Permeability issues in whole-cell bioprocesses and cellular membrane engineering

Nutrient uptake and waste excretion are among the many important functions of the cellular membrane. While permitting nutrients into the cell, the cellular membrane system evolves to guide against noxious agents present in the environment from entering the intracellular milieu. The semipermeable nature of the membrane is at odds with biomolecular engineers in their endeavor of using microbes as cell factory. The cellular membrane often retards the entry of substrate into the cellular systems and prevents the product from being released from the cellular system for an easy recovery. Consequently, productivities of whole-cell bioprocesses such as biocatalysis, fermentation, and bioremediations are severely compromised. For example, the rate of whole-cell biocatalysis is usually 1–2 orders of magnitude slower than that of the isolated enzymes. When product export cannot keep pace with the production rate, intracellular product accumulation quickly leads to a halt of production due to product inhibition. While permeabilization via chemical or physical treatment of cell membrane is effective in small-scale process, large-scale implementation is problematic. Molecular engineering approach recently emerged as a much better alternative. Armed with increasingly sophisticated tools, biomolecular engineers are following nature’s ingenuity to derive satisfactory solutions to the permeability problem. This review highlights these exciting molecular engineering achievements.     

Abstraction and reformulation in artificial intelligence

This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.     

Synthesizing and staining manganese oxide nanoparticles for cytotoxicity and cellular uptake investigation

Background

For decades, contrast agents have been used to reduce longitudinal (T₁) or transverse (T₂) relaxation times. High toxicity of gadolinium-based contrast agents leads researchers to new T₁ contrast agents. Manganese oxide (MnO) nanoparticle (NP) with the lower peril and good enough signal change ability has been offered as a new possibility for magnetic resonance imaging (MRI).

Methods

The synthesized NPs were investigated for physicochemical and biological properties by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscope, dynamic light scattering (DLS), inductively coupled plasma, enzyme-linked immunosorbent assay, and 3 T magnetic resonance imaging.

Results

Due to physical contact importance of T₁ contrast agents with tissues' protons, extremely thin layer of the surfactant, less than 2 nm, was coated on NPs for aqueous stabilizing. The hydrophilic gentisic acid with low Dalton, around 154, did that role truly. Moreover, decreasing NP size to 5 nm which increases available surface for the proton relaxation is another important parameter to reach an appropriate longitudinal relaxation rate. The NPs didn't reveal any side effects on the cells, and cellular uptake was considerable.

Conclusions

The synthesized NPs represented a promising result in comparison to clinical gadolinium chelates, due to higher r₁ relaxivity and lower toxicity.

General significance

In addition to considerable signal change and cellular uptake, Prussian blue was tried on MnO NPs for the initial time, which can be observed within cells by pale blue color.     

Distinct neurocognitive strategies for comprehensions of human and artificial intelligence

Although humans have inevitably interacted with both human and artificial intelligence in real life situations, it is unknown whether the human brain engages homologous neurocognitive strategies to cope with both forms of intelligence. To investigate this, we scanned subjects, using functional MRI, while they inferred the reasoning processes conducted by human agents or by computers. We found that the inference of reasoning processes conducted by human agents but not by computers induced increased activity in the precuneus but decreased activity in the ventral medial prefrontal cortex and enhanced functional connectivity between the two brain areas. The findings provide evidence for distinct neurocognitive strategies of taking others' perspective and inhibiting the process referenced to the self that are specific to the comprehension of human intelligence.     

On artificial immune systems and swarm intelligence

This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are many aspects of AIS that have direct parallels with SI and examine the role of AIS and SI in science and also in engineering, with the primary focus being on the immune system. We explore how in some ways, algorithms from each area are similar, but we also advocate, and explain, that rather than being competitors, AIS and SI are complementary tools and can be used effectively together to solve complex engineering problems.     

Modeling and artificial intelligence approaches to enzyme systems

Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.     

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Background  

All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.     

A hybrid asymptotic-Kalman observer for bioprocesses

The exponential observers (extended Kalman or Luenberger observers, high gain observers) allow the use of a tuning parameter for managing the rate of convergence of the state estimate towards the true state. But their results are strongly dependent on the model quality (especially the kinetic model in bioprocesses). On the other hand, asymptotic observers (like the observer of Bastin and Dochain) have a rate of convergence which is a function of the experimental conditions (namely the dilution rate). However, this lack of tuning parameter is compensated by the absence of need for any kinetic model. In this paper, a hybrid technique is proposed which allows to jointly estimate the state and identify on-line the confidence on the kinetic model. The two limit cases (100 and 0 confidence) allow to recover rigorously the extended Kalman filter and the asymptotic observer of Bastin and Dochain. A simulation example (a fed-batch bacterial culture) is proposed and exhibits very satisfactory results.     

Synthesizing insight: artificial life as thought experimentation in biology

What is artificial life? Much has been said about this interesting collection of efforts to artificially simulate and synthesize lifelike behavior and processes, yet we are far from having a robust philosophical understanding of just what Alifers are doing and why it ought to interest philosophers of science, and philosophers of biology in particular. In this paper, I first provide three introductory examples from the particular subset of artificial life I focus on, known as ‘soft Alife’ (s-Alife), and follow up with a more in-depth review of the Avida program, which serves as my case study of s-Alife. Next, I review three well-known accounts of thought experiments, and then offer my own synthesized account, to make the argument that s-Alife functions as thought experimentation in biology. I draw a comparison between the methodology of the thought-experimental world that yields real-world results, and the s-Alife research that informs our understanding of natural life. I conclude that the insights provided by s-Alife research have the potential to fundamentally alter our understanding of the nature of organic life and thus deserve the attention of both philosophers and natural scientists.     

