ TH cells without glucocorticoid excess had a significantly longer overall survival compared to patients with T cell-depleted ACC and hypercortisolism (121 vs. 27 months, p = 0.004). Hence, the impact of glucocorticoids might to some extent be responsible for the modest immunogenicity in ACC as hypercortisolism was reversely correlated with the number of CD4+ TH cells. Accordingly, CD3+ T cells co-cultured with steroidogenic NCI-H295R ACC cells demonstrated in vitro an enhanced anti-tumoral cytotoxicity by secreting 747.96 ±225.53 pg/ml IFN-γ in a therapeutically hormone-depleted microenvironment (by incubation with metyrapone), versus only 276.02 ±117.46 pg/ml IFN-γ in a standard environment with glucocorticoid excess.
Other potential biomarkers to predict response to immunotherapies are the immunomodulatory checkpoint molecules, programmed cell death 1 (PD-1) and its ligand PD-L1, since both are targets of antibodies used therapeutically in different cancer entities. In a subcohort of 129 ACCs, expressions of both molecules were heterogeneous (PD-1 17.4 %, range 1-15; PD-L1 24.4 %, range 1 - 90) and rather low. Interestingly, PD-1 expression significantly influenced ACC patients´ overall (HR: 0.21 95 % CI 0.53-0.84) and progression- free survival (HR: 0.30 95 % CI 0.13-0.72) independently of established factors, like ENSAT tumor stage, resection status, Ki67 proliferation index and glucocorticoid excess, while PD-L1 had no impact.
In conclusion, this study provides several potential explanations for the heterogeneous results of the immune checkpoint therapy in advanced ACC. In addition, the establishment of PD-1 as prognostic marker can be easily applied in routine clinical care, because it is nowadays anyway part of a detailed histo-pathological work-up. Furthermore, these results provide the rationale and will pave the way towards a combination therapy using immune checkpoint inhibitors as well as glucocorticoid blockers. This will increase the likelihood of re-activating the immunological anti-tumor potential in ACC. However, this will have to be demonstrated by additional preclinical in vivo experiments and finally in clinical trials with patients.
Additive manufacturing processes such as 3D printing are booming in the industry due to their high degree of freedom in terms of geometric shapes and available materials. Focusing on patient-specific medicine, 3D printing has also proven useful in the Life Sciences, where it exploits the shape fidelity for individualized tissues in the field of bioprinting. In parallel, the current systems of bioreactor technology have adapted to the new manufacturing technology as well and 3D-printed bioreactors are increasingly being developed. For the first time, this work combines the manufacturing of the tissue and a tailored bioreactor, significantly streamlining the overall process and optimally merging the two processes. This way the production of the tissues can be individualized by customizing the reactor to the tissue and the patient-specific wound geometry. For this reason, a common basis and guideline for the cross-device and cross-material use of 3D printers was created initially. Their applicability was demonstrated by the iterative development of a perfusable bioreactor system, made from polydimethylsiloxane (PDMS) and a lignin-based filament, into which a biological tissue of flexible shape can be bioprinted. Cost-effective bioink-replacements and in silico computational fluid dynamics simulations were used for material sustainability and shape development. Also, nutrient distribution and shear stress could be predicted in this way pre-experimentally.
As a proof of functionality and adaptability of the reactor, tissues made from a nanocellulose-based Cellink® Bioink, as well as an alginate-based ink mixed with Me-PMeOx100-b-PnPrOzi100-EIP (POx) (Alginate-POx bioink) were successfully cultured dynamically in the bioreactor together with C2C12 cell line. Tissue maturation was further demonstrated using hMSC which were successfully induced to adipocyte differentiation. For further standardization, a mobile electrical device for automated media exchange was developed, improving handling in the laboratory and thus reduces the probability of contamination.
Introduction.
Mobile health (mHealth) integrates mobile devices into healthcare, enabling remote monitoring, data collection, and personalized interventions. Machine Learning (ML), a subfield of Artificial Intelligence (AI), can use mHealth data to confirm or extend domain knowledge by finding associations within the data, i.e., with the goal of improving healthcare decisions. In this work, two data collection techniques were used for mHealth data fed into ML systems: Mobile Crowdsensing (MCS), which is a collaborative data gathering approach, and Ecological Momentary Assessments (EMA), which capture real-time individual experiences within the individual’s common environments using questionnaires and sensors. We collected EMA and MCS data on tinnitus and COVID-19. About 15 % of the world’s population suffers from tinnitus.
