bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.02.17.480405

Reducing structural non-identifiabilities in upstream bioprocess models using profile-likelihood

Babel, H.·Omar, O.·Paul, A. J.·Baer, J.
View original source ↗Export metadata ↓

Abstract

Process models are increasingly used to support upstream process development in the biopharmaceutical industry for process optimization, scale-up and to reduce experimental effort. Parametric unstructured models based biological mechanisms are highly promising, since they do not require large amounts of data. The critical part in the application is the certainty of the parameter estimates, since uncertainty of the parameter estimates propagates to model predictions and can increase the risk associated with those predictions. Currently Fisher-Information-Matrix based approximations or Monte-Carlo approaches are used to estimate parameter confidence intervals and regularization approaches to decrease parameter uncertainty. Here we apply profile likelihood to determine parameter identifiability of a recent upstream process model. We have investigated the effect of data amount on identifiability and found out that addition of data reduces non-identifiability. The likelihood profiles of non-identifiable parameters were then used to uncover structural model changes. These changes effectively alleviate the remaining non-identifiabilities except for a single parameter out of 21 total parameters. We present the first application of profile likelihood to a complete upstream process model. Profile likelihood is a highly suitable method to determine parameter confidence intervals in upstream process models and provides reliable estimates even with non-linear models and limited data.

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Babel, H., Omar, O., Paul, A. J., Baer, J.. 2022-02-19. Reducing structural non-identifiabilities in upstream bioprocess models using profile-likelihood. https://doi.org/10.1101/2022.02.17.480405

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Selected for the surface: adaptive laboratory evolution for accelerated cathodic biofilm development in Cupriavidus necator

Cupriavidus necator is a metabolically versatile facultative chemolithoautotroph and a promising chassis for sustainable bioproduction from CO2. Efficient coupling of its metabolism to renewable electricity through microbial electrosynthesis (MES) requires the organism to form stable, electroactive biofilms on cathodes. This step is hindered under oxic conditions (oxic MES, oMES) by electrochemically generated reactive oxygen species (ROS), mass-transfer limitations and poor biofilm stability. Systematic strategies to improve C. necator biofilm formation on cathodes have been lacking. Here we used adaptive laboratory evolution (ALE) to address this gap. The progenitor strain colonised graphite cathodes only after an initial heterotrophic phase and formed spatially uneven biofilms whose distribution mirrored the local flow field, as resolved by micro-particle tracking velocimetry. Populations were then selected over five passages under controlled, oxygen-limited cathodic conditions. Resequencing revealed that the recovered population was genetically heterogeneous, and its dominant isolate, strain 890, was characterised in a microfluidic oMES platform. Strain 890 reached the target electrode coverage in about one day instead of six and maintained a robust biofilm at the progenitor's steady-state biovolume, whereas the multi-strain pool peaked higher but proved unstable. Relative to the progenitor, the accelerated biofilm phenotype of strain 890 is driven by a single novel mutation in a bifunctional diguanylate cyclase/phosphodiesterase locus, which likely encodes a central regulator of the planktonic-to-biofilm transition. The steady-state biovolume itself remained bounded, most probably due to a mass-transfer ceiling set by the counter-directional supply of H2 and O2. This work shows that ALE renders the initial-attachment barrier of cathodic biofilm formation genetically tractable, providing strain 890 as a robust chassis and a well-defined starting point for oMES-based CO2 valorisation.

bioengineering↗
bioRxiv2026

Engineering a High-Fidelity CRISPR-Cas13d System for Targeted RNA Depletion in Plasmodium falciparum

Functional genomics in Plasmodium falciparum is severely constrained by the evolutionary absence of an endogenous RNA interference pathway and the requirement for time-consuming direct target locus modification by conventional conditional genetic engineering systems. To overcome these limitations, we established a programmable post-transcriptional gene silencing system in P. falciparum using High-Fidelity RfxCas13d (HF-RfxCas13d), an RNA-guided RNA nuclease that has been previously tested in several eukaryotic hosts. Using CRISPR-SpyCas9 we integrated a HF-RfxCas13d expression cassette at the dispensable pfs47 locus and generated a stable marker-free transgenic line capable of constitutive HF-RfxCas13d cytosolic expression without impairing intraerythrocytic asexual growth. Episomal delivery of single or dual guide RNAs (gRNAs) achieved significant target mRNA depletion (41%-76%) across three endogenous loci atp4, ap2-exp, ap2-g genes. Applying this system to the master transcriptional regulator of gametocytogenesis (ap2-g) resulted in transcript depletion that functionally impaired sexual differentiation in committed parasites, reducing significantly the Sexual Conversion Rate a 70%. Overall, our novel HF-Cas13d line provides a flexible, scalable, and non-locus-modifying addition to the P. falciparum genetic toolbox to accelerate functional genomics and malaria drug target identification.

bioengineering↗
bioRxiv2026

DECODE: DEcomposition of COupled and DEcoupled Brain States: Separating Structure-Coupled and Structure-Decoupled Functional Organization in Alzheimers Disease

Brain functional activity emerges within an underlying structural scaffold. While structural network connectivity provides a relatively stable anatomical backbone for large-scale communication, functional interactions are high-dimensional and temporally dynamic, forming recurrent patterns that can be represented in a latent space of transient functional states. Yet, how these transient functional states are organized and the extent to which they remain constrained by structural network connectivity or become relatively decoupled from structural organization over time remain poorly understood. To address this gap, we introduce DECODE (DEcomposition of COupled and DEcoupled Brain States), a data-driven latent-space framework that uses independent component analysis (ICA) and multimodal data to decompose resting-state dynamic functional network connectivity into a lower-dimensional representation of transient functional states. These states capture temporally varying structure-coupled and structure-decoupled patterns using diffusion MRI-derived structural network connectivity priors. Structure-coupled states reflect functional configurations more strongly anchored to major structural motifs, whereas structure-decoupled states represent configurations less constrained by the identified structural organization, while potentially remaining within structurally defined dynamical bounds. DECODE identifies reproducible structure-coupled and structure-decoupled states, with mean squared error reconstruction of dynamic information < 0.1 and adjusted R-squared = 0.4. Applied to Alzheimers disease (AD), DECODE revealed stronger group differences (FDR-corrected p-value 0.05) in both connectivity organization and temporal dynamics for decoupled states. These findings suggest that functional states less constrained by identified structural organization may be more vulnerable to AD-related disruption, whereas structure-coupled states may be relatively preserved. More broadly, DECODE provides a reproducible and generalizable framework for quantifying how functional dynamics are shaped by, or diverge from, structural organization by incorporating a latent representation of the structural connectome as an informative prior. Our findings established structural and functional alignment variation by distinguishing dynamic functional patterns closely coupled with structure from those that deviate from it.

bioengineering↗