Biology subjects

Faisal, A.

Publications and source records attributed to Faisal, A..

12 recordsLinked to original sources

bioRxiv2022

CanSeer: A Method for Development and Clinical Translation of Personalized Cancer Therapeutics

Computational modeling and analysis of biomolecular network models annotated with cancer patient-specific multi-omics data can enable the development of personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain to be fully translated. In this work, we present "CanSeer" a novel multi-stage methodology for developing in silico models towards clinical translation of personalized cancer therapeutics. The proposed methodology integrates state-of-the-art dynamical analysis of biomolecular network models with patient-specific genomic and transcriptomic data to assess the individualized therapeutic responses to targeted drugs and their combinations. CanSeers translational approach employs transcriptomic data (RNA-seq based gene expressions) with genomic profile (CNVs, SMs, and SVs). Specifically, patient-specific cancer driver genes are identified, followed by the selection of druggable and/or clinically actionable targets for therapeutic interventions. To exemplify CanSeer, we have designed three case studies including (i) lung squamous cell carcinoma, (ii) breast invasive carcinoma, and (iii) ovarian serous cystadenocarcinoma. The case study on lung squamous cell carcinoma concluded that restoration of Tp53 activity together with an inhibition of EGFR as an efficacious combinatorial treatment for patients with Tp53 and EGFR cancer driver genes. The findings from the cancer case study helped identify personalized treatments including APR-246, APR-246+palbociclib, APR-246+osimertinib, APR-246+afatinib, APR-246+osimertinib+dinaciclib, and APR-246+afatinib+dinaciclib. The second case study on breast invasive carcinoma revealed CanSeers potential to elucidate drug resistance against targeted drugs and their combinations including KU-55933, afuresertib, ipatasertib, and KU-55933+afuresertib. Lastly, the ovarian cancer case study revealed the combinatorial efficacy of APR-246+carmustine, and APR-246+dinaciclib for treating ovarian serous cystadenocarcinoma. Taken together, CanSeer outlines a novel method for systematic identification of optimal tailored treatments with mechanistic insights into patient-to-patient variability of therapeutic response, drug resistance mechanism, and cytotoxicity profiling towards personalized medicine.

systems biology↗

bioRxiv2021

Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

While aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data, such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genomewide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.

bioinformatics↗

bioRxiv2021

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a "zero-code" environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISONs modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.

systems biology↗

bioRxiv2021

Polycomb requires Tcp-1η chaperonin for maintaining gene silencing in Drosophila

In metazoans, heritable states of cell type specific gene expression patterns linked with specialization of various cell types constitute transcriptional cellular memory. Evolutionarily conserved Polycomb group (PcG) and trithorax group (trxG) proteins contribute to the transcriptional cellular memory by maintaining heritable patterns of repressed and active expression states, respectively. Although chromatin structure and modifications appear to play a fundamental role in maintenance of repression by PcG, the precise targeting mechanism and the specificity factors that bind PcG complexes to a defined region in chromosomes remain elusive. Here we report a serendipitous discovery that uncovers a direct molecular interaction between Polycomb (PC) and TCP-1 Ring Complex (TRiC) chaperonin subunit, Tcp-1{eta} in Drosophila. Tcp-1{eta} interacts with PC at chromatin to maintain repressed states of homeotic and non-homeotic targets of PcG, which supports a strong genetic interaction observed between Pc and Tcp-1{eta} mutants. Depletion of Tcp-1{eta} results in dissociation of PC from chromatin and redistribution of an abundant amount of PC in cytoplasm. We propose that Tcp-1{eta} is an important modulator of PC, which helps PC recruitment at chromatin and compromising Tcp-1{eta} can directly influence an evolutionary highly conserved epigenetic network that supervises the appropriate cellular identities during development and homoeostasis of an organism. Significance StatementSilencing of key developmental genes, e.g, Hox genes, by PcG is a hallmark of differential gene expression patterns associated with cell fate determination. Here we describe a previously unknown molecular and genetic interaction of Polycomb (PC) with Tcp-1{eta} subunit of TRiC chaperonin complex in Drosophila. Compromising Tcp-1{eta} function results in de-repression of PcG targets and a concomitant loss of PC from chromatin. Moreover, depletion of Tcp-1{eta} leads to redistribution of PC in cytoplasm. Molecular interaction of PC with Tcp-1{eta} highlights a novel factor which helps PC recruitment at chromatin. We propose that Tcp-1{eta} chaperonin is one of the specificity factors and part of the targeting mechanism that binds PC to specific regions on chromosomes.