Quantitative microscopy and artificial intelligence: some philosophical reflections

The interdisciplinary field of quantitative microscopy (computer-aided microscopy) and artificial image understanding systems is explored, with an emphasis on the philosophical aspects of pathology and artificial intelligence. Three methodological problems of traditional diagnostic pathology are identified: those of validity, variability and organisation. Quantitative microscopy is a potential research strategy for solving these problems. In practice, however, the quantitative microscopy program is handicapped by the difficulty of building artificial image-understanding systems. We discuss the segmentation problem in image understanding, and four general strategies, three cognitivistic and one connectionistic, are reviewed.     

Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics

An epidemic caused by COVID-19 in China turned into pandemic within a short duration affecting countries worldwide. Researchers and companies around the world are working on all the possible strategies to develop a curative or preventive strategy for the same, which includes vaccine development, drug repurposing, plasma therapy, and drug discovery based on Artificial intelligence. Therapeutic approaches based on Computational biology and Machine-learning algorithms are specially considered, with a view that these could provide a fast and accurate outcome in the present scenario. As an effort towards developing possible therapeutics for COVID-19, we have used machine-learning algorithms for the generation of alignment kernels from diverse viral sequences of Covid-19 reported from India, China, Italy and USA. Using these diverse sequences we have identified the conserved motifs and subsequently a peptide library was designed against them. Of these, 4 peptides have shown strong binding affinity against the main protease of SARS-CoV-2 (M^pro) and also maintained their stability and specificity under physiological conditions as observed through MD Simulations. Our data suggest that these evolutionary peptides against COVID-19 if found effective may provide cross-protection against diverse Covid-19 variants.     

ATP Photosynthetic vesicles for light-driven bioprocesses

We prepared ATP photosynthetic vesicles from inside-out membranes of Escherichia coli cells that express delta-rhodopsin (a novel light-driven H⁺ transporter) and TF₀F₁-ATP synthase (a thermo-stable ATP synthase). These vesicles showed light-dependent ATP synthesis. Furthermore, coupling the ATP photosynthetic vesicles with an ATP-hydrolyzing hexokinase enabled light-dependent glucose consumption. The ATP photosynthetic vesicles indicate their potential to applied to light-driven ATP-regenerating bioprocess for various ATP-hydrolyzing bioproductions.     

Biotechnological potential of inulin for bioprocesses

Inulin consists of linear chains of β-2,1-linked d-fructofuranose molecules terminated by a glucose residue through a sucrose-type linkage at the reducing end. In this review article, inulin and its applications in bioprocesses are overviewed. The tubers of many plants, such as Jerusalem artichoke, chicory, dahlia, and yacon contain a large amount of inulin. Inulin can be actively hydrolyzed by microbial inulinases to produce fructose, glucose and inulooligosaccharides (IOS). The fructose and glucose formed can be further transformed into ethanol, single-cell protein, single cell oil and other useful products by different microorganisms. IOS formed have many functions. Therefore, inulin can be widely used in food, feed, pharmaceutical, chemical and biofuels industries.     

Hexahedral modular bioreactor for solid state bioprocesses

The design of a modular bioreactor for solid state fermentation is a promising development because it keeps the homogeneity of the bed at optimal levels. This study determines the optimum geometry of elementary modules of hexahedral bioreactors subjected to constant volume. The bioreactors have a square section and do not need an external cooling system, because the optimization limits the temperature of the bed to 35°C. The geometric optimization followed the Constructal principle of minimum heat resistance. The numerical simulations take into account the following parameters: inlet air temperature and velocity, and module volume. Once the elementary module has been selected, the total volume of the bioreactor can be calculated.     

A grounded theory of abstraction in artificial intelligence

In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.     

CAMBIO: software for modelling and simulation of bioprocesses

CAMBIO, a software package devoted to bioprocess modelling,which runs on Apollo computers, is described. This softwareenables bioengineers to easily and interactively design appropriatemathematical models directly from their perception of the process.CAMBIO provides the user with a set of design symbols and mnemonicicons in order to interactively design a functional diagram.This diagram has to exhibit the most relevant components withtheir related interactions through biological and physico-chemicalreactions. Then, CAMBIO automatically generates the dynamicalmaterial balance equations of the process in the form of analgebraic-differential system by taking advantage of the knowledgeinvolved in the functional diagram. The model may be used forcontrol design purpose or completed by kinetics expressionswith a view to simulation. CAMBIO offers facilities to generatea simulation model (for coding of kinetics, introducing auxiliaryvariables, etc.). This model is automatically interfaced witha specialized simulation software which allows an immediatevisualization of the process dynamical behaviour under variousoperational conditions (possibly involving feedback controlstrategies). An example of an application dealing with yeastfermentation is given. Received on June 14, 1990; accepted on January 11, 1991