Materials & Methods.
This thesis investigates the challenges of ML systems when using MCS and EMA data. It asks: How can ML confirm or broad domain knowledge? Domain knowledge refers to expertise and understanding in a specific field, gained through experience and education. Are ML systems always superior to simple heuristics and if yes, how can one reach explainable AI (XAI) in the presence of mHealth data? An XAI method enables a human to understand why a model makes certain predictions. Finally, which guidelines can be beneficial for the use of ML within the mHealth domain? In tinnitus research, ML discerns gender, temperature, and season-related variations among patients. In the realm of COVID-19, we collaboratively designed a COVID-19 check app for public education, incorporating EMA data to offer informative feedback on COVID-19-related matters. This thesis uses seven EMA datasets with more than 250,000 assessments. Our analyses revealed a set of challenges: App user over-representation, time gaps, identity ambiguity, and operating system specific rounding errors, among others. Our systematic review of 450 medical studies assessed prior utilization of XAI methods.
Results.
ML models predict gender and tinnitus perception, validating gender-linked tinnitus disparities. Using season and temperature to predict tinnitus shows the association of these variables with tinnitus. Multiple assessments of one app user can constitute a group. Neglecting these groups in data sets leads to model overfitting. In select instances, heuristics outperform ML models, highlighting the need for domain expert consultation to unveil hidden groups or find simple heuristics.
Conclusion.
This thesis suggests guidelines for mHealth related data analyses and improves estimates for ML performance. Close communication with medical domain experts to identify latent user subsets and incremental benefits of ML is essential.
Glioblastoma (GBM) sind bösartige hirneigene Tumore, deren schlechte Prognose einer innovativen Therapie bedarf. Aus diesem Grund wurde ein neuer Therapieansatz entwickelt, der auf einer lokalen Ultraschall-vermittelten Zytostatika Applikation beruht. Hierfür wurden stabile Microbubbles (MB) bestehend aus Phospholipiden synthetisiert. Es konnte gezeigt werden, dass MB als auch fokussierter Ultraschall niedriger Intensität (LIFU) keinen negativen Einfluss auf GBM-Zellen hat. MB hingegen konnten mittels LIFU destruiert werden, wodurch das in den MB eingeschlossene Chemotherapeutikum freigesetzt werden kann. Es wurden verschiedene Platin(II)- und Palladium(II)-Komplexe auf GBM Zellen getestet. Zur Beladung der MB wurde Doxorubicin (Dox) verwendet. Es konnte eine Beladungseffizienz der MB mit Dox von 52 % erreicht werden, auch eine Aufreinigung dieser mittel Ionenaustausch-Chromatographie und Dialyse war erfolgreich. Die Austestung der mit Dox beladenen MB (MBDox) erfolgte auf GBM-Zellen in 2D- und 3D-Zelkulturmodellen. Dabei zeigte sich, dass die Behandlung mit MBDox und LIFU für 48 h eine zytotoxische Wirkung hatte, die sich signifikant von der Behandlung mit MBDox ohne LIFU unterschied. Zur Austestung der MBDox in 3D-Zellkulturmodellen wurden zwei Scaffold-Systeme eingesetzt. Es zeigte sich in den Versuchen, dass MBDox mit LIFU im Vergleich zu MBDox ohne LIFU Applikation einen zytotoxischen Effekt auf GBM-Zellen haben. Somit konnte die Wirksamkeit der Zytostatika Applikation mittels MB und LIFU in 2D- und 3D-Zellkulturmodellen erfolgreich etabliert werden. Als weiterer Schritt wurden zwei 3D in vitro Modelle erarbeitet. Dabei wurden zunächst organotypische hippocampale Slice Kulturen (organotypic hippocampal brain slice cultures, OHSC) aus der Maus hergestellt und anschließend mit fluoreszent-markierten Mikrotumoren aus GBM-Zelllinien, Primärzellen (PZ) und aus Patienten generierten GBM-Organoiden hergestellt. Diese GBM-Modelle wurden mit Tumor Treating Fields (TTFields) behandelt. Dabei war eine Abnahme der Tumorgröße von Mikrotumoren aus GBM-Zellen und PZ unter TTFields-Behandlung für 72 h messbar. Als weiteres in vitro Modell wurden humane Tumorschnitte aus intraoperativ entferntem GBM-Patientenmaterial hergestellt. Die Schnitte wiesen ein heterogenes Ansprechen nach 72 h TTFields-Applikation auf. Dies spiegelt die Heterogenität des GBM sehr gut wider und bestärkt die Eignung des Modelles zur Untersuchung von neuen Therapieansätzen zur Behandlung von GBM.
