| 1. |
- Bartoszek, Krzysztof, 1984-, et al.
(författare)
-
A novel algorithm to reconstruct phylogenies using gene sequences and expression data
- 2014
-
Ingår i: International Proceedings of Chemical, Biological & Environmental Engineering; Environment, Energy and Biotechnology III. ; , s. 8-12
-
Konferensbidrag (refereegranskat)abstract
- Phylogenies based on single loci should be viewed with caution and the best approach for obtaining robust trees is to examine numerous loci across the genome. It often happens that for the same set of species trees derived from different genes are in conflict between each other. There are several methods that combine information from different genes in order to infer the species tree. One novel approach is to use informationfrom different -omics. Here we describe a phylogenetic method based on an Ornstein–Uhlenbeck process that combines sequence and gene expression data. We test our method on genes belonging to the histidine biosynthetic operon. We found that the method provides interesting insights into selection pressures and adaptive hypotheses concerning gene expression levels.
|
|
| 2. |
- Bartoszek, Krzysztof, 1984-, et al.
(författare)
-
Influenza differentiation and evolution
- 2010
-
Ingår i: Acta Physica Polonica B Proceedings Supplement. ; 3:2, s. 417-452
-
Konferensbidrag (refereegranskat)abstract
- The aim of the study is to do a very wide analysis of HA, NA and M influenza gene segments to find short nucleotide regions,which differentiate between strains (i.e. H1, H2, ... e.t.c.), hosts, geographic regions, time when sequence was found and combination of time and region using a simple methodology. Finding regions differentiating between strains has as its goal the construction of a Luminex microarray which will allow quick and efficient strain recognition. Discovery for the other splitting factors could shed lighton structures significant for host specificity and on the history of influenza evolution. A large number of places in the HA, NA and M gene segments were found that can differentiate between hosts, regions, time and combination of time and region. Also very good differentiation between different Hx strains can be seen.We link one of our findings to a proposed stochastic model of creation of viral phylogenetic trees.
|
|
| 3. |
- Bartoszek, Krzysztof, et al.
(författare)
-
MODELLING TRAIT-DEPENDENT SPECIATION WITH APPROXIMATE BAYESIAN COMPUTATION
- 2019
-
Ingår i: ACTA PHYSICA POLONICA B PROCEEDINGS SUPPLEMENT. - : JAGIELLONIAN UNIV. ; , s. 25-47
-
Konferensbidrag (refereegranskat)abstract
- Phylogeny is the field of modelling the temporal discrete dynamics of speciation. Complex models can nowadays be studied using the Approximate Bayesian Computation approach which avoids likelihood calculations. The fields progression is hampered by the lack of robust software to estimate the numerous parameters of the speciation process. In this work, we present an R package, pcmabc, publicly available on CRAN, based on Approximate Bayesian Computations, that implements three novel phylogenetic algorithms for trait-dependent speciation modelling. Our phylogenetic comparative methodology takes into account both the simulated traits and phylogeny, attempting to estimate the parameters of the processes generating the phenotype and the trait. The user is not restricted to a predefined set of models and can specify a variety of evolutionary and branching models. We illustrate the software with a simulation-reestimation study focused around the branching Ornstein-Uhlenbeck process, where the branching rate depends non-linearly on the value of the driving Ornstein-Uhlenbeck process. Included in this work is a tutorial on how to use the software.
|
|
| 4. |
- Bellini, Emanuele, et al.
(författare)
-
Resilience learning through self adaptation in digital twins of human-cyber-physical systems
- 2021
-
Ingår i: Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR). - : IEEE. - 9781665402859 - 9781665402866 ; , s. 168-173
-
Konferensbidrag (refereegranskat)abstract
- Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and implications, which in turn require the adoption of advanced monitoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms, data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation to time-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.
|
|
| 5. |
- Da Lio, Mauro, et al.
(författare)
-
A Mental Simulation Approach for Learning Neural-Network Predictive Control (in Self-Driving Cars)
- 2020
-
Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 192041-192064
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel approach to learning predictive motor control via mental simulations. The method, inspired by learning via mental imagery in natural Cognition, develops in two phases: first, the learning of predictive models based on data recorded in the interaction with the environment; then, at a deferred time, the synthesis of inverse models via offline episodic simulations. Parallelism with human-engineered control-theoretic workflow (mathematical modeling the direct dynamics followed by optimal control inversion) is established. Compared to the latter human-directed synthesis, the mental simulation approach increases autonomy: a robotic agent can learn predictive models and synthesize inverse ones with a large degree of independence. Human modeling is still needed but limited to providing efficient templates for the forward and inverse neural networks and a few other directives. One could consider these templates as the efficient brain network typologies that evolution produced to permit live beings quickly and efficiently learning. The structure of the neural networks both forward and inverse ones; is made of interpretable local models which follows the cerebellar organization (and are also similar to local model approaches known in the literature). We demonstrate the learning of a first-round model (contrasted to Model Predictive Control) for lateral vehicle dynamics. Then, we demonstrate a second learning iteration, where the forward/inverse neural models are significantly improved.
|
|
| 6. |
- Xiao, Hui, et al.
(författare)
-
Multi-omic analysis of signalling factors in inflammatory comorbidities
- 2018
-
Ingår i: BMC Bioinformatics. - : BMC. - 1471-2105. ; 19
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundInflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.ResultsWe first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi-omic features including both gene expression and DNA methylation. We introduce a phylogenetic-based multi-omic method to study the relationships between multi-omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre-estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.ConclusionsApplying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF- gene family, the IL-17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.
|
|
| 7. |
- Niemi, MEK, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