genetics↗

bioRxiv2020

In silico Drosophila Patient Model Reveals Optimal Combinatorial Therapies for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

systems biology↗

bioRxiv2020

Neuroinflammation in the normal appearing white matter of multiple sclerosis brain causes abnormalities at the node of Ranvier

Changes to the structure of nodes of Ranvier in the normal-appearing white matter (NAWM) of MS brains are associated with chronic inflammation. We show that the paranodal domains in MS NAWM are longer on average than control, with Kv1.2 channels dislocated into the paranode. These pathological features are reproduced in a model of chronic meningeal inflammation generated by the injection of lentiviral vectors for the lymphotoxin- (LT) and interferon-{gamma} (IFN{gamma}) genes. We show that tumour necrosis factor (TNF), IFN{gamma} and glutamate can provoke paranodal elongation in cerebellar slice cultures, which could be reversed by an NMDA blocker. When these changes were inserted into a computational model to simulate axonal conduction, a rapid decrease in velocity was observed, reaching conduction failure in small diameter axons. We suggest that glial cells activated by proinflammatory cytokines can produce high levels of glutamate, which triggers paranodal pathology, contributing to axonal damage and conduction deficits.

neuroscience↗

bioRxiv2019

Dynamic time-locking mechanism in the cortical representation of spoken words

Human speech has a unique capacity to carry and communicate rich meanings. However, it is not known how the highly dynamic and variable perceptual signal is mapped to existing linguistic and semantic representations. In this novel approach, we utilized the natural acoustic variability of sounds and mapped them to magnetoencephalography (MEG) data using physiologically-inspired machine-learning models. We aimed at determining how well the models, differing in their representation of temporal information, serve to decode and reconstruct spoken words from MEG recordings in 16 healthy volunteers. We discovered that time-locking of the cortical activation to the unfolding speech input is crucial for the encoding of the acoustic-phonetic features. In contrast, time-locking was not highlighted in cortical processing of non-speech environmental sounds that conveyed the same meanings as the spoken words, including human-made sounds with temporal modulation content similar to speech. The amplitude envelope of the spoken words was particularly well reconstructed based on cortical evoked responses. Our results indicate that speech is encoded cortically with especially high temporal fidelity. This mechanism may contribute to the frequently reported entrainment of the cortical oscillations to the amplitude envelope of speech. Furthermore, the phoneme content was reflected in cortical evoked responses simultaneously with the spectrotemporal features, pointing to an instantaneous transformation of the unfolding acoustic features into linguistic representations during speech processing.\n\nSummary: Computational modeling of cortical responses to spoken words highlights the relevance of temporal tracking of spectrotemporal features, which is likely pivotal for transforming the acoustic-phonetic features into linguistic representations.

neuroscience↗

bioRxiv2018

Reconstructing meaning from bits of information

We can easily identify a dog merely by the sound of barking or an orange by its citrus scent. In this work, we study the neural underpinnings of how the brain combines bits of information into meaningful object representations. Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of individual semantic features (e.g., \"barks\", \"has citrus scent\"). Here, participants received clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We used machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. We discovered that the recorded brain patterns were best decoded using a combination of not only the three semantic features that were presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to observe how fragmented information is combined into a complete semantic representation of an object and suggest neuroanatomical underpinnings for this process.

neuroscience↗

bioRxiv2018

Cracking the problem of neural representations of abstract words: grounding word meanings in language itself

In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. Here, we built a model of the semantic space based on word statistics in a large text corpus, which was able to decode items from brain signals. In the model, word abstractness emerged from the statistical regularities of the language environment. This salient property of the model co-varied, at 280-420 ms after word presentation, with activity in the left-hemisphere frontal, anterior temporal and superior parietal cortex that have been linked with processing of abstract words. In light of these results, we propose that the neural encoding of word meanings is importantly grounded in language through statistical regularities.