The platelet cytoskeleton ensures normal size and discoid shape under resting conditions and undergoes immediate reorganization in response to changes in the extracellular environment through integrin-based adhesion sites, resulting in actomyosin-mediated contractile forces. Mutations in the contractile protein non-muscle myosin heavy chain IIA display, among others, macrothrombocytopenia and a mild to moderate bleeding tendency in human patients. It is insufficiently understood which factors contribute to the hemostatic defect found in MYH9-related disease patients. Therefore, a better understanding of the underlying biophysical mechanisms in thrombus formation and stabilization is warranted.
This thesis demonstrates that an amino acid exchange at the positions 702, 1424 and 1841 in the heavy chain of the contractile protein non-muscle myosin IIA, caused by heterozygous point mutations in the gene, resulted in macrothrombocytopenia and increased bleeding in mice, reflecting the clinical hallmark of the MYH9-related disease in human patients. Basic characterization of biological functions of Myh9 mutant platelets revealed overall normal surface glycoprotein expression and agonist-induced activation when compared to wildtype platelets. However, myosin light chain phosphorylation after thrombin-activation was reduced in mutant platelets, resulting in less contractile forces and a defect in clot retraction. Altered biophysical characteristics with lower adhesion and interaction forces of Myh9 mutant platelets led to reduced thrombus formation and stability. Platelets from patients with the respective mutations recapitulated the findings obtained with murine platelets, such as impaired thrombus formation and stiffness.
Besides biological and biophysical characterization of mutant platelets from mice and men, treatment options were investigated to prevent increased bleeding caused by reduced platelet forces. The antifibrinolytic agent tranexamic acid was applied to stabilize less compact thrombi, which are presumably more vulnerable to fibrinolysis. The hemostatic function in Myh9 mutant mice was improved by interfering with the fibrinolytic system. These results show the beneficial effect of fibrin stabilization to reduce bleeding in MYH9-related disease.
Studies on the role of cytoskeletal-regulatory and -crosslinking proteins in platelet function
(2023)
Cytoskeletal reorganization in platelets is highly regulated and important for proper platelet function during activation and aggregation at sites of vascular injury. In this thesis, the role of three different cytoskeletal-regulatory and -crosslinking proteins was studied in platelet physiology using megakaryocyte- and platelet-specific knockout mice. The generation of branched actin filaments is regulated by nucleation promoting factors (NPF) and the Arp2/3 complex.
(1.) The WAVE complex is a NPF, which upregulates the Arp2/3 complex activity at the plasma membrane. As shown in this thesis, the loss of the WAVE complex subunit Cyfip1 in mice did not alter platelet production and had only a minor impact on platelet activation. However, Cyfip1 played an essential role for branching of actin filaments and consequently for lamellipodia formation in vitro. The importance of lamellipodia for thrombus formation and stability has been controversially discussed. Cyfip1-deficient platelets were able to form a stable thrombus ex vivo and in vivo and a hemostatic plug comparable to controls. Moreover, Cyfip1-deficient mice maintained vascular integrity at the site of inflammation. These data show that platelet lamellipodia formation is not required for hemostatic function and pathophysiological thrombus formation.
(2.) The WASH complex is another NPF, which mediates actin filament polymerization on endosomal vesicles via the Arp2/3 complex. Loss of the WASH complex subunit Strumpellin led to a decreased protein abundance of the WASH protein and to a 20% reduction in integrin αIIbβ3 surface expression on platelets and megakaryocytes, whereas the expression of other surface receptors as well as the platelet count, size, ex vivo thrombus formation and bleeding time remained unaltered. These data point to a distinct role of Strumpellin in maintaining integrin αIIbβ3 expression and provide new insights into regulatory mechanisms of platelet integrins.
(3.) MACF1 has been described as a cytoskeletal crosslinker of microtubules and F-actin. However, MACF1-deficient mice displayed no alterations in platelet production, activation, thrombus formation and hemostatic function. Further, no compensatory up- or downregulation of other proteins could be found that contain an F-actin- and a microtubule-binding domain. These data indicate that MACF1 is dispensable for platelet biogenesis, activation and thrombus formation. Nevertheless, functional redundancy among different proteins mediating the cytoskeletal crosstalk may exist.
Neurobiology is widely supported by bioinformatics. Due to the big amount of data generated from the biological side a computational approach is required. This thesis presents four different cases of bioinformatic tools applied to the service of Neurobiology.
The first two tools presented belong to the field of image processing. In the first case, we make use of an algorithm based on the wavelet transformation to assess calcium activity events in cultured neurons. We designed an open source tool to assist neurobiology researchers in the analysis of calcium imaging videos. Such analysis is usually done manually which is time consuming and highly subjective. Our tool speeds up the work and offers the possibility of an unbiased detection of the calcium events. Even more important is that our algorithm not only detects the neuron spiking activity but also local spontaneous activity which is normally discarded because it is considered irrelevant. We showed that this activity is determinant in the calcium dynamics in neurons and it is involved in important functions like signal modulation and memory and learning.
The second project is a segmentation task. In our case we are interested in segmenting the neuron nuclei in electron microscopy images of c.elegans. Marking these structures is necessary in order to reconstruct the connectome of the organism. C.elegans is a great study case due to the simplicity of its nervous system (only 502 neurons). This worm, despite its simplicity has taught us a lot about neuronal mechanisms. There is still a lot of information we can extract from the c.elegans, therein lies the importance of reconstructing its connectome. There is a current version of the c.elegans connectome but it was done by hand and on a single subject which leaves a big room for errors. By automatizing the segmentation of the electron microscopy images we guarantee an unbiased approach and we will be able to verify the connectome on several subjects.
For the third project we moved from image processing applications to biological modeling. Because of the high complexity of even small biological systems it is necessary to analyze them with the help of computational tools. The term in silico was coined to refer to such computational models of biological systems. We designed an in silico model of the TNF (Tumor necrosis factor) ligand and its two principal receptors. This biological system is of high relevance because it is involved in the inflammation process. Inflammation is of most importance as protection mechanism but it can also lead to complicated diseases (e.g. cancer). Chronic inflammation processes can be particularly dangerous in the brain. In order to better understand the dynamics that govern the TNF system we created a model using the BioNetGen language. This is a rule based language that allows one to simulate systems where multiple agents are governed by a single rule. Using our model we characterized the TNF system and hypothesized about the relation of the ligand with each of the two receptors. Our hypotheses can be later used to define drug targets in the system or possible treatments for chronic inflammation or lack of the inflammatory response.
The final project deals with the protein folding problem. In our organism proteins are folded all the time, because only in their folded conformation are proteins capable of doing their job (with some very few exceptions). This folding process presents a great challenge for science because it has been shown to be an NP problem. NP means non deterministic Polynomial time problem. This basically means that this kind of problems cannot be efficiently solved. Nevertheless, somehow the body is capable of folding a protein in just milliseconds. This phenomenon puzzles not only biologists but also mathematicians. In mathematics NP problems have been studied for a long time and it is known that given the solution to one NP problem we could solve many of them (i.e. NP-complete problems). If we manage to understand how nature solves the protein folding problem then we might be able to apply this solution to many other problems. Our research intends to contribute to this discussion. Unfortunately, not to explain how nature solves the protein folding problem, but to explain that it does not solve the problem at all. This seems contradictory since I just mentioned that the body folds proteins all the time, but our hypothesis is that the organisms have learned to solve a simplified version of the NP problem. Nature does not solve the protein folding problem in its full complexity. It simply solves a small instance of the problem. An instance which is as simple as a convex optimization problem. We formulate the protein folding problem as an optimization problem to illustrate our claim and present some toy examples to illustrate the formulation. If our hypothesis is true, it means that protein folding is a simple problem. So we just need to understand and model the conditions of the vicinity inside the cell at the moment the folding process occurs. Once we understand this starting conformation and its influence in the folding process we will be able to design treatments for amyloid diseases such as Alzheimer's and Parkinson's.
In summary this thesis project contributes to the neurobiology research field from four different fronts. Two are practical contributions with immediate benefits, such as the calcium imaging video analysis tool and the TNF in silico model. The neuron nuclei segmentation is a contribution for the near future. A step towards the full annotation of the c.elegans connectome and later for the reconstruction of the connectome of other species. And finally, the protein folding project is a first impulse to change the way we conceive the protein folding process in nature. We try to point future research in a novel direction, where the amino code is not the most relevant characteristic of the process but the conditions within the cell.