neuroscience↗

bioRxiv2018

Deep Learning personalised, closed-loop Brain-Computer Interfaces for multi-way classification

Exoskeletons and robotic devices are for many motor disabled people the only way to interact with their envi-ronment. Our lab previously developed a gaze guided assistive robotic system for grasping. It is well known that the same natural task can require different interactions described by different dynamical systems that would require different robotic controllers and their selection by the user in a self paced way. Therefore, we investigated different ways to achieve transitions between multiple states, finding that eye blinks were the most reliable to transition from off to control modes (binary classification) compared to voice and electromyography. In this paper be expanded on this work by investigating brain signals as sources for control mode switching. We developed a Brain Computer Interface (BCI) that allows users to switch between four control modes in self paced way in real time. Since the system is devised to be used in domestic environments in a user friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (ConvNets), known by their capability to find the optimal features for a classification task, which we hypothesised would add flexibility to the system in terms of which mental activities the user could perform to control it. We tested our system using the Cybathlon BrainRunners computer game, which represents all the challenges inherent to real time control. Our preliminary results show that an efficient architecture (SmallNet) composed by a convolutional layer, a fully connected layer and a sigmoid classification layer, is able to classify 4 mental activities that the user chose to perform. For his preferred mental activities, we run and validated the system online and retrained the system using online collected EEG data. We achieved 47, 6% accuracy in online operation in the 4-way classification task. In particular we found that models trained with online collected data predicted better the behaviour of the system in real time suggesting, as a side note, that similar (ConvNets based) offline classifying methods present in literature might find a decay in performance when applied online. To the best of our knowledge this is the first time such an architecture is tested in an online operation task. While compared to our previous method relying on blinks with this one we reduced in less than half (1.6 times) the accuracy but increased by 2 the amount of states among which we can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self paced way.

bioengineering↗

bioRxiv2018

Natural Gaze Data Driven Wheelchair

Natural eye movements during navigation have long been considered to reflect planning processes and link to users future action intention. We investigate here whether natural eye movements during joystick-based navigation of wheel-chairs follow identifiable patterns that are predictive of joystick actions. To place eye movements in context with driving intentions, we combine our eye tracking with a 3D depth camera system, which allows us to identify which eye movements have the floor as gaze target and distinguish them from other non-navigation related eye movements. We find consistent patterns of eye movements on the floor predictive of steering commands issued by the driver in all subjects. Based on this empirical data we developed two gaze decoders using supervised machine learning techniques and enabled each of these drivers to then steer the wheelchair by imagining they were using a joystick to trigger appropriate natural eye movements via motor imagery. We show that all subjects are able to navigate their wheelchair \"by eye\" learning it within a short time span of minutes. Our work shows that simple gaze-based decoding without need for artificial user interfaces suffices to restore mobility and increasing participation in daily life.

bioengineering↗

bioRxiv2017

The role of sensorimotor variability and computation in falls in the elderly

The relationship between sensorimotor variability and falls in elderly has not been well investigated. We designed and used a motor task having shared biomechanics of walking and obstacle negotiation to quantify sensorimotor variability related to locomotion across age. We also applied sensory psychophysics to pinpoint specific sensory systems associated with sensorimotor variability. We found that sensorimotor variability in foot placement increases continuously with age. We further showed that increased sensory variability, specifically increased proprioceptive variability, the vital cause of more variable foot placement in the elderly. Notably, elderly participants relied more on the vision to judge their own foots height compared to the young, suggesting a shift in multisensory integration strategy to compensate for degenerated proprioception. We further modelled the probability of tripping-over based on the relationship between sensorimotor variability and age and found a good correspondence between model prediction and community-based data. We revealed increased sensorimotor variability, modulated by sensation precision, a potentially vital mechanism of raised tripping-over and thus fall events in the elderly. Therefore, our tasks, which quantify sensorimotor variability, can be used for trip-over probability assessment and, with adjustments, potentially applied as a training program to mitigate trip-over risk.

neuroscience↗